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.
š¤ Build a chatbot using Langchain, OpenAI ChatGPT, and Pinecone.
š ChatGPT can answer from internal company documents or knowledge base.
š” 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.
š¤ In this video, we combine GPT4 conversational capability with our own document semantic search using Pinecone.
š We create an index and process documents using the Sentence Transformer model and store vectors to perform semantic search.
š¬ 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.
š Splitting the text into chunks with overlapping context.
š§© Using Pinecone to create embeddings and index the documents.
š 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.
š We are using Langchain chatbot instead of plain OpenAI for our project, as it supports conversation chains and memory.
š¬ To create the chatbot, we require different prompts for system messages, human queries, and chat responses.
š„ļø 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.
š Maintaining only the recent three or four conversations in a chatbot.
ā” Using conversation buffer window memory to store the last three messages.
š¬ 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.
āØ The conversation chain uses Pinecone indexing to find matching documents based on a given query.
š¬ The conversation chain predicts responses using ChatGPT and stores them in session variables for display.
š 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.
š¤ The video discusses the use of chatbots powered by Langchain, ChatGPT, Pinecone, and Streamlit to answer questions from a knowledge base.
š The chatbot can generate refined queries based on user conversations and current queries, providing more meaningful search results.
š The video mentions the availability of code and blog posts for further information on Langchain, Pinecone, and other related topics.
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