generic-llm-memorizer 0.2.0

A library for managing LLM conversations and context

Generic LLM Memorizer

A library for AI agent long-term memory. Provides two tools — memorize and search — that any agent (pydantic-ai, Google ADK, etc.) can use to store and retrieve conversational knowledge.

Features

• Fact Extraction — Automatically extracts relevant facts from conversations (preferences, skills, context, events, demographics)
• User Profile — Builds and maintains a user profile (name, age, hobbies, occupation)
• Conversation Summary — Generates concise summaries of each conversation
• SKOS Knowledge Base — Stores ontological concepts following the SKOS standard
• Semantic Search — Hybrid search combining:
  • Dense vector search (cosine similarity on embeddings) — requires optional rag install
  • Keyword search (TF-IDF) — built-in
  • LLM reranking — re-ranks top results by semantic relevance
• Persistence — JSON files, one directory per user (pluggable StorageBackend)

Installation

uv sync
# Optional: enable RAG with local sentence-transformers
uv sync --group rag

Quick Start

import asyncio
from pydantic import BaseModel
from generic_llm_memorizer import Memorizer

# Your LLM call function (OpenAI, Ollama, Anthropic, etc.)
async def my_llm_call(prompt: str, system_prompt: str, response_model: type[BaseModel]):
    # Call your LLM API and return an instance of response_model
    ...

async def main():
    mem = Memorizer(user_id="user_123", llm_call=my_llm_call)

    # Tool 1: memorize a conversation
    result = await mem.memorize(
        conversation=[
            "User: Bonjour, je m'appelle Marie et je suis développeuse Python",
            "Assistant: Enchantée Marie !",
        ],
        session_id="session_1",
    )
    print(f"Facts added: {result.facts_added}")
    print(f"Profile updated: {result.profile_updated}")
    print(f"Summary: {result.summary}")

    # Tool 2: search stored memory
    search_result = await mem.search("What does the user do?")
    print(search_result.to_context_string())

asyncio.run(main())

With RAG (Dense Vector Search)

pip install generic-llm-memorizer[rag]

from generic_llm_memorizer import Memorizer
from generic_llm_memorizer.embeddings import SentenceTransformerEmbeddings

mem = Memorizer(
    user_id="user_123",
    llm_call=my_llm_call,
    embedder=SentenceTransformerEmbeddings(),
)
# memorize() will now also build a vector index
# search() will use hybrid vector + keyword matching

With a custom embedding API

from generic_llm_memorizer.embeddings import OpenAIEmbeddings

async def my_embed_fn(texts: list[str]) -> list[list[float]]:
    # call your embedding API
    ...

mem = Memorizer(
    user_id="user_123",
    llm_call=my_llm_call,
    embedder=OpenAIEmbeddings(embed_fn=my_embed_fn),
)

API

Memorizer

Memorizer(
    user_id: str,
    llm_call: LLMCall,
    store: StorageBackend | None = None,       # defaults to FileStorageBackend
    system_prompt: str | None = None,
    batch_size: int = 10,
    max_batches: int = 3,
    embedder: EmbeddingProvider | None = None,  # enables RAG vector search
)

memorize(conversation, session_id) -> MemorizeResult

Extract facts, profile, summary, and SKOS concepts from a conversation.

• conversation: list[str] — Messages in "role: content" format
• session_id: str — Session identifier for persistence
• Returns MemorizeResult(facts_added, profile_updated, summary, skos_concepts_added)

search(query, scope=ALL, top_k=10, use_llm_rerank=True) -> SearchResult

Search stored memory. Uses hybrid vector + keyword + optional LLM rerank.

• query: str — Natural language query
• scope: SearchScope — FACTS, SKOS, PROFILE, or ALL
• top_k: int — Max results to return
• use_llm_rerank: bool — Whether to rerank with the LLM
• Returns SearchResult(facts, skos_concepts, user_profile) with to_context_string()

Storage

from generic_llm_memorizer import FileStorageBackend

store = FileStorageBackend(base_path="./my_memories")
mem = Memorizer(user_id="user_123", llm_call=my_llm_call, store=store)

Persisted to ./my_memories/{user_id}/:

• memory.json — User profile and facts
• skos_knowledge_db.json — SKOS ontology
• {session_id}/conversation.json — Conversation history

RAG (Optional)

Provider	Install	Class
Sentence Transformers (local)	pip install generic-llm-memorizer[rag]	SentenceTransformerEmbeddings
OpenAI / any API	(included)	OpenAIEmbeddings

Architecture

Memorizer
├── memorize()
│   ├── _extract_facts()      → LLM → Facts
│   ├── _extract_user_profile() → LLM → User
│   ├── _summarize()          → LLM → Summary
│   ├── _build_skos()         → LLM → SkosKnowledgeDatabase
│   └── persist + rebuild indices
│
├── search()
│   ├── VectorIndex.search()  → cosine similarity on embeddings (RAG)
│   ├── MemoryIndex.search()  → TF-IDF keyword matching
│   ├── _merge_search_results() → dedup + merge
│   └── LLM rerank (optional)
│
├── FileStorageBackend        → JSON files per user
└── EmbeddingProvider (RAG)   → SentenceTransformer / OpenAI / custom

Development

# Run all unit tests
pytest

# Run with coverage
pytest --cov=generic_llm_memorizer

# Run integration tests (requires Ollama with gemma4:26b)
pytest tests/test_with_agent.py -v

Dependencies

• Python 3.12+
• pydantic
• scikit-learn
• sentence-transformers (optional, for RAG)