panda
b280c2b453
Add rag submodule for semantic JRXML chunk retrieval, refactor retrieve node to use RAGSearcher, and fix missing api_key in Anthropic SDK client initialization. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
"""大语言模型工厂：支持 OpenAI 兼容的云端 API、Anthropic 兼容 API 和本地 Ollama。"""
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import os
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from typing import Any
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from dotenv import load_dotenv
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load_dotenv()
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def get_llm():
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backend = os.getenv("LLM_BACKEND", "cloud")
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if backend == "local":
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from langchain_ollama import ChatOllama
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model = os.getenv("LOCAL_LLM_MODEL", "qwen2.5-coder:7b")
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return ChatOllama(model=model, temperature=0.1)
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provider = os.getenv("LLM_PROVIDER", "openai")
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if provider == "anthropic":
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from anthropic import Anthropic
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api_key = os.getenv("OPENAI_API_KEY", "")
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base_url = os.getenv("OPENAI_BASE_URL", "https://api.minimaxi.com/anthropic")
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model = os.getenv("LLM_MODEL", "minimax-2.7")
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temperature = 0.1
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max_tokens = 4096
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os.environ["NO_PROXY"] = "*"
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client = Anthropic(api_key=api_key, base_url=base_url, timeout=120)
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class MiniMaxLLM:
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def invoke(self, prompt: str) -> Any:
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resp = client.messages.create(
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model=model,
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max_tokens=max_tokens,
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temperature=temperature,
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messages=[{"role": "user", "content": [{"type": "text", "text": prompt}]}],
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)
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for block in resp.content:
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if block.type == "text":
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return type("Response", (), {"content": block.text})()
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return type("Response", (), {"content": ""})()
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def get_num_tokens(self, text: str) -> int:
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return client.count_tokens(text)
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return MiniMaxLLM()
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else:
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from langchain_openai import ChatOpenAI
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return ChatOpenAI(
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model=os.getenv("LLM_MODEL", "gpt-4o"),
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
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temperature=0.1,
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)
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def get_llm_for_correction():
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return get_llm() |