ChatPDF
ChatPDF is a Python-based RAG (Retrieval-Augmented Generation) system designed to run locally with open-source LLMs and embedding models, enabling question-answering over PDF, DOCX, Markdown, and TXT files. It supports GraphRAG, multiple LLM backends (Ollama, Deepseek, Qwen), and includes optimizations for Chinese and mixed-language document processing without requiring third-party agent libraries.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | shibing624/ChatPDF |
| Owner | shibing624 |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 854 |
| Forks | 144 |
| Open issues | 3 |
| Latest release | 1.1.0 (2024-09-06) |
| Last updated | 2025-04-02 |
| Source | https://github.com/shibing624/ChatPDF |
What ChatPDF is
Implements native RAG with local inference using pluggable embedding models, BM25 keyword matching, semantic similarity, optional reranking, and context expansion. Architecture supports async concurrent API requests, streaming responses via Gradio UI, and customizable LLM/embedding/reranker model selection through configuration parameters.
Get the ChatPDF source
Clone the repository and explore it locally.
git clone https://github.com/shibing624/ChatPDF.gitcd ChatPDF# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- GPU/CUDA availability required for acceptable inference latency; CPU-only deployments will be slow. NVIDIA GPU strongly recommended; test with target LLM model sizes before commitment.
- Model selection is critical: memory footprint, inference speed, and RAG-specific tuning (e.g., 200k context models mentioned) vary significantly. Requires benchmarking with your document corpus.
- Embedding model choice (local text2vec vs. HuggingFace vs. sentence-transformers) directly impacts retrieval quality; Chinese pipelines need validation on your language mix.
- Dependency management: Python, CUDA toolkit version, and LLM framework versions (Ollama, transformers, torch) must be aligned; refer to requirements.txt and test in target OS (WSL recommended for Windows).
- Reranker models and context expansion parameters require tuning per use case; default settings may not optimize for your document types or query patterns.
When to avoid it — and what to weigh
- Need Managed Hosted Solution — If your team lacks GPU infrastructure, CUDA expertise, or DevOps capacity to deploy and maintain local LLMs. Managed services (OpenAI, Azure, Anthropic) require less operational overhead.
- Require Enterprise Support & SLAs — This is a community-maintained project (854 stars, single primary maintainer contact listed). No commercial support, security bulletins, or guaranteed response times. Mission-critical workflows should use commercially backed alternatives.
- Large-Scale Production at Latency Budget — Deploying across thousands of concurrent users with strict <500ms response SLAs. Local LLM inference is inherently resource-constrained; horizontal scaling requires significant engineering beyond the project scope.
- Require Extensive Pre-built Integrations — If your stack depends on Slack, Salesforce, Jira, or other third-party connectors, this project requires custom integration work. No built-in connectors documented.
License & commercial use
Apache License 2.0 (Apache-2.0) is a permissive OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions.
Apache 2.0 permits commercial deployment. README explicitly states "可免费用做商业用途" (free for commercial use) with requirement to include ChatPDF link and license in product attribution. No commercial support or warranty included; reliance on community support only. Review liability and indemnification carefully for mission-critical deployments.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Local-by-design reduces data exfiltration risk vs. cloud APIs, but introduces new concerns: (1) GPU server security and network isolation required if exposed to internet; (2) no mention of input sanitization for document parsing (PDF/DOCX attack surface); (3) API key management for optional OpenAI/Deepseek backends not discussed; (4) no authentication or audit logging in Gradio UI. For production, add WAF, input validation, secrets management, and access controls externally.
Alternatives to consider
LangChain + LlamaIndex + local LLMs
More mature ecosystems with extensive integrations, better documentation, and larger communities. Heavier dependencies but wider industry adoption and easier hiring.
Ollama alone + custom RAG logic
Simpler, lighter-weight alternative if you prefer to build retrieval yourself. Less opinionated; more control but higher engineering burden.
Managed RAG services (Azure AI Search, AWS Kendra, Pinecone + LLM APIs)
Outsource infrastructure, scaling, and security concerns. Higher cost but operational simplicity and enterprise SLAs; better for teams without ML DevOps expertise.
Build on ChatPDF with DEV.co software developers
ChatPDF is ideal for teams seeking sovereign, offline document Q&A. Assess GPU resources, model selection, and integration scope with our team before deployment.
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ChatPDF FAQ
Can I use ChatPDF without a GPU?
Does ChatPDF send my documents to external services?
How do I add support for non-English documents?
What is GraphRAG and is it required?
Work with a software development agency
From first prototype to production, DEV.co delivers software development services around tools like ChatPDF. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across rag frameworks and beyond.
Ready to Deploy Local RAG?
ChatPDF is ideal for teams seeking sovereign, offline document Q&A. Assess GPU resources, model selection, and integration scope with our team before deployment.