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Code-Confluence Multi-Language Library Feature Schema

website·Indexed 31 days ago

https://docs.unoplat.io/

Documentation for the Code-Confluence Multi-Language Library Feature Schema v4.1, providing JSON schemas for framework and library feature signatures. It includes construct category definitions, constructQueryDef objects, and refinements for auto-generated Tree-sitter queries, with framework definitions for Python and TypeScript libraries such as FastAPI, Pydantic, Next.js, and Firebase.

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What's inside Code-Confluence

  1. Overview of SQLModel for Python

    SQLModel is a library designed for interacting with SQL databases from Python code. It combines SQLAlchemy and Pydantic to provide type-safe database operations with automatic validation, ensuring simplicity and robustness in database model definitions.

  5. Configure CallExpression operations in Code-Confluence Schema v4.1

    When defining an operation with the concept set to CallExpression, the schema enforces specific constraints to handle ambiguity and targeting:

    1. Target Level: Must be set to function.
    2. Base Confidence: A base_confidence value (between 0.0 and 1.0) is required. This represents the contributor-authored confidence in the pattern.
    3. Disambiguation: If base_confidence is less than 0.7, the notes field is mandatory and must contain disambiguation guidance.
    4. Query Refinement: If a match_policy is specified within the construct_query, the concept must be CallExpression.

  6. Use Axios for promise-based HTTP requests

    Axios is a promise-based HTTP client used for making outbound HTTP requests. Requests can be made using the global axios object, the axios.request method, or by creating a custom instance via axios.create.
    import axios from 'axios';

// Simple request
axios.get('https://api.example.com/data');

// Custom instance
const instance = axios.create({
  baseURL: 'https://api.example.com'
});
instance.get('/user');

  7. Configure operation concepts and target levels

    Operations in the library schema must be categorized by a concept and a target_level. The schema enforces specific pairings to ensure consistency:

    ConceptRequired Target LevelNotes
    CallExpressionfunctionRequires base_confidence.
    InheritanceclassUsed for base class identification.
    FunctionDefinitionfunctionUsed for function-based signatures.
    AnnotationLike(As specified)Used for decorator/annotation patterns.

    Special Constraints for CallExpression:

    • Confidence: Must include a base_confidence (0.0 to 1.0).
    • Ambiguity: If base_confidence is less than 0.7, the notes field is mandatory to provide disambiguation guidance.

  8. Define REST API routes in FastAPI

    FastAPI uses HTTP verb decorators to register route handlers. These decorators can be applied to instances of fastapi.FastAPI (or fastapi.applications.FastAPI) and fastapi.APIRouter (or fastapi.routing.APIRouter).

    Supported HTTP methods include:

    • get(): Handle GET requests
    • post(): Handle POST requests
    • put(): Handle PUT requests
    • delete(): Handle DELETE requests
    • patch(): Handle PATCH requests
    • head(): Handle HEAD requests
    • options(): Handle OPTIONS requests
    • trace(): Handle TRACE requests

  9. Create a Zustand state store

    Zustand provides lightweight client state containers. Stores can be created using the create API for React-integrated stores or createStore (available in zustand and zustand/vanilla) for vanilla JavaScript stores.
    import { create } from 'zustand'

const useStore = create((set) => ({
  count: 0,
  inc: () => set((state) => ({ count: state.count + 1 })),
}))

  10. Construct PydanticAI Agents and Durable TemporalAgent Wrappers

    PydanticAI allows for the construction of agents to perform language-model inference. For durable execution, PydanticAI provides a TemporalAgent wrapper. Valid constructors for these agents are located in the pydantic_ai.Agent and pydantic_ai.durable_exec.temporal.TemporalAgent paths.
    from pydantic_ai import Agent
from pydantic_ai.durable_exec.temporal import TemporalAgent

# Example of agent construction
agent = Agent('openai:gpt-4o')
# Example of a durable Temporal wrapper
temporal_agent = TemporalAgent(agent)

  11. Construct a GraphQL client using the gql Python library

    The gql library (version 3.5.3) provides a Client class used to establish outbound GraphQL access. To instantiate a client, use gql.Client or gql.client.Client. This is the primary entry point for setting up GraphQL operations in Python.
    from gql import Client
# Initialize the GraphQL client
client = Client(transport=your_transport_here)

  12. Define data models using Pydantic BaseModel

    To create a data model with automatic validation in Pydantic, inherit from the BaseModel class. This allows you to define the schema of your data using type hints, and Pydantic will ensure that any data instantiated into the model conforms to those types.
    from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str