The VectorMemorySearch Node

The VectorMemorySearch Node

This guide provides a comprehensive, code-validated overview of the VectorMemorySearch node. It details the node's precise execution logic, properties, and best practices for implementing powerful Retrieval-Augmented Generation (RAG).

Core Purpose

The VectorMemorySearch node is the primary tool for RAG in WilmerAI. It performs a powerful, relevance-based keyword search against a discussion's dedicated vector memory database. This allows an agent to instantly retrieve specific facts, topics, or details from any point in a long conversation's history to inform its next response.

Note: This node is stateful and requires an active [DiscussionId] in the conversation to function. Without one, it will return the error message "Cannot perform VectorMemorySearch without a discussionId." and will not perform a search.

Example: "[DiscussionId]my_chat_2123[/DiscussionId] How are you?" That discussionid can be anywhere in the conversation. Wilmer will find it and strip it out, then utilitize it. In this case, your discussionid is my_chat_2123.

Internal Execution Flow

1. Input Parsing: The node takes the input string and splits it into a list of keywords using the semicolon (;) as a delimiter.
2. Query Sanitization: Each keyword is individually sanitized to be safe for a database full-text search query.
3. Query Construction: The sanitized keywords are joined with OR logic to form a single query.
4. Database Execution: The query is executed against the memories_fts virtual table in the discussion's SQLite database.
5. Ranking and Retrieval: The database uses the bm25 algorithm to score results by relevance. It retrieves the memory_text column, which contains the LLM-generated summary of the original conversation chunk.
6. Result Aggregation: The summaries from the top limit results are joined into a single string, separated by \n\n---\n\n.

Data Flow

• Direct Output ({agent#Output}): The node always returns the aggregated string of relevant memory summaries as its standard output. If the VectorMemorySearch is the first node, this string is available to subsequent nodes via {agent1Output}. If no memories are found, it returns the string: "No relevant memories found in the vector database for the given keywords."

Node Properties

Workflow Strategy and Annotated Example

Place this node early in a workflow to gather relevant facts before the main response generation node. This allows the LLM to use the retrieved context to formulate a more informed answer.

[
  {
    "title": "Step 1: Identify key topics in the user's prompt",
    "type": "Standard",
    "prompt": "Read the user's last message: '{chat_user_prompt_last_one}'. List the key nouns, entities, and concepts as a semicolon-separated list.",
    "returnToUser": false
    // This node acts as a pre-processor to generate clean keywords for the search.
  },
  {
    "title": "Step 2: Use identified topics to search vector memory",
    "type": "VectorMemorySearch",
    "input": "{agent1Output}",
    "limit": 3
    // --- DATA GATHERING ---
    // It takes the keywords from the previous node and executes the search.
    // The result will be stored in {agent2Output}.
  },
  {
    "title": "Step 3: Respond to the user with retrieved context",
    "type": "Standard",
    "returnToUser": true,
    "systemPrompt": "You have the following memories retrieved from storage:\n<context>\n{agent2Output}\n</context>\n\nUse this context to formulate an answer to the user's last message: {chat_user_prompt_last_one}"
    // The final node uses the retrieved memories to provide a fact-based response.
  }
]