pdf-agent

pdf-agent

ragpdflangchainlanggraphdocument-qapgvector
Details
Visibility
public
Deploys
0
Build Number
75524c39
Updated
May 12, 2026
Contributors
Repository
Integrations
OpenAIPostgreSQL
Capabilities
Ingest PDF documents and store them as vector embeddings
Answer questions about uploaded documents using semantic search
Maintain conversation history across multiple turns
More blueprints
AGENT.md

AI PDF Chatbot

Upload one or more PDF documents and ask questions about their content. The agent retrieves the most relevant passages using vector search and generates grounded answers via GPT-4o. This is a fork of the original ai-pdf-chatbot-langchain by mayooear.

How it works

  1. Ingest — PDFs are parsed, chunked, embedded with text-embedding-3-small, and stored in a pgvector database.
  2. Retrieve — When you ask a question, the top-k most relevant chunks are retrieved using cosine similarity search.
  3. Generate — The retrieval graph passes the chunks as context to GPT-4o to produce a grounded answer with source citations.

Usage

Open the frontend at the agent URL, upload up to 5 PDFs (10 MB each), then type your question in the chat box. The agent will answer based on the content of your documents.

Limitations

  • PDF only — other file formats are not supported.
  • Max 5 files per upload, 10 MB per file.
  • The underlying LangGraph dev server is an in-memory store; ingested documents persist only for the lifetime of the running container unless a persistent volume is attached to the postgres knowledge store.