how to use async ainvoke

[BioStarr]

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Example Code

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True,output_key="answer")
callback = StreamHandler(st.empty())
llm = ChatAnthropic(temperature=0.5, max_tokens=1000, model_name=claude-2,streaming=True,callbacks=[callback])
qa = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vector_store.as_retriever(),memory=memory,verbose=True)
result = qa.invoke({"question": prompt, "chat_history": [(message["role"], message["content"]) for message in st.session_state.messages]})
full_response = result["answer"]

Description

I would like to change from invoke to ainvoke, in short to use async properly so i can better optimize my app.

change result = qa.invoke to qa.ainvoke
What would be best approach to do so?

System Info

langchain 0.1.6
langchain-community 0.0.19
langchain-core 0.1.23
langchain-experimental 0.0.50
langchain-openai 0.0.5
langchainhub 0.1.13
Windows OS 11

[dosubot[bot]]

Hey @BioStarr, great to see you diving into another LangChain adventure! Hope this one's as fun as the last.

Based on the context provided, it seems like you're trying to understand how to use the LangChain framework in the context of your provided code. Here's a breakdown of how it's used:

1. PyPDFLoader: This class is used to load PDF files into a list of documents. It reads the PDF file, chunks the file by page, and stores page numbers in metadata. You can find more about this in the LangChain documentation.

2. ChatOpenAI: This is a language model from OpenAI. It's used to generate responses to queries. The temperature parameter controls the randomness of the model's output. A lower value makes the output more deterministic, while a higher value makes it more random.

3. RecursiveCharacterTextSplitter: This class is used to split the loaded documents into chunks. The chunk_size parameter determines the size of each chunk, and the chunk_overlap parameter determines how much overlap there is between chunks.

4. OpenAIEmbeddings: This class is used to generate embeddings for the chunks. These embeddings are used to index the chunks in the knowledge base.

5. Chroma: This class is used to create a knowledge base from the chunks and their embeddings. The persist_directory parameter is the directory where the knowledge base is saved.

6. PromptTemplate: This class is used to create a template for the prompts that are sent to the language model. The input_variables parameter is a list of the variables that are included in the template.

7. ConversationBufferMemory: This class is used to create a memory buffer for the conversation. The memory_key parameter is the key that is used to store the conversation history in the memory buffer.

8. ConversationalRetrievalChain: This class is used to create a retrieval chain for the conversation. The llm parameter is the language model, the memory parameter is the memory buffer, and the retriever parameter is the knowledge base.

In your terminal example, you're asking the AI model a question ("How do I delete a staff account"), and the model is generating a response based on the knowledge base and the conversation history.

To properly change from using invoke to ainvoke in the LangChain framework to optimize your application, you need to follow these steps:

1. Ensure Asynchronous Support: Your application components and the environment must support asynchronous operations. This includes any external APIs or databases your chain interacts with.

2. Update Chain Calls: Replace synchronous invoke calls with asynchronous ainvoke calls. This involves changing the syntax to use await when calling ainvoke. For example, if you previously had:

   result = my_chain.invoke(input_data)

   You should change it to:

   result = await my_chain.ainvoke(input_data)

3. Async Callbacks: Ensure that any callbacks used with the chain are also asynchronous. The ainvoke method uses AsyncCallbackManager instead of CallbackManager, which means your callbacks should be able to handle asynchronous operations.

4. Error Handling: Asynchronous operations should use try/except blocks to handle exceptions. Note that ainvoke uses await run_manager.on_chain_error(e) for error handling within its asynchronous context.

5. Asynchronous Entry Point: The entry point of your application (or the part that calls ainvoke) must be asynchronous. This might require you to update your application's main function or the event loop handling to support asynchronous calls.

6. Adjustments for Asynchronous Operations: Depending on your application's architecture, you might need to make additional adjustments to fully benefit from asynchronous operations. This includes optimizing I/O operations, managing asynchronous tasks, and ensuring that your application's architecture supports concurrent operations without causing resource contention or deadlocks.

Here's a simplified example demonstrating how to switch from invoke to ainvoke:

Before (synchronous):

def handle_request(input_data):
    result = my_chain.invoke(input_data)
    # Process result
    return result

After (asynchronous):

async def handle_request(input_data):
    result = await my_chain.ainvoke(input_data)
    # Process result
    return result

Remember, the transition to asynchronous operations can significantly improve the scalability and performance of your application, especially for I/O-bound tasks. However, it requires careful consideration of error handling, task management, and overall application architecture to avoid common pitfalls associated with asynchronous programming.

Yes, the LangChain framework supports asynchronous operations. The components designed to be used asynchronously are primarily the functions for running language models (_arun_llm) and chains (_arun_chain), as well as a more general function (_arun_llm_or_chain) that can handle either a language model or a chain asynchronously. These functions are evident from the provided context, which includes the use of await and async def indicating asynchronous operation capabilities.

Regarding similar issues in the LangChain repository, there are a few that might be relevant to your situation:

• ConversationalRetrievalChain did not look into chat history while making an answer
• ConversationalRetrievalChain + Memory
• Random question and Answer generation while using ConversationalRetrievalChain
• Issue: View context used in query in ConversationalRetrievalChain
• LangChain memory, not returning source doc, repeating question

These issues might provide some additional insights into how to use the LangChain framework. However, it's always a good idea to update to the latest version of the software, as it may have fixed the issue you're experiencing.

Sources

• Output being rephrased
• libs/langchain/langchain/chains/base.py
• libs/langchain/langchain/smith/evaluation/runner_utils.py

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[karaposu]

We need a better example