Forge AI Platform: A Local Intelligent Document Workflow Platform Based on RAG

Forge AI Platform is a local intelligent document workflow automation platform based on RAG technology, designed to address the needs for intelligent Q&A, automatic summarization, and knowledge extraction that traditional document tools cannot meet. Built with the FastAPI+React+ChromaDB+Ollama tech stack, its core features include local deployment (to protect data privacy), RAG-enhanced conversations, semantic retrieval, and multi-PDF document processing capabilities, enabling users to complete AI tasks without relying on external APIs.

Traditional document tools often only provide basic search and reading functions, making it difficult to meet users' needs for intelligent Q&A, automatic summarization, and knowledge extraction. As an open-source project, Forge AI Platform is designed to solve this pain point by combining RAG technology with modern web development stacks to create a fully functional AI document workflow platform, with a particular focus on local deployment to protect data privacy.

The platform covers the entire document processing workflow, with core functions including:

• Batch upload and automatic parsing/indexing of multiple PDFs
• Quick extraction of core points via document summarization
• Key information extraction (identifying structured data such as concepts, dates, and names)
• Study note generation (converted into an easy-to-review format)
• Feature interview question generation (creating assessment questions based on document content) These functions reflect in-depth thinking about practical application scenarios.

RAG (Retrieval-Augmented Generation) is the technical core of the platform, which can alleviate the knowledge cutoff and hallucination issues of traditional LLMs. Its implementation process is as follows:

1. Document parsing: Process PDFs and extract text
2. Vectorization: Convert text into high-dimensional vectors using an embedding model
3. Storage and retrieval: Store vectors in the ChromaDB vector database and provide efficient similarity retrieval
4. Answer generation: Inject relevant retrieved fragments as context into prompts to guide the LLM to generate document-based answers The advantage of semantic retrieval is that it can find conceptually relevant content even if keywords do not match.

Ollama integration is a technical highlight of the project, simplifying the operation of local large models (supporting open-source models such as Llama and Mistral). Its advantages include:

• Data privacy: Documents do not leave the local machine
• Cost control: No API call fees required
• Offline availability: No reliance on the network
• Free choice of models Limitations: Running models with 7B+ parameters requires sufficient GPU memory or RAM, which may restrict usage for some users.

The project provides two deployment methods:

• Frontend: Deployed on Vercel, users can directly access the online demo
• Backend: Needs to be run locally; the documentation recommends using ngrok for intranet penetration to achieve public network access Ngrok is convenient for development and testing scenarios, but Docker containerization or cloud server hosting is recommended for production environments.

The platform adapts to multiple scenarios:

• Researchers: Quickly analyze literature, extract information, and generate reviews
• Students: Understand complex materials and generate review notes
• Enterprise users: Process internal documents, build knowledge bases, and perform intelligent Q&A The open-source nature allows users to customize and extend (e.g., add document format support, integrate other AI models).

Forge AI Platform provides a complete solution for local intelligent document processing, combining rich functionality with data privacy protection. It is recommended that users choose an appropriate local model based on their needs (considering hardware conditions), and prioritize containerization or cloud server deployment for production environments to ensure stability.