workflow-plugin-vectorstore/plugin.contracts.json at main · GoCodeAlone/workflow-plugin-vectorstore · GitHub

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{
    "version": "1",
    "plugin": "workflow-plugin-vectorstore",
    "modules": {
        "vectorstore.provider": {
            "description": "Initializes a Pinecone adapter and registers it by module name for use by vector step types. Milvus support is planned but not yet implemented.",
            "required_inputs": {
                "provider": {
                    "type": "string",
                    "description": "Backend provider. Currently only 'pinecone' is supported (milvus is planned but not yet implemented)."
                },
                "api_key": {
                    "type": "string",
                    "description": "API key for authenticating with the vector database service."
                }
            },
            "optional_inputs": {
                "host": {
                    "type": "string",
                    "description": "Custom host URL for self-hosted or private-link endpoints."
                },
                "namespace": {
                    "type": "string",
                    "description": "Default namespace for all vector operations in this module instance."
                },
                "index_name": {
                    "type": "string",
                    "description": "Default index name for operations that do not specify one explicitly."
                }
            },
            "outputs": {}
        }
    },
    "steps": {
        "step.vector_upsert": {
            "description": "Upsert vectors (with IDs, values, and metadata) into the configured vector store index.",
            "required_inputs": {
                "module": {
                    "type": "string",
                    "description": "Name of the vectorstore.provider module instance to use."
                },
                "vectors": {
                    "type": "slice",
                    "description": "Array of vector objects, each with 'id' (string), 'values' ([]float), and optional 'metadata' (map)."
                }
            },
            "optional_inputs": {},
            "outputs": {
                "upserted_count": {
                    "type": "int",
                    "description": "Number of vectors successfully upserted."
                }
            }
        },
        "step.vector_query": {
            "description": "Perform a similarity search and return the top-K nearest matches.",
            "required_inputs": {
                "module": {
                    "type": "string",
                    "description": "Name of the vectorstore.provider module instance to use."
                },
                "vector": {
                    "type": "slice",
                    "description": "The query vector (array of float values) to search with."
                }
            },
            "optional_inputs": {
                "top_k": {
                    "type": "int",
                    "description": "Maximum number of results to return.",
                    "default": 10
                },
                "filter": {
                    "type": "map",
                    "description": "Optional metadata filter map to narrow the search."
                }
            },
            "outputs": {
                "matches": {
                    "type": "slice",
                    "description": "Matched vectors, each with 'id', 'score', 'values', and 'metadata'."
                },
                "count": {
                    "type": "int",
                    "description": "Total number of matches returned."
                }
            }
        },
        "step.vector_fetch": {
            "description": "Fetch vectors by their IDs from the vector store.",
            "required_inputs": {
                "module": {
                    "type": "string",
                    "description": "Name of the vectorstore.provider module instance to use."
                },
                "ids": {
                    "type": "slice",
                    "description": "List of vector IDs to fetch."
                }
            },
            "optional_inputs": {},
            "outputs": {
                "vectors": {
                    "type": "slice",
                    "description": "Fetched vectors, each with 'id', 'values', and 'metadata'."
                },
                "count": {
                    "type": "int",
                    "description": "Number of vectors returned."
                }
            }
        },
        "step.vector_delete": {
            "description": "Delete vectors by IDs or by a metadata filter. Exactly one of 'ids' or 'filter' must be provided.",
            "required_inputs": {
                "module": {
                    "type": "string",
                    "description": "Name of the vectorstore.provider module instance to use."
                }
            },
            "optional_inputs": {
                "ids": {
                    "type": "slice",
                    "description": "List of vector IDs to delete. Mutually exclusive with 'filter'."
                },
                "filter": {
                    "type": "map",
                    "description": "Metadata filter for bulk deletion. Mutually exclusive with 'ids'."
                }
            },
            "outputs": {
                "deleted_count": {
                    "type": "int",
                    "description": "Number of vectors deleted (set when deleting by IDs)."
                },
                "deleted_by_filter": {
                    "type": "bool",
                    "description": "True when deletion was performed by metadata filter."
                }
            }
        },
        "step.vector_create_index": {
            "description": "Create a new vector index in the configured vector store.",
            "required_inputs": {
                "module": {
                    "type": "string",
                    "description": "Name of the vectorstore.provider module instance to use."
                },
                "name": {
                    "type": "string",
                    "description": "Name of the index to create."
                },
                "dimension": {
                    "type": "int",
                    "description": "Vector dimension (must match your embedding model's output size)."
                },
                "cloud": {
                    "type": "string",
                    "description": "Cloud provider: 'aws', 'gcp', or 'azure'."
                }
            },
            "optional_inputs": {
                "metric": {
                    "type": "string",
                    "description": "Similarity metric: 'cosine', 'dotproduct', or 'euclidean'.",
                    "default": "cosine"
                },
                "region": {
                    "type": "string",
                    "description": "Cloud region (e.g. 'us-east-1')."
                }
            },
            "outputs": {
                "created": {
                    "type": "string",
                    "description": "Name of the newly created index."
                }
            }
        },
        "step.vector_list_indexes": {
            "description": "List all vector indexes available in the configured vector store.",
            "required_inputs": {
                "module": {
                    "type": "string",
                    "description": "Name of the vectorstore.provider module instance to use."
                }
            },
            "optional_inputs": {},
            "outputs": {
                "indexes": {
                    "type": "slice",
                    "description": "List of index descriptors, each with 'name', 'dimension', 'metric', and 'vector_count'."
                },
                "count": {
                    "type": "int",
                    "description": "Total number of indexes returned."
                }
            }
        },
        "step.vector_describe_index": {
            "description": "Describe an existing vector index, returning its dimension, metric, and vector count.",
            "required_inputs": {
                "module": {
                    "type": "string",
                    "description": "Name of the vectorstore.provider module instance to use."
                },
                "name": {
                    "type": "string",
                    "description": "Name of the index to describe."
                }
            },
            "optional_inputs": {},
            "outputs": {
                "name": {
                    "type": "string",
                    "description": "Index name."
                },
                "dimension": {
                    "type": "int",
                    "description": "Vector dimension of the index."
                },
                "metric": {
                    "type": "string",
                    "description": "Distance metric used by the index."
                },
                "vector_count": {
                    "type": "int",
                    "description": "Total number of vectors stored in the index."
                }
            }
        }
    }
}