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Merge branch 'main' into main

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zrguo
2024-12-06 11:38:27 +08:00
committed by GitHub
parent 335179196a 3264fa0577
commit ad991f904d
3 changed files with 55 additions and 19 deletions
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6
README.md
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@@ -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

2
lightrag/kg/oracle_impl.py
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@@ -143,7 +143,7 @@ class OracleDB:
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:

66
lightrag/llm.py
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@@ -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, Union
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, 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