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
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@@ -162,7 +162,6 @@ async def azure_openai_complete_if_cache(
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if prompt is not None:
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messages.append({"role": "user", "content": prompt})
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hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
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if hashing_kv is not None:
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# Calculate args_hash only when using cache
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args_hash = compute_args_hash(model, messages)
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@@ -373,7 +372,6 @@ async def hf_model_if_cache(
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
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if hashing_kv is not None:
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# Calculate args_hash only when using cache
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args_hash = compute_args_hash(model, messages)
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@@ -491,7 +489,6 @@ async def ollama_model_if_cache(
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
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if hashing_kv is not None:
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# Calculate args_hash only when using cache
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args_hash = compute_args_hash(model, messages)
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@@ -652,7 +649,6 @@ async def lmdeploy_model_if_cache(
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
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if hashing_kv is not None:
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# Calculate args_hash only when using cache
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args_hash = compute_args_hash(model, messages)
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