pdf-agent
ragpdflangchainlanggraphdocument-qapgvector
- Visibility
- public
- Deploys
- 0
- Build Number
- 75524c39
- Updated
- May 12, 2026
Ingest PDF documents and store them as vector embeddings
Answer questions about uploaded documents using semantic search
Maintain conversation history across multiple turns
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
- Ingest — PDFs are parsed, chunked, embedded with
text-embedding-3-small, and stored in a pgvector database. - Retrieve — When you ask a question, the top-k most relevant chunks are retrieved using cosine similarity search.
- 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.
- Visibility
- public
- Deploys
- 0
- Build Number
- 75524c39
- Updated
- May 12, 2026
Ingest PDF documents and store them as vector embeddings
Answer questions about uploaded documents using semantic search
Maintain conversation history across multiple turns