43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
|
from server.chat.knowledge_base_chat import knowledge_base_chat
|
||
|
|
from configs import VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, MAX_TOKENS, LLM_MODELS
|
||
|
|
import json
|
||
|
|
import asyncio
|
||
|
|
from typing import List, Tuple, Any
|
||
|
|
from pydantic import BaseModel, Field
|
||
|
|
|
||
|
|
|
||
|
|
class PolicyKnowledgeInput(BaseModel):
|
||
|
|
location: str = Field(description="The policy related query to be searched")
|
||
|
|
|
||
|
|
|
||
|
|
async def policy_knowledgebase_search_iter(query: str) -> tuple[str | Any, list[Any] | Any]:
|
||
|
|
response = await knowledge_base_chat(query=query,
|
||
|
|
fileName=None,
|
||
|
|
knowledge_base_name_list = ["t_policy_total_bge_new_v1"],
|
||
|
|
model_name=LLM_MODELS[0],
|
||
|
|
temperature=0.01,
|
||
|
|
history=[],
|
||
|
|
top_k=VECTOR_SEARCH_TOP_K,
|
||
|
|
max_tokens=MAX_TOKENS,
|
||
|
|
prompt_name="default",
|
||
|
|
score_threshold=SCORE_THRESHOLD,
|
||
|
|
stream=False)
|
||
|
|
|
||
|
|
contents = ""
|
||
|
|
docs = []
|
||
|
|
async for data in response.body_iterator: # 这里的data是一个json字符串
|
||
|
|
data = json.loads(data)
|
||
|
|
print("data>>>>>", data)
|
||
|
|
contents = data["answer"] if "answer" in data.keys() else contents
|
||
|
|
docs = data["docs"] if "docs" in data.keys() else docs
|
||
|
|
return contents, docs
|
||
|
|
|
||
|
|
|
||
|
|
def policy_knowledgebase_search(query: str) -> tuple[str | Any, list[Any] | Any]:
|
||
|
|
return asyncio.run(policy_knowledgebase_search_iter(query))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
result = policy_knowledgebase_search("大数据男女比例")
|
||
|
|
print("答案:", result)
|