diff --git a/llama-index-packs/llama-index-packs-koda-retriever/.gitignore b/llama-index-packs/llama-index-packs-koda-retriever/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..990c18de229088f55c6c514fd0f2d49981d1b0e7
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/.gitignore
@@ -0,0 +1,153 @@
+llama_index/_static
+.DS_Store
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+bin/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+etc/
+include/
+lib/
+lib64/
+parts/
+sdist/
+share/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+.ruff_cache
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+notebooks/
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pyvenv.cfg
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Jetbrains
+.idea
+modules/
+*.swp
+
+# VsCode
+.vscode
+
+# pipenv
+Pipfile
+Pipfile.lock
+
+# pyright
+pyrightconfig.json
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/BUILD b/llama-index-packs/llama-index-packs-koda-retriever/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..0896ca890d8bffd60a44fa824f8d57fecd73ee53
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/BUILD
@@ -0,0 +1,3 @@
+poetry_requirements(
+    name="poetry",
+)
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/Makefile b/llama-index-packs/llama-index-packs-koda-retriever/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..b9eab05aa370629a4a3de75df3ff64cd53887b68
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/Makefile
@@ -0,0 +1,17 @@
+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help:	## Show all Makefile targets.
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format:	## Run code autoformatters (black).
+	pre-commit install
+	git ls-files | xargs pre-commit run black --files
+
+lint:	## Run linters: pre-commit (black, ruff, codespell) and mypy
+	pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test:	## Run tests via pytest.
+	pytest tests
+
+watch-docs:	## Build and watch documentation.
+	sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/README.md b/llama-index-packs/llama-index-packs-koda-retriever/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..582d67388a106fdc7365191788db359d7234eb7f
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/README.md
@@ -0,0 +1,77 @@
+# Koda Retriever
+
+This retriever is a custom fine-tunable Hybrid Retriever that dynamically determines the optimal alpha for a given query.
+An LLM is used to categorize the query and therefore determine the optimal alpha value, as each category has a preset/provided alpha value.
+It is recommended that you run tests on your corpus of data and queries to determine categories and corresponding alpha values for your use case.
+
+![koda-retriever-mascot](https://i.imgur.com/224ocIw.jpeg)
+
+### Disclaimer
+
+_The default categories and alpha values are not recommended for production use_
+
+## Introduction
+
+Alpha tuning in hybrid retrieval for RAG models refers to the process of adjusting the weight (alpha) given to different components of a hybrid search strategy. In RAG, the retrieval component is crucial for fetching relevant context from a knowledge base, which the generation component then uses to produce answers. By fine-tuning the alpha parameter, the balance between the retrieved results from dense vector search methods and traditional sparse methods can be optimized. This optimization aims to enhance the overall performance of the system, ensuring that the retrieval process effectively supports the generation of accurate and contextually relevant responses.
+
+### Simply explained
+
+Imagine you're playing a game where someone whispers a sentence to you, and you have to decide whether to draw a picture of exactly what they said, or draw a picture of what you think they mean. Alpha tuning is like finding the best rule for when to draw exactly what's said and when to think deeper about the meaning. It helps us get the best mix, so the game is more fun and everyone understands each other better!
+
+## Usage Snapshot
+
+Koda Retriever is compatible with all other retrieval interfaces and objects that would normally be able to interact with an LI-native [retriever](https://docs.llamaindex.ai/en/stable/module_guides/querying/retriever/root.html).
+
+Please see the [examples](./examples/) folder for more specific examples.
+
+```python
+# Setup
+from llama_index.packs.koda_retriever import KodaRetriever
+from llama_index.core import VectorStoreIndex
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai.base import OpenAIEmbedding
+from llama_index.core.postprocessor import LLMRerank
+from llama_index.core import Settings
+
+Settings.llm = OpenAI()
+Settings.embed_model = OpenAIEmbedding()
+vector_store = PineconeVectorStore(pinecone_index=index, text_key="summary")
+vector_index = VectorStoreIndex.from_vector_store(
+    vector_store=vector_store, embed_model=Settings.embed_model
+)
+
+reranker = LLMRerank(llm=Settings.llm)  # optional
+retriever = KodaRetriever(
+    index=vector_index, llm=Settings.llm, reranker=reranker, verbose=True
+)
+
+# Retrieval
+query = "What was the intended business model for the parks in the Jurassic Park lore?"
+
+results = retriever.retrieve(query)
+
+# Query Engine
+query_engine = RetrieverQueryEngine.from_args(retriever=retriever)
+
+response = query_engine.query(query)
+```
+
+### Prerequisites
+
+- Vector Store Index w/ hybrid search enabled
+- LLM (or any model to route/classify prompts)
+
+Please note that you will also need vector AND text representations of your data for a hybrid retriever to work. It is not uncommon for some vector databases to only store the vectors themselves, in which case an error will occur downstream if you try to run any hybrid queries.
+
+## Setup
+
+## Citations
+
+Idea & original implementation sourced from the following docs:
+
+- https://blog.llamaindex.ai/llamaindex-enhancing-retrieval-performance-with-alpha-tuning-in-hybrid-search-in-rag-135d0c9b8a00
+- https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167
+
+## Buy me a coffee
+
+[Thanks!](https://www.buymeacoffee.com/nodice)
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/examples/README.md b/llama-index-packs/llama-index-packs-koda-retriever/examples/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..aa395f80e54897a9023fd4434f6c0450ac366de5
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/examples/README.md
@@ -0,0 +1,8 @@
+# Notebooks as a guide
+
+For now, the following guides exist:
+
+- [Alpha Tuning](alpha_tuning.ipynb): Showcases a (simple) example on how to provide custom categories/alpha values for alpha tuning.
+- [Quickstart](quickstart.ipynb): A quickstart notebook showing how Koda Retriever functions with default values and basic fixtures.
+
+Both are recommended to review. More notebooks will be added as this project grows.
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/examples/alpha_evaluation.ipynb b/llama-index-packs/llama-index-packs-koda-retriever/examples/alpha_evaluation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6ec22ffa63f0104a47c4cd31c9d01eec2f4a29fb
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/examples/alpha_evaluation.ipynb
@@ -0,0 +1,740 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluating RAG w/ Alpha Tuning\n",
+    "Evaluation is a crucial piece of development when building a RAG pipeline. Likewise, alpha tuning can be a time consuming exercise to build out, so what does the performance benefit look like for all of this extra effort? Let's dig into that.\n",
+    "\n",
+    "### Fixtures\n",
+    "- For our dataset: Several research papers on AI (same used in the blog post referenced below) /data\n",
+    "- Our vector db: [Pinecone](https://www.pinecone.io/)\n",
+    "- For our embedding model: [ada-002](https://platform.openai.com/docs/models/embeddings) \n",
+    "- For our LLM: [GPT-3.5 Turbo](https://platform.openai.com/docs/models/gpt-3-5-turbo)\n",
+    "\n",
+    "These fixtures were chosen because they're well integrated within LlamaIndex to make this notebook very transferrable to those looking to reproduce this.\n",
+    "Likewise, Pinecone supports hybrid search, which is a requirement for hybrid searches to be possible. Koda Retriever will also be used!\n",
+    "\n",
+    "### Testing\n",
+    "\n",
+    "Koda Retriever was largely inspired from the alpha tuning [blog post written by Ravi Theja](https://blog.llamaindex.ai/llamaindex-enhancing-retrieval-performance-with-alpha-tuning-in-hybrid-search-in-rag-135d0c9b8a00) from Llama Index. For that reason, we'll follow a similar pattern and evaluate with:\n",
+    "- MRR (Mean Reciprocal Rank)\n",
+    "- Hit Rate\n",
+    "\n",
+    "### Agenda:\n",
+    "- Fixture Setup\n",
+    "- Data Ingestion\n",
+    "- Synthetic Query Generation\n",
+    "- Alpha Mining & Evaluation\n",
+    "- Koda Retriever vs Vanilla Hybrid Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import all the necessary modules\n",
+    "from llama_index.llms.openai import OpenAI\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+    "from llama_index.core.postprocessor import LLMRerank\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.vector_stores.pinecone import PineconeVectorStore\n",
+    "from llama_index.core import Settings\n",
+    "from llama_index.packs.koda_retriever import KodaRetriever\n",
+    "from llama_index.core.evaluation import RetrieverEvaluator\n",
+    "from llama_index.core import SimpleDirectoryReader\n",
+    "import os\n",
+    "from pinecone import Pinecone\n",
+    "from llama_index.core.node_parser import SemanticSplitterNodeParser\n",
+    "from llama_index.core.ingestion import IngestionPipeline\n",
+    "from llama_index.core.retrievers import VectorIndexRetriever\n",
+    "from llama_index.core.evaluation import generate_qa_embedding_pairs\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fixture Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pc = Pinecone(api_key=os.environ.get(\"PINECONE_API_KEY\"))\n",
+    "index = pc.Index(\"llama2-paper\")  # this was previously created in my pinecone account\n",
+    "\n",
+    "Settings.llm = OpenAI()\n",
+    "Settings.embed_model = OpenAIEmbedding()\n",
+    "\n",
+    "# if you recreate this using the default dataset in pinecone, you'll want to set `text_key = \"summary\"`\n",
+    "vector_store = PineconeVectorStore(pinecone_index=index)\n",
+    "vector_index = VectorStoreIndex.from_vector_store(\n",
+    "    vector_store=vector_store, embed_model=Settings.embed_model\n",
+    ")\n",
+    "\n",
+    "reranker = LLMRerank(llm=Settings.llm)  # optional\n",
+    "\n",
+    "koda_retriever = KodaRetriever(\n",
+    "    index=vector_index,\n",
+    "    llm=Settings.llm,\n",
+    "    reranker=reranker,  # optional\n",
+    "    verbose=True,\n",
+    "    similarity_top_k=10,\n",
+    ")\n",
+    "\n",
+    "vanilla_retriever = vector_index.as_retriever()\n",
+    "\n",
+    "pipeline = IngestionPipeline(\n",
+    "    transformations=[Settings.embed_model], vector_store=vector_store\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Ingestion\n",
+    "\n",
+    "Three research papers in `/data` are going to be ingested into our Pinecone instance.\n",
+    "\n",
+    "Our chunking strategy will solely be [semantic](https://docs.llamaindex.ai/en/stable/examples/node_parsers/semantic_chunking.html) - although this is not recommended for production. For production use cases it is recommended more analysis and other chunking strategies are considered for production use cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f5e9c3aec13441194a2ee4ba2f63f82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upserted vectors:   0%|          | 0/26 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "181d68e22edd4cfcb577876550d68ad8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upserted vectors:   0%|          | 0/23 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d94c8389fdc9470ab39ce7d33143dcd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upserted vectors:   0%|          | 0/37 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Simple function to load documents from a file - also taken from the source blog post\n",
+    "\n",
+    "\n",
+    "def load_documents(file_path, num_pages=None):\n",
+    "    if num_pages:\n",
+    "        documents = SimpleDirectoryReader(input_files=[file_path]).load_data()[\n",
+    "            :num_pages\n",
+    "        ]\n",
+    "    else:\n",
+    "        documents = SimpleDirectoryReader(input_files=[file_path]).load_data()\n",
+    "    return documents\n",
+    "\n",
+    "\n",
+    "# Load the documents\n",
+    "doc1 = load_documents(\n",
+    "    \"/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf\",\n",
+    "    num_pages=9,\n",
+    ")\n",
+    "doc2 = load_documents(\n",
+    "    \"/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf\",\n",
+    "    num_pages=7,\n",
+    ")\n",
+    "doc3 = load_documents(\n",
+    "    \"/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf\",\n",
+    "    num_pages=12,\n",
+    ")\n",
+    "docs = [doc1, doc2, doc3]\n",
+    "nodes = list()\n",
+    "\n",
+    "node_parser = SemanticSplitterNodeParser(\n",
+    "    embed_model=Settings.embed_model, breakpoint_percentile_threshold=95\n",
+    ")\n",
+    "# ingestion\n",
+    "for doc in docs:\n",
+    "    _nodes = node_parser.build_semantic_nodes_from_documents(\n",
+    "        documents=doc,\n",
+    "    )\n",
+    "    nodes.extend(_nodes)\n",
+    "\n",
+    "    pipeline.run(nodes=_nodes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Synthetic Query Generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[TextNode(id_='7fc3876d-f73f-4b4e-8710-53923a6f6ef3', embedding=[0.0002587825874798, 0.021306069567799568, 0.01790156401693821, -0.02210138365626335, 0.0058183567598462105, 0.02769649401307106, -8.795501344138756e-06, -0.003264977131038904, -0.031338199973106384, -0.039430879056453705, 0.02178046852350235, 0.03580312803387642, -0.014127305708825588, 0.023873401805758476, 0.0033399739768356085, 0.011253009550273418, 0.017301589250564575, 0.0054451171308755875, 0.01838991418480873, -0.01890617236495018, -0.0030644044745713472, -0.02380363829433918, -0.025436125695705414, -0.012773874215781689, -0.003517873352393508, -0.0016778354765847325, 0.04827701300382614, -0.018655018880963326, -0.007332245819270611, -0.002000496257096529, 0.013415707275271416, -0.005291635170578957, -0.007192716933786869, -0.010471646673977375, -0.0005084085860289633, 0.018948029726743698, 0.0006897962302900851, -0.00768804457038641, 0.019143370911478996, -0.0163667444139719, 0.02378968521952629, 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'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f0f9267c-b964-422b-833a-42f6938bf154', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '1', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='5b1f3212d587e9ddbb9c98457483a2dc4b77023a3789de555c853505279e2ddf')}, text='DenseXRetrieval: What Retrieval Granularity Should We Use?\\nTong Chen♣∗Hongwei Wang♢Sihao Chen♡Wenhao Yu♢\\nKaixin Ma♢Xinran Zhao♠Hongming Zhang♢Dong Yu♢\\n♣University of Washington♢Tencent AI Lab\\n♡University of Pennsylvania♠Carnegie Mellon University\\nAbstract\\nDense retrieval has become a prominent\\nmethod to obtain relevant context or world\\nknowledge in open-domain NLP tasks. When\\nwe use a learned dense retriever on a retrieval\\ncorpus at inference time, an often-overlooked\\ndesign choice is the retrieval unit in which the\\ncorpus is indexed, e.g. document, passage, or\\nsentence. We discover that the retrieval unit\\nchoice significantly impacts the performance\\nof both retrieval and downstream tasks. Dis-\\ntinct from the typical approach of using pas-\\nsages or sentences, we introduce a novel re-\\ntrieval unit, proposition , for dense retrieval.\\nPropositions are defined as atomic expressions\\nwithin text, each encapsulating a distinct fac-\\ntoid and presented in a concise, self-contained\\nnatural language format. We conduct an empir-\\nical comparison of different retrieval granular-\\nity. Our results reveal that proposition-based\\nretrieval significantly outperforms traditional\\npassage or sentence-based methods in dense\\nretrieval. Moreover, retrieval by proposition\\nalso enhances the performance of downstream\\nQA tasks, since the retrieved texts are more\\ncondensed with question-relevant information,\\nreducing the need for lengthy input tokens and\\nminimizing the inclusion of extraneous, irrele-\\nvant information.\\n1 Introduction\\nDense retrievers are a popular class of techniques\\nfor accessing external information sources for\\nknowledge-intensive tasks (Karpukhin et al., 2020).\\nBefore we use a learned dense retriever to retrieve\\nfrom a corpus, an imperative design decision we\\nhave to make is the retrieval unit – i.e. the granu-\\nlarity at which we segment and index the retrieval\\n∗Work was done during internship at Tencent AI Lab,\\nBellevue.\\n/githubhttps://github.com/ct123098/\\nfactoid-wikiQuestion: What is the angle of the Tower of Pisa?\\nPassage\\nRetrievalPrior to restoration work performed be-\\ntween 1990 and 2001, the tower leaned at\\nan angle of 5.5 degrees, but thetower now\\nleans atabout 3.99 degrees. This means\\nthe top of the Leaning Tower of Pisa is dis-\\nplaced horizontally 3.9 meters (12 ft 10 in)\\nfrom the center.\\nSentence\\nRetrievalPrior to restoration work performed be-\\ntween 1990 and 2001, the tower leaned at\\nan angle of 5.5 degrees, but thetower now\\nleans atabout 3.99 degrees.\\nProposition\\nRetrievalThe Lean ingTower ofPisa now leans at\\nabout 3.99 degrees.\\nContriever GTR3040506070Recall@5 (%)Passage Retrieval\\nContriever GTR010203040EM@100 (%)Question AnsweringPassage Sentence Proposition\\nFigure 1: ( Top) An example of three granularities of\\nretrieval units of Wikipedia text when using dense re-\\ntrieval. ( Bottom ) We observe that retrieving by proposi-\\ntions yields the best retrieval performance in both pas-\\nsage retrieval task and downstream open-domain QA\\ntask, e.g. with Contriever (Izacard et al., 2022) or GTR\\n(Ni et al., 2022) as the backbone retriever. 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f0f9267c-b964-422b-833a-42f6938bf154', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '1', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='5b1f3212d587e9ddbb9c98457483a2dc4b77023a3789de555c853505279e2ddf')}, text='In practice, the choice of re-\\ntrieval unit, e.g. documents, fixed-length passage\\nchunks or sentences, etc, is usually pre-determined\\nbased on how the dense retrieval model is instanti-\\nated or trained (Lewis et al., 2020; Lee et al., 2021a;\\nSanthanam et al., 2022; Ni et al., 2022).\\nIn this paper, we investigate an overlooked re-\\nsearch question with dense retrieval inference – at\\nwhat retrieval granularity should we segment and\\nindex the retrieval corpus? We discover that se-\\nlecting the proper retrieval granularity at inference\\ntime can be a simple yet effective strategy for im-arXiv:2312.06648v2  [cs.CL]  12 Dec 2023', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='30def863-9c49-4e2c-b5c8-444dee5c2596', embedding=[0.013406731188297272, -0.0025405220221728086, -0.005960776936262846, -0.02823173813521862, -0.005010799039155245, 0.028552858158946037, -0.031121812760829926, 0.007185044232755899, -0.015386966988444328, -0.017795361578464508, 0.017929160967469215, 0.033556967973709106, -0.013681020587682724, 0.011767684482038021, 0.01328631117939949, 0.00404744129627943, -0.0005586137995123863, 0.015159507282078266, -0.000599171849898994, -0.007907562889158726, -0.011439874768257141, -0.002764636417850852, -0.03652732074260712, -0.013212720863521099, -0.01129938568919897, -0.023896627128124237, 0.03171053156256676, 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'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='e8b0bc3f3b1f4bbe1dfde088d5a7e583cbae7005dfe00beed170f68e0103fa13')}, text='1. Prior to restoration work performed between 1990 and 2001, the Leaning Tower of Pisa leaned at an angle of 5.5 degrees.2. The Leaning Tower of Pisa now leans at about 3.99 degrees.3. The top of the Leaning Tower of Pisa is displaced horizontally 3.9 meters (12 ft 10 in) from the center.Prior to restoration work performed between 1990 and 2001, the tower leaned at an angle of 5.5 degrees , // but the tower now leans at about 3.99 degrees. // This means the top of the Learning Tower of Pisa is displaced horizontally 3.9 meters (12 ft 10 in) from the center.WIkipedia\\nFactoidWiki\\n?\\n✓AnswerQueryQA ModelRetrieval Units\\nRetrieval Units\\nPassage RetrievalBC\\nDProposition-izer\\n?QueryCorpus\\nPassagesSentencesPropositionsRetrieval UnitsRetrieverAFigure 2: We discover that segmenting and indexing a retrieval corpus on the proposition level can be a simple yet\\neffective strategy to increase dense retrievers’ generalization performance at inference time (A, B) . 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-0.020161829888820648, -0.012692694552242756], metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='141406b1-9e50-4dae-a060-c0bb2438afd7', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '2', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='e8b0bc3f3b1f4bbe1dfde088d5a7e583cbae7005dfe00beed170f68e0103fa13')}, text='We empirically\\ncompare the retrieval and downstream open-domain QA tasks performance when dense retrievers work with\\nWikipedia indexed at the level of 100-word passage, sentence or proposition (C, D) .\\nproving dense retrievers’ retrieval and downstream\\ntask performance. We illustrate our intuition with\\nan example of open-domain question-answering\\n(QA) in Table 1. The example shows retrieved text\\nby the same model at three different granularities.\\nThepassage , which represents a coarser retrieval\\nunit with a longer context, is theoretically able to\\nprovide more relevant information for the ques-\\ntion. However, a passage often includes extraneous\\ndetails (e.g., restoration period and horizontal dis-\\nplacement in the example of Table 1) that could po-\\ntentially distract both the retriever and the language\\nmodel in downstream tasks (Shi et al., 2023; Yu\\net al., 2023b). On the other hand, sentence -level in-\\ndexing provides a finer-grained approach but does\\nnot entirely address the issue (Akkalyoncu Yilmaz\\net al., 2019; Yang et al., 2020). This is because sen-\\ntences can still be complex and compounded, and\\nthey are often not self-contained, lacking necessary\\ncontextual information (e.g., in the example of Ta-\\nble 1, “the tower” is coreference of “Pisa Tower”)\\nfor judging the query-document relevance.\\nTo address these shortcomings of typical re-\\ntrieval units such as passages or sentences, we\\npropose using proposition as a novel retrieval unit\\nfor dense retrieval. Propositions are defined as\\natomic expressions within text, each encapsulat-\\ning a distinct factoid and presented in a concise,\\nself-contained natural language format. 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-0.013767368160188198, -0.03333296999335289], metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='141406b1-9e50-4dae-a060-c0bb2438afd7', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '2', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='e8b0bc3f3b1f4bbe1dfde088d5a7e583cbae7005dfe00beed170f68e0103fa13')}, text='We show\\nan example proposition in Table 1. The proposi-\\ntion describes the information regarding the Tower\\nof Pisa’s current leaning angle in a self-contained\\nway and precisely responds to what the question\\nis querying. We provide a more detailed definition\\nand description of proposition in §2.To validate the efficacy of using proposition as\\na retrieval unit for dense retrievers inference, we\\nfirst process and index an English Wikipedia dump\\nwith all documents segmented into propositions,\\nwhich we refer to as FACTOID WIKI. Then we con-\\nduct experiments on five different open-domain QA\\ndatasets and empirically compare the performance\\nof six dual-encoder retrievers when Wikipedia is\\nindexed by passage, sentence, and our proposed\\nproposition. Our evaluation is twofold: we exam-\\nine both the retrieval performance and the impact\\non downstream QA tasks. Notably, our findings in-\\ndicate that proposition-based retrieval outperforms\\nsentence and passage-based methods, especially in\\nterms of generalization, as discussed in §5. This\\nsuggests that propositions, being both compact and\\nrich in context, enable dense retrievers to access\\nprecise information while maintaining adequate\\ncontext. The average improvement over passage-\\nbased retrieval of Recall@20 is +10.1 on unsu-\\npervised dense retrievers and +2.2 on supervised\\nretrievers. Furthermore, we observe a distinct ad-\\nvantage in downstream QA performance when us-\\ning proposition-based retrieval, as elaborated in §6.\\nGiven the often limited input token length in lan-\\nguage models, propositions inherently provide a\\nhigher density of question-relevant information.\\nOur main contributions in the paper are:\\n•We propose using propositions as retrieval units\\nwhen indexing a retrieval corpus to improve the\\ndense retrieval performance.\\n•We introduce FACTOID WIKI, a processed En-\\nglish Wikipedia dump, where each page is seg-\\nmented into multiple granularities: 100-word pas-\\nsages, sentences and propositions.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='cd18d159-63fc-429a-8ee7-c6623289f47c', embedding=[0.0026314205024391413, 0.008107159286737442, 0.015464916825294495, -0.023503949865698814, -0.011642971076071262, 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RelatedNodeInfo(node_id='9dd35c38-7a58-4f0c-8640-035588ef3181', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '3', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='9d9d4f826991fff6bc427e2d674017a933ae5befd05b8f195e6552e20841838f')}, text='•We discover that retrieval by proposition outper-\\nforms passage or sentence retrieval in terms of\\ngeneralization for passage retrieval and accuracy\\nfor downstream question-answering given the\\nsame input token limit.\\n2 Proposition as a Retrieval Unit\\nThe goal of our study is to understand how the gran-\\nularity of a retrieval corpus influences the dense\\nretrieval models’ performance empirically. Aside\\nfrom commonly-used retrieval units such as 100-\\nword passage (Karpukhin et al., 2020) or sentence,\\nwe propose using proposition as an alternative re-\\ntrieval unit choice. Here, propositions represent\\natomic expressions of meanings in text (Min et al.,\\n2023) that are defined by the three principles below.\\n1.Each proposition should correspond to a distinct\\npiece of meaning in text, where the composition\\nof all propositions would represent the seman-\\ntics of the entire text.\\n2.A proposition should be minimal , i.e. it cannot\\nbe further split into separate propositions.\\n3.A proposition should be contextualized and self-\\ncontained (Choi et al., 2021). A proposition\\nshould include all the necessary context from the\\ntext (e.g. coreference) to interpret its meaning.\\nThe use of proposition as a retrieval unit is inspired\\nby a recent line of work (Min et al., 2023; Kamoi\\net al., 2023; Chen et al., 2023a,b), which finds suc-\\ncess in representing and evaluating text semantics\\nat the level of propositions. We demonstrate the\\nconcept of proposition and how a passage can be\\nsplit into its set of propositions by an example on\\nthe left side of Figure 2. The passage contains\\nthree propositions, each of which corresponds to\\na distinct factoid about the Leaning Tower of Pisa :\\nthe angle before the restoration, the current an-\\ngle, and the horizontal displacement. Within each\\nproposition, necessary context from the passage is\\nincorporated so that the meaning of the proposition\\ncan be interpreted independently of the original\\ntext, e.g. ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='d6c9ba0d-8dc2-484d-8052-0ec23b9531c6', embedding=[0.022917799651622772, 0.0048320661298930645, 0.008166192099452019, -0.03617146611213684, -0.002529931953176856, 0.02420174889266491, -0.010313009843230247, -0.007848655804991722, -0.004438598174601793, -0.03600579500198364, 0.018693193793296814, 0.03219536691904068, 0.0015686957631260157, 0.01014733873307705, 0.0017136577516794205, 0.004952867981046438, 0.014758511446416378, -0.00797981210052967, 0.0008607117924839258, -0.018444687128067017, -0.002403952879831195, -0.026438305154442787, -0.0008300799527205527, -0.003983003087341785, 0.002585155423730612, -0.0038725559134036303, 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'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='9d9d4f826991fff6bc427e2d674017a933ae5befd05b8f195e6552e20841838f')}, text='the reference of the tower is resolved into\\nits full mention, the Leaning Tower of Pisa , in the\\nfirst proposition. We expect each proposition to de-\\nscribe exactly one contextualized atomic fact, and\\nso our intuition is that propositions would suitably\\nwork as a retrieval unit for information-seeking\\nquestions.3 F ACTOID WIKI: Proposition-Level\\nIndex and Retrieval for Wikipedia\\nWe empirically compare the use of 100-word pas-\\nsages, sentences, and propositions as retrieval units\\non Wikipedia, a commonly-used retrieval source\\nfor knowledge-intensive NLP tasks (Petroni et al.,\\n2021). To allow for a fair comparison across gran-\\nularities, we process an English Wikipedia dump\\nfrom 2021-10-13, as used by Bohnet et al. 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hash='9d9d4f826991fff6bc427e2d674017a933ae5befd05b8f195e6552e20841838f')}, text='(2022).\\nWe segment each document text into three different\\ngranularities: 100-word passages, sentences, and\\npropositions. We include the details on passage-\\nand sentence-level segmentation of the corpus in\\nAppendix A.\\nParsing Passage to Propositions. To segment\\nthe Wikipedia pages into propositions, we finetune\\na text generation model, which we refer to as the\\nPropositionizer . The Propositionizer takes a pas-\\nsage as input and generates the list of propositions\\nwithin the passage. Following Chen et al. (2023b),\\nwe train the Propositionizer with a two-step distil-\\nlation process. We first prompt GPT-4 (OpenAI,\\n2023) with an instruction containing the propo-\\nsition definition and 1-shot demonstrations. We\\ninclude the details of the prompt in Figure 8. We\\nstart with a set of 42k passages and use GPT-4 to\\ngenerate the seed set of paragraph-to-propositions\\npairs. Next, we use the seed set to finetune a Flan-\\nT5-large model (Chung et al., 2022).\\nWe refer to the processed corpus as FACTOID -\\nWIKI. The resulting statistics of FACTOID WIKIare\\nshown in Table 1.\\n# units Avg. # words\\nPassage 41,393,528 58.5\\nSentence 114,219,127 21.0\\nProposition 256,885,003 11.2\\nTable 1: Statistics of text units in the English Wikipedia\\ndump from 2021-10-13.\\n4 Experimental Settings\\nTo evaluate the impact of the three retrieval unit\\nchoices, we conduct experiments on five differ-\\nent open-domain QA datasets with FACTOID WIKI.\\nWith each dataset, we evaluate both passage re-\\ntrieval and downstream QA performance when\\ndense retrievers work with Wikipedia indexed in\\ndifferent granularities.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='4525756a-3534-4060-b180-5a4de64148d3', embedding=[0.008869639597833157, 0.006982050836086273, 0.02033725194633007, -0.03131097927689552, -0.01179912406951189, 0.02461308240890503, -0.004674997180700302, 0.011724703013896942, 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node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '4', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='1928bf5325134c57e92ba605a0122861090d316cd8df33428488226f27407fcf')}, text='4.1 Open-Domain QA Datasets\\nWe evaluate on five different open-domain QA\\ndatasets with Wikipedia as the retrieval source:\\nNatural Questions (NQ) (Kwiatkowski et al.,\\n2019), TriviaQA (TQA) (Joshi et al., 2017),\\nWeb Questions (WebQ) (Berant et al., 2013),\\nSQuAD (Rajpurkar et al., 2016), and Entity Ques-\\ntions (EQ) (Sciavolino et al., 2021).\\n4.2 Dense Retrieval Models\\nWe compare the performance of the six following\\nsupervised or unsupervised dense retriever mod-\\nels. 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-0.021271206438541412, -0.011081406846642494], metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='1a9766fe-9b47-4a7e-b166-1ced85aa7e89', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '4', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='1928bf5325134c57e92ba605a0122861090d316cd8df33428488226f27407fcf')}, text='Here, supervised models refer to ones that\\nhave used human-labeled query-passage pairs as\\nsupervision during training, and vice versa.\\n•SimCSE (Gao et al., 2021) is a BERT-base (De-\\nvlin et al., 2019) encoder trained on unlabeled\\nsentence randomly sampled from Wikipedia.\\n•Contriever (Izacard et al., 2022) is an unsuper-\\nvised retriever, instantiated with a BERT-base\\nencoder. Contriever is contrastively trained by\\nsegment pairs constructed from unlabeled docu-\\nments from Wikipedia and web crawl data.\\n•DPR (Karpukhin et al., 2020) is a dual-encoder\\nBERT-base model finetuned on five open-domain\\nQA datasets, which includes four of the datasets\\n(NQ, TQA, WebQ and SQuAD) in our evalua-\\ntion.\\n•ANCE (Xiong et al., 2020) used the same setting\\nfrom DPR and trained the model with Approxi-\\nmate nearest neighbor Negative Contrastive Esti-\\nmation (ANCE), a hard negatives-based training\\napproach.\\n•TAS-B (Hofstätter et al., 2021) is a dual-\\nencoder BERT-base model distilled from a\\nteacher model with cross-attention trained on MS\\nMARCO (Nguyen et al., 2016).\\n•GTR (Hofstätter et al., 2021) is a T5-base en-\\ncoder (Raffel et al., 2020) pretrained on unla-\\nbeled pairs of online forum QA data, and fine-\\ntuned on MS MARCO and Natural Question.\\n4.3 Passage Retrieval Evaluation\\nWe evaluate retrieval performance at the passage\\nlevel when the corpus is indexed at the passage,\\nsentence, or proposition level respectively. 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='1a9766fe-9b47-4a7e-b166-1ced85aa7e89', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '4', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='1928bf5325134c57e92ba605a0122861090d316cd8df33428488226f27407fcf')}, text='For sen-\\ntence and proposition level retrieval, we follow the\\nsetting introduced in Lee et al. (2021b), where the\\nscore of the passage is based on the maximum sim-\\nilarity score between the query and all sentencesor propositions in a passage. In practice, we first\\nretrieve a slightly larger number of text units, map\\neach unit to the source passage, and return the top- k\\nunique passages. We use Recall@ kas our evalu-\\nation metric, which is defined as the percentage\\nof questions for which the correct answer is found\\nwithin the top- kretrieved passages.\\n4.4 Downstream QA Evaluation\\nTo understand the implications of using different\\nretrieval units on the downstream open-domain\\nQA tasks, we evaluate the use of retrieval mod-\\nels in retrieve-then-read setup (Izacard and Grave,\\n2021). With the retrieve-then-read setting, a re-\\ntrieval model first retrieves ktext units given the\\nquery. The kretrieved text units are then used as\\ninput along with the query to a reader model to\\nderive the final answer. Typically, the choice of\\nkis subject to the reader model’s maximum input\\nlength constraint, or the limit of compute budget,\\nwhich scales with the number of input tokens.\\nFor this reason, we follow an evaluation setup\\nwhere the maximum number of retrieved words\\nis capped at l= 100 or500, i.e. only the top l\\nwords from passage, sentence, or proposition level\\nretrieval are feed into the reader model as input. We\\nevaluate the percentage of questions for which the\\npredicted answer exactly matches (EM) the ground\\ntruth. We denote our metric as EM @ l. For our\\nevaluation, we use T5-large size UnifiedQA-v2 as\\nthe reader model (Khashabi et al., 2022).\\n5 Results: Passage Retrieval\\nIn this section, we report and discuss the retrieval\\ntasks performance. Our results show that despite\\nnone of the models being trained with proposition-\\nlevel data, all the retrieval models demonstrated\\non-par or superior performance when the corpus is\\nindexed at the proposition level.\\n5.1 Passage Retrieval Performance\\nWe report our evaluation results in Table 2. We\\nobserve that retrieval by propositions outperforms\\nretrieval by sentence or passage on most tasks for\\nboth unsupervised and supervised retrievers.\\nWith all dense retrievers tested, proposition-\\nlevel retrieval consistently outperforms sentence\\nand passage-level retrieval on average across the\\nfive datasets. With the unsupervised retrievers, i.e.\\nSimCSE and Contriever, we see an averaged Re-\\ncall@5 improvement of +12.0and+9.3(35.0%', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='734fd952-e539-4831-abd1-9d4b8c40c70e', embedding=[-0.0028304224833846092, 0.020411530509591103, 0.02135794796049595, -0.0276862271130085, -0.008482438512146473, 0.023236654698848724, -0.0016226813895627856, -0.0031800316646695137, -0.05116301774978638, -0.03856296092271805, 0.007691403850913048, 0.034042760729789734, -0.006533102132380009, 0.011561824940145016, 0.00706634484231472, 0.013327528722584248, 0.012522367760539055, 0.005727941635996103, 0.013433470390737057, -0.03299746289849281, 0.010121011175215244, 0.011794897727668285, -0.040427543222904205, -0.017487525939941406, -0.0006219689967110753, 0.009598363190889359, 0.01938035897910595, -0.020129017531871796, -0.015326304361224174, 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hash='018012a71a381235ffdc3c821575951155eb76fdb835ac822f91de982c8c00ef')}, text='Retriever GranularityNQ TQA WebQ SQuAD EQ Avg.\\nR@5 R@20 R@5 R@20 R@5 R@20 R@5 R@20 R@5 R@20 R@5 R@20\\nUnsupervised Dense Retrievers\\nSimCSE Passage 28.8 44.3 44.9 59.4 39.8 56.0 29.5 45.5 28.4 40.3 34.3 49.1\\nSentence 35.5 53.1 50.5 64.3 45.3 64.1 37.1 52.3 36.3 50.1 40.9 56.8\\nProposition 41.1 58.9 52.4 66.5 50.0 66.8 38.7 53.9 49.5 62.2 46.3 61.7\\nContriever Passage 42.5 63.8 58.1 73.7 37.1 60.6 40.8 59.8 36.3 56.3 43.0 62.8\\nSentence 46.4 66.8 60.6 75.7 41.7 63.1 45.1 63.5 42.7 61.3 47.3 66.1\\nProposition 50.1 70.0 65.1 77.9 45.9 66.8 50.7 67.7 51.7 70.1 52.7 70.5\\nSupervised Dense Retrievers\\nDPR Passage 66.0 78.0 71.6 80.2 62.9 74.9 38.3 53.9 47.5 60.4 57.3 69.5\\nSentence 66.0 78.0 71.8 80.5 64.1 74.4 40.3 55.9 53.7 66.0 59.2 71.0\\nProposition 65.4 77.7 70.7 79.6 62.8 75.1 41.4 57.2 59.4 71.3 59.9 72.2\\nANCE Passage 70.7 81.4 73.9 81.4 65.7 77.2 43.3 58.6 57.0 69.1 62.1 73.5\\nSentence 70.3 81.6 73.9 81.5 65.2 77.4 45.8 60.7 61.4 72.8 63.3 74.8\\nProposition 69.9 81.1 72.8 80.6 65.1 77.1 46.2 61.9 66.7 76.6 64.1 75.5\\nTAS-B Passage 64.2 77.9 70.4 79.3 65.1 77.0 54.3 69.2 72.2 81.3 65.2 76.9\\nSentence 64.0 78.4 71.4 80.2 63.9 76.7 58.9 72.3 72.7 82.0 66.2 77.9\\nProposition 63.8 78.6 71.4 80.0 63.8 76.8 59.8 73.4 75.1 83.3 66.8 78.4\\nGTR Passage 66.3 78.4 70.1 79.4 63.3 76.5 54.4 68.1 71.7 80.5 65.2 76.6\\nSentence 66.4 79.4 71.6 80.9 62.2 76.8 60.9 73.4 72.5 81.3 66.7 78.4\\nProposition 66.5 79.6 72.2 80.9 63.2 77.4 63.3 75.0 74.9 83.0 68.0 79.2\\nTable 2: Passage retrieval performance (Recall@ k= 5, 20) on five different open-domain QA datasets when\\npre-trained dense retrievers work with the three different granularity from the retrieval corpus. 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'last_accessed_date': None}, hash='018012a71a381235ffdc3c821575951155eb76fdb835ac822f91de982c8c00ef')}, text='101102103\\nPopularity204060Recall@5\\nSimCSE\\n101102103\\nPopularity204060Recall@5\\nContriever\\n101102103\\nPopularity405060Recall@5\\nDPR\\n101102103\\nPopularity40506070Recall@5\\nANCE\\n101102103\\nPopularity50607080Recall@5\\nTAS-B\\n101102103\\nPopularity50607080Recall@5\\nGTRPassage Sentence Proposition\\nFigure 3: Document retrieval recall vs. the popularity of the target entity in each question from the EntityQuestions\\ndataset. The popularity of each entity (i.e. smaller value ⇒less common entities, and vice versa) is estimated by\\nthe occurrence of the entity in its top-1000 passage retrieved by BM25. On queries with less common entities, we\\nobserve that retrieving by proposition shows a larger advantage over retrieval by proposition.\\nand 22.5% relative improvement) respectively over\\nfive datasets.\\nWith the supervised retrievers, proposition-level\\nretrieval still shows an advantage on average, yet\\nthe sizes of improvements are smaller. We hypothe-\\nsize that this is due to these retrievers being trained\\non query-passage pairs. For instance, with DPR\\nand ANCE, which have been trained on NQ, TQA,\\nWebQ, and SQuAD, we observe that proposition\\nand sentence level retrieval perform slightly worse\\ncompared to passage level on three out of the four\\ndatasets, with the exception of SQuAD. As shown\\nin Table 2, all supervised retrievers demonstrate\\ncomparable performance across three levels of re-\\ntrieval granularity in NQ, TQA, and WebQ.\\nHowever, on datasets that the retriever model has\\nnotseen during training, we observe that retrievalby proposition demonstrates a clear advantage. For\\ninstance, most notably on SQuAD or EntityQues-\\ntions, we observe that proposition-based retrieval\\nsignificantly outperforms the other two granulari-\\nties. We see 17-25% Recall@5 relative improve-\\nment on EntityQuestions with relatively weak re-\\ntrievers like DPR and ANCE. Furthermore, the\\nRecall@5 of retrieval by proposition on SQuAD\\nimproved most on TAS-B and GTR, with 10-16%\\nrelative improvements.\\n5.2 Retrieval by Proposition ⇒Better\\nCross-Task Generalization\\nOur results indicate that the advantage of retrieval\\nby proposition becomes most visible in cross-\\ntask generalization settings. We observe that on\\nSQuAD and EntityQuestions, retrieval by proposi-', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='7f12db15-67f1-4c23-8f75-f869b40e0ec1', embedding=[-0.0009645023965276778, 0.008141971193253994, 0.014426511712372303, -0.022121582180261612, -0.019775353372097015, 0.028029050678014755, 0.003896415000781417, -0.004720387980341911, -0.044606272131204605, -0.03868483752012253, 0.006350877229124308, 0.0331544429063797, -0.010907169431447983, -0.006584801711142063, -0.007639207877218723, 0.006015701685100794, 0.008267662487924099, 0.006497516296803951, 0.000718794297426939, -0.02579454705119133, 0.017135847359895706, 0.007632225286215544, -0.027749737724661827, -0.019049139693379402, 0.006075055804103613, 0.008288610726594925, 0.016674980521202087, -0.01568342000246048, -0.0177922323346138, 0.0019185305573046207, 0.005481516011059284, 0.0011888255830854177, 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text='Retriever GranularityNQ TQA WebQ SQuAD EQ Avg.\\nEM EM EM EM EM EM\\n@100 @500 @100 @500 @100 @500 @100 @500 @100 @500 @100 @500\\nUnsupervised Dense Retrievers\\nSimCSE Passage 8.1 16.3 22.6 33.7 7.7 14.9 9.8 17.8 10.9 17.5 11.8 20.0\\nSentence 10.1 18.0 27.2 37.2 9.6 15.6 17.3 24.8 13.0 19.8 15.4 23.1\\nProposition 12.7 20.2 28.4 37.7 11.2 17.2 18.0 25.1 18.3 25.0 17.7 25.0\\nContriever Passage 11.1 22.4 25.7 41.4 6.8 14.9 15.6 27.7 10.9 21.5 14.0 25.6\\nSentence 13.8 23.9 30.5 44.2 9.1 17.2 22.6 32.8 12.2 22.2 17.6 28.1\\nProposition 16.5 26.1 37.7 48.7 13.3 19.9 25.6 34.4 16.1 27.3 21.8 31.3\\nSupervised Dense Retrievers\\nDPR Passage 24.8 36.1 40.3 51.0 14.0 22.2 12.4 21.7 18.6 25.9 22.0 31.4\\nSentence 27.6 35.9 44.6 52.8 16.3 23.7 18.6 26.1 21.8 28.2 25.8 33.3\\nProposition 28.3 34.3 45.7 51.9 19.0 23.8 19.8 26.3 26.3 31.9 27.8 33.6\\nANCE Passage 27.1 38.3 43.1 53.1 15.2 23.0 15.3 26.0 23.4 31.1 24.8 34.3\\nSentence 30.1 37.3 47.0 54.7 16.6 23.8 22.9 30.5 25.9 32.0 28.5 35.7\\nProposition 29.8 37.0 47.4 53.5 19.3 24.1 22.9 30.1 29.1 33.7 29.7 35.7\\nTAS-B Passage 21.1 33.9 39.3 50.5 13.1 20.7 23.9 34.6 30.9 37.3 25.7 35.4\\nSentence 24.6 33.9 43.6 52.3 14.4 21.4 33.8 40.5 31.4 36.1 29.6 36.8\\nProposition 26.6 34.0 44.9 51.8 18.1 23.7 34.2 38.9 34.2 37.8 31.6 37.2\\nGTR Passage 23.4 34.5 38.7 49.3 13.1 20.1 23.9 33.8 31.3 36.7 26.1 34.9\\nSentence 26.8 35.1 43.9 52.2 15.9 21.6 35.6 41.3 31.3 35.1 30.7 37.1\\nProposition 29.5 34.4 45.9 52.6 18.7 23.8 37.0 40.4 34.1 37.1 33.0 37.7\\nTable 3: Open-domain QA performance (EM = Exact Match) under retrieve-then-read setting where the number of\\nretrieved words to the reader QA model is limited at l= 100 or500. We use UnifedQA V2 (Khashabi et al., 2022)\\nas the reader model. 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metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='0e004335-5d12-4b35-9d4b-97a20f20d1e9', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '6', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf', 'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='b1611f44ac87d4ea86479b8f4ac78374a979e6c56c3c01a17d77da386e21e5a0')}, text='The first lwords from the concatenated top retrieved text unit are feed as input to the reader\\nmodel. Underline denotes cases where the training split of the target dataset was included in the training data of the\\ndense retriever. In most cases, we see better QA performance with smaller retrieval units.\\ntion brings more performance gain over retrieval\\nby passage.\\nTo better understand where the improvements\\ncan be attributed, we conduct an additional analysis\\non EntityQuestions. As EntityQuestions features\\nquestions targeting the properties of longer-tail enti-\\nties, we study how the retrieval performance under\\nthree different granularities is affected by the popu-\\nlarity of the target entity in question, i.e. whether\\nthe entity appears frequently in Wikipedia or not.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='be4d00fd-5a15-4726-9738-7c09c39d51a2', embedding=[-0.0034460308961570263, 0.006676031742244959, 0.010871199890971184, -0.03531746193766594, -0.017867792397737503, 0.023233704268932343, -0.013470531441271305, 0.0035679833963513374, -0.03765895217657089, -0.032223351299762726, 0.012536723166704178, 0.029909737408161163, -0.006839796435087919, 0.009700454771518707, -0.0016655235085636377, 0.023373078554868698, 0.0243068877607584, 0.0011811977019533515, 0.006310173776000738, -0.017143046483397484, 0.006073237396776676, 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'file_type': 'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='b1611f44ac87d4ea86479b8f4ac78374a979e6c56c3c01a17d77da386e21e5a0')}, text='We estimate the popularity of each entity with the\\nfollowing method. Given the surface form of an en-\\ntity, we use BM25 to retrieve the top-1000 relevant\\npassages from Wikipedia. We use the number of\\noccurrences of the entity in its top-1000 passages\\nas an estimate of its popularity. With the 20,000\\ntest queries in EntityQuestion, around 25% of the\\ntarget entities have a popularity value of less or\\nequal to 3.\\nFigure 3 shows the passage retrieval perfor-\\nmance vs. the popularity of the target entity in\\neach question. Across all 6 dense retrievers, we ob-\\nserve that retrieving by proposition shows a much\\nlarger advantage over retrieving by passage with\\nquestions targeting less common entities. As the\\npopularity of entities increases, the performancegap decreases. Our findings indicate that the per-\\nformance gain from retrieval by proposition can\\nmostly be attributed to queries for long-tailed infor-\\nmation. This echoes our observation that retrieval\\nby proposition improves the cross-task generaliza-\\ntion performance of dense retrievers.\\n6 Results: Open-Domain QA\\nIn this section, we study how the choice of retrieval\\ngranularity affects downstream open-domain QA\\ntasks. We show that retrieval by proposition leads\\nto strong downstream QA performance in the\\nretrieve-then-read setting, where the number of re-\\ntrieved tokens for input to the reader QA model is\\ncapped at l= 100 or500words.\\n6.1 QA Performance\\nTable 3 shows the evaluation results. Across dif-\\nferent retrievers, we observe higher QA perfor-\\nmance in terms of the EM@ lmetric on average\\nwhen using propositions as the retrieval unit. The\\nunsupervised retrievers, SimCSE and Contriever,\\ndemonstrate improvements of +5.9 and +7.8 in\\nthe EM@100 score (50% and 55% relative im-\\nprovement), respectively. The supervised retriev-\\ners, DPR, ANCE, TAS-B, and GTR, improve +5.8,', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='b012b845-fbaf-4e78-ba29-49e0895c3f1d', embedding=[0.0006340122199617326, 0.006230080034583807, 0.00833611749112606, -0.03342573344707489, -0.014884473755955696, 0.016062771901488304, 0.009047159925103188, -0.023200275376439095, -0.034698836505413055, -0.023565955460071564, 0.013232148252427578, 0.04661725088953972, -0.02097911760210991, -0.0024039982818067074, 0.004513422027230263, 0.02933555096387863, 0.02061343938112259, 0.009676939807832241, 0.021087465807795525, -0.027927011251449585, 0.010408296249806881, -0.00385994091629982, -0.014572969637811184, -0.014315640553832054, 0.007963667623698711, 0.0012798751704394817, 0.021222902461886406, -0.015128259547054768, -0.015751266852021217, 0.00025098081096075475, 0.017728639766573906, 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text='0 200 400\\n#Words506070Recall (%)GTR / NQ\\n0 200 400\\n#Words6070Recall (%)GTR / TQA\\n0 200 400\\n#Words506070Recall (%)GTR / WebQ\\n0 200 400\\n#Words405060Recall (%)GTR / SQuAD\\n0 200 400\\n#Words607080Recall (%)GTR / EQPassage Sentence PropositionFigure 4: Recall of the gold answer in the retrieved text limited to first kwords for the GTR retriever. Finer-grained\\nretrieval has a higher recall across all numbers of words.\\n+4.9, +5.9, and +6.9 EM@100 (26%, 19%, 22%,\\n26% relative improvement), respectively. Similar\\nto our observations from passage retrieval evalu-\\nations, we find retrieval by proposition becomes\\nmore beneficial to downstream QA performance\\nwhen the retriever has not been trained on the target\\ndataset. In other cases, retrieval by proposition still\\nholds an advantage, but with a smaller margin on\\naverage.\\n6.2 Propositions ⇒Higher Density of\\nQuestion-Related Information\\nIntuitively, compared to sentences or passages as\\nretrieval units, the advantage of propositions is that\\nthe retrieved propositions have a higher density\\nof relevant information to the query. With finer-\\ngrained retrieval units, the correct answer to the\\nquery would more likely appear in the top- lre-\\ntrieved words by a dense retriever.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='f46009f3-c11f-424a-878f-07629b7108e7', embedding=[0.0053114695474505424, 0.020414577797055244, 0.014459189958870411, -0.026574382558465004, -0.00486515648663044, 0.018970021978020668, 0.004067924804985523, -0.02241787686944008, -0.046253055334091187, -0.0303356796503067, 0.015563049353659153, 0.04058385267853737, -0.022240715101361275, 0.00823124498128891, 0.0038975761272013187, 0.016530629247426987, 0.027133125811815262, 0.026751546189188957, 0.020101135596632957, -0.02623368613421917, -0.004105401691049337, -0.01597188599407673, -0.028482288122177124, -0.0112157529219985, 0.00679350271821022, 0.0037374484818428755, 0.033306561410427094, -0.028455032035708427, 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hash='9396ea4b6f9a053b9469d4bcc24a6510bd66b1806bc8c4e67b1102eced19f6ca')}, text='We illustrate this phenomenon by an analysis\\nshown in Figure 4. Here, we investigate the posi-\\ntion at which the ground truth answer appears in\\nthe top- lretrieved words. Specifically, we calcu-\\nlate the recall of the gold answer within the initial l\\nretrieved words with GTR working with Wikipedia\\nindexed in three different granularities.\\nWe show the results in Figure 4 and Figure 7 with\\nlranging from 0 to 500 across all five datasets. For\\na fixed retrieved word budget, proposition retrieval\\ndemonstrates a higher success rate compared to\\nsentence and passage retrieval methods. The most\\nsignificant improvement of proposition retrieval\\nover passage retrieval occurs within the range of\\n100-200 words, which corresponds to roughly 10\\npropositions, 5 sentences, or 2 passages. As the\\nword count further increases, the recall rates of\\nthe three granularity converge since all question-\\nrelevant information is included in the retrieved\\ntext.6.3 Error Case Study\\nTo understand the source of errors from each type\\nof retrieval granularity, we present and discuss four\\ntypical examples of mistakes in Table 4. With each\\nexample, we show the question and its correspond-\\ning top-1 retrieved text unit by the GTR retriever\\nacross the three granularities.\\nWe observe that with passage-level retrieval, the\\nambiguity of an entity or its references presents a\\nchallenge for dense retrievers, which echoes find-\\nings from (Min et al., 2020). For instance, in exam-\\nple Q1, the question asks for “ Super Bowl 50 ”, but\\nthe retrieved passage and sentence refers to “ Super\\nBowl 5 ”. In Example Q2, passage retrieval fails\\nto identify the part referring to the correct “ atomic\\nnumber ”. Instead, the top-1 retrieved passage men-\\ntions “ atomic number ” in a different and irrelevant\\ncontext to the question. Retrieval by sentences can\\nalso have a similar problem as retrieval by passages\\nlike Example Q1. Also, retrieval by sentences faces\\nanother challenge of lacking context. In Example\\nQ3, sentence-based retrieval fails as the correct sen-\\ntence in the retrieved passage uses “ it” to refer to\\nthe pericardial sac.\\nRetrieval by propositions tackles the aforemen-\\ntioned problems by ensuring each retrieval unit\\ncontains one piece of fact only and necessary con-\\ntext is incorporated in the propositions. However,\\nproposition-based retrieval faces challenges with\\nquestions that involve multi-hop reasoning over\\nlong-range textual analysis. In Example Q4, the\\nretrieved passage separately describes the actor’s\\nname and the character they portray. There is not\\na single proposition that entails both the question\\nand the answer.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='0ee0b6e3-50d7-41b6-acdf-d71cd63a8165', embedding=[-0.03645169362425804, 0.003004427533596754, 0.004107233136892319, -0.031236588954925537, 0.012808081693947315, 0.018131272867321968, -0.008221221156418324, 0.012524358928203583, -0.022535739466547966, -0.04290977865457535, 0.026778079569339752, 0.04215317964553833, 0.006208136677742004, 0.009856007993221283, -0.017807016149163246, 0.0069917538203299046, -0.0006299333763308823, 0.006907312199473381, 0.004934759344905615, -0.014767124317586422, -0.015050847083330154, -0.012206858955323696, -0.0182123351842165, -0.03855935111641884, 0.00785643421113491, -0.011294891126453876, 0.03464126959443092, -0.02599446102976799, -0.0003804088046308607, -0.008687338791787624, 0.01571286842226982, 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Work\\nRecent works on dense retrievers typically adopt\\na dual-encoder architecture (Yih et al., 2011;\\nReimers and Gurevych, 2019; Karpukhin et al.,\\n2020; Ni et al., 2022). 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Sentence Retrieval Proposition Retrieval\\nQ1: What was the theme of Super Bowl 50?\\nTitle: Super Bowl X ✗\\nThe overall theme of the Super Bowl enter-\\ntainment was to celebrate the United States\\nBicentennial. Each Cowboys and Steelers\\nplayer wore a special patch with the Bicen-\\ntennial logo on their jerseys...Title: Super Bowl X ✗\\nThe overall theme of the Super Bowl\\nentertainment was to celebrate the\\nUnited States Bicentennial.Title: Super Bowl XLV ✓\\n... As this was the 50th Super Bowl game,\\nthe league [SuperBowl 50] emphasized\\nthe\"golden anniver sary\" with various gold-\\nthemed initiatives during the 2015 season, as\\nwell as...\\nQ2: The atomic number of indium which belongs to 5th period is?\\nTitle: Period 5 element ✗\\nThe periodic table is laid out in rows to illus-\\ntrate recurring (periodic) trends in the chemi-\\ncal behaviour of the elements as their atomic\\nnumber increases: ...Title: Period 5 element ✓\\nIndium is a chemical element with the\\nsymbol In and atomic number 49.Title: Period 5 element ✓\\nIndium is a chemical element with the sym-\\nbol In and [Indium has a] atomic number 49.\\nThis rare, very soft, malleable ...\\nQ3: What is the function of the pericardial sac?\\nTitle: Pericardium ✓\\nThe pericardium, also called pericardial sac\\n... It separates the heart from interference of\\nother structures, protects itagainst infection\\nandblunt trauma, andlubricates theheart’s\\nmovements.Title: Pericardium ✗\\nThe pericardium, also called pericar-\\ndial sac, is a double-walled sac con-\\ntaining the heart and the roots of the\\ngreat vessels.Title: Cardiac muscle ✓\\nOn the outer aspect of the myocardium is the\\nepicardium which forms part of thepericar-\\ndialsacthatsurrounds, protects, andlubri-\\ncates theheart.\\nQ4: What is the main character’s name in layer cake?\\nTitle: Layer Cake (film) ✓\\n... The film’s plot revolves around a London-\\nbased criminal, played by Daniel Craig, ...\\nCraig’s character is unnamed in the film and\\nis listed in the credits as \"XXXX\" .Title: Angelic Layer ✗\\nThe primary protagonist is Misaki\\nSuzuhara.Title: Plot twist ✗\\nSometimes the audience may discover that\\nthe true identity of a character is , in fact,\\nunknown [in Layer Cake] , as in Layer Cake\\nor the eponymous assassins in V for Vendetta\\nand The Day of the Jackal.\\nTable 4: Example cases where top-1 retrieved text unit of each retrieval granularity fails to provide the correct\\nanswer. 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text is the correct answer. The gray text is the context of propositions, but it is for illustration\\npurpose only and not provided to the retrievers and downstream QA models.\\neach query and document is encoded into a low-\\ndimensional feature vector respectively, and their\\nrelevance is measured by a non-parametric similar-\\nity function between the embedding vectors (Muss-\\nmann and Ermon, 2016). Due to the limited expres-\\nsivity from the similarity function, dual encoder\\nmodels often generalize poorly to new tasks with\\nscarce training data (Thakur et al., 2021). To this\\nend, previous studies use techniques such as data\\naugmentation (Wang et al., 2022; Yu et al., 2023a;\\nIzacard et al., 2022; Gao and Callan, 2022; Lin\\net al., 2023; Dai et al., 2023), continual pre-training\\n(Chang et al., 2020; Sachan et al., 2021; Oguz et al.,\\n2022), task-aware training (Xin et al., 2022; Cheng\\net al., 2023), hybrid sparse-dense retrieval (Luan\\net al., 2021; Chen et al., 2022) or mixed strategy re-\\ntrieval (Ma et al., 2022, 2023) and so on to improve\\ncross-task generalization performance of dense re-\\ntrievers.\\nThe motivation of our work echoes in part with\\nmulti-vector retrieval, e.g. ColBERT (Khattab and\\nZaharia, 2020), DensePhrase (Lee et al., 2021a,b),\\nME-BERT (Luan et al., 2021), MVR (Zhang et al.,\\n2022), where the retrieval model learns to encodea candidate retrieval unit into multiple vectors to\\nincrease model expressivity and improve retrieval\\ngranularity (Seo et al., 2019; Humeau et al., 2019).\\nOur work instead focuses on the setting where we\\ndo not update the dense retriever model or its pa-\\nrameters. We show that segmenting the retrieval\\ncorpus into finer-grained units of proposition can\\nbe a simple and orthogonal strategy for improving\\nthe generalization of dual encoders dense retrievers\\nat inference time.\\nThe idea of using propositions as a unit of text\\nrepresentation dates back to the Pyramid method\\nin summarization evaluation (Nenkova and Passon-\\nneau, 2004), where model generated summary is\\nevaluated by each proposition. Proposition extrac-\\ntion from text has been a long-standing task in NLP,\\nwith earlier formulations focusing on a structured\\nrepresentation of propositions, e.g. Open Infor-\\nmation Extraction (Etzioni et al., 2008), Semantic\\nRole Labeling (Gildea and Jurafsky, 2000). More\\nrecent studies have found success in extracting\\npropositions in natural language form via few-shot\\nprompting with large language models (Min et al.,\\n2023; Kamoi et al., 2023), or finetuning smaller', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='4c065caf-cdca-4118-8715-0c54b73ffe9d', embedding=[-0.014725388027727604, 0.017516199499368668, 0.011794334277510643, -0.032283660024404526, 0.0015128583181649446, 0.030881242826581, -0.009024559520184994, -0.005753419362008572, -0.03377022221684456, -0.04557858034968376, 0.007664213888347149, 0.03472386673092842, -0.00463849725201726, 0.0006026014452800155, -0.0055675990879535675, -0.00037668069126084447, 0.010854714550077915, 0.0032536096405237913, 0.007488911505788565, -0.02778189815580845, 0.0077413469552993774, -0.008582797832787037, -0.02443012036383152, -0.01136659737676382, 0.00926297064870596, 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'application/pdf', 'file_size': 1103758, 'creation_date': '2024-02-25', 'last_modified_date': '2023-12-13', 'last_accessed_date': None}, hash='ba68c5dea85255f379f37c7dfa21981d34f553629d0d9c49f58b055c073f9f13')}, text='compact-sized models (Chen et al., 2023b).\\nRetrieve-then-read , or more broadly – retrieval\\naugmented generation, has recently merged as a\\npopular paradigm for open-domain question an-\\nswering (Lewis et al., 2021; Jiang et al., 2023;\\nAsai et al., 2023). While earlier works provide\\nup to the top 100 retrieved passages for the down-\\nstream reader (Izacard and Grave, 2021; Kedia\\net al., 2022), the amount of allowed context is\\nsignificantly reduced when using recent large lan-\\nguage models (e.g. top 10) (Touvron et al., 2023;\\nYu et al., 2023b), due to their limited context win-\\ndow length and inability to reason over long con-\\ntext (Liu et al., 2023). Recent efforts have tried to\\nimprove the quality of the reader context by filter-\\ning or compressing the retrieved documents (Wang\\net al., 2023; Xu et al., 2023). Our work offers a\\nnew perspective by leveraging a new retrieval unit,\\nthe proposition that not only reduces the context\\nlength but also offers greater information density,\\neffectively addressing the issue.\\n8 Conclusion\\nWe propose the use of propositions as retrieval units\\nfor indexing corpus to improve dense retrieval per-\\nformance at inference time. Through our experi-\\nments on five open-domain QA datasets with six\\ndifferent dense retrievers, we discovered that re-\\ntrieval by proposition outperforms passage or sen-\\ntence in both passage retrieval accuracy and down-\\nstream QA performance with a fixed retrieved word\\nbudget. We introduce FACTOID WIKI, an indexed\\nversion of the English Wikipedia dump, where text\\nfrom 6 million pages is segmented into 250 million\\npropositions. We hope that FACTOID WIKI, along\\nwith our findings in the paper, will facilitate future\\nresearch on information retrieval.\\nLimitations\\nThe scope of our current study on the granular-\\nity of retrieval corpus has the following limita-\\ntions. (1) Retrieval Corpus – Our study only focus\\non Wikipedia as the retrieval corpus, due to the\\nfact that most open-domain QA datasets adopts\\nWikipedia as the retrieval corpus. (2) Types of\\ndense retrievers evaluated – In the current version\\nof the paper, we only evaluate on 6 types of popular\\ndense retrievers, most of which follow bi- or dual-\\nencoder architecture. In future versions, we will\\ninclude and discuss results on a broader range of\\ndense retrievers. (3) Language – Our current studyis limited to English Wikipedia only. We leave the\\nexploration on other languages to future work.\\nReferences\\nZeynep Akkalyoncu Yilmaz, Wei Yang, Haotian Zhang,\\nand Jimmy Lin. 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Semantic parsing on freebase from\\nquestion-answer pairs. In Proceedings of the 2013\\nconference on empirical methods in natural language\\nprocessing , pages 1533–1544.\\nBernd Bohnet, Vinh Q Tran, Pat Verga, Roee Aharoni,\\nDaniel Andor, Livio Baldini Soares, Jacob Eisenstein,\\nKuzman Ganchev, Jonathan Herzig, Kai Hui, et al.\\n2022. Attributed question answering: Evaluation and\\nmodeling for attributed large language models. arXiv\\npreprint arXiv:2212.08037 .\\nWei-Cheng Chang, X Yu Felix, Yin-Wen Chang, Yim-\\ning Yang, and Sanjiv Kumar. 2020. Pre-training tasks\\nfor embedding-based large-scale retrieval. In Inter-\\nnational Conference on Learning Representations .\\nSihao Chen, Senaka Buthpitiya, Alex Fabrikant, Dan\\nRoth, and Tal Schuster. 2023a. PropSegmEnt: A\\nlarge-scale corpus for proposition-level segmentation\\nand entailment recognition. In Findings of the As-\\nsociation for Computational Linguistics: ACL 2023 ,\\npages 8874–8893, Toronto, Canada. Association for\\nComputational Linguistics.\\nSihao Chen, Hongming Zhang, Tong Chen, Ben Zhou,\\nWenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang,\\nDan Roth, and Dong Yu. 2023b. 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'2023-12-13', 'last_accessed_date': None}, hash='ba68c5dea85255f379f37c7dfa21981d34f553629d0d9c49f58b055c073f9f13')}, text='Xilun Chen, Kushal Lakhotia, Barlas Oguz, Anchit\\nGupta, Patrick Lewis, Stan Peshterliev, Yashar\\nMehdad, Sonal Gupta, and Wen-tau Yih. 2022.\\nSalient phrase aware dense retrieval: Can a dense\\nretriever imitate a sparse one? In Findings of the\\nAssociation for Computational Linguistics: EMNLP\\n2022 , pages 250–262, Abu Dhabi, United Arab Emi-\\nrates. Association for Computational Linguistics.\\nHao Cheng, Hao Fang, Xiaodong Liu, and Jianfeng\\nGao. 2023. Task-aware specialization for efficient\\nand robust dense retrieval for open-domain question', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='b98981f1-616d-4ed3-b6e6-d305dd73bacd', embedding=[-0.0030695877503603697, 0.00523564824834466, 0.014442693442106247, -0.03696560114622116, 0.008197018876671791, 0.014277791604399681, -0.006118562538176775, 0.022481681779026985, -0.020186791196465492, -0.03990635648369789, 0.0023155033122748137, 0.023264966905117035, -0.014181598089635372, 0.012257738038897514, 0.006008627824485302, 0.0039061233401298523, 0.01636655442416668, -0.010849197395145893, -0.01041632890701294, -0.007695441134274006, -0.008471855893731117, -0.008856628090143204, -0.01504733506590128, -0.007716053631156683, -0.014470177702605724, 0.007386249024420977, 0.03512419015169144, -0.026123274117708206, -0.035646382719278336, 0.0038236721884459257, 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hash='4c1e0bb3a13f4c656b05edcf75196711b7a56cd8e5b0ce1e76f1c2caf19a570e')}, text='LLaMA Beyond English: An Empirical Study on Language Capability Transfer\\nJun Zhao*, Zhihao Zhang*, Luhui Gao, Qi Zhang†, Tao Gui, Xuanjing Huang\\n1School of Computer Science, Fudan University\\n{zhaoj19,zhangzhihao19,qz,tgui }@fudan.edu.cn\\nAbstract\\nIn recent times, substantial advancements have been wit-\\nnessed in large language models (LLMs), exemplified by\\nChatGPT, showcasing remarkable proficiency across a range\\nof complex tasks. However, many mainstream LLMs (e.g.\\nLLaMA) are pretrained on English-dominant corpus, which\\nlimits their performance in other non-English languages.\\nIn this paper, we focus on how to effectively transfer the\\ncapabilities of language generation and following instruc-\\ntions to a non-English language. To answer this question,\\nwe conduct an extensive empirical investigation based on\\nLLaMA, accumulating over 1440 GPU hours. We analyze\\nthe impact of key factors such as vocabulary extension,\\nfurther pretraining, and instruction tuning on transfer. To\\naccurately assess the model’s level of knowledge, we employ\\nfour widely used standardized testing benchmarks: C-Eval,\\nMMLU, AGI-Eval, and GAOKAO-Bench. Furthermore, a\\ncomprehensive evaluation of the model’s response quality is\\nconducted, considering aspects such as accuracy, fluency, in-\\nformativeness, logical coherence, and harmlessness, based on\\nLLM-Eval, a benchmarks consisting instruction tasks from\\n17 diverse categories. Our evaluation results demonstrate that\\ncomparable performance to state-of-the-art transfer models\\ncan be achieved with less than 1%of the pretraining data,\\nboth in terms of knowledge alignment and response quality.\\nFurthermore, the experimental outcomes across the thirteen\\nlow-resource languages also exhibit similar trends. We antic-\\nipate that the conclusions revealed by the experiments will\\naid the community in developing non-English LLMs.\\nIntroduction\\nFor decades, researchers in Natural Language Processing\\n(NLP) have been exploring the fundamental principles of\\nintelligence (Bubeck et al. 2023). The recent advances in\\nlarge language models (LLMs) seem to have revealed a\\nglimmer of hope. Benefitting from the unprecedented scales\\nof model size and training data, many LLMs like ChatGPT\\n(OpenAI 2022), PaLM (Anil et al. 2023), LLaMA (Touvron\\net al. 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hash='4c1e0bb3a13f4c656b05edcf75196711b7a56cd8e5b0ce1e76f1c2caf19a570e')}, text='2023a), and others have emerged strong capabilities\\nin reasoning (Cobbe et al. 2021), planning (Huang et al.\\n2022), and learning from experience (Dong et al. 2023)\\nat or surpassing human levels. These general capabilities\\nalso provide a foundation for LLMs to address intricate\\n*These authors contributed equally.\\n†Corresponding Author\\nVocabulary  \\nExtensionFurther  \\nPretrainingInstruction\\nTuning\\nVocabulary  \\nExtensionFurther  \\nPretrainingInstruction\\nTuning\\nVocabulary  \\nExtensionFurther  \\nPretrainingInstruction\\nTuningLLaMA\\nEnglishLLaMA\\nChineseLLaMA\\nHindi\\nLLaMA\\nArabicFigure 1: Pretrained LLaMA models, which are primarily\\ntrained on English-dominated corpus (as depicted on the\\nleft), are not inherently proficient in handling non-English\\nlanguages. We aim to investigate the necessity of vocabulary\\nextension, further pretraining, and instruction tuning, as\\nwell as to what extent they influence the capability\\ntransfer. This exploration enables us to efficiently transfer\\nLLaMA’s language capabilities to non-English languages\\n(as illustrated on the right), minimizing costs in the process.\\nreal-world tasks, such as successfully completing the entire\\nUniform Bar Examination (UBE) (Katz et al. 2023) or\\ncoding based on natural language instructions (StabilityAI\\n2023).\\nMany well-known LLMs are capable of comprehending\\ninput and generating responses across different languages,\\nthanks to their pretraining on a diverse mix of corpus\\nfrom multiple languages. However, due to the imbalanced\\ndistribution of language resources, collecting extensive\\ntraining data for all languages is nearly impossible (Ranta\\nand Goutte 2021). Taking the representative LLM BLOOM\\n(Scao et al. 2023) as an example, it has been pretrained on\\n46 natural languages. Yet, this number accounts for only\\n0.66% of the roughly 7,000 languages currently in use.\\nMoreover, within the corpus of these 46 languages, there\\nexists extreme imbalance, with the high-resource English\\ntexts being 2.8 million times more than that of the low-\\nresource Chitumbuka language. This is not an isolated case.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='36633fb0-5c90-44f7-a556-33b646c34387', embedding=[-0.007932406850159168, 0.0179782435297966, 0.009524524211883545, -0.018922243267297745, 0.002976413816213608, 0.011010969989001751, -0.0319550596177578, 0.021331554278731346, -0.009179331362247467, -0.028559483587741852, 0.02048618160188198, 0.015188517048954964, -0.006946139968931675, 0.009461121633648872, -0.010785537771880627, -0.015540755353868008, 0.017597826197743416, -0.014392457902431488, 0.003899278352037072, -0.008700286969542503, 0.011292761191725731, 0.0006410737405531108, -0.019795794039964676, -0.006903871428221464, 0.0028725035954266787, 0.018668631091713905, 0.009461121633648872, -0.029813451692461967, -0.007460407912731171, -0.012694669887423515, 0.010574194602668285, -0.01572391949594021, -0.02513572759926319, 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hash='7ef086c22d20757226b9a957dbd2a70c9559f5360de5856e647208344563671f')}, text='been pretrained primarily on English-dominated corpus,\\nsupplemented with limited data from 20 related languages\\nthat utilize the Latin and Cyrillic scripts. As a result,\\nLLaMA exhibits inferior performance in contexts involving\\nnon-English languages where it has not undergone suffi-\\ncient training. Some researchers collect large-scale data for\\nspecific languages of interest and retrain an LLM (Team\\n2023a). However, this inevitably leads to high computational\\nand data collection costs, which is not suitable for low-\\nresource languages. While Cui, Yang, and Yao (2023b)\\nextend original vocabulary and further pretrain LLaMA with\\n30B Chinese tokens by LoRA (Hu et al. 2021), reporting\\npromising results. 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='337f3e70-1044-4c26-b98b-3fe8505a71b3', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '2', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_type': 'application/pdf', 'file_size': 1719588, 'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='7ef086c22d20757226b9a957dbd2a70c9559f5360de5856e647208344563671f')}, text='Nonetheless, a fine-grained systematic\\ninvestigation of the transfer process remains lacking.\\nIn this work, we take a step towards gaining a compre-\\nhensive understanding of the language capability transfer\\nin LLMs. As shown in figure 1, we empirically investigate\\nseveral key aspects based on LLaMA:\\n(1)The impact of vocabulary extension on transfer.\\nWe find that further pretraining with 0.5 billion Chinese\\ntokens on the original vocabulary significantly outperforms\\nperformance on the extended vocabulary, even though the\\nlatter has been further pretrained on over 30 billion tokens.\\nThis suggests that vocabulary extension might not be a\\nsuitable choice for small-scale incremental pretraining in the\\norder of tens of billions.\\n(2)Training scales required for effective transfer. We\\nfind that further Chinese pretraining with 100 billion tokens\\nor fewer is insufficient to significantly improve LLaMA’s\\nknowledge level. However, enhancing LLaMA’s response\\nquality (i.e., language generation capability), requires only\\nhundreds of thousands of instruction data rather than a large-\\nscale further pretraining.\\n(3)The effect of transfer training on the original English\\ncapabilities. We find that exclusive reliance on Chinese cor-\\npora for transfer training markedly compromises LLaMA’s\\noriginal English proficiency, a concern alleviated effectively\\nthrough multilingual joint training.\\nThe aforementioned findings enable us to transfer\\nLLaMA’s capabilities of language generation and following\\ninstructions to non-English languages at minimal cost.\\nBased on evaluation results from four widely used\\nstandardized testing benchmarks (C-Eval, GAOKAO-\\nBench, MMLU, AGI-Eval) and an instruction evaluation\\nbenchmark LLM-Eval, we achieve comparable knowledge\\nlevel and response quality to the state-of-the-art Open\\nChinese LLaMA, while using less than 1%of the training\\ndata. Furthermore, extension experiments on another 13\\nlow-resource languages also exhibit similar trends. We\\naim for the experimental results and analyses in this paper\\nto provide assistance and guidance to the community in\\nconstructing non-English LLMs.\\nBackground and Overview\\nIn this subsection, we firstly present the essential steps to\\ndevelop an instruction-following LLM. Subsequently, we\\nreview common practices of extrapolating this model to\\na non-English language and provide an overview of our\\nempirical research conducted for the model extrapolation.Step 1: Pretraining to acquire language capability\\nand knowledge\\nAs a significant source of foundational capabilities for a\\nLLM, pretraining aims to predict the next token based on\\nthe prefix sequences. Formally, given a large corpus D, the\\ntraining objective is to minimize the following loss:\\nLpretrain =X\\nx∈DX\\nilogpθ(xi|x1, ..., x i−1), (1)\\nwhere x={x1, ..., x n}denotes an input token sequence.\\nBy pretraining on massive text data ranging from billions\\nto trillions of tokens, LLMs are capable of capturing\\nintricate language structures, semantics, and contextual\\nrelationships, thereby acquiring strong language generation\\ncapabilities. Additionally, these LLMs also learn how to\\ncomprehend concepts, facts, and the connections between\\nthem, leading to a broad understanding of world knowledge.\\nStep 2: Instruction tuning for aligning with human\\nintent\\nInstruction tuning (SFT) aims to further enhance the capabil-\\nity of LLMs to follow instructions. Its training data consists\\nof many instruction-response pairs. The model needs to\\nlearn to accurately respond to instructions, rather than\\nmerely continuing from the preceding text. Formally, given\\nan instruction dataset D′={(I, Y)}, where Irepresents\\na task instruction and Yrepresents a desired response, the\\ntraining objective of instruction tuning is to minimize the\\nfollowing loss:\\nLins=−logpθ(Y|I), (2)\\nBy tuning on diverse instruction tasks, the model is able\\nto better comprehend and follow human instructions, and\\ngeneralize to unseen instructions.\\nExtrapolating LLMs to non-English languages\\nLLMs acquire language generation and instruction-\\nfollowing capabilities through pretraining and instruction\\ntuning. 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metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='337f3e70-1044-4c26-b98b-3fe8505a71b3', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '2', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_type': 'application/pdf', 'file_size': 1719588, 'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='7ef086c22d20757226b9a957dbd2a70c9559f5360de5856e647208344563671f')}, text='However, English holds a dominant position in the\\nfield of natural language processing, possessing the most\\nabundant collection of text data from various domains.\\nLLMs trained on English-dominant corpora exhibit inferior\\nperformance on other non-English languages. Extrapolating\\nLLMs to non-English languages poses a highly valuable\\nresearch challenge. Common extrapolation approaches\\nconsist of the following three steps: (1) extending the\\nvocabulary to add tokens of the target language, and\\nthus enhancing encoding expressiveness to that language.\\n(2) further pretraining to transfer language generation\\ncapabilities of LLMs to the target language. The required\\ntraining scale for this step is generally on the order of\\nbillions of tokens, significantly less than the trillions of\\ntokens needed for training from scratch. (3) conducting\\nSFT in the target language to transfer instruction-following\\ncapabilities of LLMs.\\nThis paper conducts a comprehensive empirical study of\\nthe aforementioned three steps, comparing the performance\\ndifferences of LLMs before and after vocabulary extension,', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='c8e64074-53fd-4d74-9e39-f2f2cd4467ff', embedding=[0.003314453875645995, 0.0070917136035859585, 0.021184667944908142, -0.02288278192281723, -0.002726377686485648, 0.008629758842289448, -0.008873341605067253, 0.0006054750410839915, -0.011726729571819305, -0.031261999160051346, 0.009179558604955673, 0.023035889491438866, -0.0070325578562915325, 0.0005363152013160288, 0.003504099790006876, -0.007011679466813803, 0.02476184070110321, -0.011504026129841805, -0.012144298292696476, -0.011016862466931343, -0.022214671596884727, 0.0011309160618111491, 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'application/pdf', 'file_size': 1719588, 'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='6d8bc2f997620f5bb0790c1ce7f08f0b0e931cbc01061563847f4b3ee1f31838')}, text='and under various pretraining and SFT scales. It analyzes the\\nnecessity of vocabulary extension and the required training\\nscale for effective transfer.\\nExperimental Setup\\nThis paper aims to explore how to effectively transfer the\\ncapabilities of language generation and following instruc-\\ntion to a non-English language. Given the rich linguistic\\nresources available in Chinese, comprehensive and in-\\ndepth empirical research can be conducted. Therefore, our\\nexperiments and analyses commence with Chinese as the\\nstarting point, and the observed phenomena are further\\nvalidated across over ten low-resource languages. In this\\nsection, we present the datasets, models, and evaluation\\nmethodology employed in our experiments.\\nModels\\nTo avoid unnecessary large-scale repetitive pretraining, we\\nemployed open-source models trained on varying scales of\\nChinese corpora. Among these, LLaMA and LLaMA2 serve\\nas checkpoints without undergoing explicit Chinese pre-\\ntraining, whereas Chinese LLaMA and Chinese LLaMA2\\nare treated as checkpoints with Chinese pretraining of 30\\nbillion tokens. The scale reaches 100 billion tokens for\\nOpen Chinese LLaMA. We employ the performance of these\\nmodels as references for analysis and comparison.\\nLLaMA (Touvron et al. 2023a): LLaMA is a series of foun-\\ndation models developed by Meta AI, trained on publicly\\navailable English-dominate corpus. The corpus includes\\nCommonCrawl, C4, Github code, Wikipedia, Books, and\\nArXiv papers, amounting to approximately 1.4 trillion\\ntokens. 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multi-\\nlingual text, contributing 4.5% of the total corpus. It covers\\n20 languages that use either the Latin or Cyrillic scripts.\\nLLaMA achieves state-of-the-art results for foundation\\nmodels of its size. For example, LLaMA-13B with just\\n13 billion parameters outperforms the much larger 175B\\nparameter GPT-3 on many NLP benchmarks. We consider\\nLLaMA-7B and LLaMA-13B in our experiments.\\nLLaMA2 (Touvron et al. 2023b): LLaMA2 is an enhanced\\nand upgraded version of LLaMA. The upgrades it has\\nreceived compared to its predecessor include a more robust\\ndata cleaning process, a new mix of publicly available\\npretraining data boasting a 40% increase in size, a doubled\\ncontext length for improved comprehension, and the imple-\\nmentation of grouped-query attention for the efficiency of\\ninference. These improvements make it a more powerful\\ntool for tackling advanced language understanding tasks. We\\nconsider LLaMA2-7B in our experiments.\\nChinese LLaMA (Cui, Yang, and Yao 2023b): Chinese\\nLLaMA is an extension of the original LLaMA, designed\\nto enhance its capability in understanding and generating\\nChinese text. The goal is achieved by integrating a Chi-\\nnese tokenizer developed using SentencePiece. 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tok-\\nenizer, with a vocabulary size of 49,953, enables improved\\nhandling of Chinese characters. In addition, it employs\\nparameter-efficient fine-tuning techniques (Hu et al. 2021)\\nto reduce memory consumption during model training. Inour experiments, we consider Chinese LLaMA 7B Plus,\\nwhich is trained on a corpus of approximately 120GB in\\nsize, equivalent to around 30 billion Chinese tokens.\\nChinese LLaMA2 (Cui, Yang, and Yao 2023a): Chinese\\nLLaMA2 is an advanced iteration of Chinese LLaMA.\\nIt utilizes the same corpus and training data as Chinese\\nLLaMA, but employs the foundational model of LLaMA2.\\nFurthermore, the construction of the new version’s vocabu-\\nlary and its code implementation have also been optimized.\\nIn our experiments, we consider Chinese LLaMA2 7B\\npretrained on 30 billion Chinese tokens.\\nOpen Chinese LLaMA (OpenLMLab 2023): Open Chinese\\nLLaMA is a larger-scale extended version of the original\\nLLaMA. To enhance the LLaMA’s capabilities of handling\\nChinese text, Open Chinese LLaMA undergoes further\\npretraining on a corpus comprising 100 billion tokens. The\\ncorpus is composed of texts collected from the internet and\\nsubjected to cleaning, along with a subset of English and\\ncode data used by the original LLAMA model.\\nDatasets\\nTo transfer the language capabilities of LLaMA to the\\nnon-English language of interest, we utilize two instruction\\ndatasets, namely BELLE and Bactrain-X, for training. The\\nformer is employed in experiments related to Chinese,\\nwhile the latter is utilized for experiments involving other\\nlanguages.\\nBELLE (Ji et al. 2023): BELLE is a large-scale Chi-\\nnese instruction tuning dataset developed by Lianjia Tech.,\\ncontaining 1.5 million instruction-following example. We\\nremoved duplicated and low-quality data, finally retaining\\n950,000 examples.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='2661d9d3-5429-4d3f-b454-ba8e4c8c2d31', embedding=[-0.01691625639796257, 0.011374479159712791, 0.017810320481657982, -0.006020739674568176, 0.006996404379606247, 0.02250770293176174, -0.015568064525723457, -0.011949234642088413, -0.013716074638068676, -0.03976455330848694, -0.0024427101016044617, 0.02053508535027504, -0.021954234689474106, 0.0062832823023200035, -0.0002762905787676573, 0.03581932187080383, 0.007400861941277981, 0.004509346093982458, -0.007819510996341705, 0.002854263409972191, -0.003249851055443287, 0.015894468873739243, -0.003514167619869113, -0.003450305899605155, -0.01133190467953682, -0.003923946991562843, 0.01734200119972229, -0.023032788187265396, -0.018888872116804123, -0.00857166014611721, 0.0014253577683120966, 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hash='6d8bc2f997620f5bb0790c1ce7f08f0b0e931cbc01061563847f4b3ee1f31838')}, text='Bactrain-X (Li et al. 2023): Bactrian-X contains instruc-\\ntions and responses across 52 languages to facilitate mul-\\ntilingual instruction tuning. It is created by translating\\n67K English instructions from Alpaca-52k (Taori et al.\\n2023) and Dolly-15k (Conover et al. 2023) datasets into\\n51 languages, then generating responses with ChatGPT.\\nIn order to objectively and comprehensively assess the\\ncapabilities of the model, we conduct evaluations from two\\nperspectives: response quality and knowledge level. For the\\nformer, we employ the LLM-Eval benchmark and translate\\nit into various low-resource languages to support multi-\\nlingual evaluation. As for the latter, we utilize four widely\\nadopted standardized testing benchmarks: C-Eval, MMLU,\\nAGI-Eval, and GAOKAO-Bench.\\nLLM-Eval (Zhang et al. 2023a): LLM-Eval is a manually\\nconstructed benchmark for instruction-following evaluation.\\nIt has 453 instruction tasks from 17 major categories, in-\\ncluding factual question answering, reading comprehension,\\nframe generation, paragraph rewriting, summarizing, math\\nproblem solving, reasoning, poetry generation, program-\\nming, and more.\\nC-Eval (Huang et al. 2023b): C-Eval is a Chinese evaluation\\nsuite with 13948 exam questions across 52 subjects and 4\\ndifficulty levels from middle school to professional exams. It\\nincludes STEM, humanities, social science and other topics.\\nC-Eval HARD is a subset of 8 challenging math and science\\nsubjects requiring advanced reasoning.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='20ed036d-e2d6-48d1-a439-1bf4e0c11b6b', embedding=[-0.00480399327352643, 0.006598548498004675, -0.0013146765995770693, -0.02836577594280243, -0.010788158513605595, 0.01229461282491684, -0.016022566705942154, -0.01921597309410572, 0.003070182166993618, -0.030573390424251556, 0.028324121609330177, 0.016466867178678513, -0.009587160311639309, -0.010808984749019146, -0.0003553532005753368, -0.0057932548224925995, 0.012023868039250374, -0.00473804259672761, 0.01092700194567442, 0.003703656606376171, 0.006376398727297783, 0.02688014693558216, -0.0162586010992527, -0.01073262095451355, -0.0008187441853806376, 0.006924831308424473, 0.013967679813504219, 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f2d3cbe5-fa54-48be-ae94-874e7bd3197a', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '4', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_type': 'application/pdf', 'file_size': 1719588, 'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='734dbbbcf139212d56384e0358b44344d6d672ec16dde4515f21b2da28b93cc2')}, text='1k SFTLLaMA (Touvron et al. 2023a) 0.482 1.194 0.858 0.614 2.970 1.224\\nLLaMA with 10Kpretrain 0.482 1.441 0.829 0.712 2.963 1.285\\nLLaMA with 100Kpretrain 0.587 1.952 0.881 0.991 2.973 1.477\\nLLaMA with 1Mpretrain 0.735 2.071 1.002 1.046 2.957 1.562\\nChinese LLaMA (Cui, Yang, and Yao 2023b) 0.509 1.205 0.811 0.726 2.970 1.244\\nOpen Chinese LLaMA (OpenLMLab 2023) 1.406 2.584 1.685 1.877 2.989 2.108\\n5k SFTLLaMA (Touvron et al. 2023a) 0.450 1.279 0.767 0.612 3.000 1.199\\nLLaMA with 10Kpretrain 0.411 1.372 0.814 0.612 2.961 1.258\\nLLaMA with 100Kpretrain 0.488 1.922 0.876 0.977 3.000 1.493\\nLLaMA with 1Mpretrain 0.682 2.085 1.039 1.008 2.969 1.623\\nChinese LLaMA (Cui, Yang, and Yao 2023b) 0.581 1.341 0.899 0.783 2.992 1.432\\nOpen Chinese LLaMA (OpenLMLab 2023) 1.295 2.481 1.667 1.884 2.969 2.245\\n950k SFTLLaMA (Touvron et al. 2023a) 1.783 2.767 2.142 2.212 2.993 2.379\\nLLaMA with 1Mpretrain 1.812 2.799 2.080 2.303 3.000 2.399\\nLLaMA-EXT with 1Mpretrain 1.591 2.726 1.918 2.164 2.998 2.279\\nChinese LLaMA (Cui, Yang, and Yao 2023b) 1.808 2.795 2.112 2.313 3.000 2.406\\nOpen Chinese LLaMA (OpenLMLab 2023) 1.890 2.858 2.189 2.390 2.993 2.464\\nLLaMA2 (Touvron et al. 2023b) 1.868 2.822 2.171 2.379 3.000 2.448\\nChinese LLaMA2 (Cui, Yang, and Yao 2023a) 1.701 2.838 2.011 2.251 3.000 2.360\\nTable 1: Response quality with different scales of further pretraining and instruction tuning (SFT). ACC., F., LC., H., INFO., and\\nA VG. 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f2d3cbe5-fa54-48be-ae94-874e7bd3197a', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '4', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_type': 'application/pdf', 'file_size': 1719588, 'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='734dbbbcf139212d56384e0358b44344d6d672ec16dde4515f21b2da28b93cc2')}, text='respectively denote accuracy, fluency, logical coherence, harmlessness, informativeness and their average. Approximately\\n1 million samples account for around 0.5 billion tokens. The pretraining scales for Chinese LLaMA and Open Chinese LLaMA\\nare 30 billion and 100 billion tokens, respectively.\\nMMLU (Hendrycks et al. 2020): MMLU measures a LLM’s\\nability to learn and apply knowledge across 57 diverse\\nsubjects including STEM, humanities, and social sciences.\\nThe test covers a wide range of difficulty levels from\\nelementary to advanced professional.\\nAGI-Eval (Zhong et al. 2023): AGIEval uses questions from\\nstandardized tests taken by millions of people, including\\ncollege entrance exams, law school admission tests, and\\nprofessional qualification exams. It has 19 tasks in both\\nEnglish and Chinese.\\nGaokao-Bench (Zhang et al. 2023b): GAOKAO-Bench\\nuses 2811 exam questions from Chinese college entrance\\nexams (Gaokao) from 2010-2022 covering all subjects. It\\nhas 1781 multiple choice, 218 fill-in-blank, and 812 open-\\nended questions across math, Chinese, English, physics, etc.\\nEvaluation Protocol\\nFor LLM-Eval, we followed the practice of Zhang et al.\\n(2023a), evaluating the response quality of a model through\\n5 scoring items: accuracy, fluency, informativeness, logical-\\nity, and harmlessness. Scores for each aspect range from 0\\nto 3. We use the prompt shown in Appendix to submit the\\ninstruction, model response, and reference answer to GPT-4\\nfor automated evaluation. Based on the results reported by\\nZhang et al. (2023a), this evaluation method demonstrates a\\nhigh degree of consistency with human evaluation.\\nFor the four standardized testing benchmarks, we calcu-\\nlate the accuracy metric for model responses. Additionally,\\nwe follow the common practice of employing a zero-shot\\nsetting for AGI-Eval and GAOKAO-Bench, while using a5-shot setting for C-Eval and MMLU.\\nMain Results\\nThe Impact of Vocabulary Extension on Transfer\\nWhen we aim to enhance the capabilities of a LLM\\nin a specific language, vocabulary extension is an intu-\\nitively reasonable approach. In this section, we evaluate\\nthe impact of vocabulary extension through the LLM-\\nEval benchmark, and the experimental results are presented\\nin table 1. Initially, we collected one million Chinese\\nsentences from the internet (approximately 0.5 billion\\ntokens) and further pretrain the original LLaMA without\\nvocabulary extension. Surprisingly, we find that this model\\nsignificantly ourperform the vocabulary-extended Chinese\\nLLaMA, across settings of 1K, 5K, and 950K instruction\\ntuning. This discovery is thought-privoking, given that the\\nChinese LLaMA underwent further Chinese pretraining on\\n30 billion tokens, a much larger volume than our 0.5 billion\\ntokens. Moreover, within the 950K setting, we include\\nresults from extending the vocabulary on original LLaMA\\nand training it with the same 0.5 billion tokens, to mitigate\\nthe influence of training data discrepancy. The outcomes\\nremain consistent. This indicates that vocabulary extension\\nis not a favorable choice within training scales of tens of\\nbillions of tokens. While we don’t negate the efficacy of\\nvocabulary extension in settings involving larger-scale pre-\\ntraining (such as trillions of tokens), as reported in other\\nliteratures (Team 2023b), this already leans more towards\\nretraining than mere language transfer.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='b316c5e3-b776-47da-8d63-ba510e173633', embedding=[0.005710112862288952, -0.0018865012098103762, 0.031217144802212715, -0.017744630575180054, -0.006579195149242878, 0.02003425918519497, -0.0017224577022716403, -0.005741525441408157, -0.02912297286093235, -0.0363827720284462, 0.005137705709785223, 0.019266394898295403, -0.0166696198284626, 0.0011622662423178554, -0.007511102128773928, 0.005957923363894224, 0.01574818417429924, 0.006306952331215143, -0.0024501830339431763, -0.010233527980744839, -0.021556025370955467, -0.006181302014738321, -0.03646653890609741, 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'application/pdf', 'file_size': 1719588, 'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='025b0b1d8c7199c2fd5c98a97bc1c92e8d5e596b743770820003522d1f8d8442')}, text='CEval GAOKAO-Bench MMLU AGIEval0.0010.0020.0030.0040.0050.00LLaMA-7B\\nChinese-LLaMA-Plus-7B\\nOpen-Chinese-LLaMA-7BLLaMA-2-7B\\nChinese-LLaMA-2-7BLLaMA-13B\\nChinese-LLaMA-Plus-13BFigure 2: Knowledge-level evaluation results on four benchmarks.\\nTraining Scales Required for Effective Transfer\\nTraining scale constitutes another significant factor influ-\\nencing the transferability of LLM capabilities, composed\\nof both pretraining scale and instruction tuning scale.\\nExperimental results are shown in table 1. Taking the\\nexample of LLaMA (with 10K, 100K, and 1M further\\npretrain) and Open Chinese LLaMA, the scale of further\\nChinese pretraining gradually increases from 0 to 100\\nbillion tokens. Under the settings of 1K and 5K instruction\\ntuning, we observed that the response quality improves\\nprogressively with the increase in the scale of further\\npretraining.1However, when the instruction tuning data\\nscale escalates to 950K, we find no significant differences\\nin response quality among the models. Consequently, we\\nhypothesize that more further pretraining could accelerate\\nthe model’s alignment with human instructions, but the mere\\ntens of billions in training scale are insufficient to enable the\\nmodel to grasp a greater amount of world knowledge. This\\nleads to their convergence at similar response levels. In other\\nwords, the enhancement in response quality primarily stems\\nfrom an improvement in language generation prowess rather\\nthan an elevation in knowledge level.\\nTo validate this standpoint, we evaluated the model’s\\nknowledge level on four widely used standardized test\\nbenchmarks. As shown in Figure 2, LLaMA 7B, Chinese\\nLLaMA 7B, and Open Chinese LLaMA 7B perform com-\\nparably on C-eval, gaokao-bench, and agi-eval, indicating no\\nsignificant differences induced by further Chinese pretrain-\\ning. It is worth noting that despite lacking further pretraining\\nin Chinese, both LLaMA2-7B and LLaMA-13B outperform\\nOpen Chinese LLaMA on C-eval, MMLU, and AGI-Eval,\\nsuggesting that trillion-level pretraining and larger model\\nsizes may indeed serve as effective pathways for enhancing\\nmodel knowledge levels.\\n1Chinese-LLaMA, however, stands as an exception due to the\\nadditional factor of vocabulary extension.L(0) L(10k) L(100k) L(1M) Open\\nChinese 10.151 8.697 6.634 5.249 3.924\\nEnglish 14.691 15.625 29.553 198.840 15.045\\nTable 2: Model perplexity with different further pretraining\\nscales. L denotes LLaMA, with the number in the\\nparentheses indicating the quantity of further pretraining\\nsamples. 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hash='025b0b1d8c7199c2fd5c98a97bc1c92e8d5e596b743770820003522d1f8d8442')}, text='Open denotes Open Chinese LLaMA.\\nHow about the Original English Capabilities\\nAnother issue of interest to us is whether the improvement\\nin Chinese proficiency has an impact on the existing\\nEnglish capabilities. To address this question, we addition-\\nally collected 200,000 Chinese samples from the internet\\nand randomly extracted 200,000 English samples from\\nthe refinedweb dataset (Penedo et al. 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hash='025b0b1d8c7199c2fd5c98a97bc1c92e8d5e596b743770820003522d1f8d8442')}, text='2023). Utilizing\\nthese samples, we evaluate the English perplexity and\\nChinese perplexity of LLaMA models trained on different-\\nscale corpora, as depicted in table 2. Our findings reveal\\nthat with the increase in further pretraining scale, the\\nperplexity of the models decreases steadily in Chinese, yet\\nnotably increases in English. This suggests that enhancing\\nthe model’s capabilities solely through a single Chinese\\ncorpus comes at the cost of sacrificing the original English\\nproficiency.\\nFurthermore, we conduct perplexity assessments for Open\\nChinese LLaMA and find that both the Chinese and English\\nperplexities remain low. This outcome is unsurprising, given\\nthat its training data incorporates both Chinese and English\\ncontent, allowing for the decreases of Chinese perplexity\\nwithout significant elevation in English perplexity. Overall,\\nexclusive reliance on Chinese corpora for transfer training\\nmarkedly compromises LLaMA’s original English profi-\\nciency, a concern alleviated effectively through multilingual\\njoint training.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='02e72d2c-2f11-4f35-9ad8-3bbf975f67b8', embedding=[0.017094474285840988, 0.014285712502896786, -0.011214889585971832, -0.022550730034708977, -0.020225774496793747, 0.00866146944463253, -0.02908211015164852, -0.0021317696664482355, -0.0020141780842095613, -0.014514177106320858, 0.03400080278515816, 0.01763203740119934, -0.02158311754465103, -0.005671279970556498, 0.011772610247135162, 0.00021649473637808114, 0.008076870813965797, -0.025157906115055084, 0.005933341570198536, -0.00751914968714118, -0.003104421077296138, 0.013775029219686985, -0.027980107814073563, -0.012316891923546791, 0.009151995182037354, 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='3ac729ef-5982-4179-a326-408649c87dbf', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '6', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llama_beyond_english.pdf', 'file_type': 'application/pdf', 'file_size': 1719588, 'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='ee6d6781356797b955c3f3d8da48931145b6648fa61117a5dc9e04759dd7ad47')}, text=\"H. A VG.\\nArbic 0.188 1.061 0.191 0.254 3.000 0.939 1.268 2.499 1.529 1.607 3.000 1.981\\nBengali 0.046 0.492 0.050 0.041 3.000 0.726 0.959 2.257 1.156 1.189 3.000 1.712\\nGujarati 0.061 0.426 0.052 0.063 2.998 0.720 0.683 1.795 0.875 0.790 2.995 1.428\\nHindi 0.131 1.064 0.147 0.162 3.000 0.901 1.014 2.342 1.238 1.240 2.998 1.766\\nIndonesian 0.398 1.266 0.544 0.438 2.995 1.128 1.659 2.751 2.026 2.012 3.000 2.290\\nMalayalam 0.101 0.621 0.103 0.103 3.000 0.786 0.906 2.427 1.182 1.197 3.000 1.742\\nMarathi 0.095 0.781 0.107 0.117 2.998 0.820 1.038 2.476 1.288 1.364 2.998 1.833\\nNepali 0.151 0.991 0.177 0.146 2.986 0.890 0.969 2.417 1.236 1.285 3.000 1.781\\nSwahili 0.083 0.712 0.090 0.086 2.998 0.794 1.569 2.707 1.955 1.907 3.000 2.228\\nTamil 0.140 0.914 0.176 0.174 2.998 0.880 0.960 2.457 1.198 1.257 2.998 1.774\\nTelugu 0.054 0.560 0.057 0.090 3.000 0.752 0.539 1.735 0.674 0.712 3.000 1.332\\nUrdu 0.057 0.573 0.052 0.071 3.000 0.751 1.038 2.443 1.285 1.335 3.000 1.820\\nVietnamese 0.105 0.623 0.126 0.117 3.000 0.794 1.361 2.595 1.665 1.710 3.000 2.066\\nAverage 0.124 0.776 0.144 0.143 2.998 0.837 1.074 2.377 1.331 1.354 2.999 1.827\\nTable 3: Evaluation results of model response quality for 13 low-resource languages on the LLM-Eval. ACC., F., LC., H.,\\nINFO., and A VG. respectively denote accuracy, fluency, logical coherence, harmlessness, informativeness and their average.\\nExtending the Analysis to Multiple Languages\\nIn the previous section, our experiments focus on Chinese.\\nTo investigate whether similar conclusions could be drawn\\nin other non-English languages, we extend our experiments\\nto 13 low-resource languages. To ensure evaluation con-\\nsistency, we translate LLM-Eval benchmark into these 13\\nlanguages and employ the same evaluation metrics. As\\nshown in table 3, a significant improvement in response\\nquality for all low-resource languages with the increase\\nin SFT data. Among these languages, Arabic, Indonesian,\\nand Vietnamese exhibited the best performance. Despite\\nall thirteen languages being low-resource, these three lan-\\nguages are more frequently used (Scao et al. 2023). As\\na result, LLaMA encounters them more often (although\\ntheir overall occurrence is small compared to English),\\nallowing the model to quickly comprehend instructions in\\nthese languages. This aligns with the conclusion drawn in\\nthe previous section.\\nIn the previous section, we observed that extending the\\nvocabulary had a negative impact on language transfer-\\nability. A plausible hypothesis is the existence of cross-\\nlingual semantic alignment within LLMs, which vocabu-\\nlary expansion might disrupt. To validate this alignment\\nhypothesis, we fine-tune LLaMA with a dataset of 1k\\ninstructions and examine the model’s output. Excitingly, we\\nobserved a certain proportion of code-switching samples. As\\ndepicted in figure 3, these samples’ model responses consist\\nof tokens from multiple languages and are semantically\\ncoherent. We have observed that code-switching occurs not\\nonly in the transfer process when Chinese is the target\\nlanguage, but also when other 13 low-resource languages\\nare target languages. As shown in figure 4, the proportion of\\nsamples with code-switching is approximately between 2%\\nto5%. This indicates that LLaMA might have learned cross-\\nlingual alignment relationships between concepts during the\\npretraining process.\\nHuman 1:  如何评价美国的医疗保障体系？请从优点和缺\\n点两个⽅⾯进⾏评价。\\n(Human 1:  How would you evaluate the healthcare system in\\nthe United States? Please provide an assessment from both\\nthe perspectives of its advantages and disadvantages. ) \\nAssistant 1:  医疗保障为我国⼈⺠养⽣和优先保障，  可以\\n找到多家科室和专科医⽣给予我们好  quality medicine\\nservice.  The medical system of the United States is very\\ngood in many aspects. ...  \\n(Assistant 1:  Healthcare coverage ensures the well-being and\\npriority protection of the people in our country . It allows us to\\naccess numerous departments and specialized doctors who\\nprovide us with high- quality medical services. The medical\\nsystem of the United States is very good in many aspects. ... ) \\nHuman 2:  请你为全球军事实⼒进⾏国家层⾯上的排序，\\n不包括⺠间组织与其他机构。\\n(Human2: Please provide a country-level ranking of global\\nmilitary capabilities, excluding non-state or ganizations and\\nother entities.)  \\nAssistant 2: ... 1. 美国：武器：  美国武器有着世界上最新\\n的轻 заряд⻋、追踪性能最强的潜⽔艇等等的 優れた武器\\n化状况。...\\n(Assistant 2: ... 1. United States: W eapons: The United States\\npossesses advanced weaponry including the world's latest\\nlightweight armored  vehicles, submarines with the highest\\ntracking capabilities, and other superior weapons . ...) Figure 3: Case study of code-switching. \", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='58c374b5-7a48-49e1-92ce-11e1781cfad5', embedding=[-0.00526037160307169, 5.0025082600768656e-05, 0.01262754201889038, 0.003729961346834898, 0.006691404618322849, 0.0071154143661260605, -0.021438995376229286, -0.020789731293916702, -0.0028985044918954372, -0.014813842251896858, 0.006512525491416454, 0.05037766322493553, -0.02067047730088234, 0.002916723722591996, -0.002855441067367792, 0.002775939181447029, 0.02190275676548481, -0.014615087769925594, 0.013217180967330933, -0.013429185375571251, -0.03702135756611824, 0.035166312009096146, -0.020259719341993332, 0.0025208708830177784, -0.031800735741853714, 0.012766670435667038, 0.02385055087506771, -0.021094487980008125, 0.0023502730764448643, -0.02883266657590866, -0.001717570936307311, -0.004856237210333347, -0.014919845387339592, 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red\\nbackground represents the non-English target language\\n(Chinese). Text with a cyan background indicates code-\\nswitching language in the model’s output, which could be\\nEnglish, Japanese, Russian or other languages.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='a9a9e883-db79-4c10-8895-1400ae90179b', embedding=[-0.0015500499866902828, -0.006273971870541573, 0.006927859969437122, -0.028060181066393852, 0.009389162063598633, 9.871194197330624e-05, -0.011702919378876686, 0.013030814938247204, -0.02639695815742016, -0.04801885411143303, 0.018644193187355995, 0.04337792843580246, -0.016068879514932632, 0.010542687028646469, 0.004439732059836388, 0.018389344215393066, 0.02261446602642536, -0.008141744881868362, -0.005791100673377514, -0.011676093563437462, -0.013949611224234104, 0.015156789682805538, -0.014311765320599079, 0.004627515561878681, -0.015706725418567657, 0.004459851887077093, 0.0317353680729866, -0.01841617003083229, 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'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='19d475056fe74953fcce7c3a1c896d28d200c12ee516fbcb6188c6f7a70ad69a')}, text='ChineseArabicBengaliGujarati Hindi\\nIndonesian\\nMalayalam\\nMarathi\\nNepali\\nSwahili\\nTamil TeluguUrduVietnamese1234\\n3.881%4.706%4.556%\\n2.078%2.613%4.235%\\n2.158%\\n4.048%\\n4.245% 2.381%\\n4.762%1.618%\\n3.302%2.108%Figure 4: Code-switching rate across languages.\\nRelated Work\\nResource Gap in LLMs\\nOne of the main challenges of LLMs is the resource\\ngap, as they are mainly pretrained on English corpus\\nand have limited access to data from other languages.\\nEnglish dominates the field of NLP as an extremely high-\\nresource language with the most raw text data from various\\ndomains, leaving few of the over 7000 languages of the\\nworld represented in the field (Joshi et al. 2020). 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hash='19d475056fe74953fcce7c3a1c896d28d200c12ee516fbcb6188c6f7a70ad69a')}, text='2023; Huang et al. 2023a). To address\\nthe resource gap, several solutions have been proposed or\\nimplemented by researchers and practitioners. One possible\\nsolution is to increase the amount of data available from\\nvarious languages and fields, and make it accessible for\\npretraining and evaluating LLMs (Lin et al. 2022; Chen et al.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='59e50564-6c6d-43c0-abf0-298f119ae1ff', embedding=[-0.01107302401214838, -0.00855272263288498, 0.035406503826379776, -0.01709185726940632, 0.000359406229108572, 0.01513539720326662, -0.0024676539469510317, 0.012390918098390102, -0.018953213468194008, -0.04646594077348709, 0.0038280056323856115, 0.04146609827876091, -0.00968040432780981, 0.0011650451924651861, 0.004405433312058449, -0.00437486357986927, 0.026738295331597328, -0.0014461165992543101, 0.0019989185966551304, -0.018898867070674896, -0.004938704427331686, -0.00012758602679241449, -0.02177921123802662, 0.005132312886416912, -0.006643814500421286, 0.029346909373998642, 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'creation_date': '2024-02-25', 'last_modified_date': '2024-01-15', 'last_accessed_date': None}, hash='19d475056fe74953fcce7c3a1c896d28d200c12ee516fbcb6188c6f7a70ad69a')}, text='2022; Cahyawijaya et al. 2023) . However, this approach\\nincurs significant computational expenses and the resource\\ngap persists. Alternatively, multilingual language models\\ntrained on texts from different languages concurrently, such\\nas mBERT (Devlin et al. 2019) and XLM-R (Conneau et al.\\n2020a), have been introduced to bridge the gap effectively.\\nCross-Lingual Transfer\\nMultilingual language models have demonstrated a high\\nlevel of zero-shot or few-shot cross-lingual transferability\\nacross a wide range of tasks (Wu and Dredze 2019;\\nPires, Schlinger, and Garrette 2019; Winata et al. 2021b).\\nThis means that they can acquire the language capability\\nfrom supervised data in one language and apply it to\\nanother without or with few additional training data. Themechanism behind the strong cross-lingual performance has\\nbeen investigated by the researchers. It has been shown\\nthat multilingual language models have inferred universal\\nrules applicable to any language (Artetxe, Ruder, and\\nYogatama 2020; Chi, Hewitt, and Manning 2020; Conneau\\net al. 2020b). Contrary to the common hypothesis that\\nmultilingual multilingual language models such as mBERT\\n(Devlin et al. 2019) rely on a shared subword vocabulary\\nand joint pretraining across multiple languages (Pires,\\nSchlinger, and Garrette 2019; Cao, Kitaev, and Klein 2020;\\nWu and Dredze 2019), researchers have developed new\\nunderstandings on the models, emphasizing the models’\\nability to learn universal semantic abstractions (Artetxe,\\nRuder, and Yogatama 2020; Chi, Hewitt, and Manning\\n2020). In terms of the factors that influence cross-lingual\\nperformance, researchers have associated transferability\\nwith parameter sharing (Conneau et al. 2020b; Dufter\\nand Sch ¨utze 2020; Wu, Papadimitriou, and Tamkin 2022)\\nand language distance (Conneau et al. 2020b; Eronen,\\nPtaszynski, and Masui 2023). We here further investigate\\nthe cross-lingual transferability of language models with\\nnew LLaMA-based experiments, presenting outcomes from\\na different aspect.\\nCode-Switching\\nCode-switching is a phenomenon in which multilingual\\nspeakers switch between languages within a single utter-\\nance. Previous work on the performance of multilingual\\nlanguage models on code-switching tasks has shown mixed\\nresults. Some studies have suggested that pretrained models\\nfine-tuned for specific code-switching scenarios can achieve\\nstate-of-the-art performance for certain language pairs such\\nas English-Spanish and English-Hindi (Khanuja et al. 2020),\\nwhile others have found that using meta-embeddings can\\nyield better results with fewer parameters (Winata, Lin,\\nand Fung 2019; Winata et al. 2019, 2021a). In another\\nline of research, code-switching-based methods have been\\npresented to improve the capability of multilingual language\\nmodels (Jiang et al. 2020; Tan and Joty 2021; Krishnan et al.\\n2021).\\nConclusions\\nIn this paper, we focus on how to effectively transfer the\\ncapabilities of language generation and following instruc-\\ntions to a non-English language. Specifically, we conducts\\na comprehensive empirical study to analyze the necessity\\nof vocabulary extension and the required training scale\\nfor effective transfer. We find that vocabulary extension is\\nuncessary and that comparable transfer performance to state-\\nof-the-art models can be achieved with less than 1%of the\\nfurther pretraining data. Additionally, we observe instances\\nof code-switching during the transfer training, suggesting\\nthat cross-lingual alignment might have been internalized\\nwithin the model. Similar results are observed from the\\nextension experiments on the 13 low-resource languages.\\nOur analysis and findings offer assistance and guidance to\\nthe community in developing non-English LLMs.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='260e10a2-2cc7-4c17-9eac-7e43b2e120ce', embedding=[-0.010972325690090656, 0.008422418497502804, -0.016591954976320267, -0.026791580021381378, -0.011119840666651726, 0.04956512525677681, -0.01272845920175314, 0.002720251679420471, -0.03338059410452843, -0.03279053419828415, 0.02908157929778099, 0.02905348129570484, 0.007087756879627705, 0.004116378258913755, -0.0010501681827008724, -0.007607572712004185, 0.010312018916010857, 0.0003007379418704659, -0.0068032629787921906, 0.008562909439206123, -0.00020810187561437488, -0.00890008732676506, 0.00878769438713789, -0.03071127086877823, -0.007965823635458946, 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'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='f14c766bcdb2eeccbad9c8763ed34fea6be283c3f4b8ee39431139afed62547e')}, text='An LLM Compiler for Parallel Function Calling\\nSehoon Kim∗1Suhong Moon∗1Ryan Tabrizi1Nicholas Lee1\\nMichael W. Mahoney1,2,3Kurt Keutzer1Amir Gholami1,2\\n1UC Berkeley2ICSI3LBNL\\n{sehoonkim, suhong.moon, rtabrizi, nicholas lee, mahoneymw, keutzer, amirgh }@berkeley.edu\\nAbstract\\nRecent language models have shown remarkable results on various complex reasoning benchmarks. The rea-\\nsoning capabilities of LLMs enable them to execute external function calls to overcome their inherent limitations,\\nsuch as knowledge cutoffs, poor arithmetic skills, or lack of access to private data. This development has allowed\\nLLMs to select and coordinate multiple functions based on the context to tackle more complex problems. However,\\ncurrent methods for multiple function calling often require sequential reasoning and acting for each function which\\ncan result in high latency, cost, and sometimes inaccurate behavior. To address this, we introduce LLMCompiler ,\\nwhich executes functions in parallel to efficiently orchestrate multiple function calling. Drawing from the principles\\nof classical compilers, LLMCompiler streamlines parallel function calling with three components: (i) an LLM Plan-\\nner, formulating execution plans; (ii) a Task Fetching Unit, dispatching function calling tasks; and (iii) an Executor,\\nexecuting these tasks in parallel. LLMCompiler automatically generates an optimized orchestration for the function\\ncalls and can be used with both open-source and closed-source models. 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-0.039871010929346085], metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='9608e049-09e1-4b29-852a-9950c5df9db0', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '1', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='f14c766bcdb2eeccbad9c8763ed34fea6be283c3f4b8ee39431139afed62547e')}, text='We have benchmarked LLMCompiler on\\na range of tasks with different patterns of function calling. We observe consistent latency speedup of up to 3.7×,\\ncost savings of up to 6.7×, and accuracy improvement of up to ∼9%compared to ReAct. Our code is available at\\nhttps://github.com/SqueezeAILab/LLMCompiler .\\n1 Introduction\\nRecent advances in the reasoning capability of Large Language Models (LLMs) have expanded the applicability of\\nLLMs beyond content generation to solving complex problems [51, 23, 57, 5, 50, 63, 8, 55, 12]; and recent works have\\nshown how this reasoning capability could be helpful in improving accuracy for solving complex and logical tasks.\\nThe reasoning capability has also allowed function (i.e., tool) calling capability where LLMs can invoke provided\\nfunctions and use the function outputs to help complete their tasks. These functions range from a simple calculator\\nthat can invoke arithmetic operations to more complex LLM-based functions.\\nThe ability of LLMs to integrate various tools and function calls could enable a fundamental shift in how we\\ndevelop LLM-based software. However, this brings up an important challenge: what is the most effective approach\\nto incorporate multiple function calls? A notable approach has been introduced in ReAct [58], where the LLM calls\\na function, analyzes the outcomes, and then reasons about the next action, which involves a subsequent function call.\\nFor a simple example illustrated in Figure 1 (Left), where the LLM is asked if Scott Derrickson and Ed Wood have the\\nsame nationality, ReAct initially analyzes the query and decides to use a search tool to search for Scott Derrickson.\\nThe result of this search (i.e., observation) is then concatenated back to the original prompt for the LLM to reason\\nabout the next action, which invokes another search tool to gather information about Ed Wood.\\nReAct has been a pioneering work in enabling function calling, and it has been integrated into several frame-\\nworks [26, 31]. 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approach for more complex applications requires considerable optimizations.\\nThis is due to the sequential nature of ReAct, where it executes function calls and reasons about their observations\\none after the other. This approach, along with the agent systems that extend ReAct [20, 57, 41, 42, 47], may lead\\nto inefficiencies in latency and cost due to the sequential function calling and repetitive LLM invocations for each\\nreasoning and action step. Furthermore, while dynamic reasoning about the observations has benefits in certain cases,\\n*Equal contribution\\n1arXiv:2312.04511v2  [cs.CL]  6 Feb 2024', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='20d63bec-b492-4698-93d0-2109270f59cf', embedding=[-0.011391918174922466, 0.009018892422318459, -0.006096103228628635, -0.03844163566827774, -0.014307748526334763, 0.028030939400196075, -0.005737713538110256, -0.008629187010228634, -0.009547777473926544, -0.03050835058093071, 0.013771903701126575, 0.02797526679933071, 0.0024669731501489878, 0.023507574573159218, -0.007223464548587799, 0.01656247116625309, 0.02101624570786953, 0.01116922963410616, 0.0017319265753030777, 0.013062084093689919, -0.016117094084620476, -0.00489219231531024, 0.012380099855363369, -0.0034586337860673666, 0.009408596903085709, 0.005480229388922453, 0.009826138615608215, -0.018733685836195946, 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'last_accessed_date': None}, hash='8edd93259f1da9535536997b3917df695121572b291062c5ca59bc2fe60db66f')}, text='ReAct LLMCompilerHotpotQA Question: Were Scott Derrickson and Ed W ood of the same nationality?\\nLLM\\nThought: I need to search Scott Derrickson.\\nAction: search(Scott \\xa0Derrickson)\\nSearch Tool\\nObservation: … Scott Derrickson (born July 16, 1966) \\nis an American filmmaker …\\nLLM\\nThought: I need to search Ed Wood.\\nAction: search(Ed \\xa0Wood)Tool invocation\\nAppended to prompt\\nSearch Tool\\nObservation: … Edward Wood Jr was an American \\nfilmmaker, actor, and ….Tool invocation\\nAppended to prompt\\nLLM\\nThought: They are both American filmmakers.\\nAction: finish(yes)LLM Planner\\n$1\\xa0=\\xa0search(Scott \\xa0Derrickson)\\n$2\\xa0=\\xa0search(Ed \\xa0Wood)\\nParallel tool invocations\\nSearch Tool\\nObservation: … Scott Derrickson (born July \\n16, 1966) is an American filmmaker …Search Tool\\nObservation: … Edward Wood Jr was an \\nAmerican filmmaker, actor, and …DAG of tasks\\nExecutorStep\\nLatency Speedup: 1.8xLLM\\nThought: They are both American filmmakers.\\nAction: finish(yes)\\nFigure 1: An illustration of the runtime dynamics of LLMCompiler , in comparison with ReAct [58], given a sample\\nquestion from the HotpotQA benchmark [54]. In LLMCompiler (Right), the Planner first decomposes the query\\ninto several tasks with inter-dependencies. The Executor then executes multiple tasks in parallel, respecting their\\ndependencies. Finally, LLMCompiler joins all observations from the tool executions to produce the final response.\\nIn contrast, sequential tool execution of the existing frameworks like ReAct (Left) leads to longer execution latency.\\nIn this example, LLMCompiler attains a latency speedup of 1.8 ×on the HotpotQA benchmark. While a 2-way\\nparallelizable question from HotpotQA is presented here for the sake of simple visual illustration, LLMCompiler is\\ncapable of managing tasks with more complex dependency patterns (Fig. 2 and Sec. 4).\\nconcatenating the outcomes of intermediate function calls could disrupt the LLM’s execution flow, potentially reduc-\\ning accuracy [53]. Common failure cases include repetitive invocation of the same function, which is also highlighted\\nin the original paper [58], and early stopping based on the partial intermediate results, as will be further discussed in\\nSec. 4.1 and Appendix. 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-0.05121900141239166], metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='40bfe53d-2d9b-4eb0-8b46-067216c081de', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '2', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='8edd93259f1da9535536997b3917df695121572b291062c5ca59bc2fe60db66f')}, text='A.1.\\nTo address this challenge, we draw inspiration from classical compilers, where optimizing instruction executions in\\ntraditional programming languages has been extensively explored. A key optimization technique in compilers involves\\nidentifying instructions that can be executed in parallel and effectively managing their dependencies. Similarly, one\\ncan envision a compiler tailored for LLM function calling, which can efficiently orchestrate various function calls\\nand their dependencies. This shares a similar philosophy with the recent studies that align LLMs with computer\\nsystems [19, 37]. To this end, we introduce LLMCompiler , a novel framework that enables parallel multi-tool\\nexecution of LLMs across different models and workloads. To the best of our knowledge, LLMCompiler is the first\\nframework to optimize the orchestration of LLM function calling that can not only improve latency and cost, but also\\naccuracy by minimizing interference from the outputs of intermediate function calls. In more detail, we make the\\nfollowing contributions:\\n• We introduce LLMCompiler , an LLM compiler that optimizes the parallel function calling performance of LLMs.\\nAt a high level, this is achieved by introducing three key components: (i) an LLM Planner (Sec. 3.1) that identifies\\nan execution flow; (ii) a Task Fetching Unit (Sec. 3.2) that dispatches the function calls in parallel; (iii) an Executor\\n(Sec. 3.3) that executes the dispatched tasks using the associated functions.\\n• We evaluate LLMCompiler onembarrassingly parallel patterns using HotpotQA [54] and Movie Recommenda-\\ntion [4], where we observe 1.80×/3.74×speedup and 3.37×/6.73×cost reduction compared to ReAct (Sec. 4.1).\\n• To test the performance on more complex patterns, we introduce a new benchmark called ParallelQA which includes\\nvarious non-trival function calling patterns. We show up to 2.27×speedup, 4.65×cost reduction, and 9%improved\\naccuracy compared to ReAct (Sec. 4.2).\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='18854f01-e037-4cf2-a4c7-f0110791c9f0', embedding=[0.0036646074149757624, -0.01784265786409378, 0.014768230728805065, -0.01139185018837452, -0.015907416120171547, 0.01630544476211071, -0.015399586409330368, -0.008585063740611076, -0.005647887475788593, -0.02219352126121521, -0.007603717036545277, 0.028658052906394005, -0.012682012282311916, -0.007130200508981943, -0.020162202417850494, 0.017746582627296448, 0.03609706833958626, 0.001000218209810555, 0.024691492319107056, 0.01329278014600277, -0.03387359902262688, 0.01616819202899933, -0.013752571307122707, -0.014699604362249374, -0.012441822327673435, 0.021452363580465317, 0.012084969319403172, -0.013731983490288258, 0.031952083110809326, -0.026173805817961693, 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'last_accessed_date': None}, hash='7541c2b7882455f0c6213e73e7128302bc1000c4815f2cbf55b873bde541d959')}, text='• We evaluate LLMCompiler ’s capability in dynamic replanning, which is achieved through a feedback loop from\\nthe Executor back to our LLM Planner. For the Game of 24 [57], which requires repeated replanning based on the\\nintermediate results, LLMCompiler demonstrates a 2 ×speedup compared to Tree-of-Thoughts (Sec. 4.3).\\n• We showcase that LLMCompiler can explore the interactive decision-making environment effectively and effi-\\nciently. On WebShop, LLMCompiler achieves up to 101.7×speedup and 25.7% improved success rate compared\\nto the baselines. (Sec. 4.4)\\n2 Related Work\\n2.1 Latency Optimization in LLMs\\nVarious studies have focused on optimizing model design [21, 11, 30, 9, 24, 10, 22, 6, 28] and systems [25, 59, 1, 2]\\nfor efficient LLM inference. Optimizations at the application level, however, are less explored. This is critical from a\\npractical point of view for situations involving black-box LLM models and services where modifications to the models\\nand the underlying inference pipeline are highly restricted.\\nSkeleton-of-Thought [34] recently proposed to reduce latency through application-level parallel decoding. This\\nmethod involves a two-step process of an initial skeleton generation phase, followed by parallel execution of skeleton\\nitems. However, it is primarily designed for embarrassingly parallel workloads and does not support problems that\\nhave inherently interdependent tasks, as it assumes no dependencies between skeleton tasks. 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limits its applica-\\nbility in complex scenarios such as coding [7, 33, 14, 3] or math [16, 15] problems, as also stated in the paper [34].\\nLLMCompiler addresses this by translating an input query into a series of tasks with inter-dependencies, thereby\\nexpanding the spectrum of problems it can handle.\\nConcurrently to our work, OpenAI has recently introduced a parallel function calling feature in their 1106 re-\\nlease, enhancing user query processing through the simultaneous generation of multiple function calls [36]. Despite\\nits potential for reducing LLM execution time, this feature has certain limitations as it is exclusively available for\\nOpenAI’s proprietary models. However, there is a growing demand for using open-source models driven by the in-\\ncreasing number of open-source LLMs as well as parameter-efficient training techniques [27, 18, 17] for finetuning\\nand customization. LLMCompiler enables efficient parallel function calling for open-source models, and also, as we\\nwill show later in Sec. 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='d517ceeb-af56-4cae-9bb5-6056419ce9ae', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '3', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='7541c2b7882455f0c6213e73e7128302bc1000c4815f2cbf55b873bde541d959')}, text='4, it can potentially achieve better latency and cost.\\n2.2 Plan and Solve Strategy\\nSeveral studies [52, 38, 40, 63, 13] have explored prompting methods of breaking down complex queries into various\\nlevels of detail to solve them, thereby improving LLM’s performance in reasoning tasks. Specifically, Decomposed\\nPrompting [20] tackles complex tasks by decomposing them into simpler sub-tasks, each optimized through LLMs\\nwith dedicated prompts. Step-Back Prompting [61] enables LLMs to abstract high-level concepts from details to\\nenhance reasoning abilities across various tasks. Plan-and-Solve Prompting [49] segments multi-step reasoning tasks\\ninto subtasks to minimize errors and improve task accuracy without manual prompting. However, these methods\\nprimarily focus on improving the accuracy of reasoning benchmarks. In contrast, LLMCompiler uses a planner to\\nidentify parallelizable patterns within queries, aiming to reduce latency while maintaining accuracy.\\nA notable work is ReWOO [53] which employs a planner to separate the reasoning process from the execution and\\nobservation phases to decrease token usage and cost as compared to ReAct. Our approach is different from ReWOO\\nin multiple aspects. First, LLMCompiler allows parallel function calling which can reduce latency as well as cost.\\nSecond, LLMCompiler supports dynamic replanning which is important for problems whose execution flow cannot\\nbe determined statically in the beginning (Sec. 4.3).\\n2.3 Tool-Augmented LLMs\\nA notable work is Toolformer [43], which produces a custom LLM output to let the LLM decide what the inputs\\nfor calling the functions should be and where to insert the result. This approach has inspired various tool calling\\nframeworks [29, 44]. ReAct [58] proposed to have LLMs interact with external environments through reasoning and\\naction generation for improved performance. Gorilla [39] introduced a finetuned LLM designed for function calling,\\nand ToolLLM [41] and RestGPT [46] have extended LLMs to support real-world APIs. Moreover, OpenAI [35]\\nreleased their own function calling capabilities, allowing their LLMs to return formatted JSON for execution.\\n3', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='7af9e82f-a329-431b-8de2-4ff2872b8c0b', embedding=[0.0036632390692830086, 0.00027508626226335764, -0.003996260929852724, -0.03691391274333, 0.004476910922676325, 0.00954433437436819, -0.014378299005329609, 0.0003686413401737809, -0.028015024960041046, -0.03166796267032623, 0.01197504997253418, 0.022137362509965897, -0.003304468235000968, 0.026641739532351494, 0.005376412533223629, 0.016163570806384087, -0.0004523259121924639, -0.006402943748980761, 0.004789333324879408, -0.015188537538051605, -0.01778404787182808, 0.008363308385014534, 0.01551812607795, -0.020832741633057594, -0.01498254481703043, 0.008109251037240028, 0.016726616770029068, -0.03235460817813873, 0.022535614669322968, 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hash='5a001a86c1b68cbeb945b1ae87f1936f7fe8e8dc8c2d5cc787ecb7af310e2fdb')}, text=\"User Input\\n“How much does \\nMicrosoft's market cap \\nneed to increase to exceed \\nApple's market cap?”$1 = search(Microsoft Market Cap)\\n$2 = search(Apple Market Cap)\\n$3 = math($1 / $2)\\n$4 = llm($3)LLM Planner\\nDAG of TasksExecutor\\nTask \\nFetching \\nUnit\\nResolves \\nDependencyFetches\\nTask\\nTools\\nsearch math\\n …llm\\nTool\\nMemoryTool\\nMemory\\nTool\\nMemoryTool\\nMemoryFigure 2: Overview of the LLMCompiler framework. The LLM Planner generates a DAG of tasks with their inter-\\ndependencies. \", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='41795406-5bbe-41b0-8eb8-764ce7d71715', embedding=[-0.008048261515796185, 0.015475301072001457, -0.007613406516611576, -0.027057617902755737, -0.008282944560050964, 0.02208785153925419, -0.009525386616587639, -0.0010198375675827265, -0.027002399787306786, -0.032469142228364944, 0.01609652303159237, 0.026049859821796417, 0.004579778295010328, 0.019271651282906532, 0.006902453955262899, 0.007565089501440525, 0.01711808517575264, -0.006405476946383715, -0.004748888313770294, -0.027457961812615395, -0.0013796279672533274, -0.0027696096803992987, 0.006743697449564934, -0.025539079681038857, -0.022653853520751, 0.00182052212767303, 0.02073497138917446, -0.025898005813360214, 0.014771251007914543, 0.002569438423961401, -0.00245382240973413, 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hash='5a001a86c1b68cbeb945b1ae87f1936f7fe8e8dc8c2d5cc787ecb7af310e2fdb')}, text='These tasks are then dispatched by the Task Fetching Unit to the Executor in parallel based on their\\ndependencies. In this example, Task $1and$2are fetched together for parallel execution of two independent search\\ntasks. After each task is performed, the results (i.e., observations) are forwarded back to the Task Fetching Unit to\\nunblock the dependent tasks after replacing their placeholder variables (e.g., the variable $1and$2in Task $3) with\\nactual values. Once all tasks have been executed, the final answer is delivered to the user.\\n3 Methodology\\nTo illustrate the components of LLMCompiler , we use a simple 2-way parallel example in Figure 2. To answer\\n“How much does Microsoft’s market cap need to increase to exceed Apple’s market cap?”, the LLM first needs\\nto conduct web searches for both companies’ market caps, followed by a division operation. While the existing\\nframeworks, including ReAct, perform these tasks sequentially, it is evident that they can be executed in parallel. 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metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='208394a1-acba-49e9-84bb-089ec30c2703', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '4', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='5a001a86c1b68cbeb945b1ae87f1936f7fe8e8dc8c2d5cc787ecb7af310e2fdb')}, text='The\\nkey question is how to automatically determine which tasks are parallelizable and which are interdependent, so we can\\norchestrate the execution of the different tasks accordingly. LLMCompiler accomplishes this through a system that\\nconsists of the following three components: an LLM Planner (Section 3.1) that generates a sequence of tasks and their\\ndependencies; a Task Fetching Unit (Section 3.2) that replaces arguments based on intermediate results and fetches the\\ntasks; and an Executor (Section 3.3) that executes the tasks with associated tools. To use LLMCompiler , users are\\nonly required to provide tool definitions, and optional in-context examples for the Planner, as will be further discussed\\nin Appendix A.3.2.\\n3.1 LLM Planner\\nThe LLM Planner is responsible for generating a sequence of tasks to be executed along with any dependency among\\nthem. For instance, Tasks $1and$2in Figure 2 are two independent searches that can be performed in parallel.\\nHowever, Task $3has a dependency on the outcomes of the first and second searches. Therefore, the Planner’s role\\nis to automatically identify the necessary tasks, their input arguments, as well as their inter-dependencies using the\\nsophisticated reasoning capability of LLMs, essentially forming a directed acyclic graph of task dependencies. If a\\ntask is dependent on a preceding task, it incorporates a placeholder variable, such as $1in Task 3 of Figure 2, which\\nwill later be substituted with the actual output from the preceding task (Sec. 3.2).\\nThe Planner in LLMCompiler leverages LLMs’ reasoning capability to decompose tasks from natural language\\ninputs. To achieve this, the Planner LLM incorporates a pre-defined prompt that guides it on how to create dependency\\ngraphs and to ensure correct syntax (see Appendix A.7 for details). Besides this, users also need to supply tool\\ndefinitions and optional in-context examples for the Planner. These examples provide detailed demonstrations of\\ntask decomposition specific to a problem, helping the Planner to better understand the rules. Further details on user-\\nsupplied information for LLMCompiler are elaborated in Appendix A.3.2. In Appendix A.3.1, we introduce an\\nadditional optimization for the Planner that streams tasks as soon as they are created, instead of waiting to complete\\nthe entire planning process.\\n3.2 Task Fetching Unit\\nThe Task Fetching Unit, inspired by the instruction fetching units in modern computer architectures, fetches tasks to\\nthe Executor as soon as they are ready for (parallel) execution based on a greedy policy. 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metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='6a0738c1-722f-45d6-8052-3cda2dc5f27b', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '5', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='bd56e4bb7cb006f9c6846e23c72a3835bb58b1ea86b4f7f1c20c7db68708f89e')}, text=\"search\\nmathsearch\\nmath(c) Which has higher total healthcare expenses, Florida \\nor New York, considering both public and private sectors?\\nsearch\\nmathsearch search\\nmathsearch\\nmath(b) If Stanford and UCLA were to merge, would they \\nhave more Nobel laureates than UC Berkeley?\\nsearchAnalyzer \\nAgent\\noutput\\noutput output(a) Analyze Apple and Microsoft's latest 10 -K \\nform and compare their sales forecast.\\nAnalyzer \\nAgentFigure 3: Examples of questions with different function calling patterns and their dependency graphs. HotpotQA\\nand Movie Recommendation datasets exhibit pattern (a), and ParallelQA dataset exhibits patterns (b) and (c), among\\nother patterns. In (a), we need to analyze each company’s latest 10-K. In (b), we need three searches for each school,\\nfollowed by one addition and one comparison operation. In (c), we need to search for each state’s annual healthcare\\nspending in each sector, sum each state’s spending, and then perform a comparison.\\nis to replace variables with the actual outputs from preceding tasks, which were initially set as placeholders by the\\nPlanner. For the example in Figure 2, the variable $1and$2in Task $3would be replaced with the actual market cap\\nof Microsoft and Apple after the search tasks are done. This can be implemented with a simple fetching and queuing\\nmechanism without a dedicated LLM.\\n3.3 Executor\\nThe Executor asynchronously executes tasks fetched from the Task Fetching Unit. As the Task Fetching Unit guaran-\\ntees all the tasks dispatched to the Executor are independent, it can simply execute them concurrently. The Executor is\\nequipped with user-provided tools, and it delegates the task to the associated tool. These tools can be simple functions\\nlike a calculator, Wikipedia search, or API calls, or they can even be LLM agents that are tailored for a specific task. As\\ndepicted in the Executor block of Figure 2, each task has dedicated memory to store its intermediate outcomes, similar\\nto what typical sequential frameworks do when aggregating observations as a single prompt [58]. Upon completion of\\nthe task, the final results are forwarded as input to the tasks dependent on them.\\n3.4 Dynamic Replanning\\nIn various applications, the execution graph may need to adapt based on intermediate results that are a priori unknown.\\nA similar analogy in programming is branching, where the path of execution is determined only during runtime,\\ndepending on which branch conditions are satisfied. Such dynamic execution patterns can also appear with LLM\\nfunction calling. For simple branching (e.g., if-else statements) one could statically compile the execution flow and\\nchoose the right dynamically based on the intermediate results. However, for more complex branching it may be\\nbetter to do a recompilation or replanning based on the intermediate results. In replanning, the Executor sends the\\nintermediate results back to our LLM Planner. Based on that, the Planner produces a new set of tasks with their\\nassociated dependencies and dispatches them to the Task Fetching Unit and then the Executor. 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hash='bd56e4bb7cb006f9c6846e23c72a3835bb58b1ea86b4f7f1c20c7db68708f89e')}, text='We show an example use case of this in Sec. 4.3 for solving the Game of 24 using the\\nTree-of-Thoughts approach.\\n4 Results\\nIn this section, we evaluate LLMCompiler using a variety of models and problem types. We use both the propri-\\netary GPT models and the open-source LLaMA-2 model, with the latter demonstrating LLMCompiler ’s capability\\nin enabling parallel function calling in open-source models. Furthermore, there are various types of parallel function\\ncalling patterns that can be addressed with LLMs. This ranges from embarrassingly parallel patterns, where all tasks\\ncan be executed in parallel without any dependencies between them, to more complex dependency patterns, as illus-\\ntrated in Figure 3. Significantly, we also assess LLMCompiler on the Game of 24 benchmark involving dynamic\\nreplanning based on intermediate results, highlighting its adaptability to dynamic dependency graphs. Finally, we\\napply LLMCompiler to the WebShop benchmark to showcase its potential in decision-making tasks. In this section,\\nwe start presenting results for simple execution patterns, and then we move to more complex ones.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='43580651-686c-4e85-9e66-eda9e6f8c8c0', embedding=[-0.006539797876030207, -0.0015215425519272685, -0.015093427151441574, -0.0327436625957489, -0.016344338655471802, 0.01983589679002762, -0.0176639836281538, -0.014447350986301899, -0.02481205388903618, -0.021952826529741287, 0.005020832642912865, 0.022365214303135872, -0.009429954923689365, -0.0014278959715738893, 0.0010885343654081225, 0.01734781824052334, 0.019725926220417023, -0.005938397720456123, 0.010990159586071968, -0.005890285596251488, -0.024152232334017754, 0.010034792125225067, -0.018956134095788002, 0.0015163876814767718, -0.009003819897770882, -0.0017002442618831992, 0.0018042005831375718, -0.0066635143011808395, 0.022365214303135872, 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'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='452f04a1073c7bb50aa9e027b7f08962fbea78b0be7cab31323cab8c01adcfbc')}, text='Table 1: Accuracy and latency comparison of LLMCompiler compared to the baseline on different benchmarks,\\nincluding HotpotQA, Movie Recommendation, our custom dataset named ParallelQA, and the Game of 24. For\\nHotpotQA and Movie Recommendation, we frequently observe looping and early stopping (Sec. 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excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='b8e0cc56-effa-41aa-8284-d8b22046ed05', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '6', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='452f04a1073c7bb50aa9e027b7f08962fbea78b0be7cab31323cab8c01adcfbc')}, text='4.1). We incorporated\\nReAct-specific prompting to minimize these behaviors as much as possible, which we denote as ReAct†. ReAct\\nindicates the original results without this prompting. We do not include the latency for the original ReAct since\\nlooping and early stopping make precise latency measurement difficult.\\nBenchmark MethodGPT (Closed-source) LLaMA-2 70B (Open-source)\\nAccuracy (%) Latency (s) Speedup Accuracy (%) Latency (s) Speedup\\nHotpotQAReAct 61.52 - - 54.74 - -\\nReAct†62.47 7.12 1.00 × 54.40 13.44 1.00 ×\\nOAI Parallel Function 62.05 4.42 1.61 × - - -\\nLLMCompiler 62.00 3.95 1.80× 57.83 9.58 1.40×\\nMovie Rec.ReAct 68.60 - - 70.00 - -\\nReAct†72.47 20.47 1.00 × 70.60 33.37 1.00 ×\\nOAI Parallel Function 77.00 7.42 2.76 × - - -\\nLLMCompiler 77.13 5.47 3.74× 77.80 11.83 2.82×\\nParallelQAReAct 89.09 35.90 1.00 × 59.59 15.47 1.00 ×\\nOAI Parallel Function 87.32 19.29 1.86 × - - -\\nLLMCompiler 89.38 16.69 2.15× 68.14 26.20 2.27×\\nGame of 24Tree-of-Thoughts 74.00 241.2 1.00 × 30.00 952.06 1.00 ×\\nLLMCompiler 75.33 83.6 2.89× 32.00 456.02 2.09×\\n4.1 Embarrassingly Parallel Function Calling\\nThe simplest scenario involves an LLM using a tool repeatedly for independent tasks such as conducting parallel\\nsearches or analyses to gather information on different topics like the pattern depicted in Figure 3 (a). While these\\ntasks are independent of each other and can be executed in parallel, ReAct, along with other LLM solutions as they\\nstand, would need to run sequentially. This leads to increased latency and token consumption due to its frequent LLM\\ninvocations for each tool usage, as also illustrated in Figure 1. In this section, we demonstrate how LLMCompiler\\ncan identify parallelizable patterns and execute independent tasks concurrently to resolve this issue using the following\\ntwo benchmarks:\\n•HotpotQA: A dataset that evaluates multi-hop reasoning [54]. We only use the comparison dev set. 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'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='b8e0cc56-effa-41aa-8284-d8b22046ed05', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '6', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='452f04a1073c7bb50aa9e027b7f08962fbea78b0be7cab31323cab8c01adcfbc')}, text='This contains\\n1.5k questions comparing two different entities, thus exhibiting a 2-way embarrassingly parallel execution pattern.\\nAn example question is shown in Figure 1.\\n•Movie Recommendation: A dataset with 500 examples that asks to identify the most similar movie out of four\\noptions to another set of four movies, exhibiting an 8-way embarrassingly parallel pattern [4].\\nExperimental Setups. As a baseline method, we compare LLMCompiler with ReAct. We follow the ReAct [58]\\nsetup using the same Wikipedia search tool that LLMs can use to search for information. We did not include the\\nlookup tool since it is not relevant to our problem setting. We have optimized the prompt and in-context examples for\\nboth ReAct and LLMCompiler to the best of our abilities. For all experiments across these datasets, we use gpt-\\n3.5-turbo (1106 release). For the experiments using GPT, we additionally report the results using OpenAI’s parallel\\nfunction calling capability, which was announced concurrently with our work. We also show how LLMCompiler\\ncan be effectively combined with the open-source LLaMA-2 70B model to provide the model with parallel function\\ncalling capabilities. For all experiments, we have measured accuracy, end-to-end latency, as well as input and output\\ntoken usage. See A.4 for more details on experimental setups.\\nAccuracy and Latency. We report the accuracy, end-to-end latency, and relative speed-up of LLMCompiler com-\\npared to ReAct in Table 1. First, we observe that ReAct consistently achieves lower accuracy compared to OpenAI\\nparallel function calling and LLMCompiler . We identify two main failure modes in ReAct: (1) the tendency for\\nredundant generation of prior function calls, a point also noted in the original ReAct paper [58]; and (2) premature\\nearly stopping based on the incomplete intermediate results. In Appendix. A.1, we offer a detailed analysis demon-\\nstrating how these two prevalent failure cases significantly hurt ReAct’s accuracy, and how they can be resolved with\\nLLMCompiler , leading to an accuracy enhancement of up to 7 – 8%. 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2: Input and output token consumption as well as the estimated cost on HotpotQA, Movie Recommendation,\\nand our custom dataset named ParallelQA. The cost is computed based on the pricing table of the GPT models used\\nfor each benchmark.\\nBenchmark Method In. Tokens Out. Tokens Cost ($/1k) Cost Red.\\nHotpotQAReAct 2900 120 5.00 1.00 ×\\nOAI Para. Func. 2500 63 2.66 1.87 ×\\nLLMCompiler 1300 80 1.47 3.37×\\nMovie Rec.ReAct 20000 230 20.46 1.00 ×\\nOAI Para. Func. 5800 160 6.14 3.33 ×\\nLLMCompiler 2800 115 3.04 6.73×\\nParallelQAReAct 46000 470 480 1.00 ×\\nOAI Para. Func. 25000 370 260 1.81 ×\\nLLMCompiler 9200 340 103 4.65×\\nexperiments in which we incorporated ReAct-specific prompts to avoid repetitive function calls and early stopping.\\nReAct†in Table 1 refers to ReAct with this ReAct-specific prompt. The ReAct-specific prompt yields a general accu-\\nracy improvement with ReAct†as compared to the original ReAct. Nevertheless, LLMCompiler still demonstrates\\non-par and better accuracy than ReAct†as such prompting does not serve as a perfect solution to completely avoiding\\nthe erroneous behavior of ReAct.\\nAdditionally, when compared to ReAct†,LLMCompiler demonstrates a noticeable speedup of 1.80×and1.40×\\non the HotpotQA benchmark with GPT and LLaMA, respectively. Similarly, LLMCompiler demonstrates 3.74×\\nand2.82×speedup on the Movie Recommendation benchmark with each model. Note that we benchmark the latency\\nofLLMCompiler against that of ReAct†since the repeating and early stopping behavior of the original ReAct as\\ndiscussed above makes its latency unpredictable and unsuitable for a fair comparison. LLMCompiler demonstrates\\na speedup of up to 35% compared to OpenAI parallel function calling whose latency gain over ReAct is 1.61×and\\n2.76×on each benchmark1.\\nCosts. Another important consideration of using LLMs is cost, which depends on the input and output token usage.\\nThe costs for GPT experiments are provided in Table 2. LLMCompiler is more cost-efficient than ReAct for cost as\\nit involves less frequent LLM invocations. Interestingly, LLMCompiler also outperforms the recent OpenAI parallel\\nfunction calling in cost efficiency. This is because LLMCompiler ’s planning phase is more prompt length efficient\\nthan that of OpenAI parallel function calling since our Planner’s in-context examples are rather short and only include\\nplans, not observations (see Appendix A.7).\\n4.2 Parallel Function Calling with Dependencies\\nThe cases considered above are rather simple, as only one tool is used and all tasks can be executed independently\\nof one another. However, similar to code execution in traditional code blocks, we may encounter function calling\\nscenarios that involve more complex dependencies. To systematically evaluate the capability to plan out function\\ncalling in scenarios that involve complex task dependencies, we have designed a custom benchmark called ParallelQA.\\nThis benchmark is designed to incorporate non-trivial function calling patterns, including three different types of\\npatterns in Figure 3 (b) and (c). Inspired by the IfQA benchmark [60], ParallelQA contains 113 examples that involve\\nmathematical questions on factual attributes of various entities. In particular, completing the task requires using\\ntwo tools (i.e., search and math tools), with the second tool’s argument depending on the result of the first tool’s\\noutput. We have meticulously included questions that are only answerable with information from Wikipedia’s first\\nparagraph, effectively factoring out the failure cases due to unsuccessful searches. See Appendix A.8 for more details\\nin ParallelQA.\\nExperimental Setups. 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text='Similar to Sec. 4.1, we use ReAct [58] as the main baseline. Here, both LLMCompiler and\\nReAct are equipped with two tools: (1) the search tool, identical to the one mentioned in Sec.4.1; and (2) the math\\n1Unfortunately, we are unable to conclude why this is the case, as OpenAI has not publicly disclosed any details about their function calling\\nmechanism. One speculation is that there might be additional overheads to validate the function and argument names and to convert them into a\\nsystem prompt. 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-0.05392996221780777], metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='8d48e4a4-a62e-4339-bbc1-b744e54ee822', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '8', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='1f88cb8d1d9fb8253751ea32dbc14b20217661f00e34f0fd1b907350f97b3d49')}, text='tool, which solves mathematical problems. The math tool is inspired by the Langchain [26]’s LLMMathChain , which\\nuses an LLM as an agent that interprets input queries and invokes the numexpr function with the appropriate formula.\\nThis enables the math chain to address a broad spectrum of math problems that are written both in mathematical and\\nverbal form. See Appendix A.4 for more details on experimental setups.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='645b79bf-9a46-4a92-82ca-6414852d9026', embedding=[-0.00673851789906621, 0.015301684848964214, -0.004551053512841463, -0.03764834254980087, -0.0160625409334898, 0.017062928527593613, -0.01241324469447136, 0.00044427369721233845, -0.05633161589503288, -0.036352068185806274, 0.027545850723981857, 0.02653137408196926, -0.0018123197369277477, -0.006051633041352034, 0.009785469621419907, 0.006351044401526451, 0.011779197491705418, 0.015484853647649288, 0.005163966212421656, 0.0013781731249764562, -0.0025978342164307833, -0.009602299891412258, -0.00588255375623703, -0.02815171703696251, -0.0011157478438690305, -0.01509033516049385, 0.020500876009464264, -0.04173442721366882, 0.0033921552821993828, 0.00877099297940731, 0.024417880922555923, -0.02619321458041668, 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-0.053457267582416534], metadata={}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='8d48e4a4-a62e-4339-bbc1-b744e54ee822', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '8', 'file_name': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_path': '/workspaces/llama_index/llama-index-packs/llama-index-packs-koda-retriever/examples/data/llm_compiler.pdf', 'file_type': 'application/pdf', 'file_size': 885090, 'creation_date': '2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='1f88cb8d1d9fb8253751ea32dbc14b20217661f00e34f0fd1b907350f97b3d49')}, text='Accuracy and Latency. As shown in the ParallelQA row of Table 1, LLMCompiler arrives at the final answer\\nwith an average speedup of 2.15×with gpt-4-turbo and 2.27×with LLaMA-2 70B by avoiding sequential execution\\nof the dependency graphs. Beyond the latency speedup, we observe higher accuracy of LLMCompiler with the\\nLLaMA-2 model as compared to that of ReAct, due to the reasons discussed in Sec. 4.1. Particularly in the LLaMA-2\\nexperiment, where LLMCompiler achieves around a 9% increase in accuracy, we noted that ∼20% of the examples\\nexperienced repetitive function calls with ReAct, aligning with our observations from the accuracy analysis detailed in\\nAppendix A.1. Additionally, a comprehensive analysis of LLMCompiler ’s failure cases is provided in Appendix A.2,\\nwhere we note minimal Planner failures, highlighting LLMCompiler ’s effectiveness in breaking down problems into\\ncomplex multi-task dependencies.\\nCost. Similar to Sec.4.1, LLMCompiler demonstrates substantial cost reductions of 4.65 ×and 2.57 ×compared\\nto ReAct and OpenAI’s parallel function calling, respectively, as indicated in Table 2. This efficiency stems from\\nLLMCompiler ’s reduced frequency of LLM invocations, which is also the case with OpenAI’s parallel function\\ncalling, which is limited to planning out immediate parallelizable tasks, not the entire dependency graph. For example,\\nin Figure 3 (c), OpenAI’s method would necessitate three distinct LLM calls for initial search tasks, following math\\ntasks, and the final math task. In contrast, LLMCompiler achieves this with a single LLM call, planning all tasks\\nconcurrently.\\n4.3 Parallel Function Calling with Replanning\\nIn the previous sections, we have discussed cases in which dependency graphs can be determined statically. However,\\nthere are cases where dependency graphs need to be constructed dynamically depending on intermediate observations.\\nHere, we consider one such dynamic approach in the context of the Game of 24 with the Tree-of-Thoughts (ToT)\\nstrategy proposed in [57]. The Game of 24 is a task to generate 24 using a set of four numbers and basic arithmetic\\noperations. For example, from the numbers 2, 4, 4, and 7, a solution could be 4×(7−4)×2=24. ToT approaches\\nthis task through two iterative LLM processes: (i) the thought proposer generates candidate partial solutions by select-\\ning two numbers and applying an operation (e.g. 2, 3, 7 from 2, 4, 4, 7 by calculating 7 - 4); (ii) the state evaluator\\nassesses the potential of each candidate. Only the promising candidates are then processed in subsequent iterations of\\nthe thought proposer and state evaluator until 24 is reached. Details about the Game of 24 benchmark and the ToT\\nstrategy can be found in Appendix A.9.\\nWhile ToT achieves significant improvement at solving the Game of 24, its sequential, breadth-first search ap-\\nproach through the state tree can be time-consuming. LLMCompiler offers a faster alternative by enabling parallel\\nexecution of the thought proposer and the subsequent feasibility evaluator, akin to a parallel beam search method.\\nExperimental Setups. Although LLMCompiler offers latency advantages, solving this problem with a single\\nstatic graph is not feasible, as the Planner cannot plan out the thought proposing stage before identifying the se-\\nlected candidates from the state evaluator of the previous iteration. Consequently, the Planner is limited to planning\\nonly within one iteration at a time. To address this, we resort to LLMCompiler ’s replanning capability. In par-\\nticular, LLMCompiler is equipped with three tools: thought proposer andstate evaluator , which are\\nboth LLMs adapted from the original ToT framework, and topkselect , which chooses the top kcandidates\\nfrom the thought proposer based on the state evaluator ’s assessment. After all these tools are executed,\\nLLMCompiler can decide to “replan” if no proposal reaches 24, triggering the Planner to devise new plans using\\nthe shortlisted states from topkselect of the previous iteration. In this way, LLMCompiler can dynamically\\nregenerate plans of each iteration, being able to tackle highly complex tasks that require iterative replanning based on\\nthe outcomes of previous plans.\\nTo evaluate LLMCompiler ’s performance on the Game of 24, we use 100 different instances of the game. For\\neach problem, we consider the output as successful if its operations are valid and yield 24 while also using the provided\\nnumbers exactly once each. Further details on experiment setups are outlined in Appendix A.4.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='352c9289-4207-40c3-a1d7-1c1f6d02281f', embedding=[-0.0011215460253879428, -0.007141557987779379, 0.004922923166304827, -0.018699420616030693, -0.006631446536630392, 0.012389414943754673, -0.01289952639490366, 0.005052198190242052, -0.027881424874067307, -0.019132666289806366, 0.0048425630666315556, 0.02210948057472706, -0.010789202526211739, 0.005251350812613964, -0.038572799414396286, 0.03348566219210625, 0.010810166597366333, 0.0046783494763076305, 0.0004860906337853521, -0.0023094764910638332, -0.029209110885858536, 0.00848322082310915, 0.0010141081875190139, -0.008706830441951752, -0.004933404736220837, 0.011453046463429928, -0.0006144919316284359, -0.027322398498654366, 0.03206014260649681, -0.03348566219210625, 0.0440233014523983, 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hash='65d42a3919b691f6c9bd51a64df4b6228357528bdd6e303b74400e9714cc14eb')}, text='Success Rate and Latency. In the last two rows of Table 1, we explore the latency and success rate of LLMCompiler\\nin comparison to the baseline described in [57] on the Game of 24 benchmark. With the gpt-4 model, LLMCompiler\\ndemonstrates a 2.89×enhancement in latency while slightly improving the success rate compared to the baseline.\\nSimilarly, when applied with the LLaMA-2 model, LLMCompiler shows a 2.01×improvement in latency, again\\nwithout compromising on success rate. These results demonstrate not only a significant latency reduction without\\nquality degradation, but also the replanning capability of LLMCompiler for solving complex problems.\\n4.4 Application: LLMCompiler in Interactive Decision Making Tasks\\nIn this section, we demonstrate that LLMCompiler can explore language-based interactive environments effectively\\nby benchmarking LLMCompiler on WebShop [56]. More details of the WebShop environment are provided in\\nAppendix A.10. As highlighted in [45, 58, 56], WebShop exhibits considerable diversity, which requires extensive\\nexploration to purchase the most appropriate item. While recent work feature advanced exploration strategies and\\nshow promising results [62, 32], their approaches are largely based on a sequential and extensive tree search that\\nincurs significant latency penalties. Here, LLMCompiler showcases an exploration strategy that is both effective and\\nefficient with the use of parallel function calling. Our method enables broader exploration of items in the environment,\\nwhich improves success rate compared to ReAct. At the same time, this exploration can be parallelized, yielding up\\nto101.7×speedup against the baselines that perform sequential exploration.\\nExperimental Setups. We evaluate LLMCompiler against three baselines on this benchmark, ReAct [58], LATS [62],\\nand LASER [32], using 500 WebShop instructions. The evaluation metrics are success rate, average score, and latency\\n(more details in Appendix A.10). For this experiment, LLMCompiler is equipped with two tools: search and\\nexplore . The search function triggers the model to generate and dispatch a query that returns a list of typically\\nten items from the Webshop environment. The explore function then clicks through links for each of the found\\nitems and retrieves information about options, prices, attributes, and features that are available. Finally, based on\\nthe gathered information, LLMCompiler decides on the item that best matches the input instruction for purchasing.\\nFurther details on experiments can be found in Appendix A.4.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='3be68799-4672-49d8-9075-283374bd66c5', embedding=[-0.009322949685156345, 0.022206351161003113, 0.0025595214683562517, -0.047129515558481216, 0.0037642125971615314, 0.013534006662666798, -0.011946816928684711, 0.0015630597481504083, -0.03774936869740486, -0.03717740625143051, 0.015342830680310726, 0.0398942194879055, -0.021119628101587296, 0.0037677872460335493, 0.010423973202705383, 0.005562312435358763, 0.009430192410945892, 0.0030456876847893, -0.0004334385448601097, -0.006781302392482758, 0.0020822922233492136, -0.017373288050293922, -0.012018311768770218, -0.024722976610064507, -0.004679348785430193, -0.015986286103725433, 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'2024-02-25', 'last_modified_date': '2024-02-07', 'last_accessed_date': None}, hash='65d42a3919b691f6c9bd51a64df4b6228357528bdd6e303b74400e9714cc14eb')}, text='Performance and Latency. Our approach significantly outperforms all baseline models as shown in Table 3. When\\nusing gpt-3.5-turbo, LLMCompiler achieves a 25.7 %and 7.5 %improvement in success rate against ReAct and\\nLATS; with gpt-4, our method improves upon ReAct and LASER by 17.6 %and 2.8 %, respectively. In terms of latency,\\nLLMCompiler exhibits a 101.7×and2.69×speedup against LATS and LASER. While we note that LLMCompiler\\nexecution is slightly slower than ReAct on this benchmark, mainly due to the Planner overhead, we also highlight that\\nthe gains in success rate far outweigh the minor latency penalty.\\nWe further delve into why LLMCompiler attains such an improved success rate and score compared to ReAct.\\nBased on our observations, we discover that the ReAct agent tends to commit to a decision with imperfect information,\\na scenario that can arise when the agent has not gathered sufficient details about the features and options available for\\nitems. This observation was also noted in [45] – without exploring more items in the environment, the agent strug-\\ngles to differentiate between seemingly similar choices, ultimately failing to make the correct decision. In contrast,\\nLLMCompiler undergoes further exploration by visiting all ten items found by search and retrieving relevant in-\\nformation about each item. We find that employing an effective search strategy is critical to decision-making tasks\\nsuch as the WebShop benchmark.\\nThe relatively high performance of LATS can also be explained in terms of its exploration scheme. In this frame-\\nwork, the agent executes a brute-force search through the state and action space of Webshop, exploring as many as 30\\ntrajectories before making the final purchase. While this approach provides richer information for decision-making,\\nthe end-to-end execution becomes prohibitively slow.\\nFinally, we report that the average scores of both LATS and LASER fall within the range of standard deviation of\\nour method’s. The average score estimates with standard deviation for LLMCompiler are72.1±4.01 and75.0±\\n1.43 for gpt-3.5-turbo and gpt-4, respectively. Further note that while the performance differences are marginal, our\\nmethod exhibits significant execution speedup, 101.7×over LATS and 2.69×over LASER.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='098584bf-93e3-4f67-b099-297ae5e30e66', embedding=[0.014445400796830654, -0.0014934545615687966, 0.0019756618421524763, -0.012981433421373367, -0.0031222046818584204, 0.027544783428311348, -0.02166116237640381, -0.0034777892287820578, -0.0319991298019886, -0.021730544045567513, 0.006861912086606026, 0.018927499651908875, -0.008596470579504967, -0.003430956043303013, -0.01104566641151905, 0.02166116237640381, 0.015402876771986485, 0.0019895383156836033, -0.0006495920242741704, -0.011434207670390606, -0.011344010941684246, -0.0055575245060026646, 0.0016460956539958715, -0.010934654623270035, -0.013009185902774334, -0.008978073485195637, 0.0040415204130113125, 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hash='dcab6538d0571537d8bc357f52693cbd57af139d5fc47d8e0a85c265f0b79856')}, text='Table 3: Performance and Latency Analysis for WebShop. We evaluate LLMCompiler with two models: gpt-4 and\\ngpt-3.5-turbo and compare LLMCompiler against three baselines: ReAct, LATS, and LASER. We report success\\nrate and average score in percentage. We reproduce the success rate and average score for ReAct, while those for\\nLATS and LASER are from their papers. N denotes the number of examples used for evaluation.\\nModel Method Succ. Rate Score Latency (s) N\\ngpt-3.5-turboReAct 19.8 54.2 5.98 500\\nLATS 38.0 75.9 1066 50\\nLLMCompiler 44.0 72.8 10.72 50\\nLLMCompiler 45.5 72.1 10.48 500\\ngpt-4-0613ReAct 35.2 58.8 19.90 500\\nLASER 50.0 75.6 72.16 500\\nLLMCompiler 52.8 75.0 26.73 500\\n5 Conclusions\\nExisting methods for invoking multiple functions with LLMs resort to sequential and dynamic reasoning, and as a\\nresult, they suffer from inefficiencies in latency, cost, and accuracy. As a solution, we introduced LLMCompiler ,\\na compiler-inspired framework that enables efficient parallel function calling across various LLMs, including open-\\nsource models like LLaMA-2 and OpenAI’s GPT series. By decomposing user inputs into tasks with defined inter-\\ndependencies and executing these tasks concurrently through its Planner, Task Fetching Unit, and Executor compo-\\nnents, LLMCompiler demonstrates substantial improvements in latency (up to 3.7×), cost efficiency (up to 6.7×),\\nand accuracy (up to ∼9%), even outperforming OpenAI’s parallel function calling feature in latency gains. Further ex-\\nploration that builds upon LLMCompiler will enhance thereby revolutionizing the future development of LLM-based\\napplications. We look forward to future work building upon our framework that will improve both the capabilities and\\nefficiencies of LLMs in executing complex, large-scale tasks, thus transforming the future development of LLM-based\\napplications.\\nAcknowledgements\\nWe appreciate the valuable feedback from Minwoo Kang. We acknowledge gracious support from Furiosa team.\\nWe also appreciate the support from Microsoft through their Accelerating Foundation Model Research, including\\ngreat support from Sean Kuno. Furthermore, we appreciate support from Google Cloud, the Google TRC team, and\\nspecifically Jonathan Caton, and Prof. David Patterson. 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hash='dcab6538d0571537d8bc357f52693cbd57af139d5fc47d8e0a85c265f0b79856')}, text='Prof. Keutzer’s lab is sponsored by the Intel corporation, Intel\\nOne-API, Intel VLAB team, the Intel One-API center of excellence, as well as funding through BDD and BAIR. We\\nalso appreciate support from Samsung including Dongkyun Kim, and David Thorsley. 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Ellick Chan, Saurabh Tangri, Andres Rodriguez, and Kittur Ganesh. Sehoon Kim and Suhong Moon\\nwould like to acknowledge the support from the Korea Foundation for Advanced Studies (KFAS). Amir Gholami was\\nsupported through funding from Samsung SAIT. Michael W. Mahoney would also like to acknowledge a J. P. Morgan\\nChase Faculty Research Award as well as the DOE, NSF, and ONR. Our conclusions do not necessarily reflect the\\nposition or the policy of our sponsors, and no official endorsement should be inferred.\\nReferences\\n[1] https://github.com/nvidia/tensorrt-llm.\\n[2] https://huggingface.co/text-generation-inference.\\n[3] Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang,\\nCarrie Cai, Michael Terry, Quoc Le, and Charles Sutton. Program synthesis with large language models, 2021.\\n[4] BIG bench authors. Beyond the imitation game: Quantifying and extrapolating the capabilities of language\\nmodels. 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hash='85632c6162c3380189296a3a7706042cf90df2e3387149924d5b486737933b5b')}, text='[5] Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz\\nLehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler. Graph of thoughts:\\nSolving elaborate problems with large language models, 2023.\\n[6] Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper.\\nAccelerating large language model decoding with speculative sampling, 2023.\\n[7] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri\\nEdwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov,\\nHeidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea\\nPower, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cum-\\nmings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-V oss, William Hebgen Guss, Alex\\nNichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders,\\nChristopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford,\\nMatthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei,\\nSam McCandlish, Ilya Sutskever, and Wojciech Zaremba. Evaluating large language models trained on code.\\n2021.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='01e351f2-ecb3-4e7d-b3ac-321e921af00d', embedding=[-0.02147151343524456, 0.0031661263201385736, 0.010033749043941498, -0.014536526054143906, 0.00182238407433033, 0.020875869318842888, -0.01801110990345478, -0.01101230550557375, -0.017415467649698257, -0.03468911722302437, 0.033384375274181366, 0.05037438124418259, -0.03571021929383278, 0.00855882279574871, 0.007016533054411411, 0.004123409744352102, 0.0171743743121624, 0.007080351933836937, 0.009771382436156273, 0.016692187637090683, 0.0026679844595491886, -0.0006005180766806006, -0.013550878502428532, -0.0025492103304713964, -0.005119693465530872, 0.014018883928656578, 0.03715677931904793, -0.03043452277779579, -0.0029214872047305107, -1.343409894616343e-05, 0.02430790849030018, -0.005296968389302492, 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Cohen. Program of thoughts prompting: Disentan-\\ngling computation from reasoning for numerical reasoning tasks, 2023.\\n[9] Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexan-\\nder Borzunov, Torsten Hoefler, and Dan Alistarh. Spqr: A sparse-quantized representation for near-lossless llm\\nweight compression, 2023.\\n[10] Elias Frantar and Dan Alistarh. Sparsegpt: Massive language models can be accurately pruned in one-shot, 2023.\\n[11] Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. GPTQ: Accurate post-training compression\\nfor generative pretrained transformers. arXiv preprint arXiv:2210.17323 , 2022.\\n[12] Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham\\nNeubig. Pal: Program-aided language models. arXiv preprint arXiv:2211.10435 , 2022.\\n[13] Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. Reasoning\\nwith language model is planning with world model, 2023.\\n[14] Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir\\nPuranik, Horace He, Dawn Song, and Jacob Steinhardt. Measuring coding challenge competence with apps.\\nNeurIPS , 2021.\\n[15] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt.\\nMeasuring massive multitask language understanding. Proceedings of the International Conference on Learning\\nRepresentations (ICLR) , 2021.\\n[16] Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob\\nSteinhardt. Measuring mathematical problem solving with the math dataset. 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Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Ges-\\nmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In Kamalika Chaud-\\nhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning ,\\nvolume 97 of Proceedings of Machine Learning Research , pages 2790–2799. PMLR, 09–15 Jun 2019.\\n[18] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu\\nChen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Repre-\\nsentations , 2022.\\n[19] Andrej Karpathy. 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hash='cee516454f7722d44cb924a591caab9086e67d2f90ab0817a0f2908d5c624b0b')}, text='[20] Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabhar-\\nwal. Decomposed prompting: A modular approach for solving complex tasks. In The Eleventh International\\nConference on Learning Representations , 2023.\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='1c6db643-c37e-488b-bb78-5b30816c7ce5', embedding=[-0.02162982150912285, -0.016810813918709755, 0.013906874693930149, -0.002905680099502206, -0.009317675605416298, 0.02409503608942032, -0.019289955496788025, 0.0026340887416154146, -0.022994741797447205, -0.031086774542927742, 0.04373318701982498, 0.040251243859529495, -0.0018837304087355733, 0.0015842834254726768, 0.005219430197030306, -0.013008533045649529, 0.009638014249503613, 0.013356727547943592, 0.005557178519666195, -0.005947155877947807, 0.024568580090999603, -0.005926264449954033, -0.020306682214140892, -0.013976513408124447, 0.003304362529888749, 0.028858331963419914, 0.03331521525979042, -0.02844049781560898, -0.0035272068344056606, -0.009046084247529507, 0.02649061009287834, 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Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, and\\nKurt Keutzer. Squeezellm: Dense-and-sparse quantization, 2023.\\n[22] Sehoon Kim, Karttikeya Mangalam, Suhong Moon, Jitendra Malik, Michael W. Mahoney, Amir Gholami, and\\nKurt Keutzer. Speculative decoding with big little decoder, 2023.\\n[23] Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models\\nare zero-shot reasoners, 2023.\\n[24] Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, and Amir Gholami. A fast\\npost-training pruning framework for transformers, 2022.\\n[25] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez,\\nHao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention.\\nInProceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles , 2023.\\n[26] Langchain. https://github.com/langchain-ai/langchain.\\n[27] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning, 2021.\\n[28] Yaniv Leviathan, Matan Kalman, and Yossi Matias. Fast inference from transformers via speculative decoding,\\n2023.\\n[29] Yaobo Liang, Chenfei Wu, Ting Song, Wenshan Wu, Yan Xia, Yu Liu, Yang Ou, Shuai Lu, Lei Ji, Shaoguang\\nMao, Yun Wang, Linjun Shou, Ming Gong, and Nan Duan. Taskmatrix.ai: Completing tasks by connecting\\nfoundation models with millions of apis, 2023.\\n[30] Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, Chuang Gan, and Song Han. Awq: Activation-\\naware weight quantization for llm compression and acceleration, 2023.\\n[31] Jerry Liu. 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Kaixin Ma, Hongming Zhang, Hongwei Wang, Xiaoman Pan, and Dong Yu. Laser: Llm agent with state-space\\nexploration for web navigation, 2023.\\n[33] Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha\\nDziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann,\\nSean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refinement with self-feedback, 2023.\\n[34] Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, and Yu Wang. Skeleton-of-thought: Large\\nlanguage models can do parallel decoding, 2023.\\n[35] OpenAI. Gpt-4 technical report, 2023.\\n[36] OpenAI. New models and developer products announced at devday, 2023.\\n[37] Charles Packer, Vivian Fang, Shishir G. Patil, Kevin Lin, Sarah Wooders, and Joseph E. Gonzalez. Memgpt:\\nTowards llms as operating systems, 2023.\\n[38] Pruthvi Patel, Swaroop Mishra, Mihir Parmar, and Chitta Baral. Is a question decomposition unit all we need? In\\nYoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 Conference on Empirical\\nMethods in Natural Language Processing , pages 4553–4569, Abu Dhabi, United Arab Emirates, December\\n2022. 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hash='cee516454f7722d44cb924a591caab9086e67d2f90ab0817a0f2908d5c624b0b')}, text='[39] Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez. Gorilla: Large language model connected\\nwith massive apis, 2023.\\n12', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]\n"
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+      "text/plain": [
+       "{'4593ea90-2088-4185-8db0-6aa65bdc106e': 'How does the choice of retrieval unit, such as document, passage, sentence, or proposition, impact the performance of dense retrieval and downstream tasks in open-domain NLP?',\n",
+       " '861e2131-add7-47cd-afdf-52b6e27562e7': 'Can you explain the significance of using proposition-based retrieval over traditional passage or sentence-based methods in dense retrieval, as highlighted in the study?',\n",
+       " 'db511668-5d08-44d2-ad99-67f8a6f1495d': 'How can the corpus be used for inference in a research study?',\n",
+       " '359a1385-6ae2-4cb7-8970-2564a4f746fd': \"As a teacher, how would you explain the importance of using diverse questions in assessments to ensure a comprehensive evaluation of students' understanding?\",\n",
+       " 'c8b0cefb-d6d0-4aca-9c0f-ce55d051fe91': 'How does the choice of retrieval unit impact the performance of dense retrieval models according to the information provided in the document?',\n",
+       " '0a15445b-f7ae-457a-9800-0f8fc29a74f7': 'What is the significance of selecting the proper retrieval granularity at inference time in dense retrieval inference, as discussed in the context information?',\n",
+       " 'cf57dc39-e3a8-46a4-8102-9fc47d5d3fac': 'What was the angle at which the Leaning Tower of Pisa leaned prior to the restoration work performed between 1990 and 2001, and what angle does it lean at now?',\n",
+       " '4f3e6d37-2a59-485e-a775-ce32db5410a8': 'How far is the top of the Leaning Tower of Pisa displaced horizontally from the center, according to the information provided?',\n",
+       " '49a62875-735c-4e90-945c-2126d4756d3a': 'How does the use of different granularities, such as passages, sentences, and propositions, impact the performance of dense retrievers in retrieval and downstream open-domain QA tasks?',\n",
+       " 'af6c593d-01e0-417c-a69e-c333d3a2aca3': 'How does the concept of using propositions as a novel retrieval unit for dense retrieval address the shortcomings of typical retrieval units like passages or sentences in providing relevant information for question-answering tasks?',\n",
+       " 'f496ef71-c554-4009-b199-9161565db4eb': 'How does the use of propositions as retrieval units improve dense retrieval performance according to the information provided in the document?',\n",
+       " 'eebe3cc4-ff0d-4ff4-bebc-523517fb3708': 'What are the key advantages of proposition-based retrieval over sentence and passage-based methods, as discussed in the document?',\n",
+       " 'f940653c-0312-42ce-af0d-9745b8ccea26': 'How does the use of proposition as a retrieval unit impact the performance of dense retrieval models compared to traditional retrieval units like passages or sentences?',\n",
+       " '8bea6d8f-b151-432b-b823-432840a24c11': 'Can you explain the three principles that define a proposition as a retrieval unit and how they contribute to the interpretation of text semantics?',\n",
+       " 'cf1254ed-e0d2-4b8e-96b7-76240fd9c9d1': 'What is the main focus of the study mentioned in the context information, and how is it related to information-seeking questions?',\n",
+       " 'cc2f85fb-03f6-481d-9ea1-3c60dc6d5858': 'How does the study compare the use of 100-word passages, sentences, and propositions as retrieval units on Wikipedia for knowledge-intensive NLP tasks?',\n",
+       " '4e82c1a4-4138-47f2-b9fc-6c84d3bea795': 'How does the Propositionizer segment Wikipedia pages into propositions, and what is the two-step distillation process involved in training the model?',\n",
+       " '123c7f05-ddd8-41b9-a4a9-24c6d5ccea0a': 'In the experimental settings, what impact is being evaluated regarding the three retrieval unit choices, and how is the evaluation conducted on open-domain QA datasets using FACTOID WIKI?',\n",
+       " '5aed46ab-d57a-46d2-a74c-5629c67a96e6': 'What are the five open-domain QA datasets evaluated in the study, and what is the common retrieval source for these datasets?',\n",
+       " '7877f4b0-1fe4-439b-8941-d1c5e5c565df': 'Can you name and briefly describe two of the dense retrieval models compared in the study?',\n",
+       " '63ea8e34-ffe4-4d76-bd74-7570e0ee4b2d': 'Compare and contrast the training methods of SimCSE, Contriever, DPR, ANCE, TAS-B, and GTR in the context of passage retrieval models. How do these models differ in terms of supervision, data sources, and training approaches?',\n",
+       " '5a36afc6-5d31-4923-84a9-802096352680': 'Discuss the evaluation criteria for passage retrieval performance at the passage, sentence, and proposition levels. How does the choice of indexing level impact the effectiveness of retrieval models? Provide examples of models mentioned in the context and their performance in different indexing levels.',\n",
+       " 'feeb318e-134a-4218-91b1-3993ca71ea3d': 'How does the use of proposition-level retrieval impact the performance of retrieval models compared to sentence or passage-level retrieval in open-domain QA tasks?',\n",
+       " '4660ca42-3219-465b-9186-4256660b8d4a': 'In what ways do dense retrievers, such as SimCSE and Contriever, demonstrate improved performance when utilizing proposition-level retrieval over sentence or passage-level retrieval in the context of downstream QA evaluation?',\n",
+       " 'a0448649-3bc1-4a09-89eb-39a58bc70e04': 'Compare the performance of unsupervised dense retrievers, such as SimCSE and Contriever, with supervised dense retrievers like DPR and ANCE, in terms of passage retrieval on various open-domain QA datasets. How does the granularity of the retrieval corpus affect the retrieval performance in each case?',\n",
+       " 'a471ef0f-731d-4606-8132-4abaa15b4268': 'Discuss the impact of including the training split of the target dataset in the training data of dense retrievers on their performance in passage retrieval. How do models like TAS-B and GTR perform in comparison to other dense retrievers across different granularity levels in the retrieval corpus?',\n",
+       " '939a846d-6db2-4058-88ad-bc35863907cf': 'Explain the concept of document retrieval recall and how it is influenced by the popularity of the target entity in each question from the EntityQuestions dataset. Discuss the advantages of retrieval by proposition over other retrieval granularities in this context.',\n",
+       " 'e0bae0b8-f93c-4bd1-8ed7-c66f18ee3f6d': 'How does the advantage of retrieval by proposition become most visible in cross-task generalization settings, as observed in the results presented? Provide examples from the SQuAD and EntityQuestions datasets to support your explanation.',\n",
+       " 'a2b96b35-4534-4bf1-b1b0-7937bacb8447': 'Compare and contrast the performance of unsupervised dense retrievers (SimCSE and Contriever) with supervised dense retrievers (DPR, ANCE, TAS-B, and GTR) in the context of open-domain QA, specifically focusing on Exact Match (EM) scores under the retrieve-then-read setting with limited retrieved words to the reader QA model at l=100 or 500.',\n",
+       " '7e8fe9ba-029e-4009-b78d-fac9489d7323': 'Analyze the impact of different retrieval methods (SimCSE, Contriever, DPR, ANCE, TAS-B, and GTR) on the performance of the UnifiedQA V2 reader model in open-domain QA tasks, considering variations in granularity (Passage, Sentence, Proposition) and retrieval limitations (l=100 or 500).',\n",
+       " '50dabb83-aae8-4061-abb2-8be60cf54ab0': 'How does the use of smaller retrieval units impact the performance of question answering in the context of dense retrievers?',\n",
+       " '311b0b04-7a78-476c-86e3-6a34c734ea75': 'In what way does the popularity of a target entity in Wikipedia affect the retrieval performance of EntityQuestions targeting the properties of longer-tail entities?',\n",
+       " 'dde52939-aaf4-44c4-8f63-4f4285f933ae': 'How does the use of BM25 help estimate the popularity of entities in the given method? Discuss the significance of using the number of occurrences of an entity in its top-1000 passages as an estimate of its popularity.',\n",
+       " 'd3f91449-4e76-4ca6-bfd4-dd38154929bd': 'Explain the findings regarding the performance gap between retrieving by proposition and retrieving by passage based on the popularity of target entities in each question. How does the retrieval by proposition contribute to improving the cross-task generalization performance of dense retrievers?',\n",
+       " '483305e3-c386-4a32-bf14-047a201762fd': 'How does the recall of the gold answer in the retrieved text vary across different numbers of words for the GTR retriever?',\n",
+       " '15594f0f-ae6e-4086-a5e0-01390aea7cc6': 'What advantage do propositions have over sentences or passages as retrieval units, in terms of the density of relevant information to the query?',\n",
+       " 'c8cb7d37-eb5b-458c-8015-008e4d56bd51': 'How does proposition retrieval demonstrate a higher success rate compared to sentence and passage retrieval methods, according to the analysis shown in Figure 4?',\n",
+       " '339172c5-3dff-499c-b45c-f146665c3b94': 'What challenges do dense retrievers face with passage-level retrieval, as illustrated in the examples provided in Table 4?',\n",
+       " 'de56b346-2719-43ea-bc97-d142ccdfa14a': 'What is the common architectural approach adopted by recent works on dense retrievers, as mentioned in the context?',\n",
+       " '33e19504-a5a6-4bfb-a7fc-4f64ce8c7e63': 'Can you explain the significance of the dual-encoder architecture in the context of dense retrievers?',\n",
+       " 'b29f57d6-097f-4be4-a114-4153cf8e8e21': 'What was the theme of Super Bowl 50 according to the passage retrieval information provided?',\n",
+       " '7755bd3b-ce1c-453a-a7c0-daad44023b4a': 'According to the proposition retrieval information, what is the function of the pericardial sac in relation to the heart?',\n",
+       " '2578891f-e45e-4907-9329-3e1246c7d6db': 'How do dual encoder models measure the relevance between queries and documents, and what are some techniques used to improve their generalization performance to new tasks?',\n",
+       " '8d79ddb7-121e-4266-87ce-6ce9386851d1': 'What is the motivation behind using propositions as a unit of text representation in dense retrievers, and how does this strategy differ from updating the dense retriever model or its parameters?',\n",
+       " 'dee3bc02-3fd6-44c8-a539-cef98107c1e1': 'How does the use of propositions as retrieval units in indexing corpus improve dense retrieval performance at inference time, according to the information provided?',\n",
+       " 'b334b9a3-308c-4bee-934e-44a1310ec1f6': 'Discuss the limitations of the current study on the granularity of retrieval corpus as outlined in the context information.',\n",
+       " 'd81db890-a614-4072-8f66-85b6618c23a9': 'How does the Self-rag model proposed by Akari Asai et al. in 2023 aim to improve document retrieval through self-reflection?',\n",
+       " '814cff30-ec93-4b50-9ed8-0c2adbc5102d': 'Discuss the significance of cross-domain modeling of sentence-level evidence for document retrieval as presented in the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.',\n",
+       " '3339b8b1-24eb-4394-81f8-85ae8a9d8a4a': 'What are the key differences between the approaches discussed in the papers by Bohnet et al. (2013) and Chen et al. (2023a) in terms of semantic parsing and proposition-level segmentation and entailment recognition?',\n",
+       " '97c296c4-96b1-4190-9e55-6652b607cdbb': 'How do the pre-training tasks for embedding-based large-scale retrieval, as outlined by Chang et al. (2020), contribute to the advancements in natural language processing discussed in the provided context information?',\n",
+       " 'c2112f32-158c-41fe-a55f-a2d5b47f8cc9': 'How does the concept of \"Salient phrase aware dense retrieval\" differ from traditional sparse retrieval methods in information retrieval systems?',\n",
+       " 'ab96b7bc-1e8b-4cd8-8bb3-08526ce968e9': 'What are the key strategies proposed by Hao Cheng et al. in their work on task-aware specialization for efficient and robust dense retrieval in open-domain question answering systems?',\n",
+       " '83326296-6a14-4c46-8a40-43950bb32616': 'How does the pretraining of mainstream LLMs, such as LLaMA, on English-dominant corpus impact their performance in non-English languages? Discuss the key factors that can affect the transfer of language generation and instruction following capabilities to a non-English language based on the empirical investigation conducted in the study.',\n",
+       " 'ebe3689f-7398-410b-9145-924c7fa10140': \"Evaluate the effectiveness of transferring language capabilities to non-English languages based on the evaluation results presented in the study. Discuss the model's performance in terms of knowledge alignment and response quality, considering factors such as accuracy, fluency, informativeness, logical coherence, and harmlessness. Additionally, analyze the experimental outcomes across low-resource languages and their implications for the development of non-English LLMs.\",\n",
+       " '7608e943-4131-4e18-9540-41c671193b32': 'How do the capabilities of Language Model Machines (LLMs) such as reasoning, planning, and learning from experience compare to human levels according to recent studies mentioned in the text?',\n",
+       " '8d2a3bb9-347e-4bc6-99db-d6157265b22f': 'Discuss the challenges faced by LLMs in handling non-English languages, particularly in terms of vocabulary extension, further pretraining, and instruction tuning.',\n",
+       " 'e4f6be09-5342-470b-beef-2715694a9f45': 'What are some key features and capabilities of the LLaMA language model as discussed in the document arXiv:2401.01055v2?',\n",
+       " 'f1a79612-c427-4750-951c-76cc70639c6d': 'How does LLaMA compare to other widely discussed language models in terms of its advancements and potential impact in the field?',\n",
+       " 'f1f900f8-64a0-4366-9d96-c1257484a452': 'How does the limited training data from 20 related languages impact the performance of LLaMA in contexts involving non-English languages?',\n",
+       " 'a2a82e36-c20d-4cc6-bd19-9094816c12e4': 'What approach did Cui, Yang, and Yao take to improve the performance of LLaMA in non-English languages, and what were the results of their efforts?',\n",
+       " 'ffd4bdb9-0b56-4b66-b97f-6898b6847ef6': 'How does the impact of vocabulary extension on transfer in LLMs differ when further pretraining is done on the original vocabulary compared to the extended vocabulary? What implications does this finding have for small-scale incremental pretraining?',\n",
+       " '74628875-49f3-4dda-8526-7611271ca871': 'Explain the importance of instruction tuning (SFT) in enhancing the capability of LLMs to follow instructions. How does training on diverse instruction tasks help the model better comprehend and respond to human instructions?',\n",
+       " '67e7db5e-4809-4ac3-bcf5-1bf1e3dfdc1c': 'How do LLMs trained on English-dominant corpora perform when extrapolated to non-English languages, and what are the common approaches used to address this challenge?',\n",
+       " '31e13ad2-746c-46f8-8508-e3234632ea3a': 'What are the three steps involved in extrapolating LLMs to non-English languages, and how does conducting a comprehensive empirical study help in comparing the performance differences before and after vocabulary extension?',\n",
+       " '0b20d7af-6c0d-49fa-b7c4-9c9448eecd53': 'How does the experimental setup in the paper aim to explore the transfer of language generation and instruction-following capabilities to non-English languages? What is the significance of starting with Chinese as the initial language for this exploration?',\n",
+       " '9f1b8673-b12a-4b4b-b2a4-f469b55f94b1': 'Compare and contrast the different models mentioned in the document, such as LLaMA, LLaMA2, Chinese LLaMA, and Open Chinese LLaMA, in terms of their pretraining scales and the impact on effective transfer of language capabilities. How do these models serve as references for analysis and comparison in the experiments conducted?',\n",
+       " 'ac318c31-8274-440d-bf6d-37342e679fcc': 'How does LLaMA-13B compare to the larger GPT-3 model in terms of performance on NLP benchmarks?',\n",
+       " 'ef7b2344-b875-473d-85fd-99edce8a95e9': 'What are the key enhancements and upgrades that LLaMA2 has received compared to its predecessor, and how do these improvements make it a more powerful tool for tackling advanced language understanding tasks?',\n",
+       " 'bf4cedd0-2722-4c22-b7c5-9ff54dc3ffde': 'How does the tokenizer with a vocabulary size of 49,953 improve the handling of Chinese characters, and what parameter-efficient fine-tuning techniques are employed to reduce memory consumption during model training?',\n",
+       " 'f1a4c785-190e-4822-8e8b-6722e5552ae2': 'Describe the differences between Chinese LLaMA, Chinese LLaMA2, and Open Chinese LLaMA in terms of their training data, vocabulary optimization, and corpus size.',\n",
+       " '48bef39d-65ae-403a-8cc1-25b93c4e2ab4': 'How does Bactrian-X facilitate multilingual instruction tuning, and what datasets were used in its creation?',\n",
+       " '8f923fe8-4be1-4158-a363-3f95a85cbd89': 'Describe the components and evaluation benchmarks of LLM-Eval, C-Eval, and C-Eval HARD as mentioned in the context information.',\n",
+       " '9fd2953b-b9bf-4599-80f3-d24dee5d77c3': 'How does the method described in the document contribute to achieving accurate results?',\n",
+       " '42c58f41-c662-4b8e-832a-cbdf2c75e2ad': 'What are the key components of the method mentioned in the context information, and how do they work together to produce an average outcome?',\n",
+       " '1357b48e-3f76-48f0-9d25-965b437ebbfa': 'Compare the response quality of LLaMA models with different scales of further pretraining and instruction tuning (SFT). How does the performance vary between models with 10Kpretrain, 100Kpretrain, and 1Mpretrain, as well as Chinese LLaMA and Open Chinese LLaMA?',\n",
+       " '9522dbae-25ae-4129-9c09-b2a62a358bc8': 'Analyze the improvements in response quality between LLaMA models such as LLaMA with 1Mpretrain, LLaMA-EXT with 1Mpretrain, LLaMA2, and Chinese LLaMA2. How do these models compare to each other and to the original LLaMA models with varying levels of pretraining?',\n",
+       " '446273bd-06dc-42f8-932c-4b938104ced6': \"How does the MMLU measure a LLM's ability to learn and apply knowledge across diverse subjects? Provide examples of the subjects covered in the test.\",\n",
+       " 'fc85e0a9-0e24-4382-b24e-a3acdbecadd4': 'Based on the experimental results presented in the document, discuss the impact of vocabulary extension on the performance of the Chinese LLaMA model in the LLM-Eval benchmark. What surprising discovery was made regarding the effectiveness of vocabulary extension in this context?',\n",
+       " '38273db4-d0cd-4364-b6b8-098eb18b4d75': 'How does training scale, including both pretraining scale and instruction tuning scale, impact the transferability of LLM capabilities according to the experimental results presented in the document? Provide examples from the study to support your answer.',\n",
+       " '3f9f9501-d12d-4c30-bb9e-2cf8d64ffd23': 'Compare and contrast the performance of LLaMA 7B, Chinese LLaMA 7B, Open Chinese LLaMA 7B, LLaMA2-7B, and LLaMA-13B on standardized test benchmarks such as C-eval, gaokao-bench, and agi-eval. What conclusions can be drawn about the effectiveness of further pretraining and model sizes in enhancing model knowledge levels based on the results presented in the document?',\n",
+       " 'b7270612-0cef-4a3f-a786-b8422a5d10ea': 'How does the study address the potential impact of improved Chinese proficiency on existing English capabilities?',\n",
+       " '9f377b74-2bd3-472a-9737-2d09e7b72b31': 'What method was used to collect Chinese and English samples for analysis in the study?',\n",
+       " 'a2e096ae-cfe7-49f6-86ca-bbdcc2f0e8be': 'How does the increase in pretraining scale affect the perplexity of LLaMA models in both English and Chinese languages, according to the findings presented in the document?',\n",
+       " '2edfa40b-3e16-4695-b4c8-66758ccf4776': 'Explain how the utilization of Open Chinese LLaMA, which incorporates both Chinese and English content in its training data, impacts the perplexity levels in both languages compared to models trained solely on Chinese corpora.',\n",
+       " '7888e078-e3db-42d3-989f-775a5977885b': 'How does the language1k SFT compare to the 65k SFT in terms of accuracy, information, and learning curve?',\n",
+       " 'bdc8dce7-d45e-4fe2-81c9-d1a8cec827c6': 'Can you explain the differences in the average accuracy, information, and learning curve between the ACC. F. INFO. LC. for the language1k SFT and the 65k SFT?',\n",
+       " 'e084df18-adb8-4a46-a23d-ca0568b5ff8e': 'How does the model LLaMA perform in terms of response quality for low-resource languages according to the evaluation results presented in Table 3? Discuss the performance of Arabic, Indonesian, and Vietnamese compared to other languages and explain the possible reasons behind their better performance.',\n",
+       " 'c03b9e79-6777-48ed-b006-0c2f95f2aa59': \"What is the significance of code-switching observed in the model LLaMA's output when fine-tuned with a dataset of 1k instructions for various low-resource languages? How does the occurrence of code-switching support the hypothesis of cross-lingual alignment relationships within LLMs, as discussed in the document?\",\n",
+       " 'a23467aa-bd92-492c-a541-315363dc2698': 'How does the model indicate non-English target language in the output?',\n",
+       " '96c8d258-02df-46fa-87da-88505e7bf88e': \"What does the cyan background represent in the model's output?\",\n",
+       " '61141a2c-b719-4769-9f67-06eca57ca0fe': 'How does the resource gap in Language Model Models (LLMs) impact their performance, especially in languages other than English?',\n",
+       " '58b1c942-cfc8-4478-9732-25cfe9dc8a31': 'Discuss the significance of code-switching rates across different languages in the context of LLMs and their training data.',\n",
+       " 'c2d0b868-f8de-4e59-8be8-487b946acb34': 'How do language models, such as LLMs, demonstrate a disparity in their capability to handle different languages according to previous findings?',\n",
+       " '94b724b0-37fc-4d23-bbaf-cfb7c3dcfbbe': 'What challenges do LLMs face when it comes to comprehending and generating non-English texts, especially in low-resource languages, as mentioned in the context information?',\n",
+       " 'ded9decc-6d53-44db-b91e-52dd8756b32f': 'What are some proposed solutions to address the resource gap in language and field data for pretraining and evaluating LLMs, as mentioned in the text?',\n",
+       " '7a2d1898-3905-4220-9fd6-5167cb96d97f': 'How have researchers and practitioners suggested increasing the amount of data available from various languages and fields for LLMs, according to the information provided?',\n",
+       " '3f9d0de8-8270-4903-9d63-69556d4642c5': 'How do multilingual language models, such as mBERT and XLM-R, bridge the resource gap and demonstrate cross-lingual transferability? Discuss the mechanisms behind their strong cross-lingual performance as highlighted in the text.',\n",
+       " 'da22d243-2321-491d-85b7-bdbdf4cc7329': 'In the context of code-switching, what are the different approaches and findings regarding the performance of multilingual language models on code-switching tasks? How have researchers proposed to improve the capability of these models in handling code-switching scenarios?',\n",
+       " '0bd09f16-e08c-4f63-90c4-81b219aceeae': 'Explain the reasoning behind the development of LLMCompiler and how it addresses the limitations of current methods for multiple function calling.',\n",
+       " '9eba5021-65f2-440c-b479-71a2d018a223': 'Describe the three components of LLMCompiler and how they work together to streamline parallel function calling.',\n",
+       " '55fb943d-2b9e-4b0c-ae07-b327bc8f9eec': 'How does LLMCompiler compare to ReAct in terms of latency speedup, cost savings, and accuracy improvement? Provide specific examples from the benchmarking results mentioned in the document.',\n",
+       " 'bcf49038-4f0d-4088-a21b-05f67a5eacea': 'Explain the approach taken by ReAct in incorporating multiple function calls for LLMs. Use the example provided in Figure 1 (Left) to illustrate how ReAct analyzes queries and reasons about the next action involving function calls.',\n",
+       " '81f6f82a-ecf1-4b6e-893f-fe33b598903c': 'How does the sequential nature of ReAct impact the scalability of more complex applications, and what optimizations are necessary to address this issue?',\n",
+       " '52f3f3ed-00b8-41c7-8f35-6fd33cfe8d66': 'In what ways can the sequential function calling and repetitive LLM invocations in agent systems that extend ReAct lead to inefficiencies in latency and cost, and how can these challenges be mitigated?',\n",
+       " '4289abce-1b35-4823-b25f-731e14ba5778': 'Compare and contrast the runtime dynamics of LLMCompiler and ReAct in handling a sample question from the HotpotQA benchmark. How does the parallel execution in LLMCompiler lead to a latency speedup compared to the sequential execution in ReAct?',\n",
+       " '351b45c6-8064-4206-9726-57a1f4ecbca6': \"Discuss the potential challenges and pitfalls that may arise in the execution flow of LLMCompiler, as mentioned in the context information. How can repetitive invocation of the same function and early stopping based on partial results impact the accuracy of LLM's responses?\",\n",
+       " '7f853613-725c-4086-8cfe-ab0ce642caf6': 'Explain the key optimization technique in compilers that involves identifying instructions that can be executed in parallel and effectively managing their dependencies. How does this technique relate to the proposed LLMCompiler framework for optimizing the orchestration of LLM function calling?',\n",
+       " '18fb096a-cf31-4592-8be5-e6e5ec31aaab': 'Compare the performance of LLMCompiler with the ReAct framework in terms of speedup, cost reduction, and accuracy when evaluating embarrassingly parallel patterns using HotpotQA and Movie Recommendation datasets. How does the introduction of the ParallelQA benchmark further demonstrate the effectiveness of LLMCompiler in handling more complex function calling patterns?',\n",
+       " 'f6e89e2c-06b6-46f6-b2e6-3bce68a3150c': 'How does the number \"2\" play a role in the context information provided for this quiz?',\n",
+       " '7de856cd-719c-44e1-80fc-28e88a80cadf': 'As a teacher/professor, how would you use the number \"2\" to create a diverse range of questions for your upcoming quiz/examination?',\n",
+       " 'f8a62a95-bc9d-4b2d-b115-df8965dd26e3': 'How does LLMCompiler demonstrate a 2× speedup compared to Tree-of-Thoughts in the context of dynamic replanning for the Game of 24? Explain the significance of this speedup in the context of interactive decision-making environments.',\n",
+       " '5471d730-3cf5-4b01-9b70-3b76d2e42c6f': 'Compare and contrast the approach of Skeleton-of-Thought with LLMCompiler in terms of reducing latency in LLM inference. Discuss the limitations of Skeleton-of-Thought and how LLMCompiler addresses these limitations for more efficient application-level optimizations.',\n",
+       " '71c61536-9eba-4918-be7e-553b8d44eac9': 'How does LLMCompiler address the limitations of handling complex scenarios such as coding and math problems?',\n",
+       " '4896fb60-e76d-4f42-bfeb-0d74f6f0997c': \"What is the significance of the parallel function calling feature introduced by OpenAI in their 1106 release, and how does it compare to LLMCompiler's approach to handling open-source models?\",\n",
+       " '04c6f822-49cb-4cce-b004-4febeb307b77': 'How does LLMCompiler differ from ReWOO in terms of reducing latency and cost in reasoning tasks? Provide specific examples of the differences in their approaches.',\n",
+       " '2da3696f-8879-4d1f-a2df-bdc1bb922d16': 'Explain the concept of Tool-Augmented LLMs and how Toolformer, ReAct, Gorilla, ToolLLM, RestGPT, and OpenAI have contributed to enhancing LLM capabilities in interacting with external environments and supporting real-world APIs.',\n",
+       " '66ec84de-6924-41ae-81de-2ee2d17ab855': 'How does the LLMCompiler framework generate a DAG of tasks with their inter-dependencies?',\n",
+       " 'ad0b9334-c0ea-40d1-8dfe-2162bb3c809c': \"Explain the process outlined in the user input for calculating the increase needed in Microsoft's market cap to exceed Apple's market cap.\",\n",
+       " '4da30433-7d9c-4f1c-80a9-c0c2e6960224': 'Explain the process of task dispatching and execution in the LLMCompiler, using the example of fetching Task $1 and Task $2 for parallel execution. How are dependencies managed in this process?',\n",
+       " 'e08f8b04-3acb-4512-b02a-d915134ac0fa': 'Compare the traditional sequential execution of tasks in frameworks like ReAct with the parallel execution approach proposed by LLMCompiler. How does parallel execution improve the efficiency of completing tasks, as illustrated in the given example of conducting web searches for market caps of Microsoft and Apple?',\n",
+       " 'dc9ec7b7-ad4d-4d4e-b1f1-4abc27b4e6b3': 'How does the LLM Planner in LLMCompiler determine which tasks are parallelizable and which are interdependent? Explain the role of the Planner in generating a sequence of tasks and their dependencies.',\n",
+       " '937d1a5b-eafb-4cd1-88b8-84bb897b8ad4': \"Describe the function of the Task Fetching Unit in LLMCompiler and how it fetches tasks for execution based on a greedy policy. How does it relate to modern computer architectures' instruction fetching units?\",\n",
+       " 'e9cb9640-0d24-4329-915c-b121a5291363': 'In the context of the document, explain the role of the Executor in the task execution process. How does it handle independent tasks and what tools can it utilize for task completion?',\n",
+       " '2c785814-3b7a-4b3a-a344-bd69728382b3': 'Discuss the concept of dynamic replanning as described in the document. Provide examples of when dynamic execution patterns may be necessary and explain how the replanning process works in the context of task execution.',\n",
+       " '7e686e5f-0951-4e2c-aac9-556321b0b269': 'How does LLMCompiler demonstrate its capability in enabling parallel function calling in open-source models, specifically with the LLaMA-2 model? Provide examples of the types of parallel function calling patterns that can be addressed with LLMs.',\n",
+       " '8e873e95-6d8f-4450-a414-1639642640df': 'How is LLMCompiler evaluated in the context of the Game of 24 benchmark and the WebShop benchmark? Discuss the adaptability of LLMCompiler to dynamic dependency graphs and its potential in decision-making tasks.',\n",
+       " 'a8cca18c-60a4-408d-adea-e55d28dd35cc': 'How does the number \"5\" relate to the context information provided?',\n",
+       " '733bd9ae-1faf-48ad-9192-356191fcd439': 'As a teacher/professor, how would you incorporate the number \"5\" into a quiz question for your students?',\n",
+       " '364db5de-0c60-4c2f-be38-d30717838c61': 'How does the accuracy and latency of LLMCompiler compare to the baseline on different benchmarks such as HotpotQA, Movie Recommendation, ParallelQA, and the Game of 24?',\n",
+       " 'd8a345d1-eaa8-42b2-86c2-6ac193a56f5c': 'What common issues are frequently observed in HotpotQA and Movie Recommendation benchmarks when using LLMCompiler, such as looping and early stopping?',\n",
+       " '7d0fb2ac-2186-488d-b959-eba2f1084af6': 'In the context of the document, what is the significance of incorporating ReAct-specific prompting (denoted as ReAct†) to minimize certain behaviors in LLMs? How does this impact the accuracy and latency of the models in benchmarks such as HotpotQA and Movie Rec.?',\n",
+       " '061f6458-d173-4104-af5f-1811cdea0b9a': 'How does the LLMCompiler address the issue of increased latency and token consumption in scenarios involving embarrassingly parallel function calling, as described in the document? Provide examples of benchmarks, such as HotpotQA, where the LLMCompiler demonstrates its ability to identify parallelizable patterns and execute tasks concurrently.',\n",
+       " '1f881cbd-3f39-4472-a354-99d34274b34c': 'Compare and contrast the experimental setups of LLMCompiler and ReAct, highlighting the differences in their approach and optimization strategies. (10 points)',\n",
+       " '17655a85-aa7c-484b-9d66-812a10e43a14': 'Discuss the main failure modes observed in ReAct and how they impact its accuracy compared to LLMCompiler and OpenAI parallel function calling. Provide examples of how these failure modes can be addressed to enhance accuracy. (15 points)',\n",
+       " '76b121be-3bf3-4502-9442-2040d4c1d9c2': 'How does LLMCompiler demonstrate a noticeable speedup compared to ReAct† on the HotpotQA benchmark with GPT and LLaMA?',\n",
+       " '46e4bf29-47e6-4cf9-869a-539fbe6ed9df': 'What is the purpose of the ParallelQA benchmark and how does it incorporate non-trivial function calling patterns?',\n",
+       " 'e3a8c2b0-84dc-469c-9f9b-f905d85bc849': 'How does the use of ReAct as a baseline compare to LLMCompiler in the context of the tools they are equipped with for the search tool and math capabilities?',\n",
+       " '7b7d1c71-c5a6-43d0-8da2-36dc716296b7': \"Why is it mentioned in the document that the reason for the inability to conclude certain aspects related to OpenAI's function calling mechanism is due to the lack of public disclosure?\",\n",
+       " 'bfed057b-e9bf-41ff-8516-7b777dc656ed': 'How would you describe the trend that has been observed with multiple runs over several days?',\n",
+       " '5b84cc0a-9011-4cd3-81f8-3a21678fd6c9': 'Why is it important to consider the consistency of the trend when analyzing data over time?',\n",
+       " '69039c36-6f93-4e62-8377-6c02843e3a62': \"How does the math tool inspired by Langchain's LLMMathChain work in solving mathematical problems? Provide a brief explanation of the process involved.\",\n",
+       " '8f1536b5-e517-4804-9476-8a6d747c3181': 'Can you explain how the math chain, using an LLM as an agent, is able to address a wide range of math problems written in both mathematical and verbal form? Provide examples to illustrate this capability.',\n",
+       " 'e7dfc45b-741f-40b4-ab3a-f67491cc2b9c': \"Explain the advantages of LLMCompiler in terms of accuracy and latency compared to ReAct and OpenAI's parallel function calling. How does LLMCompiler achieve cost reductions in comparison to these methods?\",\n",
+       " '01446f3e-e554-43c3-9cfd-a6924ed147ce': 'Describe the dynamic approach of parallel function calling with replanning in the context of the Game of 24 using the Tree-of-Thoughts (ToT) strategy. How does LLMCompiler enable faster execution in solving the Game of 24 compared to a sequential, breadth-first search approach?',\n",
+       " 'df7e6592-5184-44fc-80cd-b10ddb416f99': 'How can context information be used to enhance understanding and interpretation of a given text or document?',\n",
+       " 'a5b44694-3b65-4dc3-97e9-fd4e590a7a54': 'In what ways can context information impact the overall meaning and significance of a piece of writing or research?',\n",
+       " 'bba97cca-4e6e-4665-b2aa-29335302ea09': 'How does LLMCompiler compare to the baseline described in [57] in terms of latency and success rate on the Game of 24 benchmark? What improvements does LLMCompiler demonstrate with the gpt-4 model and the LLaMA-2 model?',\n",
+       " '5a8e1afe-bac9-41f7-b605-74fdb3a80f76': \"In the application of LLMCompiler in interactive decision-making tasks on the WebShop environment, how does LLMCompiler's exploration strategy differ from previous approaches such as ReAct, LATS, and LASER? What tools does LLMCompiler utilize in this experiment, and how do they contribute to the decision-making process in purchasing items from the WebShop environment?\",\n",
+       " '22d605af-31b6-4bbd-a334-9b7ac4803f46': \"How does LLMCompiler compare to baseline models in terms of performance and latency, specifically when using gpt-3.5-turbo and gpt-4? What factors contribute to LLMCompiler's success rate improvement and latency speedup over ReAct, LATS, and LASER?\",\n",
+       " '27bc6d90-b9b9-4449-840a-84d649a2b6a9': 'Discuss the exploration strategies employed by LLMCompiler, ReAct, LATS, and LASER in the context of decision-making tasks such as the WebShop benchmark. How does the exploration approach of each model impact their performance and execution speed?',\n",
+       " 'e8e9dfb0-7d3b-4dea-927d-7750cac0ca5c': 'How can context information be used to enhance understanding and interpretation of a given document?',\n",
+       " 'e2882317-0e9c-4e1f-b5df-f0a3eebac1dd': 'In what ways can context information impact the overall meaning and significance of a text?',\n",
+       " 'f89f6263-161d-4d25-9ee1-9cb26677396b': 'Compare the performance of LLMCompiler with the gpt-3.5-turbo model and the gpt-4-0613 model in terms of success rate, average score, and latency. How does LLMCompiler outperform the baselines ReAct, LATS, and LASER according to the data provided in Table 3?',\n",
+       " 'e78e4e37-5c61-4e04-a5f0-dd2d6ce79d45': 'Explain how LLMCompiler addresses the inefficiencies in latency, cost, and accuracy associated with existing methods for invoking multiple functions with LLMs. Describe the components of LLMCompiler and how they work together to achieve substantial improvements in latency, cost efficiency, and accuracy, as mentioned in the conclusion section of the document.',\n",
+       " '01d6674c-d743-47e0-9470-e6a29137305a': \"How does Prof. Keutzer's lab receive support from various organizations and companies, and what are some of the specific sponsors mentioned in the context information?\",\n",
+       " 'acc66062-dcad-4e96-b7ed-654bd8b6e320': \"Can you explain the significance of the funding received from Intel, Samsung, BDD, and BAIR for Prof. Keutzer's lab and its research projects?\",\n",
+       " 'bccc1eb4-da39-4c32-8a37-7dc75a3cab3a': 'How did Sehoon Kim and Suhong Moon acknowledge support for their work, and what organizations provided this support?',\n",
+       " '183d7c93-2ee0-40b9-bf67-306a4ee30cff': 'What are some of the references mentioned in the document, and how do they contribute to the research discussed?',\n",
+       " '4224b89f-a956-475b-8545-6111e73013bd': 'Compare and contrast the approaches described in the papers by Maciej Besta et al. and Charlie Chen et al. for utilizing large language models in solving complex problems. How do they differ in their methodologies and objectives?',\n",
+       " '3a7812d6-62e2-40d2-93ae-07cd7dea740e': 'Discuss the findings and implications of the research conducted by Mark Chen et al. on evaluating large language models trained on code. What are the key takeaways from their study and how do they contribute to the field of natural language processing?',\n",
+       " 'ff2099db-1339-4774-9871-f0d09b6320c8': 'In the field of natural language processing, what are some recent advancements in model compression techniques as discussed in the papers by Dettmers et al. (2023) and Frantar et al. (2022)?',\n",
+       " '6a53fb2c-0eb8-4a84-a05a-456b519da62d': 'How are mathematical problem-solving skills being measured in recent research, as highlighted in the papers by Hendrycks et al. (2021) and Frantar et al. (2023)?',\n",
+       " '485e3101-6bc1-420c-83d5-67ab1a3158dc': 'What are some key techniques discussed in the papers by Neil Houlsby et al. and Edward J Hu et al. for improving transfer learning and adaptation of large language models in the field of NLP?',\n",
+       " '8d78f4bd-ea27-448c-928f-f541de3421ef': 'How does Andrej Karpathy contribute to the discussion on large language models, and what insights does he provide in his work that may be relevant to the advancements in NLP research?',\n",
+       " '5da7cf07-1103-492c-b5df-24ff52a3fa05': 'How does the approach of \"decomposed prompting\" proposed by Khot et al. aim to solve complex tasks?',\n",
+       " '512c76a9-28dc-4187-812b-3c5016f60d25': 'What is the significance of using a modular approach in solving complex tasks, as discussed in the paper presented at The Eleventh International Conference on Learning Representations in 2023?',\n",
+       " 'd13dc852-fcd9-4cda-bc5d-724048e0a15c': 'In the field of natural language processing, what are some recent advancements and techniques mentioned in the provided context information that aim to improve the efficiency and performance of large language models?',\n",
+       " '011b45f3-cb7d-4ddc-8775-a4cce3736774': 'How do the approaches of \"dense-and-sparse quantization\" and \"speculative decoding\" contribute to the optimization and acceleration of language models, as discussed in the papers by Sehoon Kim, Amir Gholami, and their collaborators?',\n",
+       " 'beb058ea-3be0-493b-ba4b-a2b56c6082b9': 'Discuss the key contributions and advancements in the field of natural language processing as highlighted in the papers by Kaixin Ma et al., Aman Madaan et al., and Xuefei Ning et al. How do these papers showcase the evolution of language models and their applications in web navigation, iterative refinement, and parallel decoding?',\n",
+       " '8efa7e38-9087-4b17-b7a9-0cb38318e91b': 'Compare and contrast the approaches and outcomes presented in the technical reports by OpenAI on GPT-4 and the new models and developer products announced at devday in 2023. How do these advancements in artificial intelligence and machine learning contribute to the development of large language models and their potential as operating systems, as discussed in the paper by Charles Packer et al.?',\n",
+       " 'dbb18913-ed8d-4eb9-88c1-5547b24c6cae': 'How does the Gorilla language model described in the document connect with massive APIs, according to the authors?',\n",
+       " '22ee045e-a3f4-4575-b96a-81268e9147e9': 'What year was the Gorilla language model introduced, as mentioned in the context information?'}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qa_dataset = generate_qa_embedding_pairs(nodes=nodes, llm=Settings.llm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Alpha Mining & Evaluation\n",
+    "\n",
+    "We're going to update the alpha values of a vector index retriever right before evaluation. \n",
+    "We'll be evaluating alpha values in increments of .1; 0 to 1 where 0 is basic text search and 1 is a pure vector search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0.0: {'hit_rate': 0.11627906976744186, 'mrr': 0.034057770394979696},\n",
+       " 0.1: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.2: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.3: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.4: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.5: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.6: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.7: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.8: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 0.9: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239},\n",
+       " 1.0: {'hit_rate': 0.9069767441860465, 'mrr': 0.7831879844961239}}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calculate_metrics(eval_results):\n",
+    "    metric_dicts = []\n",
+    "    for eval_result in eval_results:\n",
+    "        metric_dict = eval_result.metric_vals_dict\n",
+    "        metric_dicts.append(metric_dict)\n",
+    "\n",
+    "    full_df = pd.DataFrame(metric_dicts)\n",
+    "\n",
+    "    hit_rate = full_df[\"hit_rate\"].mean()\n",
+    "    mrr = full_df[\"mrr\"].mean()\n",
+    "    return hit_rate, mrr\n",
+    "\n",
+    "\n",
+    "async def alpha_mine(\n",
+    "    qa_dataset,\n",
+    "    vector_store_index,\n",
+    "    alpha_values=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "):\n",
+    "    retriever = VectorIndexRetriever(\n",
+    "        index=vector_store_index,\n",
+    "        vector_store_query_mode=\"hybrid\",\n",
+    "        alpha=0.0,  # this will change\n",
+    "        similarity_top_k=10,\n",
+    "    )\n",
+    "\n",
+    "    results = dict()\n",
+    "\n",
+    "    for alpha in alpha_values:\n",
+    "        retriever._alpha = alpha\n",
+    "        retriever_evaluator = RetrieverEvaluator.from_metric_names(\n",
+    "            metric_names=[\"mrr\", \"hit_rate\"], retriever=retriever\n",
+    "        )\n",
+    "        eval_results = await retriever_evaluator.aevaluate_dataset(dataset=qa_dataset)\n",
+    "\n",
+    "        hit_rate, mrr = calculate_metrics(eval_results)\n",
+    "\n",
+    "        results[alpha] = {\"hit_rate\": hit_rate, \"mrr\": mrr}\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "results = await alpha_mine(qa_dataset=qa_dataset, vector_store_index=vector_index)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusions\n",
+    "\n",
+    "As seen above, the alpha values between .1 and 1 are basically the same. This was a single dataset that was tested on and more than likely you'll want to test on multiple datasets for multiple purposes. For our purposes, the default .5 for most hybrid retrievers probably works well - although as shown in the [original blog post](https://blog.llamaindex.ai/llamaindex-enhancing-retrieval-performance-with-alpha-tuning-in-hybrid-search-in-rag-135d0c9b8a00) that started this llama pack, datasets can have wildly different results among their alpha values. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus: Koda Retriever vs Vanilla Hybrid Retriever\n",
+    "\n",
+    "Finally, we'll evaluate and compare a vanilla hybrid retriever against our koda retriever.\n",
+    "This koda retriever will use default alpha values and categories provided in the alpha pack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "async def compare_retrievers(retrievers, qa_dataset):\n",
+    "    results = dict()\n",
+    "\n",
+    "    for name, retriever in retrievers.items():\n",
+    "        retriever_evaluator = RetrieverEvaluator.from_metric_names(\n",
+    "            metric_names=[\"mrr\", \"hit_rate\"], retriever=retriever\n",
+    "        )\n",
+    "\n",
+    "        eval_results = await retriever_evaluator.aevaluate_dataset(dataset=qa_dataset)\n",
+    "\n",
+    "        hit_rate, mrr = calculate_metrics(eval_results)\n",
+    "\n",
+    "        results[name] = {\"hit_rate\": hit_rate, \"mrr\": mrr}\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "retrievers = {\"vanilla\": vanilla_retriever, \"koda\": koda_retriever}\n",
+    "\n",
+    "results = await compare_retrievers(retrievers=retrievers, qa_dataset=qa_dataset)\n",
+    "results"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/examples/alpha_tuning.ipynb b/llama-index-packs/llama-index-packs-koda-retriever/examples/alpha_tuning.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0839f2c976c3eb08bda5103ff61290a9ffcc6257
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/examples/alpha_tuning.ipynb
@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Alpha Tuning w/ Koda Retriever\n",
+    "\n",
+    "*For this example non-production ready infrastructure is leveraged*\n",
+    "More specifically, the default sample data provided in a free start instance of [Pinecone](https://www.pinecone.io/) is used. This data consists of movie scripts and their summaries embedded in a free Pinecone vector database.\n",
+    "\n",
+    "### Agenda\n",
+    "- Fixture Setup\n",
+    "- Alpha Tuning Setup\n",
+    "- Koda Retriever: Retrieval \n",
+    "\n",
+    "A quick overview/visual on how alpha tuning works: (excuse the weird colors, my color settings on Windows was weirdly interacting w/ Google Sheets and made some colors useless)\n",
+    "![alpha-tuning](https://i.imgur.com/zxCXqGb.png)\n",
+    "\n",
+    "### Example Context\n",
+    "Let's say you're building a query engine or agent that is expected to answer questions on Deep Learning, AI, RAG Architecture, and adjacent topics. As a result of this, your team has narrowed down three main query classifications and associated alpha values with those classifications. Your alpha values were determined by incrementally decreasing the alpha value from 1 to 0 and for each new alpha value, several queries are run and evaluated. Repeating this process for each category should yield an optimal alpha value for each category over your data. This process should be repeated periodically as your data expands or changes. \n",
+    "\n",
+    "With the categories and corresponding alpha values uncovered, these are our categories:\n",
+    "- Concept Seeking Queries *(α: 0.2)*\n",
+    "- Fact Seeking Queries *(α: .6)*\n",
+    "- Queries w/ Misspellings *(α: 1)*\n",
+    "\n",
+    "Clearly, these categories have very different biases towards one end of the retrieval spectrum. The default for Llama Index hybrid retrievers is 0.5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import all the necessary modules\n",
+    "from llama_index.llms.openai import OpenAI\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+    "from llama_index.core.postprocessor import LLMRerank\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.vector_stores.pinecone import PineconeVectorStore\n",
+    "from llama_index.core import Settings\n",
+    "from llama_index.core.query_engine import RetrieverQueryEngine\n",
+    "from llama_index.packs.koda_retriever import KodaRetriever\n",
+    "import os\n",
+    "from pinecone import Pinecone"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Building *required objects* for a Koda Retriever.\n",
+    "- Vector Index\n",
+    "- LLM/Model\n",
+    "\n",
+    "Other objects are *optional*, and will be used if provided:\n",
+    "- Reranker\n",
+    "- Custom categories & corresponding alpha weights\n",
+    "- A custom model trained on the custom info above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pc = Pinecone(api_key=os.environ.get(\"PINECONE_API_KEY\"))\n",
+    "index = pc.Index(\"sample-movies\")\n",
+    "\n",
+    "Settings.llm = OpenAI()\n",
+    "Settings.embed_model = OpenAIEmbedding()\n",
+    "\n",
+    "vector_store = PineconeVectorStore(pinecone_index=index, text_key=\"summary\")\n",
+    "vector_index = VectorStoreIndex.from_vector_store(\n",
+    "    vector_store=vector_store, embed_model=Settings.embed_model\n",
+    ")\n",
+    "\n",
+    "reranker = LLMRerank(llm=Settings.llm)  # optional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining Categories & Alpha Values\n",
+    "\n",
+    "We need to first input our categories and alpha values in a format that Koda can understand.\n",
+    "\n",
+    "### Important Considerations:\n",
+    "If you provide these custom categories and no custom model, these values will be input as few-shot context training for whatever model is provided to Koda Retriever. Otherwise, if a custom model is provided and has been trained on the data that would otherwise be provided below, ensure the keys of the categories dictionary matches the expected labels of the custom model. Likewise, do NOT provide any examples or a description."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# It is easiest and recommended to define your alpha categories and values in a dictionary\n",
+    "# It must follow this format:\n",
+    "# {\n",
+    "#     \"category_name\": {\n",
+    "#         \"alpha\": 0.5,  # alpha value - always required\n",
+    "#         \"description\": \"description of the category\", # optional if providing fine tuned model\n",
+    "#         \"examples\": [ # optional if providing fine tuned model\n",
+    "#             \"example 1\", # provide at least 1 example\n",
+    "#             \"example 2\"\n",
+    "#         ]\n",
+    "\n",
+    "categories = {  # key, #alpha, description [and examples]\n",
+    "    \"concept seeking query\": {\n",
+    "        \"alpha\": 0.2,\n",
+    "        \"description\": \"Abstract questions, usually on a specific topic, that require multiple sentences to answer\",\n",
+    "        \"examples\": [\n",
+    "            \"What is the dual-encoder architecture used in recent works on dense retrievers?\",\n",
+    "            \"Why should I use semantic search to rank results?\",\n",
+    "        ],\n",
+    "    },\n",
+    "    \"fact seeking query\": {\n",
+    "        \"alpha\": 0.6,\n",
+    "        \"description\": \"Queries with a single, clear answer\",\n",
+    "        \"examples\": [\n",
+    "            \"What is the total number of propositions the English Wikipedia dump is segmented into in FACTOID WIKI?\",\n",
+    "            \"How many documents are semantically ranked?\",\n",
+    "        ],\n",
+    "    },\n",
+    "    \"queries with misspellings\": {\n",
+    "        \"alpha\": 1,\n",
+    "        \"description\": \"Queries with typos, transpositions and common misspellings introduced\",\n",
+    "        \"examples\": [\n",
+    "            \"What is the advntage of prposition retrieval over sentnce or passage retrieval?\",\n",
+    "            \"Ho w mny documents are samantically r4nked\",\n",
+    "        ],\n",
+    "    },\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building Koda Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = KodaRetriever(\n",
+    "    index=vector_index,\n",
+    "    llm=Settings.llm,\n",
+    "    matrix=categories,  # koda now knows to use these categories\n",
+    "    reranker=reranker,  # optional\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Retrieving w/ Koda Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[NodeWithScore(node=TextNode(id_='33', embedding=None, metadata={'box-office': 1113138548.0, 'title': 'Jurassic Park', 'year': 1993.0}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='A theme park showcasing genetically engineered dinosaurs turns into a nightmare when the creatures escape their enclosures, forcing the visitors to fight for survival.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=8.0),\n",
+       " NodeWithScore(node=TextNode(id_='7', embedding=None, metadata={'box-office': 1671537444.0, 'title': 'Jurassic World', 'year': 2015.0}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Set in a fully functioning dinosaur theme park on Isla Nublar, Jurassic World faces chaos when a genetically engineered dinosaur, the Indominus Rex, escapes. Stars Chris Pratt and Bryce Dallas Howard.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=7.0)]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"Can you explain the Jurassic Park as a business as it was supposed to operate inside the movie's lore or timeline?\"\n",
+    "results = retriever.retrieve(query)\n",
+    "\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Those results don't look quite palletteable though. For that, lets look into making the response more *natural*. For that we'll likely need a Query Engine."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Query Engine w/ Koda Retriever\n",
+    "\n",
+    "Query Engines are [Llama Index abstractions](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/root.html) that combine retrieval and synthesization of an LLM to interpret the results given by a retriever into a natural language response to the original query. They are themselves an end-to-end pipeline from query to natural langauge response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Jurassic Park was intended to be a theme park that featured genetically engineered dinosaurs as its main attraction. Visitors were meant to experience the thrill of seeing these creatures up close in a controlled environment. However, due to unforeseen circumstances in the movie's storyline, the dinosaurs escaped their enclosures, leading to chaos and danger for the visitors.\""
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_engine = RetrieverQueryEngine.from_args(retriever=retriever)\n",
+    "\n",
+    "response = query_engine.query(query)\n",
+    "\n",
+    "str(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf b/llama-index-packs/llama-index-packs-koda-retriever/examples/data/dense_x_retrieval.pdf
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diff --git a/llama-index-packs/llama-index-packs-koda-retriever/examples/quickstart.ipynb b/llama-index-packs/llama-index-packs-koda-retriever/examples/quickstart.ipynb
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--- /dev/null
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@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Koda Retriever: Quickstart\n",
+    "\n",
+    "*For this example non-production ready infrastructure is leveraged, and default categories (and corresponding alpha values) are used.*\n",
+    "\n",
+    "More specifically, the default sample data provided in a free start instance of [Pinecone](https://www.pinecone.io/) is used. This data consists of movie scripts and their summaries embedded in a free Pinecone vector database.\n",
+    "\n",
+    "### Agenda:\n",
+    "- Setup\n",
+    "- Koda Retriever: Retrieval \n",
+    "- Koda Retriever: Query Engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import all the necessary modules\n",
+    "from llama_index.llms.openai import OpenAI\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+    "from llama_index.core.postprocessor import LLMRerank\n",
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.vector_stores.pinecone import PineconeVectorStore\n",
+    "from llama_index.core import Settings\n",
+    "from llama_index.core.query_engine import RetrieverQueryEngine\n",
+    "from llama_index.packs.koda_retriever import KodaRetriever\n",
+    "import os\n",
+    "from pinecone import Pinecone"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Building *required objects* for a Koda Retriever.\n",
+    "- Vector Index\n",
+    "- LLM/Model\n",
+    "\n",
+    "Other objects are *optional*, and will be used if provided:\n",
+    "- Reranker\n",
+    "- Custom categories & corresponding alpha weights\n",
+    "- A custom model trained on the custom info above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pc = Pinecone(api_key=os.environ.get(\"PINECONE_API_KEY\"))\n",
+    "index = pc.Index(\"sample-movies\")\n",
+    "\n",
+    "Settings.llm = OpenAI()\n",
+    "Settings.embed_model = OpenAIEmbedding()\n",
+    "\n",
+    "vector_store = PineconeVectorStore(pinecone_index=index, text_key=\"summary\")\n",
+    "vector_index = VectorStoreIndex.from_vector_store(\n",
+    "    vector_store=vector_store, embed_model=Settings.embed_model\n",
+    ")\n",
+    "\n",
+    "reranker = LLMRerank(llm=Settings.llm)  # optional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building Koda Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = KodaRetriever(\n",
+    "    index=vector_index,\n",
+    "    llm=Settings.llm,\n",
+    "    reranker=reranker,  # optional\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Retrieving w/ Koda Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[NodeWithScore(node=TextNode(id_='7', embedding=None, metadata={'box-office': 1671537444.0, 'title': 'Jurassic World', 'year': 2015.0}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Set in a fully functioning dinosaur theme park on Isla Nublar, Jurassic World faces chaos when a genetically engineered dinosaur, the Indominus Rex, escapes. Stars Chris Pratt and Bryce Dallas Howard.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=8.0)]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"How many Jurassic Park movies are there?\"\n",
+    "results = retriever.retrieve(query)\n",
+    "\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Those results don't look quite palletteable though. For that, lets look into making the response more *natural*. For that we'll likely need a Query Engine."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Query Engine w/ Koda Retriever\n",
+    "\n",
+    "Query Engines are [Llama Index abstractions](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/root.html) that combine retrieval and synthesization of an LLM to interpret the results given by a retriever into a natural language response to the original query. They are themselves an end-to-end pipeline from query to natural langauge response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'There are five Jurassic Park movies.'"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_engine = RetrieverQueryEngine.from_args(retriever=retriever)\n",
+    "\n",
+    "response = query_engine.query(query)\n",
+    "\n",
+    "str(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/BUILD b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..db46e8d6c978c67e301dd6c47bee08c1b3fd141c
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/BUILD
@@ -0,0 +1 @@
+python_sources()
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/__init__.py b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff4c1cdffacf25a40656517f16ba7c1065987e11
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/__init__.py
@@ -0,0 +1,5 @@
+from llama_index.packs.koda_retriever.base import KodaRetriever
+from llama_index.packs.koda_retriever.matrix import AlphaMatrix
+from llama_index.packs.koda_retriever.constants import DEFAULT_CATEGORIES
+
+__all__ = ["KodaRetriever", "AlphaMatrix", "DEFAULT_CATEGORIES"]
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/base.py b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c03d93d675f5647b0d460df02c241c3a26aea9d
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/base.py
@@ -0,0 +1,258 @@
+"""
+Defines a custom hybrid retriever for flexible alpha tuning.
+
+Idea & original implementation sourced from the following docs:
+    - https://blog.llamaindex.ai/llamaindex-enhancing-retrieval-performance-with-alpha-tuning-in-hybrid-search-in-rag-135d0c9b8a00
+    - https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167
+
+Author: no_dice
+"""
+
+from typing import Optional, List, Any
+from llama_index.core.vector_stores.types import VectorStoreQueryMode
+from llama_index.core.retrievers import BaseRetriever
+from llama_index.core.retrievers import VectorIndexRetriever
+from llama_index.core import VectorStoreIndex
+from llama_index.core.postprocessor.types import BaseNodePostprocessor
+from llama_index.core.llms import LLM
+from llama_index.core.schema import NodeWithScore, QueryType
+from llama_index.core.llama_pack.base import BaseLlamaPack
+from llama_index.core.base.response.schema import RESPONSE_TYPE
+from .constants import CATEGORIZER_PROMPT, DEFAULT_CATEGORIES
+import logging
+from .matrix import AlphaMatrix
+
+
+class KodaRetriever(BaseRetriever):
+    """
+    Custom Hybrid Retriever that dynamically determines the optimal alpha for a given query.
+    An LLM is used to categorize the query and therefore determine the optimal alpha value, as each category has a preset/provided alpha value.
+    It is recommended that you run tests on your corpus of data and queries to determine categories and corresponding alpha values for your use case.
+
+    KodaRetriever is built from BaseRetriever, and therefore is a llama-index compatible drop-in replacement for any hybrid retriever.
+
+    Auto-routing is NOT enabled without providing an LLM.
+    If no LLM is provided, the default alpha value will be used for all queries and no alpha tuning will be done.
+    Reranking will be done automatically if a reranker is provided.
+    If no matrix is provided, a default matrix is leveraged. (Not recommended for production use)
+
+    Args:
+        index (VectorStoreIndex): The index to be used for retrieval
+        llm (LLM, optional): The LLM to be used for auto-routing. Defaults to None.
+        reranker (BaseNodePostprocessor, optional): The reranker to be used for postprocessing. Defaults to None.
+        default_alpha (float, optional): The default alpha value to be used if no LLM is provided. Defaults to .5.
+        matrix (dict or AlphaMatrix, optional): The matrix to be used for auto-routing. Defaults to AlphaMatrix(data=DEFAULT_CATEGORIES).
+        verbose (bool, optional): Whether to log verbose output. Defaults to False.
+        **kwargs: Additional arguments for VectorIndexRetriever
+
+    Returns:
+        KodaRetriever
+
+    Examples:
+        >>> # Example 1 - provide your own LLM
+        >>> retriever = KodaRetriever( # woof woof
+                            index=vector_index
+                            , llm=Settings.llm
+                            , verbose=True
+                        )
+        >>> results = retriever.retrieve("What is the capital of France?")
+
+        >>> # Example 2 - set custom alpha values
+        >>> matrix_data = { # this is just dummy data, alpha values were randomly chosen
+                "positive sentiment": {
+                    "alpha": .2
+                    , "description": "Queries expecting a positive answer"
+                    , "examples": [
+                        "I love this product"
+                        , "This is the best product ever"
+                    ]
+                }
+                , "negative sentiment": {
+                    "alpha": .7
+                    , "description": "Queries expecting a negative answer"
+                    , "examples": [
+                        "I hate this product"
+                        , "This is the worst product ever"
+                    ]
+                }
+            }
+
+        >>> retriever = KodaRetriever( # woof woof
+                            index=vector_index
+                            , llm=Settings.llm
+                            , matrix=matrix_data
+                            , verbose=True
+                        )
+        >>> results = retriever.retrieve("What happened on Y2K?")
+
+    """
+
+    def __init__(
+        self,
+        index: VectorStoreIndex,
+        llm: Optional[LLM] = None,  # if I could, I'd default to
+        reranker: Optional[BaseNodePostprocessor] = None,
+        default_alpha: float = 0.5,
+        matrix: dict or AlphaMatrix = DEFAULT_CATEGORIES,  # type: ignore
+        verbose: bool = False,
+        **kwargs,  # kwargs for VectorIndexRetriever
+    ):
+        super().__init__()
+
+        self.index = index
+        self.retriever = VectorIndexRetriever(
+            index=index,
+            vector_store_query_mode=VectorStoreQueryMode.HYBRID,
+            alpha=default_alpha,
+            **kwargs,  # filters, etc, added here
+        )
+        self.default_alpha = default_alpha
+        self.reranker = reranker
+        self.llm = llm
+        self.verbose = verbose
+
+        if isinstance(matrix, dict):
+            matrix = AlphaMatrix(data=matrix)
+
+        self.matrix = matrix
+
+    def categorize(self, query: str) -> str:
+        """Categorizes a query using the provided LLM and matrix. If no LLM is provided, the default alpha value will be used."""
+        if not self.llm:
+            err = "LLM is required for auto-routing. During instantiation, please provide an LLM or use direct routing."
+            raise TypeError(err)
+
+        prompt = CATEGORIZER_PROMPT.format(
+            question=query, category_info=self.matrix.get_all_category_info()
+        )
+
+        response = str(self.llm.complete(prompt))  # type: ignore
+
+        if response not in self.matrix.get_categories():
+            raise ValueError(
+                f"LLM classified question in a category that is not registered. {response} not in {self.matrix.get_categories()}"
+            )
+
+        return response
+
+    async def a_categorize(self, query: str) -> str:
+        """(async) Categorizes a query using the provided LLM and matrix. If no LLM is provided, the default alpha value will be used."""
+        if not self.llm:
+            err = "LLM is required for auto-routing. During instantiation, please provide an LLM or use direct routing."
+            raise TypeError(err)
+
+        prompt = CATEGORIZER_PROMPT.format(
+            question=query, category_info=self.matrix.get_all_category_info()
+        )
+
+        response = await self.llm.acomplete(prompt)  # type: ignore
+        response = str(response)
+
+        if response not in self.matrix.get_categories():
+            raise ValueError(
+                f"LLM classified question in a category that is not registered. {response!s} not in {self.matrix.get_categories()}"
+            )
+
+        return response
+
+    def category_retrieve(self, category: str, query: QueryType) -> List[NodeWithScore]:
+        """Updates the alpha and retrieves results for a query using the provided category and query. If no LLM is provided, the default alpha value will be used."""
+        alpha = self.matrix.get_alpha(category)
+        self.retriever._alpha = (
+            alpha  # updates alpha according to classification of query
+        )
+
+        return self.retriever.retrieve(str_or_query_bundle=query)
+
+    async def a_category_retrieve(
+        self, category: str, query: QueryType
+    ) -> List[NodeWithScore]:
+        """(async) Updates the alpha and retrieves results for a query using the provided category and query. If no LLM is provided, the default alpha value will be used."""
+        alpha = self.matrix.get_alpha(category)
+        self.retriever._alpha = (
+            alpha  # updates alpha according to classification of query
+        )
+
+        return await self.retriever.aretrieve(str_or_query_bundle=query)
+
+    def _retrieve(self, query: QueryType) -> List[NodeWithScore]:
+        """llama-index compatible retrieve method that auto-determines the optimal alpha for a query and then retrieves results for a query."""
+        if not self.llm:
+            warning = f"LLM is not provided, skipping route categorization. Default alpha of {self.default_alpha} will be used."
+            logging.warning(warning)
+
+            results = self.retriever.retrieve(query)
+        else:
+            category = self.categorize(query)  # type: ignore
+            results = self.category_retrieve(category, query)
+            if self.verbose:
+                logging.info(
+                    f"Query categorized as {category} with alpha of {self.matrix.get_alpha(category)}"
+                )
+
+        if self.reranker:
+            if self.verbose:
+                logging.info("Reranking results")
+            results = self.reranker.postprocess_nodes(nodes=results, query_str=query)
+
+        return results
+
+    async def _aretrieve(self, query: QueryType) -> List[NodeWithScore]:
+        """(async) llama-index compatible retrieve method that auto-determines the optimal alpha for a query and then retrieves results for a query."""
+        if not self.llm:
+            warning = f"LLM is not provided, skipping route categorization. Default alpha of {self.default_alpha} will be used."
+            logging.warning(warning)
+
+            results = await self.retriever.aretrieve(query)
+        else:
+            category = await self.a_categorize(query)  # type: ignore
+            results = await self.a_category_retrieve(category, query)
+            if self.verbose:
+                logging.info(
+                    f"Query categorized as {category} with alpha of {self.matrix.get_alpha(category)}"
+                )
+
+        if self.reranker:
+            if self.verbose:
+                logging.info("Reranking results")
+            results = self.reranker.postprocess_nodes(nodes=results, query_str=query)
+
+        return results
+
+
+class KodaRetrieverPack(BaseLlamaPack):
+    def __init__(
+        self,
+        index: VectorStoreIndex,
+        llm: Optional[LLM] = None,  # if I could, I'd default to
+        reranker: Optional[BaseNodePostprocessor] = None,
+        default_alpha: float = 0.5,
+        matrix: dict or AlphaMatrix = DEFAULT_CATEGORIES,  # type: ignore
+        verbose: bool = False,
+        **kwargs,  # kwargs for VectorIndexRetriever
+    ) -> None:
+        """Init params."""
+        self.retriever = KodaRetriever(
+            index=index,
+            llm=llm,
+            reranker=reranker,
+            default_alpha=default_alpha,
+            matrix=matrix,
+            verbose=verbose,
+            **kwargs,
+        )
+
+    def get_modules(self) -> dict:
+        """Get modules."""
+        return {
+            "retriever": self.retriever,
+            "retriever_cls": KodaRetriever,
+        }
+
+    def run(self, query_str: str, **kwargs: Any) -> RESPONSE_TYPE:
+        """Run method."""
+        return self.retriever.retrieve(query_str, **kwargs)
+
+    async def arun(self, query_str: str, **kwargs: Any) -> RESPONSE_TYPE:
+        """Asynchronous run method."""
+        return await self.retriever.aretrieve(query_str, **kwargs)
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/constants.py b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..12e9d05fa258225fcc8215ba9a3b5d71dc50352d
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/constants.py
@@ -0,0 +1,62 @@
+CATEGORIZER_PROMPT = """
+
+You are an AI tool meant to categorize questions into a set of curated categories.
+
+Here are the categories and additional information for each category you're meant to categorize the question below into: (Examples may be separated by a ;) \n
+{category_info}
+
+When responding with the selected category above, please ONLY respond with the category.
+Do not respond with your explanation or context, your response must only contain a category above.
+
+Please categorize this question into one of the categories above:\n
+{question}
+
+""".strip()
+
+DEFAULT_CATEGORIES = {  # key, #alpha, description [and examples]
+    "web search query": {
+        "alpha": 0,
+        "description": "Shortened queries similar to those commonly entered into a search engine (often an incomplete sentence)",
+        "examples": [
+            "Transfer capabilities of LLaMA language model to non-English languages",
+            "Best retrieval concept queries",
+        ],
+    },
+    "concept seeking query": {
+        "alpha": 0.2,
+        "description": "Abstract questions, usually on a specific topic, that require multiple sentences to answer",
+        "examples": [
+            "What is the dual-encoder architecture used in recent works on dense retrievers?",
+            "Why should I use semantic search to rank results?",
+        ],
+    },
+    "fact seeking query": {
+        "alpha": 0.6,
+        "description": "Queries with a single, clear answer",
+        "examples": [
+            "What is the total number of propositions the English Wikipedia dump is segmented into in FACTOID WIKI?",
+            "How many documents are semantically ranked?",
+        ],
+    },
+    "keyword query": {
+        "alpha": 0.4,
+        "description": "Short queries that consist of only the important identifier words.",
+        "examples": ["GTR retriever recall rate", "semantic ranker"],
+    },
+    "queries with misspellings": {
+        "alpha": 1,
+        "description": "Queries with typos, transpositions and common misspellings introduced",
+        "examples": [
+            "What is the advntage of prposition retrieval over sentnce or passage retrieval?",
+            "Ho w mny documents are samantically r4nked",
+        ],
+    },
+    "exact sub-string searches": {
+        "alpha": 0,
+        "description": "Longer queries that are exact sub-strings from the original paragraph",
+        "examples": [
+            "first kwords for the GTR retriever. Finer-grained",
+            "enables you to maximize the quality and value of your LLM investments most efficiently by feeding only relevant information",
+        ],
+    },
+}
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/matrix.py b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ea62991d95f5305ad4a49d5bcbcca56455e66e1
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/llama_index/packs/koda_retriever/matrix.py
@@ -0,0 +1,97 @@
+from pydantic import BaseModel
+from typing import Optional, Dict, List
+
+
+class AlphaMatrix(BaseModel):
+    """
+    This class is not necessary to understand to use a KodaRetriever - as it will be automatically instantiated if a dictionary is provided.
+
+    Pydantic class to enforce the required fields for a KodaRetriever
+    Its best to just instantiate this using a dictionary, don't both trying to instantiate by declaring any AlphaCategory objects.
+
+    Example:
+        >>> data = {
+                "normal query": { # examples is not required if you aren't using few-shot auto-routing
+                    "alpha": .5
+                    , "description": "This is a normal query" # desc is not required if you aren't using few-shot auto-routing
+                    , "examples": ["This is a normal query", "Another normal query"]
+                }
+            }
+        >>> matrix = AlphaMatrix(data=data) # arg must be named matrix for the retriever to use it
+    """
+
+    class AlphaCategory(BaseModel):
+        """
+        Subclass to enforce the required fields for a category in the AlphaMatrix - necessary for nesting in the AlphaMatrix class
+        You should not have to really touch this, as it is only used for type checking and validation.
+        """
+
+        alpha: float
+        description: Optional[
+            str
+        ] = None  # optional if providing a custom LLM, its presumed this was part of your training data for the custom model
+        examples: Optional[
+            List[str]
+        ] = None  # if not providing a custom model, this is required
+
+    data: Dict[str, AlphaCategory]
+
+    def get_alpha(self, category: str) -> float:
+        """Simple helper function to get the alpha value for a given category."""
+        if category not in self.data:
+            err = f"Provided category '{category}' cannot be found"
+            raise ValueError(err)
+
+        return self.data.get(category).alpha  # type: ignore
+
+    def get_examples(self, category: str) -> List[str]:
+        """Simple helper function to get the examples for a given category."""
+        if category not in self.data:
+            err = f"Provided category '{category}' cannot be found"
+            raise ValueError(err)
+
+        return self.data.get(category).examples  # type: ignore
+
+    def get_description(self, category: str) -> str:
+        """Simple helper function to get the description for a given category."""
+        if category not in self.data:
+            err = f"Provided category '{category}' cannot be found"
+            raise ValueError(err)
+
+        return self.data.get(category).description  # type: ignore
+
+    def get_categories(self) -> list:
+        """Simple helper function to get the categories for a given category."""
+        return list(self.data.keys())
+
+    def format_category(self, category: str) -> str:
+        """Simple helper function to format the category information for a given category."""
+        if category not in self.data:
+            err = f"Provided category '{category}' cannot be found"
+            raise ValueError(err)
+
+        description = self.get_description(category)
+        examples = self.get_examples(category)
+
+        category_info = f"""
+        - {category}:
+            description: {description}
+        """.strip()
+
+        if examples:
+            examples = "; ".join(examples)
+            example_info = f"""
+            examples:
+                {examples}
+            """
+            category_info = f"{category_info}\n{example_info}"
+
+        return category_info
+
+    def get_all_category_info(self) -> str:
+        """Simple helper function to get the category information for all categories."""
+        categories = []
+        for category in self.get_categories():
+            category_info = self.format_category(category)
+            categories.append(category_info)
+        return "\n".join(categories)
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/poetry.lock b/llama-index-packs/llama-index-packs-koda-retriever/poetry.lock
new file mode 100644
index 0000000000000000000000000000000000000000..d4036ab5ac73400ca91ac699a3a2921a584baf80
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/poetry.lock
@@ -0,0 +1,4578 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
+version = "3.9.3"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"},
+    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"},
+    {file = "aiohttp-3.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:82e6aa28dd46374f72093eda8bcd142f7771ee1eb9d1e223ff0fa7177a96b4a5"},
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[[package]]
+name = "zipp"
+version = "3.17.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
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+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.8.1,<3.12"
+content-hash = "5a5c98da7cd54d5d135ca318040921073ab4ea00cf6f880f6afc4e2780dbcbdf"
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-koda-retriever/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..0ee676967206e84c7b43f10490432c15b23edd5f
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/pyproject.toml
@@ -0,0 +1,58 @@
+[build-system]
+build-backend = "poetry.core.masonry.api"
+requires = ["poetry-core"]
+
+[tool.codespell]
+check-filenames = true
+check-hidden = true
+# Feel free to un-skip examples, and experimental, you will just need to
+# work through many typos (--write-changes and --interactive will help)
+skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb"
+
+[tool.llamahub]
+contains_example = true
+import_path = "llama_index.packs.koda_retriever"
+
+[tool.llamahub.class_authors]
+KodaRetrieverPack = "no_dice"
+
+[tool.mypy]
+disallow_untyped_defs = true
+# Remove venv skip when integrated with pre-commit
+exclude = [".venv", "_static", "build", "examples", "notebooks", "venv"]
+ignore_missing_imports = true
+python_version = "3.8"
+
+[tool.poetry]
+authors = ["Patrick Miramontes <patdjm7@gmail.com>"]
+description = "Custom hybrid retriever with alpha tuning and routing."
+license = "MIT"
+name = "llama-index-packs-koda-retriever"
+packages = [{include = "llama_index/"}]
+readme = "README.md"
+version = "0.1.0"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<3.12"
+llama-index-core = "^0.10.0"
+llama-index-readers-wikipedia = "^0.1.3"
+wikipedia = "^1.4.0"
+
+[tool.poetry.group.dev.dependencies]
+black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"}
+codespell = {extras = ["toml"], version = ">=v2.2.6"}
+ipython = "8.10.0"
+jupyter = "^1.0.0"
+mypy = "0.991"
+pre-commit = "3.2.0"
+pylint = "2.15.10"
+pytest = "7.2.1"
+pytest-mock = "3.11.1"
+ruff = "0.0.292"
+tree-sitter-languages = "^1.8.0"
+types-Deprecated = ">=0.1.0"
+types-PyYAML = "^6.0.12.12"
+types-protobuf = "^4.24.0.4"
+types-redis = "4.5.5.0"
+types-requests = "2.28.11.8"  # TODO: unpin when mypy>0.991
+types-setuptools = "67.1.0.0"
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/tests/BUILD b/llama-index-packs/llama-index-packs-koda-retriever/tests/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..5c8342c912668214be70e1dc952174cff35e6b46
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/tests/BUILD
@@ -0,0 +1,13 @@
+python_test_utils(
+    name="test_utils",
+)
+
+python_sources()
+
+python_tests(
+    name="tests0",
+    dependencies=[
+        "llama-index-packs/llama-index-packs-koda-retriever/tests/koda_mocking.py",
+        "llama-index-packs/llama-index-packs-koda-retriever/tests/monkeypatch.py",
+    ],
+)
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/tests/__init__.py b/llama-index-packs/llama-index-packs-koda-retriever/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/tests/conftest.py b/llama-index-packs/llama-index-packs-koda-retriever/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..7af44aedf99eef4fa66bd01b70acb5bb99d66e24
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/tests/conftest.py
@@ -0,0 +1,45 @@
+from llama_index.core.embeddings.mock_embed_model import MockEmbedding
+from llama_index.core.postprocessor import LLMRerank
+from llama_index.core import Settings
+from llama_index.packs.koda_retriever import (
+    KodaRetriever,
+    AlphaMatrix,
+    DEFAULT_CATEGORIES,
+)
+import pytest
+
+from tests.koda_mocking import KVMockLLM
+from tests.monkeypatch import monkey_patch_vector_store_index
+
+
+@pytest.fixture()
+def setup() -> dict:  # monkey
+    """Sets up fixtures for simple vector stores to be used within retriever testing."""
+    Settings.llm = KVMockLLM()
+    Settings.embed_model = MockEmbedding(8)
+
+    # add text nodes custom
+    vector_index = monkey_patch_vector_store_index()
+
+    shots = AlphaMatrix(
+        data=DEFAULT_CATEGORIES
+    )  # this could also just be a dictionary i guess
+
+    reranker = LLMRerank(llm=Settings.llm)
+
+    retriever = KodaRetriever(
+        index=vector_index,
+        llm=Settings.llm,
+        reranker=reranker,
+        matrix=shots,
+        verbose=True,
+    )
+
+    return {
+        "retriever": retriever,
+        "llm": Settings.llm,
+        "reranker": reranker,
+        "embed_model": Settings.embed_model,
+        "vector_index": vector_index,
+        "matrix": shots,
+    }
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/tests/koda_mocking.py b/llama-index-packs/llama-index-packs-koda-retriever/tests/koda_mocking.py
new file mode 100644
index 0000000000000000000000000000000000000000..80a52ac3a3f9cebe77a3587646ce35d36b0b5395
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/tests/koda_mocking.py
@@ -0,0 +1,47 @@
+"""
+This module contains a class (KVMockLLM) that slightly extends the Llama Index MockLLM
+class further by allowing the user to specify a dictionary of prompt-response pairs.
+This allows the user to specify the response that the mock LLM should return based on the prompt.
+
+This should really only be used for testing purposes to simulate the behavior of a real LLM.
+Maybe this would be useful in the LlamaIndex repo itself?
+
+AUTHOR: no_dice
+"""
+
+from llama_index.core.llms.mock import MockLLM
+from llama_index.core.base.llms.types import CompletionResponse
+import random
+
+
+PROMPT_RESPONSES = {
+    "What are LLMs good at?": "concept seeking query",
+    "How many Jurassic Park movies are there?": "fact seeking query",
+}
+
+
+class KVMockLLM(MockLLM):
+    """Simple mock LLM that returns a response based on the prompt."""
+
+    prompt_responses: dict = PROMPT_RESPONSES
+    strict: bool = False
+    default_response: str = "concept seeking query"
+
+    @classmethod
+    def class_name(cls) -> str:
+        return "KVMockLLM"
+
+    def random_prompt(self) -> str:
+        """Returns a random prompt from the prompt_responses dictionary."""
+        return random.choice(list(self.prompt_responses.keys()))
+
+    def complete(self, prompt: str, **kwargs) -> CompletionResponse:
+        """Returns a response that was matched from the given prompt."""
+        if self.strict:
+            if prompt not in self.prompt_responses:
+                err = f"Prompt '{prompt}' not found in prompt_responses. Please recreate this MockLLM with the expected prompts and responses."
+                raise ValueError(err)
+
+        response = self.prompt_responses.get(prompt, self.default_response)
+
+        return CompletionResponse(text=response)
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/tests/monkeypatch.py b/llama-index-packs/llama-index-packs-koda-retriever/tests/monkeypatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5694de890c180055e7030f57a6625da35963427
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/tests/monkeypatch.py
@@ -0,0 +1,176 @@
+"""
+This is a monkeypatch script that will allow us to SIMULATE hybrid search functionality to the SimpleVectorStore class.
+Emphasis on simulate, as we are not actually adding the functionality to the class, but rather
+just allowing the class to accept VectorStoreQuery objects with the mode set to VectorStoreQueryMode.HYBRID.
+
+This also flows upstream to the VectorStoreIndex class - which will default use SimpleVectorStore index if you don't provide
+your own Vector Store. This is why we are monkeypatching the SimpleVectorStore class and not the VectorStoreIndex class.
+
+I didn't write MOST of this code, I just copied it from the llama_index library and modified it to fit my needs.
+
+AUTHOR: no_dice
+"""
+
+import logging
+from typing import Any, Callable, List, Mapping, Optional, cast
+from llama_index.core.indices.query.embedding_utils import (
+    get_top_k_embeddings,
+    get_top_k_embeddings_learner,
+    get_top_k_mmr_embeddings,
+)
+from llama_index.core import VectorStoreIndex
+from llama_index.core.vector_stores import SimpleVectorStore
+from llama_index.core.vector_stores.types import (
+    MetadataFilters,
+    VectorStoreQuery,
+    VectorStoreQueryMode,
+    VectorStoreQueryResult,
+)
+from llama_index.core.readers import StringIterableReader
+
+logger = logging.getLogger(__name__)
+
+LEARNER_MODES = {
+    VectorStoreQueryMode.SVM,
+    VectorStoreQueryMode.LINEAR_REGRESSION,
+    VectorStoreQueryMode.LOGISTIC_REGRESSION,
+}
+
+MMR_MODE = VectorStoreQueryMode.MMR
+
+NAMESPACE_SEP = "__"
+DEFAULT_VECTOR_STORE = "default"
+
+LEARNER_MODES = {
+    VectorStoreQueryMode.SVM,
+    VectorStoreQueryMode.LINEAR_REGRESSION,
+    VectorStoreQueryMode.LOGISTIC_REGRESSION,
+}
+
+
+def _build_metadata_filter_fn(
+    metadata_lookup_fn: Callable[[str], Mapping[str, Any]],
+    metadata_filters: Optional[MetadataFilters] = None,
+) -> Callable[[str], bool]:
+    """Build metadata filter function."""
+    filter_list = metadata_filters.legacy_filters() if metadata_filters else []
+    if not filter_list:
+        return lambda _: True
+
+    def filter_fn(node_id: str) -> bool:
+        metadata = metadata_lookup_fn(node_id)
+        for filter_ in filter_list:
+            metadata_value = metadata.get(filter_.key, None)
+            if metadata_value is None:
+                return False
+            elif isinstance(metadata_value, list):
+                if filter_.value not in metadata_value:
+                    return False
+            elif isinstance(metadata_value, (int, float, str, bool)):
+                if metadata_value != filter_.value:
+                    return False
+        return True
+
+    return filter_fn
+
+
+def monkey_query(
+    self,
+    query: VectorStoreQuery,
+    **kwargs: Any,
+):
+    """Get nodes for response."""
+    # Prevent metadata filtering on stores that were persisted without metadata.
+    if (
+        query.filters is not None
+        and self._data.embedding_dict
+        and not self._data.metadata_dict
+    ):
+        raise ValueError(
+            "Cannot filter stores that were persisted without metadata. "
+            "Please rebuild the store with metadata to enable filtering."
+        )
+    # Prefilter nodes based on the query filter and node ID restrictions.
+    query_filter_fn = _build_metadata_filter_fn(
+        lambda node_id: self._data.metadata_dict[node_id], query.filters
+    )
+
+    if query.node_ids is not None:
+        available_ids = set(query.node_ids)
+
+        def node_filter_fn(node_id: str) -> bool:
+            return node_id in available_ids
+
+    else:
+
+        def node_filter_fn(node_id: str) -> bool:
+            return True
+
+    node_ids = []
+    embeddings = []
+    # TODO: consolidate with get_query_text_embedding_similarities
+    for node_id, embedding in self._data.embedding_dict.items():
+        if node_filter_fn(node_id) and query_filter_fn(node_id):
+            node_ids.append(node_id)
+            embeddings.append(embedding)
+
+    query_embedding = cast(List[float], query.query_embedding)
+
+    if query.mode in LEARNER_MODES:
+        top_similarities, top_ids = get_top_k_embeddings_learner(
+            query_embedding,
+            embeddings,
+            similarity_top_k=query.similarity_top_k,
+            embedding_ids=node_ids,
+        )
+    elif query.mode == MMR_MODE:
+        mmr_threshold = kwargs.get("mmr_threshold", None)
+        top_similarities, top_ids = get_top_k_mmr_embeddings(
+            query_embedding,
+            embeddings,
+            similarity_top_k=query.similarity_top_k,
+            embedding_ids=node_ids,
+            mmr_threshold=mmr_threshold,
+        )
+    elif query.mode == VectorStoreQueryMode.DEFAULT:
+        top_similarities, top_ids = get_top_k_embeddings(
+            query_embedding,
+            embeddings,
+            similarity_top_k=query.similarity_top_k,
+            embedding_ids=node_ids,
+        )
+    elif query.mode == VectorStoreQueryMode.HYBRID:  # where I made my changes
+        top_similarities, top_ids = get_top_k_embeddings(
+            query_embedding,
+            embeddings,
+            similarity_top_k=query.similarity_top_k,
+            embedding_ids=node_ids,
+        )
+    else:
+        raise ValueError(f"Invalid query mode: {query.mode}")
+
+    return VectorStoreQueryResult(similarities=top_similarities, ids=top_ids)
+
+
+SimpleVectorStore.query = (
+    monkey_query  # very important, this is where the monkeypatching happens
+)
+
+
+def monkey_patch_vector_store_index() -> VectorStoreIndex:  # I did write this part..
+    """Returns a monkey-patched vector store index to simulate hybrid retrieval behavior."""
+    import sys
+
+    print(sys.executable)
+
+    loader = StringIterableReader()
+
+    documents = loader.load_data(
+        texts=[
+            "This is a test document.",
+            "This is another test document.",
+            "This is a third test document.",
+        ]
+    )
+
+    return VectorStoreIndex.from_documents(documents)
diff --git a/llama-index-packs/llama-index-packs-koda-retriever/tests/test_koda_retriever.py b/llama-index-packs/llama-index-packs-koda-retriever/tests/test_koda_retriever.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc573e84159d56910b473ff7be8599c3491d6981
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-koda-retriever/tests/test_koda_retriever.py
@@ -0,0 +1,59 @@
+from asyncio import run
+from llama_index.packs.koda_retriever import KodaRetriever
+from llama_index.core.indices.base import BaseIndex
+from llama_index.core.llms.llm import BaseLLM
+
+
+def test_init(setup):
+    retriever = setup.get("retriever")
+
+    assert isinstance(
+        retriever.index, BaseIndex
+    ), "index should be an instance of BaseIndex"
+    assert isinstance(retriever.llm, BaseLLM), "llm should be an instance of LLM"
+    assert isinstance(
+        retriever, KodaRetriever
+    ), "retriever should be an instance of KodaRetriever"
+
+
+def test_retrieve(setup):
+    retriever = setup.get("retriever")
+    llm = setup.get("llm")
+    query = llm.random_prompt()
+    results = retriever.retrieve(query)
+
+    assert isinstance(results, list), "retrieve should return a list"
+
+
+def test_a_retrieve(setup):
+    retriever = setup.get("retriever")
+    llm = setup.get("llm")
+    query = llm.random_prompt()
+    results = run(retriever.aretrieve(query))
+
+    assert isinstance(results, list), "aretrieve should return a list"
+
+
+def test_categorize(setup):
+    retriever = setup.get("retriever")
+    expected_categories = setup.get("matrix").get_categories()
+
+    llm = setup.get("llm")
+    query = llm.random_prompt()
+    category = retriever.categorize(query)
+
+    assert isinstance(category, str), "categorize should return a string"
+    assert (
+        category in expected_categories
+    ), "categorize should return a category from the matrix"
+
+
+def test_category_retrieve(setup):
+    retriever = setup.get("retriever")
+    llm = setup.get("llm")
+    query = llm.random_prompt()
+    category = llm.prompt_responses.get(query, "concept seeking query")
+
+    results = retriever.category_retrieve(category, query)
+
+    assert isinstance(results, list), "category_retrieve should return a list"
diff --git a/poetry.lock b/poetry.lock
index f2ecc57b37cca5b561e83a6df33325492c82b674..323bcf5c5d9f69176383bb1b556d18540281e2bb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2175,6 +2175,21 @@ llama-index-core = ">=0.10.1,<0.11.0"
 onnxruntime = ">=1.17.0,<2.0.0"
 tokenizers = ">=0.15.1,<0.16.0"
 
+[[package]]
+name = "llama-index-vector-stores-pinecone"
+version = "0.1.3"
+description = "llama-index vector_stores pinecone integration"
+optional = false
+python-versions = ">=3.8.1,<3.13"
+files = [
+    {file = "llama_index_vector_stores_pinecone-0.1.3-py3-none-any.whl", hash = "sha256:776c233bccc6d43c8e60751b1a1330586b0909124ddb4a6efc275f92ad042b81"},
+    {file = "llama_index_vector_stores_pinecone-0.1.3.tar.gz", hash = "sha256:c4c48bcb025730cfd470ae9bf535bfa33b0bd06a637804033d77aa9b5ec2455f"},
+]
+
+[package.dependencies]
+llama-index-core = ">=0.10.11.post1,<0.11.0"
+pinecone-client = ">=3.0.2,<4.0.0"
+
 [[package]]
 name = "llama-parse"
 version = "0.3.4"
@@ -3326,6 +3341,26 @@ files = [
     {file = "pickleshare-0.7.5.tar.gz", hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca"},
 ]
 
+[[package]]
+name = "pinecone-client"
+version = "3.0.3"
+description = "Pinecone client and SDK"
+optional = false
+python-versions = ">=3.8,<3.13"
+files = [
+    {file = "pinecone_client-3.0.3-py3-none-any.whl", hash = "sha256:940c942aeb259145e1cd6d3f214ad977dbb4dc2e626b3528fb5015c64c3e6190"},
+    {file = "pinecone_client-3.0.3.tar.gz", hash = "sha256:2ad3ef7627edc4d9ee248d9781861c4341d6d27a15bc05f6bef53d958837d374"},
+]
+
+[package.dependencies]
+certifi = ">=2019.11.17"
+tqdm = ">=4.64.1"
+typing-extensions = ">=3.7.4"
+urllib3 = ">=1.26.0"
+
+[package.extras]
+grpc = ["googleapis-common-protos (>=1.53.0)", "grpc-gateway-protoc-gen-openapiv2 (==0.1.0)", "grpcio (>=1.44.0)", "lz4 (>=3.1.3)", "protobuf (>=3.20.0,<3.21.0)"]
+
 [[package]]
 name = "pkgutil-resolve-name"
 version = "1.3.10"
@@ -3711,6 +3746,7 @@ python-versions = ">=3.8"
 files = [
     {file = "PyMuPDFb-1.23.22-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9085a1e2fbf16f2820f9f7ad3d25e85f81d9b9eb0409110c1670d4cf5a27a678"},
     {file = "PyMuPDFb-1.23.22-py3-none-macosx_11_0_arm64.whl", hash = "sha256:01016dd33220cef4ecaf929d09fd27a584dc3ec3e5c9f4112dfe63613ea35135"},
+    {file = "PyMuPDFb-1.23.22-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cf50e814db91f2a2325219302fbac229a23682c372cf8232aabd51ea3f18210e"},
     {file = "PyMuPDFb-1.23.22-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3ffa713ad18e816e584c8a5f569995c32d22f8ac76ab6e4a61f2d2983c4b73d9"},
     {file = "PyMuPDFb-1.23.22-py3-none-win32.whl", hash = "sha256:d00e372452845aea624659c302d25e935052269fd3aafe26948301576d6f2ee8"},
     {file = "PyMuPDFb-1.23.22-py3-none-win_amd64.whl", hash = "sha256:7c9c157281fdee9f296e666a323307dbf74cb38f017921bb131fa7bfcd39c2bd"},
@@ -4614,36 +4650,48 @@ python-versions = ">=3.7"
 files = [
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+    {file = "SQLAlchemy-2.0.27-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6c7a596d0be71b7baa037f4ac10d5e057d276f65a9a611c46970f012752ebf2d"},
     {file = "SQLAlchemy-2.0.27-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:954d9735ee9c3fa74874c830d089a815b7b48df6f6b6e357a74130e478dbd951"},
+    {file = "SQLAlchemy-2.0.27-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:5cd20f58c29bbf2680039ff9f569fa6d21453fbd2fa84dbdb4092f006424c2e6"},
     {file = "SQLAlchemy-2.0.27-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:03f448ffb731b48323bda68bcc93152f751436ad6037f18a42b7e16af9e91c07"},
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diff --git a/pyproject.toml b/pyproject.toml
index 4485f445e267186e70d88e9bb95f43e29f262c45..e3dbda8e1b3e4c43ee9807873230fdf3948374b4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -60,6 +60,8 @@ llama-index-indices-managed-llama-cloud = "^0.1.2"
 llama-index-core = "^0.10.12"
 llama-index-multi-modal-llms-openai = "^0.1.3"
 llama-index-cli = "^0.1.2"
+llama-index-vector-stores-pinecone = "^0.1.3"
+pinecone-client = "^3.0.3"
 
 [tool.poetry.group.dev.dependencies]
 black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"}