291 lines
11 KiB
Python
291 lines
11 KiB
Python
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import json
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import re
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import warnings
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from typing import Dict
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from langchain.callbacks.manager import AsyncCallbackManagerForChainRun, CallbackManagerForChainRun
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from langchain.chains.llm import LLMChain
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from langchain.pydantic_v1 import Extra, root_validator
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from langchain.schema import BasePromptTemplate
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from langchain.schema.language_model import BaseLanguageModel
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from typing import List, Any, Optional
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from langchain.prompts import PromptTemplate
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from server.chat.knowledge_base_chat import knowledge_base_chat
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from configs import VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD
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import asyncio
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from server.agent import model_container
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async def search_knowledge_base_iter(database: str, query: str) -> str:
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response = await knowledge_base_chat(query=query,
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knowledge_base_name=database,
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model_name=model_container.MODEL.model_name,
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temperature=0.01,
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history=[],
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top_k=VECTOR_SEARCH_TOP_K,
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max_tokens=None,
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prompt_name="default",
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score_threshold=SCORE_THRESHOLD,
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stream=False)
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contents = ""
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async for data in response.body_iterator: # 这里的data是一个json字符串
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data = json.loads(data)
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contents += data["answer"]
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docs = data["docs"]
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return contents
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async def search_knowledge_multiple(queries) -> List[str]:
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# queries 应该是一个包含多个 (database, query) 元组的列表
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tasks = [search_knowledge_base_iter(database, query) for database, query in queries]
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results = await asyncio.gather(*tasks)
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# 结合每个查询结果，并在每个查询结果前添加一个自定义的消息
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combined_results = []
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for (database, _), result in zip(queries, results):
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message = f"\n查询到 {database} 知识库的相关信息:\n{result}"
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combined_results.append(message)
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return combined_results
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def search_knowledge(queries) -> str:
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responses = asyncio.run(search_knowledge_multiple(queries))
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# 输出每个整合的查询结果
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contents = ""
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for response in responses:
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contents += response + "\n\n"
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return contents
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_PROMPT_TEMPLATE = """
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用户会提出一个需要你查询知识库的问题，你应该对问题进行理解和拆解，并在知识库中查询相关的内容。
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对于每个知识库，你输出的内容应该是一个一行的字符串，这行字符串包含知识库名称和查询内容，中间用逗号隔开，不要有多余的文字和符号。你可以同时查询多个知识库，下面这个例子就是同时查询两个知识库的内容。
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例子:
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robotic,机器人男女比例是多少
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bigdata,大数据的就业情况如何
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这些数据库是你能访问的，冒号之前是他们的名字，冒号之后是他们的功能，你应该参考他们的功能来帮助你思考
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{database_names}
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你的回答格式应该按照下面的内容，请注意```text 等标记都必须输出，这是我用来提取答案的标记。
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Question: ${{用户的问题}}
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```text
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${{知识库名称,查询问题,不要带有任何除了,之外的符号}}
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```output
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数据库查询的结果
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这是一个完整的问题拆分和提问的例子：
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问题: 分别对比机器人和大数据专业的就业情况并告诉我哪儿专业的就业情况更好？
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```text
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robotic,机器人专业的就业情况
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bigdata,大数据专业的就业情况
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现在，我们开始作答
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问题: {question}
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"""
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PROMPT = PromptTemplate(
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input_variables=["question", "database_names"],
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template=_PROMPT_TEMPLATE,
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)
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class LLMKnowledgeChain(LLMChain):
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llm_chain: LLMChain
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llm: Optional[BaseLanguageModel] = None
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"""[Deprecated] LLM wrapper to use."""
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prompt: BasePromptTemplate = PROMPT
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"""[Deprecated] Prompt to use to translate to python if necessary."""
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database_names: Dict[str, str] = None
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input_key: str = "question" #: :meta private:
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output_key: str = "answer" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator(pre=True)
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def raise_deprecation(cls, values: Dict) -> Dict:
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if "llm" in values:
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warnings.warn(
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"Directly instantiating an LLMKnowledgeChain with an llm is deprecated. "
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"Please instantiate with llm_chain argument or using the from_llm "
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"class method."
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)
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if "llm_chain" not in values and values["llm"] is not None:
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prompt = values.get("prompt", PROMPT)
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values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
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return values
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Expect output key.
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:meta private:
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"""
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return [self.output_key]
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def _evaluate_expression(self, queries) -> str:
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try:
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output = search_knowledge(queries)
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except Exception as e:
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output = "输入的信息有误或不存在知识库,错误信息如下:\n"
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return output + str(e)
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return output
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def _process_llm_result(
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self,
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llm_output: str,
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run_manager: CallbackManagerForChainRun
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) -> Dict[str, str]:
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run_manager.on_text(llm_output, color="green", verbose=self.verbose)
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llm_output = llm_output.strip()
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# text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
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text_match = re.search(r"```text(.*)", llm_output, re.DOTALL)
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if text_match:
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expression = text_match.group(1).strip()
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cleaned_input_str = (expression.replace("\"", "").replace("“", "").
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replace("”", "").replace("```", "").strip())
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lines = cleaned_input_str.split("\n")
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# 使用逗号分割每一行，然后形成一个（数据库，查询）元组的列表
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queries = [(line.split(",")[0].strip(), line.split(",")[1].strip()) for line in lines]
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run_manager.on_text("知识库查询询内容:\n\n" + str(queries) + " \n\n", color="blue", verbose=self.verbose)
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output = self._evaluate_expression(queries)
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run_manager.on_text("\nAnswer: ", verbose=self.verbose)
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run_manager.on_text(output, color="yellow", verbose=self.verbose)
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answer = "Answer: " + output
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elif llm_output.startswith("Answer:"):
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answer = llm_output
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elif "Answer:" in llm_output:
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answer = llm_output.split("Answer:")[-1]
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else:
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return {self.output_key: f"输入的格式不对:\n {llm_output}"}
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return {self.output_key: answer}
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async def _aprocess_llm_result(
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self,
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llm_output: str,
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run_manager: AsyncCallbackManagerForChainRun,
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) -> Dict[str, str]:
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await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
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llm_output = llm_output.strip()
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text_match = re.search(r"```text(.*)", llm_output, re.DOTALL)
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if text_match:
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expression = text_match.group(1).strip()
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cleaned_input_str = (
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expression.replace("\"", "").replace("“", "").replace("”", "").replace("```", "").strip())
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lines = cleaned_input_str.split("\n")
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queries = [(line.split(",")[0].strip(), line.split(",")[1].strip()) for line in lines]
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await run_manager.on_text("知识库查询询内容:\n\n" + str(queries) + " \n\n", color="blue",
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verbose=self.verbose)
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output = self._evaluate_expression(queries)
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await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
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await run_manager.on_text(output, color="yellow", verbose=self.verbose)
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answer = "Answer: " + output
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elif llm_output.startswith("Answer:"):
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answer = llm_output
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elif "Answer:" in llm_output:
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answer = "Answer: " + llm_output.split("Answer:")[-1]
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else:
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raise ValueError(f"unknown format from LLM: {llm_output}")
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return {self.output_key: answer}
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def _call(
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self,
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inputs: Dict[str, str],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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_run_manager.on_text(inputs[self.input_key])
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self.database_names = model_container.DATABASE
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data_formatted_str = ',\n'.join([f' "{k}":"{v}"' for k, v in self.database_names.items()])
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llm_output = self.llm_chain.predict(
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database_names=data_formatted_str,
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question=inputs[self.input_key],
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stop=["```output"],
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callbacks=_run_manager.get_child(),
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)
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return self._process_llm_result(llm_output, _run_manager)
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async def _acall(
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self,
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inputs: Dict[str, str],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
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await _run_manager.on_text(inputs[self.input_key])
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self.database_names = model_container.DATABASE
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data_formatted_str = ',\n'.join([f' "{k}":"{v}"' for k, v in self.database_names.items()])
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llm_output = await self.llm_chain.apredict(
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database_names=data_formatted_str,
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question=inputs[self.input_key],
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stop=["```output"],
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callbacks=_run_manager.get_child(),
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)
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return await self._aprocess_llm_result(llm_output, inputs[self.input_key], _run_manager)
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@property
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def _chain_type(self) -> str:
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return "llm_knowledge_chain"
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate = PROMPT,
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**kwargs: Any,
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):
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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return cls(llm_chain=llm_chain, **kwargs)
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def knowledge_search_more(query: str):
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model = model_container.MODEL
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llm_knowledge = LLMKnowledgeChain.from_llm(model, verbose=True, prompt=PROMPT)
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ans = llm_knowledge.run(query)
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return ans
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if __name__ == "__main__":
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result = knowledge_search_more("机器人和大数据在代码教学上有什么区别")
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print(result)
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# 这是一个正常的切割
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# queries = [
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# ("bigdata", "大数据专业的男女比例"),
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# ("robotic", "机器人专业的优势")
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# ]
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# result = search_knowledge(queries)
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# print(result)
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