87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
from langchain_core.runnables import RunnableLambda
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from langchain_openai import ChatOpenAI
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from llama_index.llms.openai_like import OpenAILike
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from app.config.ai_configs import ai_configs
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from app.utils.LLamaIndexEmbeddings import LLamaIndexEmbeddings
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def create_llm(
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platform_code='huoshan-doubao',
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temperature=0.5,
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disable_streaming=False,
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):
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_ai_config = ai_configs.get(platform_code)
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if _ai_config is None:
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raise Exception('Unknown platform code', platform_code)
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return ChatOpenAI(
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base_url=_ai_config.get('url').replace("chat/completions", ""),
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api_key=_ai_config.get('key'),
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model=_ai_config.get('model'),
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temperature=temperature,
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disable_streaming=disable_streaming,
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extra_body={"enable_thinking": False, "thinking": {"type": "disabled"}}
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)
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def create_llama_index_llm(platform_code='huoshan-doubao', temperature=0.5):
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_ai_config = ai_configs.get(platform_code)
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if _ai_config is None:
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raise Exception('Unknown platform code', platform_code)
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return OpenAILike(
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api_base=_ai_config.get('url').replace("chat/completions", ""),
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api_key=_ai_config.get('key'),
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model=_ai_config.get('model'),
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temperature=temperature,
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max_tokens=None, # 不限制最大token
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is_chat_model=True, # 明确指定是聊天模型
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timeout=120.0, # 增加超时时间
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)
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def create_embeddings(platform_code="bailian-embedding"):
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"""
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创建自定义嵌入模型实例
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参数:
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platform_code: 平台代码，用于从默认配置中查找对应平台的API信息
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返回:
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LLamaIndexEmbeddings类的实例，用于生成文本嵌入向量
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异常:
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当找不到对应平台代码的配置时抛出异常
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"""
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# 从默认配置中获取指定平台的AI配置信息
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_ai_config = ai_configs.get(platform_code)
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# 检查配置是否存在
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if _ai_config is None:
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raise Exception('Unknown platform code', platform_code)
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# 创建并返回自定义嵌入模型实例
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return LLamaIndexEmbeddings(
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base_url=_ai_config.get('url').replace("/embeddings", ""), # API基础URL
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api_key=_ai_config.get('key'), # API密钥
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model=_ai_config.get('model') # 嵌入模型名称
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)
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def chain_log(format_func=None):
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"""创建一个函数，用于在链中打印上一个管道的结果"""
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def func(val):
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print("\033[34m chain log==>>", format_func(val) if format_func is not None else val, '\033[0m')
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return val
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return func
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def runnable_chain_log(format_func=None):
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"""创建一个Runnable对象，用于在链中打印上一个管道的结果，如果上一个管道是字典对象，那么打印这个字典对象需要使用runnable_chain_log"""
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return RunnableLambda(chain_log(format_func))
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