bump create-llama to 0.11 and update event handler (#260)

.changeset/slow-papayas-camp.md

+---
+"create-llama": patch
+---
+
+Use callback manager properly

.changeset/sour-donkeys-develop.md

+---
+"create-llama": patch
+---
+
+Bump create-llama version to 0.11.1

helpers/python.ts

@@ -12,6 +12,7 @@ import {
   InstallTemplateArgs,
   ModelConfig,
   TemplateDataSource,
+  TemplateType,
   TemplateVectorDB,
 } from "./types";
 
@@ -26,6 +27,7 @@ const getAdditionalDependencies = (
   vectorDb?: TemplateVectorDB,
   dataSources?: TemplateDataSource[],
   tools?: Tool[],
+  templateType?: TemplateType,
 ) => {
   const dependencies: Dependency[] = [];
 
@@ -128,7 +130,7 @@ const getAdditionalDependencies = (
         case "llamacloud":
           dependencies.push({
             name: "llama-index-indices-managed-llama-cloud",
-            version: "^0.2.7",
+            version: "^0.3.0",
           });
           break;
       }
@@ -147,77 +149,99 @@ const getAdditionalDependencies = (
     case "ollama":
       dependencies.push({
         name: "llama-index-llms-ollama",
-        version: "0.1.2",
+        version: "0.3.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-ollama",
-        version: "0.1.2",
+        version: "0.3.0",
       });
       break;
     case "openai":
-      dependencies.push({
-        name: "llama-index-agent-openai",
-        version: "0.2.6",
-      });
+      if (templateType !== "multiagent") {
+        dependencies.push({
+          name: "llama-index-llms-openai",
+          version: "^0.2.0",
+        });
+        dependencies.push({
+          name: "llama-index-embeddings-openai",
+          version: "^0.2.3",
+        });
+        dependencies.push({
+          name: "llama-index-agent-openai",
+          version: "^0.3.0",
+        });
+      }
       break;
     case "groq":
+      // Fastembed==0.2.0 does not support python3.13 at the moment
+      // Fixed the python version less than 3.13
+      dependencies.push({
+        name: "python",
+        version: "^3.11,<3.13",
+      });
       dependencies.push({
         name: "llama-index-llms-groq",
-        version: "0.1.4",
+        version: "0.2.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-fastembed",
-        version: "^0.1.4",
+        version: "^0.2.0",
       });
       break;
     case "anthropic":
+      // Fastembed==0.2.0 does not support python3.13 at the moment
+      // Fixed the python version less than 3.13
+      dependencies.push({
+        name: "python",
+        version: "^3.11,<3.13",
+      });
       dependencies.push({
         name: "llama-index-llms-anthropic",
-        version: "0.1.10",
+        version: "0.3.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-fastembed",
-        version: "^0.1.4",
+        version: "^0.2.0",
       });
       break;
     case "gemini":
       dependencies.push({
         name: "llama-index-llms-gemini",
-        version: "0.1.10",
+        version: "0.3.4",
       });
       dependencies.push({
         name: "llama-index-embeddings-gemini",
-        version: "0.1.6",
+        version: "^0.2.0",
       });
       break;
     case "mistral":
       dependencies.push({
         name: "llama-index-llms-mistralai",
-        version: "0.1.17",
+        version: "0.2.1",
       });
       dependencies.push({
         name: "llama-index-embeddings-mistralai",
-        version: "0.1.4",
+        version: "0.2.0",
       });
       break;
     case "azure-openai":
       dependencies.push({
         name: "llama-index-llms-azure-openai",
-        version: "0.1.10",
+        version: "0.2.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-azure-openai",
-        version: "0.1.11",
+        version: "0.2.4",
       });
       break;
     case "t-systems":
       dependencies.push({
         name: "llama-index-agent-openai",
-        version: "0.2.2",
+        version: "0.3.0",
       });
       dependencies.push({
         name: "llama-index-llms-openai-like",
-        version: "0.1.3",
+        version: "0.2.0",
       });
       break;
   }
@@ -227,7 +251,7 @@ const getAdditionalDependencies = (
 
 const mergePoetryDependencies = (
   dependencies: Dependency[],
-  existingDependencies: Record<string, Omit<Dependency, "name">>,
+  existingDependencies: Record<string, Omit<Dependency, "name"> | string>,
 ) => {
   for (const dependency of dependencies) {
     let value = existingDependencies[dependency.name] ?? {};
@@ -246,7 +270,13 @@ const mergePoetryDependencies = (
       );
     }
 
-    existingDependencies[dependency.name] = value;
+    // Serialize separately only if extras are provided
+    if (value.extras && value.extras.length > 0) {
+      existingDependencies[dependency.name] = value;
+    } else {
+      // Otherwise, serialize just the version string
+      existingDependencies[dependency.name] = value.version;
+    }
   }
 };
 
@@ -388,6 +418,7 @@ export const installPythonTemplate = async ({
     vectorDb,
     dataSources,
     tools,
+    template,
   );
 
   if (observability && observability !== "none") {

templates/components/engines/python/agent/engine.py

 import os
 
-from app.engine.index import get_index
+from app.engine.index import IndexConfig, get_index
 from app.engine.tools import ToolFactory
 from llama_index.core.agent import AgentRunner
+from llama_index.core.callbacks import CallbackManager
 from llama_index.core.settings import Settings
 from llama_index.core.tools.query_engine import QueryEngineTool
 
 
-def get_chat_engine(filters=None, params=None):
+def get_chat_engine(filters=None, params=None, event_handlers=None):
     system_prompt = os.getenv("SYSTEM_PROMPT")
     top_k = int(os.getenv("TOP_K", 0))
     tools = []
+    callback_manager = CallbackManager(handlers=event_handlers or [])
 
     # Add query tool if index exists
-    index = get_index()
+    index_config = IndexConfig(callback_manager=callback_manager, **(params or {}))
+    index = get_index(index_config)
     if index is not None:
         query_engine = index.as_query_engine(
             filters=filters, **({"similarity_top_k": top_k} if top_k != 0 else {})
@@ -28,5 +31,6 @@ def get_chat_engine(filters=None, params=None):
         llm=Settings.llm,
         tools=tools,
         system_prompt=system_prompt,
+        callback_manager=callback_manager,
         verbose=True,
     )

templates/components/engines/python/chat/engine.py

 import os
 
-from app.engine.index import get_index
+from app.engine.index import IndexConfig, get_index
 from app.engine.node_postprocessors import NodeCitationProcessor
 from fastapi import HTTPException
+from llama_index.core.callbacks import CallbackManager
 from llama_index.core.chat_engine import CondensePlusContextChatEngine
+from llama_index.core.memory import ChatMemoryBuffer
+from llama_index.core.settings import Settings
 
 
-def get_chat_engine(filters=None, params=None):
+def get_chat_engine(filters=None, params=None, event_handlers=None):
     system_prompt = os.getenv("SYSTEM_PROMPT")
     citation_prompt = os.getenv("SYSTEM_CITATION_PROMPT", None)
     top_k = int(os.getenv("TOP_K", 0))
+    llm = Settings.llm
+    memory = ChatMemoryBuffer.from_defaults(
+        token_limit=llm.metadata.context_window - 256
+    )
+    callback_manager = CallbackManager(handlers=event_handlers or [])
 
     node_postprocessors = []
     if citation_prompt:
         node_postprocessors = [NodeCitationProcessor()]
         system_prompt = f"{system_prompt}\n{citation_prompt}"
 
-    index = get_index(params)
+    index_config = IndexConfig(callback_manager=callback_manager, **(params or {}))
+    index = get_index(index_config)
     if index is None:
         raise HTTPException(
             status_code=500,
@@ -29,8 +38,11 @@ def get_chat_engine(filters=None, params=None):
         filters=filters, **({"similarity_top_k": top_k} if top_k != 0 else {})
     )
 
-    return CondensePlusContextChatEngine.from_defaults(
+    return CondensePlusContextChatEngine(
+        llm=llm,
+        memory=memory,
         system_prompt=system_prompt,
         retriever=retriever,
         node_postprocessors=node_postprocessors,
+        callback_manager=callback_manager,
     )

templates/components/vectordbs/python/llamacloud/index.py

 import logging
 import os
-from llama_index.indices.managed.llama_cloud import LlamaCloudIndex
+from typing import Optional
+
+from llama_index.core.callbacks import CallbackManager
 from llama_index.core.ingestion.api_utils import (
     get_client as llama_cloud_get_client,
 )
+from llama_index.indices.managed.llama_cloud import LlamaCloudIndex
+from pydantic import BaseModel, Field, validator
 
 logger = logging.getLogger("uvicorn")
 
 
-def get_client():
-    return llama_cloud_get_client(
-        os.getenv("LLAMA_CLOUD_API_KEY"),
-        os.getenv("LLAMA_CLOUD_BASE_URL"),
+class LlamaCloudConfig(BaseModel):
+    # Private attributes
+    api_key: str = Field(
+        default=os.getenv("LLAMA_CLOUD_API_KEY"),
+        exclude=True,  # Exclude from the model representation
+    )
+    base_url: Optional[str] = Field(
+        default=os.getenv("LLAMA_CLOUD_BASE_URL"),
+        exclude=True,
     )
+    organization_id: Optional[str] = Field(
+        default=os.getenv("LLAMA_CLOUD_ORGANIZATION_ID"),
+        exclude=True,
+    )
+    # Configuration attributes, can be set by the user
+    pipeline: str = Field(
+        description="The name of the pipeline to use",
+        default=os.getenv("LLAMA_CLOUD_INDEX_NAME"),
+    )
+    project: str = Field(
+        description="The name of the LlamaCloud project",
+        default=os.getenv("LLAMA_CLOUD_PROJECT_NAME"),
+    )
+
+    # Validate and throw error if the env variables are not set before starting the app
+    @validator("pipeline", "project", "api_key", pre=True, always=True)
+    @classmethod
+    def validate_env_vars(cls, value):
+        if value is None:
+            raise ValueError(
+                "Please set LLAMA_CLOUD_INDEX_NAME, LLAMA_CLOUD_PROJECT_NAME and LLAMA_CLOUD_API_KEY"
+                " to your environment variables or config them in .env file"
+            )
+        return value
 
+    def to_client_kwargs(self) -> dict:
+        return {
+            "api_key": self.api_key,
+            "base_url": self.base_url,
+        }
 
-def get_index(params=None):
-    configParams = params or {}
-    pipelineConfig = configParams.get("llamaCloudPipeline", {})
-    name = pipelineConfig.get("pipeline", os.getenv("LLAMA_CLOUD_INDEX_NAME"))
-    project_name = pipelineConfig.get("project", os.getenv("LLAMA_CLOUD_PROJECT_NAME"))
-    api_key = os.getenv("LLAMA_CLOUD_API_KEY")
-    base_url = os.getenv("LLAMA_CLOUD_BASE_URL")
-    organization_id = os.getenv("LLAMA_CLOUD_ORGANIZATION_ID")
-
-    if name is None or project_name is None or api_key is None:
-        raise ValueError(
-            "Please set LLAMA_CLOUD_INDEX_NAME, LLAMA_CLOUD_PROJECT_NAME and LLAMA_CLOUD_API_KEY"
-            " to your environment variables or config them in .env file"
-        )
-
-    index = LlamaCloudIndex(
-        name=name,
-        project_name=project_name,
-        api_key=api_key,
-        base_url=base_url,
-        organization_id=organization_id,
+
+class IndexConfig(BaseModel):
+    llama_cloud_pipeline_config: LlamaCloudConfig = Field(
+        default=LlamaCloudConfig(),
+        alias="llamaCloudPipeline",
+    )
+    callback_manager: Optional[CallbackManager] = Field(
+        default=None,
     )
 
+    def to_index_kwargs(self) -> dict:
+        return {
+            "name": self.llama_cloud_pipeline_config.pipeline,
+            "project_name": self.llama_cloud_pipeline_config.project,
+            "api_key": self.llama_cloud_pipeline_config.api_key,
+            "base_url": self.llama_cloud_pipeline_config.base_url,
+            "organization_id": self.llama_cloud_pipeline_config.organization_id,
+            "callback_manager": self.callback_manager,
+        }
+
+
+def get_index(config: IndexConfig = None):
+    if config is None:
+        config = IndexConfig()
+    index = LlamaCloudIndex(**config.to_index_kwargs())
+
     return index
+
+
+def get_client():
+    config = LlamaCloudConfig()
+    return llama_cloud_get_client(**config.to_client_kwargs())

templates/components/vectordbs/python/none/index.py

-import os
 import logging
+import os
 from datetime import timedelta
+from typing import Optional
 
-from cachetools import cached, TTLCache
-from llama_index.core.storage import StorageContext
+from cachetools import TTLCache, cached
+from llama_index.core.callbacks import CallbackManager
 from llama_index.core.indices import load_index_from_storage
+from llama_index.core.storage import StorageContext
+from pydantic import BaseModel, Field
 
 logger = logging.getLogger("uvicorn")
 
 
-@cached(
-    TTLCache(maxsize=10, ttl=timedelta(minutes=5).total_seconds()),
-    key=lambda *args, **kwargs: "global_storage_context",
-)
-def get_storage_context(persist_dir: str) -> StorageContext:
-    return StorageContext.from_defaults(persist_dir=persist_dir)
+class IndexConfig(BaseModel):
+    callback_manager: Optional[CallbackManager] = Field(
+        default=None,
+    )
 
 
-def get_index(params=None):
+def get_index(config: IndexConfig = None):
+    if config is None:
+        config = IndexConfig()
     storage_dir = os.getenv("STORAGE_DIR", "storage")
     # check if storage already exists
     if not os.path.exists(storage_dir):
@@ -25,6 +28,16 @@ def get_index(params=None):
     # load the existing index
     logger.info(f"Loading index from {storage_dir}...")
     storage_context = get_storage_context(storage_dir)
-    index = load_index_from_storage(storage_context)
+    index = load_index_from_storage(
+        storage_context, callback_manager=config.callback_manager
+    )
     logger.info(f"Finished loading index from {storage_dir}")
     return index
+
+
+@cached(
+    TTLCache(maxsize=10, ttl=timedelta(minutes=5).total_seconds()),
+    key=lambda *args, **kwargs: "global_storage_context",
+)
+def get_storage_context(persist_dir: str) -> StorageContext:
+    return StorageContext.from_defaults(persist_dir=persist_dir)

templates/types/extractor/fastapi/app/engine/index.py

 import logging
+from typing import Optional
+
+from llama_index.core.callbacks import CallbackManager
 from llama_index.core.indices import VectorStoreIndex
-from app.engine.vectordb import get_vector_store
+from pydantic import BaseModel, Field
 
+from app.engine.vectordb import get_vector_store
 
 logger = logging.getLogger("uvicorn")
 
 
-def get_index(params=None):
+class IndexConfig(BaseModel):
+    callback_manager: Optional[CallbackManager] = Field(
+        default=None,
+    )
+
+
+def get_index(config: IndexConfig = None):
+    if config is None:
+        config = IndexConfig()
     logger.info("Connecting vector store...")
     store = get_vector_store()
     # Load the index from the vector store
     # If you are using a vector store that doesn't store text,
     # you must load the index from both the vector store and the document store
-    index = VectorStoreIndex.from_vector_store(store)
+    index = VectorStoreIndex.from_vector_store(
+        store, callback_manager=config.callback_manager
+    )
     logger.info("Finished load index from vector store.")
     return index

templates/types/extractor/fastapi/app/services/extractor.py

 import logging
+
 from app.engine import get_query_engine
 from app.services.model import IMPORTS
 
@@ -33,4 +34,4 @@ class ExtractorService:
         query_engine = get_query_engine(schema_model)
         response = await query_engine.aquery(query)
         output_data = response.response.dict()
-        return schema_model(**output_data).json(indent=2)
+        return schema_model(**output_data).model_dump_json(indent=2)

templates/types/extractor/fastapi/pyproject.toml

@@ -13,7 +13,7 @@ python = "^3.11,<4.0"
 fastapi = "^0.109.1"
 uvicorn = { extras = ["standard"], version = "^0.23.2" }
 python-dotenv = "^1.0.0"
-llama-index = "^0.10.58"
+llama-index = "^0.11.1"
 cachetools = "^5.3.3"
 reflex = "^0.5.9"
 

templates/types/streaming/fastapi/app/api/routers/chat.py

@@ -38,11 +38,10 @@ async def chat(
         logger.info(
             f"Creating chat engine with filters: {str(filters)}",
         )
-        chat_engine = get_chat_engine(filters=filters, params=params)
-
         event_handler = EventCallbackHandler()
-        chat_engine.callback_manager.handlers.append(event_handler)  # type: ignore
-
+        chat_engine = get_chat_engine(
+            filters=filters, params=params, event_handlers=[event_handler]
+        )
         response = await chat_engine.astream_chat(last_message_content, messages)
         process_response_nodes(response.source_nodes, background_tasks)
 

templates/types/streaming/fastapi/app/api/services/file.py

@@ -5,8 +5,7 @@ from io import BytesIO
 from pathlib import Path
 from typing import Any, List, Tuple
 
-
-from app.engine.index import get_index
+from app.engine.index import IndexConfig, get_index
 from llama_index.core import VectorStoreIndex
 from llama_index.core.ingestion import IngestionPipeline
 from llama_index.core.readers.file.base import (
@@ -77,7 +76,8 @@ class PrivateFileService:
         file_data, extension = PrivateFileService.preprocess_base64_file(base64_content)
 
         # Add the nodes to the index and persist it
-        current_index = get_index(params)
+        index_config = IndexConfig(**params)
+        current_index = get_index(index_config)
 
         # Insert the documents into the index
         if isinstance(current_index, LlamaCloudIndex):

templates/types/streaming/fastapi/app/engine/index.py

 import logging
+from typing import Optional
+
+from llama_index.core.callbacks import CallbackManager
 from llama_index.core.indices import VectorStoreIndex
-from app.engine.vectordb import get_vector_store
+from pydantic import BaseModel, Field
 
+from app.engine.vectordb import get_vector_store
 
 logger = logging.getLogger("uvicorn")
 
 
-def get_index(params=None):
+class IndexConfig(BaseModel):
+    callback_manager: Optional[CallbackManager] = Field(
+        default=None,
+    )
+
+
+def get_index(config: IndexConfig = None):
+    if config is None:
+        config = IndexConfig()
     logger.info("Connecting vector store...")
     store = get_vector_store()
     # Load the index from the vector store
     # If you are using a vector store that doesn't store text,
     # you must load the index from both the vector store and the document store
-    index = VectorStoreIndex.from_vector_store(store)
+    index = VectorStoreIndex.from_vector_store(
+        store, callback_manager=config.callback_manager
+    )
     logger.info("Finished load index from vector store.")
     return index

templates/types/streaming/fastapi/pyproject.toml

@@ -14,7 +14,7 @@ fastapi = "^0.109.1"
 uvicorn = { extras = ["standard"], version = "^0.23.2" }
 python-dotenv = "^1.0.0"
 aiostream = "^0.5.2"
-llama-index = "0.10.58"
+llama-index = "0.11.1"
 cachetools = "^5.3.3"
 
 [build-system]