Vector Store Integration

Query external vector stores as part of multi-source RAG or from inside Flow Designer workflows.

Findable provides two paths for querying external vector stores:

1. Vector Retriever data source (type: 'vectorretriever' in dataSources[]) — configure directly on a chat to query a vector store as part of multi-source RAG
2. Flow Designer tool nodes (integrationType: 'vector_store') — use a vector store as a tool inside a Flow Designer workflow

Both approaches share the same IDataConnection registry for managing credentials and endpoints.

Supported Providers

Tier 1 — High Priority (DataPlatformType values in IDataConnection):

Provider	Platform Type	Description
Pinecone	pinecone	Managed vector database
pgvector	pgvector	PostgreSQL vector extension
Weaviate	weaviate	Open-source vector database
Qdrant	qdrant	High-performance vector similarity engine
Chroma	chroma	Open-source embedding database
Elasticsearch	elasticsearch	Full-text + vector search
OpenSearch	opensearch	AWS-managed search and analytics
Redis	redis	In-memory vector search

Tier 2 — Enterprise/Cloud (VECTOR_STORE_PROVIDER in Flow Designer):

Oracle Vector, IBM watsonx, AWS Kendra, Vertex AI Search, Milvus, MongoDB Atlas

Tier 3 — Specialized:

Vespa, LanceDB, FAISS, Marqo

Data Connection Fields (Vector Stores)

Vector store connections are managed via the unified IDataConnection interface in the dataconnections Cosmos container. When type is a vector store platform, these additional fields apply:

Field	Type	Description
index	string?	Index / collection / class name (Pinecone index, Weaviate class, Qdrant collection)
namespace	string?	Namespace / partition (Pinecone namespace, Redis prefix)
verifySSL	boolean?	Verify SSL certificates (default: true)
maxRetries	number?	Maximum retry attempts
connectionString	string?	Full connection string (alternative to individual fields)

Authentication types vary by provider — see getAuthTypesForPlatform() in dataConnectionTypes.ts.

Vector Retriever Data Source

To query a vector store directly from a chat’s RAG pipeline, add a vectorretriever entry to the chat’s dataSources[]:

Field	Type	Description
type	'vectorretriever'	Identifies this as a vector retriever source
vectorConnectionId	string	FK to IDataConnection in the data connections registry
vectorTopK	number?	Number of results (default: 5)
vectorSearchType	'vector' | 'semantic' | 'hybrid' | 'keyword'	Search strategy
vectorMinScore	number?	Minimum similarity score (0–1)

The orchestrator queries the vector store in parallel with other data sources and merges results into the RAG context.

Flow Designer Vector Store Config (IVectorStoreConfig)

For Flow Designer tool nodes with integrationType: 'vector_store', the configuration has three parts:

Connection (IVectorStoreConnectionConfig):

• endpoint, apiKey, index, environment, projectId, region
• SSL/TLS, connection pool settings

Search (IVectorStoreSearchConfig):

• topK, minScore, searchType (vector / semantic / hybrid / keyword)
• similarityMetric (cosine / euclidean / dotProduct / l2)
• Metadata filters, namespace filters
• Reranking support (enableReranking, rerankingModel)
• Query expansion (enableQueryExpansion, expansionPrompt)
• Hybrid weights (keywordWeight, vectorWeight)

Embedding (optional):

• modelEndpointId — FK to AI model endpoint for query vectorization
• embeddingDimensions, batchSize

See Vector Store Quick Reference and Provider Template for detailed setup.