c7score

The c7score package is used to evaluate the quality of Upstash's Context7 code snippets.

Metrics

c7score uses the following five metrics to grade quality. The metrics can be divided into two groups: LLM analysis and rule-based text analysis.

1. LLM Analysis
   • Metric 1 (Question-Snippet Comparison): How well the snippets answer common developer questions.
   • Metric 2 (LLM substitute for human eval): Evaluates snippet relevancy, clarity, and correctness.
2. Text Analysis
   • Metric 3 (Formatting): Determines whether the snippets have the expected format.
   • Metric 4 (Project Metadata): Checks for irrelevant project information.
   • Metric 5 (Initialization): Looks for basic import and installation statements.

Exports

1. getScore - evaluates a single library based on 5 metrics
2. compareLibraries - evaluates two similar libraries based on 5 metrics
3. scoreQA - evaluates how well code snippets answer provided questions.

Usage

Requirements: The .env file must always have the following:

CONTEXT7_API_TOKEN=...

This library can be used with both Vertex AI and the Gemini API. To use Vertex AI, the .env file must contain the following:

VERTEX_AI=true
GOOGLE_CLOUD_PROJECT=...
GOOGLE_APPLICATION_CREDENTIALS=path_to_credentials

If using the Gemini API:

GEMINI_API_TOKEN=...

import { getScore, compareLibraries, scoreQA } from "@upstash/c7score";

await getScore(
  "/websites/python_langchain",
  `1. What is a selector, and how do I use it?
   2. What is the input token limit for Gemini 2.5 Pro?`,
  { 
    report: {
      console: true,
      folderPath: "results",
    },
    weights: {
      question: 0.8,
      llm: 0.05,
      formatting: 0.05,
      metadata: 0.05,
      initialization: 0.05,
    },
    prompts: {
      questionEvaluation: `Evaluate ...`,
    },
  }
);

await compareLibraries(
  "/tailwindlabs/tailwindcss.com",
  "/websites/tailwindcss",
  `1. How can I install rust?
   2. How can I install Tailwind CSS v4.1 using npm?`;
  {
    report: {
      console: true
    },
    llm: {
      temperature: 0.95,
      topP: 0.8,
      topK: 45
    },
    prompts: {   
      questionEvaluation: `Evaluate ...`
    }
  }
);

await scoreQA(
    "How can I install LangChain Core?",
    `
    ========================
    CODE SNIPPETS
    ========================
    TITLE: Install LangGraph and LangChain dependencies
    DESCRIPTION: This snippet demonstrates how to install 
    the necessary Python and JavaScript/TypeScript packages 
    for LangGraph and LangChain, including Anthropic 
    integrations for model access.
    ...
    `
)

Configuration

For getScore and compareLibraries:

{
  report: {
    console: boolean;
    folderPath: string;
    humanReadable: boolean;
    returnScore: boolean;
  };
  weights: {
    question: number;
    llm: number;
    formatting: number;
    metadata: number;
    initialization: number;
  };
  llm: {
    temperature: number;
    topP: number;
    topK: number;
    candidateCount: number;
    seed: number;
  };
  prompts: {
    searchTopics: string;
    questionEvaluation: string;
    llmEvaluation: string;
  };
}

For scoreQA:

{
  report: {
    console: boolean;
  }
  llm: {
    temperature: number;
    topP: number;
    topK: number;
    candidateCount: number;
    seed: number;
  };
  prompts: {
    questionEvaluation: string;
  }
}

Configuration Details

• compareLibraries
  • must have two libraries that have the same product
  • will output results to result-compare.json and result-compare-LIBRARY_NAME.txt
• getScore
  • will output machine-readable results to result.json and human-readable results to result-LIBRARY_NAME.txt in the specified directory
• scoreQA only returns the score and explanations or logs to the console.
• report
  • console: true prints results to the console.
  • folderPath specifies the folder for human-readable and machine-readable results (the folder must already exist).
    • The machine-readable file will add or update the libraries.
  • humanReadable writes the results to a txt file.
  • returnScore returns the average score as a number for getScore and an object for compareLibraries.
• weights
  • Specifies weight breakdown for evaluation metrics. If changing the weights, all must have an associated value (can be 0) and must sum to 1.
• llm
  • LLM configuration options for Gemini
  • Specific default numbers used to create more reproducible results.
• prompts
  • Replaces the current prompts. It is not recommended to change the final output result instructions or score maximum (e.g., 100 -> 10)
  • Each prompt accepts different placeholders, but they must be formatted as {{variableName}} with the correct associated variable name in the prompt (see Placeholder Reference).

Placeholder Reference

Prompt	For getScore	For compareLibraries	For scoreQA
searchTopics	{{product}}, {{questions}}	–	–
questionEvaluation	{{contexts}}, {{questions}}	{{contexts[0]}}, {{contexts[1]}}, {{questions}}	{{context}}, {{question}}
llmEvaluation	{{snippets}}, {{snippetDelimiter}}	{{snippets[0]}}, {{snippets[1]}}, {{snippetDelimiter}}	–

Default Values

For getScore and compareLibraries:

{
    report: {
        console: true,
        humanReadable: false,
        returnScore: false,
    },
    weights: {
        question: 0.8,
        llm: 0.05,
        formatting: 0.05,
        metadata: 0.05,
        initialization: 0.05,
    },
    llm: {
        temperature: 0,
        topP: 0.1,
        topK: 1,
        candidateCount: 1,
        seed: 42,
    }
}

For scoreQA:

{
    report: {
        console: true,
    },
    llm: {
        temperature: 0,
        topP: 0.1,
        topK: 1,
        candidateCount: 1,
        seed: 42,
    }
}

• Note: scoreQA will always return the scores as objects

Example Outputs

Example output from scoreQA:

Score: 75
Explanation: The provided context contains several code snippets that show how to install `@langchain/core` for JavaScript/TypeScript using npm, yarn, pnpm, and bun.
However, it fails to provide the equivalent `pip` command for installing the `langchain-core` Python package, thus only partially answering the question.

Example output from getScore (compareLibraries is the same):

== Average Score ==

92

== Questions Score ==

100

== Questions Explanation ==

The context provides clear definitions and multiple comprehensive code examples that explain what a selector is in LangChain.
It thoroughly demonstrates how to use various types of selectors, such as `SemanticSimilarityExampleSelector` and `LengthBasedExampleSelector`, by integrating them into few-shot prompt templates.

== LLM Score ==

46

== LLM Explanation ==

The score is low due to a high rate of duplicate snippets, with over a quarter being identical copies. Additionally, a majority of snippets fail the syntax criterion due to errors, use of placeholders, and formatting issues that also impact clarity.

== Formatting Score ==

0

== Project Metadata Score ==

100

== Initialization Score ==

100