-
Notifications
You must be signed in to change notification settings - Fork 1
/
agent.py
39 lines (31 loc) · 1.3 KB
/
agent.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
import os
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.prompts import load_prompt
from langchain.schema import HumanMessage, SystemMessage
from langchain.vectorstores.chroma import Chroma
from langchain_community.chat_models import GigaChat
from langchain_community.embeddings.gigachat import GigaChatEmbeddings
load_dotenv()
class ScientificAIAgent:
def __init__(self):
self.credentials = os.environ['GIGACHAT_CRED']
self.embeddings = GigaChatEmbeddings(
credentials=self.credentials,
verify_ssl_certs=False,
scope='GIGACHAT_API_CORP')
self.llm = GigaChat(credentials=self.credentials,
verify_ssl_certs=False,
scope='GIGACHAT_API_CORP')
def response_to_user_request(
self, user_id: int, user_text_request: str) -> str:
vectordb = Chroma(
persist_directory="./data",
embedding_function=self.embeddings)
qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=vectordb.as_retriever(search_kwargs={'k': 12}),
return_source_documents=True)
result = qa_chain.invoke({'query': user_text_request})
return result['result']