Initial setup: Knowledge base RAG system with LlamaIndex and ChromaDB

- Add Python project with uv package manager
- Implement LlamaIndex + ChromaDB RAG pipeline
- Add sentence-transformers for local embeddings (all-MiniLM-L6-v2)
- Create MCP server with semantic search, indexing, and stats tools
- Add Markdown chunker with heading/wikilink/frontmatter support
- Add Dockerfile and docker-compose.yaml for self-hosted deployment
- Include sample Obsidian vault files for testing
- Add .gitignore and .env.example

pyproject.toml

@@ -0,0 +1,36 @@
+[project]
+name = "knowledge-rag"
+version = "0.1.0"
+description = "RAG system for Obsidian vault knowledge base with MCP server"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "llama-index>=0.10.0",
+    "llama-index-vector-stores-chroma>=0.1.0",
+    "chromadb>=0.4.0",
+    "sentence-transformers>=2.2.0",
+    "mcp>=1.0.0",
+    "python-dotenv>=1.0.0",
+    "pydantic>=2.0.0",
+    "watchdog>=3.0.0",
+    "httpx>=0.25.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0.0",
+    "pytest-asyncio>=0.21.0",
+    "ruff>=0.1.0",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.ruff]
+line-length = 100
+target-version = "py311"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "N", "W"]
+ignore = ["E501"]