mirror of
https://github.com/tkenaz/breathe-memory.git
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4e671a0a83
Context optimization and associative memory for LLM applications. Two-phase system: SYNAPSE (pre-generation memory injection) + GraphCompactor (structured context compression). - Interface-based, storage-agnostic, LLM-agnostic - Memory Nexus: PostgreSQL + pgvector reference backend - Zero mandatory dependencies beyond stdlib - 28 tests passing, clean install verified Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
"""
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Full integration — BREATHE + Memory Nexus with PostgreSQL.
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Prerequisites:
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pip install breathe-memory[pg]
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createdb breathe_demo
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psql breathe_demo < schema.sql # see memory_nexus/postgres.py for DDL
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Run with: python examples/with_postgres.py
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"""
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import asyncio
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import os
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from breathe import Synapse, BreatheConfig
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from breathe.config import ENGLISH
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from memory_nexus import PostgresMemoryStore
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DSN = os.environ.get("DATABASE_URL", "postgresql://localhost/breathe_demo")
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async def main():
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# Initialize Memory Nexus store
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# Default: all-MiniLM-L6-v2 (384-dim, fast, good for prototyping)
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# Production: model_name="intfloat/multilingual-e5-large" (1024-dim)
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# — remember to create your table with vector(1024) instead of vector(384)
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store = PostgresMemoryStore(
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dsn=DSN,
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model_name="sentence-transformers/all-MiniLM-L6-v2",
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min_similarity=0.55,
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)
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await store.initialize()
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# Store some memories
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await store.store(
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"FastAPI is a modern Python web framework for building APIs with automatic OpenAPI docs.",
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metadata=None,
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)
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await store.store(
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"PostgreSQL with pgvector extension enables vector similarity search in SQL.",
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metadata=None,
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)
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await store.store(
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"Redis is an in-memory data structure store used for caching and pub/sub.",
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metadata=None,
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)
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# Initialize SYNAPSE — store acts as both VectorSearchClient and can be
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# wrapped in a MemoryRepository adapter for graph BFS
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config = BreatheConfig(
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language_packs=[ENGLISH],
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default_language="en",
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min_similarity=0.55,
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)
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synapse = Synapse(
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vector_client=store, # Memory Nexus as semantic search backend
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config=config,
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enable_model=False,
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)
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await synapse.initialize()
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "What should I use for caching in FastAPI?"},
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]
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print("Query:", messages[-1]["content"])
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print()
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enriched = await synapse.inject(messages)
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print("Enriched message:")
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print(enriched[-1]["content"])
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print()
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# Search memories directly
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results = await store.search("caching database", limit=3)
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print(f"Direct search results ({len(results)} found):")
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for node in results:
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print(f" [{node.importance:.2f}] {node.summary[:80]}...")
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await store.close()
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if __name__ == "__main__":
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asyncio.run(main())
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