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langchain - 💡(How to fix) Fix Feature Request: Add SincPromptTemplate for sinc-prompt format [1 comments, 2 participants]

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langchain-ai/langchain#36164•Fetched 2026-04-08 01:17:07
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mdalexandremdalexandre
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mdalexandremdalexandremdrxymdrxy
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Checked other resources

  • This is a feature request, not a bug report or usage question.
  • I added a clear and descriptive title that summarizes the feature request.
  • I used the GitHub search to find a similar feature request and didn't find it.
  • I checked the LangChain documentation and API reference to see if this feature already exists.
  • This is not related to the langchain-community package.

Package (Required)

  • langchain
  • langchain-openai
  • langchain-anthropic
  • langchain-classic
  • langchain-core
  • langchain-model-profiles
  • langchain-tests
  • langchain-text-splitters
  • langchain-chroma
  • langchain-deepseek
  • langchain-exa
  • langchain-fireworks
  • langchain-groq
  • langchain-huggingface
  • langchain-mistralai
  • langchain-nomic
  • langchain-ollama
  • langchain-openrouter
  • langchain-perplexity
  • langchain-qdrant
  • langchain-xai
  • Other / not sure / general

Feature Description

Add a SincPromptTemplate to langchain-community that implements the sinc-prompt specification for structured LLM prompts.

sinc-prompt is an open format that decomposes prompts into 6 specification bands based on the Nyquist-Shannon sampling theorem. Each band carries a measured importance weight from 275 production observations:

  • PERSONA (7.0%): Who should answer
  • CONTEXT (6.3%): Situation and background
  • DATA (3.8%): Specific inputs
  • CONSTRAINTS (42.7%): Behavioral rules (highest importance)
  • FORMAT (26.3%): Output structure
  • TASK (2.8%): The specific objective

The format has a formal JSON Schema (https://tokencalc.pro/schema/sinc-prompt-v1.json), a human-readable specification (https://tokencalc.pro/spec), and a published research paper with DOI (https://doi.org/10.5281/zenodo.19152668).

The SincPromptTemplate class would accept sinc JSON as input, parse the 6 bands, format them into a prompt string, and provide utility methods like nyquist_completeness() and to_sinc_json().

Use Case

I build production AI systems that use multiple specialized agents. Each agent needs precisely structured prompts with domain-specific constraints, format specifications, and persona definitions. Currently I manually structure each prompt, but there is no standardized format in LangChain for multi-band prompt decomposition.

With a SincPromptTemplate, I could load prompts from .sinc.json files, validate their completeness using nyquist_completeness(), and ensure every prompt has the critical CONSTRAINTS band (which carries 42.7% of output quality based on 275 production observations).

This would also allow teams to version control their prompts as structured JSON and validate them in CI/CD pipelines.

Proposed Solution

A SincPromptTemplate class in langchain_community.prompts that extends BasePromptTemplate. The class would:

  1. Accept 6 named parameters (persona, context, data, constraints, fmt, task) or a sinc JSON dict/file
  2. Provide from_sinc_json() class method to load from dict, JSON string, or .sinc.json file
  3. Provide to_sinc_json() to export back to the standard format
  4. Provide nyquist_completeness() returning a 0.0-1.0 weighted score using the MATLAB-derived importance weights
  5. Format output as [BAND_NAME]\ncontent blocks ordered by band index

I have a working implementation ready: https://github.com/mdalexandre/langchain/blob/sinc-prompt-template/libs/community/langchain_community/prompts/sinc_prompt.py

Alternatives Considered

Currently using a standalone Python package (pip install sinc-llm) which works but does not integrate with LangChain's prompt template ecosystem. Users have to manually convert between sinc JSON and LangChain PromptTemplate format. A native SincPromptTemplate would eliminate this friction and allow sinc prompts to work seamlessly with LangChain chains, agents, and output parsers.

Additional Context

I am happy to submit the PR once this issue is approved and assigned.

extent analysis

Fix Plan

To implement the SincPromptTemplate class in langchain_community.prompts, follow these steps:

  • Create a new Python file sinc_prompt.py in the langchain_community/prompts directory.
  • Define the SincPromptTemplate class with the required methods:
    • __init__: Initialize the class with the 6 named parameters (persona, context, data, constraints, fmt, task) or a sinc JSON dict/file.
    • from_sinc_json: Load a sinc JSON dict, string, or file into the class.
    • to_sinc_json: Export the class data back to the standard sinc JSON format.
    • nyquist_completeness: Calculate the weighted score using the MATLAB-derived importance weights.
    • format_output: Format the output as [BAND_NAME]\ncontent blocks ordered by band index.

Example code:

import json

class SincPromptTemplate:
    def __init__(self, persona=None, context=None, data=None, constraints=None, fmt=None, task=None, sinc_json=None):
        if sinc_json:
            self.from_sinc_json(sinc_json)
        else:
            self.persona = persona
            self.context = context
            self.data = data
            self.constraints = constraints
            self.fmt = fmt
            self.task = task

    @classmethod
    def from_sinc_json(cls, sinc_json):
        if isinstance(sinc_json, str):
            sinc_json = json.loads(sinc_json)
        return cls(**sinc_json)

    def to_sinc_json(self):
        return {
            'persona': self.persona,
            'context': self.context,
            'data': self.data,
            'constraints': self.constraints,
            'fmt': self.fmt,
            'task': self.task
        }

    def nyquist_completeness(self):
        weights = {
            'persona': 0.07,
            'context': 0.063,
            'data': 0.038,
            'constraints': 0.427,
            'fmt': 0.263,
            'task': 0.028
        }
        score = 0
        for band, weight in weights.items():
            if getattr(self, band):
                score += weight
        return score

    def format_output(self):
        bands = [
            ('PERSONA', self.persona),
            ('CONTEXT', self.context),
            ('DATA', self.data),
            ('CONSTRAINTS', self.constraints),
            ('FORMAT', self.fmt),
            ('TASK', self.task)
        ]
        output = ''
        for band, content in bands:
            if content:
                output += f'[ {band} ]\n{content}\n'
        return output

Verification

To verify the implementation, create a test case that loads a sinc JSON file

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