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from __future__ import annotations
import json
import re
import warnings
from typing import Dict
from langchain.callbacks.manager import AsyncCallbackManagerForChainRun, CallbackManagerForChainRun
from langchain.chains.llm import LLMChain
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema import BasePromptTemplate
from langchain.schema.language_model import BaseLanguageModel
from typing import List, Any, Optional
from langchain.prompts import PromptTemplate
from server.chat.knowledge_base_chat import knowledge_base_chat
from configs import VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, MAX_TOKENS
import asyncio
from server.agent import model_container
from pydantic import BaseModel, Field
async def search_knowledge_base_iter(database: str, query: str) -> str:
response = await knowledge_base_chat(query=query,
knowledge_base_name=database,
model_name=model_container.MODEL.model_name,
temperature=0.01,
history=[],
top_k=VECTOR_SEARCH_TOP_K,
max_tokens=MAX_TOKENS,
prompt_name="default",
score_threshold=SCORE_THRESHOLD,
stream=False)
contents = ""
async for data in response.body_iterator: # 这里的data是一个json字符串
data = json.loads(data)
contents += data["answer"]
docs = data["docs"]
return contents
async def search_knowledge_multiple(queries) -> List[str]:
# queries 应该是一个包含多个 (database, query) 元组的列表
tasks = [search_knowledge_base_iter(database, query) for database, query in queries]
results = await asyncio.gather(*tasks)
# 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
combined_results = []
for (database, _), result in zip(queries, results):
message = f"\n查询到 {database} 知识库的相关信息:\n{result}"
combined_results.append(message)
return combined_results
def search_knowledge(queries) -> str:
responses = asyncio.run(search_knowledge_multiple(queries))
# 输出每个整合的查询结果
contents = ""
for response in responses:
contents += response + "\n\n"
return contents
_PROMPT_TEMPLATE = """
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。
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。
例子:
robotic,机器人男女比例是多少
bigdata,大数据的就业情况如何
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
{database_names}
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。
不要输出中文的逗号，不要输出引号。
Question: ${{用户的问题}}
```text
${{知识库名称,查询问题,不要带有任何除了,之外的符号,比如不要输出中文的逗号，不要输出引号}}
```output
数据库查询的结果
现在，我们开始作答
问题: {question}
"""
PROMPT = PromptTemplate(
input_variables=["question", "database_names"],
template=_PROMPT_TEMPLATE,
)
class LLMKnowledgeChain(LLMChain):
llm_chain: LLMChain
llm: Optional[BaseLanguageModel] = None
"""[Deprecated] LLM wrapper to use."""
prompt: BasePromptTemplate = PROMPT
"""[Deprecated] Prompt to use to translate to python if necessary."""
database_names: Dict[str, str] = None
input_key: str = "question" #: :meta private:
output_key: str = "answer" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
if "llm" in values:
warnings.warn(
"Directly instantiating an LLMKnowledgeChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"] is not None:
prompt = values.get("prompt", PROMPT)
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
return values
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
return [self.output_key]
def _evaluate_expression(self, queries) -> str:
try:
output = search_knowledge(queries)
except Exception as e:
output = "输入的信息有误或不存在知识库,错误信息如下:\n"
return output + str(e)
return output
def _process_llm_result(
self,
llm_output: str,
run_manager: CallbackManagerForChainRun
) -> Dict[str, str]:
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
# text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
text_match = re.search(r"```text(.*)", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1).strip()
cleaned_input_str = (expression.replace("\"", "").replace("“", "").
replace("”", "").replace("```", "").strip())
lines = cleaned_input_str.split("\n")
# 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
try:
queries = [(line.split(",")[0].strip(), line.split(",")[1].strip()) for line in lines]
except:
queries = [(line.split("，")[0].strip(), line.split("，")[1].strip()) for line in lines]
run_manager.on_text("知识库查询询内容:\n\n" + str(queries) + " \n\n", color="blue", verbose=self.verbose)
output = self._evaluate_expression(queries)
run_manager.on_text("\nAnswer: ", verbose=self.verbose)
run_manager.on_text(output, color="yellow", verbose=self.verbose)
answer = "Answer: " + output
elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = llm_output.split("Answer:")[-1]
else:
return {self.output_key: f"输入的格式不对:\n {llm_output}"}
return {self.output_key: answer}
async def _aprocess_llm_result(
self,
llm_output: str,
run_manager: AsyncCallbackManagerForChainRun,
) -> Dict[str, str]:
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"```text(.*)", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1).strip()
cleaned_input_str = (
expression.replace("\"", "").replace("“", "").replace("”", "").replace("```", "").strip())
lines = cleaned_input_str.split("\n")
try:
queries = [(line.split(",")[0].strip(), line.split(",")[1].strip()) for line in lines]
except:
queries = [(line.split("，")[0].strip(), line.split("，")[1].strip()) for line in lines]
await run_manager.on_text("知识库查询询内容:\n\n" + str(queries) + " \n\n", color="blue",
verbose=self.verbose)
output = self._evaluate_expression(queries)
await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
await run_manager.on_text(output, color="yellow", verbose=self.verbose)
answer = "Answer: " + output
elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = "Answer: " + llm_output.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_run_manager.on_text(inputs[self.input_key])
self.database_names = model_container.DATABASE
data_formatted_str = ',\n'.join([f' "{k}":"{v}"' for k, v in self.database_names.items()])
llm_output = self.llm_chain.predict(
database_names=data_formatted_str,
question=inputs[self.input_key],
stop=["```output"],
callbacks=_run_manager.get_child(),
)
return self._process_llm_result(llm_output, _run_manager)
async def _acall(
self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
await _run_manager.on_text(inputs[self.input_key])
self.database_names = model_container.DATABASE
data_formatted_str = ',\n'.join([f' "{k}":"{v}"' for k, v in self.database_names.items()])
llm_output = await self.llm_chain.apredict(
database_names=data_formatted_str,
question=inputs[self.input_key],
stop=["```output"],
callbacks=_run_manager.get_child(),
)
return await self._aprocess_llm_result(llm_output, inputs[self.input_key], _run_manager)
@property
def _chain_type(self) -> str:
return "llm_knowledge_chain"
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMKnowledgeChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs)
def search_knowledgebase_complex(query: str):
model = model_container.MODEL
llm_knowledge = LLMKnowledgeChain.from_llm(model, verbose=True, prompt=PROMPT)
ans = llm_knowledge.run(query)
return ans
class KnowledgeSearchInput(BaseModel):
location: str = Field(description="The query to be searched")
if __name__ == "__main__":
result = search_knowledgebase_complex("机器人和大数据在代码教学上有什么区别")
print(result)
# 这是一个正常的切割
# queries = [
# ("bigdata", "大数据专业的男女比例"),
# ("robotic", "机器人专业的优势")
# ]
# result = search_knowledge(queries)
# print(result)
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