how to remove sentence that we dont want from output prompt template

[barbuscanian]

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Example Code

def lead_gen(sensor_type, description):

    # Define a preset prompt
    prompt_template = """
    Offer a direct and professional recommendation based on the provided data. Craft the output as a concise suggestion, maintaining an expert tone with a maximum of 3 sentences and short 1 paragraph.

    {input_c}

    Please note the significance of the sensor_type and column titles outlined below:
    - powermeter: Captures power consumption data.
    - watermeter: Records water consumption data.
    - Waterlevel: Detects water height at specific locations.

    Understand the implications of the following column titles:
    - today_total_usage: Current sensor reading for today.
    - dod: Day-over-day percentage growth comparison.
    - wow: Week-over-week percentage growth comparison.
    - mom: Month-over-month percentage growth comparison.
    - qoq: Quarter-over-quarter percentage growth comparison.
    - yoy: Year-over-year percentage growth comparison.
    - current_usage_pattern: Compares current month's usage with historical patterns.
    - today_usage_anomaly: Indicates if today detected some usage has different pattern than usual or not.
    - today_update_anomaly: Indicates if device information is updated or not updated today.

    """

    prompt = PromptTemplate.from_template(prompt_template)

    llm_chain = LLMChain(llm=llm, prompt=prompt)

    input_c = f'{sensor_type}. {description}'

    # Wrap the invocation with the callback to track cost
    with get_openai_callback() as cb:
        response = llm_chain.invoke(input_c)

    total_cost = cb.total_cost * 15741 # Retrieve the total cost from the callback

    return response, total_cost #, tokens_used  # Return both response and total cost


sensor_type = 'powermeter'
input_inf = 'today_total_usage: 164.39, dod: -89, wow: -29.08, mom: 3.18, qoq:0, yoy:0, current_usage_pattern: 0.3, today_usage_anomaly: true, today_update_anomaly: updated'

response = lead_gen(sensor_type, input_inf)

print(response[0]['text'])

Description

output is : Based on the provided data, I recommend investigating the anomaly in today's power consumption pattern, as it is significantly lower compared to the previous day and week. Additionally, ensure that the device information is up to date to accurately monitor and analyze power usage.

i want to remove sentence 'Based on the provided data' from the output and prompt template, i try to remove sentence 'Based on the provided data' still get the output with that sentence.

what should i do? any tips?

System Info

(from pydantic<3,>=1->langchain) (0.6.0)

[dosubot[bot]]

Hey @barbuscanian! 👋 I'm Dosu, your friendly neighborhood bot here to assist with bugs, answer questions, and help guide you towards becoming a contributor while we wait for a human maintainer. I'm diving into your issue now and will get back to you with some insights pronto! 🚀

[dosubot[bot]]

To remove the sentence "Based on the provided data" from both the output and the prompt template in your script, you can implement a function that specifically targets and removes this sentence from any text it's applied to. Here's a concise way to do it:

def remove_unwanted_sentence(text, sentence="Based on the provided data"):
    """Remove a specific sentence from the text."""
    return text.replace(sentence, "")

# Apply this function to your prompt template or any output text where you want the sentence removed.

This function remove_unwanted_sentence can be used to preprocess any string, including your prompt template or the output text, to ensure the unwanted sentence is removed before processing or displaying the text. Simply call this function with the text you need to clean up as its argument.

For example, if you're working with a prompt template or an output that includes the unwanted sentence, you can do the following:

prompt_template = """Your original prompt template here"""
cleaned_prompt = remove_unwanted_sentence(prompt_template)

This approach ensures that the specific sentence is removed from your text, allowing you to control the content of both your prompts and the generated outputs more precisely.

Details

Sources

• libs/langchain/langchain/output_parsers/prompts.py
• libs/langchain/langchain/chains/llm.py
• libs/community/tests/integration_tests/callbacks/test_openai_callback.py
• libs/core/langchain_core/language_models/llms.py
• libs/community/langchain_community/callbacks/openai_info.py

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.

To continue the conversation, mention @dosu.

[barbuscanian]

@dosu

[dosubot[bot]]

To address the issue of removing the sentence "Based on the provided data" from your output, you can directly manipulate the text after it's generated by the language model. This approach involves creating a function that searches for and removes the specified sentence from any given text. Here's how you can implement this:

def remove_unwanted_sentence(text, unwanted_sentence="Based on the provided data"):
    return text.replace(unwanted_sentence, "")

response = lead_gen(sensor_type, input_inf)
cleaned_response = remove_unwanted_sentence(response[0]['text'])
print(cleaned_response)

This function, remove_unwanted_sentence, will strip the unwanted sentence from the output, ensuring that the specific phrase is not included in the final text presented. It's a straightforward and effective way to ensure that your output text meets your requirements without needing to adjust the underlying model or its training data.

Details

Sources

• how to remove sentence that we dont want from output prompt template
• libs/langchain/langchain/memory/prompt.py
• templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py
• libs/community/langchain_community/callbacks/manager.py
• libs/langchain/langchain/chains/llm.py
• libs/core/langchain_core/language_models/llms.py

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.

To continue the conversation, mention @dosu.

[barbuscanian]

doesn't work, still same output with first sentence "based on the provided data" @dosu

[dosubot[bot]]

To effectively remove the sentence "Based on the provided data" from your output, despite your efforts with the replace() method, consider the possibility that this sentence might be added by the language model based on its training and the input it receives. Language models generate text that can include common phrases or sentences if those are frequently present in their training data and the input prompt suggests such a sentence might be appropriate.

Here's a refined approach using regular expressions to ensure all possible variations of the unwanted sentence are removed, accounting for additional spaces or different capitalization:

import re

output_text = "Your script output here. Based on the provided data, some recommendations."
cleaned_output = re.sub(r"\bBased on the provided data\b", "", output_text, flags=re.I)

This method uses re.sub() to replace occurrences of the unwanted sentence with an empty string, where flags=re.I makes the pattern matching case-insensitive to catch variations in capitalization.

It's important to note that the LangChain framework and the LLMChain class do not inherently add any predefined text to the outputs. The appearance of the unwanted sentence is likely influenced by the language model's interpretation of the input prompt or its internal processing logic. Therefore, applying a post-processing step like the one above directly to the model's output is a practical solution.

If you continue to encounter issues, it may be necessary to review the implementation details of the language model's generation process and the prompt preparation logic to identify where the sentence is being introduced. This could reveal configuration options or parameters that control the output more granularly.

Details

Sources

• how to remove sentence that we dont want from output prompt template
• libs/langchain/langchain/retrievers/document_compressors/chain_extract_prompt.py
• libs/langchain/langchain/chains/llm.py
• libs/core/langchain_core/language_models/llms.py
• libs/core/langchain_core/prompts/prompt.py
• libs/core/langchain_core/runnables/base.py

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.

To continue the conversation, mention @dosu.

[eyurtsev]

Modify the prompt / use a better model / use a regular expression like dosu suggested / or pass the output through another LLM with instructions about what kind of patterns to remove. I'd start with modifying the prompt.

[Huzaifa-ali]

Hi there, I would recommend in such cases add a prompt
"Don't start with "according to provided context" , "based on the provided data" "
It will work in my case
Try experiment with prompt to avoid this.