Local_Pdf_Chat_RAG

Local PDF Chat RAG

A transparent, runnable Python implementation for learning and inspecting RAG

English | 简体中文

Local PDF Chat RAG is an educational and reference implementation for developers who want to inspect the complete retrieval-augmented generation pipeline. Document loading, chunking, embeddings, FAISS, BM25, hybrid retrieval, reranking, and answer generation are split into readable and replaceable modules. The repository includes both a Gradio UI and a FastAPI interface.

This repository is intended for learning and experimentation. It is not a production-ready knowledge-base service. Add authentication, tenant isolation, persistence, evaluation, security controls, and deployment governance before using it with real business data.

Usage and maintenance

Repository snapshot as of 2026-08-31:

Signal	Verifiable snapshot
Adoption	951 stars and 179 forks
Recent GitHub traffic	105 unique visitors and 78 unique cloners in the latest 14-day window, through 2026-08-30
Release history	2 published releases; current version: v2.1.0
Maintenance activity	5 commits in the last 90 days, with the latest main update on 2026-08-16
Continuous integration	Latest main CI run passed; source compilation and credential-free tests run on pull requests
Issue and PR handling	38 issues closed and 0 open; 9 pull requests merged and 1 under review

Recent maintenance includes reviewing and merging an external retrieval fix, publishing bilingual documentation, adding credential-free tests and GitHub Actions CI, adopting the MIT license, and documenting a private security-reporting process.

Stars, forks, and traffic are dated GitHub snapshots. Traffic and clone counts indicate repository interest, not verified installations or production deployments. See the repository, Actions, and Releases for the current state.

Why this project

• Inspectable pipeline: core modules follow the order in which a RAG request is processed.
• Hybrid retrieval: combines FAISS dense retrieval with BM25 keyword retrieval.
• Optional reranking: supports a CrossEncoder or model-based relevance scoring.
• Multiple model backends: local Ollama, SiliconFlow, and OpenAI-compatible APIs.
• Document support: PDF, TXT, Markdown, DOCX, XLS/XLSX, and PPTX.
• Two interfaces: a Gradio web application and a FastAPI REST API.
• Verifiable maintenance: automated tests, GitHub Actions CI, contribution guidance, and a security-reporting process.

RAG pipeline

flowchart LR
    A[Documents] --> B[Parsing]
    B --> C[Chunking]
    C --> D[Embeddings]
    D --> E[FAISS]
    C --> F[BM25]
    E --> G[Hybrid retrieval]
    F --> G
    G --> H[Reranking]
    H --> I[Context building]
    I --> J[LLM generation]
    J --> K[Answer and sources]

Quick start

1. Create an environment

git clone https://github.com/weiwill88/Local_Pdf_Chat_RAG.git
cd Local_Pdf_Chat_RAG

python3.10 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
python -m pip install --upgrade pip
pip install -r requirements.txt

2. Configure one model backend

cp example.env .env

Edit .env and choose at least one option:

• set SILICONFLOW_API_KEY;
• set MAGICK_API_KEY, its endpoint, and model name; or
• start Ollama locally and pull the model configured in .env.

Keep real credentials in your local .env file. Values beginning with Your_ are treated as placeholders and are not valid credentials.

3. Start the web UI

python rag_demo.py

The application first tries http://127.0.0.1:17995, then ports 17996–17999 if needed.

4. Start the REST API

python api_router.py

Main endpoints:

• GET /api/status: runtime and provider configuration status;
• POST /api/upload: upload and process a document;
• POST /api/ask: ask a question against processed documents.

Repository layout

├── config.py                  # Environment, model, and RAG settings
├── rag_demo.py                # Gradio web UI
├── api_router.py              # FastAPI interface
├── core/
│   ├── document_loader.py     # Document extraction
│   ├── text_splitter.py       # Text chunking
│   ├── embeddings.py          # Embeddings
│   ├── vector_store.py        # FAISS index
│   ├── bm25_index.py          # BM25 index
│   ├── retriever.py           # Hybrid and recursive retrieval
│   ├── reranker.py            # Result reranking
│   └── generator.py           # Context and answer generation
├── features/                  # Web search and optional extensions
├── tests/                     # Tests that require no external credentials
└── .github/                   # CI, issue forms, and pull request template

Tests

pip install -r requirements-dev.txt
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest

The current suite covers:

• configuration and default backend selection;
• TXT, Markdown, and unsupported-file loading behavior;
• BM25 and hybrid-result merging;
• clean, network-free failure when an API key is missing.

GitHub Actions compiles the Python sources and runs the test suite for every pull request.

Configuration

See example.env for the complete example. Common variables include:

Variable	Purpose
SILICONFLOW_API_KEY	SiliconFlow API credential
SILICONFLOW_MODEL_NAME	SiliconFlow model ID
MAGICK_API_KEY	OpenAI-compatible provider credential
MAGICK_API_URL	Provider base URL or full Chat Completions URL
MAGICK_MODEL_NAME	Provider model ID
OLLAMA_MODEL_NAME	Local Ollama model name
SERPAPI_KEY	Optional web-search credential
RERANK_METHOD	cross_encoder or llm

Known limitations

• PDF extraction reads the text layer and does not provide general-purpose OCR.
• Excel and PowerPoint extraction focuses on text rather than visual layout.
• The index is currently in process memory and must be rebuilt after restart.
• Embedding and reranking models may be downloaded on first use.
• Cloud model and web-search requests send the relevant query to third-party services; review your data boundary first.

Contributing

Reproducible bug reports, documentation improvements, and focused pull requests are welcome. Read CONTRIBUTING.md and CODE_OF_CONDUCT.md before contributing.

Do not open a public issue for a vulnerability. Follow SECURITY.md instead.

Releases and maintenance

• Changelog: CHANGELOG.md
• Releases: GitHub Releases
• Maintainer: Will Wei

License

Released under the MIT License.