microsoft/semantic-kernel · error · AgentInitializationException

Missing or malformed 'vector_store_ids' in

Error message

Missing or malformed 'vector_store_ids' in: {spec}

What it means

Raised by the 'file_search' tool builder when a ToolSpec's options lack a usable vector_store_ids value. The builder requires a non-empty list whose first element is truthy, because OpenAI file search needs at least one existing vector store id to query.

Solutions

1. Create the vector store via the OpenAI client and capture the returned id, then pass it as a list.
2. Ensure options={'vector_store_ids': ['vs_abc123']} (a non-empty list with a real id).
3. Validate vector_store_ids is a list and its first element is a non-empty string before building the tool.
4. Check the key spelling is exactly 'vector_store_ids' (snake_case).

Example fix

# before
tool = ToolSpec(type="file_search", options={"vector_store_ids": vs_id})  # vs_id is a str or None
# after
vs = await client.beta.vector_stores.create(name="docs")
tool = ToolSpec(type="file_search", options={"vector_store_ids": [vs.id]})

vs_ids = spec.options.get("vector_store_ids")
if not vs_ids or not isinstance(vs_ids, list) or not vs_ids[0]:
    raise ValueError("file_search requires options['vector_store_ids'] as a non-empty list of store ids.")
# all good -> build the tool

def is_valid_vector_store_ids(v) -> bool:
    return isinstance(v, list) and len(v) > 0 and all(isinstance(i, str) and i for i in v)

When it happens

Trigger: Defining a declarative tool of type 'file_search' where options.vector_store_ids is missing, is not a list, or is an empty/None-first-element list (e.g. {'vector_store_ids': [None]} or {'vector_store_ids': []}).

Common situations: Forgetting to create the vector store first and capture its id; passing a single string instead of a list; referencing a variable that is None at spec-build time; typos like 'vectorStoreIds'.

Understand the failure class

• Parsing and encoding errors: unexpected token, malformed input — why parsers reject input and how to find the real culprit.

Related errors

• Tool spec must include a 'type' field.
• Unsupported tool type
• Client cannot be None
• Kernel instance is required for tool resolution.
• Missing 'type' field in agent definition.

AI-assisted analysis of microsoft/semantic-kernel@c028a0c7dc (2026-08-13). Data as JSON: /api/errors/836c5fcbea5ec9c2. Report an issue: GitHub.

Appendix: source

Thrown at python/semantic_kernel/agents/open_ai/openai_assistant_agent.py:115

        _TOOL_BUILDERS[tool_type.lower()] = fn
        return fn

    return decorator


# Update _code_interpreter
@_register_tool("code_interpreter")
def _code_interpreter(spec: ToolSpec, kernel: Kernel | None = None) -> tuple[list[AssistantToolParam], ToolResources]:
    file_ids = spec.options.get("file_ids")
    return OpenAIAssistantAgent.configure_code_interpreter_tool(file_ids=file_ids)


# Update _file_search
@_register_tool("file_search")
def _file_search(spec: ToolSpec, kernel: Kernel | None = None) -> tuple[list[AssistantToolParam], ToolResources]:
    vector_store_ids = spec.options.get("vector_store_ids")
    if not vector_store_ids or not isinstance(vector_store_ids, list) or not vector_store_ids[0]:
        raise AgentInitializationException(f"Missing or malformed 'vector_store_ids' in: {spec}")
    return OpenAIAssistantAgent.configure_file_search_tool(vector_store_ids=vector_store_ids)


def _build_tool(spec: ToolSpec, kernel: "Kernel") -> tuple[list[AssistantToolParam], ToolResources]:
    if not spec.type:
        raise AgentInitializationException("Tool spec must include a 'type' field.")

    try:
        builder = _TOOL_BUILDERS[spec.type.lower()]
    except KeyError as exc:
        raise AgentInitializationException(f"Unsupported tool type: {spec.type}") from exc

    sig = inspect.signature(builder)
    return builder(spec) if len(sig.parameters) == 1 else builder(spec, kernel)  # type: ignore[call-arg]


# endregion

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