The Best Embedding Model for Conversational AI in 2026

The Conversational Retrieval Problem

When someone types a query into a search engine, they often use keywords — they’ve learned to “speak search.” But when someone talks to a chatbot or virtual assistant, they communicate naturally: “I can’t figure out how to cancel my subscription, I’ve been trying for like 20 minutes,” or “what happens if I miss a payment?” or “is there a way to get a refund after 30 days?”

These natural language queries need to be matched to documentation that was written in formal, structured language:

Benchmark Performance in the Conversational Domain

The conversational domain shows zembed-1’s largest performance gaps across competitors. Against voyage-4-nano, zembed-1 leads by +33.1%. Against OpenAI text-embedding-3-large, the advantage is +35.0%. Even against Cohere Embed v4, zembed-1 outperforms by +26.9%.

These are not marginal improvements — they represent a qualitative difference in how well the system understands conversational queries. For chatbot and virtual assistant applications, these numbers translate directly into user satisfaction, deflection rates, and the quality of answers that downstream language models can generate.

Why zembed-1 Excels at Conversational Retrieval

zELO Training on Real Query-Document Pairs

The zELO training methodology trains zembed-1 by having document candidates compete in pairwise relevance judgements rather than matching on surface-level similarity. This approach specifically captures the semantic bridge between how people ask questions and how documents answer them — even when the vocabulary is completely different.

The model learns that “my package hasn’t arrived” maps to “shipment tracking and delivery issues” documentation, that “I forgot my password” maps to “account recovery procedures,” and that “the app keeps crashing on my phone” maps to “mobile application troubleshooting guides” — because real query-document pairs teach it these relationships.

Distilled from a Reranker That Understands Intent

zembed-1 is distilled from zerank-2, ZeroEntropy’s state-of-the-art reranker. Rerankers are specifically trained to evaluate whether a document truly addresses a user’s query — a task that requires understanding intent, not just vocabulary. This lineage gives zembed-1 a unique advantage in conversational settings where intent interpretation is everything.

Handles Conversational Query Diversity

zembed-1 was trained with over 50% non-English data, meaning its conversational retrieval performance extends across languages. A multilingual customer support system can serve users in their native language with the same retrieval quality — whether the customer writes in Spanish, French, Arabic, or Japanese.

Conversational AI Use Cases

Designing Better Conversational RAG Systems with zembed-1

A well-designed conversational RAG pipeline with zembed-1 typically looks like this:

1. User query arrives in natural language — “can I add my spouse to my insurance plan mid-year?”
2. zembed-1 encodes the query using encode_query() which optimizes the encoding for retrieval
3. zembed-1 retrieves the top-k most relevant documents from the knowledge base vector store
4. A language model generates a response grounded in the retrieved context

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "zeroentropy/zembed-1",
    trust_remote_code=True,
    model_kwargs={"torch_dtype": "bfloat16"},
)

# Conversational support query
query_embeddings = model.encode_query(
    "can i add my spouse to my plan after the open enrollment period ended?"
)

knowledge_base = [
    "Qualifying Life Events: Outside of Open Enrollment, you may make changes to your health insurance plan within 30 days of a qualifying life event. Qualifying life events include marriage, birth of a child, loss of other coverage, and divorce...",
    "Open Enrollment Period: Our annual Open Enrollment runs from November 1 to November 30. During this time, you can add dependents, change plan tiers, or update coverage...",
    "Dependent Coverage: You may add eligible dependents to your health insurance plan. Eligible dependents include your legal spouse, domestic partner (where applicable), and children up to age 26...",
]

document_embeddings = model.encode_document(knowledge_base)
similarities = model.similarity(query_embeddings, document_embeddings)
# zembed-1 correctly surfaces the Qualifying Life Events document as most relevant

from sentence_transformers import SentenceTransformer
from openai import OpenAI  # or any LLM client

embed_model = SentenceTransformer(
    "zeroentropy/zembed-1",
    trust_remote_code=True,
    model_kwargs={"torch_dtype": "bfloat16"},
)
llm = OpenAI()

def conversational_rag(
    user_message: str,
    conversation_history: list[dict],
    knowledge_base_embeddings,
    knowledge_base_texts: list[str],
    top_k: int = 4,
) -> str:
    # Include recent history in the query for contextual retrieval
    recent_context = " ".join(
        [m["content"] for m in conversation_history[-3:]] + [user_message]
    )

    # Retrieve relevant knowledge using the enriched query
    q_emb = embed_model.encode_query(recent_context)
    scores = embed_model.similarity(q_emb, knowledge_base_embeddings)[0]
    top_idx = scores.argsort(descending=True)[:top_k]
    retrieved_context = "\n\n".join([knowledge_base_texts[i] for i in top_idx])

    # Generate a grounded answer
    messages = conversation_history + [{
        "role": "user",
        "content": f"Context:\n{retrieved_context}\n\nQuestion: {user_message}"
    }]
    response = llm.chat.completions.create(model="gpt-4o", messages=messages)
    return response.choices[0].message.content

# Example
history = []
answer = conversational_rag(
    "Can I add my spouse to my plan mid-year?",
    history,
    knowledge_base_embeddings,
    knowledge_base_texts,
)

The ROI of Better Conversational Retrieval

The business impact of zembed-1’s conversational retrieval advantage is concrete:

Higher automation rates: When the retrieval is right, the chatbot can answer correctly. Every additional percentage point of automation rate reduces human agent costs.

Better user experience: Users who get accurate answers the first time don’t escalate, don’t abandon, and are more likely to trust the system.

Reduced hallucination: Language models hallucinate when they don’t have the right context. Better retrieval means the LLM has what it needs to answer accurately, dramatically reducing hallucination rates.

Multilingual support at no extra cost: zembed-1’s multilingual training means you get the same conversational retrieval quality in every language — no separate models, no translation overhead.

from zeroentropy import ZeroEntropy
zclient = ZeroEntropy()
response = zclient.models.embed(
model="zembed-1",
input_type="query", # "query" or "document"
input="What is retrieval augmented generation?", # string or list[str]
dimensions=2560, # optional: must be one of [2560, 1280, 640, 320, 160, 80, 40]
encoding_format="float", # "float" or "base64"
latency="fast", # "fast" or "slow"; omit for auto
)