LangChain + Vector DB + GPT 3.5 Turbo => Semantic Search Chat App (with full Code)

Build a Chat App using LangChain Framework, Pinecone Vector Database and GPT 3.5 Turbo Language Model | Langchain OpenAi Generation AI Project for Beginners 

The goal of this Langchain OpenAi Project for Student is to develop a robust Semantic Search application capable of providing precise answers to user queries. This is achieved by leveraging the SQuAD 2.0 dataset, Pinecone vector database, LangChain tools and agents, and the OpenAI GPT Chat API. The application will create a searchable space for the SQuAD 2.0 dataset and enable interactive querying using natural language processing capabilities.

Key Components

1. SQuAD 2.0 Dataset: A large-scale dataset for reading comprehension and question-answering that contains more than 150,000 question-answer pairs on 500+ articles.
2. Pinecone Vector Database: A managed vector database service optimized for machine learning models. It allows for efficient similarity search, which is crucial for finding the most relevant answers to queries.
3. LangChain: A library of tools for building applications with language models. LangChain will be used for managing conversational context and integrating with the OpenAI GPT API.
4. OpenAI GPT Chat API: A state-of-the-art language model that can understand context, answer questions, and maintain a coherent conversation.

Project Workflow | Lang Chain Generation AI Project for Beginners

Data Preparation and Indexing

• Data Ingestion: Load the SQuAD 2.0 dataset and preprocess it to extract the questions, answers, and contextual paragraphs.
• Vectorization: Use OpenAI’s embedding models to convert textual data into high-dimensional vectors.
• Upsert into Pinecone: Upsert the vectorized data into a Pinecone index, which will allow us to perform semantic searches over the dataset.

Semantic Search Agent Development in LangChain

• LangChain Agent: Create a LangChain Tool for Query from the Pinecone DB and a LangChain Agent that uses the Q&A Tool.
• Query Processing: Use LangChain’s conversational agents to process the query and generate a suitable prompt for the OpenAI GPT model.
• Vector Search: Convert the processed query into a vector using OpenAI embeddings and perform a similarity search in the Pinecone index to retrieve the most relevant context passages.
• Answer Generation: Pass the context passages to the OpenAI GPT Chat API to generate a natural language answer.
• Conversational Memory: Employ LangChain’s conversational memory to maintain the context of the conversation, allowing for follow-up questions and clarifications without losing the thread of the discussion.