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from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from models import api_model
def chain():
#Tạo prompt cho LangChains
prompt = PromptTemplate(
template = """<|im_start|>system\nYou are a chatbot that takes care of and helps customers find information. If the customer wants to ask for information, use the following information to answer the question (If you don't know the answer, say you don't know, don't try to make up the answer). If not, respond to customers like a regular chatbot.\n
{context}<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant""",
input_variables = ["context", "question"],
)
#Embedding bằng FAISS
db = FAISS.load_local('datasource/db_faiss', api_model.embeddings, allow_dangerous_deserialization=True)
llm_chain = RetrievalQA.from_chain_type(
llm = api_model.model,
chain_type= "stuff",
retriever = db.as_retriever(search_kwargs = {"k":10}, max_tokens_limit=1024),
return_source_documents = False,
chain_type_kwargs= {'prompt': prompt}
)
return llm_chain
def chat(request):
llm_chain = chain()
response = llm_chain.invoke(request)
return str(response['result'])
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