Method create

opencontractserver/llms/api.py:545–598

Create a vector store using the specified framework. Args: framework: "pydantic_ai" user_id: Filter by user ID corpus_id: Filter by corpus ID document_id: Filter by document ID embedder_path: Path to embedder model to use

(
        *,
        framework: Optional[FrameworkType] = None,
        user_id: Optional[Union[str, int]] = None,
        corpus_id: Optional[Union[str, int]] = None,
        document_id: Optional[Union[str, int]] = None,
        embedder_path: Optional[str] = None,
        must_have_text: Optional[str] = None,
        embed_dim: int = 384,
        **kwargs,
    )

    @staticmethod
    def create(
        *,
        framework: Optional[FrameworkType] = None,
        user_id: Optional[Union[str, int]] = None,
        corpus_id: Optional[Union[str, int]] = None,
        document_id: Optional[Union[str, int]] = None,
        embedder_path: Optional[str] = None,
        must_have_text: Optional[str] = None,
        embed_dim: int = 384,
        **kwargs,
    ) -> Any:
        """
        Create a vector store using the specified framework.

        Args:
            framework: "pydantic_ai"
            user_id: Filter by user ID
            corpus_id: Filter by corpus ID
            document_id: Filter by document ID
            embedder_path: Path to embedder model to use
            must_have_text: Filter by text content
            embed_dim: Embedding dimension to use (384, 768, 1024, 1536, 3072, or 4096)
            **kwargs: Additional framework-specific arguments

        Returns:
            Framework-specific vector store instance

        Examples:
            # Pydantic AI vector store
            store = vector_stores.create("pydantic_ai", corpus_id=123)

            # Pydantic AI vector store
            store = vector_stores.create("pydantic_ai", document_id=456)
        """
        # Resolve default framework if caller did not specify one
        if framework is None:
            framework = getattr(
                settings, "LLMS_DOCUMENT_AGENT_FRAMEWORK", AgentFramework.PYDANTIC_AI
            )

        # Normalize framework
        if isinstance(framework, str):
            framework = AgentFramework(framework)

        return UnifiedVectorStoreFactory.create_vector_store(
            framework=framework,
            user_id=user_id,
            corpus_id=corpus_id,
            document_id=document_id,
            embedder_path=embedder_path,
            must_have_text=must_have_text,
            embed_dim=embed_dim,
            **kwargs,
        )


def _resolve_tools(tools: list[ToolType]) -> list[CoreTool]: