Field "chat_history" in prompt uses a MessagesPlaceholder

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Error occurred: Error [InputFormatError]: Error: Field "chat_history" in prompt uses a MessagesPlaceholder, which expects an array of BaseMessages as an input value. Received:

const {OpenAI} = require('langchain/llms/openai'); const {initializeAgentExecutorWithOptions} = require('langchain/agents');

const {PineconeStore} = require('langchain/vectorstores/pinecone'); const {OpenAIEmbeddings} = require('langchain/embeddings/openai'); const admin = require("firebase-admin"); const {Pinecone} = require('@pinecone-database/pinecone');

const {BufferMemory} = require('langchain/memory'); // Import BufferMemory const {FirestoreChatMessageHistory} = require('langchain/stores/message/firestore'); // Import FirestoreChatMessageHistory const {PromptTemplate} = require("langchain/prompts");

const {ChainTool} = require("langchain/tools"); const {ConversationalRetrievalQAChain} = require('langchain/chains'); const {response} = require("express"); const {ChatOpenAI} = require("@langchain/openai");

const CONDENSE_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.

Chat History: {chat_history} Follow Up Input: {question} Standalone question:

const QA_PROMPT = `You are {author}. Use the following pieces of context to answer the question at the end.

{context} Question: {question} Helpful answer ALWAYS output in markdown!!!

const runChatbot = async (sessionId, uid, chatbot, question,history) => { try {

const sanitizedQuestion = question.trim().replaceAll('\n', ' ');

//Retrieve the chatbot from firestore const chatbotReference = await admin.firestore().collection('chatbots').doc(chatbot).get(); const chatbotData = chatbotReference.data();

// Transform the chat history to the format expected by LangChain const transformedHistory = Array.isArray(history) ? history.map(msg => { const prefix = msg.type === 'userMessage' ? 'Human:' : 'Assistant:'; return ${prefix} ${msg.message}; }).join('\n') : '';

// Pinecone setup and initialization const pinecone = new Pinecone({ apiKey: process.env.PINECONE_API_KEY, environment: process.env.PINECONE_ENVIRONMENT, });

const index = pinecone.Index(process.env.PINECONE_INDEX_NAME);

// Create a vector store const vectorStore = await PineconeStore.fromExistingIndex( new OpenAIEmbeddings({}), { pineconeIndex: index, textKey: 'text', namespace: "marting_lings", } );

const model = new ChatOpenAI({ temperature: 0.5, modelName: chatbot.activeModel, openAIApiKey: "****"

});

// Create a config for the FirestoreChatMessageHistory const firestoreConfig = { collectionName: "chathistory", sessionId: sessionId, userId: uid, config: {projectId: "unity-352c6"}, };

// Initialize the FirestoreChatMessageHistory for chat history // Here a new sub collection for messages was made const chatHistory = new FirestoreChatMessageHistory(firestoreConfig);

// Create a BufferMemory instance with FirestoreChatMessageHistory const memory = new BufferMemory({ chatHistory, memoryKey: "chat_history", inputKey: "question", outputKey: "text", });

// Create a conversational chain const retrievalChain = ConversationalRetrievalQAChain.fromLLM( model, vectorStore.asRetriever(), { qaTemplate: QA_PROMPT.replace('{author}', chatbot.name), questionGeneratorTemplate: CONDENSE_PROMPT, returnSourceDocuments: true, memory, // Add memory to the chain }, );

const qaTool = new ChainTool({ name: Books of ${chatbot}-qa, description: Books of the ${chatbot} QA - useful for when you need to ask questions about the opinion of ${chatbot}., chain: retrievalChain, returnDirect: true, verbose: true })

const tools = [qaTool]

const executor = await initializeAgentExecutorWithOptions(tools, model, { agentType: 'openai-functions', memory, returnIntermediateSteps: true, agentArgs: { prefix: You are ${chatbot} answering questions about ${chatbot} you can use tools to read his books answer question with this information., } })

console.log(await transformedHistory)

const response = await executor.call( {

    author: chatbot,
    question: sanitizedQuestion,
    chat_history: transformedHistory
},

)

console.log(response)

} catch (error) { console.error('Error occurred:', error) // Handle the error as needed: log, throw, or respond gracefully. // Example: throw the error to propagate it to the caller throw error }

} module.exports = { runChatbot };

you response: Hi @RedouanAssakali! Good to see you again. Thanks for reaching out with another issue. Let's see what we can do to help.

The error message you're encountering, "Field 'chat_history' in prompt uses a MessagesPlaceholder, which expects an array of BaseMessages as an input value", is indicating that the 'chat_history' field is expected to be an array of BaseMessages, but it's not receiving that.

In your code, you have defined 'chat_history' as a memory key in BufferMemory and also used it as a MessagesPlaceholder in memoryPrompts. However, it seems like 'chat_history' is not being populated with an array of BaseMessages before being used in the executor.invoke() method.

To fix this issue, you need to ensure that 'chat_history' is being populated with an array of BaseMessages before it's used. If 'chat_history' is supposed to store the history of chat messages, you might need to update it after each executor.invoke() call.

The 'chat_history' field in the langchainjs framework is expected to be an array of message objects. Each message object is either of type 'HumanMessage' or 'AIMessage'. Both types of messages have a 'content' field which contains the message text and an 'additional_kwargs' field which is an object that can contain any additional information related to the message.

Here is the expected format:

[ HumanMessage { content: 'message text', additional_kwargs: {} }, AIMessage { content: 'message text', additional_kwargs: {} } ]

The 'chat_history' field in the langchainjs framework is typically populated by using the 'addMessage', 'addUserMessage', and 'addAIChatMessage' methods of the 'BaseChatMessageHistory' and 'BaseListChatMessageHistory' classes.

For example, to add a user message to the chat history, you would use the 'addUserMessage' method, passing in the message as a string. This method creates a new 'HumanMessage' instance with the provided message and adds it to the chat history.

this.addUserMessage("Hello, world!");

Similarly, to add an AI chat message to the chat history, you would use the 'addAIChatMessage' method, passing in the message as a string. This method creates a new 'AIMessage' instance with the provided message and adds it to the chat history.

this.addAIChatMessage("Hello, user!");

The 'addMessage' method can be used to add any message (either a user message or an AI message) to the chat history, as long as the message is an instance of 'BaseMessage'.

this.addMessage(new HumanMessage("Hello, world!")); this.addMessage(new AIMessage("Hello, user!"));

You can refer to the following sources for more information:

[examples/src/agents/structured_chat_with_memory.ts](https://github.com/langchain-ai/langchainjs/blob/main/examples/src/agents/structured_chat_with_memory.ts)
[examples/src/memory/chat_messages.ts](https://github.com/langchain-ai/langchainjs/blob/main/examples/src/memory/chat_messages.ts)
[langchain-core/src/chat_history.ts](https://github.com/langchain-ai/langchainjs/blob/main/langchain-core/src/chat_history.ts)

If you need further clarification or if there's anything else you'd like to know, feel free to ask. Sources

[examples/src/agents/structured_chat_with_memory.ts](https://github.com/langchain-ai/langchainjs/blob/main/examples/src/agents/structured_chat_with_memory.ts)
[examples/src/memory/chat_messages.ts](https://github.com/langchain-ai/langchainjs/blob/main/examples/src/memory/chat_messages.ts)
[langchain-core/src/chat_history.ts](https://github.com/langchain-ai/langchainjs/blob/main/langchain-core/src/chat_history.ts)

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.

What i think the problem is: @dosu-bot i think the problem is that chat_history is empty

langchain-ai / langchainjs

Field "chat_history" in prompt uses a MessagesPlaceholder #4117

Sources