Merge pull request #408 from magicyuan876/main

修复 args_hash在使用常规缓存时候才计算导致embedding缓存时没有计算的bug
2024-12-06 10:42:42 +08:00
parent 2a2756d9d1 7c4bbe2474
commit 8a6796b9dc
2 changed files with 54 additions and 18 deletions
--- a/README.md
+++ b/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

--- a/lightrag/llm.py
+++ b/lightrag/llm.py
@@ -1,12 +1,16 @@
-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
+
 import aioboto3
 import aiohttp
 import numpy as np
 import ollama
-
+import torch
 from openai import (
    AsyncOpenAI,
    APIConnectionError,
@@ -14,10 +18,7 @@ from openai import (
    Timeout,
    AsyncAzureOpenAI,
 )
-
-import base64
-import struct
-
+from pydantic import BaseModel, Field
 from tenacity import (
    retry,
    stop_after_attempt,
@@ -25,9 +26,7 @@ from tenacity import (
    retry_if_exception_type,
 )
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-from pydantic import BaseModel, Field
-from typing import List, Dict, Callable, Any
+
 from .base import BaseKVStorage
 from .utils import (
    compute_args_hash,
@@ -66,7 +65,11 @@ 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}
@@ -86,7 +89,6 @@ 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"]
@@ -159,7 +161,11 @@ 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}
@@ -178,7 +184,7 @@ 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"]
@@ -271,6 +277,9 @@ 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}
@@ -290,7 +299,6 @@ 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"]
@@ -343,6 +351,11 @@ 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,
@@ -360,6 +373,9 @@ 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}
@@ -379,7 +395,6 @@ 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"]
@@ -448,6 +463,11 @@ 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,
@@ -468,7 +488,11 @@ 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}
@@ -488,7 +512,6 @@ 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"]
@@ -542,6 +565,11 @@ 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,
@@ -620,7 +648,11 @@ 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}
@@ -640,7 +672,6 @@ 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"]
@@ -831,7 +862,8 @@ 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