增加了知识图谱导出excel
This commit is contained in:
chentianrui
@@ -1,111 +1,118 @@
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import requests
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from typing import Any, Dict, List, Optional
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from langchain_core.language_models import BaseLLM
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.outputs import LLMResult, Generation
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from openai import OpenAI
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import httpx
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import logging
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# import requests
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# from typing import Any, Dict, List, Optional
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# from langchain_core.language_models import BaseLLM
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# from langchain_core.callbacks import CallbackManagerForLLMRun
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# from langchain_core.outputs import LLMResult, Generation
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# from openai import OpenAI
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# import httpx
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# import logging
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# class OpenAiLLM:
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# # class OpenAiLLM:
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# # def __init__(self, url, api_key, model_name):
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# # self._api_key = api_key
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# # self._url = url
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# # self._model = model_name
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# # def generate(self, prompt):
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# # client = OpenAI(api_key=self._api_key, base_url=self._url)
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# # try:
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# # # 创建 Completion 请求
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# # completion = client.chat.completions.create(
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# # model=self._model,
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# # messages=[
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# # {"role": "system", "content": "You are a helpful assistant"},
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# # {"role": "user", "content": prompt}
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# # ],
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# # timeout=httpx.Timeout(300.0),
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# # temperature=0.7,
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# # )
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# # return completion.choices[0].message.content
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# # except Exception as e:
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# # logging.error(f"LLM调用出错: {e}")
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# # return f"模型调用失败: {str(e)}"
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# # llm = OpenAiLLM(
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# # url="http://172.20.0.145:9995/v1",
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# # api_key="xxx",
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# # model_name="deepseek-r1-distill-qwen2.5-32b",
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# # )
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# class Embedding:
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# def __init__(self, url, api_key, model_name):
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# self._api_key = api_key
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# self._url = url
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# self._model = model_name
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# def generate(self, prompt):
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# def embed(self, text):
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# # 使用OpenAI客户端
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# client = OpenAI(api_key=self._api_key, base_url=self._url)
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# try:
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# # 创建 Completion 请求
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# completion = client.chat.completions.create(
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# model=self._model,
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# messages=[
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# {"role": "system", "content": "You are a helpful assistant"},
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# {"role": "user", "content": prompt}
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# ],
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# timeout=httpx.Timeout(300.0),
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# temperature=0.7,
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# )
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# return completion.choices[0].message.content
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# # 调用embeddings API
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# response = client.embeddings.create(model=self._model, input=text, timeout=httpx.Timeout(60.0))
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# # 返回嵌入向量
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# return response.data[0].embedding
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# except Exception as e:
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# logging.error(f"LLM调用出错: {e}")
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# return f"模型调用失败: {str(e)}"
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# logging.error(f"嵌入模型调用出错: {e}")
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# raise RuntimeError(f"嵌入请求失败: {str(e)}")
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# llm = OpenAiLLM(
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# url="http://172.20.0.145:9995/v1",
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# api_key="xxx",
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# model_name="deepseek-r1-distill-qwen2.5-32b",
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# embedding = Embedding(url="http://172.20.0.145:9995/v1", api_key="xxx", model_name="bge-m3")
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# class SiliconFlowLLM(BaseLLM):
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# """自定义硅基流动大模型调用类"""
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# api_url: str
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# api_key: str
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# model: str
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# def _generate(
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# self,
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# prompts: List[str],
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# stop: Optional[List[str]] = None,
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# run_manager: Optional[CallbackManagerForLLMRun] = None,
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# **kwargs: Any,
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# ) -> LLMResult:
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# from langchain_core.outputs import Generation, LLMResult
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# generations = []
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# for prompt in prompts:
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# try:
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# headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
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# payload = {
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# "model": self.model,
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# "messages": [{"role": "user", "content": prompt}],
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# }
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# response = requests.post(self.api_url, json=payload, headers=headers)
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# response.raise_for_status()
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# text = response.json()["choices"][0]["message"]["content"]
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# generations.append([Generation(text=text)])
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# except Exception as e:
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# raise Exception(f"调用硅基流动API失败: {str(e)}")
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# return LLMResult(generations=generations)
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# @property
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# def _llm_type(self) -> str:
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# return "siliconflow"
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# llm = SiliconFlowLLM(
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# api_url="https://api.siliconflow.cn/v1/chat/completions",
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# api_key="sk-bbeamiumkouptsrueilgufqqyuumelcsivxwjbdugqwsqhwj",
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# model="Qwen/Qwen2.5-72B-Instruct",
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# )
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from equipment_calculation.main import bcl_calculate
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class Embedding:
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def __init__(self, url, api_key, model_name):
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self._api_key = api_key
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self._url = url
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self._model = model_name
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input_dir = "project2json/outputs/merged"
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output_dir = "project2json/outputs/bclresults"
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def embed(self, text):
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# 使用OpenAI客户端
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client = OpenAI(api_key=self._api_key, base_url=self._url)
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try:
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# 调用embeddings API
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response = client.embeddings.create(model=self._model, input=text, timeout=httpx.Timeout(60.0))
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# 返回嵌入向量
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return response.data[0].embedding
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except Exception as e:
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logging.error(f"嵌入模型调用出错: {e}")
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raise RuntimeError(f"嵌入请求失败: {str(e)}")
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embedding = Embedding(url="http://172.20.0.145:9995/v1", api_key="xxx", model_name="bge-m3")
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class SiliconFlowLLM(BaseLLM):
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"""自定义硅基流动大模型调用类"""
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api_url: str
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api_key: str
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model: str
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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from langchain_core.outputs import Generation, LLMResult
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generations = []
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for prompt in prompts:
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try:
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headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
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payload = {
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"model": self.model,
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"messages": [{"role": "user", "content": prompt}],
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}
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response = requests.post(self.api_url, json=payload, headers=headers)
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response.raise_for_status()
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text = response.json()["choices"][0]["message"]["content"]
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generations.append([Generation(text=text)])
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except Exception as e:
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raise Exception(f"调用硅基流动API失败: {str(e)}")
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return LLMResult(generations=generations)
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@property
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def _llm_type(self) -> str:
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return "siliconflow"
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llm = SiliconFlowLLM(
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api_url="https://api.siliconflow.cn/v1/chat/completions",
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api_key="sk-bbeamiumkouptsrueilgufqqyuumelcsivxwjbdugqwsqhwj",
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model="Qwen/Qwen2.5-72B-Instruct",
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)
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bcl_calculate(input_dir, output_dir)
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