Python API¶

Hub: documentation home. CLI interpolation is different: CLI.

Application code installs vectorsmith. Do not import Engine.

pip install "vectorsmith[qdrant]"                 # connect()
pip install "vectorsmith[qdrant,langchain]"       # load_tools (LangChain / LangGraph)

Env for these APIs: os.environ, then env=, then env_file= (file wins). That is not how the CLI works — CLI.

Always await …aclose() when finished (closes store clients).

`connect`¶

from vectorsmith import connect

vs = connect(
    "tools.invoices.yaml",
    "tools.tickets.yaml",
    env_file=".env",           # optional
    env={"QDRANT_URL": "…"},   # optional; overrides os.environ, then env_file overlays
)

Returns BoundTools. Compiles each YAML in-process (no serve subprocess). Duplicate tool names: last file wins for call().

`BoundTools`¶

Member	Role
`names`	Tool names in load order
`schemas`	MCP dicts: `name`, `description`, `inputSchema`
`as_anthropic()`	`{name, description, input_schema}` for `messages.create`
`as_langchain()`	LangChain `Toolset` (`vectorsmith[langchain]`)
`as_openai_agents()`	OpenAI Agents `FunctionTool` list (`vectorsmith[openai-agents]`)
`await call(name, args)`	Run a tool; `args` is a mapping (omit optional keys or pass `None` to drop them)
`await aclose()`	Close engines

rows = await vs.call("search_invoices", {"query": "Globex", "limit": 3})

Unknown name → KeyError.

`load_tools` (LangChain / LangGraph)¶

from vectorsmith import load_tools
# same function:
from vectorsmith.langchain import load_tools
from vectorsmith.langgraph import load_tools

tools = load_tools("tools.invoices.yaml", env_file=".env")

Requires langchain-core (vectorsmith[langchain] or [langgraph]). Returns a Toolset (a list of LangChain StructuredTools) with aclose().

Equivalent: connect(...).as_langchain().

Pass into create_agent / create_react_agent / ToolNode. Mix with your own @tools.

OpenAI Agents SDK¶

from vectorsmith.openai_agents import load_tools

vs = load_tools("tools.yaml", env_file=".env")
# vs is a list of FunctionTool; splat into Agent(tools=[*vs, ...])
await vs.aclose()

Requires vectorsmith[openai-agents]. Optional YAML fields are not OpenAI strict-mode schemas; the adapter sets strict_json_schema=False.

Equivalent: connect(...).as_openai_agents().

Anthropic Messages API¶

from vectorsmith.anthropic import load_tools

vs = load_tools("tools.yaml", env_file=".env")
resp = client.messages.create(..., tools=vs.tools, messages=...)
output = await vs.execute(block.name, block.input)  # JSON string
await vs.aclose()

Requires vectorsmith[anthropic] (or pip install anthropic plus vectorsmith[qdrant]). vs.tools is the list of API dicts; execute dispatches tool_use.

Return envelope¶

call() / MCP tool results look like:

Field	Meaning
`rows`	Projected records
`count`	Length of `rows`
`truncated`	Hit `limit`
`may_be_incomplete`	Pipeline over-fetch cap; caveat the answer
`search_mode`	`dense` · `hybrid` · `none`
`warnings`	e.g. `VB4001`–`VB4003`
`latency_ms`	Engine timing
`exact_search` / `compiled_query`	Present on some results

Authoring (`vectorsmith_core`)¶

The compiler module ships inside pip install vectorsmith (not a second PyPI project). For CI and validate-like scripts — not for binding schemas onto an LLM:

from vectorsmith_core import load_project

project = load_project("tools.yaml", env={"QDRANT_URL": "http://localhost:6333"})
project.mcp_tool_schemas()
project.issues   # VBxxxx

Engine is not part of this public surface.

Extras¶

Store extras (pick one or more): vector stores — qdrant, pgvector, chroma, pinecone, weaviate, milvus. Each extra is the store client plus FastEmbed.

Agent extras (not databases):

Extra	What you get
`langchain`	`langchain-core`
`langgraph`	`langchain-core` + `langgraph`
`openai-agents`	OpenAI Agents SDK
`anthropic`	Anthropic SDK

Guides: integrations. Examples: examples/.

Python API¶

connect¶

BoundTools¶

load_tools (LangChain / LangGraph)¶