llm

app.py

@@ -1,5 +1,41 @@
 import gradio as gr
+from langchain import PromptTemplate, LLMChain
+import os
+from langchain.vectorstores import Chroma
+from getpass import getpass
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
 
+model_name = "text-embedding-ada-002"
+# get openai api key from platform.openai.com
+OPENAI_API_KEY = token
+
+OAIembeddings = OpenAIEmbeddings(
+    model=model_name, openai_api_key=OPENAI_API_KEY, disallowed_special=()
+)
+
+load_vector_store = Chroma(persist_directory="iupui_openai_store_final", embedding_function=OAIembeddings)
+prompt_template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
+retriever = load_vector_store.as_retriever(search_kwargs={"k":5})
+llm = ChatOpenAI(temperature=0.7, model='gpt-3.5-turbo',openai_api_key=OPENAI_API_KEY)
+
+def get_response(input):
+  query = input
+  chain_type_kwargs = {"prompt": prompt}
+  qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs=chain_type_kwargs, verbose=True)
+  response = qa(query)
+  return (response['result'], response['source_documents'])
+    
 input = gr.Text(
                 label="Prompt",
                 show_label=False,