Update app.py

app.py

@@ -47,116 +47,119 @@ async def echo(websocket):
 async def main():
     async with serve(echo, "0.0.0.0", 7860):
         await asyncio.Future()
-
-if not os.path.isdir('database'):
-    os.system("unzip database.zip")
-
-loader = DirectoryLoader('./database', glob="./*.txt", loader_cls=TextLoader)
-
-documents = loader.load()
-
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-splits = text_splitter.split_documents(documents)
-
-print()
-print("-------")
-print("TextSplitter, DirectoryLoader")
-print("-------")
-
-persist_directory = 'db'
-
-# embedding = HuggingFaceInferenceAPIEmbeddings(api_key=os.environ["HUGGINGFACE_API_KEY"], model=)
-model_name = "BAAI/bge-large-en"
-model_kwargs = {'device': 'cpu'}
-encode_kwargs = {'normalize_embeddings': True}
-embedding = HuggingFaceBgeEmbeddings(
-    model_name=model_name,
-    model_kwargs=model_kwargs,
-    encode_kwargs=encode_kwargs,
-    show_progress=True,
-)
-
-print()
-print("-------")
-print("Embeddings")
-print("-------")
-
-vectorstore = Chroma.from_documents(documents=splits, embedding=embedding)
-
-def format_docs(docs):
-    return "\n\n".join(doc.page_content for doc in docs)
-
-retriever = vectorstore.as_retriever()
-
-prompt = hub.pull("rlm/rag-prompt")
-llm = HuggingFaceEndpoint(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1")
-rag_chain = (
-    {"context": retriever | format_docs, "question": RunnablePassthrough()}
-    | prompt
-    | llm
-    | StrOutputParser()
-)
-
-print()
-print("-------")
-print("Retriever, Prompt, LLM, Rag_Chain")
-print("-------")
-
-### Contextualize question ###
-contextualize_q_system_prompt = """Given a chat history and the latest user question \
-which might reference context in the chat history, formulate a standalone question \
-which can be understood without the chat history. Do NOT answer the question, \
-just reformulate it if needed and otherwise return it as is."""
-contextualize_q_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", contextualize_q_system_prompt),
-        MessagesPlaceholder("chat_history"),
-        ("human", "{input}"),
-    ]
-)
-history_aware_retriever = create_history_aware_retriever(
-    llm, retriever, contextualize_q_prompt
-)
-
-
-### Answer question ###
-qa_system_prompt = """You are an assistant for question-answering tasks. \
-Use the following pieces of retrieved context to answer the question. \
-If you don't know the answer, just say that you don't know. \
-Use three sentences maximum and keep the answer concise.\
-
-{context}"""
-qa_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", qa_system_prompt),
-        MessagesPlaceholder("chat_history"),
-        ("human", "{input}"),
-    ]
-)
-question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
-
-rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
-
-
-### Statefully manage chat history ###
-store = {}
-
-
-def get_session_history(session_id: str) -> BaseChatMessageHistory:
-    if session_id not in store:
-        store[session_id] = ChatMessageHistory()
-    return store[session_id]
-
-
-conversational_rag_chain = RunnableWithMessageHistory(
-    rag_chain,
-    get_session_history,
-    input_messages_key="input",
-    history_messages_key="chat_history",
-    output_messages_key="answer",
-)
-
-asyncio.run(main())
+def g():
+    if not os.path.isdir('database'):
+        os.system("unzip database.zip")
+    
+    loader = DirectoryLoader('./database', glob="./*.txt", loader_cls=TextLoader)
+    
+    documents = loader.load()
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    splits = text_splitter.split_documents(documents)
+    
+    print()
+    print("-------")
+    print("TextSplitter, DirectoryLoader")
+    print("-------")
+    
+    persist_directory = 'db'
+    
+    # embedding = HuggingFaceInferenceAPIEmbeddings(api_key=os.environ["HUGGINGFACE_API_KEY"], model=)
+    model_name = "BAAI/bge-large-en"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': True}
+    embedding = HuggingFaceBgeEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs,
+        show_progress=True,
+    )
+    
+    print()
+    print("-------")
+    print("Embeddings")
+    print("-------")
+    
+    vectorstore = Chroma.from_documents(documents=splits, embedding=embedding)
+    
+    def format_docs(docs):
+        return "\n\n".join(doc.page_content for doc in docs)
+    
+    retriever = vectorstore.as_retriever()
+    
+    prompt = hub.pull("rlm/rag-prompt")
+    llm = HuggingFaceEndpoint(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1")
+    rag_chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    
+    print()
+    print("-------")
+    print("Retriever, Prompt, LLM, Rag_Chain")
+    print("-------")
+    
+    ### Contextualize question ###
+    contextualize_q_system_prompt = """Given a chat history and the latest user question \
+    which might reference context in the chat history, formulate a standalone question \
+    which can be understood without the chat history. Do NOT answer the question, \
+    just reformulate it if needed and otherwise return it as is."""
+    contextualize_q_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", contextualize_q_system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human", "{input}"),
+        ]
+    )
+    history_aware_retriever = create_history_aware_retriever(
+        llm, retriever, contextualize_q_prompt
+    )
+    
+    
+    ### Answer question ###
+    qa_system_prompt = """You are an assistant for question-answering tasks. \
+    Use the following pieces of retrieved context to answer the question. \
+    If you don't know the answer, just say that you don't know. \
+    Use three sentences maximum and keep the answer concise.\
+    
+    {context}"""
+    qa_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", qa_system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human", "{input}"),
+        ]
+    )
+    question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+    
+    rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
+    
+    
+    ### Statefully manage chat history ###
+    store = {}
+    
+    
+    def get_session_history(session_id: str) -> BaseChatMessageHistory:
+        if session_id not in store:
+            store[session_id] = ChatMessageHistory()
+        return store[session_id]
+    
+    
+    conversational_rag_chain = RunnableWithMessageHistory(
+        rag_chain,
+        get_session_history,
+        input_messages_key="input",
+        history_messages_key="chat_history",
+        output_messages_key="answer",
+    )
+
+def f():
+    asyncio.run(main())
+Process(f).start()
+Process(g).start()
 """
 websocket
 streamlit app ~> backend