Merge branch 'HKUDS:main' into main

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
 
-__version__ = "1.0.1"
+__version__ = "1.0.2"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/lightrag.py

@@ -1,5 +1,6 @@
 import asyncio
 import os
+from tqdm.asyncio import tqdm as tqdm_async
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
 from functools import partial
@@ -242,7 +243,9 @@ class LightRAG:
             logger.info(f"[New Docs] inserting {len(new_docs)} docs")
 
             inserting_chunks = {}
-            for doc_key, doc in new_docs.items():
+            for doc_key, doc in tqdm_async(
+                new_docs.items(), desc="Chunking documents", unit="doc"
+            ):
                 chunks = {
                     compute_mdhash_id(dp["content"], prefix="chunk-"): {
                         **dp,
@@ -304,6 +307,108 @@ class LightRAG:
             tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
         await asyncio.gather(*tasks)
 
+    def insert_custom_kg(self, custom_kg: dict):
+        loop = always_get_an_event_loop()
+        return loop.run_until_complete(self.ainsert_custom_kg(custom_kg))
+
+    async def ainsert_custom_kg(self, custom_kg: dict):
+        update_storage = False
+        try:
+            # Insert entities into knowledge graph
+            all_entities_data = []
+            for entity_data in custom_kg.get("entities", []):
+                entity_name = f'"{entity_data["entity_name"].upper()}"'
+                entity_type = entity_data.get("entity_type", "UNKNOWN")
+                description = entity_data.get("description", "No description provided")
+                source_id = entity_data["source_id"]
+
+                # Prepare node data
+                node_data = {
+                    "entity_type": entity_type,
+                    "description": description,
+                    "source_id": source_id,
+                }
+                # Insert node data into the knowledge graph
+                await self.chunk_entity_relation_graph.upsert_node(
+                    entity_name, node_data=node_data
+                )
+                node_data["entity_name"] = entity_name
+                all_entities_data.append(node_data)
+                update_storage = True
+
+            # Insert relationships into knowledge graph
+            all_relationships_data = []
+            for relationship_data in custom_kg.get("relationships", []):
+                src_id = f'"{relationship_data["src_id"].upper()}"'
+                tgt_id = f'"{relationship_data["tgt_id"].upper()}"'
+                description = relationship_data["description"]
+                keywords = relationship_data["keywords"]
+                weight = relationship_data.get("weight", 1.0)
+                source_id = relationship_data["source_id"]
+
+                # Check if nodes exist in the knowledge graph
+                for need_insert_id in [src_id, tgt_id]:
+                    if not (
+                        await self.chunk_entity_relation_graph.has_node(need_insert_id)
+                    ):
+                        await self.chunk_entity_relation_graph.upsert_node(
+                            need_insert_id,
+                            node_data={
+                                "source_id": source_id,
+                                "description": "UNKNOWN",
+                                "entity_type": "UNKNOWN",
+                            },
+                        )
+
+                # Insert edge into the knowledge graph
+                await self.chunk_entity_relation_graph.upsert_edge(
+                    src_id,
+                    tgt_id,
+                    edge_data={
+                        "weight": weight,
+                        "description": description,
+                        "keywords": keywords,
+                        "source_id": source_id,
+                    },
+                )
+                edge_data = {
+                    "src_id": src_id,
+                    "tgt_id": tgt_id,
+                    "description": description,
+                    "keywords": keywords,
+                }
+                all_relationships_data.append(edge_data)
+                update_storage = True
+
+            # Insert entities into vector storage if needed
+            if self.entities_vdb is not None:
+                data_for_vdb = {
+                    compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
+                        "content": dp["entity_name"] + dp["description"],
+                        "entity_name": dp["entity_name"],
+                    }
+                    for dp in all_entities_data
+                }
+                await self.entities_vdb.upsert(data_for_vdb)
+
+            # Insert relationships into vector storage if needed
+            if self.relationships_vdb is not None:
+                data_for_vdb = {
+                    compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
+                        "src_id": dp["src_id"],
+                        "tgt_id": dp["tgt_id"],
+                        "content": dp["keywords"]
+                        + dp["src_id"]
+                        + dp["tgt_id"]
+                        + dp["description"],
+                    }
+                    for dp in all_relationships_data
+                }
+                await self.relationships_vdb.upsert(data_for_vdb)
+        finally:
+            if update_storage:
+                await self._insert_done()
+
     def query(self, query: str, param: QueryParam = QueryParam()):
         loop = always_get_an_event_loop()
         return loop.run_until_complete(self.aquery(query, param))

lightrag/operate.py

@@ -1,6 +1,7 @@
 import asyncio
 import json
 import re
+from tqdm.asyncio import tqdm as tqdm_async
 from typing import Union
 from collections import Counter, defaultdict
 import warnings
@@ -342,11 +343,15 @@ async def extract_entities(
         )
         return dict(maybe_nodes), dict(maybe_edges)
 
-    # use_llm_func is wrapped in ascynio.Semaphore, limiting max_async callings
-    results = await asyncio.gather(
-        *[_process_single_content(c) for c in ordered_chunks]
-    )
-    print()  # clear the progress bar
+    results = []
+    for result in tqdm_async(
+        asyncio.as_completed([_process_single_content(c) for c in ordered_chunks]),
+        total=len(ordered_chunks),
+        desc="Extracting entities from chunks",
+        unit="chunk",
+    ):
+        results.append(await result)
+
     maybe_nodes = defaultdict(list)
     maybe_edges = defaultdict(list)
     for m_nodes, m_edges in results:
@@ -354,18 +359,38 @@ async def extract_entities(
             maybe_nodes[k].extend(v)
         for k, v in m_edges.items():
             maybe_edges[tuple(sorted(k))].extend(v)
-    all_entities_data = await asyncio.gather(
-        *[
-            _merge_nodes_then_upsert(k, v, knowledge_graph_inst, global_config)
-            for k, v in maybe_nodes.items()
-        ]
-    )
-    all_relationships_data = await asyncio.gather(
-        *[
-            _merge_edges_then_upsert(k[0], k[1], v, knowledge_graph_inst, global_config)
-            for k, v in maybe_edges.items()
-        ]
-    )
+    logger.info("Inserting entities into storage...")
+    all_entities_data = []
+    for result in tqdm_async(
+        asyncio.as_completed(
+            [
+                _merge_nodes_then_upsert(k, v, knowledge_graph_inst, global_config)
+                for k, v in maybe_nodes.items()
+            ]
+        ),
+        total=len(maybe_nodes),
+        desc="Inserting entities",
+        unit="entity",
+    ):
+        all_entities_data.append(await result)
+
+    logger.info("Inserting relationships into storage...")
+    all_relationships_data = []
+    for result in tqdm_async(
+        asyncio.as_completed(
+            [
+                _merge_edges_then_upsert(
+                    k[0], k[1], v, knowledge_graph_inst, global_config
+                )
+                for k, v in maybe_edges.items()
+            ]
+        ),
+        total=len(maybe_edges),
+        desc="Inserting relationships",
+        unit="relationship",
+    ):
+        all_relationships_data.append(await result)
+
     if not len(all_entities_data):
         logger.warning("Didn't extract any entities, maybe your LLM is not working")
         return None

lightrag/storage.py

@@ -1,6 +1,7 @@
 import asyncio
 import html
 import os
+from tqdm.asyncio import tqdm as tqdm_async
 from dataclasses import dataclass
 from typing import Any, Union, cast
 import networkx as nx
@@ -95,9 +96,16 @@ class NanoVectorDBStorage(BaseVectorStorage):
             contents[i : i + self._max_batch_size]
             for i in range(0, len(contents), self._max_batch_size)
         ]
-        embeddings_list = await asyncio.gather(
-            *[self.embedding_func(batch) for batch in batches]
-        )
+        embedding_tasks = [self.embedding_func(batch) for batch in batches]
+        embeddings_list = []
+        for f in tqdm_async(
+            asyncio.as_completed(embedding_tasks),
+            total=len(embedding_tasks),
+            desc="Generating embeddings",
+            unit="batch",
+        ):
+            embeddings = await f
+            embeddings_list.append(embeddings)
         embeddings = np.concatenate(embeddings_list)
         for i, d in enumerate(list_data):
             d["__vector__"] = embeddings[i]