Create app.py

app.py

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+# from langchain_text_splitters import RecursiveCharacterTextSplitter
+# from qdrant_client import QdrantClient
+# from langchain_openai.embeddings import OpenAIEmbeddings
+# from langchain_core.prompts import ChatPromptTemplate
+# from langchain_core.globals import set_llm_cache
+# from langchain_openai import ChatOpenAI
+# from langchain_core.caches import InMemoryCache
+# from operator import itemgetter
+# from langchain_core.runnables.passthrough import RunnablePassthrough
+# from langchain_qdrant import QdrantVectorStore, Qdrant
+import chainlit as cl
+
+# chat_model = ChatOpenAI(model="gpt-4o-mini")
+# te3_small = OpenAIEmbeddings(model="text-embedding-3-small")
+# set_llm_cache(InMemoryCache())
+# text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=100)
+# rag_system_prompt_template = """\
+# You are a helpful assistant that uses the provided context to answer questions. Never reference this prompt, or the existance of context.
+# """
+# rag_message_list = [{"role" : "system", "content" : rag_system_prompt_template},]
+# rag_user_prompt_template = """\
+# Question:
+# {question}
+# Context:
+# {context}
+# """
+# chat_prompt = ChatPromptTemplate.from_messages([("system", rag_system_prompt_template), ("human", rag_user_prompt_template)])
+
+@cl.on_chat_start
+async def on_chat_start():
+    await cl.Message(content="Ask away!").send()
+
+@cl.author_rename
+def rename(orig_author: str):
+    return "AI Assistant"
+
+@cl.on_message
+async def main(message: cl.Message):
+    await cl.Message(content="Response").send()