Agent Memory & Semantic Vector Search Configuration Example

AI-generated memory architecture outline and Chroma Python sketch. It is an example to test, not a verified persistent-memory plugin.

Tags: plugin, memory, vector-db, chromadb, embeddings, context, rag

Category: productivity

Listing attribution: SimplyUtils

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Source

Repository

Agent Memory & Semantic Vector Search Configuration Example

Overview

The Agent Memory & Vector Plugin provides persistent semantic memory across coding sessions. It indexes repository architectural decisions, past bug fixes, and user preferences into a local vector database (ChromaDB or Qdrant), allowing agents to query context dynamically.

Architecture

[ Agent Prompt ] --> [ Semantic Search Query ] --> [ Local Chroma Vector Index ]
                                                                |
                                                      [ Top K Relevant Snippets ]
                                                                |
                                                      [ Injected Context ]

Plugin Usage Example (Python / Chroma)

import chromadb
from chromadb.utils import embedding_functions

client = chromadb.PersistentClient(path="./.agents/memory_db")
collection = client.get_or_create_collection(name="repo_memory")

def recall_past_decisions(query: str, top_k: int = 3):
    results = collection.query(query_texts=[query], n_results=top_k)
    return results["documents"][0]

def store_decision(doc_id: str, decision_text: str, metadata: dict):
    collection.upsert(
        documents=[decision_text],
        metadatas=[metadata],
        ids=[doc_id]
    )

Key Benefits

  • Zero Hallucination of Project Rules: Recalls exact historical rationale behind specific refactors or architecture constraints.
  • Session-to-Session Persistence: Retains context even when switching branches or restarting the IDE.