Choosing and Configuring an Embedding Model for RAG

How to Choose an Embedding Model for RAG?

We often encounter situations where a RAG system works, but retrieval returns irrelevant documents. Most often, the problem is the embedding model. It may not suit your corpus language or query context. Changing the model can boost recall by 10–15% without changing the architecture. We offer a free audit; paid tuning starts at $3,000. Our engineers have 10 years of experience in NLP. They have implemented over 50 RAG projects for Enterprise. An error in model selection can cost up to 40% of answer accuracy. We guarantee that after tuning to your domain, recall will increase by at least 10%. Contact us for a free audit.

Step-by-Step Model Selection

1. Analyze your corpus: Determine language, size, and domain specificity.
2. Define latency and privacy: Decide if you need on-premise or can use APIs.
3. Select candidate models: Choose 2-3 from the table below.
4. Evaluate on your data: Use RAGAS to measure context_recall and context_precision.
5. Pick the winner: We help you finalize and integrate.

Model Comparison on MTEB

The embedding model is one of the most critical components of a RAG system. Retrieval quality directly depends on how well the model represents texts in vector space. Changing the embedding model can yield a greater recall improvement than optimizing chunking or search parameters.

Candidate Models

Proprietary API models:

• text-embedding-3-large (OpenAI, dim=3072): Best overall on MTEB benchmarks.
• text-embedding-3-small (OpenAI, dim=1536): Good price/quality ratio.
• embed-v3 (Cohere): Strong on retrieval; supports input_type parameter.

Open models (self-hosted):

• BAAI/bge-m3 (dim=1024): Multilingual, supports dense+sparse+colbert.
• BAAI/bge-large-en-v1.5 (dim=1024): Best for English.
• intfloat/multilingual-e5-large (dim=1024): Good on Russian.
• nomic-ai/nomic-embed-text-v1.5 (dim=768): Supports matryoshka.

Performance on BEIR (Retrieval nDCG@10)

Data from MTEB leaderboard:

For Russian-language tasks, the picture differs. We recommend testing on your own domain. We conduct a trial on your corpus and provide a report.

Configuring Popular Embedding Models

Cohere Embed v3 with input_type

Cohere embed-v3 requires specifying input_type for optimal retrieval. Using the correct type increases recall by 8–15%:

import cohere

co = cohere.Client(api_key="...")

def embed_documents(texts: list[str]) -> list[list[float]]:
    response = co.embed(
        texts=texts,
        model="embed-multilingual-v3.0",
        input_type="search_document",
    )
    return response.embeddings

def embed_query(query: str) -> list[float]:
    response = co.embed(
        texts=[query],
        model="embed-multilingual-v3.0",
        input_type="search_query",
    )
    return response.embeddings[0]

Self-hosted BGE-M3

BGE-M3 is the most versatile open-source model. It supports dense, sparse (SPLADE), and ColBERT-style multi-vector retrieval from a single model. Infrastructure savings: one model instead of three.

from FlagEmbedding import BGEM3FlagModel

model = BGEM3FlagModel(
    "BAAI/bge-m3",
    use_fp16=True,
    device="cuda",
)

# Dense embeddings (for standard ANN search)
dense_embeddings = model.encode(
    texts,
    batch_size=32,
    max_length=8192,
    return_dense=True,
    return_sparse=False,
    return_colbert_vecs=False,
)["dense_vecs"]

# Sparse embeddings (for BM25-like search)
sparse_embeddings = model.encode(
    texts,
    return_dense=False,
    return_sparse=True,
)["lexical_weights"]

# Hybrid retrieval score
def compute_bge_m3_score(query_dense, doc_dense, query_sparse, doc_sparse,
                          alpha=0.5) -> float:
    dense_score = np.dot(query_dense, doc_dense)
    sparse_score = sum(
        query_sparse.get(token, 0) * doc_sparse.get(token, 0)
        for token in query_sparse
    )
    return alpha * dense_score + (1 - alpha) * sparse_score

Dimensionality Reduction with Matryoshka

Nomic Embed and other models support matryoshka embeddings. You can use the first N dimensions without retraining. This reduces vector DB RAM requirements by 2× with only 2–5% quality loss. For instance, text-embedding-3-large can output 1536 dimensions instead of 3072:

from openai import OpenAI

client = OpenAI()

response = client.embeddings.create(
    model="text-embedding-3-large",
    input=texts,
    dimensions=1536,
)

Practical Choice and Quality Factors

Key Factors Beyond Model Choice

Besides model selection, important factors include text preprocessing, chunk length, and strategies for merging sparse+dense results. We account for all these when tuning RAG for your domain.

Decision Guidelines

• Confidential/on-prem data: Use BGE-M3 or E5-mistral-7b.
• Best Russian support: Test BGE-M3, multilingual-e5-large, and text-embedding-3-large on your own data. There is no universal winner.
• Minimal latency: Prefer text-embedding-3-small (API) or nomic-embed-text-v1.5 (self-hosted).
• Hybrid sparse+dense: BGE-M3 is the only open model with native dual support.

Comparison: Cohere embed-v3 with input_type outperforms without it by 8–15% in recall. BGE-M3 is 2× faster than OpenAI when self-hosted with GPU.

Evaluation on Your Domain

from ragas import evaluate
from ragas.metrics import context_recall, context_precision

for model_name in ["text-embedding-3-small", "text-embedding-3-large"]:
    retriever = build_retriever(model_name)
    scores = evaluate(test_dataset, metrics=[context_recall, context_precision],
                      retriever=retriever)
    print(f"{model_name}: recall={scores['context_recall']:.3f}, "
          f"precision={scores['context_precision']:.3f}")

Turnkey RAG Setup and Pricing

What's Included

• Analysis of your corpus and use cases.
• Selection and testing of 2–3 embedding models.
• Indexing configuration (chunk size, overlap, vector DB).
• Integration with your backend (API, gRPC).
• Documentation and team training.
• 2-week post-release support.

Timeline and Cost

• Embedding model setup and indexing: 2–5 days.
• Comparative testing of 2–3 models: 3–5 days.
• Total: 1–2 weeks.
• Typical cost: $3,000–$6,000 depending on complexity.
• We will assess your task for free in one business day.

Contact us, and we will select the optimal model for your RAG. We guarantee a recall improvement of at least 10% on your metrics.