RAG Integration for Tech Orchestrator

Write a prompt that will write a program. It will use https://github.com/kliewerdaniel/tech-company-orchestrator as a base to be improved on. Use the following RAG set up to help langchain openai chromadb pinecone-client tiktoken sentence-transformers python-dotenv PyPDF2 langchain-community langchain-openai langchain-chroma import os import sys import glob from dotenv import load_dotenv # Load environment variables from .env file load_dotenv() # Updated imports from langchain_openai.embeddings import OpenAIEmbeddings from langchain_chroma.vectorstores import Chroma from langchain_openai.llms import OpenAI from langchain.chains import RetrievalQA # Updated document loaders from langchain_community.document_loaders import TextLoader, PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter def main(): # Load OpenAI API key openai_api_key = os.getenv("OPENAI_API_KEY") if not openai_api_key: print("Please set your OPENAI_API_KEY in the .env file.") sys.exit(1) # Define the folder path (change 'data' to your folder name) folder_path = './data' if not os.path.exists(folder_path): print(f"Folder '{folder_path}' does not exist.") sys.exit(1) # Read all files in the folder documents = [] for filepath in glob.glob(os.path.join(folder_path, '**/*.*'), recursive=True): if os.path.isfile(filepath): ext = os.path.splitext(filepath)[1].lower() try: if ext == '.txt': loader = TextLoader(filepath, encoding='utf-8') documents.extend(loader.load_and_split()) elif ext == '.pdf': loader = PyPDFLoader(filepath) documents.extend(loader.load_and_split()) else: print(f"Unsupported file format: {filepath}") except Exception as e: print(f"Error reading '{filepath}': {e}") if not documents: print("No documents found in the folder.") sys.exit(1) # Split documents into chunks text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) texts = text_splitter.split_documents(documents) # Initialize embeddings and vector store embeddings = OpenAIEmbeddings() vector_store = Chroma(embedding_function=embeddings, persist_directory="./chroma_store") # Add texts to vector store in batches batch_size = 500 # Adjust this number as needed for i in range(0, len(texts), batch_size): batch_texts = texts[i:i+batch_size] vector_store.add_documents(batch_texts) # No need to call persist() anymore # vector_store.persist() # Remove or comment out this line # Set up retriever retriever = vector_store.as_retriever(search_kwargs={"k": 3}) # Set up the language model llm = OpenAI(temperature=0.7) # Create the RetrievalQA chain qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", # Options: 'stuff', 'map_reduce', 'refine', 'map_rerank' retriever=retriever ) # Interactive prompt for user queries print("The system is ready. You can now ask questions about the content.") while True: query = input("Enter your question (or type 'exit' to quit): ") if query.lower() in ('exit', 'quit'): break try: response = qa_chain.run(query) print(f"\nAnswer: {response}\n") except Exception as e: print(f"An error occurred: {e}\n") if __name__ == "__main__": main() I want you to come up with ideas on how to incorporate the RAG and repo and then use the one that is best.

### Prompt You are a developer improving upon the [Tech Company Orchestrator](https://github.com/kliewerdaniel/tech-company-orchestrator) by integrating a Retrieval-Augmented Generation (RAG) setup. Your goal is to enhance the repository with better document processing, retrieval, and AI-based Q&A functionality, while leveraging the following technologies: - **langchain**: Chain models and retrieval processes. - **openai**: Generate embeddings and answer questions. - **chromadb**: Store and retrieve vectors. - **pinecone-client**: An optional alternative for scalable vector storage. - **tiktoken**: Efficient token handling. - **sentence-transformers**: Additional embeddings options. - **python-dotenv**: Manage environment variables. - **PyPDF2**: Parse PDFs for document ingestion. - **langchain-community**: Extend loaders and utility methods. Your task is to propose and implement improvements to the repository using this RAG stack. Include features that would benefit a tech company managing diverse datasets, making information retrieval and AI interaction seamless. --- ### Ideas for Incorporating RAG and Repo #### 1. **Unified Vector Store with Hybrid Retrieval** - **Enhancement**: Support both `Chroma` and `Pinecone` as interchangeable backends for vector storage. - **Details**: Add configuration for switching between local (`Chroma`) and scalable cloud (`Pinecone`) vector storage, ensuring flexibility. - **Use case**: Handles growing datasets efficiently, adapting to scale. #### 2. **Multi-Embedding Strategy** - **Enhancement**: Enable dual embeddings using `OpenAI` for general context and `sentence-transformers` for domain-specific content. - **Details**: Store two embeddings for each document. Choose retrieval strategy dynamically based on user queries. - **Use case**: Improves retrieval accuracy for varied types of questions. #### 3. **Enhanced PDF Processing** - **Enhancement**: Use `PyPDF2` to extract metadata and structured content for better indexing. - **Details**: Allow extraction of titles, authors, and table contents to create metadata-driven search filters. - **Use case**: Improves discoverability of documents in complex datasets. #### 4. **Token Cost Optimization** - **Enhancement**: Integrate `tiktoken` for token counting during document splitting and chunking. - **Details**: Optimize chunk sizes to avoid exceeding token limits, lowering costs. - **Use case**: Makes RAG pipelines cost-efficient. #### 5. **Topic-Based Retrieval Chains** - **Enhancement**: Build topic-specific retrievers using metadata tags. - **Details**: Dynamically create chains that restrict retrieval to subsets of data tagged with certain keywords. - **Use case**: Handles multi-domain content efficiently. #### 6. **Automated Dataset Ingestion Pipeline** - **Enhancement**: Create a CLI tool for ingesting new documents, processing embeddings, and updating the vector store. - **Details**: Support hot-reloading of vector stores when new data is ingested. - **Use case**: Simplifies maintaining up-to-date datasets. #### 7. **Improved User Interface for Q&A** - **Enhancement**: Replace CLI with a simple web UI or API endpoint using Flask or FastAPI. - **Details**: Provide a form-based interface for asking questions and displaying results. - **Use case**: Improves user experience for non-technical users. --- ### Best Idea: **Unified Vector Store with Multi-Embedding and Enhanced Retrieval** **Rationale**: This idea integrates hybrid storage, improves accuracy with dual embeddings, and scales well for both small and large datasets. It ensures flexibility and extensibility while keeping costs manageable. --- ### Implementation Plan #### Key Features: 1. **Dual Vector Store**: - Implement an abstraction layer to toggle between `Chroma` (local) and `Pinecone` (cloud). 2. **Multi-Embedding**: - Generate and store embeddings using both `OpenAI` and `sentence-transformers`. - Choose retrieval strategy based on query type (semantic vs domain-specific). 3. **Efficient Document Processing**: - Use `PyPDF2` for PDF metadata and text extraction. - Incorporate `tiktoken` to ensure token limits are respected during chunking. 4. **Hot-Reloading Pipeline**: - Add a CLI tool to streamline the ingestion of new documents into the vector store. 5. **Interactive API**: - Build a FastAPI interface for querying and retrieval, making the system accessible via HTTP requests. #### Updated Code Outline: The `main` function will split into modular components: - **`load_documents()`**: Handle file ingestion. - **`process_documents()`**: Create embeddings and update vector stores. - **`query_system()`**: Handle Q&A queries via an API. Would you like me to proceed with the implementation code?

### Prompt You are a developer improving upon the [Tech Company Orchestrator](https://github.com/kliewerdaniel/tech-company-orchestrator) by integrating a Retrieval-Augmented Generation (RAG) setup. Your goal is to enhance the repository with better document processing, retrieval, and AI-based Q&A functionality, while leveraging the following technologies: - **langchain**: Chain models and retrieval processes. - **openai**: Generate embeddings and answer questions. - **chromadb**: Store and retrieve vectors. - **pinecone-client**: An optional alternative for scalable vector storage. - **tiktoken**: Efficient token handling. - **sentence-transformers**: Additional embeddings options. - **python-dotenv**: Manage environment variables. - **PyPDF2**: Parse PDFs for document ingestion. - **langchain-community**: Extend loaders and utility methods. Your task is to propose and implement improvements to the repository using this RAG stack. Include features that would benefit a tech company managing diverse datasets, making information retrieval and AI interaction seamless. --- ### Ideas for Incorporating RAG and Repo #### 1. **Unified Vector Store with Hybrid Retrieval** - **Enhancement**: Support both `Chroma` and `Pinecone` as interchangeable backends for vector storage. - **Details**: Add configuration for switching between local (`Chroma`) and scalable cloud (`Pinecone`) vector storage, ensuring flexibility. - **Use case**: Handles growing datasets efficiently, adapting to scale. #### 2. **Multi-Embedding Strategy** - **Enhancement**: Enable dual embeddings using `OpenAI` for general context and `sentence-transformers` for domain-specific content. - **Details**: Store two embeddings for each document. Choose retrieval strategy dynamically based on user queries. - **Use case**: Improves retrieval accuracy for varied types of questions. #### 3. **Enhanced PDF Processing** - **Enhancement**: Use `PyPDF2` to extract metadata and structured content for better indexing. - **Details**: Allow extraction of titles, authors, and table contents to create metadata-driven search filters. - **Use case**: Improves discoverability of documents in complex datasets. #### 4. **Token Cost Optimization** - **Enhancement**: Integrate `tiktoken` for token counting during document splitting and chunking. - **Details**: Optimize chunk sizes to avoid exceeding token limits, lowering costs. - **Use case**: Makes RAG pipelines cost-efficient. #### 5. **Topic-Based Retrieval Chains** - **Enhancement**: Build topic-specific retrievers using metadata tags. - **Details**: Dynamically create chains that restrict retrieval to subsets of data tagged with certain keywords. - **Use case**: Handles multi-domain content efficiently. #### 6. **Automated Dataset Ingestion Pipeline** - **Enhancement**: Create a CLI tool for ingesting new documents, processing embeddings, and updating the vector store. - **Details**: Support hot-reloading of vector stores when new data is ingested. - **Use case**: Simplifies maintaining up-to-date datasets. #### 7. **Improved User Interface for Q&A** - **Enhancement**: Replace CLI with a simple web UI or API endpoint using Flask or FastAPI. - **Details**: Provide a form-based interface for asking questions and displaying results. - **Use case**: Improves user experience for non-technical users. --- ### Best Idea: **Unified Vector Store with Multi-Embedding and Enhanced Retrieval** **Rationale**: This idea integrates hybrid storage, improves accuracy with dual embeddings, and scales well for both small and large datasets. It ensures flexibility and extensibility while keeping costs manageable. --- ### Implementation Plan #### Key Features: 1. **Dual Vector Store**: - Implement an abstraction layer to toggle between `Chroma` (local) and `Pinecone` (cloud). 2. **Multi-Embedding**: - Generate and store embeddings using both `OpenAI` and `sentence-transformers`. - Choose retrieval strategy based on query type (semantic vs domain-specific). 3. **Efficient Document Processing**: - Use `PyPDF2` for PDF metadata and text extraction. - Incorporate `tiktoken` to ensure token limits are respected during chunking. 4. **Hot-Reloading Pipeline**: - Add a CLI tool to streamline the ingestion of new documents into the vector store. 5. **Interactive API**: - Build a FastAPI interface for querying and retrieval, making the system accessible via HTTP requests. #### Updated Code Outline: The `main` function will split into modular components: - **`load_documents()`**: Handle file ingestion. - **`process_documents()`**: Create embeddings and update vector stores. - **`query_system()`**: Handle Q&A queries via an API. Would you like me to proceed with the implementation code?

Certainly! Below is the implementation code that incorporates the enhancements discussed. The code is modularized into separate functions for better readability and maintainability. It includes: - Dual vector store support (`Chroma` and `Pinecone`) - Multi-embedding strategy using both OpenAI and Sentence Transformers - Efficient document processing with `PyPDF2` and `tiktoken` - Hot-reloading dataset ingestion pipeline - An interactive API using FastAPI for querying --- ### **Prerequisites** Ensure you have the following packages installed: ```bash pip install langchain openai chromadb pinecone-client tiktoken sentence-transformers python-dotenv PyPDF2 fastapi uvicorn ``` --- ### **Code Implementation** ```python # main.py import os import sys import glob import asyncio from dotenv import load_dotenv from typing import List, Dict from fastapi import FastAPI import uvicorn # Load environment variables from .env file load_dotenv() # Imports for embeddings and vector stores from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings from langchain.vectorstores import Chroma, Pinecone from langchain.llms import OpenAI from langchain.chains import RetrievalQA # Document loaders and text splitters from langchain.document_loaders import TextLoader, PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter # Tokenization import tiktoken # Initialize FastAPI app = FastAPI() # Global variables vector_store = None retriever = None qa_chain = None # Function to load documents from a directory def load_documents(folder_path: str) -> List: documents = [] for filepath in glob.glob(os.path.join(folder_path, '**/*.*'), recursive=True): if os.path.isfile(filepath): ext = os.path.splitext(filepath)[1].lower() try: if ext == '.txt': loader = TextLoader(filepath, encoding='utf-8') documents.extend(loader.load_and_split()) elif ext == '.pdf': loader = PyPDFLoader(filepath) pdf_documents = loader.load() documents.extend(pdf_documents) else: print(f"Unsupported file format: {filepath}") except Exception as e: print(f"Error reading '{filepath}': {e}") return documents # Function to split documents while respecting token limits def split_documents(documents: List, chunk_size: int = 1000, chunk_overlap: int = 200) -> List: text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) texts = text_splitter.split_documents(documents) return texts # Function to initialize embeddings def initialize_embeddings() -> Dict: embeddings = { 'openai': OpenAIEmbeddings(), 'sentence_transformer': HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2') } return embeddings # Function to initialize vector store def initialize_vector_store(embeddings: Dict, use_pinecone: bool = False): global vector_store if use_pinecone: import pinecone pinecone_api_key = os.getenv("PINECONE_API_KEY") pinecone_environment = os.getenv("PINECONE_ENVIRONMENT") if not pinecone_api_key or not pinecone_environment: print("Please set your PINECONE_API_KEY and PINECONE_ENVIRONMENT in the .env file.") sys.exit(1) pinecone.init(api_key=pinecone_api_key, environment=pinecone_environment) index_name = 'tech-company-orchestrator' vector_store = Pinecone(index_name=index_name, embedding_function=embeddings['openai'].embed_query) else: vector_store = Chroma(embedding_function=embeddings['openai'], persist_directory="./chroma_store") # Function to add documents to vector store def add_documents_to_vector_store(texts: List, embeddings: Dict): global vector_store # Create embeddings using both OpenAI and Sentence Transformers for embedding_name, embedding_function in embeddings.items(): # Add documents to the vector store vector_store.add_documents(texts, embedding=embedding_function) # Function to initialize the retriever and QA chain def initialize_qa_chain(embeddings: Dict): global vector_store, retriever, qa_chain retriever = vector_store.as_retriever(search_kwargs={"k": 3}) llm = OpenAI(temperature=0.7) qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever ) # Endpoint for querying the system @app.post("/query") async def query_system(query: str): global qa_chain try: response = qa_chain.run(query) return {"answer": response} except Exception as e: return {"error": str(e)} # Function to process new documents and update vector store def process_new_documents(folder_path: str): documents = load_documents(folder_path) if not documents: print("No documents found.") return texts = split_documents(documents) embeddings = initialize_embeddings() add_documents_to_vector_store(texts, embeddings) vector_store.persist() initialize_qa_chain(embeddings) print("Documents processed and vector store updated.") # Main function to start the API def main(): folder_path = './data' use_pinecone = os.getenv("USE_PINECONE", "false").lower() == "true" openai_api_key = os.getenv("OPENAI_API_KEY") if not openai_api_key: print("Please set your OPENAI_API_KEY in the .env file.") sys.exit(1) embeddings = initialize_embeddings() initialize_vector_store(embeddings, use_pinecone) process_new_documents(folder_path) uvicorn.run(app, host="0.0.0.0", port=8000) if __name__ == "__main__": main() ``` --- ### **Explanation** #### **1. Modular Functions** - **`load_documents()`**: Loads and reads documents from the specified folder. Supports `.txt` and `.pdf` files using `TextLoader` and `PyPDFLoader`. - **`split_documents()`**: Splits documents into chunks while respecting token limits to optimize costs. Uses `RecursiveCharacterTextSplitter`. - **`initialize_embeddings()`**: Initializes both OpenAI and Sentence Transformer embeddings for a multi-embedding strategy. - **`initialize_vector_store()`**: Sets up the vector store. It can toggle between `Chroma` for local storage and `Pinecone` for scalable cloud storage based on the `USE_PINECONE` environment variable. - **`add_documents_to_vector_store()`**: Adds documents to the vector store using both embeddings. - **`initialize_qa_chain()`**: Sets up the retriever and the QA chain for querying. - **`process_new_documents()`**: Orchestrates the document loading, splitting, and adding to the vector store. It also re-initializes the QA chain after updating the vector store. #### **2. Interactive API with FastAPI** - **FastAPI Endpoints**: Provides an HTTP interface for querying the system. - **`/query`**: Accepts a `POST` request with a query string and returns the answer from the QA chain. - **Running the API**: The `main()` function initializes everything and runs the FastAPI app using `uvicorn`. #### **3. Hot-Reloading Dataset Ingestion** - When `process_new_documents()` is called, it processes any new documents added to the `./data` folder and updates the vector store accordingly. #### **4. Environment Variables** - **`.env` File**: Used to securely manage API keys and configurations. - `OPENAI_API_KEY` - `PINECONE_API_KEY` - `PINECONE_ENVIRONMENT` - `USE_PINECONE` (set to `"true"` or `"false"`) --- ### **Usage Instructions** 1. **Set Up Environment Variables** Create a `.env` file in the root directory: ```env OPENAI_API_KEY=your_openai_api_key PINECONE_API_KEY=your_pinecone_api_key PINECONE_ENVIRONMENT=your_pinecone_environment USE_PINECONE=false ``` 2. **Prepare Your Data** - Place your `.txt` and `.pdf` files inside the `./data` directory. 3. **Run the Application** ```bash python main.py ``` 4. **Interact with the API** - Use `curl`, Postman, or any HTTP client to send a `POST` request to `http://localhost:8000/query`. ```bash curl -X POST "http://localhost:8000/query" -H "Content-Type: application/json" -d '{"query": "Your question here"}' ``` 5. **Adding New Documents** - Add new documents to the `./data` folder. - Restart the application or modify the code to watch the folder for changes and call `process_new_documents()` accordingly. --- ### **Optional Enhancements** - **Automatic Folder Monitoring** Implement a file system watcher to automatically process new documents without restarting the application. - **Advanced Query Handling** Modify the `/query` endpoint to accept additional parameters, such as: - Number of results to return (`k`) - Choice of embedding model - Specific metadata filters - **Authentication** Secure the API endpoints by adding authentication mechanisms like API keys or OAuth tokens. - **Dockerization** Create a `Dockerfile` to containerize the application for easier deployment. --- ### **Conclusion** This implementation provides a scalable and flexible RAG system suitable for a tech company managing diverse datasets. It leverages the strengths of both OpenAI and HuggingFace embeddings, supports scalable vector storage options, and offers an accessible API for seamless integration. Feel free to ask if you need further assistance or additional features!

(venv) daniel@Daniels-MacBook-Pro rag % python3 main.py /Users/daniel/RAG/main.py:16: LangChainDeprecationWarning: Importing OpenAIEmbeddings from langchain.embeddings is deprecated. Please replace deprecated imports: >> from langchain.embeddings import OpenAIEmbeddings with new imports of: >> from langchain_community.embeddings import OpenAIEmbeddings You can use the langchain cli to **automatically** upgrade many imports. Please see documentation here <https://python.langchain.com/docs/versions/v0_2/> from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings /Users/daniel/RAG/main.py:16: LangChainDeprecationWarning: Importing HuggingFaceEmbeddings from langchain.embeddings is deprecated. Please replace deprecated imports: >> from langchain.embeddings import HuggingFaceEmbeddings with new imports of: >> from langchain_community.embeddings import HuggingFaceEmbeddings You can use the langchain cli to **automatically** upgrade many imports. Please see documentation here <https://python.langchain.com/docs/versions/v0_2/> from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings /Users/daniel/RAG/main.py:17: LangChainDeprecationWarning: Importing Chroma from langchain.vectorstores is deprecated. Please replace deprecated imports: >> from langchain.vectorstores import Chroma with new imports of: >> from langchain_community.vectorstores import Chroma You can use the langchain cli to **automatically** upgrade many imports. Please see documentation here <https://python.langchain.com/docs/versions/v0_2/> from langchain.vectorstores import Chroma, Pinecone /Users/daniel/RAG/main.py:17: LangChainDeprecationWarning: Importing Pinecone from langchain.vectorstores is deprecated. Please replace deprecated imports: >> from langchain.vectorstores import Pinecone with new imports of: >> from langchain_community.vectorstores import Pinecone You can use the langchain cli to **automatically** upgrade many imports. Please see documentation here <https://python.langchain.com/docs/versions/v0_2/> from langchain.vectorstores import Chroma, Pinecone /Users/daniel/RAG/venv/lib/python3.11/site-packages/langchain/llms/__init__.py:549: LangChainDeprecationWarning: Importing LLMs from langchain is deprecated. Importing from langchain will no longer be supported as of langchain==0.2.0. Please import from langchain-community instead: `from langchain_community.llms import OpenAI`. To install langchain-community run `pip install -U langchain-community`. warnings.warn( /Users/daniel/RAG/venv/lib/python3.11/site-packages/langchain/llms/__init__.py:549: LangChainDeprecationWarning: Importing LLMs from langchain is deprecated. Importing from langchain will no longer be supported as of langchain==0.2.0. Please import from langchain-community instead: `from langchain_community.llms import OpenAI`. To install langchain-community run `pip install -U langchain-community`. warnings.warn( /Users/daniel/RAG/main.py:22: LangChainDeprecationWarning: Importing TextLoader from langchain.document_loaders is deprecated. Please replace deprecated imports: >> from langchain.document_loaders import TextLoader with new imports of: >> from langchain_community.document_loaders import TextLoader You can use the langchain cli to **automatically** upgrade many imports. Please see documentation here <https://python.langchain.com/docs/versions/v0_2/> from langchain.document_loaders import TextLoader, PyPDFLoader /Users/daniel/RAG/main.py:22: LangChainDeprecationWarning: Importing PyPDFLoader from langchain.document_loaders is deprecated. Please replace deprecated imports: >> from langchain.document_loaders import PyPDFLoader with new imports of: >> from langchain_community.document_loaders import PyPDFLoader You can use the langchain cli to **automatically** upgrade many imports. Please see documentation here <https://python.langchain.com/docs/versions/v0_2/> from langchain.document_loaders import TextLoader, PyPDFLoader /Users/daniel/RAG/main.py:65: LangChainDeprecationWarning: The class `OpenAIEmbeddings` was deprecated in LangChain 0.0.9 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-openai package and should be used instead. To use it run `pip install -U :class:`~langchain-openai` and import as `from :class:`~langchain_openai import OpenAIEmbeddings``. 'openai': OpenAIEmbeddings(), /Users/daniel/RAG/main.py:66: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFaceEmbeddings``. 'sentence_transformer': HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2') modules.json: 100%|█████████████████████████████| 349/349 [00:00<00:00, 448kB/s] config_sentence_transformers.json: 100%|████████| 116/116 [00:00<00:00, 518kB/s] README.md: 100%|███████████████████████████| 10.7k/10.7k [00:00<00:00, 33.6MB/s] sentence_bert_config.json: 100%|██████████████| 53.0/53.0 [00:00<00:00, 325kB/s] config.json: 100%|█████████████████████████████| 612/612 [00:00<00:00, 5.01MB/s] model.safetensors: 100%|███████████████████| 90.9M/90.9M [00:13<00:00, 6.50MB/s] tokenizer_config.json: 100%|███████████████████| 350/350 [00:00<00:00, 2.82MB/s] vocab.txt: 100%|█████████████████████████████| 232k/232k [00:00<00:00, 2.44MB/s] tokenizer.json: 100%|████████████████████████| 466k/466k [00:00<00:00, 6.17MB/s] special_tokens_map.json: 100%|██████████████████| 112/112 [00:00<00:00, 985kB/s] 1_Pooling/config.json: 100%|███████████████████| 190/190 [00:00<00:00, 1.71MB/s] /Users/daniel/RAG/main.py:84: LangChainDeprecationWarning: The class `Chroma` was deprecated in LangChain 0.2.9 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-chroma package and should be used instead. To use it run `pip install -U :class:`~langchain-chroma` and import as `from :class:`~langchain_chroma import Chroma``. vector_store = Chroma(embedding_function=embeddings['openai'], persist_directory="./chroma_store") Traceback (most recent call last): File "/Users/daniel/RAG/main.py", line 142, in <module> main() File "/Users/daniel/RAG/main.py", line 138, in main process_new_documents(folder_path) File "/Users/daniel/RAG/main.py", line 123, in process_new_documents add_documents_to_vector_store(texts, embeddings) File "/Users/daniel/RAG/main.py", line 92, in add_documents_to_vector_store vector_store.add_documents(texts, embedding=embedding_function) File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/langchain_core/vectorstores/base.py", line 287, in add_documents return self.add_texts(texts, metadatas, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 313, in add_texts raise e File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 299, in add_texts self._collection.upsert( File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/chromadb/api/models/Collection.py", line 343, in upsert self._client._upsert( File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/chromadb/telemetry/opentelemetry/__init__.py", line 150, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/chromadb/api/segment.py", line 103, in wrapper return self._rate_limit_enforcer.rate_limit(func)(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/chromadb/rate_limit/simple_rate_limit/__init__.py", line 23, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/chromadb/api/segment.py", line 523, in _upsert validate_batch( File "/Users/daniel/RAG/venv/lib/python3.11/site-packages/chromadb/api/types.py", line 827, in validate_batch raise ValueError( ValueError: Batch size 10812 exceeds maximum batch size 5461

I'm sorry to hear you're encountering this error. The `ValueError: Batch size 10812 exceeds maximum batch size 5461` indicates that when adding documents to the vector store, the batch size exceeds ChromaDB's maximum allowed batch size. This issue arises because ChromaDB imposes a limit on the number of documents that can be added in a single batch operation. In your code, you're trying to add all documents at once without batching them into smaller chunks that respect this limit. Additionally, you're seeing several deprecation warnings related to `langchain` imports. The `langchain` library has undergone significant changes, and many modules have been moved to separate packages under `langchain-community`. Below, I'll provide an updated version of your code that: 1. **Fixes the batch size error** by adding documents in smaller batches. 2. **Updates deprecated imports** to comply with the latest `langchain` library structure. 3. **Includes error handling** to make debugging easier. 4. **Improves code modularity and readability**. --- ### **Updated Code** ```python # main.py import os import sys import glob from dotenv import load_dotenv from typing import List, Dict from fastapi import FastAPI import uvicorn # Load environment variables from .env file load_dotenv() # Imports for embeddings and vector stores from langchain_openai.embeddings import OpenAIEmbeddings from langchain_huggingface.embeddings import HuggingFaceEmbeddings from langchain_chroma.vectorstores import Chroma from langchain_pinecone.vectorstores import Pinecone from langchain_openai.llms import OpenAI from langchain.chains import RetrievalQA # Document loaders and text splitters from langchain_community.document_loaders import TextLoader, PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter # Initialize FastAPI app = FastAPI() # Global variables vector_store = None retriever = None qa_chain = None # Function to load documents from a directory def load_documents(folder_path: str) -> List: documents = [] for filepath in glob.glob(os.path.join(folder_path, '**/*.*'), recursive=True): if os.path.isfile(filepath): ext = os.path.splitext(filepath)[1].lower() try: if ext == '.txt': loader = TextLoader(filepath, encoding='utf-8') documents.extend(loader.load_and_split()) elif ext == '.pdf': loader = PyPDFLoader(filepath) pdf_documents = loader.load() documents.extend(pdf_documents) else: print(f"Unsupported file format: {filepath}") except Exception as e: print(f"Error reading '{filepath}': {e}") return documents # Function to split documents def split_documents(documents: List, chunk_size: int = 1000, chunk_overlap: int = 200) -> List: text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) texts = text_splitter.split_documents(documents) return texts # Function to initialize embeddings def initialize_embeddings() -> Dict: embeddings = { 'openai': OpenAIEmbeddings(), 'sentence_transformer': HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2') } return embeddings # Function to initialize vector store def initialize_vector_store(embedding_function, use_pinecone: bool = False): global vector_store if use_pinecone: import pinecone pinecone_api_key = os.getenv("PINECONE_API_KEY") pinecone_environment = os.getenv("PINECONE_ENVIRONMENT") if not pinecone_api_key or not pinecone_environment: print("Please set your PINECONE_API_KEY and PINECONE_ENVIRONMENT in the .env file.") sys.exit(1) pinecone.init(api_key=pinecone_api_key, environment=pinecone_environment) index_name = 'tech-company-orchestrator' vector_store = Pinecone.from_existing_index(index_name, embedding_function=embedding_function) else: vector_store = Chroma(embedding_function=embedding_function, persist_directory="./chroma_store") # Function to add documents to vector store in batches def add_documents_to_vector_store(texts: List, embedding_function, batch_size: int = 500): global vector_store total_texts = len(texts) for i in range(0, total_texts, batch_size): batch_texts = texts[i:i+batch_size] try: vector_store.add_documents(batch_texts, embedding=embedding_function) print(f"Added batch {i//batch_size + 1} of {((total_texts - 1) // batch_size) + 1}") except Exception as e: print(f"Error adding batch {i//batch_size + 1}: {e}") # Function to initialize the retriever and QA chain def initialize_qa_chain(): global vector_store, retriever, qa_chain retriever = vector_store.as_retriever(search_kwargs={"k": 3}) llm = OpenAI(temperature=0.7) qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever ) # Endpoint for querying the system @app.post("/query") async def query_system(query: str): global qa_chain try: response = qa_chain.run(query) return {"answer": response} except Exception as e: return {"error": str(e)} # Function to process new documents and update vector store def process_new_documents(folder_path: str, embeddings: Dict): documents = load_documents(folder_path) if not documents: print("No documents found.") return texts = split_documents(documents) # Use OpenAI embeddings for the vector store embedding_function = embeddings['openai'] add_documents_to_vector_store(texts, embedding_function) vector_store.persist() print("Documents processed and vector store updated.") # Main function to start the API def main(): folder_path = './data' use_pinecone = os.getenv("USE_PINECONE", "false").lower() == "true" openai_api_key = os.getenv("OPENAI_API_KEY") if not openai_api_key: print("Please set your OPENAI_API_KEY in the .env file.") sys.exit(1) embeddings = initialize_embeddings() initialize_vector_store(embedding_function=embeddings['openai'], use_pinecone=use_pinecone) process_new_documents(folder_path, embeddings) initialize_qa_chain() uvicorn.run(app, host="0.0.0.0", port=8000) if __name__ == "__main__": main() ``` --- ### **Explanation of Changes** #### **1. Batching Documents** - **Modified `add_documents_to_vector_store()`**: - Added a `batch_size` parameter (defaulted to 500). - The function now processes the texts in batches, adding each batch separately to avoid exceeding ChromaDB's maximum batch size. - Included error handling within the batching loop to catch and report any issues per batch. #### **2. Updated Imports** - **Replaced Deprecated Imports**: - Updated imports to use the new package structure as per the deprecation warnings. - For example, `from langchain.embeddings import OpenAIEmbeddings` is replaced with `from langchain_openai.embeddings import OpenAIEmbeddings`. - Similarly updated imports for HuggingFace embeddings, Chroma vector store, and other modules. #### **3. Adjusted Function Parameters** - **Passed `embedding_function` Explicitly**: - Modified functions to accept `embedding_function` as a parameter where needed. - Ensures the correct embedding function is used consistently across vector store initialization and document addition. #### **4. Initialization Order** - **Ensured Correct Initialization Sequence**: - Moved `initialize_qa_chain()` to be called after processing new documents, ensuring the vector store is populated before setting up the retriever and QA chain. #### **5. Removed Unnecessary Embeddings in Vector Store** - **Simplified to Single Embedding for Vector Store**: - For the vector store, using a single embedding function (`OpenAIEmbeddings`) to prevent conflicts. - You can extend this to use multi-embedding strategies, but it requires managing separate vector stores or indices. #### **6. Added Progress Logging** - **Print Statements for Batch Progress**: - Added print statements within the batching loop to track progress and make debugging easier. --- ### **Addressing the Batch Size Error** By processing documents in batches of a size that does not exceed ChromaDB's maximum batch size (5461), we prevent the `ValueError` from occurring. The default batch size is set to 500, which is well within the limit. --- ### **Handling Deprecation Warnings** The deprecation warnings suggest that the modules have moved to different packages. Updating the imports according to the latest `langchain` documentation resolves these warnings. - **Embeddings**: - `from langchain.embeddings import OpenAIEmbeddings` ➔ `from langchain_openai.embeddings import OpenAIEmbeddings` - `from langchain.embeddings import HuggingFaceEmbeddings` ➔ `from langchain_huggingface.embeddings import HuggingFaceEmbeddings` - **Vector Stores**: - `from langchain.vectorstores import Chroma` ➔ `from langchain_chroma.vectorstores import Chroma` - `from langchain.vectorstores import Pinecone` ➔ `from langchain_pinecone.vectorstores import Pinecone` - **LLMs**: - `from langchain.llms import OpenAI` ➔ `from langchain_openai.llms import OpenAI` - **Document Loaders**: - `from langchain.document_loaders import TextLoader, PyPDFLoader` ➔ `from langchain_community.document_loaders import TextLoader, PyPDFLoader` --- ### **Updated Requirements** Ensure you have the updated packages installed: ```bash pip install langchain-openai langchain-huggingface langchain-chroma langchain-pinecone langchain-community openai chromadb pinecone-client tiktoken sentence-transformers python-dotenv PyPDF2 fastapi uvicorn ``` --- ### **Usage Instructions** 1. **Update Environment Variables** Ensure your `.env` file includes the necessary API keys and settings: ```env OPENAI_API_KEY=your_openai_api_key PINECONE_API_KEY=your_pinecone_api_key # Only if using Pinecone PINECONE_ENVIRONMENT=your_pinecone_env # Only if using Pinecone USE_PINECONE=false # Set to "true" if you want to use Pinecone ``` 2. **Prepare Data** Place your `.txt` and `.pdf` files inside the `./data` directory. 3. **Run the Application** ```bash python main.py ``` 4. **Interact with the API** Use an HTTP client to send a `POST` request to `http://localhost:8000/query`: ```bash curl -X POST "http://localhost:8000/query" -H "Content-Type: application/json" -d '{"query": "Your question here"}' ``` --- ### **Additional Recommendations** - **Monitor ChromaDB Limitations**: While the default batch size is set to 500, if you encounter the same error again, you may need to reduce this number further. - **Multi-Embedding Strategy**: Implementing a multi-embedding strategy requires more complex management of vector stores. Consider maintaining separate vector stores for each embedding type or using advanced techniques to combine embeddings. - **Stay Updated with Library Changes**: The `langchain` ecosystem is evolving rapidly. Regularly check the documentation and update your code accordingly to accommodate any breaking changes or deprecations. --- ### **Conclusion** The updated code should resolve the batch size error and eliminate the deprecation warnings. By processing documents in manageable batches and updating imports to align with the latest library versions, the application becomes more robust and maintainable. If you have any further questions or need assistance with additional features, feel free to ask!