syndarix/mcp-servers/knowledge-base/server.py

"""
Syndarix Knowledge Base MCP Server.

Provides RAG capabilities with:
- pgvector for semantic search
- Per-project collection isolation
- Hybrid search (vector + keyword)
- Chunking strategies for code, markdown, and text

Per ADR-008: Knowledge Base RAG Architecture.
"""

import os

from fastmcp import FastMCP

# Create MCP server
mcp = FastMCP(
    "syndarix-knowledge-base",
    description="RAG with pgvector for semantic search",
)

# Configuration
DATABASE_URL = os.getenv("DATABASE_URL")
REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/0")


@mcp.tool()
async def search_knowledge(
    project_id: str,
    query: str,
    top_k: int = 10,
    search_type: str = "hybrid",
    filters: dict | None = None,
) -> dict:
    """
    Search the project knowledge base.

    Args:
        project_id: UUID of the project (scopes to project collection)
        query: Search query text
        top_k: Number of results to return
        search_type: Search type (semantic, keyword, hybrid)
        filters: Optional filters (file_type, path_prefix, etc.)

    Returns:
        List of matching documents with scores
    """
    # TODO: Implement pgvector search
    # 1. Generate query embedding via LLM Gateway
    # 2. Search project-scoped collection
    # 3. Apply filters
    # 4. Return results with scores
    return {
        "status": "not_implemented",
        "project_id": project_id,
        "query": query,
    }


@mcp.tool()
async def ingest_document(
    project_id: str,
    content: str,
    source_path: str,
    doc_type: str = "text",
    metadata: dict | None = None,
) -> dict:
    """
    Ingest a document into the knowledge base.

    Args:
        project_id: UUID of the project
        content: Document content
        source_path: Original file path for reference
        doc_type: Document type (code, markdown, text)
        metadata: Additional metadata

    Returns:
        Ingestion result with chunk count
    """
    # TODO: Implement document ingestion
    # 1. Apply chunking strategy based on doc_type
    # 2. Generate embeddings for chunks
    # 3. Store in project collection
    return {
        "status": "not_implemented",
        "project_id": project_id,
        "source_path": source_path,
    }


@mcp.tool()
async def ingest_repository(
    project_id: str,
    repo_path: str,
    include_patterns: list[str] | None = None,
    exclude_patterns: list[str] | None = None,
) -> dict:
    """
    Ingest an entire repository into the knowledge base.

    Args:
        project_id: UUID of the project
        repo_path: Path to the repository
        include_patterns: Glob patterns to include (e.g., ["*.py", "*.md"])
        exclude_patterns: Glob patterns to exclude (e.g., ["node_modules/*"])

    Returns:
        Ingestion summary with file and chunk counts
    """
    # TODO: Implement repository ingestion
    return {
        "status": "not_implemented",
        "project_id": project_id,
        "repo_path": repo_path,
    }


@mcp.tool()
async def delete_document(
    project_id: str,
    source_path: str,
) -> dict:
    """
    Delete a document from the knowledge base.

    Args:
        project_id: UUID of the project
        source_path: Original file path

    Returns:
        Deletion result
    """
    # TODO: Implement document deletion
    return {
        "status": "not_implemented",
        "project_id": project_id,
        "source_path": source_path,
    }


@mcp.tool()
async def get_collection_stats(project_id: str) -> dict:
    """
    Get statistics for a project's knowledge base collection.

    Args:
        project_id: UUID of the project

    Returns:
        Collection statistics (document count, chunk count, etc.)
    """
    # TODO: Implement collection stats
    return {
        "status": "not_implemented",
        "project_id": project_id,
    }


if __name__ == "__main__":
    mcp.run()