Searchspace

The search space defines the hyperparameter ranges and choices that the optimizer explores to find optimal RAG configurations.

Overview

RAGOpt uses a RAGSearchSpace to define all tunable parameters including:

Chunking parameters (size, overlap)
Retrieval settings (k, search type)
Model selections (LLM, embeddings, vector store, reranker)
Generation parameters (temperature)

Important: You typically don’t need to customize the search space manually. The default configuration or YAML-based config is sufficient for most use cases.

How It Works

Under the hood, the search space:

Defines parameter types (continuous, categorical, boolean)
Sets valid ranges and choices
Handles encoding/decoding for Bayesian Optimization
Manages component pricing information

The optimizer uses this to:

Sample configurations efficiently (Sobol, Random, QMC)
Convert between tensor representations and RAGConfig objects
Evaluate configurations during optimization

Default Configuration

RAGOpt provides sensible defaults out of the box:

from rag_opt.search_space import RAGSearchSpace

# Use default search space
search_space = RAGSearchSpace.get_default_search_space_config()

Default ranges include:

chunk_size: 200-2000 tokens
chunk_overlap: 0-500 tokens
k: 1-20 retrieved documents
temperature: 0.0-2.0
search_type: similarity, mmr, bm25, hybrid
vector_stores: FAISS, Chroma, Pinecone, Weaviate
embeddings: OpenAI, HuggingFace, Sentence Transformers
llms: OpenAI GPT, Anthropic Claude

YAML Configuration

For custom configurations, define a YAML file:

# rag_config.yaml
chunk_size:
  searchspace_type: continuous
  bounds: [200, 2000]
  dtype: int

chunk_overlap:
  searchspace_type: continuous
  bounds: [0, 500]
  dtype: int

k:
  searchspace_type: continuous
  bounds: [1, 20]
  dtype: int

temperature:
  searchspace_type: continuous
  bounds: [0.0, 2.0]
  dtype: float

search_type:
  searchspace_type: categorical
  choices: ["similarity", "mmr", "bm25", "hybrid"]

llm:
  searchspace_type: categorical
  choices:
    openai:
      provider: openai
      models: ["gpt-3.5-turbo", "gpt-4"]
      pricing:
        gpt-3.5-turbo:
          input: 0.0015
          output: 0.002
    anthropic:
      provider: anthropic
      models: ["claude-3-7-sonnet-latest"]
      pricing:
        claude-3-7-sonnet-latest:
          input: 0.003
          output: 0.015

embedding:
  searchspace_type: categorical
  choices:
    openai:
      provider: openai
      models: ["text-embedding-ada-002"]
    huggingface:
      provider: huggingface
      models: ["all-MiniLM-L6-v2"]

vector_store:
  searchspace_type: categorical
  choices:
    faiss:
      provider: faiss
    chroma:
      provider: chroma

use_reranker:
  searchspace_type: boolean
  allow_multiple: true

reranker:
  searchspace_type: categorical
  choices:
    cross_encoder:
      provider: cross_encoder
      models: ["msmarco-MiniLM-L-6-v3"]

Load it in the optimizer:

from rag_opt.search_space import RAGSearchSpace

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

Parameter Types

Continuous Parameters

Numeric ranges for values like chunk_size, temperature:

chunk_size:
  searchspace_type: continuous
  bounds: [200, 2000]
  dtype: int

Categorical Parameters

Discrete choices like models or search strategies:

search_type:
  searchspace_type: categorical
  choices: ["similarity", "mmr", "bm25"]

Boolean Parameters

Binary decisions like enabling reranking:

use_reranker:
  searchspace_type: boolean
  allow_multiple: true

Pricing Configuration

Include cost information for optimization:

llm:
  choices:
    openai:
      models: ["gpt-4"]
      pricing:
        gpt-4:
          input: 0.03 # per 1K tokens
          output: 0.06 # per 1K tokens

This enables cost-aware optimization in multi-objective scenarios.

Sampling Methods

The search space supports multiple sampling strategies:

from rag_opt import SamplerType
# Sobol sampling (default, best for optimization)
configs = search_space.sample(n_samples=10, sampler_type=SamplerType.SOBOL)

# Random sampling
configs = search_space.sample(n_samples=10, sampler_type=SamplerType.RANDOM)

Getting started

RAG Configurations

Optimization Workflow

Evaluation & Metrics

Overview

How It Works

Default Configuration

YAML Configuration

Parameter Types

Continuous Parameters

Categorical Parameters

Boolean Parameters

Pricing Configuration

Sampling Methods

​Overview

​How It Works

​Default Configuration

​YAML Configuration

​Parameter Types

​Continuous Parameters

​Categorical Parameters

​Boolean Parameters

​Pricing Configuration

​Sampling Methods

Overview

How It Works

Default Configuration

YAML Configuration

Parameter Types

Continuous Parameters

Categorical Parameters

Boolean Parameters

Pricing Configuration

Sampling Methods