Merge branch 'main' into main

README.md

@@ -12,7 +12,7 @@
     </p>
      <p>
           <img src='https://img.shields.io/github/stars/hkuds/lightrag?color=green&style=social' />
-        <img src="https://img.shields.io/badge/python->=3.10-blue">
+        <img src="https://img.shields.io/badge/python-3.10-blue">
         <a href="https://pypi.org/project/lightrag-hku/"><img src="https://img.shields.io/pypi/v/lightrag-hku.svg"></a>
         <a href="https://pepy.tech/project/lightrag-hku"><img src="https://static.pepy.tech/badge/lightrag-hku/month"></a>
     </p>

lightrag/lightrag.py

@@ -323,7 +323,7 @@ class LightRAG:
         )
 
     async def ainsert(
-        self, string_or_strings, split_by_character, split_by_character_only
+        self, string_or_strings, split_by_character=None, split_by_character_only=False
     ):
         """Insert documents with checkpoint support
 
@@ -466,6 +466,73 @@ class LightRAG:
                     # Ensure all indexes are updated after each document
                     await self._insert_done()
 
+    def insert_custom_chunks(self, full_text: str, text_chunks: list[str]):
+        loop = always_get_an_event_loop()
+        return loop.run_until_complete(
+            self.ainsert_custom_chunks(full_text, text_chunks)
+        )
+
+    async def ainsert_custom_chunks(self, full_text: str, text_chunks: list[str]):
+        update_storage = False
+        try:
+            doc_key = compute_mdhash_id(full_text.strip(), prefix="doc-")
+            new_docs = {doc_key: {"content": full_text.strip()}}
+
+            _add_doc_keys = await self.full_docs.filter_keys([doc_key])
+            new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
+            if not len(new_docs):
+                logger.warning("This document is already in the storage.")
+                return
+
+            update_storage = True
+            logger.info(f"[New Docs] inserting {len(new_docs)} docs")
+
+            inserting_chunks = {}
+            for chunk_text in text_chunks:
+                chunk_text_stripped = chunk_text.strip()
+                chunk_key = compute_mdhash_id(chunk_text_stripped, prefix="chunk-")
+
+                inserting_chunks[chunk_key] = {
+                    "content": chunk_text_stripped,
+                    "full_doc_id": doc_key,
+                }
+
+            _add_chunk_keys = await self.text_chunks.filter_keys(
+                list(inserting_chunks.keys())
+            )
+            inserting_chunks = {
+                k: v for k, v in inserting_chunks.items() if k in _add_chunk_keys
+            }
+            if not len(inserting_chunks):
+                logger.warning("All chunks are already in the storage.")
+                return
+
+            logger.info(f"[New Chunks] inserting {len(inserting_chunks)} chunks")
+
+            await self.chunks_vdb.upsert(inserting_chunks)
+
+            logger.info("[Entity Extraction]...")
+            maybe_new_kg = await extract_entities(
+                inserting_chunks,
+                knowledge_graph_inst=self.chunk_entity_relation_graph,
+                entity_vdb=self.entities_vdb,
+                relationships_vdb=self.relationships_vdb,
+                global_config=asdict(self),
+            )
+
+            if maybe_new_kg is None:
+                logger.warning("No new entities and relationships found")
+                return
+            else:
+                self.chunk_entity_relation_graph = maybe_new_kg
+
+            await self.full_docs.upsert(new_docs)
+            await self.text_chunks.upsert(inserting_chunks)
+
+        finally:
+            if update_storage:
+                await self._insert_done()
+
     async def _insert_done(self):
         tasks = []
         for storage_inst in [

lightrag/operate.py

@@ -4,7 +4,6 @@ import re
 from tqdm.asyncio import tqdm as tqdm_async
 from typing import Union
 from collections import Counter, defaultdict
-import warnings
 from .utils import (
     logger,
     clean_str,
@@ -611,15 +610,22 @@ async def kg_query(
         logger.warning("low_level_keywords and high_level_keywords is empty")
         return PROMPTS["fail_response"]
     if ll_keywords == [] and query_param.mode in ["local", "hybrid"]:
-        logger.warning("low_level_keywords is empty")
-        return PROMPTS["fail_response"]
-    else:
-        ll_keywords = ", ".join(ll_keywords)
+        logger.warning(
+            "low_level_keywords is empty, switching from %s mode to global mode",
+            query_param.mode,
+        )
+        query_param.mode = "global"
     if hl_keywords == [] and query_param.mode in ["global", "hybrid"]:
-        logger.warning("high_level_keywords is empty")
-        return PROMPTS["fail_response"]
-    else:
-        hl_keywords = ", ".join(hl_keywords)
+        logger.warning(
+            "high_level_keywords is empty, switching from %s mode to local mode",
+            query_param.mode,
+        )
+        query_param.mode = "local"
+
+    ll_keywords = ", ".join(ll_keywords) if ll_keywords else ""
+    hl_keywords = ", ".join(hl_keywords) if hl_keywords else ""
+
+    logger.info("Using %s mode for query processing", query_param.mode)
 
     # Build context
     keywords = [ll_keywords, hl_keywords]
@@ -685,78 +691,52 @@ async def _build_query_context(
     # ll_entities_context, ll_relations_context, ll_text_units_context = "", "", ""
     # hl_entities_context, hl_relations_context, hl_text_units_context = "", "", ""
 
-    ll_kewwords, hl_keywrds = query[0], query[1]
-    if query_param.mode in ["local", "hybrid"]:
-        if ll_kewwords == "":
-            ll_entities_context, ll_relations_context, ll_text_units_context = (
-                "",
-                "",
-                "",
-            )
-            warnings.warn(
-                "Low Level context is None. Return empty Low entity/relationship/source"
-            )
-            query_param.mode = "global"
-        else:
-            (
-                ll_entities_context,
-                ll_relations_context,
-                ll_text_units_context,
-            ) = await _get_node_data(
-                ll_kewwords,
-                knowledge_graph_inst,
-                entities_vdb,
-                text_chunks_db,
-                query_param,
-            )
-    if query_param.mode in ["global", "hybrid"]:
-        if hl_keywrds == "":
-            hl_entities_context, hl_relations_context, hl_text_units_context = (
-                "",
-                "",
-                "",
-            )
-            warnings.warn(
-                "High Level context is None. Return empty High entity/relationship/source"
-            )
-            query_param.mode = "local"
-        else:
-            (
-                hl_entities_context,
-                hl_relations_context,
-                hl_text_units_context,
-            ) = await _get_edge_data(
-                hl_keywrds,
-                knowledge_graph_inst,
-                relationships_vdb,
-                text_chunks_db,
-                query_param,
-            )
-            if (
-                hl_entities_context == ""
-                and hl_relations_context == ""
-                and hl_text_units_context == ""
-            ):
-                logger.warn("No high level context found. Switching to local mode.")
-                query_param.mode = "local"
-    if query_param.mode == "hybrid":
+    ll_keywords, hl_keywords = query[0], query[1]
+
+    if query_param.mode == "local":
+        entities_context, relations_context, text_units_context = await _get_node_data(
+            ll_keywords,
+            knowledge_graph_inst,
+            entities_vdb,
+            text_chunks_db,
+            query_param,
+        )
+    elif query_param.mode == "global":
+        entities_context, relations_context, text_units_context = await _get_edge_data(
+            hl_keywords,
+            knowledge_graph_inst,
+            relationships_vdb,
+            text_chunks_db,
+            query_param,
+        )
+    else:  # hybrid mode
+        (
+            ll_entities_context,
+            ll_relations_context,
+            ll_text_units_context,
+        ) = await _get_node_data(
+            ll_keywords,
+            knowledge_graph_inst,
+            entities_vdb,
+            text_chunks_db,
+            query_param,
+        )
+        (
+            hl_entities_context,
+            hl_relations_context,
+            hl_text_units_context,
+        ) = await _get_edge_data(
+            hl_keywords,
+            knowledge_graph_inst,
+            relationships_vdb,
+            text_chunks_db,
+            query_param,
+        )
         entities_context, relations_context, text_units_context = combine_contexts(
             [hl_entities_context, ll_entities_context],
             [hl_relations_context, ll_relations_context],
             [hl_text_units_context, ll_text_units_context],
         )
-    elif query_param.mode == "local":
-        entities_context, relations_context, text_units_context = (
-            ll_entities_context,
-            ll_relations_context,
-            ll_text_units_context,
-        )
-    elif query_param.mode == "global":
-        entities_context, relations_context, text_units_context = (
-            hl_entities_context,
-            hl_relations_context,
-            hl_text_units_context,
-        )
     return f"""
 -----Entities-----
 ```csv