Building a Chatbot with Langchain, ChatGPT, Pinecone, and Streamlit
Learn how to build a chatbot using Langchain, ChatGPT, Pinecone, and Streamlit. Answer questions using our own documents and handle follow-up questions.
00:00:00 In this video, we build a chatbot using Langchain, ChatGPT, Pinecone, and Streamlit. The chatbot answers questions using our own documents and can handle follow-up questions.
🤖 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.
00:04:23 Combining GPT4 conversational capability with a custom document semantic search using Pinecone. Indexing logic and document processing using Langchain and Sentence Transformer embedding. Answering follow-up questions based on the Pinecone index knowledge base.
🤖 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.
00:08:47 Learn how to split and index documents using Langchain, ChatGPT, Pinecone, and Streamlit for a semantic search chatbot.
📚 Splitting the text into chunks with overlapping context.
🧩 Using Pinecone to create embeddings and index the documents.
🔎 Performing similarity search to find related chunks.
00:13:11 Creating a chatbot using Langchain for conversation handling and OpenAI GPT-3.5 as the chat model. Streamlit used for UI. Discussing buffer memory and conversation window memory.
📚 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.
00:17:36 Maintaining recent conversations with a chatbot using a conversation buffer window memory. Instructions for truthfully answering questions provided. Implementation using Langchain, ChatGPT, Pinecone, and Streamlit.
🔍 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.
00:22:00 This video discusses a chatbot that answers queries from a knowledge base using Langchain, ChatGPT, Pinecone, and Streamlit. It explains how the conversation chain predicts responses based on the input variable and stores the query and response in session variables.
✨ 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.
00:26:22 This video demonstrates the process of using Langchain, ChatGPT, Pinecone, and Streamlit to create a chatbot that answers questions based on a knowledge base. The chatbot refines user queries to generate more meaningful queries for document matching.
🤖 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.
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