Chat with JSON object

[KeenanFernandes2000]

I have an endpoint that obtains my users information when they are logged in, I want to take that information and use it as part of the chatbots memory or context how can i do that.

[dosubot[bot]]

🤖

Hey there, @KeenanFernandes2000! 🌟 Long time, no chat! Hope you're coding away to glory and your projects are as exciting as ever. What's cooking this time in the LangChain kitchen?

To integrate user data into the chatbot's context using the LangChain Javascript framework, you can utilize the createPrompt method of the StructuredChatAgent class. This method allows you to create a prompt with a list of tools and arguments. One of the arguments you can pass is inputVariables, which is a list of input variables the final prompt will expect. You can include your user data as input variables here.

Here is an example of how you can do this:

const tools = [...]; // Your list of tools
const userData = {...}; // Your user data

const prompt = StructuredChatAgent.createPrompt(tools, {
  inputVariables: ["input", "agent_scratchpad", ...Object.keys(userData)],
});

In this example, inputVariables includes the keys of the userData object, which means the final prompt will expect these user data variables.

Please note that you will need to ensure that these variables are provided when the prompt is used. For example, if you are using the createStructuredChatAgent function, you will need to include these variables in the input when invoking the agent:

const agentExecutor = new AgentExecutor({
  agent,
  tools,
});

const result = await agentExecutor.invoke({
  input: "what is LangChain?",
  ...userData,
});

In this example, userData is spread into the input object, providing the user data variables to the agent.

You can find more details about the createPrompt method in the langchain/src/agents/structured_chat/index.ts file.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• langchain/src/agents/structured_chat/index.ts

About Dosu

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[KeenanFernandes2000]

@dosu-bot i want to implement the json data similar to how the PDFLoader works is that possible? and without and agent.

[dosubot[bot]]

🤖

Sure, you can implement the user's JSON data from an endpoint into the chatbot's memory or context, similar to how the PDFLoader works, without using an agent. You can use the ChatMessageHistory class provided by the LangChainJS framework. This class is responsible for storing and loading chat messages from persistent storage.

Here's an example of how you can do this:

import { ChatMessageHistory, HumanMessage } from "langchain/stores/message/in_memory";
import axios from 'axios';

// Create an instance of ChatMessageHistory
const chatMessageHistory = new ChatMessageHistory();

// Fetch JSON data from the endpoint
axios.get('your-endpoint-url')
  .then(response => {
    // Assuming the response data is an array of messages
    const messages = response.data;

    // Add each message to the chatbot's memory
    messages.forEach(message => {
      chatMessageHistory.addMessage(new HumanMessage(message));
    });
  })
  .catch(error => {
    console.error(error);
  });

In this example, axios is used to fetch the JSON data from the endpoint. You can replace 'your-endpoint-url' with the actual URL of your endpoint. The forEach loop is used to add each message from the fetched JSON data to the chatbot's memory.

Please note that this example assumes that the fetched JSON data is an array of messages. If your data structure is different, you may need to adjust the code accordingly. You can find more information about this in the LangChainJS documentation.

As for the ChatMessageHistory class, it provides methods for getting messages (getMessages()), adding a message (addMessage(message: BaseMessage)), adding a user message (addUserMessage(message: string)), adding an AI chat message (addAIChatMessage(message: string)), and clearing all messages (clear()). These methods are expected to be implemented in the classes that extend BaseChatMessageHistory.

The actual storage and retrieval of messages will depend on the specific implementations of these methods in the classes that extend BaseChatMessageHistory and BaseListChatMessageHistory. You can find more information about this in the LangChainJS source code.

Sources

• docs/core_docs/docs/use_cases/chatbots/memory_management.mdx
• langchain-core/src/chat_history.ts

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[KeenanFernandes2000]

@dosu-bot i have another issue that I wanted to check with you I have a pdfLoader code below for reference:

const docs = await loader.load();
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 0,
});

const texts = await splitter.splitDocuments(docs);

const vectorStore = await FaissStore.fromDocuments(
  texts,
  new OpenAIEmbeddings()
);

const retriever = vectorStore.asRetriever();

const SYSTEM_TEMPLATE = `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
{context}`;

const messages = [
  SystemMessagePromptTemplate.fromTemplate(SYSTEM_TEMPLATE),
  HumanMessagePromptTemplate.fromTemplate("{question}"),
];

const prompt = ChatPromptTemplate.fromMessages(messages);

const chain = RunnableSequence.from([
  {
    context: retriever.pipe(formatDocumentsAsString),
    question: new RunnablePassthrough(),
  },
  prompt,
  model,
  new StringOutputParser(),
]);

however if i upload a pdf that is graphics based and not text this code throws an error how do i fix this

[dosubot[bot]]

🤖

To integrate JSON data into the chatbot's context similar to how the PDFLoader works, you can create a custom loader that fetches the user data from your endpoint and formats it into a format that the chatbot can understand. Here's a basic example of how you might do this:

class UserLoader extends Loader {
  constructor(endpoint) {
    super();
    this.endpoint = endpoint;
  }

  async load() {
    const response = await fetch(this.endpoint);
    const data = await response.json();

    // Format the data into a format that the chatbot can understand
    const formattedData = this.formatData(data);

    return formattedData;
  }

  formatData(data) {
    // Implement this method to format the data into a format that the chatbot can understand
  }
}

You can then use this UserLoader in the same way you would use the PDFLoader:

const loader = new UserLoader('https://my-api.com/user');
const docs = await loader.load();
// ...

Regarding your second issue, the PDFLoader currently only supports text-based PDFs. If you try to load a graphics-based PDF, it will throw an error because it cannot extract the text from the graphics.

To handle this, you could add a check in your code to determine if the PDF is text-based or graphics-based before trying to load it. If it's graphics-based, you could handle it differently, perhaps by showing an error message to the user or by using a different method to extract the text from the graphics.

Unfortunately, extracting text from graphics is a complex task that typically involves OCR (Optical Character Recognition), and it's beyond the scope of the PDFLoader. You might want to look into dedicated OCR libraries or services if you need to handle graphics-based PDFs.

I hope this helps! If you have any further questions, feel free to ask.

Sources

• langchain/src/agents/toolkits/json/prompt.ts
• examples/src/memory/dynamodb-store.ts

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.