AI Knowledge Retrieval Chatbot Langchain and Voiceflow, the perfect combo
AI Summary
- Introduction
- Dona, a senior software engineer and AI specialist from Sydney, greets viewers and wishes them a happy new year.
- Announces a tutorial on building a knowledge retrieval chatbot.
- Chatbot Capabilities
- Answers questions about specific documents, FAQ pages, and resources like Notion or Confluence.
- Can be deployed to production and embedded in any website.
- Requirements and Tools
- No coding skills needed; minor code modifications required.
- Tools used: Voiceflow for interface, Langchain for knowledge retrieval, Langserve for API exposure, and Repl.it for hosting.
- Demonstration
- Uses an article from MedicalExpress about iStar, a tool for detecting tumors with AI vision.
- Shows chatbot answering a question based on the article content.
- Voiceflow Setup
- Briefly explains the Voiceflow setup.
- Includes an “ask a question” button and an intent that triggers the question block.
- Captures user’s question and sends it to a custom API via a POST request.
- Building the API
- Uses Langserve and Repl.it to create and host the API.
- Ingests documents from URLs, selecting specific CSS classes to extract content.
- Chunks content and saves it in a vector store.
- Uses Langchain hub for prompts and GPT-3.5 turbo for the LLM.
- Sets up a RAG chain with LCEL for document retrieval.
- Exposes the RAG chain with a FastAPI route.
- Deployment
- Forks the Repl.it project and sets up a new vector store.
- Adds OpenAI API key to secrets.
- Installs dependencies with
poetry install.- Loads documents from a directory or a URL.
- Deploys the API, setting up auto-scaling and build settings.
- Copies the deployed API URL to Voiceflow.
- Post-Deployment
- Advises turning off the server when not in use to save credits.
- Conclusion
- Encourages viewers to access links in the video description for a summarized document.
- Mentions future videos on enhancing chatbot features.
- Invites questions and comments from viewers.