F048: Multi-Vector-DB Support

Status: P1 Shipped (v0.12.0) Priority: High Category: Infrastructure

Problem

CSTP is hardcoded to ChromaDB via direct HTTP API calls in query_service.py and decision_service.py. This creates:

Vendor lock-in: Users must run ChromaDB even if they already have another vector DB
No testing without Docker: No in-memory backend for unit tests
Manual hybrid search: CSTP implements hybrid retrieval (F017) in Python by combining semantic + keyword results, when some backends (Weaviate, Qdrant) support this natively
Embedding coupling: Embedding generation (currently Gemini) is interleaved with storage logic

Solution

Extract vector operations behind a VectorStore abstract interface with pluggable backends, and similarly abstract embedding generation behind an EmbeddingProvider interface.

Architecture

┌─────────────────────────────────────────────┐
│              CSTP Services                   │
│  query_service.py  decision_service.py       │
├─────────────────────────────────────────────┤
│           VectorStore Interface              │
│  upsert() query() delete() count() reset()  │
├──────┬──────┬──────┬──────┬──────┬──────────┤
│Chroma│Weavi-│pgvec-│Qdrant│Pine- │ Memory   │
│  DB  │ ate  │ tor  │      │ cone │ (test)   │
└──────┴──────┴──────┴──────┴──────┴──────────┘

┌─────────────────────────────────────────────┐
│         EmbeddingProvider Interface          │
│  embed(texts) -> list[list[float]]          │
├──────┬──────┬──────┬────────────────────────┤
│Gemini│OpenAI│Ollama│sentence-transformers   │
└──────┴──────┴──────┴────────────────────────┘

VectorStore Interface

python

from abc import ABC, abstractmethod
from dataclasses import dataclass

@dataclass(slots=True)
class VectorResult:
    """Single result from vector similarity search."""
    id: str
    document: str
    metadata: dict[str, Any]
    distance: float

class VectorStore(ABC):
    """Abstract vector store interface for decision storage and retrieval."""

    @abstractmethod
    async def initialize(self) -> None:
        """Initialize connection, create collection if needed."""
        ...

    @abstractmethod
    async def upsert(
        self,
        id: str,
        document: str,
        embedding: list[float],
        metadata: dict[str, Any],
    ) -> bool:
        """Insert or update a document with its embedding and metadata."""
        ...

    @abstractmethod
    async def query(
        self,
        embedding: list[float],
        n_results: int = 10,
        where: dict[str, Any] | None = None,
    ) -> list[VectorResult]:
        """Find similar documents by embedding vector.
        
        Args:
            embedding: Query vector.
            n_results: Maximum results.
            where: Metadata filters (backend translates to native syntax).
        """
        ...

    @abstractmethod
    async def hybrid_query(
        self,
        text: str,
        embedding: list[float],
        n_results: int = 10,
        where: dict[str, Any] | None = None,
        semantic_weight: float = 0.7,
    ) -> list[VectorResult]:
        """Hybrid search combining semantic + keyword.
        
        Backends with native hybrid (Weaviate, Qdrant) use it directly.
        Others fall back to manual merge.
        """
        ...

    @abstractmethod
    async def delete(self, ids: list[str]) -> bool:
        """Delete documents by ID."""
        ...

    @abstractmethod
    async def count(self) -> int:
        """Return total document count."""
        ...

    @abstractmethod
    async def reset(self) -> bool:
        """Delete and recreate the collection."""
        ...

    @abstractmethod
    async def close(self) -> None:
        """Clean up connections."""
        ...

EmbeddingProvider Interface

python

class EmbeddingProvider(ABC):
    """Abstract embedding generation interface."""

    @abstractmethod
    async def embed(self, texts: list[str]) -> list[list[float]]:
        """Generate embeddings for one or more texts.
        
        Args:
            texts: Input texts to embed.
            
        Returns:
            List of embedding vectors (one per input text).
        """
        ...

    @property
    @abstractmethod
    def dimensions(self) -> int:
        """Return the embedding dimensionality."""
        ...

    @property
    @abstractmethod
    def model_name(self) -> str:
        """Return the model identifier."""
        ...

Backend Implementations

ChromaDB (P0 - extract existing)

Move current query_service.py and decision_service.py HTTP calls into vectordb/chromadb.py. Zero behavior change, pure refactor.

python

class ChromaDBStore(VectorStore):
    """ChromaDB via HTTP API (v2)."""
    
    def __init__(self, url: str, collection: str, tenant: str, database: str):
        self.url = url
        self.collection = collection
        # ... existing HTTP logic moved here

    async def hybrid_query(self, text, embedding, n_results, where, semantic_weight):
        # Manual merge (current F017 implementation)
        semantic = await self.query(embedding, n_results * 2, where)
        keyword = await self._keyword_search(text, n_results * 2, where)
        return self._merge_results(semantic, keyword, semantic_weight)

Weaviate (P1)

Native hybrid search, built-in vectorization, multi-tenancy.

python

class WeaviateStore(VectorStore):
    """Weaviate via REST/GraphQL API."""

    def __init__(self, url: str, collection: str, api_key: str | None = None):
        self.url = url
        self.collection = collection
        self.api_key = api_key

    async def query(self, embedding, n_results, where):
        # nearVector query
        payload = {
            "nearVector": {"vector": embedding},
            "limit": n_results,
        }
        if where:
            payload["where"] = self._translate_where(where)
        # ... GraphQL or REST v2 API call

    async def hybrid_query(self, text, embedding, n_results, where, semantic_weight):
        # Native hybrid - single API call
        payload = {
            "hybrid": {
                "query": text,
                "vector": embedding,
                "alpha": semantic_weight,  # 0=BM25, 1=vector
            },
            "limit": n_results,
        }
        if where:
            payload["where"] = self._translate_where(where)
        # ... single request, Weaviate handles fusion internally

Weaviate advantages for CSTP:

hybrid_query is a single API call (vs ChromaDB's manual 2-query merge)
alpha parameter maps directly to CSTP's hybrid_weight config
Multi-tenancy maps to per-agent isolation (F038 federation)
Built-in text2vec-* modules can replace external embedding providers
Batch import API for efficient reindexing
Cross-reference properties could support F045 (graph edges)

Filter translation:

python

def _translate_where(self, cstp_where: dict) -> dict:
    """Translate CSTP where clause to Weaviate filter format."""
    # CSTP: {"category": "architecture", "stakes": {"$in": ["high", "medium"]}}
    # Weaviate: {"operator": "And", "operands": [
    #   {"path": ["category"], "operator": "Equal", "valueText": "architecture"},
    #   {"path": ["stakes"], "operator": "ContainsAny", "valueTextArray": ["high", "medium"]}
    # ]}

pgvector (P1)

PostgreSQL with pgvector extension. Zero extra infrastructure for Postgres users.

python

class PgVectorStore(VectorStore):
    """PostgreSQL + pgvector extension."""

    def __init__(self, dsn: str, table: str = "decisions"):
        self.dsn = dsn
        self.table = table

    async def query(self, embedding, n_results, where):
        # SQL with cosine distance
        sql = f"""
            SELECT id, document, metadata, 
                   1 - (embedding <=> $1::vector) as similarity
            FROM {self.table}
            WHERE {self._build_where(where)}
            ORDER BY embedding <=> $1::vector
            LIMIT $2
        """

    async def hybrid_query(self, text, embedding, n_results, where, semantic_weight):
        # pg_trgm for keyword + pgvector for semantic
        sql = f"""
            WITH semantic AS (
                SELECT id, document, metadata,
                       1 - (embedding <=> $1::vector) as score
                FROM {self.table} WHERE {self._build_where(where)}
                ORDER BY embedding <=> $1::vector LIMIT $3
            ),
            keyword AS (
                SELECT id, document, metadata,
                       ts_rank(to_tsvector(document), plainto_tsquery($2)) as score
                FROM {self.table} WHERE {self._build_where(where)}
                ORDER BY score DESC LIMIT $3
            )
            SELECT id, document, metadata,
                   ({semantic_weight} * COALESCE(s.score, 0) + 
                    {1 - semantic_weight} * COALESCE(k.score, 0)) as combined
            FROM semantic s FULL OUTER JOIN keyword k USING (id)
            ORDER BY combined DESC LIMIT $3
        """

Qdrant (P2)

python

class QdrantStore(VectorStore):
    """Qdrant via REST API."""

    async def hybrid_query(self, text, embedding, n_results, where, semantic_weight):
        # Qdrant has native hybrid via "fusion" in query API
        payload = {
            "prefetch": [
                {"query": embedding, "using": "dense", "limit": n_results * 2},
                {"query": text, "using": "sparse", "limit": n_results * 2},
            ],
            "query": {"fusion": "rrf"},  # Reciprocal Rank Fusion
            "limit": n_results,
        }

In-Memory (P1 - testing)

python

class MemoryStore(VectorStore):
    """In-memory vector store for testing. No external dependencies."""

    def __init__(self):
        self._docs: dict[str, dict] = {}

    async def query(self, embedding, n_results, where):
        # Brute-force cosine similarity
        results = []
        for id, doc in self._docs.items():
            if self._matches_where(doc["metadata"], where):
                dist = self._cosine_distance(embedding, doc["embedding"])
                results.append(VectorResult(id, doc["document"], doc["metadata"], dist))
        results.sort(key=lambda r: r.distance)
        return results[:n_results]

Embedding Providers

a2a/cstp/embeddings/
├── __init__.py             # ABC + factory
├── gemini.py               # Current (768 dims, free tier)
├── openai.py               # text-embedding-3-small/large
├── ollama.py               # Local models (nomic-embed-text, etc.)
├── sentence_transformers.py # Local HuggingFace models
└── weaviate_builtin.py     # Weaviate's text2vec modules (no separate call needed)

Configuration

env

# Vector store
VECTOR_BACKEND=chromadb                    # chromadb | weaviate | pgvector | qdrant | pinecone | memory
VECTOR_URL=http://chromadb:8000            # Backend URL
VECTOR_COLLECTION=decisions                # Collection/table name
VECTOR_API_KEY=                            # For cloud backends (Pinecone, Weaviate Cloud)

# Embeddings
EMBEDDING_PROVIDER=gemini                  # gemini | openai | ollama | sentence_transformers | weaviate
EMBEDDING_MODEL=text-embedding-004         # Provider-specific model name
EMBEDDING_DIMENSIONS=768                   # Output dimensions
EMBEDDING_URL=                             # For Ollama/custom endpoints

# Weaviate-specific
WEAVIATE_TEXT2VEC_MODULE=text2vec-openai   # Built-in vectorizer (optional)
WEAVIATE_MULTI_TENANCY=false              # Enable per-agent tenants

Factory

python

def create_vector_store() -> VectorStore:
    backend = os.getenv("VECTOR_BACKEND", "chromadb")
    url = os.getenv("VECTOR_URL", "http://chromadb:8000")
    collection = os.getenv("VECTOR_COLLECTION", "decisions")
    api_key = os.getenv("VECTOR_API_KEY")

    match backend:
        case "chromadb":
            return ChromaDBStore(url=url, collection=collection)
        case "weaviate":
            return WeaviateStore(url=url, collection=collection, api_key=api_key)
        case "pgvector":
            return PgVectorStore(dsn=url, table=collection)
        case "qdrant":
            return QdrantStore(url=url, collection=collection, api_key=api_key)
        case "pinecone":
            return PineconeStore(api_key=api_key, index=collection)
        case "memory":
            return MemoryStore()
        case _:
            raise ValueError(f"Unknown vector backend: {backend}")

def create_embedding_provider() -> EmbeddingProvider:
    provider = os.getenv("EMBEDDING_PROVIDER", "gemini")
    model = os.getenv("EMBEDDING_MODEL", "text-embedding-004")

    match provider:
        case "gemini":
            return GeminiEmbeddings(model=model)
        case "openai":
            return OpenAIEmbeddings(model=model)
        case "ollama":
            url = os.getenv("EMBEDDING_URL", "http://localhost:11434")
            return OllamaEmbeddings(url=url, model=model)
        case "weaviate":
            return WeaviateBuiltinEmbeddings()  # No-op, Weaviate handles it
        case _:
            raise ValueError(f"Unknown embedding provider: {provider}")

Docker Compose Examples

ChromaDB (current, default):

yaml

services:
  cstp:
    environment:
      VECTOR_BACKEND: chromadb
      VECTOR_URL: http://chromadb:8000
      EMBEDDING_PROVIDER: gemini
  chromadb:
    image: chromadb/chroma:latest

Weaviate (hybrid search, built-in embeddings):

yaml

services:
  cstp:
    environment:
      VECTOR_BACKEND: weaviate
      VECTOR_URL: http://weaviate:8080
      EMBEDDING_PROVIDER: weaviate
      WEAVIATE_TEXT2VEC_MODULE: text2vec-openai
  weaviate:
    image: semitechnologies/weaviate:latest
    environment:
      ENABLE_MODULES: text2vec-openai
      DEFAULT_VECTORIZER_MODULE: text2vec-openai

pgvector (no extra services):

yaml

services:
  cstp:
    environment:
      VECTOR_BACKEND: pgvector
      VECTOR_URL: postgresql://user:pass@postgres:5432/cstp
      EMBEDDING_PROVIDER: ollama
      EMBEDDING_URL: http://ollama:11434
  postgres:
    image: pgvector/pgvector:pg16

Fully local (no cloud APIs):

yaml

services:
  cstp:
    environment:
      VECTOR_BACKEND: qdrant
      VECTOR_URL: http://qdrant:6333
      EMBEDDING_PROVIDER: ollama
      EMBEDDING_MODEL: nomic-embed-text
  qdrant:
    image: qdrant/qdrant:latest
  ollama:
    image: ollama/ollama:latest

Phases

P1: Abstraction + ChromaDB extraction ✅ Shipped

[x] Define VectorStore and EmbeddingProvider ABCs
[x] Extract ChromaDB logic from query_service.py and decision_service.py into vectordb/chromadb.py
[x] Extract Gemini embedding logic into embeddings/gemini.py
[x] Add MemoryStore for testing
[x] Factory with env-based backend selection
[x] Refactor reindex_service.py to use VectorStore interface
[x] Update all tests to use MemoryStore + factory injection (446 tests pass)
Zero behavior change - existing ChromaDB deployments work unchanged

Implementation notes (P1):

hybrid_query() was kept off the ABC — hybrid search stays orchestrated in the dispatcher (semantic via VectorStore + BM25 via bm25_index.py), matching existing behavior
EmbeddingProvider.embed() takes a single string (not batch) — embed_batch() provided as sequential default
VectorStore.close() is a non-abstract default no-op — backends override only if they hold resources
MemoryStore implements full ChromaDB-style where-clause matching: $gte, $lte, $gt, $lt, $ne, $in, $nin, $contains, $or, $and

P2: Weaviate + pgvector

Implement WeaviateStore with native hybrid search
Implement PgVectorStore with pgvector extension
Add OllamaEmbeddings for fully local stack
Docker compose examples for each
Integration tests per backend

P3: Qdrant + Pinecone + OpenAI embeddings

Implement remaining backends
OpenAIEmbeddings provider
Cloud deployment guides

P4: Backend-specific optimizations

Weaviate multi-tenancy for agent isolation (F038)
Weaviate cross-references for decision graph edges (F045)
pgvector partitioning for large decision sets
Batch import APIs for efficient reindexing

Migration Guide

For existing ChromaDB users: nothing changes. Set VECTOR_BACKEND=chromadb (or leave unset, it's the default).

For new deployments: choose based on your stack:

Already running	Recommended backend	Why
Nothing	ChromaDB	Simplest, lightweight
PostgreSQL	pgvector	No extra infra
Kubernetes	Weaviate or Qdrant	Production-grade, scalable
Cloud-only	Pinecone or Weaviate Cloud	Managed, zero-ops
Air-gapped / local	Qdrant + Ollama	Fully offline

Integration Points

F002 (Query): query_service.py uses VectorStore.query() / hybrid_query()
F007 (Record): decision_service.py uses VectorStore.upsert()
F017 (Hybrid Retrieval): Backends with native hybrid skip manual merge
F038 (Federation): Weaviate multi-tenancy for per-agent isolation
F045 (Graph): Weaviate cross-references for graph edges alongside vectors
F046 (Pre-Action): Uses query internally, benefits from faster backends
F047 (Session Context): Bulk query benefits from backend optimizations

F048: Multi-Vector-DB Support ​

Problem ​

Solution ​

Architecture ​

VectorStore Interface ​

EmbeddingProvider Interface ​

Backend Implementations ​

ChromaDB (P0 - extract existing) ​

Weaviate (P1) ​

pgvector (P1) ​

Qdrant (P2) ​

In-Memory (P1 - testing) ​

Embedding Providers ​

Configuration ​

Factory ​

Docker Compose Examples ​

Phases ​

P1: Abstraction + ChromaDB extraction ✅ Shipped ​

P2: Weaviate + pgvector ​

P3: Qdrant + Pinecone + OpenAI embeddings ​

P4: Backend-specific optimizations ​

Migration Guide ​

Integration Points ​

F048: Multi-Vector-DB Support

Problem

Solution

Architecture

VectorStore Interface

EmbeddingProvider Interface

Backend Implementations

ChromaDB (P0 - extract existing)

Weaviate (P1)

pgvector (P1)

Qdrant (P2)

In-Memory (P1 - testing)

Embedding Providers

Configuration

Factory

Docker Compose Examples

Phases

P1: Abstraction + ChromaDB extraction ✅ Shipped

P2: Weaviate + pgvector

P3: Qdrant + Pinecone + OpenAI embeddings

P4: Backend-specific optimizations

Migration Guide

Integration Points