fix: 修复Ollama嵌入模型接口和前台不一致的问题 issue#65
This commit is contained in:
zstar
parent
c1c517c4c0
commit
3496fff83c
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@ -52,16 +52,8 @@ class TenantLLMService(CommonService):
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@classmethod
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@DB.connection_context()
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def get_my_llms(cls, tenant_id):
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fields = [
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cls.model.llm_factory,
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LLMFactories.logo,
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LLMFactories.tags,
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cls.model.model_type,
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cls.model.llm_name,
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cls.model.used_tokens
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]
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objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(
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cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
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fields = [cls.model.llm_factory, LLMFactories.logo, LLMFactories.tags, cls.model.model_type, cls.model.llm_name, cls.model.used_tokens]
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objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
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return list(objs)
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@ -117,8 +109,7 @@ class TenantLLMService(CommonService):
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model_config = {"llm_factory": llm[0].fid, "api_key": "", "llm_name": mdlnm, "api_base": ""}
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if not model_config:
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if mdlnm == "flag-embedding":
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model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "",
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"llm_name": llm_name, "api_base": ""}
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model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "", "llm_name": llm_name, "api_base": ""}
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else:
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if not mdlnm:
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raise LookupError(f"Type of {llm_type} model is not set.")
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@ -127,43 +118,32 @@ class TenantLLMService(CommonService):
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@classmethod
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@DB.connection_context()
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def model_instance(cls, tenant_id, llm_type,
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llm_name=None, lang="Chinese"):
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def model_instance(cls, tenant_id, llm_type, llm_name=None, lang="Chinese"):
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model_config = TenantLLMService.get_model_config(tenant_id, llm_type, llm_name)
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if llm_type == LLMType.EMBEDDING.value:
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if model_config["llm_factory"] not in EmbeddingModel:
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return
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return EmbeddingModel[model_config["llm_factory"]](
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model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
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return EmbeddingModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
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if llm_type == LLMType.RERANK:
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if model_config["llm_factory"] not in RerankModel:
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return
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return RerankModel[model_config["llm_factory"]](
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model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
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return RerankModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
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if llm_type == LLMType.IMAGE2TEXT.value:
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if model_config["llm_factory"] not in CvModel:
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return
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return CvModel[model_config["llm_factory"]](
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model_config["api_key"], model_config["llm_name"], lang,
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base_url=model_config["api_base"]
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)
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return CvModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], lang, base_url=model_config["api_base"])
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if llm_type == LLMType.CHAT.value:
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if model_config["llm_factory"] not in ChatModel:
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return
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return ChatModel[model_config["llm_factory"]](
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model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
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return ChatModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
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if llm_type == LLMType.SPEECH2TEXT:
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if model_config["llm_factory"] not in Seq2txtModel:
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return
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return Seq2txtModel[model_config["llm_factory"]](
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key=model_config["api_key"], model_name=model_config["llm_name"],
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lang=lang,
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base_url=model_config["api_base"]
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)
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return Seq2txtModel[model_config["llm_factory"]](key=model_config["api_key"], model_name=model_config["llm_name"], lang=lang, base_url=model_config["api_base"])
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if llm_type == LLMType.TTS:
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if model_config["llm_factory"] not in TTSModel:
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return
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@ -187,7 +167,7 @@ class TenantLLMService(CommonService):
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LLMType.IMAGE2TEXT.value: tenant.img2txt_id,
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LLMType.CHAT.value: tenant.llm_id if not llm_name else llm_name,
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LLMType.RERANK.value: tenant.rerank_id if not llm_name else llm_name,
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LLMType.TTS.value: tenant.tts_id if not llm_name else llm_name
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LLMType.TTS.value: tenant.tts_id if not llm_name else llm_name,
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}
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mdlnm = llm_map.get(llm_type)
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@ -198,17 +178,13 @@ class TenantLLMService(CommonService):
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llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(mdlnm)
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try:
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num = cls.model.update(
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used_tokens=cls.model.used_tokens + used_tokens
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).where(
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cls.model.tenant_id == tenant_id,
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cls.model.llm_name == llm_name,
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cls.model.llm_factory == llm_factory if llm_factory else True
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).execute()
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num = (
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cls.model.update(used_tokens=cls.model.used_tokens + used_tokens)
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.where(cls.model.tenant_id == tenant_id, cls.model.llm_name == llm_name, cls.model.llm_factory == llm_factory if llm_factory else True)
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.execute()
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)
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except Exception:
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logging.exception(
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"TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s",
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tenant_id, llm_name)
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logging.exception("TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s", tenant_id, llm_name)
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return 0
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return num
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@ -216,11 +192,7 @@ class TenantLLMService(CommonService):
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@classmethod
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@DB.connection_context()
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def get_openai_models(cls):
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objs = cls.model.select().where(
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(cls.model.llm_factory == "OpenAI"),
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~(cls.model.llm_name == "text-embedding-3-small"),
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~(cls.model.llm_name == "text-embedding-3-large")
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).dicts()
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objs = cls.model.select().where((cls.model.llm_factory == "OpenAI"), ~(cls.model.llm_name == "text-embedding-3-small"), ~(cls.model.llm_name == "text-embedding-3-large")).dicts()
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return list(objs)
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@ -229,79 +201,59 @@ class LLMBundle:
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self.tenant_id = tenant_id
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self.llm_type = llm_type
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self.llm_name = llm_name
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self.mdl = TenantLLMService.model_instance(
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tenant_id, llm_type, llm_name, lang=lang)
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assert self.mdl, "Can't find model for {}/{}/{}".format(
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tenant_id, llm_type, llm_name)
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self.mdl = TenantLLMService.model_instance(tenant_id, llm_type, llm_name, lang=lang)
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assert self.mdl, "Can't find model for {}/{}/{}".format(tenant_id, llm_type, llm_name)
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model_config = TenantLLMService.get_model_config(tenant_id, llm_type, llm_name)
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self.max_length = model_config.get("max_tokens", 8192)
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def encode(self, texts: list):
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embeddings, used_tokens = self.mdl.encode(texts)
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if not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, used_tokens):
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logging.error(
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"LLMBundle.encode can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
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if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
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logging.error("LLMBundle.encode can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
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return embeddings, used_tokens
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def encode_queries(self, query: str):
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emd, used_tokens = self.mdl.encode_queries(query)
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if not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, used_tokens):
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logging.error(
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"LLMBundle.encode_queries can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
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if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
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logging.error("LLMBundle.encode_queries can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
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return emd, used_tokens
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def similarity(self, query: str, texts: list):
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sim, used_tokens = self.mdl.similarity(query, texts)
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if not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, used_tokens):
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logging.error(
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"LLMBundle.similarity can't update token usage for {}/RERANK used_tokens: {}".format(self.tenant_id, used_tokens))
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if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
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logging.error("LLMBundle.similarity can't update token usage for {}/RERANK used_tokens: {}".format(self.tenant_id, used_tokens))
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return sim, used_tokens
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def describe(self, image, max_tokens=300):
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txt, used_tokens = self.mdl.describe(image, max_tokens)
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if not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, used_tokens):
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logging.error(
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"LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
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if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
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logging.error("LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
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return txt
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def transcription(self, audio):
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txt, used_tokens = self.mdl.transcription(audio)
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if not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, used_tokens):
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logging.error(
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"LLMBundle.transcription can't update token usage for {}/SEQUENCE2TXT used_tokens: {}".format(self.tenant_id, used_tokens))
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if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
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logging.error("LLMBundle.transcription can't update token usage for {}/SEQUENCE2TXT used_tokens: {}".format(self.tenant_id, used_tokens))
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return txt
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def tts(self, text):
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for chunk in self.mdl.tts(text):
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if isinstance(chunk, int):
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if not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, chunk, self.llm_name):
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logging.error(
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"LLMBundle.tts can't update token usage for {}/TTS".format(self.tenant_id))
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if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, chunk, self.llm_name):
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logging.error("LLMBundle.tts can't update token usage for {}/TTS".format(self.tenant_id))
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return
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yield chunk
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def chat(self, system, history, gen_conf):
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txt, used_tokens = self.mdl.chat(system, history, gen_conf)
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if isinstance(txt, int) and not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, used_tokens, self.llm_name):
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logging.error(
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"LLMBundle.chat can't update token usage for {}/CHAT llm_name: {}, used_tokens: {}".format(self.tenant_id, self.llm_name,
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used_tokens))
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if isinstance(txt, int) and not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, self.llm_name):
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logging.error("LLMBundle.chat can't update token usage for {}/CHAT llm_name: {}, used_tokens: {}".format(self.tenant_id, self.llm_name, used_tokens))
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return txt
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def chat_streamly(self, system, history, gen_conf):
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for txt in self.mdl.chat_streamly(system, history, gen_conf):
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if isinstance(txt, int):
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if not TenantLLMService.increase_usage(
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self.tenant_id, self.llm_type, txt, self.llm_name):
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logging.error(
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"LLMBundle.chat_streamly can't update token usage for {}/CHAT llm_name: {}, content: {}".format(self.tenant_id, self.llm_name,
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txt))
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if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, txt, self.llm_name):
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logging.error("LLMBundle.chat_streamly can't update token usage for {}/CHAT llm_name: {}, content: {}".format(self.tenant_id, self.llm_name, txt))
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return
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yield txt
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@ -0,0 +1,110 @@
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import requests
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import time
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# Ollama配置
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OLLAMA_HOST = "http://localhost:11434" # 默认Ollama地址
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MODEL_NAME = "bge-m3" # 使用的embedding模型
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TEXT_TO_EMBED = "测试文本"
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# 定义接口URL和对应的请求体结构
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ENDPOINTS = {
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"api/embeddings": {
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"url": f"{OLLAMA_HOST}/api/embeddings", # 原生API路径
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"payload": {"model": MODEL_NAME, "prompt": TEXT_TO_EMBED}, # 原生API用prompt字段
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},
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"v1/embeddings": {
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"url": f"{OLLAMA_HOST}/v1/embeddings", # OpenAI兼容API路径
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"payload": {"model": MODEL_NAME, "input": TEXT_TO_EMBED}, # OpenAI兼容API用input字段
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},
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}
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headers = {"Content-Type": "application/json"}
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def test_endpoint(endpoint_name, endpoint_info):
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"""测试单个端点并返回结果"""
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print(f"\n测试接口: {endpoint_name}")
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url = endpoint_info["url"]
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payload = endpoint_info["payload"]
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try:
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start_time = time.time()
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response = requests.post(url, headers=headers, json=payload)
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response_time = time.time() - start_time
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print(f"状态码: {response.status_code}")
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print(f"响应时间: {response_time:.3f}秒")
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try:
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data = response.json()
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# 处理不同接口的响应结构差异
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embedding = None
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if endpoint_name == "api/embeddings":
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embedding = data.get("embedding") # 原生API返回embedding字段
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elif endpoint_name == "v1/embeddings":
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embedding = data.get("data", [{}])[0].get("embedding") # OpenAI兼容API返回data数组中的embedding
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if embedding:
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print(f"Embedding向量长度: {len(embedding)}")
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return {
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"endpoint": endpoint_name,
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"status_code": response.status_code,
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"response_time": response_time,
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"embedding_length": len(embedding),
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"embedding": embedding[:5],
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}
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else:
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print("响应中未找到'embedding'字段")
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return {"endpoint": endpoint_name, "status_code": response.status_code, "error": "No embedding field in response"}
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except ValueError:
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print("响应不是有效的JSON格式")
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return {"endpoint": endpoint_name, "status_code": response.status_code, "error": "Invalid JSON response"}
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except Exception as e:
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print(f"请求失败: {str(e)}")
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return {"endpoint": endpoint_name, "error": str(e)}
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def compare_endpoints():
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"""比较两个端点的性能"""
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results = []
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print("=" * 50)
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print(f"开始比较Ollama的embeddings接口，使用模型: {MODEL_NAME}")
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print("=" * 50)
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for endpoint_name, endpoint_info in ENDPOINTS.items():
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results.append(test_endpoint(endpoint_name, endpoint_info))
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print("\n" + "=" * 50)
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print("比较结果摘要:")
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print("=" * 50)
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successful_results = [res for res in results if "embedding_length" in res]
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if len(successful_results) == 2:
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if successful_results[0]["embedding_length"] == successful_results[1]["embedding_length"]:
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print(f"两个接口返回的embedding维度相同: {successful_results[0]['embedding_length']}")
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else:
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print("两个接口返回的embedding维度不同:")
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for result in successful_results:
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print(f"- {result['endpoint']}: {result['embedding_length']}")
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print("\nEmbedding前5个元素示例:")
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for result in successful_results:
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print(f"- {result['endpoint']}: {result['embedding']}")
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faster = min(successful_results, key=lambda x: x["response_time"])
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slower = max(successful_results, key=lambda x: x["response_time"])
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print(f"\n更快的接口: {faster['endpoint']} ({faster['response_time']:.3f}秒 vs {slower['response_time']:.3f}秒)")
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else:
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print("至少有一个接口未返回有效的embedding数据")
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for result in results:
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if "error" in result:
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print(f"- {result['endpoint']} 错误: {result['error']}")
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if __name__ == "__main__":
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compare_endpoints()
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@ -0,0 +1,64 @@
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import numpy as np
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from abc import ABC
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from ollama import Client
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class Base(ABC):
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def __init__(self, key, model_name):
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pass
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def encode(self, texts: list):
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raise NotImplementedError("Please implement encode method!")
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def encode_queries(self, text: str):
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raise NotImplementedError("Please implement encode method!")
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def total_token_count(self, resp):
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try:
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return resp.usage.total_tokens
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except Exception:
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pass
|
||||
try:
|
||||
return resp["usage"]["total_tokens"]
|
||||
except Exception:
|
||||
pass
|
||||
return 0
|
||||
|
||||
|
||||
class OllamaEmbed(Base):
|
||||
def __init__(self, model_name, **kwargs):
|
||||
self.client = Client(host="http://localhost:11434", **kwargs)
|
||||
self.model_name = model_name
|
||||
|
||||
def encode(self, texts: list):
|
||||
arr = []
|
||||
tks_num = 0
|
||||
for txt in texts:
|
||||
res = self.client.embeddings(prompt=txt, model=self.model_name)
|
||||
arr.append(res["embedding"])
|
||||
tks_num += 128
|
||||
return np.array(arr), tks_num
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 初始化嵌入模型
|
||||
embedder = OllamaEmbed(model_name="bge-m3")
|
||||
|
||||
# 测试文本
|
||||
test_texts = ["测试文本"]
|
||||
|
||||
# 获取嵌入向量和token计数
|
||||
embeddings, total_tokens = embedder.encode(test_texts)
|
||||
|
||||
# 打印结果
|
||||
print(f"Total tokens used: {total_tokens}")
|
||||
print("\nEmbedding vectors:")
|
||||
for i, (text, embedding) in enumerate(zip(test_texts, embeddings)):
|
||||
print(f"\nText {i + 1}: '{text}'")
|
||||
print(f"Embedding shape: {embedding.shape}")
|
||||
print(f"First 5 values: {embedding[:5]}")
|
||||
print(f"Embedding dtype: {embedding.dtype}")
|
||||
|
||||
# 打印完整的第一个embedding向量
|
||||
print("\nComplete first embedding vector:")
|
||||
print(embeddings[0])
|
||||
|
|
@ -5,23 +5,17 @@ import requests
|
|||
url = "https://api.siliconflow.cn/v1/embeddings"
|
||||
api_key = "你的API密钥" # 替换为你的API密钥
|
||||
|
||||
payload = {
|
||||
"model": "BAAI/bge-m3",
|
||||
"input": "Silicon flow embedding online: fast, affordable, and high-quality embedding services. come try it out!"
|
||||
}
|
||||
headers = {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
payload = {"model": "BAAI/bge-m3", "input": "测试文本"}
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
|
||||
response = requests.request("POST", url, json=payload, headers=headers)
|
||||
|
||||
print(response.text)
|
||||
|
||||
# print(response.text.data)
|
||||
# print(response.text.data)
|
||||
|
||||
# embedding_resp = response
|
||||
# embedding_data = embedding_resp.json()
|
||||
# q_1024_vec = embedding_data["data"][0]["embedding"]
|
||||
|
||||
# print("q_1024_vec", q_1024_vec)
|
||||
# print("q_1024_vec", q_1024_vec)
|
||||
|
|
|
|||
|
|
@ -248,21 +248,20 @@ def perform_parse(doc_id, doc_info, file_info, embedding_config, kb_info):
|
|||
if not embedding_api_base.startswith(("http://", "https://")):
|
||||
embedding_api_base = "http://" + embedding_api_base
|
||||
|
||||
# --- URL 拼接优化 (处理 /v1) ---
|
||||
endpoint_segment = "embeddings"
|
||||
full_endpoint_path = "v1/embeddings"
|
||||
# 移除末尾斜杠以方便判断
|
||||
normalized_base_url = embedding_api_base.rstrip("/")
|
||||
|
||||
if normalized_base_url.endswith("/v1"):
|
||||
# 如果 base_url 已经是 http://host/v1 形式
|
||||
embedding_url = normalized_base_url + "/" + endpoint_segment
|
||||
# 如果请求url端口号为11434，则认为是ollama模型，采用ollama特定的api
|
||||
is_ollama = "11434" in normalized_base_url
|
||||
if is_ollama:
|
||||
# Ollama 的特殊接口路径
|
||||
embedding_url = normalized_base_url + "/api/embeddings"
|
||||
elif normalized_base_url.endswith("/v1"):
|
||||
embedding_url = normalized_base_url + "/embeddings"
|
||||
elif normalized_base_url.endswith("/embeddings"):
|
||||
# 如果 base_url 已经是 http://host/embeddings 形式(比如硅基流动API，无需再进行处理)
|
||||
embedding_url = normalized_base_url
|
||||
else:
|
||||
# 如果 base_url 是 http://host 或 http://host/api 等其他形式
|
||||
embedding_url = normalized_base_url + "/" + full_endpoint_path
|
||||
embedding_url = normalized_base_url + "/v1/embeddings"
|
||||
|
||||
print(f"[Parser-INFO] 使用 Embedding 配置: URL='{embedding_url}', Model='{embedding_model_name}', Key={embedding_api_key}")
|
||||
|
||||
|
|
@ -535,8 +534,14 @@ def perform_parse(doc_id, doc_info, file_info, embedding_config, kb_info):
|
|||
|
||||
embedding_resp.raise_for_status()
|
||||
embedding_data = embedding_resp.json()
|
||||
q_1024_vec = embedding_data["data"][0]["embedding"]
|
||||
|
||||
# 对ollama嵌入模型的接口返回值进行特殊处理
|
||||
if is_ollama:
|
||||
q_1024_vec = embedding_data.get("embedding")
|
||||
else:
|
||||
q_1024_vec = embedding_data["data"][0]["embedding"]
|
||||
print(f"[Parser-INFO] 获取embedding成功，长度: {len(q_1024_vec)}")
|
||||
|
||||
# 检查向量维度是否为1024
|
||||
if len(q_1024_vec) != 1024:
|
||||
error_msg = f"[Parser-ERROR] Embedding向量维度不是1024，实际维度: {len(q_1024_vec)}, 建议使用bge-m3模型"
|
||||
|
|
|
|||
Loading…
Reference in New Issue