mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-26 16:53:36 +08:00
支持deepseek客户端
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glide-the
parent
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commit
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@ -0,0 +1,59 @@
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from typing import Dict, List, Type
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import openai
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from httpx import Timeout
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from model_providers.core.model_runtime.errors.invoke import (
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InvokeAuthorizationError,
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InvokeBadRequestError,
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InvokeConnectionError,
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InvokeError,
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InvokeRateLimitError,
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InvokeServerUnavailableError,
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)
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class _CommonDeepseek:
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def _to_credential_kwargs(self, credentials: dict) -> dict:
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"""
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Transform credentials to kwargs for model instance
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:param credentials:
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:return:
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"""
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credentials_kwargs = {
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"api_key": credentials["api_key"],
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"timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
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"max_retries": 1,
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}
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if "base_url" in credentials and credentials["base_url"]:
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credentials_kwargs["base_url"] = credentials["base_url"]
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return credentials_kwargs
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@property
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def _invoke_error_mapping(self) -> Dict[Type[InvokeError], List[Type[Exception]]]:
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"""
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Map model invoke error to unified error
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The key is the error type thrown to the caller
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The value is the error type thrown by the model,
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which needs to be converted into a unified error type for the caller.
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:return: Invoke error mapping
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"""
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return {
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InvokeConnectionError: [openai.APIConnectionError, openai.APITimeoutError],
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InvokeServerUnavailableError: [openai.InternalServerError],
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InvokeRateLimitError: [openai.RateLimitError],
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InvokeAuthorizationError: [
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openai.AuthenticationError,
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openai.PermissionDeniedError,
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],
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InvokeBadRequestError: [
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openai.BadRequestError,
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openai.NotFoundError,
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openai.UnprocessableEntityError,
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openai.APIError,
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],
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}
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@ -0,0 +1,18 @@
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import logging
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from model_providers.core.model_runtime.model_providers.__base.model_provider import (
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ModelProvider,
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)
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logger = logging.getLogger(__name__)
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class DeepseekProvider(ModelProvider):
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def validate_provider_credentials(self, credentials: dict) -> None:
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"""
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Validate provider credentials
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if validate failed, raise exception
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:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
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"""
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pass
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@ -0,0 +1,44 @@
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provider: deepseek
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label:
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en_US: Deepseek
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icon_large:
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en_US: icon_l_en.svg
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icon_small:
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en_US: icon_s_en.svg
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background: "#F9FAFB"
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help:
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title:
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en_US: How to integrate with Deepseek
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zh_Hans: 如何集成 Deepseek
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url:
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en_US: "deepseek"
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supported_model_types:
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- llm
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configurate_methods:
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- customizable-model
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model_credential_schema:
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model:
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label:
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en_US: Model Name
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zh_Hans: 模型名称
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placeholder:
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en_US: Enter your model name
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zh_Hans: 输入模型名称
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credential_form_schemas:
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- variable: api_key
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label:
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en_US: API Key
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type: secret-input
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required: true
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placeholder:
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zh_Hans: 在此输入您的 API Key
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en_US: Enter your API Key
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- variable: base_url
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label:
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zh_Hans: API Base
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en_US: API Base
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type: text-input
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required: false
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placeholder:
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zh_Hans: 在此输入您的 API Base
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en_US: Enter your API Base
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@ -27,9 +27,8 @@ class _CommonOllama:
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"max_retries": 1,
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}
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if "openai_api_base" in credentials and credentials["openai_api_base"]:
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credentials["openai_api_base"] = credentials["openai_api_base"].rstrip("/")
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credentials_kwargs["base_url"] = credentials["openai_api_base"] + "/v1"
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if "base_url" in credentials and credentials["base_url"]:
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credentials_kwargs["base_url"] = credentials["base_url"]
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return credentials_kwargs
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@ -1,6 +1,7 @@
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import logging
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from collections.abc import Generator
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from typing import List, Optional, Union, cast
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from decimal import Decimal
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import tiktoken
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from openai import OpenAI, Stream
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@ -39,7 +40,7 @@ from model_providers.core.model_runtime.entities.model_entities import (
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FetchFrom,
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I18nObject,
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ModelType,
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PriceConfig,
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PriceConfig, ModelFeature, ModelPropertyKey, DefaultParameterName, ParameterRule, ParameterType,
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)
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from model_providers.core.model_runtime.errors.validate import (
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CredentialsValidateFailedError,
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@ -1116,47 +1117,223 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
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return num_tokens
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def get_customizable_model_schema(
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self, model: str, credentials: dict
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self, model: str, credentials: dict
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) -> AIModelEntity:
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"""
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OpenAI supports fine-tuning of their models. This method returns the schema of the base model
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but renamed to the fine-tuned model name.
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Get customizable model schema.
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:param model: model name
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:param credentials: credentials
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:return: model schema
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"""
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if not model.startswith("ft:"):
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base_model = model
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else:
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# get base_model
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base_model = model.split(":")[1]
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extras = {}
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# get model schema
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models = self.predefined_models()
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model_map = {model.model: model for model in models}
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if base_model not in model_map:
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raise ValueError(f"Base model {base_model} not found")
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base_model_schema = model_map[base_model]
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base_model_schema_features = base_model_schema.features or []
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base_model_schema_model_properties = base_model_schema.model_properties or {}
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base_model_schema_parameters_rules = base_model_schema.parameter_rules or []
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if "vision_support" in credentials and credentials["vision_support"] == "true":
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extras["features"] = [ModelFeature.VISION]
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entity = AIModelEntity(
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model=model,
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label=I18nObject(zh_Hans=model, en_US=model),
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model_type=ModelType.LLM,
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features=[feature for feature in base_model_schema_features],
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fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
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model_properties={
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key: property
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for key, property in base_model_schema_model_properties.items()
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ModelPropertyKey.MODE: credentials.get("mode"),
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ModelPropertyKey.CONTEXT_SIZE: int(
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credentials.get("context_size", 4096)
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),
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},
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parameter_rules=[rule for rule in base_model_schema_parameters_rules],
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pricing=base_model_schema.pricing,
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parameter_rules=[
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ParameterRule(
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name=DefaultParameterName.TEMPERATURE.value,
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use_template=DefaultParameterName.TEMPERATURE.value,
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label=I18nObject(en_US="Temperature"),
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type=ParameterType.FLOAT,
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help=I18nObject(
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en_US="The temperature of the model. "
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"Increasing the temperature will make the model answer "
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"more creatively. (Default: 0.8)"
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),
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default=0.8,
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min=0,
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max=2,
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),
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ParameterRule(
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name=DefaultParameterName.TOP_P.value,
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use_template=DefaultParameterName.TOP_P.value,
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label=I18nObject(en_US="Top P"),
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type=ParameterType.FLOAT,
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help=I18nObject(
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en_US="Works together with top-k. A higher value (e.g., 0.95) will lead to "
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"more diverse text, while a lower value (e.g., 0.5) will generate more "
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"focused and conservative text. (Default: 0.9)"
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),
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default=0.9,
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min=0,
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max=1,
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),
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ParameterRule(
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name="top_k",
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label=I18nObject(en_US="Top K"),
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type=ParameterType.INT,
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help=I18nObject(
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en_US="Reduces the probability of generating nonsense. "
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"A higher value (e.g. 100) will give more diverse answers, "
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"while a lower value (e.g. 10) will be more conservative. (Default: 40)"
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),
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default=40,
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min=1,
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max=100,
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),
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ParameterRule(
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name="repeat_penalty",
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label=I18nObject(en_US="Repeat Penalty"),
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type=ParameterType.FLOAT,
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help=I18nObject(
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en_US="Sets how strongly to penalize repetitions. "
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"A higher value (e.g., 1.5) will penalize repetitions more strongly, "
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"while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)"
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),
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default=1.1,
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min=-2,
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max=2,
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),
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ParameterRule(
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name="num_predict",
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use_template="max_tokens",
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label=I18nObject(en_US="Num Predict"),
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type=ParameterType.INT,
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help=I18nObject(
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en_US="Maximum number of tokens to predict when generating text. "
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"(Default: 128, -1 = infinite generation, -2 = fill context)"
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),
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default=128,
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min=-2,
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max=int(credentials.get("max_tokens", 4096)),
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),
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ParameterRule(
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name="mirostat",
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label=I18nObject(en_US="Mirostat sampling"),
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type=ParameterType.INT,
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help=I18nObject(
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en_US="Enable Mirostat sampling for controlling perplexity. "
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"(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"
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),
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default=0,
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min=0,
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max=2,
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),
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ParameterRule(
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name="mirostat_eta",
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label=I18nObject(en_US="Mirostat Eta"),
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type=ParameterType.FLOAT,
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help=I18nObject(
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en_US="Influences how quickly the algorithm responds to feedback from "
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"the generated text. A lower learning rate will result in slower adjustments, "
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"while a higher learning rate will make the algorithm more responsive. "
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"(Default: 0.1)"
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),
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default=0.1,
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precision=1,
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),
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ParameterRule(
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name="mirostat_tau",
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label=I18nObject(en_US="Mirostat Tau"),
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type=ParameterType.FLOAT,
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help=I18nObject(
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en_US="Controls the balance between coherence and diversity of the output. "
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"A lower value will result in more focused and coherent text. (Default: 5.0)"
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),
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default=5.0,
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precision=1,
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),
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ParameterRule(
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name="num_ctx",
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label=I18nObject(en_US="Size of context window"),
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type=ParameterType.INT,
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help=I18nObject(
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en_US="Sets the size of the context window used to generate the next token. "
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"(Default: 2048)"
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),
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default=2048,
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min=1,
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),
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ParameterRule(
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name="num_gpu",
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label=I18nObject(en_US="Num GPU"),
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type=ParameterType.INT,
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help=I18nObject(
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en_US="The number of layers to send to the GPU(s). "
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"On macOS it defaults to 1 to enable metal support, 0 to disable."
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),
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default=1,
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min=0,
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max=1,
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),
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ParameterRule(
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name="num_thread",
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label=I18nObject(en_US="Num Thread"),
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type=ParameterType.INT,
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help=I18nObject(
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en_US="Sets the number of threads to use during computation. "
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"By default, Ollama will detect this for optimal performance. "
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"It is recommended to set this value to the number of physical CPU cores "
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"your system has (as opposed to the logical number of cores)."
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),
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min=1,
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),
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ParameterRule(
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name="repeat_last_n",
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label=I18nObject(en_US="Repeat last N"),
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type=ParameterType.INT,
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help=I18nObject(
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en_US="Sets how far back for the model to look back to prevent repetition. "
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"(Default: 64, 0 = disabled, -1 = num_ctx)"
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),
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default=64,
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min=-1,
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),
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ParameterRule(
|
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name="tfs_z",
|
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label=I18nObject(en_US="TFS Z"),
|
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type=ParameterType.FLOAT,
|
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help=I18nObject(
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en_US="Tail free sampling is used to reduce the impact of less probable tokens "
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"from the output. A higher value (e.g., 2.0) will reduce the impact more, "
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"while a value of 1.0 disables this setting. (default: 1)"
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),
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default=1,
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precision=1,
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),
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ParameterRule(
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name="seed",
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label=I18nObject(en_US="Seed"),
|
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type=ParameterType.INT,
|
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help=I18nObject(
|
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en_US="Sets the random number seed to use for generation. Setting this to "
|
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"a specific number will make the model generate the same text for "
|
||||
"the same prompt. (Default: 0)"
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),
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default=0,
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),
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ParameterRule(
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name="format",
|
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label=I18nObject(en_US="Format"),
|
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type=ParameterType.STRING,
|
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help=I18nObject(
|
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en_US="the format to return a response in."
|
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" Currently the only accepted value is json."
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),
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options=["json"],
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),
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],
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pricing=PriceConfig(
|
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input=Decimal(credentials.get("input_price", 0)),
|
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output=Decimal(credentials.get("output_price", 0)),
|
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unit=Decimal(credentials.get("unit", 0)),
|
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currency=credentials.get("currency", "USD"),
|
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),
|
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**extras,
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)
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return entity
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@ -27,7 +27,7 @@ model_credential_schema:
|
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zh_Hans: 输入模型名称
|
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credential_form_schemas:
|
||||
|
||||
- variable: openai_api_base
|
||||
- variable: base_url
|
||||
label:
|
||||
zh_Hans: API Base
|
||||
en_US: API Base
|
||||
@ -35,4 +35,4 @@ model_credential_schema:
|
||||
required: false
|
||||
placeholder:
|
||||
zh_Hans: 在此输入您的 API Base
|
||||
en_US: Enter your API Base
|
||||
en_US: Enter your API Base
|
||||
|
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@ -0,0 +1,11 @@
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|
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deepseek:
|
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model_credential:
|
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- model: 'deepseek-chat'
|
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model_type: 'llm'
|
||||
model_credentials:
|
||||
base_url: 'https://api.deepseek.com'
|
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api_key: 'sk-dcb625fcbc1e497d80b7b9493b51d758'
|
||||
|
||||
|
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|
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@ -0,0 +1,21 @@
|
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from langchain.chains import LLMChain
|
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from langchain_core.prompts import PromptTemplate
|
||||
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
|
||||
import pytest
|
||||
import logging
|
||||
|
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logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
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def test_llm(init_server: str):
|
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llm = ChatOpenAI(model_name="deepseek-chat", openai_api_key="sk-dcb625fcbc1e497d80b7b9493b51d758", openai_api_base=f"{init_server}/deepseek/v1")
|
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template = """Question: {question}
|
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|
||||
Answer: Let's think step by step."""
|
||||
|
||||
prompt = PromptTemplate.from_template(template)
|
||||
|
||||
llm_chain = LLMChain(prompt=prompt, llm=llm)
|
||||
responses = llm_chain.run("你好")
|
||||
logger.info("\033[1;32m" + f"llm_chain: {responses}" + "\033[0m")
|
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@ -4,7 +4,7 @@ ollama:
|
||||
- model: 'llama3'
|
||||
model_type: 'llm'
|
||||
model_credentials:
|
||||
openai_api_base: 'http://172.21.80.1:11434'
|
||||
base_url: 'http://172.21.80.1:11434/v1'
|
||||
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,11 @@
|
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|
||||
deepseek:
|
||||
model_credential:
|
||||
- model: 'deepseek-chat'
|
||||
model_type: 'llm'
|
||||
model_credentials:
|
||||
base_url: 'https://api.deepseek.com'
|
||||
api_key: 'sk-dcb625fcbc1e497d80b7b9493b51d758'
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,39 @@
|
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import asyncio
|
||||
import logging
|
||||
|
||||
import pytest
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
from model_providers import BootstrapWebBuilder, _to_custom_provide_configuration
|
||||
from model_providers.core.model_manager import ModelManager
|
||||
from model_providers.core.model_runtime.entities.model_entities import ModelType
|
||||
from model_providers.core.provider_manager import ProviderManager
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_ollama_provider_manager_models(logging_conf: dict, providers_file: str) -> None:
|
||||
logging.config.dictConfig(logging_conf) # type: ignore
|
||||
# 读取配置文件
|
||||
cfg = OmegaConf.load(
|
||||
providers_file
|
||||
)
|
||||
# 转换配置文件
|
||||
(
|
||||
provider_name_to_provider_records_dict,
|
||||
provider_name_to_provider_model_records_dict,
|
||||
) = _to_custom_provide_configuration(cfg)
|
||||
# 创建模型管理器
|
||||
provider_manager = ProviderManager(
|
||||
provider_name_to_provider_records_dict=provider_name_to_provider_records_dict,
|
||||
provider_name_to_provider_model_records_dict=provider_name_to_provider_model_records_dict,
|
||||
)
|
||||
|
||||
provider_model_bundle_llm = provider_manager.get_provider_model_bundle(
|
||||
provider="deepseek", model_type=ModelType.LLM
|
||||
)
|
||||
predefined_models = (
|
||||
provider_model_bundle_llm.model_type_instance.predefined_models()
|
||||
)
|
||||
|
||||
logger.info(f"predefined_models: {predefined_models}")
|
||||
@ -4,7 +4,7 @@ ollama:
|
||||
- model: 'llama3'
|
||||
model_type: 'llm'
|
||||
model_credentials:
|
||||
openai_api_base: 'http://172.21.80.1:11434'
|
||||
base_url: 'http://172.21.80.1:11434/v1'
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user