Update app.py

app.py

@@ -12,39 +12,39 @@ import uuid
 import chainlit as cl
 import os
 
-# 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)])
+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():
-    # qdrant_client = QdrantClient(url=os.environ["QDRANT_ENDPOINT"], api_key=os.environ["QDRANT_API_KEY"])
-    # qdrant_store = Qdrant(
-    #     client=qdrant_client,
-    #     collection_name="kai_test_docs",
-    #     embeddings=te3_small
-    # )
-    # retriever = qdrant_store.as_retriever()
+    qdrant_client = QdrantClient(url=os.environ["QDRANT_ENDPOINT"], api_key=os.environ["QDRANT_API_KEY"])
+    qdrant_store = Qdrant(
+        client=qdrant_client,
+        collection_name="kai_test_docs",
+        embeddings=te3_small
+    )
+    retriever = qdrant_store.as_retriever()
 
-    # global retrieval_augmented_qa_chain
-    # retrieval_augmented_qa_chain = (
-    #     {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-    #     | RunnablePassthrough.assign(context=itemgetter("context"))
-    #     | chat_prompt
-    #     | chat_model
-    # )
+    global retrieval_augmented_qa_chain
+    retrieval_augmented_qa_chain = (
+        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+        | RunnablePassthrough.assign(context=itemgetter("context"))
+        | chat_prompt
+        | chat_model
+    )
 
     await cl.Message(content="YAsk away!").send()
 
@@ -54,6 +54,5 @@ def rename(orig_author: str):
 
 @cl.on_message
 async def main(message: cl.Message):
-    # response = retrieval_augmented_qa_chain.invoke({"question": message.content})
-    # await cl.Message(content=response.content).send()
-    await cl.Message(content="Message response").send()
+    response = retrieval_augmented_qa_chain.invoke({"question": message.content})
+    await cl.Message(content=response.content).send()