diff --git a/llama-index-packs/llama-index-packs-finchat/.gitignore b/llama-index-packs/llama-index-packs-finchat/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..990c18de229088f55c6c514fd0f2d49981d1b0e7
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/.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-finchat/BUILD b/llama-index-packs/llama-index-packs-finchat/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..0896ca890d8bffd60a44fa824f8d57fecd73ee53
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/BUILD
@@ -0,0 +1,3 @@
+poetry_requirements(
+ name="poetry",
+)
diff --git a/llama-index-packs/llama-index-packs-finchat/Makefile b/llama-index-packs/llama-index-packs-finchat/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..b9eab05aa370629a4a3de75df3ff64cd53887b68
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/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-finchat/README.md b/llama-index-packs/llama-index-packs-finchat/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..fda963f89b8d8518c29f0c177124ffcb72b60c21
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/README.md
@@ -0,0 +1,61 @@
+# Finance Chat Llama Pack based on OpenAIAgent
+
+This LlamaPack implements a hierarchical agent based on LLM for financial chat and information extraction purposed.
+
+LLM agent is connected to various open financial apis as well as daily updated SP500 postgres SQL database storing
+opening & closing price, volume as well as past earnings.
+
+Based on the query, the agent reasons and routes to available tools or runs SQL query to retrieve information and
+combine information to answer.
+
+### Installation
+
+```bash
+pip install llama-index llama-index-tools-finchat
+```
+
+## CLI Usage
+
+You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
+
+```bash
+llamaindex-cli download-llamapack FinanceChatPack --download-dir ./finchat_pack
+```
+
+You can then inspect the files at `./finchat_pack` and use them as a template for your own project.
+
+## Code Usage
+
+You can download the pack to a the `./finchat_pack` directory:
+
+```python
+from llama_index.core.llama_pack import download_llama_pack
+
+FinanceChatPack = download_llama_pack("FinanceChatPack", "./finchat_pack")
+```
+
+To use this tool, you'll need a few API keys:
+
+- POLYGON_API_KEY -- <https://polygon.io/>
+- FINNHUB_API_KEY -- <https://finnhub.io/>
+- ALPHA_VANTAGE_API_KEY -- <https://www.alphavantage.co/>
+- NEWSAPI_API_KEY -- <https://newsapi.org/>
+- POSTGRES_DB_URI -- 'postgresql://postgres.xhlcobfkbhtwmckmszqp:fingptpassword#123@aws-0-us-east-1.pooler.supabase.com:5432/postgres' (You can also host your own postgres SQL DB with the same table signatures. To use different signatures, modification is required in giving query examples for SQL code generation.)
+
+```python
+fin_agent = FinanceChatPack(
+ POLYGON_API_KEY,
+ FINNHUB_API_KEY,
+ ALPHA_VANTAGE_API_KEY,
+ NEWSAPI_API_KEY,
+ OPENAI_API_KEY,
+)
+```
+
+From here, you can use the pack, or inspect and modify the pack in `./finchat_pack`.
+
+The `run()` function chats with the agent and sends the response of the input query.
+
+```python
+response = fin_agent.run("<query>")
+```
diff --git a/llama-index-packs/llama-index-packs-finchat/examples/BUILD b/llama-index-packs/llama-index-packs-finchat/examples/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..db46e8d6c978c67e301dd6c47bee08c1b3fd141c
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/examples/BUILD
@@ -0,0 +1 @@
+python_sources()
diff --git a/llama-index-packs/llama-index-packs-finchat/examples/example.py b/llama-index-packs/llama-index-packs-finchat/examples/example.py
new file mode 100644
index 0000000000000000000000000000000000000000..b414f15ebc821a71eaf6941d3ccc716eaae2176d
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/examples/example.py
@@ -0,0 +1,37 @@
+"""
+This example demonstrates how to set up and test chatting with a finance agent using the FinanceChatPack.
+It involves collecting necessary API keys and initializing the FinanceChatPack with these keys and a PostgreSQL database URI.
+"""
+
+import getpass
+from llama_index.packs.finchat import FinanceChatPack
+
+# Prompting the user to enter all necessary API keys for finance data access and OpenAI
+openai_api_key = getpass.getpass("Enter your OpenAI API key: ")
+polygon_api_key = getpass.getpass("Enter your Polygon API key: ")
+finnhub_api_key = getpass.getpass("Enter your Finnhub API key: ")
+alpha_vantage_api_key = getpass.getpass("Enter your Alpha Vantage API key: ")
+newsapi_api_key = getpass.getpass("Enter your NewsAPI API key: ")
+
+# PostgreSQL database URI for storing and accessing financial data
+postgres_db_uri = "postgresql://postgres.xhlcobfkbhtwmckmszqp:fingptpassword#123@aws-0-us-east-1.pooler.supabase.com:5432/postgres"
+
+# Initializing the FinanceChatPack with the collected API keys and database URI
+finance_chat_pack = FinanceChatPack(
+ polygon_api_key=polygon_api_key,
+ finnhub_api_key=finnhub_api_key,
+ alpha_vantage_api_key=alpha_vantage_api_key,
+ newsapi_api_key=newsapi_api_key,
+ openai_api_key=openai_api_key,
+ postgres_db_uri=postgres_db_uri,
+)
+
+# Notifying the user that the FinanceChatPack has been initialized and is ready for testing
+print(
+ "FinanceChatPack initialized successfully. Ready for testing chat interactions with the finance agent."
+)
+
+
+user_query = "Find similar companies to Rivian?"
+response = finance_chat_pack.run(user_query)
+print("Finance agent response:", response)
diff --git a/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/BUILD b/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..db46e8d6c978c67e301dd6c47bee08c1b3fd141c
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/BUILD
@@ -0,0 +1 @@
+python_sources()
diff --git a/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/__init__.py b/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e85603546160de936731808f4b5af04c42acac38
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/__init__.py
@@ -0,0 +1,3 @@
+from llama_index.packs.finchat.base import FinanceChatPack
+
+__all__ = ["FinanceChatPack"]
diff --git a/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/base.py b/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a7082d7fe64714fbc8b9b22770e2c72193c2b3d
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/llama_index/packs/finchat/base.py
@@ -0,0 +1,375 @@
+"""Finance Chat LlamaPack class."""
+
+from typing import Optional, List, Any
+
+# The following imports have been adjusted to fix the ModuleNotFoundError
+from llama_index.core.llama_pack.base import BaseLlamaPack
+from llama_index.llms.openai import OpenAI
+from llama_index.tools.finance import FinanceAgentToolSpec
+from llama_index.core.tools.tool_spec.base import BaseToolSpec
+from llama_index.core.readers.base import BaseReader
+from llama_index.core.utilities.sql_wrapper import SQLDatabase
+from llama_index.core.tools.query_engine import QueryEngineTool
+from llama_index.agent.openai import OpenAIAgent
+from llama_index.core.schema import Document
+from llama_index.core.base.llms.types import ChatMessage
+from sqlalchemy import MetaData, text
+from sqlalchemy.engine import Engine
+from sqlalchemy.exc import NoSuchTableError
+from sqlalchemy.schema import CreateTable
+
+
+class SQLDatabaseToolSpec(BaseToolSpec, BaseReader):
+ """
+ A tool to query and retrieve results from a SQL Database.
+
+ Args:
+ sql_database (Optional[SQLDatabase]): SQL database to use,
+ including table names to specify.
+ See :ref:`Ref-Struct-Store` for more details.
+
+ OR
+
+ engine (Optional[Engine]): SQLAlchemy Engine object of the database connection.
+
+ OR
+
+ uri (Optional[str]): uri of the database connection.
+
+ OR
+
+ scheme (Optional[str]): scheme of the database connection.
+ host (Optional[str]): host of the database connection.
+ port (Optional[int]): port of the database connection.
+ user (Optional[str]): user of the database connection.
+ password (Optional[str]): password of the database connection.
+ dbname (Optional[str]): dbname of the database connection.
+
+ """
+
+ spec_functions = ["run_sql_query", "describe_tables", "list_tables"]
+
+ def __init__(
+ self,
+ sql_database: Optional[SQLDatabase] = None,
+ engine: Optional[Engine] = None,
+ uri: Optional[str] = None,
+ scheme: Optional[str] = None,
+ host: Optional[str] = None,
+ port: Optional[str] = None,
+ user: Optional[str] = None,
+ password: Optional[str] = None,
+ dbname: Optional[str] = None,
+ *args: Optional[Any],
+ **kwargs: Optional[Any],
+ ) -> None:
+ """Initialize with parameters."""
+ if sql_database:
+ self.sql_database = sql_database
+ elif engine:
+ self.sql_database = SQLDatabase(engine, *args, **kwargs)
+ elif uri:
+ self.uri = uri
+ self.sql_database = SQLDatabase.from_uri(uri, *args, **kwargs)
+ elif scheme and host and port and user and password and dbname:
+ uri = f"{scheme}://{user}:{password}@{host}:{port}/{dbname}"
+ self.uri = uri
+ self.sql_database = SQLDatabase.from_uri(uri, *args, **kwargs)
+ else:
+ raise ValueError(
+ "You must provide either a SQLDatabase, "
+ "a SQL Alchemy Engine, a valid connection URI, or a valid "
+ "set of credentials."
+ )
+ self._metadata = MetaData()
+ self._metadata.reflect(bind=self.sql_database.engine)
+
+ def run_sql_query(self, query: str) -> Document:
+ r"""Runs SQL query on the provided SQL database, returning a Document storing all the rows separated by \n.
+
+ Args:
+ query (str): SQL query in text format which can directly be executed using SQLAlchemy engine.
+
+ Returns:
+ Document: Document storing all the output result of the sql-query generated.
+ """
+ with self.sql_database.engine.connect() as connection:
+ if query is None:
+ raise ValueError("A query parameter is necessary to filter the data")
+ else:
+ result = connection.execute(text(query))
+ all_doc_str = ""
+ for item in result.fetchall():
+ if all_doc_str:
+ all_doc_str += "\n"
+ # fetch each item
+ doc_str = ", ".join([str(entry) for entry in item])
+ all_doc_str += doc_str
+ return Document(text=all_doc_str)
+
+ def list_tables(self) -> List[str]:
+ """
+ Returns a list of available tables in the database.
+ """
+ return [x.name for x in self._metadata.sorted_tables]
+
+ def describe_tables(self, tables: Optional[List[str]] = None) -> str:
+ """
+ Describes the specified tables in the database.
+
+ Args:
+ tables (List[str]): A list of table names to retrieve details about
+ """
+ table_names = tables or [table.name for table in self._metadata.sorted_tables]
+ table_schemas = []
+
+ for table_name in table_names:
+ table = next(
+ (
+ table
+ for table in self._metadata.sorted_tables
+ if table.name == table_name
+ ),
+ None,
+ )
+ if table is None:
+ raise NoSuchTableError(f"Table '{table_name}' does not exist.")
+ schema = str(CreateTable(table).compile(self.sql_database._engine))
+ table_schemas.append(f"{schema}\n")
+
+ return "\n".join(table_schemas)
+
+ def get_table_info(self) -> str:
+ """Construct table info for the all tables in DB which includes information about the columns of the table and also shows top row of the table."""
+ all_table_info = ""
+ for table_name in self.list_tables():
+ table_info = self.sql_database.get_single_table_info(table_name)
+ table_info += f"\n\nHere is the DDL statement for this table:\n"
+ table_info += self.describe_tables([table_name])
+ _, output = self.sql_database.run_sql(f"SELECT * FROM {table_name} LIMIT 1")
+ table_info += f"\nTop row of {table_name}:\n\n"
+ for colname in output["col_keys"]:
+ table_info += colname + "\t"
+ table_info += "\n"
+ for data in output["result"]:
+ for val in data:
+ table_info += str(val) + "\t"
+ table_info += "\n"
+ all_table_info += f"\n{table_info}\n"
+ return all_table_info
+
+
+class FinanceChatPack(BaseLlamaPack):
+ def __init__(
+ self,
+ polygon_api_key: str,
+ finnhub_api_key: str,
+ alpha_vantage_api_key: str,
+ newsapi_api_key: str,
+ openai_api_key: str,
+ postgres_db_uri: str,
+ gpt_model_name: str = "gpt-4-0613",
+ ):
+ llm = OpenAI(temperature=0, model=gpt_model_name, api_key=openai_api_key)
+ self.db_tool_spec = SQLDatabaseToolSpec(uri=postgres_db_uri)
+ self.fin_tool_spec = FinanceAgentToolSpec(
+ polygon_api_key, finnhub_api_key, alpha_vantage_api_key, newsapi_api_key
+ )
+
+ self.db_table_info = self.db_tool_spec.get_table_info()
+ prefix_messages = self.construct_prefix_db_message(self.db_table_info)
+ # add some role play in the system .
+ database_agent = OpenAIAgent.from_tools(
+ [
+ tool
+ for tool in self.db_tool_spec.to_tool_list()
+ if tool.metadata.name == "run_sql_query"
+ ],
+ prefix_messages=prefix_messages,
+ llm=llm,
+ verbose=True,
+ )
+ database_agent_tool = QueryEngineTool.from_defaults(
+ database_agent,
+ name="database_agent",
+ description=""""
+ This agent analyzes a text query and add further explanations and thoughts to help a data scientist who has access to following tables:
+
+ {table_info}
+
+ Be concise and do not lose any information about original query while passing to the data scientist.
+ """,
+ )
+
+ fin_api_agent = OpenAIAgent.from_tools(
+ self.fin_tool_spec.to_tool_list(),
+ system_prompt=f"""
+ You are a helpful AI financial assistant designed to understand the intent of the user query and then use relevant tools/apis to help answer it.
+ You can use more than one tool/api only if needed, but final response should be concise and relevant. If you are not able to find
+ relevant tool/api, respond respectfully suggesting that you don't know. Think step by step""",
+ llm=llm,
+ verbose=True,
+ )
+
+ fin_api_agent_tool = QueryEngineTool.from_defaults(
+ fin_api_agent,
+ name="fin_api_agent",
+ description=f"""
+ This agent has access to another agent which can access certain open APIs to provide information based on user query.
+ Analyze the query and add any information if needed which can help to decide which API to call.
+ Be concise and do not lose any information about original query.
+ """,
+ )
+
+ self.fin_hierarchical_agent = OpenAIAgent.from_tools(
+ [database_agent_tool, fin_api_agent_tool],
+ system_prompt="""
+ You are a specialized financial assistant with access to certain tools which can access open APIs and SP500 companies database containing information on
+ daily opening price, closing price, high, low, volume, reported earnings, estimated earnings since 2010 to 2023. Before answering query you should check
+ if the question can be answered via querying the database or using specific open APIs. If you try to find answer via querying database first and it did
+ not work out, think if you can use other tool APIs available before replying gracefully.
+ """,
+ llm=llm,
+ verbose=True,
+ )
+
+ def construct_prefix_db_message(self, table_info: str) -> str:
+ system_prompt = f"""
+ You are a smart data scientist working in a reputed trading firm like Jump Trading developing automated trading algorithms. Take a deep breathe and think
+ step by step to design queries over a SQL database.
+
+ Here is a complete description of tables in SQL database you have access to:
+
+ {table_info}
+
+ Use responses to past questions also to guide you.
+
+
+ """
+
+ prefix_messages = []
+ prefix_messages.append(ChatMessage(role="system", content=system_prompt))
+
+ prefix_messages.append(
+ ChatMessage(
+ role="user",
+ content="What is the average price of Google in the month of July in 2023",
+ )
+ )
+ prefix_messages.append(
+ ChatMessage(
+ role="assistant",
+ content="""
+ SELECT AVG(close) AS AvgPrice
+ FROM stock_data
+ WHERE stock = 'GOOG'
+ AND date >= '2023-07-01'
+ AND date <= '2023-07-31';
+ """,
+ )
+ )
+
+ prefix_messages.append(
+ ChatMessage(
+ role="user",
+ content="Which stock has the maximum % change in any month in 2023",
+ )
+ )
+ # prefix_messages.append(ChatMessage(role="user", content="Which stocks gave more than 2% return constantly in month of July from past 5 years"))
+ prefix_messages.append(
+ ChatMessage(
+ role="assistant",
+ content="""
+ WITH MonthlyPrices AS (
+ SELECT
+ stock,
+ EXTRACT(YEAR FROM date) AS year,
+ EXTRACT(MONTH FROM date) AS month,
+ FIRST_VALUE(close) OVER (PARTITION BY stock, EXTRACT(YEAR FROM date), EXTRACT(MONTH FROM date) ORDER BY date ASC) AS opening_price,
+ LAST_VALUE(close) OVER (PARTITION BY stock, EXTRACT(YEAR FROM date), EXTRACT(MONTH FROM date) ORDER BY date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS closing_price
+ FROM
+ stock_data
+ WHERE
+ EXTRACT(YEAR FROM date) = 2023
+ ),
+ PercentageChanges AS (
+ SELECT
+ stock,
+ year,
+ month,
+ CASE
+ WHEN opening_rice IS NULL OR closing_price IS NULL THEN NULL
+ WHEN opening_price = 0 THEN NULL
+ ELSE ((closing_price - opening_price) / opening_price) * 100
+ END AS pct
+ FROM
+ MonthlyPrices
+ )
+ SELECT *
+ FROM
+ PercentageChanges
+ WHERE pct IS NOT NULL
+ ORDER BY
+ pct DESC
+ LIMIT 1;
+ """,
+ )
+ )
+
+ prefix_messages.append(
+ ChatMessage(
+ role="user",
+ content="How many times Microsoft beat earnings estimates in 2022",
+ )
+ )
+ prefix_messages.append(
+ ChatMessage(
+ role="assistant",
+ content="""
+ SELECT
+ COUNT(*)
+ FROM
+ earnings
+ WHERE
+ stock = 'MSFT' AND reported > estimated and EXTRACT(YEAR FROM date) = 2022
+ """,
+ )
+ )
+
+ prefix_messages.append(
+ ChatMessage(
+ role="user",
+ content="Which stocks have beaten earnings estimate by more than 1$ consecutively from last 4 reportings?",
+ )
+ )
+ prefix_messages.append(
+ ChatMessage(
+ role="assistant",
+ content="""
+ WITH RankedEarnings AS(
+ SELECT
+ stock,
+ date,
+ reported,
+ estimated,
+ RANK() OVER (PARTITION BY stock ORDER BY date DESC) as ranking
+ FROM
+ earnings
+ )
+ SELECT
+ stock
+ FROM
+ RankedEarnings
+ WHERE
+ ranking <= 4 AND reported - estimated > 1
+ GROUP BY
+ stock
+ HAVING COUNT(*) = 4
+ """,
+ )
+ )
+
+ return prefix_messages
+
+ def run(self, query: str):
+ return self.fin_hierarchical_agent.chat(query)
diff --git a/llama-index-packs/llama-index-packs-finchat/pyproject.toml b/llama-index-packs/llama-index-packs-finchat/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..b5e23a3bb299f38f895604f55f9fc20cd00eebe0
--- /dev/null
+++ b/llama-index-packs/llama-index-packs-finchat/pyproject.toml
@@ -0,0 +1,61 @@
+[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.finchat"
+
+[tool.llamahub.class_authors]
+FinanceChatPack = "345ishaan"
+
+[tool.mypy]
+disallow_untyped_defs = true
+# Remove venv skip when integrated with pre-commit
+exclude = ["_static", "build", "examples", "notebooks", "venv"]
+ignore_missing_imports = true
+python_version = "3.8"
+
+[tool.poetry]
+authors = ["Ishan Gupta <345ishaan@gmail.com>"]
+description = "llama-index packs implementation of a hierarchical agent for finance chat."
+keywords = ["agent", "finance", "finchat"]
+license = "MIT"
+maintainers = ["345ishaan"]
+name = "llama-index-packs-finchat"
+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"
+tavily-python = "^0.3.1"
+llama-index-agent-openai = "^0.1.5"
+llama-index-tools-finance = "^0.1.0"
+
+[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"