Using LLM response again, or possibly save it as a variable.

[3veryDay]

Hi, I am a beginner who just started with Langflow a couple of days ago.
I successfully got my LLM to answer at the format I want to.
It is supposed to seperate a given text into beginning, reason1, reason2, end.

I want to use the given parts to ask seperate questions.
I want to achieve this by saving the given text into seperate text files, or variables.
and use these by connecting a new prompt template.

however, with my restricted knowledge, I can't seem to figure this out.
can anybody help me?

[dosubot[bot]]

Hey @smart8ss! Great to see you diving into Langflow and using it for something cool. Hope you're doing fantastic! 😄👋

Based on your request, it seems like you're trying to separate an LLM response into different parts (beginning, reason1, reason2, and end), save these parts into separate text files or variables, and then use them in a new prompt template in Langflow. Here's a step-by-step guide on how you can achieve this:

1. Separate the LLM Response: The first step is to separate the LLM response into the parts you mentioned. This process depends on how the response is structured. If there are clear delimiters or patterns, you can use Python's string methods or regular expressions to split the response.

2. Save to Text Files or Variables: Once you have the parts separated, you can either save them to text files using Python's file handling methods or store them in variables for immediate use.

   • Saving to Text Files:

     parts = {'beginning': beginning, 'reason1': reason1, 'reason2': reason2, 'end': end}
     for part_name, content in parts.items():
         with open(f"{part_name}.txt", "w") as file:
             file.write(content)

   • Storing in Variables:

     # Assuming the variables beginning, reason1, reason2, and end are already defined
     # Just proceed to use them as needed

3. Use in New Prompt Template: To use these parts in a new prompt template in Langflow, you can either read the saved text files or use the variables directly to construct your new prompt. If you're using the Langflow API or working within its framework, you'll likely need to update or create a new PromptTemplate object with these parts as input variables.

   • Reading from Text Files (if saved to files):

     parts = ['beginning', 'reason1', 'reason2', 'end']
     content = {}
     for part in parts:
         with open(f"{part}.txt", "r") as file:
             content[part] = file.read()

   • Constructing New Prompt Template:
     Assuming you have a function or method to create or update a prompt template, you would pass the content of these parts as arguments or parameters. This step is highly dependent on the specifics of the Langflow application's API or the structure of your project.

     If you're working with the provided code, you might use the add_new_variables_to_template function to add these parts as new variables to your template. You would need to ensure that input_variables, custom_fields, and template are correctly defined and passed along with the name of the template you're updating or creating.

     # Example of adding a new variable to a template
     # This is a simplified example and might need adjustments based on your actual template structure and requirements
     input_variables = ['beginning', 'reason1', 'reason2', 'end']  # These would be your separated parts
     custom_fields = {}  # Assuming this is defined elsewhere in your code
     template = {}  # Assuming this is your template structure
     name = "YourTemplateName"  # The name of your template
     
     for variable in input_variables:
         add_new_variables_to_template([variable], custom_fields, template, name)

This approach assumes familiarity with Python's file handling for saving and reading text files, as well as basic understanding of manipulating dictionaries and objects in Python. Adjustments may be necessary based on the specific structure of your Langflow application and the format of the LLM responses.

For more details, you can refer to the source code in the Langflow repository. Specifically, you might find the following files useful:

• src/backend/base/langflow/base/prompts/utils.py for understanding how to manipulate PromptTemplate objects.
• src/backend/base/langflow/api/v1/callback.py for understanding how the LLM response is structured.

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

Sources

• src/backend/base/langflow/base/prompts/utils.py
• src/backend/base/langflow/api/v1/callback.py

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[Cristhianzl]

Hi! 👋

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