Flowise

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{
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    "description": "A chain that automatically picks an appropriate retriever from multiple different vector databases",
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    "categories": "ChatOpenAI,Multi Retrieval QA Chain,Pinecone,Chroma,Supabase,Langchain",
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    "framework": "Langchain",
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                "id": "vectorStoreRetriever_0",
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                "label": "Vector Store Retriever",
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                "version": 1,
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                "name": "vectorStoreRetriever",
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                "type": "VectorStoreRetriever",
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                "baseClasses": ["VectorStoreRetriever"],
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                "category": "Retrievers",
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                "description": "Store vector store as retriever. Used with MultiRetrievalQAChain",
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                "inputParams": [
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                    {
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                        "label": "Retriever Name",
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                        "name": "name",
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                        "type": "string",
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                        "placeholder": "netflix movies",
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                        "id": "vectorStoreRetriever_0-input-name-string"
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                    },
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                    {
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                        "label": "Retriever Description",
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                        "name": "description",
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                        "type": "string",
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                        "rows": 3,
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                        "description": "Description of when to use the vector store retriever",
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                        "placeholder": "Good for answering questions about netflix movies",
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                        "id": "vectorStoreRetriever_0-input-description-string"
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                    }
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                ],
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                "inputAnchors": [
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                    {
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                        "label": "Vector Store",
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                        "name": "vectorStore",
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                        "type": "VectorStore",
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                        "id": "vectorStoreRetriever_0-input-vectorStore-VectorStore"
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                    }
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                ],
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                "inputs": {
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                    "vectorStore": "{{supabase_0.data.instance}}",
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                    "name": "aqua teen",
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                    "description": "Good for answering questions about Aqua Teen Hunger Force theme song"
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                },
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                "outputAnchors": [
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                    {
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                        "id": "vectorStoreRetriever_0-output-vectorStoreRetriever-VectorStoreRetriever",
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                        "name": "vectorStoreRetriever",
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                        "label": "VectorStoreRetriever",
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                        "type": "VectorStoreRetriever"
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                    }
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                ],
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                "outputs": {},
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                "selected": false
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            },
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            "id": "multiRetrievalQAChain_0",
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                "id": "multiRetrievalQAChain_0",
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                "label": "Multi Retrieval QA Chain",
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                "name": "multiRetrievalQAChain",
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                "type": "MultiRetrievalQAChain",
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                "baseClasses": ["MultiRetrievalQAChain", "MultiRouteChain", "BaseChain", "BaseLangChain"],
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                "category": "Chains",
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                "description": "QA Chain that automatically picks an appropriate vector store from multiple retrievers",
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                "inputParams": [
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                    {
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                        "label": "Return Source Documents",
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                        "name": "returnSourceDocuments",
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                        "type": "boolean",
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                        "optional": true
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                    }
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                ],
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                "inputAnchors": [
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                    {
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                        "label": "Language Model",
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                        "name": "model",
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                        "type": "BaseLanguageModel",
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                        "id": "multiRetrievalQAChain_0-input-model-BaseLanguageModel"
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                    },
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                    {
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                        "label": "Vector Store Retriever",
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                        "name": "vectorStoreRetriever",
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                        "type": "VectorStoreRetriever",
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                        "list": true,
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                        "id": "multiRetrievalQAChain_0-input-vectorStoreRetriever-VectorStoreRetriever"
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                    },
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                    {
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                        "label": "Input Moderation",
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                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
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                        "name": "inputModeration",
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                        "type": "Moderation",
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                        "optional": true,
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                        "list": true,
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                        "id": "multiRetrievalQAChain_0-input-inputModeration-Moderation"
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                    }
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                ],
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                "inputs": {
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                    "inputModeration": "",
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                    "model": "{{chatOpenAI_0.data.instance}}",
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                    "vectorStoreRetriever": [
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                        "{{vectorStoreRetriever_0.data.instance}}",
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                        "{{vectorStoreRetriever_1.data.instance}}",
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                        "{{vectorStoreRetriever_2.data.instance}}"
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                    ]
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                },
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                "outputAnchors": [
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                    {
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                        "id": "multiRetrievalQAChain_0-output-multiRetrievalQAChain-MultiRetrievalQAChain|MultiRouteChain|BaseChain|BaseLangChain",
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                        "name": "multiRetrievalQAChain",
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                        "label": "MultiRetrievalQAChain",
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                        "type": "MultiRetrievalQAChain | MultiRouteChain | BaseChain | BaseLangChain"
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                    }
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                "outputs": {},
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        {
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            "width": 300,
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            "id": "vectorStoreRetriever_1",
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            "type": "customNode",
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                "id": "vectorStoreRetriever_1",
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                "label": "Vector Store Retriever",
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                "version": 1,
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                "name": "vectorStoreRetriever",
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                "type": "VectorStoreRetriever",
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                "baseClasses": ["VectorStoreRetriever"],
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                "category": "Retrievers",
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                "description": "Store vector store as retriever. Used with MultiRetrievalQAChain",
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                "inputParams": [
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                    {
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                        "label": "Retriever Name",
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                        "name": "name",
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                        "type": "string",
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                        "placeholder": "netflix movies",
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                        "id": "vectorStoreRetriever_1-input-name-string"
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                    },
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                    {
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                        "label": "Retriever Description",
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                        "name": "description",
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                        "type": "string",
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                        "rows": 3,
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                        "description": "Description of when to use the vector store retriever",
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                        "placeholder": "Good for answering questions about netflix movies",
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                        "id": "vectorStoreRetriever_1-input-description-string"
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                    }
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                ],
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                "inputAnchors": [
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                    {
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                        "label": "Vector Store",
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                        "name": "vectorStore",
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                        "type": "VectorStore",
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                        "id": "vectorStoreRetriever_1-input-vectorStore-VectorStore"
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                    }
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                ],
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                "inputs": {
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                    "vectorStore": "{{chroma_0.data.instance}}",
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                    "name": "mst3k",
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                    "description": "Good for answering questions about Mystery Science Theater 3000 theme song"
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                },
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                "outputAnchors": [
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                    {
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                        "id": "vectorStoreRetriever_1-output-vectorStoreRetriever-VectorStoreRetriever",
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                        "name": "vectorStoreRetriever",
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                        "label": "VectorStoreRetriever",
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                        "type": "VectorStoreRetriever"
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                    }
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                ],
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                "outputs": {},
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                "selected": false
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            },
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            "id": "vectorStoreRetriever_2",
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            "type": "customNode",
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            "data": {
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                "id": "vectorStoreRetriever_2",
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                "label": "Vector Store Retriever",
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                "version": 1,
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                "name": "vectorStoreRetriever",
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                "type": "VectorStoreRetriever",
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                "baseClasses": ["VectorStoreRetriever"],
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                "category": "Retrievers",
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                "description": "Store vector store as retriever. Used with MultiRetrievalQAChain",
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                "inputParams": [
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                    {
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                        "label": "Retriever Name",
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                        "name": "name",
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                        "type": "string",
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                        "placeholder": "netflix movies",
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                        "id": "vectorStoreRetriever_2-input-name-string"
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                    },
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                    {
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                        "label": "Retriever Description",
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                        "name": "description",
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                        "type": "string",
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                        "rows": 3,
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                        "description": "Description of when to use the vector store retriever",
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                        "placeholder": "Good for answering questions about netflix movies",
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                        "id": "vectorStoreRetriever_2-input-description-string"
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                    }
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                ],
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                "inputAnchors": [
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                    {
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                        "label": "Vector Store",
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                        "name": "vectorStore",
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                        "type": "VectorStore",
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                        "id": "vectorStoreRetriever_2-input-vectorStore-VectorStore"
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                    }
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                ],
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                "inputs": {
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                    "vectorStore": "{{pinecone_0.data.instance}}",
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                    "name": "animaniacs",
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                    "description": "Good for answering questions about Animaniacs theme song"
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                },
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                "outputAnchors": [
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                    {
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                        "id": "vectorStoreRetriever_2-output-vectorStoreRetriever-VectorStoreRetriever",
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                        "name": "vectorStoreRetriever",
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                        "label": "VectorStoreRetriever",
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                        "type": "VectorStoreRetriever"
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                    }
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                ],
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                "outputs": {},
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                "selected": false
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            },
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        },
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        {
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            "width": 300,
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            "id": "openAIEmbeddings_0",
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                "id": "openAIEmbeddings_0",
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                "label": "OpenAI Embeddings",
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                "name": "openAIEmbeddings",
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                "type": "OpenAIEmbeddings",
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                "baseClasses": ["OpenAIEmbeddings", "Embeddings"],
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                "category": "Embeddings",
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                "description": "OpenAI API to generate embeddings for a given text",
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                "inputParams": [
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                    {
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                        "label": "Connect Credential",
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                        "name": "credential",
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                        "type": "credential",
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                        "credentialNames": ["openAIApi"],
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                        "id": "openAIEmbeddings_0-input-credential-credential"
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                    },
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                    {
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                        "label": "Model Name",
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                        "name": "modelName",
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                        "type": "asyncOptions",
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                        "loadMethod": "listModels",
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                        "default": "text-embedding-ada-002",
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                        "id": "openAIEmbeddings_0-input-modelName-options"
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                    },
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                    {
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                        "label": "Strip New Lines",
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                        "name": "stripNewLines",
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                        "type": "boolean",
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "openAIEmbeddings_0-input-stripNewLines-boolean"
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                    },
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                        "label": "Batch Size",
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                        "name": "batchSize",
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                        "type": "number",
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "openAIEmbeddings_0-input-batchSize-number"
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                        "label": "Timeout",
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                        "id": "openAIEmbeddings_0-input-timeout-number"
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                        "label": "BasePath",
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                        "name": "basepath",
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                        "type": "string",
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                        "optional": true,
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                        "id": "openAIEmbeddings_0-input-basepath-string"
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                ],
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                "inputs": {
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                    "stripNewLines": "",
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                    "modelName": "text-embedding-ada-002"
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                        "id": "openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings",
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                        "name": "openAIEmbeddings",
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                        "label": "OpenAIEmbeddings",
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                        "type": "OpenAIEmbeddings | Embeddings"
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                    }
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                ],
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                "outputs": {},
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                "selected": false
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            },
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        },
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        {
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            "width": 300,
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            "height": 574,
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            "id": "chatOpenAI_0",
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            },
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            "type": "customNode",
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            "data": {
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                "id": "chatOpenAI_0",
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                "label": "ChatOpenAI",
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                "version": 6.0,
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                "name": "chatOpenAI",
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                "type": "ChatOpenAI",
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                "baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
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                "category": "Chat Models",
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                "description": "Wrapper around OpenAI large language models that use the Chat endpoint",
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                "inputParams": [
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                    {
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                        "label": "Connect Credential",
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                        "name": "credential",
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                        "type": "credential",
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                        "credentialNames": ["openAIApi"],
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                        "id": "chatOpenAI_0-input-credential-credential"
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                    },
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                    {
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                        "label": "Model Name",
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                        "name": "modelName",
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                        "type": "asyncOptions",
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                        "loadMethod": "listModels",
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                        "default": "gpt-3.5-turbo",
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                        "id": "chatOpenAI_0-input-modelName-options"
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                    },
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                    {
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                        "label": "Temperature",
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                        "name": "temperature",
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                        "type": "number",
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                        "step": 0.1,
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                        "default": 0.9,
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                        "optional": true,
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                        "id": "chatOpenAI_0-input-temperature-number"
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                    },
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                    {
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                        "label": "Max Tokens",
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                        "name": "maxTokens",
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                        "type": "number",
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                        "step": 1,
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-maxTokens-number"
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                    },
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                    {
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                        "label": "Top Probability",
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                        "name": "topP",
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                        "type": "number",
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                        "step": 0.1,
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-topP-number"
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                    },
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                    {
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                        "label": "Frequency Penalty",
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                        "name": "frequencyPenalty",
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                        "type": "number",
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                        "step": 0.1,
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-frequencyPenalty-number"
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                    },
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                    {
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                        "label": "Presence Penalty",
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                        "name": "presencePenalty",
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                        "type": "number",
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                        "step": 0.1,
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-presencePenalty-number"
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                    },
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                    {
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                        "label": "Timeout",
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                        "name": "timeout",
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                        "type": "number",
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                        "step": 1,
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-timeout-number"
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                    {
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                        "label": "BasePath",
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                        "name": "basepath",
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                        "type": "string",
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-basepath-string"
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                    },
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                    {
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                        "label": "BaseOptions",
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                        "name": "baseOptions",
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                        "type": "json",
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-baseOptions-json"
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                    },
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                    {
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                        "label": "Allow Image Uploads",
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                        "name": "allowImageUploads",
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                        "type": "boolean",
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                        "description": "Automatically uses gpt-4-vision-preview when image is being uploaded from chat. Only works with LLMChain, Conversation Chain, ReAct Agent, and Conversational Agent",
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                        "default": false,
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                        "optional": true,
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                        "id": "chatOpenAI_0-input-allowImageUploads-boolean"
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                    },
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                    {
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                        "label": "Image Resolution",
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                        "description": "This parameter controls the resolution in which the model views the image.",
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                        "name": "imageResolution",
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                        "type": "options",
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                        "options": [
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                            {
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                                "label": "Low",
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                                "name": "low"
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                            },
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                            {
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                                "label": "High",
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                                "name": "high"
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                            },
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                            {
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                                "label": "Auto",
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                                "name": "auto"
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                            }
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                        ],
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                        "default": "low",
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                        "optional": false,
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                        "additionalParams": true,
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                        "id": "chatOpenAI_0-input-imageResolution-options"
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                    }
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                ],
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                "inputAnchors": [
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                    {
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                        "label": "Cache",
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                        "name": "cache",
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                        "type": "BaseCache",
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                        "optional": true,
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                        "id": "chatOpenAI_0-input-cache-BaseCache"
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                    }
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                ],
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                "inputs": {
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                    "cache": "",
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                    "modelName": "gpt-3.5-turbo",
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                    "presencePenalty": "",
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                    "basepath": "",
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                    "baseOptions": "",
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                    "allowImageUploads": true,
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                    "imageResolution": "low"
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                },
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                "outputAnchors": [
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                    {
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                        "id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
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                        "name": "chatOpenAI",
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                        "label": "ChatOpenAI",
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                        "type": "ChatOpenAI | BaseChatModel | BaseLanguageModel | Runnable"
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                    }
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                ],
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                "outputs": {},
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                "selected": false
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        },
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        {
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            "width": 300,
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            "height": 555,
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            "id": "pinecone_0",
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                "y": -333.57075989595313
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            },
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            "type": "customNode",
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            "data": {
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                "id": "pinecone_0",
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                "label": "Pinecone",
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                "version": 2,
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                "name": "pinecone",
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                "type": "Pinecone",
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                "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
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                "category": "Vector Stores",
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                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
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                "inputParams": [
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                    {
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                        "label": "Connect Credential",
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                        "name": "credential",
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                        "type": "credential",
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                        "credentialNames": ["pineconeApi"],
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                        "id": "pinecone_0-input-credential-credential"
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                    },
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                    {
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                        "label": "Pinecone Index",
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                        "name": "pineconeIndex",
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                        "type": "string",
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                        "id": "pinecone_0-input-pineconeIndex-string"
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                    },
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                    {
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                        "label": "Pinecone Namespace",
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                        "name": "pineconeNamespace",
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                        "type": "string",
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                        "placeholder": "my-first-namespace",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-pineconeNamespace-string"
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                    },
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                    {
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                        "label": "Pinecone Metadata Filter",
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                        "name": "pineconeMetadataFilter",
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                        "type": "json",
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "pinecone_0-input-pineconeMetadataFilter-json"
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                    },
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                    {
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                        "label": "Top K",
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                        "name": "topK",
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                        "description": "Number of top results to fetch. Default to 4",
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                        "placeholder": "4",
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                        "type": "number",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-topK-number"
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                    },
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                    {
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                        "label": "Search Type",
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                        "name": "searchType",
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                        "type": "options",
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                        "default": "similarity",
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                        "options": [
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                            {
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                                "label": "Similarity",
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                                "name": "similarity"
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                            },
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                            {
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                                "label": "Max Marginal Relevance",
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                                "name": "mmr"
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                            }
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                        ],
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-searchType-options"
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                    },
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                    {
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                        "label": "Fetch K (for MMR Search)",
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                        "name": "fetchK",
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                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
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                        "placeholder": "20",
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                        "type": "number",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-fetchK-number"
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                    {
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                        "label": "Lambda (for MMR Search)",
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                        "name": "lambda",
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                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
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                        "placeholder": "0.5",
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                        "type": "number",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-lambda-number"
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                        "type": "Document",
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                        "list": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-document-Document"
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                        "label": "Embeddings",
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                        "name": "embeddings",
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                        "type": "Embeddings",
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                        "id": "pinecone_0-input-embeddings-Embeddings"
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                    }
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                    "embeddings": "{{openAIEmbeddings_0.data.instance}}",
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                        "name": "output",
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                        "label": "Output",
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                                "id": "pinecone_0-output-retriever-Pinecone|VectorStoreRetriever|BaseRetriever",
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                                "name": "retriever",
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                                "label": "Pinecone Retriever",
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                                "type": "Pinecone | VectorStoreRetriever | BaseRetriever"
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                            {
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                                "id": "pinecone_0-output-vectorStore-Pinecone|VectorStore",
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                                "name": "vectorStore",
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                                "label": "Pinecone Vector Store",
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                                "type": "Pinecone | VectorStore"
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                            }
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                        ],
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                        "default": "retriever"
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                    }
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                ],
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                "outputs": {
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                    "output": "vectorStore"
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                "selected": false
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            "selected": false,
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            "height": 654,
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                "name": "chroma",
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                "type": "Chroma",
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                "baseClasses": ["Chroma", "VectorStoreRetriever", "BaseRetriever"],
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                "category": "Vector Stores",
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                "description": "Upsert embedded data and perform similarity search upon query using Chroma, an open-source embedding database",
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                "inputParams": [
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                    {
736
                        "label": "Connect Credential",
737
                        "name": "credential",
738
                        "type": "credential",
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                        "description": "Only needed if you have chroma on cloud services with X-Api-key",
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                        "optional": true,
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                        "credentialNames": ["chromaApi"],
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                        "id": "chroma_0-input-credential-credential"
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                    },
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                    {
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                        "label": "Collection Name",
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                        "name": "collectionName",
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                        "type": "string",
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                        "id": "chroma_0-input-collectionName-string"
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                    },
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                    {
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                        "label": "Chroma URL",
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                        "name": "chromaURL",
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                        "type": "string",
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                        "optional": true,
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                        "id": "chroma_0-input-chromaURL-string"
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                    },
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                    {
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                        "label": "Chroma Metadata Filter",
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                        "name": "chromaMetadataFilter",
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                        "type": "json",
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "chroma_0-input-chromaMetadataFilter-json"
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                    },
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                    {
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                        "label": "Top K",
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                        "name": "topK",
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                        "description": "Number of top results to fetch. Default to 4",
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                        "placeholder": "4",
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                        "type": "number",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "chroma_0-input-topK-number"
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                        "label": "Document",
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                        "name": "document",
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                        "type": "Document",
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                        "list": true,
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                        "optional": true,
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                        "id": "chroma_0-input-document-Document"
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                    {
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                        "label": "Embeddings",
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                        "name": "embeddings",
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                        "type": "Embeddings",
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                        "id": "chroma_0-input-embeddings-Embeddings"
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                    }
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                ],
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                "inputs": {
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                    "document": "",
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                    "embeddings": "{{openAIEmbeddings_0.data.instance}}",
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                    "collectionName": "",
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                    "chromaURL": "",
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                    "chromaMetadataFilter": "",
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                    "topK": ""
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                },
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                "outputAnchors": [
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                        "name": "output",
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                        "label": "Output",
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                        "type": "options",
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                        "options": [
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                            {
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                                "id": "chroma_0-output-retriever-Chroma|VectorStoreRetriever|BaseRetriever",
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                                "name": "retriever",
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                                "label": "Chroma Retriever",
810
                                "type": "Chroma | VectorStoreRetriever | BaseRetriever"
811
                            },
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                            {
813
                                "id": "chroma_0-output-vectorStore-Chroma|VectorStore",
814
                                "name": "vectorStore",
815
                                "label": "Chroma Vector Store",
816
                                "type": "Chroma | VectorStore"
817
                            }
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                        ],
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                        "default": "retriever"
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                    }
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                ],
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                "outputs": {
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                    "output": "vectorStore"
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                "selected": false
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            "positionAbsolute": {
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                "y": 242.72988251281214
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            "width": 300,
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            "height": 753,
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            "id": "supabase_0",
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            "position": {
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                "y": 920.6999513218148
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                "label": "Supabase",
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                "version": 1,
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                "name": "supabase",
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                "type": "Supabase",
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                "baseClasses": ["Supabase", "VectorStoreRetriever", "BaseRetriever"],
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                "category": "Vector Stores",
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                "description": "Upsert embedded data and perform similarity search upon query using Supabase via pgvector extension",
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                "inputParams": [
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                    {
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                        "label": "Connect Credential",
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                        "name": "credential",
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                        "type": "credential",
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                        "credentialNames": ["supabaseApi"],
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                        "id": "supabase_0-input-credential-credential"
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                    },
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                    {
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                        "label": "Supabase Project URL",
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                        "name": "supabaseProjUrl",
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                        "type": "string",
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                        "id": "supabase_0-input-supabaseProjUrl-string"
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                    },
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                    {
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                        "label": "Table Name",
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                        "name": "tableName",
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                        "type": "string",
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                        "id": "supabase_0-input-tableName-string"
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                    },
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                    {
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                        "label": "Query Name",
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                        "name": "queryName",
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                        "type": "string",
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                        "id": "supabase_0-input-queryName-string"
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                    },
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                    {
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                        "label": "Supabase Metadata Filter",
880
                        "name": "supabaseMetadataFilter",
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                        "type": "json",
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                        "optional": true,
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                        "additionalParams": true,
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                        "id": "supabase_0-input-supabaseMetadataFilter-json"
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                    },
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                    {
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                        "label": "Top K",
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                        "name": "topK",
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                        "description": "Number of top results to fetch. Default to 4",
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                        "placeholder": "4",
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                        "type": "number",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "supabase_0-input-topK-number"
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                    {
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                        "label": "Search Type",
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                        "name": "searchType",
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                        "type": "options",
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                        "default": "similarity",
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                        "options": [
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                            {
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                                "label": "Similarity",
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                                "name": "similarity"
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                            },
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                            {
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                                "label": "Max Marginal Relevance",
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                                "name": "mmr"
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                            }
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                        ],
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-searchType-options"
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                    },
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                    {
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                        "label": "Fetch K (for MMR Search)",
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                        "name": "fetchK",
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                        "description": "Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR",
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                        "placeholder": "20",
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                        "type": "number",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-fetchK-number"
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                    },
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                    {
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                        "label": "Lambda (for MMR Search)",
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                        "name": "lambda",
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                        "description": "Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR",
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                        "placeholder": "0.5",
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                        "type": "number",
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                        "additionalParams": true,
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                        "optional": true,
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                        "id": "pinecone_0-input-lambda-number"
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                    }
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                        "label": "Document",
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                        "name": "document",
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                        "type": "Document",
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                        "list": true,
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                        "optional": true,
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                        "id": "supabase_0-input-document-Document"
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                    },
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                        "label": "Embeddings",
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                        "name": "embeddings",
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                        "type": "Embeddings",
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                        "id": "supabase_0-input-embeddings-Embeddings"
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                    }
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                ],
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                "inputs": {
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                    "document": "",
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                    "embeddings": "{{openAIEmbeddings_0.data.instance}}",
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                    "supabaseProjUrl": "",
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                    "tableName": "",
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                    "queryName": "",
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                    "supabaseMetadataFilter": "",
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                    "topK": "",
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                    "searchType": "similarity",
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                    "fetchK": "",
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                    "lambda": ""
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                },
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                "outputAnchors": [
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                    {
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                        "name": "output",
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                        "label": "Output",
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                        "type": "options",
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                        "options": [
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                            {
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                                "id": "supabase_0-output-retriever-Supabase|VectorStoreRetriever|BaseRetriever",
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                                "name": "retriever",
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                                "label": "Supabase Retriever",
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                                "type": "Supabase | VectorStoreRetriever | BaseRetriever"
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                            },
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                            {
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                                "id": "supabase_0-output-vectorStore-Supabase|VectorStore",
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                                "name": "vectorStore",
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                                "label": "Supabase Vector Store",
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                                "type": "Supabase | VectorStore"
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                            }
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                        ],
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                        "default": "retriever"
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                    }
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                ],
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                    "output": "vectorStore"
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            "target": "multiRetrievalQAChain_0",
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