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memory_vector.py
ChromaDB-backed vector store for memory entries.
Shares the EmbeddingClient with RAG to save memory.
Stores pre-computed embeddings (ChromaDB does not manage embedding).
"""
import logging
from typing import List, Dict, Optional
logger = logging.getLogger(__name__)
class MemoryVectorStore:
"""Vector index over memory entries for semantic retrieval."""
COLLECTION_NAME = "odysseus_memories"
def __init__(self, data_dir: str, embedding_model=None):
self._model = embedding_model
self._collection = None
self._healthy = False
self._initialize()
def _initialize(self):
try:
from src.chroma_client import get_chroma_client
if self._model is None:
from src.embeddings import get_embedding_client
self._model = get_embedding_client()
if self._model is None:
raise RuntimeError("No embedding backend available")
logger.info(f"MemoryVectorStore using embeddings: {self._model.url}")
client = get_chroma_client()
self._collection = client.get_or_create_collection(
name=self.COLLECTION_NAME,
metadata={"hnsw:space": "cosine"},
)
self._healthy = True
count = self._collection.count()
logger.info(f"MemoryVectorStore ready (entries={count})")
except Exception as e:
logger.error(f"MemoryVectorStore init failed: {e}")
@property
def healthy(self) -> bool:
return self._healthy
def _embed(self, texts: List[str]) -> List[List[float]]:
vecs = self._model.encode(texts, normalize_embeddings=True)
return vecs.tolist()
def count(self) -> int:
"""Return the number of stored vectors."""
if not self._healthy:
return 0
return self._collection.count()
def add(self, memory_id: str, text: str):
"""Add a single memory entry to the vector index."""
if not self._healthy:
return
# Skip if already exists
existing = self._collection.get(ids=[memory_id])
if existing["ids"]:
return
embeddings = self._embed([text])
self._collection.add(
ids=[memory_id],
embeddings=embeddings,
documents=[text],
metadatas=[{"source": "memory"}],
)
def remove(self, memory_id: str):
"""Remove a memory entry. O(1) — no rebuild needed."""
if not self._healthy:
return
try:
self._collection.delete(ids=[memory_id])
except Exception as e:
logger.warning(f"memory remove {memory_id}: {e}")
def search(self, query: str, k: int = 8) -> List[Dict]:
"""Search for the most relevant memory IDs by semantic similarity.
Returns list of {"memory_id": str, "score": float}.
ChromaDB cosine distance = 1 - cosine_similarity.
We convert back: similarity = 1.0 - distance.
"""
if not self._healthy or self._collection.count() == 0:
return []
embeddings = self._embed([query])
actual_k = min(k, self._collection.count())
results = self._collection.query(
query_embeddings=embeddings,
n_results=actual_k,
)
out = []
for idx, mid in enumerate(results["ids"][0]):
distance = results["distances"][0][idx]
out.append({
"memory_id": mid,
"score": round(1.0 - distance, 4),
})
return out
def find_similar(self, text: str, threshold: float = 0.92) -> Optional[str]:
"""Check if a near-duplicate exists. Returns memory_id if found, else None."""
if not self._healthy or self._collection.count() == 0:
return None
embeddings = self._embed([text])
results = self._collection.query(
query_embeddings=embeddings,
n_results=1,
)
if results["ids"][0]:
distance = results["distances"][0][0]
similarity = 1.0 - distance
if similarity >= threshold:
return results["ids"][0][0]
return None
def rebuild(self, memories: List[Dict]):
"""Rebuild the entire index from a list of memory entries.
Each entry must have 'id' and 'text' keys."""
if not self._healthy:
return
from src.chroma_client import get_chroma_client
# Delete and recreate collection for a clean rebuild
client = get_chroma_client()
try:
client.delete_collection(self.COLLECTION_NAME)
except Exception:
pass
self._collection = client.get_or_create_collection(
name=self.COLLECTION_NAME,
metadata={"hnsw:space": "cosine"},
)
texts = []
ids = []
for mem in memories:
text = mem.get("text", "").strip()
mid = mem.get("id", "")
if text and mid:
texts.append(text)
ids.append(mid)
if texts:
# Batch in chunks of 100 to avoid oversized requests
for i in range(0, len(texts), 100):
batch_texts = texts[i:i + 100]
batch_ids = ids[i:i + 100]
embeddings = self._embed(batch_texts)
self._collection.add(
ids=batch_ids,
embeddings=embeddings,
documents=batch_texts,
metadatas=[{"source": "memory"}] * len(batch_ids),
)
logger.info(f"MemoryVectorStore rebuilt with {len(ids)} entries")
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