Help Needed: Handling Custom Object in Prompt Input for Email Summarization

[ealss101]

Hi everyone,

I'm working on an email summarization project using LangFlow, but I'm running into an issue with how my input data is being passed to the prompt. Specifically, the input contains a custom object (HumanMessage) that is not in standard JSON format. Here's an example of the problematic input:

{
  'messages': [
    HumanMessage(
      content='hello world',
      additional_kwargs={'sender': 'Emi Sanchez <[email protected]>'},
      response_metadata={}
    )
  ]
}

I'm trying to use this input in the following prompt template:

You are a helpful email assistant who summarizes email content.

Here is the email content: {content}

However, the input does not map correctly to the {content} variable in the template because of the custom object structure. LangFlow throws an error when trying to process this.

---

What I Need Help With

1. Extracting the content field:

   • I need to preprocess the input to extract the content field from the messages[0] object, so only the plain email text ("hello world") is passed into the prompt.

2. Simplifying the Input for the Prompt:

   • I want to restructure the input into something like:

     {
       "content": "hello world",
       "sender": "Emi Sanchez <[email protected]>"
     }

3. Best Practices in LangFlow:

   • Is there a way to use the Filter Data component or another built-in tool in LangFlow to preprocess this data without resorting to custom Python scripts?
   • Is there a way that the agent can simply interpret the prompt better?

---

What I’ve Tried

• Using the Filter Data component to extract messages[0].content, but it does not return any data.
• Adding a custom Python REPL node to handle the extraction, but I’d like to avoid writing custom scripts if possible.

---

Questions

1. How can I extract and restructure the content and sender fields from the HumanMessage object within LangFlow?
2. Is there a recommended approach to handle nested fields in the input JSON-like structure using LangFlow components?
3. Are there built-in tools in LangFlow for flattening or cleaning inputs for prompt templates?

Any help or pointers would be greatly appreciated. Thanks in advance!

Emi

[dosubot[bot]]

To handle your email summarization project in LangFlow and address the issues with the HumanMessage object, you can follow these steps:

1. Extracting and Restructuring Fields:

   • You can use the to_lc_message method in the Message class to convert a HumanMessage to a BaseMessage and extract the content and sender fields. Here's a function to help with this:

     def extract_human_message_fields(message):
         human_message = message.to_lc_message()
         if isinstance(human_message, HumanMessage):
             content = human_message.content
             sender = message.additional_kwargs.get('sender', '')
             return {"content": content, "sender": sender}
         return None

2. Using LangFlow Components:

   • The Filter Data component can be configured to handle nested data structures using JQ queries. However, for your specific case, you might need to preprocess the data using a custom component or utility function to extract and restructure the fields as shown above.

3. Built-in Tools for Flattening or Cleaning Inputs:

   • LangFlow provides utilities like flatten_list and serialize_field for handling complex data objects. These can be used to flatten and serialize your data for prompt input ^[1][2].

4. Best Practices:

   • If you want to avoid custom scripts, consider creating a custom component in LangFlow that uses the above extraction logic. This way, you can preprocess the data within LangFlow's environment without external scripts.

By following these steps, you should be able to preprocess your input data effectively for use in your prompt template.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}