Semantic Search MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Semantic Search MCP Serverfind where we handle JWT token validation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CodeSight
AI-powered document search engine — hybrid BM25 + vector + RRF retrieval with pluggable LLM answer synthesis.
Quick Start
# Install
pip install -e ".[dev]"
# Index a folder of documents
python -m codesight index /path/to/documents
# Search (hybrid BM25 + vector)
python -m codesight search "payment terms" /path/to/documents
# Filter by file type
python -m codesight search "auth" /path/to/code --glob '*.py'
# Ask a question (requires LLM API key — see Configuration)
python -m codesight ask "What are the payment terms?" /path/to/documents
# Machine-readable output
python -m codesight search "query" /path --json
# Check index status
python -m codesight status /path/to/documents
# Launch the web chat UI
pip install -e ".[demo]"
python -m codesight demoPython API
from codesight import CodeSight
engine = CodeSight("/path/to/documents")
engine.index() # Index all files
results = engine.search("payment terms") # Hybrid search
answer = engine.ask("What are the payment terms?") # Search + LLM answer
status = engine.status() # Index freshness checkSupported Formats
Format | Extension | Parser |
| pymupdf | |
Word |
| python-docx |
PowerPoint |
| python-pptx |
Code |
| Built-in (10 languages) |
Text |
| Built-in |
Architecture
Document Parsing: PDF, DOCX, PPTX text extraction with page/section metadata
Chunking: Language-aware regex splitting (code) + paragraph-aware splitting (documents)
Embeddings:
all-MiniLM-L6-v2via sentence-transformers (local, no API key)Vector Store: LanceDB (serverless, file-based)
Keyword Search: SQLite FTS5 sidecar
Retrieval: Hybrid BM25 + vector with RRF merge
Answer Synthesis: Pluggable LLM backend (Claude, Azure OpenAI, OpenAI, Ollama)
See ARCHITECTURE.md for the full system tour.
Configuration
Variable | Default | Description |
| — | Required for Claude backend ( |
|
| LLM backend: |
|
| Where indexes are stored |
|
| Embedding model |
|
| LLM model for answers |
|
| Index freshness threshold (seconds) |
|
| Logging verbosity |
See .env.example for all options.
Stack
Python 3.11+
LanceDB + SQLite FTS5
sentence-transformers
Anthropic Claude API / Azure OpenAI / OpenAI / Ollama
Streamlit (web chat UI)
pymupdf, python-docx, python-pptx (document parsing)
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