This repository demonstrates a workflow that integrates LangChain with a vector store (Pinecone) to enable semantic search and question answering using large language models (LLMs).
The workflow processes PDF documents to create embeddings, stores them in a vector store, and then uses these embeddings to provide accurate answers to user questions through semantic search.
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Document Input:
- Multiple PDF documents are the source of information.
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Chunking Text:
- Each PDF document is split into smaller chunks of text to facilitate efficient processing.
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Embeddings Creation:
- Each chunk of text is processed to create embeddings using a large language model (LLM). Embeddings are vector representations of the text chunks that capture the semantic meaning of the content.
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Storing Embeddings:
- The embeddings are stored in a vector store (Pinecone), which acts as a knowledge base for the documents.
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Question Embedding:
- When a user asks a question (e.g., "What is a neural network?"), the question is converted into an embedding using the LLM.
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Semantic Search:
- The question embedding is used to perform a semantic search against the embeddings stored in the vector store. This retrieves the most relevant text chunks based on the semantic similarity to the question.
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Ranking Results:
- The retrieved results are ranked based on their relevance to the question.
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Answer Generation:
- The LLM uses the ranked results to generate an answer to the user's question.
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User Interaction:
- The user receives the answer generated by the LLM.
- LangChain: A framework for developing applications with LLMs.
- Pinecone: A vector database service for storing and searching embeddings.
- Large Language Models (LLMs): Used to create embeddings and generate answers.
- Python 3.8 or higher
- Pip (Python package installer)
- Access to Pinecone and a large language model API (such as OpenAI's GPT-4)
- Clone the Repository:
git clone https://github.com/yourusername/langchain-pinecone-workflow.git cd langchain-pinecone-workflow
- Install the requirements file:
pip install -r requirements.txt
- Set Up Environment Variables:
Create a .env file in the project root and add your API keys and other configuration details: PINECONE_API_KEY=your_pinecone_api_key OPENAI_API_KEY=your_openai_api_key