Merge branch 'main' into pkaushal/vectordb-chroma

README.md

@@ -594,7 +594,7 @@ if __name__ == "__main__":
 | **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation |     |
 | **vector\_db\_storage\_cls\_kwargs** | `dict` | Additional parameters for vector database (currently not used) |     |
 | **enable\_llm\_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
-| **addon\_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese"}`: sets example limit and output language | `example_number: all examples, language: English` |
+| **addon\_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"]}`: sets example limit and output language | `example_number: all examples, language: English` |
 | **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
 | **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains three parameters:<br>- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.<br>- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.<br>- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |
 

lightrag/lightrag.py

@@ -50,16 +50,17 @@ from .storage import (
 def lazy_external_import(module_name: str, class_name: str):
     """Lazily import a class from an external module based on the package of the caller."""
 
+    # Get the caller's module and package
+    import inspect
+
+    caller_frame = inspect.currentframe().f_back
+    module = inspect.getmodule(caller_frame)
+    package = module.__package__ if module else None
+
     def import_class(*args, **kwargs):
-        import inspect
         import importlib
 
-        # Get the caller's module and package
-        caller_frame = inspect.currentframe().f_back
-        module = inspect.getmodule(caller_frame)
-        package = module.__package__ if module else None
-
-        # Import the module using importlib with package context
+        # Import the module using importlib
         module = importlib.import_module(module_name, package=package)
 
         # Get the class from the module and instantiate it

lightrag/llm.py

@@ -30,6 +30,7 @@ from .utils import (
     wrap_embedding_func_with_attrs,
     locate_json_string_body_from_string,
     safe_unicode_decode,
+    logger,
 )
 
 import sys
@@ -63,12 +64,18 @@ async def openai_complete_if_cache(
         AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
     )
     kwargs.pop("hashing_kv", None)
+    kwargs.pop("keyword_extraction", None)
     messages = []
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
 
+    # 添加日志输出
+    logger.debug("===== Query Input to LLM =====")
+    logger.debug(f"Query: {prompt}")
+    logger.debug(f"System prompt: {system_prompt}")
+    logger.debug("Full context:")
     if "response_format" in kwargs:
         response = await openai_async_client.beta.chat.completions.parse(
             model=model, messages=messages, **kwargs

lightrag/operate.py

@@ -260,6 +260,9 @@ async def extract_entities(
     language = global_config["addon_params"].get(
         "language", PROMPTS["DEFAULT_LANGUAGE"]
     )
+    entity_types = global_config["addon_params"].get(
+        "entity_types", PROMPTS["DEFAULT_ENTITY_TYPES"]
+    )
     example_number = global_config["addon_params"].get("example_number", None)
     if example_number and example_number < len(PROMPTS["entity_extraction_examples"]):
         examples = "\n".join(
@@ -272,7 +275,7 @@ async def extract_entities(
         tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"],
         record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"],
         completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"],
-        entity_types=",".join(PROMPTS["DEFAULT_ENTITY_TYPES"]),
+        entity_types=",".join(entity_types),
         language=language,
     )
     # add example's format
@@ -283,7 +286,7 @@ async def extract_entities(
         tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"],
         record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"],
         completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"],
-        entity_types=",".join(PROMPTS["DEFAULT_ENTITY_TYPES"]),
+        entity_types=",".join(entity_types),
         examples=examples,
         language=language,
     )
@@ -412,15 +415,17 @@ async def extract_entities(
     ):
         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
-    if not len(all_relationships_data):
+    if not len(all_entities_data) and not len(all_relationships_data):
         logger.warning(
-            "Didn't extract any relationships, maybe your LLM is not working"
+            "Didn't extract any entities and relationships, maybe your LLM is not working"
         )
         return None
 
+    if not len(all_entities_data):
+        logger.warning("Didn't extract any entities")
+    if not len(all_relationships_data):
+        logger.warning("Didn't extract any relationships")
+
     if entity_vdb is not None:
         data_for_vdb = {
             compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
@@ -630,6 +635,13 @@ async def _build_query_context(
                 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":
         entities_context, relations_context, text_units_context = combine_contexts(
             [hl_entities_context, ll_entities_context],
@@ -865,7 +877,7 @@ async def _get_edge_data(
     results = await relationships_vdb.query(keywords, top_k=query_param.top_k)
 
     if not len(results):
-        return None
+        return "", "", ""
 
     edge_datas = await asyncio.gather(
         *[knowledge_graph_inst.get_edge(r["src_id"], r["tgt_id"]) for r in results]

lightrag/prompt.py

@@ -8,7 +8,7 @@ PROMPTS["DEFAULT_RECORD_DELIMITER"] = "##"
 PROMPTS["DEFAULT_COMPLETION_DELIMITER"] = "<|COMPLETE|>"
 PROMPTS["process_tickers"] = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]
 
-PROMPTS["DEFAULT_ENTITY_TYPES"] = ["organization", "person", "geo", "event"]
+PROMPTS["DEFAULT_ENTITY_TYPES"] = ["organization", "person", "geo", "event", "category"]
 
 PROMPTS["entity_extraction"] = """-Goal-
 Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.
@@ -268,14 +268,19 @@ PROMPTS[
 Question 1: {original_prompt}
 Question 2: {cached_prompt}
 
-Please evaluate:
+Please evaluate the following two points and provide a similarity score between 0 and 1 directly:
 1. Whether these two questions are semantically similar
 2. Whether the answer to Question 2 can be used to answer Question 1
-
-Please provide a similarity score between 0 and 1, where:
-0: Completely unrelated or answer cannot be reused
+Similarity score criteria:
+0: Completely unrelated or answer cannot be reused, including but not limited to:
+   - The questions have different topics
+   - The locations mentioned in the questions are different
+   - The times mentioned in the questions are different
+   - The specific individuals mentioned in the questions are different
+   - The specific events mentioned in the questions are different
+   - The background information in the questions is different
+   - The key conditions in the questions are different
 1: Identical and answer can be directly reused
 0.5: Partially related and answer needs modification to be used
-
 Return only a number between 0-1, without any additional content.
 """

lightrag/storage.py

@@ -107,10 +107,16 @@ class NanoVectorDBStorage(BaseVectorStorage):
             embeddings = await f
             embeddings_list.append(embeddings)
         embeddings = np.concatenate(embeddings_list)
-        for i, d in enumerate(list_data):
-            d["__vector__"] = embeddings[i]
-        results = self._client.upsert(datas=list_data)
-        return results
+        if len(embeddings) == len(list_data):
+            for i, d in enumerate(list_data):
+                d["__vector__"] = embeddings[i]
+            results = self._client.upsert(datas=list_data)
+            return results
+        else:
+            # sometimes the embedding is not returned correctly. just log it.
+            logger.error(
+                f"embedding is not 1-1 with data, {len(embeddings)} != {len(list_data)}"
+            )
 
     async def query(self, query: str, top_k=5):
         embedding = await self.embedding_func([query])

lightrag/utils.py

@@ -17,6 +17,17 @@ import tiktoken
 
 from lightrag.prompt import PROMPTS
 
+
+class UnlimitedSemaphore:
+    """A context manager that allows unlimited access."""
+
+    async def __aenter__(self):
+        pass
+
+    async def __aexit__(self, exc_type, exc, tb):
+        pass
+
+
 ENCODER = None
 
 logger = logging.getLogger("lightrag")
@@ -42,9 +53,17 @@ class EmbeddingFunc:
     embedding_dim: int
     max_token_size: int
     func: callable
+    concurrent_limit: int = 16
+
+    def __post_init__(self):
+        if self.concurrent_limit != 0:
+            self._semaphore = asyncio.Semaphore(self.concurrent_limit)
+        else:
+            self._semaphore = UnlimitedSemaphore()
 
     async def __call__(self, *args, **kwargs) -> np.ndarray:
-        return await self.func(*args, **kwargs)
+        async with self._semaphore:
+            return await self.func(*args, **kwargs)
 
 
 def locate_json_string_body_from_string(content: str) -> Union[str, None]: