graph-rag-memory 0.1.0

A BYOK Hierarchical Graph Memory library for AI agents.

Graph-Memory

A blazingly fast, agentic memory library for Chatbots. Graph-Memory combines an L1 Short-Term Buffer with a background L2 Hierarchical Topic Graph and Quantitative JSON State.

Features

• < 200ms Retrieval Latency: Never wait for an LLM to read chat history.
• Async Graph Construction: Builds a conceptual memory graph in the background without blocking the user.
• Dual Semantic Search: Prevents "Catastrophic Misrouting" by searching against both Topic Labels and Expanded Topic Descriptions.
• Bring Your Own Key (BYOK): Use your own free local embeddings (e.g., HuggingFace) and your own LLM provider (Groq, OpenAI, Gemini) to avoid vendor lock-in and high SaaS fees.

Installation

pip install graph-rag-memory

Quick Start (BYOK)

Because this library requires no paid SaaS subscription, you must inject your own LLM and Embedding functions.

import os
from pydantic import BaseModel
from typing import Optional, Any
from groq import Groq
from sentence_transformers import SentenceTransformer
from graph_memory import MemoryClient

# 1. Define your free local embedder
embedder = SentenceTransformer('all-MiniLM-L6-v2')
def embed_func(text: str) -> list[float]:
    return embedder.encode(text).tolist()

# 2. Define your LLM generator (e.g., using Groq for blazing speed)
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

def llm_func(sys_prompt: str, user_prompt: str, schema: Optional[BaseModel] = None) -> Any:
    # If a Pydantic schema is provided, force JSON output
    # (Implementation omitted for brevity, use instructor or standard JSON mode)
    pass

# 3. Initialize the Memory Client
memory = MemoryClient(
    llm_generate=llm_func,
    embed_text=embed_func,
    use_query_expansion=True # Turn off for absolute maximum speed
)

# 4. Use it in your chatbot loop
user_id = "user_123"

# Add messages (silently batches to the Graph in the background)
memory.add_message(user_id, "user", "I bought a new Tesla Model 3 today!")

# Retrieve instant, highly accurate context for your chatbot's prompt
context = memory.retrieve_context(user_id, "What car do I drive?")

print(context['short_term_history'])
print(context['long_term_graph_context'])
print(context['global_state'])