Documentation Index
Fetch the complete documentation index at: https://ragopt.aboneda.com/llms.txt
Use this file to discover all available pages before exploring further.
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()
- 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"]
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
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)
