redis.set randomly stops working

[jacobtt21]

It's seemingly random but when I sometimes set some data in the redis db, it just doesn't save. I see the key in the database but not the value.

await redis.set(email, {
  name: name
},
{
  ex: 60 * 60 * 24,
  nx: true
})

when I check the data browser in my dashboard, I can find the key but the value looks like this:

{}

[jacobtt21]

It happens randomly too and I cannot predict when and why it happens

[ogzhanolguncu]

In NextJS if you don't await correctly in your serverless functions sometimes lambdas don't work properly maybe thats the case? If it persists contact us via support@upstash.com.

[jacobtt21]

How do you correctly use await?

[ogzhanolguncu]

What I meant was that sometimes, floating promises (promises that you don't await) behave differently in serverless functions. Perhaps that was the issue?

[myhendry]

I encounter similar issue as the others

when i use nextjs runtime = "nodejs", my chatbot can work properly. but when i use runtime="edge", it works fine in dev environment however, when pushed onto vercel, it leads to very unpredictable outcome. sometimes, the new chat is written into upstash redis but sometimes, it doesn't. anyone knows how can i resolve this issue? below is my api/chat/route.ts code...

export const runtime = "nodejs";

interface NextExtendedRequest extends NextRequest {
  json: () => Promise<{
    messages: VercelChatMessage[];
    sessionId: string;
    loadMessages: boolean;
  }>;
}

export async function POST(req: NextExtendedRequest) {
  try {
    const { messages, sessionId } = await req.json();

    const { stream, handlers } = LangChainStream();

    const prompt = ChatPromptTemplate.fromMessages([
      SystemMessagePromptTemplate.fromTemplate(
        "You are a professor and your reply will be short and concise."
      ),
      new MessagesPlaceholder("history"),
      HumanMessagePromptTemplate.fromTemplate("{input}"),
    ]);

    const memory = new BufferMemory({
      chatHistory: new UpstashRedisChatMessageHistory({
        sessionId: sessionId,
        client: Redis.fromEnv(),
      }),
      aiPrefix: "assistant",
      humanPrefix: "user",
      memoryKey: "history",
      returnMessages: true,
    });

    const model = new ChatOpenAI({
      model: "gpt-3.5-turbo",
      temperature: 0,
      streaming: true,
      apiKey: process.env.OPENAI_API_KEY,
    });

    const chain = new ConversationChain({ llm: model, memory, prompt });

    const latestMessage = messages[messages.length - 1].content;

    chain.call({
      input: latestMessage,
      callbacks: [handlers],
    });

    return new StreamingTextResponse(stream);
  } catch (e: any) {
    console.log("error", e);
    return NextResponse.json({ error: e.message }, { status: e.status ?? 500 });
  }
}

[ogzhanolguncu]

@myhendry can you try converting chain.call to this chain.invoke then await? Sometimes floating promises in edge environments cause that issue.

[myhendry]

@myhendry can you try converting chain.call to this chain.invoke then await? Sometimes floating promises in edge environments cause that issue.

@ogzhanolguncu hi, im using runtime="nodejs" environment instead of runtime="edge". let me try using invoke. so code will be... i dont need the "await" keyword before chain.invoke right? thanks

 chain.invoke({
      input: latestMessage,
      callbacks: [handlers],
    });

[ogzhanolguncu]

No, if you are streaming you don't have to. If you are not streaming you have to await till you get a result back.

[github-actions]

This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 30 days.

[ElectricCodeGuy]

Not sure if this is still relevant but i would suggest you update to teh newest version of ai package from vercel and try using the callback function onFinal. So like this here:

 const outputStreamChain = ChatPromptTemplate.fromMessages([
      ['system', systemPromptTemplate],
      new MessagesPlaceholder('chat_history'),
      ['human', '{question}']
    ]);
    const mainModel = new ChatAnthropic({
      model: 'claude-3-opus-20240229',
      maxTokens: 4000,
      temperature: 0,
      streaming: true
    });
 const rewriteArticleChain = outputStreamChain.pipe(mainModel);

    const fullInput = {
      question: messages[messages.length - 1].content,
      chat_history: formattedChatHistory,
      })
    };

    const stream = await rewriteArticleChain.stream(fullInput);

let partialCompletion = '';

 const aiStream = LangChainAdapter.toAIStream(stream, {
      onToken: (token: string) => {
        partialCompletion += token;
      },
      onFinal: () => {
        try {
          saveChatToRedis(
            chatSessionId,
            userId,
            messages[messages.length - 1].content,
            partialCompletion,
            Array.from(uniqueReferences)
          );
        } catch (error) {
          console.error('Error saving chat to database:', error);
        }
      }
    });

 return new StreamingTextResponse(aiStream, {
      headers: {
        'Content-Type': 'text/plain; charset=utf-8'
      }
    });

This should work just fine i have used it for many month. Also the Langchain buffer memory i would not use. I would in general avoid using langchain overall and just stick with the AI package from vercel, it contains everything you need :)

The function:

 saveChatToRedis(
            chatSessionId,
            userId,
            messages[messages.length - 1].content,
            partialCompletion,
            Array.from(uniqueReferences)
          );

is just a basic function that takes these input and stores the value in upstash/redis

The partialcompletion is if the stream is suddenly cut off or the user stops it, it then still stores what ever that have been generated in the chat memory.