Building a Chatbot with Langchain, ChatGPT, Pinecone, and Streamlit

🤖 Build a chatbot using Langchain, OpenAI ChatGPT, and Pinecone.

📚 ChatGPT can answer from internal company documents or knowledge base.

💡 Refine queries to get relevant context for semantic search in Pinecone.

🤖 In this video, we combine GPT4 conversational capability with our own document semantic search using Pinecone.

🔍 We create an index and process documents using the Sentence Transformer model and store vectors to perform semantic search.

💬 We utilize Streamlit to build a conversational chat application that retrieves answers from the Pinecone index knowledge base.

📚 Splitting the text into chunks with overlapping context.

🧩 Using Pinecone to create embeddings and index the documents.

🔎 Performing similarity search to find related chunks.

📚 We are using Langchain chatbot instead of plain OpenAI for our project, as it supports conversation chains and memory.

💬 To create the chatbot, we require different prompts for system messages, human queries, and chat responses.

🖥️ We are using Streamlit for the user interface, where we display the responses and queries in a visually appealing way.

🔍 Maintaining only the recent three or four conversations in a chatbot.

⚡ Using conversation buffer window memory to store the last three messages.

💬 Combining system prompts, message placeholders, and conversation history in chat prompt templates.

✨ The conversation chain uses Pinecone indexing to find matching documents based on a given query.

💬 The conversation chain predicts responses using ChatGPT and stores them in session variables for display.

🔍 The refined prompt transforms the current query using previous conversation logs to enable semantic search.

🤖 The video discusses the use of chatbots powered by Langchain, ChatGPT, Pinecone, and Streamlit to answer questions from a knowledge base.

🔍 The chatbot can generate refined queries based on user conversations and current queries, providing more meaningful search results.

📚 The video mentions the availability of code and blog posts for further information on Langchain, Pinecone, and other related topics.