Multi Rag Platform
C+ 73 completed
Other
containerized / json · small
59
Files
15,380
LOC
1
Frameworks
7
Languages
Pipeline State
completedRun ID
#395163Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
50.67Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:31Deduplication group #48148
Member of a group with 4 similar repo(s) — canonical #94557 view group →
Top concepts (2)
Project DescriptionWeb Backend
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AI Prompt
Create a general-purpose, containerized RAG (Retrieval-Augmented Generation) platform. I need it to support managing multiple independent document libraries, allowing for features like mixed retrieval (vector + BM25) and answer verification. The platform must expose a REST API for querying and also include an MCP interface for AI Agents to call. Since data privacy is key, please ensure it supports local LLMs like Ollama with Qwen2.5. The deployment should be easy with Docker Compose, and it should be optimized for Chinese documents.
python fastapi rag llm docker api retrieval ai-agent ollama chinese
Generated by gemma4:latest
Catalog Information
通用的多资料库 RAG (检索增强生成) 训练和部署平台，支持管理多个独立的文档资料库，提供 REST API 和 MCP 接口给 AI Agent 调用。
Description
通用的多资料库 RAG (检索增强生成) 训练和部署平台，支持管理多个独立的文档资料库，提供 REST API 和 MCP 接口给 AI Agent 调用。
Novelty
3/10Tags
python fastapi rag llm docker api retrieval ai-agent ollama chinese
Technologies
fastapi langchain pydantic
Claude Models
claude-opus-4-6
Quality Score
C+
72.8/100
Structure
60
Code Quality
84
Documentation
80
Testing
50
Practices
73
Security
92
Dependencies
60
Strengths
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
- Containerized deployment (Docker)
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No CI/CD configuration \u2014 manual testing and deployment
- 2 bare except/catch blocks swallowing errors
- 456 duplicate lines detected \u2014 consider DRY refactoring
Recommendations
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
- Replace bare except/catch blocks with specific exception types
Security & Health
5.8h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility · open methodology · https://repobility.com/research/
Unknown
License
5.8%
Duplication
Languages
Frameworks
FastAPI
Concepts (2)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| auto_description | Project Description | 通用的多资料库 RAG (检索增强生成) 训练和部署平台，支持管理多个独立的文档资料库，提供 REST API 和 MCP 接口给 AI Agent 调用。 | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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