VecSearch Flow

The VecSearch flow implements a Retrieval-Augmented Generation (RAG) pipeline with conditional nodes for query rephrasing, store discovery, retrieval, and document grading. It is exposed through the AgentSpec REST API under the name vecsearch_flow.

flowchart TD
    prompt["Fetch system prompt (optimizer_vs-tools-default)"] --> build["Build AgentSpec Flow"]
    build --> load["Load into runtime"]
    load --> session["Create flow session"]
    session --> input["User query"]
    input --> rephrase{"Rephrase enabled?"}
    rephrase -->|Yes| rephrase_node["Rephrase query (optimizer_vs-rephrase)"]
    rephrase -->|No| discovery
    rephrase_node --> discovery{"Discovery enabled?"}
    discovery -->|Yes| discovery_node["Discover vector stores (optimizer_vs-discovery)"]
    discovery -->|No| retriever
    discovery_node --> retriever["Retrieve documents (optimizer_vs-retriever)"]
    retriever --> grade{"Grading enabled?"}
    grade -->|Yes| grade_node["Grade relevance (optimizer_vs-grade)"]
    grade -->|No| format
    grade_node --> format["LLM generates answer from retrieved documents"]
    format --> finish["Return answer"]

• The system prompt is fetched from the MCP server (optimizer_vs-tools-default). If unavailable, a default instruction is used.
• build_vecsearch_flow creates a portable AgentSpec Flow with conditional nodes based on the user’s vector search settings (rephrase, discovery, grade).
• Rephrase (optimizer_vs-rephrase) rewrites the user question using conversation history to improve retrieval quality.
• Discovery (optimizer_vs-discovery) lists available vector stores when AutoRAG is enabled, allowing the LLM to select the most relevant store.
• Retriever (optimizer_vs-retriever) performs the core vector similarity search and always runs.
• Grade (optimizer_vs-grade) filters retrieved documents for relevance before answer generation.
• The final LLM node generates the answer using the system prompt and the retrieved (or graded) documents.
• Unlike NL2SQL, VecSearch does not require a database connection name — it operates entirely through vector search MCP tools.