added rest

app.py

@@ -22,20 +22,20 @@ from langchain_community.document_loaders import PyPDFLoader
 load_dotenv()
 
 
-# RAG_PROMPT = """
+RAG_PROMPT = """
 
-# CONTEXT:
-# {context}
+CONTEXT:
+{context}
 
-# QUERY:
-# {question}
+QUERY:
+{question}
 
-# You house builder and can only provide your answers from the context. 
-# You can only provide a response in danish
+You house builder and can only provide your answers from the context. 
+You can only provide a response in danish
 
-# Don't tell in your response that you are getting it from the context.
+Don't tell in your response that you are getting it from the context.
 
-# """
+"""
 
 
 text_splitter = RecursiveCharacterTextSplitter(
@@ -87,33 +87,33 @@ loader = UnstructuredPDFLoader("br_femogfirs.pdf",  strategy="fast")
 data = loader.load_and_split(text_splitter)
 # data = loader.load()
 
-# embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
 
-# vector_store = Pinecone.from_documents(data, embedding_model, index_name="bygnings-regl-rag-1")
-# retriever = vector_store.as_retriever()
+vector_store = Pinecone.from_documents(data, embedding_model, index_name="bygnings-regl-rag-1")
+retriever = vector_store.as_retriever()
 
-# rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
+rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
 
-# model = ChatOpenAI(model="gpt-3.5-turbo")
+model = ChatOpenAI(model="gpt-3.5-turbo")
 
 @cl.on_chat_start
 async def main():
     mecanic_qa_chain = ""
-    # mecanic_qa_chain = (
-    #     {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-    #     | RunnablePassthrough.assign(context=itemgetter("context"))
-    #     | rag_prompt | model | StrOutputParser()
-    # )
+    mecanic_qa_chain = (
+        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+        | RunnablePassthrough.assign(context=itemgetter("context"))
+        | rag_prompt | model | StrOutputParser()
+    )
 
     cl.user_session.set("runnable", mecanic_qa_chain)
 
 @cl.on_message
 async def on_message(message: cl.Message):
     runnable = cl.user_session.get("runnable")
-    # msg = cl.Message(content="")
+    msg = cl.Message(content="")
 
-    # async for chunk in runnable.astream(
-    #     {"question":message.content},
-    #     config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
-    # ):
-    #     await msg.stream_token(chunk)
+    async for chunk in runnable.astream(
+        {"question":message.content},
+        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+    ):
+        await msg.stream_token(chunk)