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:

LlamaIndex (Medium priority)

Proper chunking configuration Metadata preservation during parsing Error handling for unsupported formats

ChromaDB (High priority)

Thread-safe client access Proper distance metric selection Metadata type compatibility

Celery (High priority)

Task routing to correct queue Error logging with context Proper serialization

Performance (Medium priority)

Batch processing for embeddings Deduplication enabled Appropriate caching

See individual topic files for detailed checklists with grep patterns.

rag-skills

安装