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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
View File

@@ -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 = {}

123
lightrag/kg/nano_vector_db_impl.py
View File

@@ -11,25 +11,19 @@ from lightrag.utils import (
)
import pipmaster as pm
from lightrag.base import BaseVectorStorage
from .shared_storage import (
get_storage_lock,
get_namespace_object,
is_multiprocess,
try_initialize_namespace,
)
if not pm.is_installed("nano-vectordb"):
pm.install("nano-vectordb")
from nano_vectordb import NanoVectorDB
from threading import Lock as ThreadLock
@final
@dataclass
class NanoVectorDBStorage(BaseVectorStorage):
def __post_init__(self):
# Initialize lock only for file operations
self._storage_lock = get_storage_lock()
self._storage_lock = ThreadLock()
# Use global config value if specified, otherwise use default
kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
@@ -45,32 +39,14 @@ class NanoVectorDBStorage(BaseVectorStorage):
)
self._max_batch_size = self.global_config["embedding_batch_num"]
# check need_init must before get_namespace_object
need_init = try_initialize_namespace(self.namespace)
self._client = get_namespace_object(self.namespace)
if need_init:
if is_multiprocess:
self._client.value = NanoVectorDB(
self.embedding_func.embedding_dim,
storage_file=self._client_file_name,
)
logger.info(
f"Initialized vector DB client for namespace {self.namespace}"
)
else:
self._client = NanoVectorDB(
self.embedding_func.embedding_dim,
storage_file=self._client_file_name,
)
logger.info(
f"Initialized vector DB client for namespace {self.namespace}"
)
with self._storage_lock:
self._client = NanoVectorDB(
self.embedding_func.embedding_dim,
storage_file=self._client_file_name,
)
def _get_client(self):
"""Get the appropriate client instance based on multiprocess mode"""
if is_multiprocess:
return self._client.value
"""Check if the shtorage should be reloaded"""
return self._client
async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
@@ -101,8 +77,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
if len(embeddings) == len(list_data):
for i, d in enumerate(list_data):
d["__vector__"] = embeddings[i]
with self._storage_lock:
results = self._get_client().upsert(datas=list_data)
results = self._get_client().upsert(datas=list_data)
return results
else:
# sometimes the embedding is not returned correctly. just log it.
@@ -115,21 +90,20 @@ class NanoVectorDBStorage(BaseVectorStorage):
embedding = await self.embedding_func([query])
embedding = embedding[0]
with self._storage_lock:
results = self._get_client().query(
query=embedding,
top_k=top_k,
better_than_threshold=self.cosine_better_than_threshold,
)
results = [
{
**dp,
"id": dp["__id__"],
"distance": dp["__metrics__"],
"created_at": dp.get("__created_at__"),
}
for dp in results
]
results = self._get_client().query(
query=embedding,
top_k=top_k,
better_than_threshold=self.cosine_better_than_threshold,
)
results = [
{
**dp,
"id": dp["__id__"],
"distance": dp["__metrics__"],
"created_at": dp.get("__created_at__"),
}
for dp in results
]
return results
@property
@@ -143,8 +117,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
ids: List of vector IDs to be deleted
"""
try:
with self._storage_lock:
self._get_client().delete(ids)
self._get_client().delete(ids)
logger.debug(
f"Successfully deleted {len(ids)} vectors from {self.namespace}"
)
@@ -158,37 +131,35 @@ class NanoVectorDBStorage(BaseVectorStorage):
f"Attempting to delete entity {entity_name} with ID {entity_id}"
)
with self._storage_lock:
# Check if the entity exists
if self._get_client().get([entity_id]):
self._get_client().delete([entity_id])
logger.debug(f"Successfully deleted entity {entity_name}")
else:
logger.debug(f"Entity {entity_name} not found in storage")
# Check if the entity exists
if self._get_client().get([entity_id]):
self._get_client().delete([entity_id])
logger.debug(f"Successfully deleted entity {entity_name}")
else:
logger.debug(f"Entity {entity_name} not found in storage")
except Exception as e:
logger.error(f"Error deleting entity {entity_name}: {e}")
async def delete_entity_relation(self, entity_name: str) -> None:
try:
with self._storage_lock:
storage = getattr(self._get_client(), "_NanoVectorDB__storage")
relations = [
dp
for dp in storage["data"]
if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name
]
logger.debug(
f"Found {len(relations)} relations for entity {entity_name}"
)
ids_to_delete = [relation["__id__"] for relation in relations]
storage = getattr(self._get_client(), "_NanoVectorDB__storage")
relations = [
dp
for dp in storage["data"]
if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name
]
logger.debug(
f"Found {len(relations)} relations for entity {entity_name}"
)
ids_to_delete = [relation["__id__"] for relation in relations]
if ids_to_delete:
self._get_client().delete(ids_to_delete)
logger.debug(
f"Deleted {len(ids_to_delete)} relations for {entity_name}"
)
else:
logger.debug(f"No relations found for entity {entity_name}")
if ids_to_delete:
self._get_client().delete(ids_to_delete)
logger.debug(
f"Deleted {len(ids_to_delete)} relations for {entity_name}"
)
else:
logger.debug(f"No relations found for entity {entity_name}")
except Exception as e:
logger.error(f"Error deleting relations for {entity_name}: {e}")

268
lightrag/kg/networkx_impl.py
View File

@@ -6,12 +6,6 @@ import numpy as np
from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
from lightrag.utils import logger
from lightrag.base import BaseGraphStorage
from .shared_storage import (
get_storage_lock,
get_namespace_object,
is_multiprocess,
try_initialize_namespace,
)
import pipmaster as pm
@@ -23,7 +17,7 @@ if not pm.is_installed("graspologic"):
import networkx as nx
from graspologic import embed
from threading import Lock as ThreadLock
@final
@dataclass
@@ -78,38 +72,23 @@ class NetworkXStorage(BaseGraphStorage):
self._graphml_xml_file = os.path.join(
self.global_config["working_dir"], f"graph_{self.namespace}.graphml"
)
self._storage_lock = get_storage_lock()
# check need_init must before get_namespace_object
need_init = try_initialize_namespace(self.namespace)
self._graph = get_namespace_object(self.namespace)
if need_init:
if is_multiprocess:
preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
self._graph.value = preloaded_graph or nx.Graph()
if preloaded_graph:
logger.info(
f"Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
)
else:
preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
self._graph = preloaded_graph or nx.Graph()
if preloaded_graph:
logger.info(
f"Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
)
self._storage_lock = ThreadLock()
with self._storage_lock:
preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
if preloaded_graph is not None:
logger.info(
f"Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
)
else:
logger.info("Created new empty graph")
self._graph = preloaded_graph or nx.Graph()
self._node_embed_algorithms = {
"node2vec": self._node2vec_embed,
}
def _get_graph(self):
"""Get the appropriate graph instance based on multiprocess mode"""
if is_multiprocess:
return self._graph.value
"""Check if the shtorage should be reloaded"""
return self._graph
async def index_done_callback(self) -> None:
@@ -117,54 +96,44 @@ class NetworkXStorage(BaseGraphStorage):
NetworkXStorage.write_nx_graph(self._get_graph(), self._graphml_xml_file)
async def has_node(self, node_id: str) -> bool:
with self._storage_lock:
return self._get_graph().has_node(node_id)
return self._get_graph().has_node(node_id)
async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
with self._storage_lock:
return self._get_graph().has_edge(source_node_id, target_node_id)
return self._get_graph().has_edge(source_node_id, target_node_id)
async def get_node(self, node_id: str) -> dict[str, str] | None:
with self._storage_lock:
return self._get_graph().nodes.get(node_id)
return self._get_graph().nodes.get(node_id)
async def node_degree(self, node_id: str) -> int:
with self._storage_lock:
return self._get_graph().degree(node_id)
return self._get_graph().degree(node_id)
async def edge_degree(self, src_id: str, tgt_id: str) -> int:
with self._storage_lock:
return self._get_graph().degree(src_id) + self._get_graph().degree(tgt_id)
return self._get_graph().degree(src_id) + self._get_graph().degree(tgt_id)
async def get_edge(
self, source_node_id: str, target_node_id: str
) -> dict[str, str] | None:
with self._storage_lock:
return self._get_graph().edges.get((source_node_id, target_node_id))
return self._get_graph().edges.get((source_node_id, target_node_id))
async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
with self._storage_lock:
if self._get_graph().has_node(source_node_id):
return list(self._get_graph().edges(source_node_id))
return None
if self._get_graph().has_node(source_node_id):
return list(self._get_graph().edges(source_node_id))
return None
async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
with self._storage_lock:
self._get_graph().add_node(node_id, **node_data)
self._get_graph().add_node(node_id, **node_data)
async def upsert_edge(
self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
) -> None:
with self._storage_lock:
self._get_graph().add_edge(source_node_id, target_node_id, **edge_data)
self._get_graph().add_edge(source_node_id, target_node_id, **edge_data)
async def delete_node(self, node_id: str) -> None:
with self._storage_lock:
if self._get_graph().has_node(node_id):
self._get_graph().remove_node(node_id)
logger.debug(f"Node {node_id} deleted from the graph.")
else:
logger.warning(f"Node {node_id} not found in the graph for deletion.")
if self._get_graph().has_node(node_id):
self._get_graph().remove_node(node_id)
logger.debug(f"Node {node_id} deleted from the graph.")
else:
logger.warning(f"Node {node_id} not found in the graph for deletion.")
async def embed_nodes(
self, algorithm: str
@@ -175,13 +144,12 @@ class NetworkXStorage(BaseGraphStorage):
# TODO: NOT USED
async def _node2vec_embed(self):
with self._storage_lock:
graph = self._get_graph()
embeddings, nodes = embed.node2vec_embed(
graph,
**self.global_config["node2vec_params"],
)
nodes_ids = [graph.nodes[node_id]["id"] for node_id in nodes]
graph = self._get_graph()
embeddings, nodes = embed.node2vec_embed(
graph,
**self.global_config["node2vec_params"],
)
nodes_ids = [graph.nodes[node_id]["id"] for node_id in nodes]
return embeddings, nodes_ids
def remove_nodes(self, nodes: list[str]):
@@ -190,11 +158,10 @@ class NetworkXStorage(BaseGraphStorage):
Args:
nodes: List of node IDs to be deleted
"""
with self._storage_lock:
graph = self._get_graph()
for node in nodes:
if graph.has_node(node):
graph.remove_node(node)
graph = self._get_graph()
for node in nodes:
if graph.has_node(node):
graph.remove_node(node)
def remove_edges(self, edges: list[tuple[str, str]]):
"""Delete multiple edges
@@ -202,11 +169,10 @@ class NetworkXStorage(BaseGraphStorage):
Args:
edges: List of edges to be deleted, each edge is a (source, target) tuple
"""
with self._storage_lock:
graph = self._get_graph()
for source, target in edges:
if graph.has_edge(source, target):
graph.remove_edge(source, target)
graph = self._get_graph()
for source, target in edges:
if graph.has_edge(source, target):
graph.remove_edge(source, target)
async def get_all_labels(self) -> list[str]:
"""
@@ -214,10 +180,9 @@ class NetworkXStorage(BaseGraphStorage):
Returns:
[label1, label2, ...] # Alphabetically sorted label list
"""
with self._storage_lock:
labels = set()
for node in self._get_graph().nodes():
labels.add(str(node)) # Add node id as a label
labels = set()
for node in self._get_graph().nodes():
labels.add(str(node)) # Add node id as a label
# Return sorted list
return sorted(list(labels))
@@ -239,88 +204,87 @@ class NetworkXStorage(BaseGraphStorage):
seen_nodes = set()
seen_edges = set()
with self._storage_lock:
graph = self._get_graph()
graph = self._get_graph()
# Handle special case for "*" label
if node_label == "*":
# For "*", return the entire graph including all nodes and edges
subgraph = (
graph.copy()
) # Create a copy to avoid modifying the original graph
else:
# Find nodes with matching node id (partial match)
nodes_to_explore = []
for n, attr in graph.nodes(data=True):
if node_label in str(n): # Use partial matching
nodes_to_explore.append(n)
# Handle special case for "*" label
if node_label == "*":
# For "*", return the entire graph including all nodes and edges
subgraph = (
graph.copy()
) # Create a copy to avoid modifying the original graph
else:
# Find nodes with matching node id (partial match)
nodes_to_explore = []
for n, attr in graph.nodes(data=True):
if node_label in str(n): # Use partial matching
nodes_to_explore.append(n)
if not nodes_to_explore:
logger.warning(f"No nodes found with label {node_label}")
return result
if not nodes_to_explore:
logger.warning(f"No nodes found with label {node_label}")
return result
# Get subgraph using ego_graph
subgraph = nx.ego_graph(graph, nodes_to_explore[0], radius=max_depth)
# Get subgraph using ego_graph
subgraph = nx.ego_graph(graph, nodes_to_explore[0], radius=max_depth)
# Check if number of nodes exceeds max_graph_nodes
max_graph_nodes = 500
if len(subgraph.nodes()) > max_graph_nodes:
origin_nodes = len(subgraph.nodes())
node_degrees = dict(subgraph.degree())
top_nodes = sorted(
node_degrees.items(), key=lambda x: x[1], reverse=True
)[:max_graph_nodes]
top_node_ids = [node[0] for node in top_nodes]
# Create new subgraph with only top nodes
subgraph = subgraph.subgraph(top_node_ids)
logger.info(
f"Reduced graph from {origin_nodes} nodes to {max_graph_nodes} nodes (depth={max_depth})"
# Check if number of nodes exceeds max_graph_nodes
max_graph_nodes = 500
if len(subgraph.nodes()) > max_graph_nodes:
origin_nodes = len(subgraph.nodes())
node_degrees = dict(subgraph.degree())
top_nodes = sorted(
node_degrees.items(), key=lambda x: x[1], reverse=True
)[:max_graph_nodes]
top_node_ids = [node[0] for node in top_nodes]
# Create new subgraph with only top nodes
subgraph = subgraph.subgraph(top_node_ids)
logger.info(
f"Reduced graph from {origin_nodes} nodes to {max_graph_nodes} nodes (depth={max_depth})"
)
# Add nodes to result
for node in subgraph.nodes():
if str(node) in seen_nodes:
continue
node_data = dict(subgraph.nodes[node])
# Get entity_type as labels
labels = []
if "entity_type" in node_data:
if isinstance(node_data["entity_type"], list):
labels.extend(node_data["entity_type"])
else:
labels.append(node_data["entity_type"])
# Create node with properties
node_properties = {k: v for k, v in node_data.items()}
result.nodes.append(
KnowledgeGraphNode(
id=str(node), labels=[str(node)], properties=node_properties
)
)
seen_nodes.add(str(node))
# Add nodes to result
for node in subgraph.nodes():
if str(node) in seen_nodes:
continue
# Add edges to result
for edge in subgraph.edges():
source, target = edge
edge_id = f"{source}-{target}"
if edge_id in seen_edges:
continue
node_data = dict(subgraph.nodes[node])
# Get entity_type as labels
labels = []
if "entity_type" in node_data:
if isinstance(node_data["entity_type"], list):
labels.extend(node_data["entity_type"])
else:
labels.append(node_data["entity_type"])
edge_data = dict(subgraph.edges[edge])
# Create node with properties
node_properties = {k: v for k, v in node_data.items()}
result.nodes.append(
KnowledgeGraphNode(
id=str(node), labels=[str(node)], properties=node_properties
)
# Create edge with complete information
result.edges.append(
KnowledgeGraphEdge(
id=edge_id,
type="DIRECTED",
source=str(source),
target=str(target),
properties=edge_data,
)
seen_nodes.add(str(node))
# Add edges to result
for edge in subgraph.edges():
source, target = edge
edge_id = f"{source}-{target}"
if edge_id in seen_edges:
continue
edge_data = dict(subgraph.edges[edge])
# Create edge with complete information
result.edges.append(
KnowledgeGraphEdge(
id=edge_id,
type="DIRECTED",
source=str(source),
target=str(target),
properties=edge_data,
)
)
seen_edges.add(edge_id)
)
seen_edges.add(edge_id)
logger.info(
f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"

61
lightrag/kg/shared_storage.py
View File

@@ -20,15 +20,12 @@ LockType = Union[ProcessLock, ThreadLock]
_manager = None
_initialized = None
is_multiprocess = None
_global_lock: Optional[LockType] = None
# shared data for storage across processes
_shared_dicts: Optional[Dict[str, Any]] = None
_share_objects: Optional[Dict[str, Any]] = None
_init_flags: Optional[Dict[str, bool]] = None # namespace -> initialized
_global_lock: Optional[LockType] = None
def initialize_share_data(workers: int = 1):
"""
Initialize shared storage data for single or multi-process mode.
@@ -53,7 +50,6 @@ def initialize_share_data(workers: int = 1):
is_multiprocess, \
_global_lock, \
_shared_dicts, \
_share_objects, \
_init_flags, \
_initialized
@@ -72,7 +68,6 @@ def initialize_share_data(workers: int = 1):
_global_lock = _manager.Lock()
# Create shared dictionaries with manager
_shared_dicts = _manager.dict()
_share_objects = _manager.dict()
_init_flags = (
_manager.dict()
) # Use shared dictionary to store initialization flags
@@ -83,7 +78,6 @@ def initialize_share_data(workers: int = 1):
is_multiprocess = False
_global_lock = ThreadLock()
_shared_dicts = {}
_share_objects = {}
_init_flags = {}
direct_log(f"Process {os.getpid()} Shared-Data created for Single Process")
@@ -99,11 +93,7 @@ def try_initialize_namespace(namespace: str) -> bool:
global _init_flags, _manager
if _init_flags is None:
direct_log(
f"Error: try to create nanmespace before Shared-Data is initialized, pid={os.getpid()}",
level="ERROR",
)
raise ValueError("Shared dictionaries not initialized")
raise ValueError("Try to create nanmespace before Shared-Data is initialized")
if namespace not in _init_flags:
_init_flags[namespace] = True
@@ -113,43 +103,9 @@ def try_initialize_namespace(namespace: str) -> bool:
return False
def _get_global_lock() -> LockType:
return _global_lock
def get_storage_lock() -> LockType:
"""return storage lock for data consistency"""
return _get_global_lock()
def get_scan_lock() -> LockType:
"""return scan_progress lock for data consistency"""
return get_storage_lock()
def get_namespace_object(namespace: str) -> Any:
"""Get an object for specific namespace"""
if _share_objects is None:
direct_log(
f"Error: try to getnanmespace before Shared-Data is initialized, pid={os.getpid()}",
level="ERROR",
)
raise ValueError("Shared dictionaries not initialized")
lock = _get_global_lock()
with lock:
if namespace not in _share_objects:
if namespace not in _share_objects:
if is_multiprocess:
_share_objects[namespace] = _manager.Value("O", None)
else:
_share_objects[namespace] = None
direct_log(
f"Created namespace: {namespace}(type={type(_share_objects[namespace])})"
)
return _share_objects[namespace]
return _global_lock
def get_namespace_data(namespace: str) -> Dict[str, Any]:
@@ -161,7 +117,7 @@ def get_namespace_data(namespace: str) -> Dict[str, Any]:
)
raise ValueError("Shared dictionaries not initialized")
lock = _get_global_lock()
lock = get_storage_lock()
with lock:
if namespace not in _shared_dicts:
if is_multiprocess and _manager is not None:
@@ -175,11 +131,6 @@ def get_namespace_data(namespace: str) -> Dict[str, Any]:
return _shared_dicts[namespace]
def get_scan_progress() -> Dict[str, Any]:
"""get storage space for document scanning progress data"""
return get_namespace_data("scan_progress")
def finalize_share_data():
"""
Release shared resources and clean up.
@@ -195,7 +146,6 @@ def finalize_share_data():
is_multiprocess, \
_global_lock, \
_shared_dicts, \
_share_objects, \
_init_flags, \
_initialized
@@ -216,8 +166,6 @@ def finalize_share_data():
# Clear shared dictionaries first
if _shared_dicts is not None:
_shared_dicts.clear()
if _share_objects is not None:
_share_objects.clear()
if _init_flags is not None:
_init_flags.clear()
@@ -234,7 +182,6 @@ def finalize_share_data():
_initialized = None
is_multiprocess = None
_shared_dicts = None
_share_objects = None
_init_flags = None
_global_lock = None