Fix LLM cache now work for nodes and edges merging

lightrag/api/__init__.py

@@ -1 +1 @@
-__api_version__ = "0142"
+__api_version__ = "0143"

lightrag/operate.py

@@ -24,8 +24,8 @@ from .utils import (
     handle_cache,
     save_to_cache,
     CacheData,
-    statistic_data,
     get_conversation_turns,
+    use_llm_func_with_cache,
 )
 from .base import (
     BaseGraphStorage,
@@ -108,6 +108,7 @@ async def _handle_entity_relation_summary(
     global_config: dict,
     pipeline_status: dict = None,
     pipeline_status_lock=None,
+    llm_response_cache: BaseKVStorage | None = None,
 ) -> str:
     """Handle entity relation summary
     For each entity or relation, input is the combined description of already existing description and new description.
@@ -125,13 +126,6 @@ async def _handle_entity_relation_summary(
     if len(tokens) < summary_max_tokens:  # No need for summary
         return description
 
-    # Update pipeline status when LLM summary is needed
-    status_message = "Use LLM to re-summary description..."
-    logger.info(status_message)
-    if pipeline_status is not None and pipeline_status_lock is not None:
-        async with pipeline_status_lock:
-            pipeline_status["latest_message"] = status_message
-            pipeline_status["history_messages"].append(status_message)
     prompt_template = PROMPTS["summarize_entity_descriptions"]
     use_description = decode_tokens_by_tiktoken(
         tokens[:llm_max_tokens], model_name=tiktoken_model_name
@@ -143,7 +137,23 @@ async def _handle_entity_relation_summary(
     )
     use_prompt = prompt_template.format(**context_base)
     logger.debug(f"Trigger summary: {entity_or_relation_name}")
-    summary = await use_llm_func(use_prompt, max_tokens=summary_max_tokens)
+
+    # Update pipeline status when LLM summary is needed
+    status_message = "Use LLM to re-summary description..."
+    logger.info(status_message)
+    if pipeline_status is not None and pipeline_status_lock is not None:
+        async with pipeline_status_lock:
+            pipeline_status["latest_message"] = status_message
+            pipeline_status["history_messages"].append(status_message)
+
+    # Use LLM function with cache
+    summary = await use_llm_func_with_cache(
+        use_prompt, 
+        use_llm_func,
+        llm_response_cache=llm_response_cache,
+        max_tokens=summary_max_tokens,
+        cache_type="extract",
+    )
     return summary
 
 
@@ -224,6 +234,7 @@ async def _merge_nodes_then_upsert(
     global_config: dict,
     pipeline_status: dict = None,
     pipeline_status_lock=None,
+    llm_response_cache: BaseKVStorage | None = None,
 ):
     """Get existing nodes from knowledge graph use name,if exists, merge data, else create, then upsert."""
     already_entity_types = []
@@ -269,7 +280,12 @@ async def _merge_nodes_then_upsert(
 
     logger.debug(f"file_path: {file_path}")
     description = await _handle_entity_relation_summary(
-        entity_name, description, global_config, pipeline_status, pipeline_status_lock
+        entity_name,
+        description,
+        global_config,
+        pipeline_status,
+        pipeline_status_lock,
+        llm_response_cache,
     )
     node_data = dict(
         entity_id=entity_name,
@@ -294,6 +310,7 @@ async def _merge_edges_then_upsert(
     global_config: dict,
     pipeline_status: dict = None,
     pipeline_status_lock=None,
+    llm_response_cache: BaseKVStorage | None = None,
 ):
     already_weights = []
     already_source_ids = []
@@ -303,7 +320,7 @@ async def _merge_edges_then_upsert(
 
     if await knowledge_graph_inst.has_edge(src_id, tgt_id):
         # Update pipeline status when an edge that needs merging is found
-        status_message = f"Merging edges: {src_id} - {tgt_id}"
+        status_message = f"Merging edges:    {src_id} - {tgt_id}"
         logger.info(status_message)
         if pipeline_status is not None and pipeline_status_lock is not None:
             async with pipeline_status_lock:
@@ -393,6 +410,7 @@ async def _merge_edges_then_upsert(
         global_config,
         pipeline_status,
         pipeline_status_lock,
+        llm_response_cache,
     )
     await knowledge_graph_inst.upsert_edge(
         src_id,
@@ -428,11 +446,9 @@ async def extract_entities(
     pipeline_status_lock=None,
     llm_response_cache: BaseKVStorage | None = None,
 ) -> None:
+
     use_llm_func: callable = global_config["llm_model_func"]
     entity_extract_max_gleaning = global_config["entity_extract_max_gleaning"]
-    enable_llm_cache_for_entity_extract: bool = global_config[
-        "enable_llm_cache_for_entity_extract"
-    ]
 
     ordered_chunks = list(chunks.items())
     # add language and example number params to prompt
@@ -483,51 +499,7 @@ async def extract_entities(
 
     graph_db_lock = get_graph_db_lock(enable_logging=False)
 
-    async def _user_llm_func_with_cache(
+    # Use the global use_llm_func_with_cache function from utils.py
-        input_text: str, history_messages: list[dict[str, str]] = None
-    ) -> str:
-        if enable_llm_cache_for_entity_extract and llm_response_cache:
-            if history_messages:
-                history = json.dumps(history_messages, ensure_ascii=False)
-                _prompt = history + "\n" + input_text
-            else:
-                _prompt = input_text
-
-            # TODO： add cache_type="extract"
-            arg_hash = compute_args_hash(_prompt)
-            cached_return, _1, _2, _3 = await handle_cache(
-                llm_response_cache,
-                arg_hash,
-                _prompt,
-                "default",
-                cache_type="extract",
-            )
-            if cached_return:
-                logger.debug(f"Found cache for {arg_hash}")
-                statistic_data["llm_cache"] += 1
-                return cached_return
-            statistic_data["llm_call"] += 1
-            if history_messages:
-                res: str = await use_llm_func(
-                    input_text, history_messages=history_messages
-                )
-            else:
-                res: str = await use_llm_func(input_text)
-            await save_to_cache(
-                llm_response_cache,
-                CacheData(
-                    args_hash=arg_hash,
-                    content=res,
-                    prompt=_prompt,
-                    cache_type="extract",
-                ),
-            )
-            return res
-
-        if history_messages:
-            return await use_llm_func(input_text, history_messages=history_messages)
-        else:
-            return await use_llm_func(input_text)
 
     async def _process_extraction_result(
         result: str, chunk_key: str, file_path: str = "unknown_source"
@@ -592,7 +564,12 @@ async def extract_entities(
             **context_base, input_text="{input_text}"
         ).format(**context_base, input_text=content)
 
-        final_result = await _user_llm_func_with_cache(hint_prompt)
+        final_result = await use_llm_func_with_cache(
+            hint_prompt,
+            use_llm_func,
+            llm_response_cache=llm_response_cache,
+            cache_type="extract",
+        )
         history = pack_user_ass_to_openai_messages(hint_prompt, final_result)
 
         # Process initial extraction with file path
@@ -602,8 +579,12 @@ async def extract_entities(
 
         # Process additional gleaning results
         for now_glean_index in range(entity_extract_max_gleaning):
-            glean_result = await _user_llm_func_with_cache(
+            glean_result = await use_llm_func_with_cache(
-                continue_prompt, history_messages=history
+                continue_prompt,
+                use_llm_func,
+                llm_response_cache=llm_response_cache,
+                history_messages=history,
+                cache_type="extract",
             )
 
             history += pack_user_ass_to_openai_messages(continue_prompt, glean_result)
@@ -622,8 +603,12 @@ async def extract_entities(
             if now_glean_index == entity_extract_max_gleaning - 1:
                 break
 
-            if_loop_result: str = await _user_llm_func_with_cache(
+            if_loop_result: str = await use_llm_func_with_cache(
-                if_loop_prompt, history_messages=history
+                if_loop_prompt,
+                use_llm_func,
+                llm_response_cache=llm_response_cache,
+                history_messages=history,
+                cache_type="extract",
             )
             if_loop_result = if_loop_result.strip().strip('"').strip("'").lower()
             if if_loop_result != "yes":
@@ -653,6 +638,7 @@ async def extract_entities(
                     global_config,
                     pipeline_status,
                     pipeline_status_lock,
+                    llm_response_cache,
                 )
                 chunk_entities_data.append(entity_data)
 
@@ -668,6 +654,7 @@ async def extract_entities(
                     global_config,
                     pipeline_status,
                     pipeline_status_lock,
+                    llm_response_cache,
                 )
                 chunk_relationships_data.append(edge_data)
 

lightrag/utils.py

@@ -12,13 +12,17 @@ import re
 from dataclasses import dataclass
 from functools import wraps
 from hashlib import md5
-from typing import Any, Callable
+from typing import Any, Callable, TYPE_CHECKING
 import xml.etree.ElementTree as ET
 import numpy as np
 import tiktoken
 from lightrag.prompt import PROMPTS
 from dotenv import load_dotenv
 
+# Use TYPE_CHECKING to avoid circular imports
+if TYPE_CHECKING:
+    from lightrag.base import BaseKVStorage
+
 # use the .env that is inside the current folder
 # allows to use different .env file for each lightrag instance
 # the OS environment variables take precedence over the .env file
@@ -908,6 +912,84 @@ def lazy_external_import(module_name: str, class_name: str) -> Callable[..., Any
     return import_class
 
 
+async def use_llm_func_with_cache(
+    input_text: str, 
+    use_llm_func: callable,
+    llm_response_cache: 'BaseKVStorage | None' = None,
+    max_tokens: int = None,
+    history_messages: list[dict[str, str]] = None,
+    cache_type: str = "extract"
+) -> str:
+    """Call LLM function with cache support
+    
+    If cache is available and enabled (determined by handle_cache based on mode),
+    retrieve result from cache; otherwise call LLM function and save result to cache.
+    
+    Args:
+        input_text: Input text to send to LLM
+        use_llm_func: LLM function to call
+        llm_response_cache: Cache storage instance
+        max_tokens: Maximum tokens for generation
+        history_messages: History messages list
+        cache_type: Type of cache
+        
+    Returns:
+        LLM response text
+    """
+    if llm_response_cache:
+        if history_messages:
+            history = json.dumps(history_messages, ensure_ascii=False)
+            _prompt = history + "\n" + input_text
+        else:
+            _prompt = input_text
+
+        arg_hash = compute_args_hash(_prompt)
+        cached_return, _1, _2, _3 = await handle_cache(
+            llm_response_cache,
+            arg_hash,
+            _prompt,
+            "default",
+            cache_type=cache_type,
+        )
+        if cached_return:
+            logger.debug(f"Found cache for {arg_hash}")
+            statistic_data["llm_cache"] += 1
+            return cached_return
+        statistic_data["llm_call"] += 1
+
+        # Call LLM
+        kwargs = {}
+        if history_messages:
+            kwargs["history_messages"] = history_messages
+        if max_tokens is not None:
+            kwargs["max_tokens"] = max_tokens
+
+        res: str = await use_llm_func(input_text, **kwargs)
+
+        # Save to cache
+        logger.info(f"Saving LLM cache for {arg_hash}")
+        await save_to_cache(
+            llm_response_cache,
+            CacheData(
+                args_hash=arg_hash,
+                content=res,
+                prompt=_prompt,
+                cache_type=cache_type,
+            ),
+        )
+        return res
+
+    # When cache is disabled, directly call LLM
+    kwargs = {}
+    if history_messages:
+        kwargs["history_messages"] = history_messages
+    if max_tokens is not None:
+        kwargs["max_tokens"] = max_tokens
+
+    logger.info(f"Call LLM function with query text lenght: {len(input_text)}")
+    return await use_llm_func(input_text, **kwargs)
+
+
 def get_content_summary(content: str, max_length: int = 250) -> str:
     """Get summary of document content