Merge remote-tracking branch 'upstream/memgraph' into add-Memgraph-graph-db

README-zh.md

@@ -30,7 +30,7 @@
       <a href="https://github.com/HKUDS/LightRAG/issues/285"><img src="https://img.shields.io/badge/💬微信群-交流-07c160?style=for-the-badge&logo=wechat&logoColor=white&labelColor=1a1a2e"></a>
     </p>
     <p>
-      <a href="README_zh.md"><img src="https://img.shields.io/badge/🇨🇳中文版-1a1a2e?style=for-the-badge"></a>
+      <a href="README-zh.md"><img src="https://img.shields.io/badge/🇨🇳中文版-1a1a2e?style=for-the-badge"></a>
       <a href="README.md"><img src="https://img.shields.io/badge/🇺🇸English-1a1a2e?style=for-the-badge"></a>
     </p>
   </div>

env.example

@@ -96,7 +96,7 @@ EMBEDDING_BINDING_API_KEY=your_api_key
 # If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
 EMBEDDING_BINDING_HOST=http://localhost:11434
 ### Num of chunks send to Embedding in single request
-# EMBEDDING_BATCH_NUM=32
+# EMBEDDING_BATCH_NUM=10
 ### Max concurrency requests for Embedding
 # EMBEDDING_FUNC_MAX_ASYNC=16
 ### Maximum tokens sent to Embedding for each chunk (no longer in use?)

lightrag/lightrag.py

@@ -201,7 +201,7 @@ class LightRAG:
     embedding_func: EmbeddingFunc | None = field(default=None)
     """Function for computing text embeddings. Must be set before use."""
 
-    embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 32)))
+    embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 10)))
     """Batch size for embedding computations."""
 
     embedding_func_max_async: int = field(

lightrag/llm/openai.py

@@ -210,9 +210,18 @@ async def openai_complete_if_cache(
         async def inner():
             # Track if we've started iterating
             iteration_started = False
+            final_chunk_usage = None
+
             try:
                 iteration_started = True
                 async for chunk in response:
+                    # Check if this chunk has usage information (final chunk)
+                    if hasattr(chunk, "usage") and chunk.usage:
+                        final_chunk_usage = chunk.usage
+                        logger.debug(
+                            f"Received usage info in streaming chunk: {chunk.usage}"
+                        )
+
                     # Check if choices exists and is not empty
                     if not hasattr(chunk, "choices") or not chunk.choices:
                         logger.warning(f"Received chunk without choices: {chunk}")
@@ -222,16 +231,31 @@ async def openai_complete_if_cache(
                     if not hasattr(chunk.choices[0], "delta") or not hasattr(
                         chunk.choices[0].delta, "content"
                     ):
-                        logger.warning(
-                            f"Received chunk without delta content: {chunk.choices[0]}"
-                        )
+                        # This might be the final chunk, continue to check for usage
                         continue
+
                     content = chunk.choices[0].delta.content
                     if content is None:
                         continue
                     if r"\u" in content:
                         content = safe_unicode_decode(content.encode("utf-8"))
+
                     yield content
+
+                # After streaming is complete, track token usage
+                if token_tracker and final_chunk_usage:
+                    # Use actual usage from the API
+                    token_counts = {
+                        "prompt_tokens": getattr(final_chunk_usage, "prompt_tokens", 0),
+                        "completion_tokens": getattr(
+                            final_chunk_usage, "completion_tokens", 0
+                        ),
+                        "total_tokens": getattr(final_chunk_usage, "total_tokens", 0),
+                    }
+                    token_tracker.add_usage(token_counts)
+                    logger.debug(f"Streaming token usage (from API): {token_counts}")
+                elif token_tracker:
+                    logger.debug("No usage information available in streaming response")
             except Exception as e:
                 logger.error(f"Error in stream response: {str(e)}")
                 # Try to clean up resources if possible

lightrag/operate.py

@@ -26,6 +26,7 @@ from .utils import (
     get_conversation_turns,
     use_llm_func_with_cache,
     update_chunk_cache_list,
+    remove_think_tags,
 )
 from .base import (
     BaseGraphStorage,
@@ -1703,7 +1704,8 @@ async def extract_keywords_only(
     result = await use_model_func(kw_prompt, keyword_extraction=True)
 
     # 6. Parse out JSON from the LLM response
-    match = re.search(r"\{.*\}", result, re.DOTALL)
+    result = remove_think_tags(result)
+    match = re.search(r"\{.*?\}", result, re.DOTALL)
     if not match:
         logger.error("No JSON-like structure found in the LLM respond.")
         return [], []

lightrag/utils.py

@@ -1465,6 +1465,11 @@ async def update_chunk_cache_list(
         )
 
 
+def remove_think_tags(text: str) -> str:
+    """Remove <think> tags from the text"""
+    return re.sub(r"^(<think>.*?</think>|<think>)", "", text, flags=re.DOTALL).strip()
+
+
 async def use_llm_func_with_cache(
     input_text: str,
     use_llm_func: callable,
@@ -1531,6 +1536,7 @@ async def use_llm_func_with_cache(
             kwargs["max_tokens"] = max_tokens
 
         res: str = await use_llm_func(input_text, **kwargs)
+        res = remove_think_tags(res)
 
         if llm_response_cache.global_config.get("enable_llm_cache_for_entity_extract"):
             await save_to_cache(
@@ -1557,8 +1563,9 @@ async def use_llm_func_with_cache(
     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)
+    logger.info(f"Call LLM function with query text length: {len(input_text)}")
+    res = await use_llm_func(input_text, **kwargs)
+    return remove_think_tags(res)
 
 
 def get_content_summary(content: str, max_length: int = 250) -> str: