indexing_engine.pyā¢6.23 kB
"""Hybrid RAG indexing engine combining vector search and knowledge graph."""
from dataclasses import dataclass
from typing import Optional
from mcp_skills.services.skill_manager import Skill
@dataclass
class ScoredSkill:
"""Skill with relevance score.
Attributes:
skill: The Skill object
score: Relevance score (0.0-1.0)
match_type: Type of match (vector, graph, hybrid)
"""
skill: Skill
score: float
match_type: str
@dataclass
class IndexStats:
"""Index statistics.
Attributes:
total_skills: Total number of indexed skills
vector_store_size: Size of vector store in bytes
graph_nodes: Number of nodes in knowledge graph
graph_edges: Number of edges in knowledge graph
last_indexed: Timestamp of last indexing operation
"""
total_skills: int
vector_store_size: int
graph_nodes: int
graph_edges: int
last_indexed: str
class IndexingEngine:
"""Build and maintain vector + KG indices for skill discovery.
Combines vector embeddings for semantic search with knowledge graph
for relationship-based discovery.
Architecture:
- Vector Store: ChromaDB or Qdrant for semantic similarity
- Knowledge Graph: NetworkX for skill relationships
- Embeddings: sentence-transformers/all-MiniLM-L6-v2
"""
def __init__(
self, vector_backend: str = "chromadb", graph_backend: str = "networkx"
) -> None:
"""Initialize indexing engine.
Args:
vector_backend: Vector store backend (chromadb, qdrant, faiss)
graph_backend: Knowledge graph backend (networkx, neo4j)
"""
self.vector_backend = vector_backend
self.graph_backend = graph_backend
# TODO: Initialize vector store and graph connections
def index_skill(self, skill: Skill) -> None:
"""Add skill to vector + KG stores.
Args:
skill: Skill object to index
"""
# TODO: Implement skill indexing
# 1. Generate embeddings for skill content
# 2. Add to vector store with metadata
# 3. Create/update graph nodes and edges
# 4. Store in metadata database
pass
def build_embeddings(self, skill: Skill) -> list[float]:
"""Generate embeddings from skill content.
Combines name, description, instructions, and examples
into embeddings using sentence-transformers.
Args:
skill: Skill to generate embeddings for
Returns:
Embedding vector as list of floats
"""
# TODO: Implement embedding generation
# 1. Concatenate skill text fields
# 2. Use sentence-transformers to generate embeddings
# 3. Return embedding vector
return []
def extract_relationships(self, skill: Skill) -> list[tuple[str, str, str]]:
"""Identify skill dependencies and relationships.
Args:
skill: Skill to extract relationships from
Returns:
List of (source_id, relation_type, target_id) tuples
"""
# TODO: Implement relationship extraction
# 1. Parse dependencies field
# 2. Identify category relationships
# 3. Detect tag-based relationships
# 4. Find toolchain associations
# 5. Return list of relationships
return []
def reindex_all(self, force: bool = False) -> IndexStats:
"""Rebuild indices from scratch.
Args:
force: Force rebuild even if indices exist
Returns:
Index statistics after rebuild
"""
# TODO: Implement full reindexing
# 1. Clear existing indices (if force=True)
# 2. Discover all skills
# 3. Generate embeddings for all skills
# 4. Build knowledge graph
# 5. Return statistics
return IndexStats(
total_skills=0,
vector_store_size=0,
graph_nodes=0,
graph_edges=0,
last_indexed="never",
)
def search(
self,
query: str,
toolchain: Optional[str] = None,
category: Optional[str] = None,
top_k: int = 10,
) -> list[ScoredSkill]:
"""Search skills using vector similarity + KG.
Hybrid search combines:
1. Vector similarity for semantic matching
2. Knowledge graph for relationship-based discovery
3. Reranking based on toolchain and category filters
Args:
query: Search query (natural language)
toolchain: Optional toolchain filter (Python, TypeScript, etc.)
category: Optional category filter (testing, debugging, etc.)
top_k: Maximum number of results
Returns:
List of ScoredSkill objects sorted by relevance
"""
# TODO: Implement hybrid search
# 1. Generate query embedding
# 2. Vector search in ChromaDB/Qdrant
# 3. Graph-based related skills
# 4. Combine and rerank results
# 5. Apply filters (toolchain, category)
# 6. Return top_k results
return []
def get_related_skills(self, skill_id: str, max_depth: int = 2) -> list[Skill]:
"""Find related skills via knowledge graph.
Traverses graph to find skills connected via dependencies,
categories, or tags.
Args:
skill_id: Starting skill ID
max_depth: Maximum traversal depth
Returns:
List of related Skill objects
"""
# TODO: Implement graph traversal
# 1. Find skill node in graph
# 2. BFS/DFS to specified depth
# 3. Collect connected skill IDs
# 4. Load Skill objects
# 5. Return list
return []
def get_stats(self) -> IndexStats:
"""Get current index statistics.
Returns:
IndexStats object with current metrics
"""
# TODO: Implement statistics gathering
return IndexStats(
total_skills=0,
vector_store_size=0,
graph_nodes=0,
graph_edges=0,
last_indexed="never",
)