LangChain Workflow with Pinecone

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).

Overview

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.

Workflow

1. Document Input:

   • Multiple PDF documents are the source of information.

2. Chunking Text:

   • Each PDF document is split into smaller chunks of text to facilitate efficient processing.

3. 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.

4. Storing Embeddings:

   • The embeddings are stored in a vector store (Pinecone), which acts as a knowledge base for the documents.

5. 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.

6. 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.

7. Ranking Results:

   • The retrieved results are ranked based on their relevance to the question.

8. Answer Generation:

   • The LLM uses the ranked results to generate an answer to the user's question.

9. User Interaction:

   • The user receives the answer generated by the LLM.

Components

• 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.

Setup and Installation

Prerequisites

• Python 3.8 or higher
• Pip (Python package installer)
• Access to Pinecone and a large language model API (such as OpenAI's GPT-4)

Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/langchain-pinecone-workflow.git
   cd langchain-pinecone-workflow
   

2. Install the requirements file:

   pip install -r requirements.txt
   

3. 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