Vector stores

Hub: documentation home · tools.yaml · Python API.

VectorSmith talks to your cluster. It does not host a database. These six backends ship in the package; pick the extra that matches connections.*.backend in tools.yaml.

What is integrated

Store	backend in YAML	Install extra	Client pulled in
Qdrant	qdrant	vectorsmith[qdrant]	qdrant-client
PostgreSQL + pgvector	pgvector	vectorsmith[pgvector]	psycopg (binary + pool)
Chroma	chroma	vectorsmith[chroma]	chromadb
Pinecone	pinecone	vectorsmith[pinecone]	pinecone
Weaviate	weaviate	vectorsmith[weaviate]	weaviate-client
Milvus	milvus	vectorsmith[milvus]	pymilvus

Every store extra also installs FastEmbed (BAAI/bge-small-en-v1.5 by default) so search can embed queries locally. You do not need a separate embed extra on the published vectorsmith package.

pip install "vectorsmith[qdrant]"     # most common
pip install "vectorsmith[pgvector]"
pip install "vectorsmith[chroma]"
pip install "vectorsmith[pinecone]"
pip install "vectorsmith[weaviate]"
pip install "vectorsmith[milvus]"

Install more than one extra if a single tools.yaml has mixed backends. The CLI (serve, validate, test) and connect() / load_tools use the same extras.

There is no first-party adapter for other stores (Elasticsearch, Redis, OpenSearch, …). Those are not integrated.

What each backend can do

Same tools (search, lookup, count, scroll, pipeline) compile against every backend. Capability gates reject YAML that the store cannot run (validate, including validate --live for hybrid/sparse).

	Qdrant	pgvector	Chroma	Pinecone	Weaviate	Milvus
Dense search	yes	yes (vector mode)	yes	yes	yes	yes
Hybrid / sparse	yes	no	no	yes	yes	yes
Nested payload paths	yes	yes	no	no	yes	yes
exists / is_null	yes	yes	no	no	yes	exists only
like	no	yes	yes	no	yes	yes
text_match in filter	yes	no	no	no	yes	no
Filtered count	yes	yes	yes	yes	yes	yes
Scroll	yes	yes	yes	no	yes	yes
Introspection	typed	typed	none	none	typed	typed
Server-side embedding	no	no	no	no	yes	no

Every backend accepts comparison ops: eq ne gt gte lt lte in nin.

pgvector table mode (mode: table or vector_column: null) is for lookup / count / scroll / pipeline only — no kind: search (VB2016).

Connection fields: tools.yaml → connections.

Mixing stores

One YAML file can declare several connections with different backends. That is still one MCP server (vectorsmith serve). Each tool names target.connection. Two Claude connectors still mean two YAML files and two serve --name processes.

Not a store extra

These extras are agent SDKs, not databases:

Extra	For
langchain / langgraph	load_tools
openai-agents	BoundTools.as_openai_agents()
anthropic	BoundTools.as_anthropic()

connect() only needs the store extra. Details: Python API.