Files
agent_alpha/pyproject.toml
T
furyhawk bb4b614711 feat: Implement RAG document and sync services with vector store integration
- Added `vectorstore.py` for managing vector storage operations using Milvus.
- Introduced `rag_document.py` for handling RAG document lifecycle, including ingestion and status updates.
- Created `rag_status.py` for streaming RAG ingestion status via Redis pub/sub.
- Developed `rag_sync.py` for managing synchronization operations and logs.
- Implemented `sync_source.py` for managing sync source configurations and triggering syncs.
- Established a lightweight task dispatcher in `dispatcher.py` for background task execution.
- Registered task functions for RAG ingestion and sync operations in `rag_tasks.py`.
- Configured task dispatcher settings in `arq_settings.py`.
- Added necessary database models and schemas for RAG document and sync operations.
2026-06-13 21:52:27 +08:00

36 lines
1.0 KiB
TOML

[project]
name = "agent-alpha"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"fastapi[standard]>=0.136.3",
"logfire[asyncpg,fastapi,httpx,sqlite3]>=4.36.0",
"pydantic-settings>=2.7.0",
"pydantic-ai-backend>=0.2.11",
"pydantic-ai-harness[code-mode]>=0.3.0",
"pydantic-ai-shields>=0.3.4",
"pydantic-ai-skills>=0.11.0",
"pydantic-ai-slim[duckduckgo,mcp,openai]>=1.107.0",
"pydantic-ai-todo>=0.2.4",
"pydantic-deep>=0.3.28",
"uvicorn[standard]>=0.34.0",
"asyncpg>=0.30.0",
"bcrypt>=4.2.0",
"redis[hiredis]>=5.2.0",
"sqlalchemy[asyncio]>=2.0.36",
"pymupdf>=1.27.2.3",
"python-docx>=1.2.0",
"langchain-text-splitters>=1.1.2",
"sentence-transformers>=5.5.1",
# Vector Database
"pymilvus>=2.5.0",
# Async Task Queue (lightweight, redis-based)
# (using redis directly instead of arq to avoid dep conflicts)
# Embeddings
"openai>=1.0.0",
# Hybrid Search
"rank-bm25>=0.2.2",
]