Vector operations allow you to store and retrieve vector embeddings from vector databases like Qdrant. These actions are essential for building semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG) applications.
storevector Stores vector embeddings in a vector database along with associated metadata.
Integration ID The vector database integration to store vectors in.
YAML Key integrationIDType string Required Yes
Vectors The vector data to store, typically as a JSON array of floats.
YAML Key vectorsType string Required Yes
Use {{ .generate_embedding }} to reference vectors generated by a previous action, such as an AI embedding model.
Fields Metadata fields to store alongside the vector. These fields can be used for filtering during retrieval.
YAML Key fieldsType map Required Yes
Options Additional storage options such as the collection name.
YAML Key optionsType map Required No
Common options:
collection — The collection name to store vectors in
Example actions: store_embedding: type: storevector config: integrationID: my_qdrant vectors: "{{ .generate_embedding }}" fields: content: "{{ param \"text\" }}" source: "user_input" timestamp: "{{ now }}" options: collection: documents next: response.success fail: response.error
fetchvectors Retrieves vectors from a vector database for similarity search. Returns the most similar vectors to the provided query vector.
Integration ID The vector database integration to query.
YAML Key integrationIDType string Required Yes
Vector The query vector to search for similar vectors. This is typically an embedding of the search query.
YAML Key vectorType string Required No
Options Query options such as collection name and result limit.
YAML Key optionsType map Required No
Common options:
collection — The collection name to search inlimit — Maximum number of results to return
Example actions: search_similar: type: fetchvectors config: integrationID: my_qdrant vector: "{{ .query_embedding }}" options: collection: documents limit: 10 next: response.results fail: response.error
Common Patterns RAG Pipeline A typical RAG (Retrieval-Augmented Generation) workflow combines vector search with an AI agent:
actions: embed_query: type: agent config: integrationID: my_openai userPrompt: "Generate an embedding for: {{ param \"question\" }}" next: action.search_docs search_docs: type: fetchvectors config: integrationID: my_qdrant vector: "{{ .embed_query }}" options: collection: knowledge_base limit: 5 next: action.generate_answer generate_answer: type: agent config: integrationID: my_openai systemPrompt: | Answer the user's question using only the provided context. Context: {{ .search_docs }} userPrompt: "{{ param \"question\" }}" next: response.answer
Document Ingestion Store documents with their embeddings for later retrieval:
actions: generate_embedding: type: agent config: integrationID: my_openai userPrompt: "Generate an embedding for: {{ param \"content\" }}" next: action.store_document store_document: type: storevector config: integrationID: my_qdrant vectors: "{{ .generate_embedding }}" fields: content: "{{ param \"content\" }}" title: "{{ param \"title\" }}" category: "{{ param \"category\" }}" options: collection: documents next: response.stored
Next Steps
AI Agents Use AI agents to generate embeddings and process search results.
Data Operations Combine vector search with traditional database queries.
Actions Overview Learn the fundamentals of ServFlow actions.
Configuration Reference Explore all ServFlow configuration options.
