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revert vector and graph use local data(single process)

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This commit is contained in:
yangdx
2025-02-28 01:14:25 +08:00
parent db2a902fcb
commit 291e0c1b14
4 changed files with 287 additions and 443 deletions
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278
lightrag/kg/faiss_impl.py
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@@ -10,19 +10,12 @@ import pipmaster as pm
from lightrag.utils import logger, compute_mdhash_id
from lightrag.base import BaseVectorStorage
from .shared_storage import (
get_namespace_data,
get_storage_lock,
get_namespace_object,
is_multiprocess,
try_initialize_namespace,
)
if not pm.is_installed("faiss"):
pm.install("faiss")
import faiss # type: ignore
from threading import Lock as ThreadLock
@final
@dataclass
@@ -51,35 +44,29 @@ class FaissVectorDBStorage(BaseVectorStorage):
self._max_batch_size = self.global_config["embedding_batch_num"]
# Embedding dimension (e.g. 768) must match your embedding function
self._dim = self.embedding_func.embedding_dim
self._storage_lock = get_storage_lock()
self._storage_lock = ThreadLock()
# check need_init must before get_namespace_object/get_namespace_data
need_init = try_initialize_namespace("faiss_indices")
self._index = get_namespace_object("faiss_indices")
self._id_to_meta = get_namespace_data("faiss_meta")
# Create an empty Faiss index for inner product (useful for normalized vectors = cosine similarity).
# If you have a large number of vectors, you might want IVF or other indexes.
# For demonstration, we use a simple IndexFlatIP.
self._index = faiss.IndexFlatIP(self._dim)
# Keep a local store for metadata, IDs, etc.
# Maps <int faiss_id> → metadata (including your original ID).
self._id_to_meta = {}
# Attempt to load an existing index + metadata from disk
with self._storage_lock:
self._load_faiss_index()
if need_init:
if is_multiprocess:
# Create an empty Faiss index for inner product (useful for normalized vectors = cosine similarity).
# If you have a large number of vectors, you might want IVF or other indexes.
# For demonstration, we use a simple IndexFlatIP.
self._index.value = faiss.IndexFlatIP(self._dim)
# Keep a local store for metadata, IDs, etc.
# Maps <int faiss_id> → metadata (including your original ID).
self._id_to_meta.update({})
# Attempt to load an existing index + metadata from disk
self._load_faiss_index()
else:
self._index = faiss.IndexFlatIP(self._dim)
self._id_to_meta.update({})
self._load_faiss_index()
def _get_index(self):
"""
Helper method to get the correct index object based on multiprocess mode.
Returns the actual index object that can be used for operations.
"""
return self._index.value if is_multiprocess else self._index
"""Check if the shtorage should be reloaded"""
return self._index
async def index_done_callback(self) -> None:
with self._storage_lock:
self._save_faiss_index()
async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
"""
@@ -134,34 +121,33 @@ class FaissVectorDBStorage(BaseVectorStorage):
# Normalize embeddings for cosine similarity (in-place)
faiss.normalize_L2(embeddings)
with self._storage_lock:
# Upsert logic:
# 1. Identify which vectors to remove if they exist
# 2. Remove them
# 3. Add the new vectors
existing_ids_to_remove = []
for meta, emb in zip(list_data, embeddings):
faiss_internal_id = self._find_faiss_id_by_custom_id(meta["__id__"])
if faiss_internal_id is not None:
existing_ids_to_remove.append(faiss_internal_id)
# Upsert logic:
# 1. Identify which vectors to remove if they exist
# 2. Remove them
# 3. Add the new vectors
existing_ids_to_remove = []
for meta, emb in zip(list_data, embeddings):
faiss_internal_id = self._find_faiss_id_by_custom_id(meta["__id__"])
if faiss_internal_id is not None:
existing_ids_to_remove.append(faiss_internal_id)
if existing_ids_to_remove:
self._remove_faiss_ids(existing_ids_to_remove)
if existing_ids_to_remove:
self._remove_faiss_ids(existing_ids_to_remove)
# Step 2: Add new vectors
index = self._get_index()
start_idx = index.ntotal
index.add(embeddings)
# Step 2: Add new vectors
index = self._get_index()
start_idx = index.ntotal
index.add(embeddings)
# Step 3: Store metadata + vector for each new ID
for i, meta in enumerate(list_data):
fid = start_idx + i
# Store the raw vector so we can rebuild if something is removed
meta["__vector__"] = embeddings[i].tolist()
self._id_to_meta.update({fid: meta})
# Step 3: Store metadata + vector for each new ID
for i, meta in enumerate(list_data):
fid = start_idx + i
# Store the raw vector so we can rebuild if something is removed
meta["__vector__"] = embeddings[i].tolist()
self._id_to_meta.update({fid: meta})
logger.info(f"Upserted {len(list_data)} vectors into Faiss index.")
return [m["__id__"] for m in list_data]
logger.info(f"Upserted {len(list_data)} vectors into Faiss index.")
return [m["__id__"] for m in list_data]
async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
"""
@@ -177,57 +163,54 @@ class FaissVectorDBStorage(BaseVectorStorage):
)
# Perform the similarity search
with self._storage_lock:
distances, indices = self._get_index().search(embedding, top_k)
distances, indices = self._get_index().search(embedding, top_k)
distances = distances[0]
indices = indices[0]
distances = distances[0]
indices = indices[0]
results = []
for dist, idx in zip(distances, indices):
if idx == -1:
# Faiss returns -1 if no neighbor
continue
results = []
for dist, idx in zip(distances, indices):
if idx == -1:
# Faiss returns -1 if no neighbor
continue
# Cosine similarity threshold
if dist < self.cosine_better_than_threshold:
continue
# Cosine similarity threshold
if dist < self.cosine_better_than_threshold:
continue
meta = self._id_to_meta.get(idx, {})
results.append(
{
**meta,
"id": meta.get("__id__"),
"distance": float(dist),
"created_at": meta.get("__created_at__"),
}
)
meta = self._id_to_meta.get(idx, {})
results.append(
{
**meta,
"id": meta.get("__id__"),
"distance": float(dist),
"created_at": meta.get("__created_at__"),
}
)
return results
return results
@property
def client_storage(self):
# Return whatever structure LightRAG might need for debugging
with self._storage_lock:
return {"data": list(self._id_to_meta.values())}
return {"data": list(self._id_to_meta.values())}
async def delete(self, ids: list[str]):
"""
Delete vectors for the provided custom IDs.
"""
logger.info(f"Deleting {len(ids)} vectors from {self.namespace}")
with self._storage_lock:
to_remove = []
for cid in ids:
fid = self._find_faiss_id_by_custom_id(cid)
if fid is not None:
to_remove.append(fid)
to_remove = []
for cid in ids:
fid = self._find_faiss_id_by_custom_id(cid)
if fid is not None:
to_remove.append(fid)
if to_remove:
self._remove_faiss_ids(to_remove)
logger.debug(
f"Successfully deleted {len(to_remove)} vectors from {self.namespace}"
)
if to_remove:
self._remove_faiss_ids(to_remove)
logger.debug(
f"Successfully deleted {len(to_remove)} vectors from {self.namespace}"
)
async def delete_entity(self, entity_name: str) -> None:
entity_id = compute_mdhash_id(entity_name, prefix="ent-")
@@ -239,23 +222,18 @@ class FaissVectorDBStorage(BaseVectorStorage):
Delete relations for a given entity by scanning metadata.
"""
logger.debug(f"Searching relations for entity {entity_name}")
with self._storage_lock:
relations = []
for fid, meta in self._id_to_meta.items():
if (
meta.get("src_id") == entity_name
or meta.get("tgt_id") == entity_name
):
relations.append(fid)
relations = []
for fid, meta in self._id_to_meta.items():
if (
meta.get("src_id") == entity_name
or meta.get("tgt_id") == entity_name
):
relations.append(fid)
logger.debug(f"Found {len(relations)} relations for {entity_name}")
if relations:
self._remove_faiss_ids(relations)
logger.debug(f"Deleted {len(relations)} relations for {entity_name}")
async def index_done_callback(self) -> None:
with self._storage_lock:
self._save_faiss_index()
logger.debug(f"Found {len(relations)} relations for {entity_name}")
if relations:
self._remove_faiss_ids(relations)
logger.debug(f"Deleted {len(relations)} relations for {entity_name}")
# --------------------------------------------------------------------------------
# Internal helper methods
@@ -265,11 +243,10 @@ class FaissVectorDBStorage(BaseVectorStorage):
"""
Return the Faiss internal ID for a given custom ID, or None if not found.
"""
with self._storage_lock:
for fid, meta in self._id_to_meta.items():
if meta.get("__id__") == custom_id:
return fid
return None
for fid, meta in self._id_to_meta.items():
if meta.get("__id__") == custom_id:
return fid
return None
def _remove_faiss_ids(self, fid_list):
"""
@@ -277,48 +254,42 @@ class FaissVectorDBStorage(BaseVectorStorage):
Because IndexFlatIP doesn't support 'removals',
we rebuild the index excluding those vectors.
"""
keep_fids = [fid for fid in self._id_to_meta if fid not in fid_list]
# Rebuild the index
vectors_to_keep = []
new_id_to_meta = {}
for new_fid, old_fid in enumerate(keep_fids):
vec_meta = self._id_to_meta[old_fid]
vectors_to_keep.append(vec_meta["__vector__"]) # stored as list
new_id_to_meta[new_fid] = vec_meta
with self._storage_lock:
keep_fids = [fid for fid in self._id_to_meta if fid not in fid_list]
# Rebuild the index
vectors_to_keep = []
new_id_to_meta = {}
for new_fid, old_fid in enumerate(keep_fids):
vec_meta = self._id_to_meta[old_fid]
vectors_to_keep.append(vec_meta["__vector__"]) # stored as list
new_id_to_meta[new_fid] = vec_meta
# Re-init index
new_index = faiss.IndexFlatIP(self._dim)
# Re-init index
self._index = faiss.IndexFlatIP(self._dim)
if vectors_to_keep:
arr = np.array(vectors_to_keep, dtype=np.float32)
new_index.add(arr)
if is_multiprocess:
self._index.value = new_index
else:
self._index = new_index
self._index.add(arr)
self._id_to_meta = new_id_to_meta
self._id_to_meta.update(new_id_to_meta)
def _save_faiss_index(self):
"""
Save the current Faiss index + metadata to disk so it can persist across runs.
"""
with self._storage_lock:
faiss.write_index(
self._get_index(),
self._faiss_index_file,
)
faiss.write_index(self._index, self._faiss_index_file)
# Save metadata dict to JSON. Convert all keys to strings for JSON storage.
# _id_to_meta is { int: { '__id__': doc_id, '__vector__': [float,...], ... } }
# We'll keep the int -> dict, but JSON requires string keys.
serializable_dict = {}
for fid, meta in self._id_to_meta.items():
serializable_dict[str(fid)] = meta
# Save metadata dict to JSON. Convert all keys to strings for JSON storage.
# _id_to_meta is { int: { '__id__': doc_id, '__vector__': [float,...], ... } }
# We'll keep the int -> dict, but JSON requires string keys.
serializable_dict = {}
for fid, meta in self._id_to_meta.items():
serializable_dict[str(fid)] = meta
with open(self._meta_file, "w", encoding="utf-8") as f:
json.dump(serializable_dict, f)
with open(self._meta_file, "w", encoding="utf-8") as f:
json.dump(serializable_dict, f)
def _load_faiss_index(self):
"""
@@ -331,31 +302,22 @@ class FaissVectorDBStorage(BaseVectorStorage):
try:
# Load the Faiss index
loaded_index = faiss.read_index(self._faiss_index_file)
if is_multiprocess:
self._index.value = loaded_index
else:
self._index = loaded_index
self._index = faiss.read_index(self._faiss_index_file)
# Load metadata
with open(self._meta_file, "r", encoding="utf-8") as f:
stored_dict = json.load(f)
# Convert string keys back to int
self._id_to_meta.update({})
self._id_to_meta = {}
for fid_str, meta in stored_dict.items():
fid = int(fid_str)
self._id_to_meta[fid] = meta
logger.info(
f"Faiss index loaded with {loaded_index.ntotal} vectors from {self._faiss_index_file}"
f"Faiss index loaded with {self._index.ntotal} vectors from {self._faiss_index_file}"
)
except Exception as e:
logger.error(f"Failed to load Faiss index or metadata: {e}")
logger.warning("Starting with an empty Faiss index.")
new_index = faiss.IndexFlatIP(self._dim)
if is_multiprocess:
self._index.value = new_index
else:
self._index = new_index
self._id_to_meta.update({})
self._index = faiss.IndexFlatIP(self._dim)
self._id_to_meta = {}