add app.py

app.py

@@ -0,0 +1,65 @@
+import os
+
+from langchain_openai import ChatOpenAI
+from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.prompts import ChatPromptTemplate
+import gradio as gr
+
+
+def format_docs(docs):
+  print(docs)
+  return "\n\n".join(doc.page_content for doc in docs)
+
+
+prompt = ChatPromptTemplate.from_messages([
+    ('system',
+     "You are a KCE chatbot, and you are assisting customers with the inquires about the company."
+     "Answer the questions witht the provided context. Do not include based on the context or based on the documents in your answer."
+     "Remember that your job is to represent KCE company."
+     "Please say you do not know if you do not know or cannot find the information needed."
+     "\n Question: {question} \nContext: {context}"),
+    ('user', "{question}")
+])
+
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+
+embeddings = OpenAIEmbeddings()
+db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+
+llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+def chat_gen(message, history):
+  history_openai_format = []
+  for human, assistant in history:
+        history_openai_format.append({"role": "user", "content": human })
+        history_openai_format.append({"role": "assistant", "content":assistant})
+        history_openai_format.append({"role": "user", "content": message})
+
+  partial_message=""
+  for chunk in rag_chain.stream(message):
+    # if chunk.choices[0].delta.content is not None:
+    partial_message = partial_message + chunk
+    yield partial_message
+
+
+initial_msg = "Hello! I am KCE assistant. You can ask me anything about KCE. I am happy to assist you."
+chatbot = gr.Chatbot(value = [[None, initial_msg]], bubble_full_width=False)
+demo = gr.ChatInterface(chat_gen, chatbot=chatbot).queue()
+
+
+try:
+    demo.launch(debug=True, share=True, show_api=False)
+    demo.close()
+except Exception as e:
+    demo.close()
+    print(e)
+    raise e

requirements.txt

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+langchain
+langchain_community
+langchain-openai
+tiktoken
+faiss-cpu
+gradio