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rag-skills

RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.

llama-farm☆ 816 仓库 Stars46 仓库 Forks

安装与使用

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技能说明

RAG Skills for LlamaFarm

Framework-specific patterns and code review checklists for the RAG component.

Extends: python-skills - All Python best practices apply here.

Component Overview

Aspect Technology Version
Python Python 3.11+
Document Processing LlamaIndex 0.13+
Vector Storage ChromaDB 1.0+
Task Queue Celery 5.5+
Embeddings Universal/Ollama/OpenAI Multiple

Directory Structure

rag/
├── api.py                 # Search and database APIs
├── celery_app.py          # Celery configuration
├── main.py                # Entry point
├── core/
│   ├── base.py            # Document, Component, Pipeline ABCs
│   ├── factories.py       # Component factories
│   ├── ingest_handler.py  # File ingestion with safety checks
│   ├── blob_processor.py  # Binary file processing
│   ├── settings.py        # Pydantic settings
│   └── logging.py         # RAGStructLogger
├── components/
│   ├── embedders/         # Embedding providers
│   ├── extractors/        # Metadata extractors
│   ├── parsers/           # Document parsers (LlamaIndex)
│   ├── retrievers/        # Retrieval strategies
│   └── stores/            # Vector stores (ChromaDB, FAISS)
├── tasks/                 # Celery tasks
│   ├── ingest_tasks.py    # File ingestion
│   ├── search_tasks.py    # Database search
│   ├── query_tasks.py     # Complex queries
│   ├── health_tasks.py    # Health checks
│   └── stats_tasks.py     # Statistics
└── utils/
    └── embedding_safety.py  # Circuit breaker, validation

Quick Reference

Topic File Key Points
LlamaIndex llamaindex.md Document parsing, chunking, node conversion
ChromaDB chromadb.md Collections, embeddings, distance metrics
Celery celery.md Task routing, error handling, worker config
Performance performance.md Batching, caching, deduplication

Core Patterns

Document Dataclass
from dataclasses import dataclass, field
from typing import Any

@dataclass
class Document:
    content: str
    metadata: dict[str, Any] = field(default_factory=dict)
    id: str = field(default_factory=lambda: str(uuid.uuid4()))
    source: str | None = None
    embeddings: list[float] | None = None
Component Abstract Base Class
from abc import ABC, abstractmethod

class Component(ABC):
    def __init__(
        self,
        name: str | None = None,
        config: dict[str, Any] | None = None,
        project_dir: Path | None = None,
    ):
        self.name = name or self.__class__.__name__
        self.config = config or {}
        self.logger = RAGStructLogger(__name__).bind(name=self.name)
        self.project_dir = project_dir

    @abstractmethod
    def process(self, documents: list[Document]) -> ProcessingResult:
        pass
Retrieval Strategy Pattern
class RetrievalStrategy(Component, ABC):
    @abstractmethod
    def retrieve(
        self,
        query_embedding: list[float],
        vector_store,
        top_k: int = 5,
        **kwargs
    ) -> RetrievalResult:
        pass

    @abstractmethod
    def supports_vector_store(self, vector_store_type: str) -> bool:
        pass
Embedder with Circuit Breaker
class Embedder(Component):
    DEFAULT_FAILURE_THRESHOLD = 5
    DEFAULT_RESET_TIMEOUT = 60.0

    def __init__(self, ...):
        super().__init__(...)
        self._circuit_breaker = CircuitBreaker(
            failure_threshold=config.get("failure_threshold", 5),
            reset_timeout=config.get("reset_timeout", 60.0),
        )
        self._fail_fast = config.get("fail_fast", True)

    def embed_text(self, text: str) -> list[float]:
        self.check_circuit_breaker()
        try:
            embedding = self._call_embedding_api(text)
            self.record_success()
            return embedding
        except Exception as e:
            self.record_failure(e)
            if self._fail_fast:
                raise EmbedderUnavailableError(str(e)) from e
            return [0.0] * self.get_embedding_dimension()

Review Checklist Summary

When reviewing RAG code:

  1. LlamaIndex (Medium priority)

    • Proper chunking configuration
    • Metadata preservation during parsing
    • Error handling for unsupported formats
  2. ChromaDB (High priority)

    • Thread-safe client access
    • Proper distance metric selection
    • Metadata type compatibility
  3. Celery (High priority)

    • Task routing to correct queue
    • Error logging with context
    • Proper serialization
  4. Performance (Medium priority)

    • Batch processing for embeddings
    • Deduplication enabled
    • Appropriate caching

See individual topic files for detailed checklists with grep patterns.

使用示例

查看来源仓库中的使用示例与配置说明。

查看来源 ↗

来源历史

收录时间
2026-01-20
索引更新时间
2026-01-30
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

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