feat(cache): 增加 LLM 相似性检查功能并优化缓存机制

- 在 embedding 缓存配置中添加 use_llm_check 参数
- 实现 LLM 相似性检查逻辑，作为缓存命中的二次验证- 优化 naive 模式的缓存处理流程
- 调整缓存数据结构，移除不必要的 model 字段

README.md

@@ -596,11 +596,7 @@ 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 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}` |
+| **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}` |
 
 ## API Server Implementation
 

lightrag/lightrag.py

@@ -87,7 +87,11 @@ class LightRAG:
     )
     # Default not to use embedding cache
     embedding_cache_config: dict = field(
-        default_factory=lambda: {"enabled": False, "similarity_threshold": 0.95}
+        default_factory=lambda: {
+            "enabled": False,
+            "similarity_threshold": 0.95,
+            "use_llm_check": False,
+        }
     )
     kv_storage: str = field(default="JsonKVStorage")
     vector_storage: str = field(default="NanoVectorDBStorage")
@@ -174,7 +178,6 @@ class LightRAG:
             if self.enable_llm_cache
             else None
         )
-
         self.embedding_func = limit_async_func_call(self.embedding_func_max_async)(
             self.embedding_func
         )
@@ -481,6 +484,7 @@ class LightRAG:
                 self.text_chunks,
                 param,
                 asdict(self),
+                hashing_kv=self.llm_response_cache,
             )
         elif param.mode == "naive":
             response = await naive_query(
@@ -489,6 +493,7 @@ class LightRAG:
                 self.text_chunks,
                 param,
                 asdict(self),
+                hashing_kv=self.llm_response_cache,
             )
         else:
             raise ValueError(f"Unknown mode {param.mode}")

lightrag/llm.py

@@ -4,8 +4,7 @@ import json
 import os
 import struct
 from functools import lru_cache
-from typing import List, Dict, Callable, Any, Union, Optional
-from dataclasses import dataclass
+from typing import List, Dict, Callable, Any, Union
 import aioboto3
 import aiohttp
 import numpy as np
@@ -27,13 +26,9 @@ from tenacity import (
 )
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-from .base import BaseKVStorage
 from .utils import (
-    compute_args_hash,
     wrap_embedding_func_with_attrs,
     locate_json_string_body_from_string,
-    quantize_embedding,
-    get_best_cached_response,
 )
 
 import sys
@@ -66,23 +61,13 @@ async def openai_complete_if_cache(
     openai_async_client = (
         AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
     )
-
+    kwargs.pop("hashing_kv", None)
     messages = []
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
 
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
-    # Handle cache
-    mode = kwargs.pop("mode", "default")
-    args_hash = compute_args_hash(model, messages)
-    cached_response, quantized, min_val, max_val = await handle_cache(
-        hashing_kv, args_hash, prompt, mode
-    )
-    if cached_response is not None:
-        return cached_response
-
     if "response_format" in kwargs:
         response = await openai_async_client.beta.chat.completions.parse(
             model=model, messages=messages, **kwargs
@@ -95,21 +80,6 @@ async def openai_complete_if_cache(
     if r"\u" in content:
         content = content.encode("utf-8").decode("unicode_escape")
 
-    # Save to cache
-    await save_to_cache(
-        hashing_kv,
-        CacheData(
-            args_hash=args_hash,
-            content=content,
-            model=model,
-            prompt=prompt,
-            quantized=quantized,
-            min_val=min_val,
-            max_val=max_val,
-            mode=mode,
-        ),
-    )
-
     return content
 
 
@@ -140,10 +110,7 @@ async def azure_openai_complete_if_cache(
         api_key=os.getenv("AZURE_OPENAI_API_KEY"),
         api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
     )
-
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
-    mode = kwargs.pop("mode", "default")
-
+    kwargs.pop("hashing_kv", None)
     messages = []
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})
@@ -151,34 +118,11 @@ async def azure_openai_complete_if_cache(
     if prompt is not None:
         messages.append({"role": "user", "content": prompt})
 
-    # Handle cache
-    args_hash = compute_args_hash(model, messages)
-    cached_response, quantized, min_val, max_val = await handle_cache(
-        hashing_kv, args_hash, prompt, mode
-    )
-    if cached_response is not None:
-        return cached_response
-
     response = await openai_async_client.chat.completions.create(
         model=model, messages=messages, **kwargs
     )
     content = response.choices[0].message.content
 
-    # Save to cache
-    await save_to_cache(
-        hashing_kv,
-        CacheData(
-            args_hash=args_hash,
-            content=content,
-            model=model,
-            prompt=prompt,
-            quantized=quantized,
-            min_val=min_val,
-            max_val=max_val,
-            mode=mode,
-        ),
-    )
-
     return content
 
 
@@ -210,7 +154,7 @@ async def bedrock_complete_if_cache(
     os.environ["AWS_SESSION_TOKEN"] = os.environ.get(
         "AWS_SESSION_TOKEN", aws_session_token
     )
-
+    kwargs.pop("hashing_kv", None)
     # Fix message history format
     messages = []
     for history_message in history_messages:
@@ -220,15 +164,6 @@ async def bedrock_complete_if_cache(
 
     # Add user prompt
     messages.append({"role": "user", "content": [{"text": prompt}]})
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
-    # Handle cache
-    mode = kwargs.pop("mode", "default")
-    args_hash = compute_args_hash(model, messages)
-    cached_response, quantized, min_val, max_val = await handle_cache(
-        hashing_kv, args_hash, prompt, mode
-    )
-    if cached_response is not None:
-        return cached_response
 
     # Initialize Converse API arguments
     args = {"modelId": model, "messages": messages}
@@ -251,15 +186,6 @@ async def bedrock_complete_if_cache(
             args["inferenceConfig"][inference_params_map.get(param, param)] = (
                 kwargs.pop(param)
             )
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
-    # Handle cache
-    mode = kwargs.pop("mode", "default")
-    args_hash = compute_args_hash(model, messages)
-    cached_response, quantized, min_val, max_val = await handle_cache(
-        hashing_kv, args_hash, prompt, mode
-    )
-    if cached_response is not None:
-        return cached_response
 
     # Call model via Converse API
     session = aioboto3.Session()
@@ -269,21 +195,6 @@ async def bedrock_complete_if_cache(
         except Exception as e:
             raise BedrockError(e)
 
-    # Save to cache
-    await save_to_cache(
-        hashing_kv,
-        CacheData(
-            args_hash=args_hash,
-            content=response["output"]["message"]["content"][0]["text"],
-            model=model,
-            prompt=prompt,
-            quantized=quantized,
-            min_val=min_val,
-            max_val=max_val,
-            mode=mode,
-        ),
-    )
-
     return response["output"]["message"]["content"][0]["text"]
 
 
@@ -315,22 +226,12 @@ async def hf_model_if_cache(
 ) -> str:
     model_name = model
     hf_model, hf_tokenizer = initialize_hf_model(model_name)
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages = []
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
-
-    # Handle cache
-    mode = kwargs.pop("mode", "default")
-    args_hash = compute_args_hash(model, messages)
-    cached_response, quantized, min_val, max_val = await handle_cache(
-        hashing_kv, args_hash, prompt, mode
-    )
-    if cached_response is not None:
-        return cached_response
-
+    kwargs.pop("hashing_kv", None)
     input_prompt = ""
     try:
         input_prompt = hf_tokenizer.apply_chat_template(
@@ -375,21 +276,6 @@ async def hf_model_if_cache(
         output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True
     )
 
-    # Save to cache
-    await save_to_cache(
-        hashing_kv,
-        CacheData(
-            args_hash=args_hash,
-            content=response_text,
-            model=model,
-            prompt=prompt,
-            quantized=quantized,
-            min_val=min_val,
-            max_val=max_val,
-            mode=mode,
-        ),
-    )
-
     return response_text
 
 
@@ -410,25 +296,14 @@ async def ollama_model_if_cache(
     # kwargs.pop("response_format", None) # allow json
     host = kwargs.pop("host", None)
     timeout = kwargs.pop("timeout", None)
-
+    kwargs.pop("hashing_kv", None)
     ollama_client = ollama.AsyncClient(host=host, timeout=timeout)
     messages = []
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})
-
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
 
-    # Handle cache
-    mode = kwargs.pop("mode", "default")
-    args_hash = compute_args_hash(model, messages)
-    cached_response, quantized, min_val, max_val = await handle_cache(
-        hashing_kv, args_hash, prompt, mode
-    )
-    if cached_response is not None:
-        return cached_response
-
     response = await ollama_client.chat(model=model, messages=messages, **kwargs)
     if stream:
         """ cannot cache stream response """
@@ -441,38 +316,7 @@ async def ollama_model_if_cache(
     else:
         result = response["message"]["content"]
         # Save to cache
-        await save_to_cache(
-            hashing_kv,
-            CacheData(
-                args_hash=args_hash,
-                content=result,
-                model=model,
-                prompt=prompt,
-                quantized=quantized,
-                min_val=min_val,
-                max_val=max_val,
-                mode=mode,
-            ),
-        )
         return result
-    result = response["message"]["content"]
-
-    # Save to cache
-    await save_to_cache(
-        hashing_kv,
-        CacheData(
-            args_hash=args_hash,
-            content=result,
-            model=model,
-            prompt=prompt,
-            quantized=quantized,
-            min_val=min_val,
-            max_val=max_val,
-            mode=mode,
-        ),
-    )
-
-    return result
 
 
 @lru_cache(maxsize=1)
@@ -547,7 +391,7 @@ async def lmdeploy_model_if_cache(
         from lmdeploy import version_info, GenerationConfig
     except Exception:
         raise ImportError("Please install lmdeploy before intialize lmdeploy backend.")
-
+    kwargs.pop("hashing_kv", None)
     kwargs.pop("response_format", None)
     max_new_tokens = kwargs.pop("max_tokens", 512)
     tp = kwargs.pop("tp", 1)
@@ -579,19 +423,9 @@ async def lmdeploy_model_if_cache(
     if system_prompt:
         messages.append({"role": "system", "content": system_prompt})
 
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
 
-    # Handle cache
-    mode = kwargs.pop("mode", "default")
-    args_hash = compute_args_hash(model, messages)
-    cached_response, quantized, min_val, max_val = await handle_cache(
-        hashing_kv, args_hash, prompt, mode
-    )
-    if cached_response is not None:
-        return cached_response
-
     gen_config = GenerationConfig(
         skip_special_tokens=skip_special_tokens,
         max_new_tokens=max_new_tokens,
@@ -607,22 +441,6 @@ async def lmdeploy_model_if_cache(
         session_id=1,
     ):
         response += res.response
-
-    # Save to cache
-    await save_to_cache(
-        hashing_kv,
-        CacheData(
-            args_hash=args_hash,
-            content=response,
-            model=model,
-            prompt=prompt,
-            quantized=quantized,
-            min_val=min_val,
-            max_val=max_val,
-            mode=mode,
-        ),
-    )
-
     return response
 
 
@@ -1052,75 +870,6 @@ class MultiModel:
         return await next_model.gen_func(**args)
 
 
-async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
-    """Generic cache handling function"""
-    if hashing_kv is None:
-        return None, None, None, None
-
-    # Get embedding cache configuration
-    embedding_cache_config = hashing_kv.global_config.get(
-        "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
-    )
-    is_embedding_cache_enabled = embedding_cache_config["enabled"]
-
-    quantized = min_val = max_val = None
-    if is_embedding_cache_enabled:
-        # Use embedding cache
-        embedding_model_func = hashing_kv.global_config["embedding_func"]["func"]
-        current_embedding = await embedding_model_func([prompt])
-        quantized, min_val, max_val = quantize_embedding(current_embedding[0])
-        best_cached_response = await get_best_cached_response(
-            hashing_kv,
-            current_embedding[0],
-            similarity_threshold=embedding_cache_config["similarity_threshold"],
-            mode=mode,
-        )
-        if best_cached_response is not None:
-            return best_cached_response, None, None, None
-    else:
-        # Use regular cache
-        mode_cache = await hashing_kv.get_by_id(mode) or {}
-        if args_hash in mode_cache:
-            return mode_cache[args_hash]["return"], None, None, None
-
-    return None, quantized, min_val, max_val
-
-
-@dataclass
-class CacheData:
-    args_hash: str
-    content: str
-    model: str
-    prompt: str
-    quantized: Optional[np.ndarray] = None
-    min_val: Optional[float] = None
-    max_val: Optional[float] = None
-    mode: str = "default"
-
-
-async def save_to_cache(hashing_kv, cache_data: CacheData):
-    if hashing_kv is None:
-        return
-
-    mode_cache = await hashing_kv.get_by_id(cache_data.mode) or {}
-
-    mode_cache[cache_data.args_hash] = {
-        "return": cache_data.content,
-        "model": cache_data.model,
-        "embedding": cache_data.quantized.tobytes().hex()
-        if cache_data.quantized is not None
-        else None,
-        "embedding_shape": cache_data.quantized.shape
-        if cache_data.quantized is not None
-        else None,
-        "embedding_min": cache_data.min_val,
-        "embedding_max": cache_data.max_val,
-        "original_prompt": cache_data.prompt,
-    }
-
-    await hashing_kv.upsert({cache_data.mode: mode_cache})
-
-
 if __name__ == "__main__":
     import asyncio
 

lightrag/operate.py

@@ -17,6 +17,10 @@ from .utils import (
     split_string_by_multi_markers,
     truncate_list_by_token_size,
     process_combine_contexts,
+    compute_args_hash,
+    handle_cache,
+    save_to_cache,
+    CacheData,
 )
 from .base import (
     BaseGraphStorage,
@@ -452,8 +456,17 @@ async def kg_query(
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
     global_config: dict,
+    hashing_kv: BaseKVStorage = None,
 ) -> str:
-    context = None
+    # Handle cache
+    use_model_func = global_config["llm_model_func"]
+    args_hash = compute_args_hash(query_param.mode, query)
+    cached_response, quantized, min_val, max_val = await handle_cache(
+        hashing_kv, args_hash, query, query_param.mode
+    )
+    if cached_response is not None:
+        return cached_response
+
     example_number = global_config["addon_params"].get("example_number", None)
     if example_number and example_number < len(PROMPTS["keywords_extraction_examples"]):
         examples = "\n".join(
@@ -471,12 +484,9 @@ async def kg_query(
         return PROMPTS["fail_response"]
 
     # LLM generate keywords
-    use_model_func = global_config["llm_model_func"]
     kw_prompt_temp = PROMPTS["keywords_extraction"]
     kw_prompt = kw_prompt_temp.format(query=query, examples=examples, language=language)
-    result = await use_model_func(
-        kw_prompt, keyword_extraction=True, mode=query_param.mode
-    )
+    result = await use_model_func(kw_prompt, keyword_extraction=True)
     logger.info("kw_prompt result:")
     print(result)
     try:
@@ -537,7 +547,6 @@ async def kg_query(
         query,
         system_prompt=sys_prompt,
         stream=query_param.stream,
-        mode=query_param.mode,
     )
     if isinstance(response, str) and len(response) > len(sys_prompt):
         response = (
@@ -550,6 +559,20 @@ async def kg_query(
             .strip()
         )
 
+    # Save to cache
+    await save_to_cache(
+        hashing_kv,
+        CacheData(
+            args_hash=args_hash,
+            content=response,
+            prompt=query,
+            quantized=quantized,
+            min_val=min_val,
+            max_val=max_val,
+            mode=query_param.mode,
+        ),
+    )
+
     return response
 
 
@@ -1013,8 +1036,17 @@ async def naive_query(
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
     global_config: dict,
+    hashing_kv: BaseKVStorage = None,
 ):
+    # Handle cache
     use_model_func = global_config["llm_model_func"]
+    args_hash = compute_args_hash(query_param.mode, query)
+    cached_response, quantized, min_val, max_val = await handle_cache(
+        hashing_kv, args_hash, query, query_param.mode
+    )
+    if cached_response is not None:
+        return cached_response
+
     results = await chunks_vdb.query(query, top_k=query_param.top_k)
     if not len(results):
         return PROMPTS["fail_response"]
@@ -1039,7 +1071,6 @@ async def naive_query(
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
-        mode=query_param.mode,
     )
 
     if len(response) > len(sys_prompt):
@@ -1054,4 +1085,18 @@ async def naive_query(
             .strip()
         )
 
+    # Save to cache
+    await save_to_cache(
+        hashing_kv,
+        CacheData(
+            args_hash=args_hash,
+            content=response,
+            prompt=query,
+            quantized=quantized,
+            min_val=min_val,
+            max_val=max_val,
+            mode=query_param.mode,
+        ),
+    )
+
     return response

lightrag/prompt.py

@@ -261,3 +261,22 @@ Do not include information where the supporting evidence for it is not provided.
 
 Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown.
 """
+
+PROMPTS[
+    "similarity_check"
+] = """Please analyze the similarity between these two questions:
+
+Question 1: {original_prompt}
+Question 2: {cached_prompt}
+
+Please evaluate:
+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
+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/utils.py

@@ -15,6 +15,8 @@ import xml.etree.ElementTree as ET
 import numpy as np
 import tiktoken
 
+from lightrag.prompt import PROMPTS
+
 ENCODER = None
 
 logger = logging.getLogger("lightrag")
@@ -314,6 +316,9 @@ async def get_best_cached_response(
     current_embedding,
     similarity_threshold=0.95,
     mode="default",
+    use_llm_check=False,
+    llm_func=None,
+    original_prompt=None,
 ) -> Union[str, None]:
     # Get mode-specific cache
     mode_cache = await hashing_kv.get_by_id(mode)
@@ -348,6 +353,37 @@ async def get_best_cached_response(
             best_cache_id = cache_id
 
     if best_similarity > similarity_threshold:
+        # If LLM check is enabled and all required parameters are provided
+        if use_llm_check and llm_func and original_prompt and best_prompt:
+            compare_prompt = PROMPTS["similarity_check"].format(
+                original_prompt=original_prompt, cached_prompt=best_prompt
+            )
+
+            try:
+                llm_result = await llm_func(compare_prompt)
+                llm_result = llm_result.strip()
+                llm_similarity = float(llm_result)
+
+                # Replace vector similarity with LLM similarity score
+                best_similarity = llm_similarity
+                if best_similarity < similarity_threshold:
+                    log_data = {
+                        "event": "llm_check_cache_rejected",
+                        "original_question": original_prompt[:100] + "..."
+                        if len(original_prompt) > 100
+                        else original_prompt,
+                        "cached_question": best_prompt[:100] + "..."
+                        if len(best_prompt) > 100
+                        else best_prompt,
+                        "similarity_score": round(best_similarity, 4),
+                        "threshold": similarity_threshold,
+                    }
+                    logger.info(json.dumps(log_data, ensure_ascii=False))
+                    return None
+            except Exception as e:  # Catch all possible exceptions
+                logger.warning(f"LLM similarity check failed: {e}")
+                return None  # Return None directly when LLM check fails
+
         prompt_display = (
             best_prompt[:50] + "..." if len(best_prompt) > 50 else best_prompt
         )
@@ -391,21 +427,33 @@ def dequantize_embedding(
     scale = (max_val - min_val) / (2**bits - 1)
     return (quantized * scale + min_val).astype(np.float32)
 
+
 async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
     """Generic cache handling function"""
     if hashing_kv is None:
         return None, None, None, None
 
+    # For naive mode, only use simple cache matching
+    if mode == "naive":
+        mode_cache = await hashing_kv.get_by_id(mode) or {}
+        if args_hash in mode_cache:
+            return mode_cache[args_hash]["return"], None, None, None
+        return None, None, None, None
+
     # Get embedding cache configuration
     embedding_cache_config = hashing_kv.global_config.get(
-        "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
+        "embedding_cache_config",
+        {"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False},
     )
     is_embedding_cache_enabled = embedding_cache_config["enabled"]
+    use_llm_check = embedding_cache_config.get("use_llm_check", False)
 
     quantized = min_val = max_val = None
     if is_embedding_cache_enabled:
         # Use embedding cache
         embedding_model_func = hashing_kv.global_config["embedding_func"]["func"]
+        llm_model_func = hashing_kv.global_config.get("llm_model_func")
+
         current_embedding = await embedding_model_func([prompt])
         quantized, min_val, max_val = quantize_embedding(current_embedding[0])
         best_cached_response = await get_best_cached_response(
@@ -413,6 +461,9 @@ async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
             current_embedding[0],
             similarity_threshold=embedding_cache_config["similarity_threshold"],
             mode=mode,
+            use_llm_check=use_llm_check,
+            llm_func=llm_model_func if use_llm_check else None,
+            original_prompt=prompt if use_llm_check else None,
         )
         if best_cached_response is not None:
             return best_cached_response, None, None, None
@@ -429,7 +480,6 @@ async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
 class CacheData:
     args_hash: str
     content: str
-    model: str
     prompt: str
     quantized: Optional[np.ndarray] = None
     min_val: Optional[float] = None
@@ -445,7 +495,6 @@ async def save_to_cache(hashing_kv, cache_data: CacheData):
 
     mode_cache[cache_data.args_hash] = {
         "return": cache_data.content,
-        "model": cache_data.model,
         "embedding": cache_data.quantized.tobytes().hex()
         if cache_data.quantized is not None
         else None,