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from __future__ import annotations
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
import sys
import os
import json
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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):
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="knowledge_base_chat",
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
_PROMPT_TEMPLATE = """
用户会提出一个需要你查询知识库的问题，你应该按照我提供的思想进行思考
Question: ${{用户的问题}}
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：
{database_names}
你的回答格式应该按照下面的内容，请注意，格式内的```text 等标记都必须输出，这是我用来提取答案的标记。
```text
${{知识库的名称}}
```
```output
数据库查询的结果
```
答案: ${{答案}}
现在，这是我的问题：
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] = model_container.DATABASE
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, dataset, query) -> str:
try:
output = asyncio.run(search_knowledge_base_iter(dataset, query))
except Exception as e:
output = "输入的信息有误或不存在知识库"
return output
return output
def _process_llm_result(
self,
llm_output: str,
llm_input: 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)
if text_match:
database = text_match.group(1).strip()
output = self._evaluate_expression(database, llm_input)
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 = "Answer: " + llm_output.split("Answer:")[-1]
else:
return {self.output_key: f"输入的格式不对: {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)
output = self._evaluate_expression(expression)
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])
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, inputs[self.input_key], _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])
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_once(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_once("大数据的男女比例")
print(result)
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