Merge branch 'main' into main

README.md

@@ -596,7 +596,11 @@ if __name__ == "__main__":
 | **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` |
 | **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
-| **embedding\_cache\_config** | `dict` | Configuration for embedding cache. Includes `enabled` (bool) to toggle cache and `similarity_threshold` (float) for cache retrieval | `{"enabled": False, "similarity_threshold": 0.95}` |
+| **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains two parameters:
+- `enabled`: Boolean value to enable/disable caching functionality. When enabled, questions and answers will be cached.
+- `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.
+
+Default: `{"enabled": False, "similarity_threshold": 0.95}` | `{"enabled": False, "similarity_threshold": 0.95}` |
 
 ## API Server Implementation
 

lightrag/kg/oracle_impl.py

@@ -143,7 +143,7 @@ async def query(
                         data = None
                 return data
 
-    async def execute(self, sql: str, data: list | dict = None):
+    async def execute(self, sql: str, data: Union[list, dict] = None):
         # logger.info("go into OracleDB execute method")
         try:
             async with self.pool.acquire() as connection:

lightrag/llm.py

@@ -1,33 +1,32 @@
-import os
+import base64
 import copy
-from functools import lru_cache
 import json
+import os
+import struct
+from functools import lru_cache
+from typing import List, Dict, Callable, Any, Union
+
 import aioboto3
 import aiohttp
 import numpy as np
 import ollama
-
+import torch
 from openai import (
     AsyncOpenAI,
     APIConnectionError,
     RateLimitError,
     Timeout,
     AsyncAzureOpenAI,
 )
-
-import base64
-import struct
-
+from pydantic import BaseModel, Field
 from tenacity import (
     retry,
     stop_after_attempt,
     wait_exponential,
     retry_if_exception_type,
 )
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-from pydantic import BaseModel, Field
-from typing import List, Dict, Callable, Any, Union
+
 from .base import BaseKVStorage
 from .utils import (
     compute_args_hash,
@@ -73,7 +72,11 @@ async def openai_complete_if_cache(
         messages.append({"role": "system", "content": system_prompt})
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
+
     if hashing_kv is not None:
+        # Calculate args_hash only when using cache
+        args_hash = compute_args_hash(model, messages)
+
         # Get embedding cache configuration
         embedding_cache_config = hashing_kv.global_config.get(
             "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
@@ -93,7 +96,6 @@ async def openai_complete_if_cache(
                 return best_cached_response
         else:
             # Use regular cache
-            args_hash = compute_args_hash(model, messages)
             if_cache_return = await hashing_kv.get_by_id(args_hash)
             if if_cache_return is not None:
                 return if_cache_return["return"]
@@ -166,7 +168,11 @@ async def azure_openai_complete_if_cache(
     messages.extend(history_messages)
     if prompt is not None:
         messages.append({"role": "user", "content": prompt})
+
     if hashing_kv is not None:
+        # Calculate args_hash only when using cache
+        args_hash = compute_args_hash(model, messages)
+
         # Get embedding cache configuration
         embedding_cache_config = hashing_kv.global_config.get(
             "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
@@ -185,7 +191,7 @@ async def azure_openai_complete_if_cache(
             if best_cached_response is not None:
                 return best_cached_response
         else:
-            args_hash = compute_args_hash(model, messages)
+            # Use regular cache
             if_cache_return = await hashing_kv.get_by_id(args_hash)
             if if_cache_return is not None:
                 return if_cache_return["return"]
@@ -278,6 +284,9 @@ async def bedrock_complete_if_cache(
 
     hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     if hashing_kv is not None:
+        # Calculate args_hash only when using cache
+        args_hash = compute_args_hash(model, messages)
+
         # Get embedding cache configuration
         embedding_cache_config = hashing_kv.global_config.get(
             "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
@@ -297,7 +306,6 @@ async def bedrock_complete_if_cache(
                 return best_cached_response
         else:
             # Use regular cache
-            args_hash = compute_args_hash(model, messages)
             if_cache_return = await hashing_kv.get_by_id(args_hash)
             if if_cache_return is not None:
                 return if_cache_return["return"]
@@ -350,6 +358,11 @@ def initialize_hf_model(model_name):
     return hf_model, hf_tokenizer
 
 
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
 async def hf_model_if_cache(
     model,
     prompt,
@@ -367,6 +380,9 @@ async def hf_model_if_cache(
     messages.append({"role": "user", "content": prompt})
 
     if hashing_kv is not None:
+        # Calculate args_hash only when using cache
+        args_hash = compute_args_hash(model, messages)
+
         # Get embedding cache configuration
         embedding_cache_config = hashing_kv.global_config.get(
             "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
@@ -386,7 +402,6 @@ async def hf_model_if_cache(
                 return best_cached_response
         else:
             # Use regular cache
-            args_hash = compute_args_hash(model, messages)
             if_cache_return = await hashing_kv.get_by_id(args_hash)
             if if_cache_return is not None:
                 return if_cache_return["return"]
@@ -455,6 +470,11 @@ async def hf_model_if_cache(
     return response_text
 
 
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
 async def ollama_model_if_cache(
     model,
     prompt,
@@ -476,7 +496,11 @@ async def ollama_model_if_cache(
     hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
+
     if hashing_kv is not None:
+        # Calculate args_hash only when using cache
+        args_hash = compute_args_hash(model, messages)
+
         # Get embedding cache configuration
         embedding_cache_config = hashing_kv.global_config.get(
             "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
@@ -496,7 +520,6 @@ async def ollama_model_if_cache(
                 return best_cached_response
         else:
             # Use regular cache
-            args_hash = compute_args_hash(model, messages)
             if_cache_return = await hashing_kv.get_by_id(args_hash)
             if if_cache_return is not None:
                 return if_cache_return["return"]
@@ -561,6 +584,11 @@ def initialize_lmdeploy_pipeline(
     return lmdeploy_pipe
 
 
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
 async def lmdeploy_model_if_cache(
     model,
     prompt,
@@ -639,7 +667,11 @@ async def lmdeploy_model_if_cache(
     hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
+
     if hashing_kv is not None:
+        # Calculate args_hash only when using cache
+        args_hash = compute_args_hash(model, messages)
+
         # Get embedding cache configuration
         embedding_cache_config = hashing_kv.global_config.get(
             "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
@@ -659,7 +691,6 @@ async def lmdeploy_model_if_cache(
                 return best_cached_response
         else:
             # Use regular cache
-            args_hash = compute_args_hash(model, messages)
             if_cache_return = await hashing_kv.get_by_id(args_hash)
             if if_cache_return is not None:
                 return if_cache_return["return"]
@@ -850,7 +881,8 @@ async def openai_embedding(
 )
 async def nvidia_openai_embedding(
     texts: list[str],
-    model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1",  # refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
+    model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1",
+    # refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
     base_url: str = "https://integrate.api.nvidia.com/v1",
     api_key: str = None,
     input_type: str = "passage",  # query for retrieval, passage for embedding