langchain-ai / langchain-nextjs-template Goto Github PK

    at connResetException (node:internal/errors:720:14)
    at abortIncoming (node:_http_server:766:17)
    at socketOnClose (node:_http_server:760:3)
    at Socket.emit (node:events:526:35)
    at TCP.<anonymous> (node:net:323:12)
    at TCP.callbackTrampoline (node:internal/async_hooks:130:17) {
  code: 'ECONNRESET'

`import { getSession } from '@/app/supabase-server';
import { Database } from '@/lib/db_types';
import { templates } from '@/lib/template';
import { nanoid } from '@/lib/utils';
import { PineconeClient } from '@pinecone-database/pinecone';
import { createServerActionClient } from '@supabase/auth-helpers-nextjs';
import { LangChainStream, Message, StreamingTextResponse } from 'ai';
import { ConversationalRetrievalQAChain } from 'langchain/chains';
import { ChatOpenAI } from 'langchain/chat_models/openai';
import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
import { BufferMemory } from 'langchain/memory';
import { PineconeStore } from 'langchain/vectorstores/pinecone';
import { cookies } from 'next/headers';
import { redirect } from 'next/navigation';
import { NextResponse } from 'next/server';
import { Configuration, OpenAIApi } from 'openai-edge';

export const runtime = 'nodejs';

const configuration = new Configuration({
  apiKey: process.env.OPENAI_API_KEY
});

const openai = new OpenAIApi(configuration);

const formatMessage = (message: Message) => {
  return `${message.role === 'user' ? 'Human' : 'Assistant'}: ${
    message.content
  }`;
};

export async function POST(req: Request) {
  const cookieStore = cookies();
  const supabase = createServerActionClient<Database>({
    cookies: () => cookieStore
  });
  const session = await getSession();
  const userId = session?.user.id;

  if (!userId) {
    return new Response('Unauthorized', {
      status: 401
    });
  }

  const json = await req.json();
  // const messages: Message[] = json.messages ?? [];
  const { messages } = json;
  const formattedPreviousMessages = messages.slice(0, -1).map(formatMessage);
  const question = messages[messages.length - 1].content;

  try {
    const sanitizedQuestion = question.trim().replaceAll('\n', ' ');
    const pinecone = new PineconeClient();
    await pinecone.init({
      environment: process.env.PINECONE_ENVIRONMENT ?? '',
      apiKey: process.env.PINECONE_API_KEY ?? ''
    });

    const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX_NAME!);

    const vectorStore = await PineconeStore.fromExistingIndex(
      new OpenAIEmbeddings(),
      { pineconeIndex }
    );

    const { stream, handlers } = LangChainStream({
      async onCompletion(completion) {
        const title = json.messages[0].content.substring(0, 100);
        const id = json.id ?? nanoid();
        const createdAt = Date.now();
        const path = `/chat/${id}`;
        const payload = {
          id,
          title,
          userId,
          createdAt,
          path,
          messages: [
            ...messages,
            {
              content: completion,
              role: 'assistant'
            }
          ]
        };

        await supabase.from('chats').upsert({ id, payload }).throwOnError();
      }
    });

    const streamingModel = new ChatOpenAI({
      modelName: 'gpt-4',
      streaming: true,
      verbose: true,
      temperature: 0
    });

    const nonStreamingModel = new ChatOpenAI({
      modelName: 'gpt-4',
      verbose: true,
      temperature: 0
    });

    const chain = ConversationalRetrievalQAChain.fromLLM(
      streamingModel,
      vectorStore.asRetriever(),
      {
        qaTemplate: templates.qaPrompt,
        questionGeneratorTemplate: templates.condensePrompt,
        memory: new BufferMemory({
          memoryKey: 'chat_history',
          inputKey: 'question', // The key for the input to the chain
          outputKey: 'text', // The key for the final conversational output of the chain
          returnMessages: true // If using with a chat model (e.g. gpt-3.5 or gpt-4)
        }),
        questionGeneratorChainOptions: {
          llm: nonStreamingModel
        }
      }
    );

    chain.call(
      {
        question: sanitizedQuestion,
        chat_history: formattedPreviousMessages.join('\n')
      },
      [handlers]
    );

    // Return the readable strea
    return new StreamingTextResponse(stream);
  } catch (error) {
    console.error('Internal server error ', error);
    return NextResponse.json('Error: Something went wrong. Try again!', {
      status: 500
    });
  }
}`

const model = new ChatBedrock({
  model: "anthropic.claude-v2",
  region: "us-west-2",
  streaming: true,
  credentials: {
   accessKeyId: process.env.AWS_ACCESS_KEY_ID,
   secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY
  }

type ConversationalRetrievalQAChainInput = {
  question: string;
  chat_history: VercelChatMessage[];
};

if(typeof window === "undefined") {
  const {Chroma} = require("langchain/vectorstores/chroma");
}

import { Message as VercelChatMessage, StreamingTextResponse } from "ai";
import { AzureCosmosDBVectorStore } from "@langchain/community/vectorstores/azure_cosmosdb";
import {
  AzureOpenAIEmbeddings,
  AzureChatOpenAI,
} from "@langchain/azure-openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { Document } from "@langchain/core/documents";
import { RunnableSequence } from "@langchain/core/runnables";
import {
  BytesOutputParser,
  StringOutputParser,
} from "@langchain/core/output_parsers";

const combineDocumentsFn = (docs: Document[]) => {
  const serializedDocs = docs.map((doc) => doc.pageContent);
  return serializedDocs.join("\n\n");
};

const formatVercelMessages = (chatHistory: VercelChatMessage[]) => {
  const formattedDialogueTurns = chatHistory.map((message) => {
    if (message.role === "user") {
      return `Human: ${message.content}`;
    } else if (message.role === "assistant") {
      return `Assistant: ${message.content}`;
    } else {
      return `${message.role}: ${message.content}`;
    }
  });
  return formattedDialogueTurns.join("\n");
};

const CONDENSE_QUESTION_TEMPLATE = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.

<chat_history>
  {chat_history}
</chat_history>

Follow Up Input: {question}
Standalone question:`;
const condenseQuestionPrompt = PromptTemplate.fromTemplate(
  CONDENSE_QUESTION_TEMPLATE
);

const ANSWER_TEMPLATE = `Answer the question based only on the following context and chat history:
<context>
  {context}
</context>

<chat_history>
  {chat_history}
</chat_history>

Question: {question}
`;
const answerPrompt = PromptTemplate.fromTemplate(ANSWER_TEMPLATE);

export async function POST(req: NextRequest) {
  try {
    const body = await req.json();
    const messages = body.messages ?? [];
    const previousMessages = messages.slice(0, -1);
    const currentMessageContent = messages[messages.length - 1].content;

    const vectorstore = new AzureCosmosDBVectorStore(
      new AzureOpenAIEmbeddings(),
      {
        databaseName: process.env.DB_NAME,
        collectionName: process.env.DB_COLLECTION_NAME,
      }
    );

    const model = new AzureChatOpenAI({
      azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,
      azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
      azureOpenAIApiDeploymentName:
        process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
      modelName: process.env.AZURE_OPENAI_MODEL_NAME,
    });
    const standaloneQuestionChain = RunnableSequence.from([
      condenseQuestionPrompt,
      model,
      new StringOutputParser(),
    ]);

    let resolveWithDocuments: (value: Document[]) => void;
    const documentPromise = new Promise<Document[]>((resolve) => {
      resolveWithDocuments = resolve;
    });

    const retriever = vectorstore.asRetriever({
      callbacks: [
        {
          handleRetrieverEnd(documents) {
            resolveWithDocuments(documents);
          },
        },
      ],
    });

    const retrievalChain = retriever.pipe(combineDocumentsFn);

    const answerChain = RunnableSequence.from([
      {
        context: RunnableSequence.from([
          (input) => input.question,
          retrievalChain,
        ]),
        chat_history: (input) => input.chat_history,
        question: (input) => input.question,
      },
      answerPrompt,
      model,
    ]);

    const conversationalRetrievalQAChain = RunnableSequence.from([
      {
        question: standaloneQuestionChain,
        chat_history: (input) => input.chat_history,
      },
      answerChain,
      new BytesOutputParser(),
    ]);

    const stream = await conversationalRetrievalQAChain.stream({
      question: currentMessageContent,
      chat_history: formatVercelMessages(previousMessages),
    });

    const documents = await documentPromise;
    console.log("documents ", documents.length);
    const serializedSources = Buffer.from(
      JSON.stringify(
        documents.map((doc) => {
          return {
            pageContent: doc.pageContent.slice(0, 50) + "...",
            metadata: doc.metadata,
          };
        })
      )
    ).toString("base64");
    const sourceMetadata = documents.map((doc) => ({
      title: doc.metadata.title, // Or whatever metadata you want
      url: doc.metadata.url,
    }));

    return new StreamingTextResponse(stream, {
      headers: {
        "x-message-index": (previousMessages.length + 1).toString(),
        "x-message-sources": serializedSources,
      },
    });
  } catch (e: any) {
    return NextResponse.json({ error: e.message }, { status: e.status ?? 500 });
  }
}

const productKnowledgeStore = new SupabaseVectorStore(
  new OpenAIEmbeddings(),
  {
    client,
    tableName: "productknowledge",
    queryName: "match_documents",
  },
);

const productKnowledgeRetriever = productKnowledgeStore.asRetriever();

the SQL
CREATE FUNCTION match_documents (

const prompt = ChatPromptTemplate.fromMessages([
  ["system", AGENT_SYSTEM_TEMPLATE],
  ...previousMessages.map((msg: { content: string }) => [
    "system",
    msg.content,
  ]), // Add previous context to the system message
  [
    "human",
    `Please formulate a detailed query aimed to provide the human with information on the following input: {input}`,
  ],
  new MessagesPlaceholder("agent_scratchpad"),
]);

const agent = await createToolCallingAgent({
  llm: chatModel,
  tools: [productKnowledge, companyKnowledge],
  prompt,
});

const agentExecutor = new AgentExecutor({
  agent,
  tools: [productKnowledge, companyKnowledge],
  // Set this if you want to receive all intermediate steps in the output of .invoke().
  returnIntermediateSteps,
});

[20:44:58.702] 
[20:44:58.765]   ▲ Next.js 14.2.3
[20:44:58.765] 
[20:44:58.836]    Creating an optimized production build ...
[20:45:29.608]  ✓ Compiled successfully
[20:45:29.610]    Linting and checking validity of types ...
[20:45:38.082] Failed to compile.
[20:45:38.083] 
[20:45:38.083] ./app/ai_sdk/agent/action.ts:18:31
[20:45:38.083] Type error: Type 'ChatPromptTemplate<any, any>' does not satisfy the constraint 'Runnable<any, any, RunnableConfig>'.
[20:45:38.083]   Property 'lc_runnable' is protected but type 'Runnable<RunInput, RunOutput, CallOptions>' is not a class derived from 'Runnable<RunInput, RunOutput, CallOptions>'.
[20:45:38.083] 
[20:45:38.204] error Command failed with exit code 1.
[20:45:38.204] info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
[20:45:38.226] Error: Command "yarn run build" exited with 1
[20:45:38.590] 

./app/ai_sdk/agent/action.ts:18:31
Type error: Type 'ChatPromptTemplate<any, any>' does not satisfy the constraint 'Runnable<any, any, RunnableConfig>'.
  Property 'lc_runnable' is protected but type 'Runnable<RunInput, RunOutput, CallOptions>' is not a class derived from 'Runnable<RunInput, RunOutput, CallOptions>'.

  16 |     const tools = [new TavilySearchResults({ maxResults: 1 })];
  17 |
> 18 |     const prompt = await pull<ChatPromptTemplate>(
     |                               ^
  19 |       "hwchase17/openai-tools-agent",
  20 |     );
  21 |

[19:16:22.433] Running build in Washington, D.C., USA (East) – iad1
[19:16:23.054] Cloning github.com/guilherme-argentino/puulpo-mvp-web-summit (Branch: main, Commit: 188b61d)
[19:16:23.418] Previous build cache not available
[19:16:24.073] Cloning completed: 1.018s
[19:16:24.420] Running "vercel build"
[19:16:24.896] Vercel CLI 33.5.3
[19:16:25.332] Warning: Detected "engines": { "node": ">=18" } in your `package.json` that will automatically upgrade when a new major Node.js Version is released. Learn More: http://vercel.link/node-version
[19:16:25.340] Installing dependencies...
[19:16:25.621] yarn install v1.22.17
[19:16:25.690] [1/5] Validating package.json...
[19:16:25.693] [2/5] Resolving packages...
[19:16:27.718] warning Resolution field "@langchain/[email protected]" is incompatible with requested version "@langchain/core@~0.1.41"
[19:16:31.917] warning Resolution field "@langchain/[email protected]" is incompatible with requested version "@langchain/core@~0.1.36"
[19:16:31.932] warning Resolution field "@langchain/[email protected]" is incompatible with requested version "@langchain/core@~0.1.41"
[19:16:34.096] [3/5] Fetching packages...
[19:16:54.951] [4/5] Linking dependencies...
[19:16:54.955] warning "ai > [email protected]" has unmet peer dependency "solid-js@^1.2".
[19:16:54.955] warning "ai > [email protected]" has unmet peer dependency "vue@>=3.2.26 < 4".
[19:16:54.955] warning "ai > [email protected]" has unmet peer dependency "svelte@^4.0.0".
[19:17:01.992] [5/5] Building fresh packages...
[19:17:02.160] success Saved lockfile.
[19:17:02.173] Done in 36.55s.
[19:17:02.321] Detected Next.js version: 14.0.1
[19:17:02.321] Running "yarn run build"
[19:17:02.534] yarn run v1.22.17
[19:17:02.562] $ next build
[19:17:04.496] Attention: Next.js now collects completely anonymous telemetry regarding usage.
[19:17:04.497] This information is used to shape Next.js' roadmap and prioritize features.
[19:17:04.497] You can learn more, including how to opt-out if you'd not like to participate in this anonymous program, by visiting the following URL:
[19:17:04.497] https://nextjs.org/telemetry
[19:17:04.499] 
[19:17:04.598]    ▲ Next.js 14.0.1
[19:17:04.601] 
[19:17:04.601]    Creating an optimized production build ...
[19:17:04.670]  ⚠ Custom webpack configuration is detected. When using a custom webpack configuration, the Webpack build worker is disabled by default. To force enable it, set the "experimental.webpackBuildWorker" option to "true". Read more: https://nextjs.org/docs/messages/webpack-build-worker-opt-out
[19:17:20.527] Failed to compile.
[19:17:20.527] 
[19:17:20.528] ./node_modules/langchain/node_modules/@langchain/openai/dist/chat_models.js:9:0
[19:17:20.528] Module not found: Package path ./output_parsers/openai_tools is not exported from package /vercel/path0/node_modules/@langchain/core (see exports field in /vercel/path0/node_modules/@langchain/core/package.json)
[19:17:20.528] 
[19:17:20.528] https://nextjs.org/docs/messages/module-not-found
[19:17:20.528] 
[19:17:20.529] Import trace for requested module:
[19:17:20.529] ./node_modules/langchain/node_modules/@langchain/openai/dist/index.js
[19:17:20.529] ./node_modules/langchain/node_modules/@langchain/openai/index.js
[19:17:20.529] ./node_modules/langchain/dist/embeddings/openai.js
[19:17:20.529] ./node_modules/langchain/embeddings/openai.js
[19:17:20.529] ./app/api/retrieval/ingest/route.ts
[19:17:20.529] ./node_modules/next/dist/build/webpack/loaders/next-edge-app-route-loader/index.js?absolutePagePath=private-next-app-dir%2Fapi%2Fretrieval%2Fingest%2Froute.ts&page=%2Fapi%2Fretrieval%2Fingest%2Froute&appDirLoader=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%3D&nextConfigOutput=&preferredRegion=&middlewareConfig=e30%3D!
[19:17:20.529] 
[19:17:20.530] 
[19:17:20.530] > Build failed because of webpack errors
[19:17:20.584] error Command failed with exit code 1.
[19:17:20.585] info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
[19:17:20.602] Error: Command "yarn run build" exited with 1
[19:17:21.168] 

TypeError [ERR_INVALID_ARG_TYPE]: The "chunk" argument must be of type string or an instance of Buffer or Uint8Array. Received an instance of Object