Vector Database Integration Analysis

1. Current System Extension Points

Existing Structure

-- Current core tables
CREATE TABLE documents (
    doc_uuid VARCHAR(36) PRIMARY KEY,
    filename VARCHAR(255),
    created_at TIMESTAMP
);

CREATE TABLE chunks (
    chunk_uuid VARCHAR(36) PRIMARY KEY,
    doc_uuid VARCHAR(36),
    content TEXT,
    sequence_num INTEGER
);

Workflow Checklist

• Prerequisites verified
• Configuration applied
• Process executed
• Results validated
• Documentation updated

Workflow Steps

1. Initialize - Set up the environment
2. Configure - Apply settings
3. Execute - Run the process
4. Validate - Check results
5. Complete - Finalize workflow

Workflow Phases

Phase 1: Initialization

Set up prerequisites and validate inputs.

Phase 2: Processing

Execute the main workflow steps.

Phase 3: Verification

Validate outputs and confirm completion.

Phase 4: Finalization

Clean up and generate reports.

2. PostgreSQL + pgvector Approach

A. Schema Extension

-- Enable pgvector extension
CREATE EXTENSION vector;

-- Add vector embeddings to chunks table
ALTER TABLE chunks 
ADD COLUMN embedding vector(1536);  -- For OpenAI embeddings
ADD COLUMN embedding_model VARCHAR(50);

-- Create vector index
CREATE INDEX ON chunks 
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

-- Add semantic search function
CREATE OR REPLACE FUNCTION semantic_chunk_search(
    query_embedding vector,
    similarity_threshold float,
    max_results int
) RETURNS TABLE (
    chunk_uuid VARCHAR,
    content TEXT,
    similarity float
) LANGUAGE plpgsql AS $$
BEGIN
    RETURN QUERY
    SELECT 
        c.chunk_uuid,
        c.content,
        1 - (c.embedding <=> query_embedding) as similarity
    FROM chunks c
    WHERE 1 - (c.embedding <=> query_embedding) > similarity_threshold
    ORDER BY c.embedding <=> query_embedding
    LIMIT max_results;
END;
$$;

B. Implementation

class PgVectorManager:
    def __init__(self, db_pool, embedding_model):
        self.db = db_pool
        self.embedding_model = embedding_model
    
    async def store_chunk_embedding(self, chunk_uuid: str, content: str):
        """Store chunk with its embedding"""
        embedding = await self.embedding_model.embed_text(content)
        
        await self.db.execute("""
            UPDATE chunks
            SET embedding = $1, embedding_model = $2
            WHERE chunk_uuid = $3
        """, embedding, self.embedding_model.name, chunk_uuid)
    
    async def similar_chunks(self, query: str, threshold: float = 0.7):
        """Find similar chunks using vector similarity"""
        query_embedding = await self.embedding_model.embed_text(query)
        
        return await self.db.fetch("""
            SELECT * FROM semantic_chunk_search($1, $2, 10)
        """, query_embedding, threshold)

Advantages of pgvector

1. Integrated Solution

   • Single database for all data
   • Transactional consistency
   • Familiar PostgreSQL tooling
   • Cost-effective

2. Performance

   • Efficient for moderate-sized collections
   • Good indexing options
   • Native SQL integration

Disadvantages of pgvector

1. Scaling Limitations
   • Less efficient for very large vector collections
   • Limited to PostgreSQL's scaling capabilities
   • Memory constraints for large embeddings

3. Weaviate Approach

A. Weaviate Schema

class WeaviateManager:
    def __init__(self, client):
        self.client = client
        
    def create_schema(self):
        """Create Weaviate schema for document chunks"""
        class_obj = {
            "class": "DocumentChunk",
            "vectorizer": "text2vec-openai",
            "properties": [
                {
                    "name": "chunk_uuid",
                    "dataType": ["string"],
                    "index": True
                },
                {
                    "name": "doc_uuid",
                    "dataType": ["string"],
                    "index": True
                },
                {
                    "name": "content",
                    "dataType": ["text"],
                    "vectorizer": "text2vec-openai"
                },
                {
                    "name": "sequence_num",
                    "dataType": ["int"],
                    "index": True
                },
                {
                    "name": "previous_chunk",
                    "dataType": ["DocumentChunk"],
                    "index": True
                },
                {
                    "name": "next_chunk",
                    "dataType": ["DocumentChunk"],
                    "index": True
                }
            ]
        }
        self.client.schema.create_class(class_obj)

B. Hybrid Implementation

class HybridStorageManager:
    def __init__(self, pg_pool, weaviate_client):
        self.pg = pg_pool
        self.weaviate = weaviate_client
    
    async def store_chunk(self, chunk: Dict):
        """Store chunk in both PostgreSQL and Weaviate"""
        # Store in PostgreSQL for transactional data
        async with self.pg.transaction():
            await self.pg.execute("""
                INSERT INTO chunks (chunk_uuid, doc_uuid, content, sequence_num)
                VALUES ($1, $2, $3, $4)
            """, chunk['uuid'], chunk['doc_uuid'], 
                 chunk['content'], chunk['sequence_num'])
        
        # Store in Weaviate for vector search
        self.weaviate.data_object.create(
            class_name="DocumentChunk",
            data_object={
                "chunk_uuid": chunk['uuid'],
                "doc_uuid": chunk['doc_uuid'],
                "content": chunk['content'],
                "sequence_num": chunk['sequence_num']
            }
        )
    
    async def semantic_search(self, query: str, limit: int = 10):
        """Perform semantic search using Weaviate"""
        return (
            self.weaviate.query
            .get("DocumentChunk", ["chunk_uuid", "content", "sequence_num"])
            .with_near_text({"concepts": [query]})
            .with_limit(limit)
            .do()
        )

Advantages of Weaviate

1. Scalability

   • Built for vector search
   • Efficient large-scale operations
   • Advanced semantic capabilities

2. Features

   • GraphQL API
   • Real-time capabilities
   • Advanced filtering

Disadvantages of Weaviate

1. Complexity
   • Separate system to maintain
   • Additional infrastructure
   • Learning curve

4. GraphRAG Implementation

A. Graph-Enhanced Retrieval

class GraphRAGManager:
    def __init__(self, vector_store, graph_store):
        self.vector_store = vector_store
        self.graph_store = graph_store
    
    async def enhanced_retrieval(self, query: str, depth: int = 2):
        """Retrieve chunks using both vector and graph relationships"""
        # Initial vector search
        similar_chunks = await self.vector_store.semantic_search(query)
        
        # Graph expansion
        enhanced_results = []
        for chunk in similar_chunks:
            # Get connected chunks from graph
            related_chunks = await self.graph_store.get_connected_chunks(
                chunk['chunk_uuid'], 
                depth=depth
            )
            enhanced_results.extend(related_chunks)
        
        return self._rank_results(enhanced_results)
    
    def _rank_results(self, chunks: List[Dict]) -> List[Dict]:
        """Rank results using both semantic and graph relevance"""
        # Implementation of ranking algorithm
        pass

B. Knowledge Graph Integration

class DocumentKnowledgeGraph:
    def __init__(self, db_connection):
        self.db = db_connection
    
    async def build_graph(self, doc_uuid: str):
        """Build knowledge graph from document chunks"""
        chunks = await self.db.fetch("""
            SELECT * FROM chunks 
            WHERE doc_uuid = $1 
            ORDER BY sequence_num
        """, doc_uuid)
        
        for chunk in chunks:
            # Extract entities and relationships
            entities = self._extract_entities(chunk['content'])
            relationships = self._extract_relationships(chunk['content'])
            
            # Store in graph structure
            await self._store_graph_elements(
                chunk['chunk_uuid'], 
                entities, 
                relationships
            )
    
    async def get_connected_chunks(self, chunk_uuid: str, depth: int):
        """Get connected chunks from knowledge graph"""
        return await self.db.fetch("""
            WITH RECURSIVE connected_chunks AS (
                SELECT c.*, 1 as depth
                FROM chunks c
                WHERE c.chunk_uuid = $1
                
                UNION
                
                SELECT c.*, cc.depth + 1
                FROM chunks c
                JOIN chunk_relationships cr ON c.chunk_uuid = cr.target_chunk_uuid
                JOIN connected_chunks cc ON cr.source_chunk_uuid = cc.chunk_uuid
                WHERE cc.depth < $2
            )
            SELECT DISTINCT * FROM connected_chunks
        """, chunk_uuid, depth)

5. Recommendations

1. Phased Implementation

2. Recommended Approach

1. Start with pgvector

   • Simplest integration
   • Maintains data consistency
   • Good performance for initial scale

2. Add GraphRAG capabilities

   • Enhance retrieval quality
   • Maintain chunk relationships
   • Enable context-aware search

3. Monitor and Scale

   • Track performance metrics
   • Evaluate need for Weaviate
   • Adjust based on usage patterns

3. Implementation Priority

1. Vector embeddings for semantic search
2. Basic RAG implementation
3. Graph relationships for chunks
4. Advanced GraphRAG features
5. Scale-based migration decisions

Would you like me to:

1. Detail the pgvector implementation?
2. Create the GraphRAG structures?
3. Design the monitoring system?
4. Develop the scaling strategy?