3- RAG Manager

The RAGPipelineManager is the heart of the optimization process. It orchestrates component loading, caching, and configuration sampling to efficiently evaluate thousands of RAG configurations.

Overview

The manager handles:

Component Loading: Initialize LLMs, embeddings, vector stores, and rerankers
Caching: Reuse components across configurations to save time and cost
Configuration Sampling: Generate RAG configs from the search space
Encoding/Decoding: Convert between RAGConfig objects and optimization tensors
Parallel Processing: Batch evaluation with thread pools

Important: You typically don’t need to interact with the RAG Manager directly. The Optimizer class handles it automatically.

How It Works

The manager operates in several key phases:

1. Component Initialization

When created, the manager:

from rag_opt.search_space import RAGSearchSpace
from rag_opt import RAGPipelineManager

search_space = RAGSearchSpace.from_yaml("./rag_config.yaml")

manager = RAGPipelineManager(
    search_space=search_space,
    eager_load=False,  # Load components on-demand
    max_workers=5      # Parallel workers
)

2. Lazy Loading & Caching

Components are loaded once and cached:

First Request: LLM(gpt-3.5) → Initialize → Cache
Second Request: LLM(gpt-3.5) → Return from Cache ✓

This dramatically reduces:

API initialization overhead
Memory usage
Evaluation time

3. Configuration Sampling

The manager samples configurations from the search space:

# Sample RAG configurations
from rag_opt import SamplerType,RAGPipelineManager
from rag_opt.search_space import RAGSearchSpace

search_space = RAGSearchSpace.from_yaml("./rag_config.yaml")
manager = RAGPipelineManager(
    search_space=search_space,
    eager_load=False,  # Load components on-demand
    max_workers=5      # Parallel workers
)

configs = manager.sample(
    n_samples=2,
    sampler_type=SamplerType.SOBOL
)

# Each config contains:
# - chunk_size, chunk_overlap, max_tokens
# - search_type, k
# - LLM, embeddings, vector store selections
# - temperature, reranker settings

4. Encoding for Optimization

Converts RAGConfig ↔ Tensor for Bayesian Optimization:

configs = manager.sample(
    n_samples=2,
    sampler_type=SamplerType.SOBOL
)
config = configs[0]

# Config → Pytorch Tensors (for optimizer)
tensor = manager.encode_rag_config_to_tensor(config)

# Tensors → Config (decode optimizer output)
config = manager.decode_sample_to_rag_config(tensor)

This allows the optimizer to work in continuous space while evaluating discrete configurations.

5. RAG Instance Creation

Creates RAGWorkflow instances with cached components:

rag = manager.create_rag_instance(
    config=rag_config,
    documents=train_docs,
    initialize=True
)

# rag now contains:
# - Cached LLM
# - Cached embeddings
# - Fresh vector store (per config)
# - Cached reranker (if enabled)

Integration with Optimizer

The optimizer uses the manager internally:

from rag_opt.optimizer import Optimizer

my_manager = RAGPipelineManager(
    search_space=search_space,
    eager_load=False,
    max_workers=5
)
optimizer = Optimizer(
    train_dataset=train_dataset,
    config_path="rag_config.yaml",
    verbose=True,
    custom_rag_pipeline_manager=my_manager
)
# Manager is created automatically
# optimizer.rag_pipeline_manager is ready to use

Create Custom Manager

class MyManager(AbstractRAGPipelineManager):
    """ Create a custom pipeline manager """
    # NOTE:: u have to create list of abstract methods. For more info see
    # https://github.com/GaiaAI-Hub/rag-opt/blob/main/src/rag_opt/_manager.py

Getting started

RAG Configurations

Optimization Workflow

Evaluation & Metrics

Overview

How It Works

1. Component Initialization

2. Lazy Loading & Caching

3. Configuration Sampling

4. Encoding for Optimization

5. RAG Instance Creation

Integration with Optimizer

Create Custom Manager

​Overview

​How It Works

​1. Component Initialization

​2. Lazy Loading & Caching

​3. Configuration Sampling

​4. Encoding for Optimization

​5. RAG Instance Creation

​Integration with Optimizer

​Create Custom Manager

Overview

How It Works

1. Component Initialization

2. Lazy Loading & Caching

3. Configuration Sampling

4. Encoding for Optimization

5. RAG Instance Creation

Integration with Optimizer

Create Custom Manager