Improve Entity Extraction Robustness for Truncated LLM Responses

lightrag/operate.py

@@ -141,18 +141,36 @@ async def _handle_single_entity_extraction(
 ):
     if len(record_attributes) < 4 or record_attributes[0] != '"entity"':
         return None
-    # add this record as a node in the G
+
+    # Clean and validate entity name
     entity_name = clean_str(record_attributes[1]).strip('"')
     if not entity_name.strip():
+        logger.warning(
+            f"Entity extraction error: empty entity name in: {record_attributes}"
+        )
         return None
+
+    # Clean and validate entity type
     entity_type = clean_str(record_attributes[2]).strip('"')
+    if not entity_type.strip() or entity_type.startswith('("'):
+        logger.warning(
+            f"Entity extraction error: invalid entity type in: {record_attributes}"
+        )
+        return None
+
+    # Clean and validate description
     entity_description = clean_str(record_attributes[3]).strip('"')
-    entity_source_id = chunk_key
+    if not entity_description.strip():
+        logger.warning(
+            f"Entity extraction error: empty description for entity '{entity_name}' of type '{entity_type}'"
+        )
+        return None
+
     return dict(
         entity_name=entity_name,
         entity_type=entity_type,
         description=entity_description,
-        source_id=entity_source_id,
+        source_id=chunk_key,
         metadata={"created_at": time.time()},
     )
 
@@ -438,47 +456,22 @@ async def extract_entities(
         else:
             return await use_llm_func(input_text)
 
-    async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
+    async def _process_extraction_result(result: str, chunk_key: str):
-        """ "Prpocess a single chunk
+        """Process a single extraction result (either initial or gleaning)
         Args:
-            chunk_key_dp (tuple[str, TextChunkSchema]):
+            result (str): The extraction result to process
-                ("chunck-xxxxxx", {"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int})
+            chunk_key (str): The chunk key for source tracking
+        Returns:
+            tuple: (nodes_dict, edges_dict) containing the extracted entities and relationships
         """
-        nonlocal processed_chunks
-        chunk_key = chunk_key_dp[0]
-        chunk_dp = chunk_key_dp[1]
-        content = chunk_dp["content"]
-        # hint_prompt = entity_extract_prompt.format(**context_base, input_text=content)
-        hint_prompt = entity_extract_prompt.format(
-            **context_base, input_text="{input_text}"
-        ).format(**context_base, input_text=content)
-
-        final_result = await _user_llm_func_with_cache(hint_prompt)
-        history = pack_user_ass_to_openai_messages(hint_prompt, final_result)
-        for now_glean_index in range(entity_extract_max_gleaning):
-            glean_result = await _user_llm_func_with_cache(
-                continue_prompt, history_messages=history
-            )
-
-            history += pack_user_ass_to_openai_messages(continue_prompt, glean_result)
-            final_result += glean_result
-            if now_glean_index == entity_extract_max_gleaning - 1:
-                break
-
-            if_loop_result: str = await _user_llm_func_with_cache(
-                if_loop_prompt, history_messages=history
-            )
-            if_loop_result = if_loop_result.strip().strip('"').strip("'").lower()
-            if if_loop_result != "yes":
-                break
-
-        records = split_string_by_multi_markers(
-            final_result,
-            [context_base["record_delimiter"], context_base["completion_delimiter"]],
-        )
-
         maybe_nodes = defaultdict(list)
         maybe_edges = defaultdict(list)
+        
+        records = split_string_by_multi_markers(
+            result,
+            [context_base["record_delimiter"], context_base["completion_delimiter"]],
+        )
+        
         for record in records:
             record = re.search(r"\((.*)\)", record)
             if record is None:
@@ -487,13 +480,14 @@ async def extract_entities(
             record_attributes = split_string_by_multi_markers(
                 record, [context_base["tuple_delimiter"]]
             )
+            
             if_entities = await _handle_single_entity_extraction(
                 record_attributes, chunk_key
             )
             if if_entities is not None:
                 maybe_nodes[if_entities["entity_name"]].append(if_entities)
                 continue
-
+                
             if_relation = await _handle_single_relationship_extraction(
                 record_attributes, chunk_key
             )
@@ -501,6 +495,58 @@ async def extract_entities(
                 maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append(
                     if_relation
                 )
+                
+        return maybe_nodes, maybe_edges
+
+    async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
+        """Process a single chunk
+        Args:
+            chunk_key_dp (tuple[str, TextChunkSchema]):
+                ("chunk-xxxxxx", {"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int})
+        """
+        nonlocal processed_chunks
+        chunk_key = chunk_key_dp[0]
+        chunk_dp = chunk_key_dp[1]
+        content = chunk_dp["content"]
+        
+        # Get initial extraction
+        hint_prompt = entity_extract_prompt.format(
+            **context_base, input_text="{input_text}"
+        ).format(**context_base, input_text=content)
+        
+        final_result = await _user_llm_func_with_cache(hint_prompt)
+        history = pack_user_ass_to_openai_messages(hint_prompt, final_result)
+        
+        # Process initial extraction
+        maybe_nodes, maybe_edges = await _process_extraction_result(final_result, chunk_key)
+        
+        # Process additional gleaning results
+        for now_glean_index in range(entity_extract_max_gleaning):
+            glean_result = await _user_llm_func_with_cache(
+                continue_prompt, history_messages=history
+            )
+            
+            history += pack_user_ass_to_openai_messages(continue_prompt, glean_result)
+            
+            # Process gleaning result separately
+            glean_nodes, glean_edges = await _process_extraction_result(glean_result, chunk_key)
+            
+            # Merge results
+            for entity_name, entities in glean_nodes.items():
+                maybe_nodes[entity_name].extend(entities)
+            for edge_key, edges in glean_edges.items():
+                maybe_edges[edge_key].extend(edges)
+                
+            if now_glean_index == entity_extract_max_gleaning - 1:
+                break
+                
+            if_loop_result: str = await _user_llm_func_with_cache(
+                if_loop_prompt, history_messages=history
+            )
+            if_loop_result = if_loop_result.strip().strip('"').strip("'").lower()
+            if if_loop_result != "yes":
+                break
+                
         processed_chunks += 1
         entities_count = len(maybe_nodes)
         relations_count = len(maybe_edges)