Why Your Embeddings Are the Wrong Shape for Your Domain

A healthcare compliance team asked their RAG system about telemedicine licensing requirements for California. The system retrieved content with high cosine similarity scores. The retrieved regulations were from 2019, superseded by COVID-19 emergency orders and subsequent permanent rule changes. The "similar" content was legally worthless - and the system had no way to signal that. High similarity does not mean correct retrieval. It means the embedding model found text that is geometrically close in a space it was trained to organize. If the model was not trained on the domain, its geometry is wrong for the domain.

This is the Semantic Compression Loss problem: when you embed domain-specific text with a general-purpose model, you lose the semantic distinctions that matter most. Regulatory filings compress toward general legal prose. Clinical trial terminology collapses into generic medical language. Financial covenant language blurs into contractual boilerplate. The vectors land in approximately the right neighborhood but not at the right address.

Teams treat embedding model selection as an infrastructure decision - pick whatever tops the MTEB leaderboard, call it done. The thesis of this article is direct: MTEB ranking is a poor predictor of domain-specific retrieval quality, and treating it as one is why RAG systems built for specialized domains fail silently. The FinMTEB benchmark (Tang & Yang, EMNLP 2025), which evaluated 15 embedding models on 64 financial domain datasets, found statistically insignificant correlation between general MTEB scores and financial domain performance. The same models that rank at the top of MTEB do not necessarily rank at the top of FinMTEB.

Embedding model selection is a domain alignment decision. The wrong default costs you retrieval quality you will not recover by tuning retrieval strategy or chunking.

How Semantic Compression Loss Accumulates

In Part 1 of this series we named the Retrieval Tax: the compounding cost your system pays when retrieval strategy mismatches your data and query type. In Part 2, we named Chunking Debt: the accumulated quality degradation from early chunking decisions that is expensive to reverse. Semantic Compression Loss is the embedding layer equivalent - and it sits between the two. Your chunks arrive at the embedding model and get projected into a vector space. What survives that projection determines everything the retriever can find.

Three mechanisms drive the compression.

1. Vocabulary Collapse: Domain Terms Mapped to Generic Proxies

General-purpose embedding models train on web crawls, Wikipedia, books, and code. They have never seen your domain's terminology at volume. When they encounter a domain-specific term, they map it to the nearest concept they do have signal for.

"EBITDA covenant breach" lands near "contract violation" in the vector space - close enough to surface for broad queries, not close enough to distinguish from a non-financial covenant. "Adverse event reporting per 21 CFR 312.32" collapses toward "medical reporting requirement." "Claim construction under Markman" maps toward "legal interpretation."

The model is not hallucinating. It is doing exactly what it was trained to do: find the nearest representation it has strong signal for. The problem is that the nearest representation in a general latent space is not the nearest concept in your domain.

This is why the FinMTEB paper found a surprising result: a simple Bag-of-Words approach outperformed sophisticated dense embeddings on financial Semantic Textual Similarity tasks. When the dense model's geometry is wrong for the domain, a model that simply matches tokens can outperform one that tries to understand meaning.

2. Context Window Truncation: Silent Information Loss

Most widely-used embedding models were built with 512-token input limits inherited from BERT-era architectures. Many are still 512 tokens in 2026. When you feed a chunk that exceeds the model's context window, the model silently truncates at the limit. No error is thrown. No warning is logged. The embedding represents whatever fit in the first 512 tokens.

The consequences compound with the Chunking Debt from Part 2. If you followed the Part 2 guidance and moved to recursive splitting at 400 tokens, you are within 512-token model limits. But if you are using 512+ token chunks for analytical queries - also recommended in Part 2 for query types that need broader context - you will silently lose the tail of every chunk on models with 512-token limits. The retrieval bias becomes systematic: document beginnings are fully represented; endings are partially or fully absent.

# Silent truncation demo - this is the wrong wayfrom sentence_transformers import SentenceTransformermodel = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")# all-MiniLM-L6-v2 has a 512-token limitlong_chunk = " ".join(["word"] * 600)  # 600 tokens - exceeds limitembedding = model.encode(long_chunk)# No error. No warning.# The embedding represents the first 512 tokens.# The last 88 tokens are silently discarded.# If the critical information is in the tail of the chunk,# it does not exist in your index.# Verify the actual token limit before committing to a model:print(f"Max sequence length: {model.max_seq_length}")  # 256 for MiniLM# Note: MiniLM's actual effective limit is 256 tokens, not 512.# The model's tokenizer may count differently from your text splitter.# Always verify with the model's own tokenizer.

The correct approach is to verify context window limits against the model's own tokenizer before ingestion, and to select a model whose context window matches or exceeds your chunk size targets.

# Right way: verify before ingestingfrom transformers import AutoTokenizerdef verify_chunk_fits(text: str, model_name: str) -> tuple[bool, int]:    """    Check whether a chunk fits within the embedding model's context window.    Use the model's own tokenizer, not a generic character count.        Returns (fits, token_count).    """    tokenizer = AutoTokenizer.from_pretrained(model_name)    tokens = tokenizer.encode(text, add_special_tokens=True)        # Get the model's actual max length    max_length = tokenizer.model_max_length        return len(tokens) <= max_length, len(tokens)def safe_embed(texts: list[str], model, model_name: str, max_tokens: int) -> list:    """    Embed a list of texts with truncation detection and logging.    Raises ValueError on any text that would be silently truncated.    """    from transformers import AutoTokenizer    tokenizer = AutoTokenizer.from_pretrained(model_name)        truncated = []    for i, text in enumerate(texts):        token_count = len(tokenizer.encode(text, add_special_tokens=True))        if token_count > max_tokens:            truncated.append((i, token_count, max_tokens))        if truncated:        details = "\n".join(            f"  Chunk {i}: {count} tokens (limit: {limit})"            for i, count, limit in truncated        )        raise ValueError(            f"The following chunks exceed the embedding model's context window "            f"and would be silently truncated:\n{details}\n"            f"Fix: re-chunk with smaller targets, or switch to a model with "            f"a larger context window (e.g., Qwen3-Embedding-8B: 32K tokens)."        )        return model.encode(texts)

3. Embedding-Chunk Size Mismatch: The Sensitivity Gap

Embedding models have distinct chunk-size sensitivity profiles. Some perform better with larger chunks that provide more context per embedding; others perform better with smaller, focused chunks. A 2025 multi-dataset analysis (arXiv:2505.21700) found that Stella benefits from larger chunks and longer-range global context, while Snowflake-Arctic-Embed performs better with smaller, fine-grained chunks for entity-based matching. Picking chunk size without considering the embedding model's sensitivity profile - or picking an embedding model without considering your target chunk size - produces a mismatch that degrades both.

This is the connector between Part 2 and Part 3 of this series. The right chunk size depends partly on what your embedding model is designed to process. If your corpus analysis says you need 1024-token chunks for analytical queries, you need an embedding model whose context window and sensitivity profile fits 1024-token inputs. The decision is not separable.

Named Concept: Semantic Compression Loss

Semantic Compression Loss is the information destroyed when domain-specific text is projected into an embedding space that was not trained to preserve the distinctions that matter for that domain. The loss has two components:

Vocabulary compression: Domain terms mapped to the nearest general-purpose proxy. The vector lands close enough to surface on broad queries; far enough to miss on precision queries.

Geometric distortion: The relative distances between domain concepts are wrong. In a general embedding space, two legal clauses about termination may be farther apart than a legal clause and an HR policy about offboarding - because the general model has weaker signal on the semantic distinction between them. The retriever finds what is close in the learned geometry, not what is close in domain meaning.

Semantic Compression Loss compounds every other quality problem in the pipeline. The Retrieval Tax from Part 1 is amplified: even the right retrieval strategy cannot compensate for a latent space where domain concepts are geometrically wrong. The Chunking Debt from Part 2 is amplified: even well-formed chunks are projected into a space that does not preserve the semantic units they contain.

The loss is not fixed by tuning retrieval hyperparameters. It is only fixed by aligning the embedding model to the domain.

Choosing the Right Embedding Model for Production

Step 1: Evaluate Against the MTEB Leaderboard - With the Right Caveats

The MTEB leaderboard (Hugging Face) is the standard starting point for embedding model comparison. As of April 2026, the top-performing models on the English retrieval tasks are:

Caveats on MTEB scores:

• MTEB scores are self-reported. Model providers submit their own results.
• General MTEB retrieval scores show statistically insignificant correlation with financial domain performance (FinMTEB, 2025). Assume the same risk for legal, medical, and other specialized domains.
• Always benchmark on your own data before committing at production scale. The leaderboard tells you where to start, not where to stop.

Step 2: Check Domain Alignment

If your corpus is in a specialized domain, check whether domain-specific models or fine-tuned variants exist before defaulting to a general-purpose model:

• Finance: Fin-E5 (fine-tuned e5-Mistral-7B on FinMTEB tasks), voyage-finance-2 (Voyage AI), finance-adapted BGE variants
• Legal: voyage-law-2 (Voyage AI), legal-BERT embeddings, fine-tuned BGE on legal corpora
• Medical/Clinical: BioGPT embeddings, PubMedBERT, clinical fine-tuned models
• Code: voyage-code-3, Gemini Embedding 2 (MTEB Code: 84.0), CodeBERT variants
• Chemistry/Scientific: ChEmbed (9% nDCG@10 gain over general baseline on chemistry tasks)

Voyage AI's specialized models (legal, finance, code) outperform generic models by 10-15% in their target domains. For domains with available specialized models, the case for defaulting to a general-purpose model is weak.

Step 3: Evaluate Against Your Actual Data

MTEB benchmarks use public datasets. Your production corpus is not a public dataset. Run a representative evaluation before committing:

import numpy as npfrom sentence_transformers import SentenceTransformerfrom sklearn.metrics.pairwise import cosine_similaritydef evaluate_embedding_model(    model_name: str,    queries: list[str],    relevant_doc_indices: list[list[int]],    corpus: list[str],    k: int = 5,) -> dict:    """    Evaluate an embedding model against your actual domain data.        Args:        model_name: HuggingFace model identifier        queries: list of representative queries from your use case        relevant_doc_indices: for each query, list of relevant corpus indices                              (use indices, not strings, to avoid equality fragility)        corpus: full document corpus to retrieve from        k: number of documents to retrieve per query        Returns:        dict with recall@k and MRR scores    """    model = SentenceTransformer(model_name)        # Embed corpus and queries    corpus_embeddings = model.encode(corpus, show_progress_bar=True)    query_embeddings = model.encode(queries)        recall_scores = []    mrr_scores = []        for query_emb, relevant_indices in zip(query_embeddings, relevant_doc_indices):        # Compute similarities and get top-k corpus indices        sims = cosine_similarity([query_emb], corpus_embeddings)[0]        top_k_indices = set(np.argsort(sims)[::-1][:k].tolist())        relevant_set = set(relevant_indices)                # Recall@k: what fraction of relevant docs were in top-k        hits = len(top_k_indices & relevant_set)        recall_scores.append(hits / len(relevant_set))                # MRR: reciprocal rank of first relevant hit        ranked = np.argsort(sims)[::-1]        for rank, idx in enumerate(ranked[:k], 1):            if idx in relevant_set:                mrr_scores.append(1 / rank)                break        else:            mrr_scores.append(0)        return {        "model": model_name,        "recall_at_k": np.mean(recall_scores),        "mrr": np.mean(mrr_scores),        "k": k,    }def compare_models(    model_names: list[str],    queries: list[str],    relevant_doc_indices: list[list[int]],    corpus: list[str],) -> list[dict]:    """    Run the evaluation across multiple candidate models and rank results.    Use this before committing to an embedding model for production.    """    results = []    for name in model_names:        try:            result = evaluate_embedding_model(                name, queries, relevant_doc_indices, corpus            )            results.append(result)            print(f"{name}: Recall@5={result['recall_at_k']:.3f}, MRR={result['mrr']:.3f}")        except Exception as e:            print(f"{name}: evaluation failed - {e}")        return sorted(results, key=lambda x: x["recall_at_k"], reverse=True)

Run this against 50-100 representative query-document pairs from your actual domain before making an architectural decision. If the general-purpose top-MTEB model and a domain-specific model are within 1-2 points of recall on your data, pick the general-purpose model for operational simplicity. If the gap is larger, the domain-specific model earns its added complexity.

Step 4: Consider Matryoshka Representation Learning for Cost Control

Matryoshka Representation Learning (MRL) is now the industry standard on new models. It trains embeddings so that the first N dimensions of a high-dimensional vector are themselves a useful lower-dimensional representation. You can truncate from 3072 dimensions to 256, 512, or 768 with graceful quality degradation rather than catastrophic loss.

This matters operationally because vector database storage and query cost scales with embedding dimensionality. A 3072-dimension index is significantly more expensive to operate than a 768-dimension index. MRL lets you choose a point on the cost-quality curve at query time, not at training time.

Models with MRL support as of 2026: Gemini Embedding 001, Qwen3-Embedding-8B, Voyage 4, Cohere embed-v4, OpenAI text-embedding-3-*, Jina v5.

# MRL usage with OpenAI text-embedding-3-largefrom openai import OpenAIclient = OpenAI()def embed_with_mrl(texts: list[str], dimensions: int = 1536) -> list[list[float]]:    """    Embed with reduced dimensions via MRL.    text-embedding-3-large native: 3072 dimensions    Reduced options: 256, 512, 768, 1024, 1536, 3072        Cost and speed scale with dimensions.    For most RAG use cases, 1536 matches ada-002 quality at half the storage cost.    For high-throughput pipelines with acceptable quality tradeoff, 768 is viable.    """    response = client.embeddings.create(        input=texts,        model="text-embedding-3-large",        dimensions=dimensions,  # MRL truncation    )    return [item.embedding for item in response.data]# Qwen3-Embedding-8B with MRL (self-hosted via vLLM or SGLang)# Supports 32 to 4096 dimensions - the widest MRL range available# vLLM and SGLang shipped first-class embedding support in Q1 2026def embed_qwen3(texts: list[str], dimensions: int = 1024) -> list[list[float]]:    """    Self-hosted Qwen3-Embedding-8B with MRL.    32K context window - handles document-level embedding without truncation.    Apache 2.0 license - commercial use permitted.    """    import requests        response = requests.post(        "http://localhost:8000/v1/embeddings",  # vLLM endpoint        json={            "model": "Qwen/Qwen3-Embedding-8B",            "input": texts,            "dimensions": dimensions,        }    )    response.raise_for_status()    return [item["embedding"] for item in response.json()["data"]]

Step 5: Fine-Tune When Domain Gaps Persist

If no pre-trained domain-specific model exists for your use case and a general-purpose model shows >5 point recall gap against domain data, fine-tuning is the correct path. Fine-tuning shows 10-30% gains for specialized domains depending on corpus size and base model. Even small amounts of labeled data yield significant gains.

The minimum viable fine-tuning dataset for an embedding model is 500-1000 (query, positive document, hard negative) triples. Hard negatives - documents that look similar but are not the correct answer - are the most important component. Without them, the model learns to find similar text but not to discriminate within the domain.

from sentence_transformers import SentenceTransformer, InputExample, lossesfrom torch.utils.data import DataLoaderdef fine_tune_embedding_model(    base_model_name: str,    training_examples: list[tuple[str, str, str]],  # (query, positive, hard_negative)    output_path: str,    epochs: int = 3,    batch_size: int = 16,    warmup_steps: int = 100,) -> SentenceTransformer:    """    Fine-tune an embedding model on domain-specific (query, positive, negative) triples.        Minimum viable dataset: 500-1000 triples.    Hard negatives matter more than dataset size.        Args:        base_model_name: starting checkpoint (e.g. "BAAI/bge-large-en-v1.5")        training_examples: list of (query, positive_doc, hard_negative_doc) tuples        output_path: where to save the fine-tuned model        epochs: 1-3 is typical; more risks overfitting on small datasets        batch_size: 16-32 for most GPU setups        warmup_steps: linear warmup for training stability        Returns:        Fine-tuned SentenceTransformer model    """    model = SentenceTransformer(base_model_name)        # Build InputExamples for triplet loss    examples = [        InputExample(texts=[query, positive, negative], label=1.0)        for query, positive, negative in training_examples    ]        dataloader = DataLoader(examples, shuffle=True, batch_size=batch_size)        # TripletLoss: pulls query closer to positive, pushes away from hard negative    loss = losses.TripletLoss(model=model)        model.fit(        train_objectives=[(dataloader, loss)],        epochs=epochs,        warmup_steps=warmup_steps,        output_path=output_path,        show_progress_bar=True,    )        return modeldef generate_hard_negatives(    queries: list[str],    corpus: list[str],    correct_indices: list[int],  # corpus index of the correct answer per query    base_model: SentenceTransformer,    n_negatives: int = 3,) -> list[tuple[str, str, str]]:    """    Generate hard negatives by finding corpus entries that are semantically    similar to the query but are NOT correct answers.        Uses corpus indices for matching - safer than string equality comparison.        These are more valuable than random negatives because they force the model    to learn fine-grained domain distinctions.    """    import numpy as np    from sklearn.metrics.pairwise import cosine_similarity        corpus_embeddings = base_model.encode(corpus)    query_embeddings = base_model.encode(queries)        triplets = []    for query, query_emb, correct_idx in zip(queries, query_embeddings, correct_indices):        sims = cosine_similarity([query_emb], corpus_embeddings)[0]                # Get top-k similar by index - candidates for hard negatives        ranked_indices = np.argsort(sims)[::-1]                negatives = []        for idx in ranked_indices:            if int(idx) != correct_idx and len(negatives) < n_negatives:                negatives.append(corpus[idx])                correct_doc = corpus[correct_idx]        for neg in negatives:            triplets.append((query, correct_doc, neg))        return triplets

Embedding Model Switching Is an Index-Level Event

One operational fact that does not appear in MTEB benchmarks: changing your embedding model requires re-indexing your entire corpus. Vectors from model A are geometrically incompatible with vectors from model B. You cannot mix embeddings from different models in the same index. Cosine similarity across different embedding spaces is undefined.

This makes embedding model selection a high-stakes early decision - analogous to Chunking Debt from Part 2. The wrong embedding model at launch creates a debt that is paid on every query until you re-index. Re-indexing at scale - millions of documents - is a non-trivial infrastructure event.

The practical consequence: if you are in the early stages of building a RAG system, invest time evaluating embedding models against your domain data before you begin large-scale ingestion. If you are already operating at scale with the wrong model, the business case for re-indexing should be built around the recall gap measured on representative queries, not intuition.

The Embedding Selection Decision Guide

flowchart TD
    A[Select embedding model] --> B{Is the corpus\nin a specialized domain?\nfinance, legal, medical,\ncode, science}
    B -- Yes --> C{Does a domain-specific\nor fine-tuned model\nexist for this domain?}
    C -- Yes --> D[Evaluate domain-specific model\nagainst your data first\ne.g. voyage-finance-2,\nvoyage-law-2, ChEmbed]
    C -- No --> E[Evaluate top general-purpose\nmodels against domain data\nqwen3-embedding-8B,\ngemini-embedding-001,\nvoyage-3-large]
    D --> F{Gap vs general-purpose\nmodel on your data?}
    E --> F
    F -- "More than 5 points recall" --> G[Use domain-specific model\nor fine-tune on domain data\n500+ triplets minimum]
    F -- "5 points or fewer" --> H[Use general-purpose model\nfor operational simplicity]
    B -- No --> I{Context window\nrequirements?}
    I -- "Chunks over 2K tokens" --> J[Qwen3-Embedding-8B\n32K context\nor jina-v3, voyage-3-large\n8-32K context]
    I -- "Chunks 2K tokens or under" --> K{Cost constraint?}
    K -- "Tight budget" --> L[jina-v3 at $0.02/1M\nor self-hosted Qwen3]
    K -- "Quality priority" --> M[gemini-embedding-001\nor voyage-3-large]
    H --> N{Need MRL for\ncost control?}
    G --> N
    J --> N
    L --> N
    M --> N
    N -- Yes --> O[Verify MRL support:\ngemini, qwen3, voyage-4,\ncohere-v4, openai-text-3-*]
    N -- No --> P[Proceed with\nfull-dimension index]

    style A fill:#4A90E2,color:#fff
    style D fill:#6BCF7F,color:#fff
    style E fill:#6BCF7F,color:#fff
    style G fill:#9B59B6,color:#fff
    style H fill:#4A90E2,color:#fff
    style J fill:#98D8C8,color:#333
    style L fill:#FFD93D,color:#333
    style M fill:#4A90E2,color:#fff
    style O fill:#98D8C8,color:#333
    style P fill:#4A90E2,color:#fff
    style B fill:#7B68EE,color:#fff
    style C fill:#7B68EE,color:#fff
    style F fill:#7B68EE,color:#fff
    style I fill:#7B68EE,color:#fff
    style K fill:#7B68EE,color:#fff
    style N fill:#7B68EE,color:#fff

Pre-Ingestion Embedding Checklist

Before embedding a single document at production scale:

Model selection:

• Checked MTEB leaderboard retrieval scores (not just overall score) for candidate models
• Checked whether domain-specific models exist for your corpus domain
• Evaluated at least 2 candidate models against 50+ representative query-document pairs from your actual data
• Selected model has context window that covers your largest chunk size target
• Confirmed model's actual token limit using its own tokenizer (not character count)

Context window:

• Maximum chunk token count verified against embedding model's max sequence length
• Truncation detection added to ingestion pipeline - silent truncation is not acceptable
• If chunks exceed context window: either re-chunk smaller or switch to longer-context model

Cost and dimensionality:

• If using MRL: tested quality at target dimensions against your evaluation set
• Vector database storage and query costs estimated at target dimensionality and corpus size
• Re-indexing plan documented: model switching triggers full corpus re-embed

Domain alignment:

• If specialized domain: domain-specific model evaluated and either adopted or rejected with documented recall delta
• If fine-tuning: minimum 500 (query, positive, hard negative) triples prepared; hard negatives generated from semantic similarity, not random sampling
• Evaluation metrics defined: Recall@5, MRR, and answer faithfulness (for end-to-end quality)

Where the Pipeline Stands

Three parts in, you now have the full upstream pipeline locked:

Part 1: Retrieval strategy - the question of which retrieval backend (vector, BM25, SQL, graph, hybrid) serves each query type, and the Retrieval Tax paid when the answer is wrong.

Part 2: Chunking - the question of how documents are split before entering the embedding model, and the Chunking Debt that accumulates from early decisions.

Part 3: Embedding - the question of which model projects your chunks into vector space, and the Semantic Compression Loss that distorts domain-specific semantics in general-purpose embedding spaces.

These three decisions interact. Your chunk size targets constrain your embedding model choices. Your embedding model's sensitivity profile informs your chunk size calibration. Your retrieval strategy operates on the vectors that embedding produces.

Part 4 covers what happens after retrieval returns: reranking, and why skipping it is the difference between 65% precision and 89% precision on the same corpus.

References

• Tang, Y., & Yang, Y. (2025). FinMTEB: Finance Massive Text Embedding Benchmark. EMNLP 2025. arXiv:2502.10990. https://arxiv.org/abs/2502.10990
• Shaukat, M.A., et al. (2026). Do We Need Domain-Specific Embedding Models? An Empirical Investigation. arXiv:2409.18511. https://arxiv.org/abs/2409.18511
• Amiri, M., & Bocklitz, T. (2025). ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings. arXiv:2508.01643. https://arxiv.org/abs/2508.01643
• Zhu, D., et al. (2024). LongEmbed: Extending Embedding Models for Long Context Retrieval. arXiv:2404.12096. https://arxiv.org/abs/2404.12096
• Anonymous. (2025). Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis. arXiv:2505.21700. https://arxiv.org/abs/2505.21700
• Kusupati, A., et al. (2022). Matryoshka Representation Learning. NeurIPS 2022. https://arxiv.org/abs/2205.13147
• Muennighoff, N., et al. (2022). MTEB: Massive Text Embedding Benchmark. EACL 2023. https://arxiv.org/abs/2210.07316
• Awesome Agents. (2026). Embedding Model Leaderboard: MTEB Rankings April 2026. https://awesomeagents.ai/leaderboards/embedding-model-leaderboard-mteb-april-2026/
• PremAI. (2026). Best Embedding Models for RAG 2026. https://blog.premai.io/best-embedding-models-for-rag-2026-ranked-by-mteb-score-cost-and-self-hosting/
• Weaviate. (2025). Why, When and How to Fine-Tune a Custom Embedding Model. https://weaviate.io/blog/fine-tune-embedding-model
• MTEB Leaderboard. Hugging Face. https://huggingface.co/spaces/mteb/leaderboard
• Voyage AI. Domain-specific embedding models: voyage-finance-2, voyage-law-2, voyage-code-3. https://docs.voyageai.com/docs/embeddings

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