Comprehensive base de vectores Tools for Every Need

Get access to base de vectores solutions that address multiple requirements. One-stop resources for streamlined workflows.

base de vectores

  • AI-powered PDF chatbot agent using LangChain and LangGraph for document ingestion and querying.
    0
    0
    What is AI PDF chatbot agent built with LangChain ?
    This AI PDF Chatbot agent is a customizable solution that enables users to upload and parse PDF documents, store vector embeddings in a database, and query these documents through a chat interface. It integrates with OpenAI or other LLM providers to generate answers with references to the relevant content. The system utilizes LangChain for language model orchestration and LangGraph for managing agent workflows. Its architecture includes a backend service that handles ingestion and retrieval graphs, a frontend with a Next.js UI to upload files and chat, and Supabase for vector storage. It supports real-time streaming responses and allows customization of retrievers, prompts, and storage configurations.
    AI PDF chatbot agent built with LangChain Core Features
    • PDF document ingestion and embedding storage
    • Conversational retrieval with OpenAI and vector search
    • Real-time streaming chat responses
    • LangGraph orchestration for agent workflows
    • Next.js frontend UI with file upload and chat
    AI PDF chatbot agent built with LangChain Pro & Cons

    The Cons

    Requires setup of vector database and API keys
    No native mobile or desktop apps, web only
    Initial setup complexity for beginners
    Chat history is session-based, not persistent by default
    Dependency on third-party APIs may incur costs

    The Pros

    Open-source and highly customizable
    Supports powerful LLMs and vector search
    Well-structured backend and frontend architecture
    Real-time streaming improves interactivity
    Comprehensive example with LangChain and LangGraph
Featured