Spaces:
Build error
Build error
File size: 2,151 Bytes
6c1e91d |
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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import pickle
import faiss
import openai
from langchain import LLMChain
from langchain.llms.openai import OpenAIChat
from langchain.prompts import Prompt
from langchain import OpenAI
from langchain.callbacks import get_openai_callback
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
history = []
index = 0
store = None
prompt = ''
llmChain = ''
k = 0
def runPrompt(user_input):
global index, k, store, prompt, llmChain
k += 1
if k <= 1: # 避免重复请求
index = faiss.read_index("after_training/training.index")
with open("after_training/faiss.pkl", "rb") as f:
store = pickle.load(f)
store.index = index
with open("training/master.txt", "r") as f:
promptTemplate = f.read()
prompt = Prompt(template=promptTemplate, input_variables=["history", "context", "question"])
llmChain = LLMChain(prompt=prompt, llm=OpenAIChat(temperature=0))
def onMessage(question, history_p):
# contexts = []
# response_prarm = OpenAI(
# temperature=0,
# openai_api_key=openai.api_key,
# model_name="gpt-3.5-turbo",
# callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
# verbose=True,
# streaming=True
# )
#
# llmChain = LLMChain(prompt=prompt, llm=response_prarm)
# ai_answer = llmChain.predict(question=question, context="\n\n".join(contexts), history=history_p,
# stop=["Human:", "AI:"])
docs = store.similarity_search(question, k=1)
contexts = []
for i, doc in enumerate(docs):
contexts.append(f"Context {i}:\n{doc.page_content}")
ai_answer = llmChain.predict(question=question, context="\n\n".join(contexts), history=history_p)
return ai_answer
answer = onMessage(user_input, history)
history.append(f"Human: {user_input}")
history.append(f"Bot: {answer}")
return answer
|