Embeddings

Embeddings are vector representations of text that capture semantic meaning. They're essential for building:

• Semantic search — Find documents based on meaning, not just keyword matching
• RAG (Retrieval-Augmented Generation) — Retrieve relevant context for your AI agents
• Similarity detection — Find similar documents, detect duplicates, or cluster content
• Classification — Use embeddings as features for downstream ML models

Pydantic AI provides a unified interface for generating embeddings across multiple providers.

Quick Start

The [Embedder][pydantic_ai.embeddings.Embedder] class is the high-level interface for generating embeddings:

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    # Embed a search query
    result = await embedder.embed_query('What is machine learning?')
    print(f'Embedding dimensions: {len(result.embeddings[0])}')
    #> Embedding dimensions: 1536

    # Embed multiple documents at once
    docs = [
        'Machine learning is a subset of AI.',
        'Deep learning uses neural networks.',
        'Python is a programming language.',
    ]
    result = await embedder.embed_documents(docs)
    print(f'Embedded {len(result.embeddings)} documents')
    #> Embedded 3 documents

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

!!! tip "Queries vs Documents" Some embedding models optimize differently for queries and documents. Use [embed_query()][pydantic_ai.embeddings.Embedder.embed_query] for search queries and [embed_documents()][pydantic_ai.embeddings.Embedder.embed_documents] for content you're indexing.

Embedding Result

All embed methods return an [EmbeddingResult][pydantic_ai.embeddings.EmbeddingResult] containing the embeddings along with useful metadata.

For convenience, you can access embeddings either by index (result[0]) or by the original input text (result['Hello world']).

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    result = await embedder.embed_query('Hello world')

    # Access embeddings - each is a sequence of floats
    embedding = result.embeddings[0]  # By index via .embeddings
    embedding = result[0]  # Or directly via __getitem__
    embedding = result['Hello world']  # Or by original input text
    print(f'Dimensions: {len(embedding)}')
    #> Dimensions: 1536

    # Check usage
    print(f'Tokens used: {result.usage.input_tokens}')
    #> Tokens used: 2

    # Calculate cost (requires `genai-prices` to have pricing data for the model)
    cost = result.cost()
    print(f'Cost: ${cost.total_price:.6f}')
    #> Cost: $0.000000

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

Choosing a model

The best embedding model depends on your language, domain, latency, deployment, and evaluation constraints. Use this table as a starting point, then check the MTEB leaderboard, the model card on the Hugging Face Hub, and your own retrieval evaluation before committing to a model for a large index.

!!! tip "Switching models later" Switching models changes the embedding space and may change the output dimension, so you'll need to re-embed your documents and update the index. Pick a model you're happy to stick with, or one that supports dimension control so you can tune the index size without changing models.

Using embeddings for RAG

For Retrieval-Augmented Generation (RAG), embeddings are one part of a larger retrieval pipeline. Pydantic AI provides [Embedder][pydantic_ai.embeddings.Embedder] for query and document embeddings, and tools for giving retrieved context to an agent. The RAG example demonstrates vector storage, retrieval, and passing retrieved context to an agent using pre-split data.

If you want a provider-managed pipeline instead, first upload or import files into a provider-managed store, then pass its ID to [FileSearchTool][pydantic_ai.builtin_tools.FileSearchTool]. The provider handles chunking, embeddings, storage, and retrieval; see the File Search Tool docs for supported providers. The rest of this section covers building your own pipeline, where these choices stay application-specific.

A typical custom RAG pipeline looks like this:

1. Split source documents into chunks when needed.
2. Embed the chunks with [embed_documents()][pydantic_ai.embeddings.Embedder.embed_documents].
3. Store each vector with metadata such as source URL, title, heading, page number, and permissions.
4. Embed the user's search text with [embed_query()][pydantic_ai.embeddings.Embedder.embed_query].
5. Search a vector index for similar chunks.
6. Optionally rerank the shortlist.
7. Pass the retrieved text to the agent through a tool.

Chunking

Chunks are the units of source text that a retrieval index stores and returns. You do not need to split every document. Chunking is useful when:

• you want to retrieve a relevant subsection instead of the full document;
• a document is longer than the embedding model's maximum input length; or
• a document contains enough independent facts or topics that one embedding may not represent them precisely.

If none of these applies, embedding the whole document can be reasonable. There is no universally best chunking strategy or chunk size: the right choice depends on the embedding model, source format, domain, and the questions users ask.

For a first implementation, use natural document boundaries with a token limit, keep source metadata and a link or identifier for the full document with every chunk, and evaluate before adding a more expensive strategy. Chunks should be large enough to answer a focused question but small enough to avoid unrelated context.

See Chroma's chunking strategy evaluation for a comparison of common strategies and reproducible evaluation code, and Hugging Face's RAG evaluation cookbook for an end-to-end RAG evaluation workflow.

Vector storage

Pydantic AI does not prescribe a vector database. Choose based on the index size, query and ingestion volume, metadata filtering and permission requirements, hybrid keyword search needs, availability requirements, and infrastructure you already operate:

• A local index or embedded database, such as FAISS, LanceDB, or SQLite with a vector extension, can suit prototypes and small indexes.
• PostgreSQL with pgvector can suit applications that already use PostgreSQL.
• A managed vector or search service can suit applications that need hosted scaling and operational support.

The index's vector dimension must match the embedding output dimension. Use a compatible model configuration when indexing and querying; matching dimensions alone do not make vectors compatible. If you change the embedding configuration, re-embed the documents and update or rebuild the index as required by your storage layer.

Hugging Face's advanced RAG cookbook introduces vector indexes, similarity choices, reranking, and other retrieval trade-offs.

Evaluation

Chunking, the embedding model, similarity metric, number of retrieved chunks, and reranking all interact. Public benchmarks can narrow the candidates, but they cannot prove which complete pipeline works best for your application. Compare changes using queries and documents representative of production, checking both that relevant text is retrieved and that irrelevant text is kept out of the agent's context.

Use Pydantic Evals to track retrieval quality across a dataset of representative queries. Hugging Face's RAG evaluation cookbook demonstrates how to build a synthetic evaluation set and evaluate generated answers.

Providers

OpenAI

[OpenAIEmbeddingModel][pydantic_ai.embeddings.openai.OpenAIEmbeddingModel] works with OpenAI's embeddings API and any OpenAI-compatible provider.

Install

To use OpenAI embedding models, you need to either install pydantic-ai, or install pydantic-ai-slim with the openai optional group:

pip/uv-add "pydantic-ai-slim[openai]"

Configuration

To use OpenAIEmbeddingModel with the OpenAI API, go to platform.openai.com and follow your nose until you find the place to generate an API key. Once you have the API key, you can set it as an environment variable:

export OPENAI_API_KEY='your-api-key'

You can then use the model:

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 1536

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

See OpenAI's embedding models for available models.

Dimension Control

OpenAI's text-embedding-3-* models support dimension reduction via the dimensions setting:

from pydantic_ai import Embedder
from pydantic_ai.embeddings import EmbeddingSettings

embedder = Embedder(
    'openai:text-embedding-3-small',
    settings=EmbeddingSettings(dimensions=256),
)


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 256

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

OpenAI-Compatible Providers {#openai-compatible}

Since [OpenAIEmbeddingModel][pydantic_ai.embeddings.openai.OpenAIEmbeddingModel] uses the same provider system as [OpenAIChatModel][pydantic_ai.models.openai.OpenAIChatModel], you can use it with any OpenAI-compatible provider:

# Using Azure OpenAI
from openai import AsyncAzureOpenAI

from pydantic_ai import Embedder
from pydantic_ai.embeddings.openai import OpenAIEmbeddingModel
from pydantic_ai.providers.openai import OpenAIProvider

azure_client = AsyncAzureOpenAI(
    azure_endpoint='https://your-resource.openai.azure.com',
    api_version='2024-02-01',
    api_key='your-azure-key',
)
model = OpenAIEmbeddingModel(
    'text-embedding-3-small',
    provider=OpenAIProvider(openai_client=azure_client),
)
embedder = Embedder(model)


# Using any OpenAI-compatible API
model = OpenAIEmbeddingModel(
    'your-model-name',
    provider=OpenAIProvider(
        base_url='https://your-provider.com/v1',
        api_key='your-api-key',
    ),
)
embedder = Embedder(model)

For providers with dedicated provider classes (like [OllamaProvider][pydantic_ai.providers.ollama.OllamaProvider] or [AzureProvider][pydantic_ai.providers.azure.AzureProvider]), you can use the shorthand syntax:

from pydantic_ai import Embedder

embedder = Embedder('azure:text-embedding-3-small')
embedder = Embedder('ollama:nomic-embed-text')

See OpenAI-compatible Models for the full list of supported providers.

Google

[GoogleEmbeddingModel][pydantic_ai.embeddings.google.GoogleEmbeddingModel] works with Google's embedding models via the Gemini API (Google AI Studio) or Google Cloud (formerly known as Vertex AI).

Install

To use Google embedding models, you need to either install pydantic-ai, or install pydantic-ai-slim with the google optional group:

pip/uv-add "pydantic-ai-slim[google]"

Configuration

To use GoogleEmbeddingModel with the Gemini API, go to aistudio.google.com and generate an API key. Once you have the API key, you can set it as an environment variable:

export GOOGLE_API_KEY='your-api-key'

You can then use the model:

from pydantic_ai import Embedder

embedder = Embedder('google:gemini-embedding-001')


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 3072

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

See the Google Embeddings documentation for available models.

Google Cloud

To use Google's embedding models via Google Cloud (formerly known as Vertex AI) instead of the Gemini API, use the google-cloud: provider prefix:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.google import GoogleEmbeddingModel
from pydantic_ai.providers.google_cloud import GoogleCloudProvider

# Using provider prefix
embedder = Embedder('google-cloud:gemini-embedding-001')

# Or with explicit provider configuration
model = GoogleEmbeddingModel(
    'gemini-embedding-001',
    provider=GoogleCloudProvider(project='my-project', location='us-central1'),
)
embedder = Embedder(model)

See the Google provider documentation for more details on Google Cloud authentication options, including application default credentials, service accounts, and API keys.

Dimension Control

Google's embedding models support dimension reduction via the dimensions setting:

from pydantic_ai import Embedder
from pydantic_ai.embeddings import EmbeddingSettings

embedder = Embedder(
    'google:gemini-embedding-001',
    settings=EmbeddingSettings(dimensions=768),
)


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 768

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

Task Conditioning

gemini-embedding-2 is conditioned on the task you're embedding for by prepending a short task instruction to the input text, rather than through the [google_task_type][pydantic_ai.embeddings.google.GoogleEmbeddingSettings.google_task_type] field used by the other Google models. Pydantic AI builds this prefix for you via the google_task setting:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.google import GoogleEmbeddingSettings

embedder = Embedder(
    'google:gemini-embedding-2',
    settings=GoogleEmbeddingSettings(google_task='question answering'),
)

google_task accepts the task names from Google's API: 'search result', 'question answering', 'fact checking', 'code retrieval', 'classification', 'clustering', and 'sentence similarity'. For the retrieval-style (asymmetric) tasks, queries and documents are prefixed differently, so the same task applies to both sides of a pair; the remaining (symmetric) tasks prefix both inputs the same way.

When you don't set google_task, gemini-embedding-2 is conditioned as 'search result'. Conditioning is on by default because Google recommends it for this model and it yields better retrieval performance than embedding raw text. To opt out and embed the text verbatim, pass google_task='raw'.

google_task only applies to gemini-embedding-2; on any other model it is ignored with a warning (those models condition via google_task_type instead). Conversely, google_task_type is ignored on gemini-embedding-2, since that model conditions through the text prefix.

Google-Specific Settings

Google models support additional settings via [GoogleEmbeddingSettings][pydantic_ai.embeddings.google.GoogleEmbeddingSettings]:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.google import GoogleEmbeddingSettings

embedder = Embedder(
    'google:gemini-embedding-001',
    settings=GoogleEmbeddingSettings(
        dimensions=768,
        google_task_type='SEMANTIC_SIMILARITY',  # Optimize for similarity comparison
    ),
)

See Google's task type documentation for available task types. By default, embed_query() uses RETRIEVAL_QUERY and embed_documents() uses RETRIEVAL_DOCUMENT.

Cohere

[CohereEmbeddingModel][pydantic_ai.embeddings.cohere.CohereEmbeddingModel] provides access to Cohere's embedding models, which offer multilingual support and various model sizes.

Install

To use Cohere embedding models, you need to either install pydantic-ai, or install pydantic-ai-slim with the cohere optional group:

pip/uv-add "pydantic-ai-slim[cohere]"

Configuration

To use CohereEmbeddingModel, go to dashboard.cohere.com/api-keys and follow your nose until you find the place to generate an API key. Once you have the API key, you can set it as an environment variable:

export CO_API_KEY='your-api-key'

You can then use the model:

from pydantic_ai import Embedder

embedder = Embedder('cohere:embed-v4.0')


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 1024

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

See the Cohere Embed documentation for available models.

Cohere-Specific Settings

Cohere models support additional settings via [CohereEmbeddingSettings][pydantic_ai.embeddings.cohere.CohereEmbeddingSettings]:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.cohere import CohereEmbeddingSettings

embedder = Embedder(
    'cohere:embed-v4.0',
    settings=CohereEmbeddingSettings(
        dimensions=512,
        cohere_truncate='END',  # Truncate long inputs instead of erroring
        cohere_max_tokens=256,  # Limit tokens per input
    ),
)

VoyageAI

[VoyageAIEmbeddingModel][pydantic_ai.embeddings.voyageai.VoyageAIEmbeddingModel] provides access to VoyageAI's embedding models, which are optimized for retrieval with specialized models for code, finance, and legal domains.

Install

To use VoyageAI embedding models, you need to install pydantic-ai-slim with the voyageai optional group:

pip/uv-add "pydantic-ai-slim[voyageai]"

Configuration

To use VoyageAIEmbeddingModel, go to dash.voyageai.com to generate an API key. Once you have the API key, you can set it as an environment variable:

export VOYAGE_API_KEY='your-api-key'

You can then use the model:

from pydantic_ai import Embedder

embedder = Embedder('voyageai:voyage-3.5')


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 1024

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

See the VoyageAI Embeddings documentation for available models.

VoyageAI-Specific Settings

VoyageAI models support additional settings via [VoyageAIEmbeddingSettings][pydantic_ai.embeddings.voyageai.VoyageAIEmbeddingSettings]:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.voyageai import VoyageAIEmbeddingSettings

embedder = Embedder(
    'voyageai:voyage-3.5',
    settings=VoyageAIEmbeddingSettings(
        dimensions=512,  # Reduce output dimensions
        voyageai_input_type='document',  # Override input type for all requests
    ),
)

Bedrock

[BedrockEmbeddingModel][pydantic_ai.embeddings.bedrock.BedrockEmbeddingModel] provides access to embedding models through AWS Bedrock, including Amazon Titan, Cohere, and Amazon Nova models.

Install

To use Bedrock embedding models, you need to either install pydantic-ai, or install pydantic-ai-slim with the bedrock optional group:

pip/uv-add "pydantic-ai-slim[bedrock]"

Configuration

Authentication with AWS Bedrock uses standard AWS credentials. See the Bedrock provider documentation for details on configuring credentials via environment variables, AWS credentials file, or IAM roles.

Ensure your AWS account has access to the Bedrock embedding models you want to use. See AWS Bedrock model access for details.

Basic Usage

from pydantic_ai import Embedder

# Using Amazon Titan
embedder = Embedder('bedrock:amazon.titan-embed-text-v2:0')


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 1024

(This example requires AWS credentials configured)

Supported Models

Bedrock supports three families of embedding models. See the AWS Bedrock documentation for the full list of available models.

Amazon Titan:

• amazon.titan-embed-text-v1 — 1536 dimensions (fixed), 8K tokens
• amazon.titan-embed-text-v2:0 — 256/384/1024 dimensions (configurable, default: 1024), 8K tokens

Cohere Embed:

• cohere.embed-english-v3 — English-only, 1024 dimensions (fixed), 512 tokens
• cohere.embed-multilingual-v3 — Multilingual, 1024 dimensions (fixed), 512 tokens
• cohere.embed-v4:0 — 256/512/1024/1536 dimensions (configurable, default: 1536), 128K tokens

Amazon Nova:

• amazon.nova-2-multimodal-embeddings-v1:0 — 256/384/1024/3072 dimensions (configurable, default: 3072), 8K tokens

Titan-Specific Settings

Titan v2 supports vector normalization for direct similarity calculations via bedrock_titan_normalize (default: True). Titan v1 does not support this setting.

from pydantic_ai import Embedder
from pydantic_ai.embeddings.bedrock import BedrockEmbeddingSettings

embedder = Embedder(
    'bedrock:amazon.titan-embed-text-v2:0',
    settings=BedrockEmbeddingSettings(
        dimensions=512,
        bedrock_titan_normalize=True,
    ),
)

!!! note Titan models do not support the truncate setting. The dimensions setting is only supported by Titan v2.

Cohere-Specific Settings

Cohere models on Bedrock support additional settings via [BedrockEmbeddingSettings][pydantic_ai.embeddings.bedrock.BedrockEmbeddingSettings]:

• bedrock_cohere_input_type — By default, embed_query() uses 'search_query' and embed_documents() uses 'search_document'. Also accepts 'classification' or 'clustering'.
• bedrock_cohere_truncate — Fine-grained truncation control: 'NONE' (default, error on overflow), 'START', or 'END'. Overrides the base truncate setting.
• bedrock_cohere_max_tokens — Limits tokens per input (default: 128000). Only supported by Cohere v4.

from pydantic_ai import Embedder
from pydantic_ai.embeddings.bedrock import BedrockEmbeddingSettings

embedder = Embedder(
    'bedrock:cohere.embed-v4:0',
    settings=BedrockEmbeddingSettings(
        dimensions=512,
        bedrock_cohere_max_tokens=1000,
        bedrock_cohere_truncate='END',
    ),
)

!!! note The dimensions and bedrock_cohere_max_tokens settings are only supported by Cohere v4. Cohere v3 models have fixed 1024 dimensions.

Nova-Specific Settings

Nova models on Bedrock support additional settings via [BedrockEmbeddingSettings][pydantic_ai.embeddings.bedrock.BedrockEmbeddingSettings]:

• bedrock_nova_truncate — Fine-grained truncation control: 'NONE' (default, error on overflow), 'START', or 'END'. Overrides the base truncate setting.
• bedrock_nova_embedding_purpose — By default, embed_query() uses 'GENERIC_RETRIEVAL' and embed_documents() uses 'GENERIC_INDEX'. Also accepts 'TEXT_RETRIEVAL', 'CLASSIFICATION', or 'CLUSTERING'.

from pydantic_ai import Embedder
from pydantic_ai.embeddings.bedrock import BedrockEmbeddingSettings

embedder = Embedder(
    'bedrock:amazon.nova-2-multimodal-embeddings-v1:0',
    settings=BedrockEmbeddingSettings(
        dimensions=1024,
        bedrock_nova_embedding_purpose='TEXT_RETRIEVAL',
        truncate=True,
    ),
)

Concurrency Settings

Models that don't support batch embedding (Titan and Nova) make individual API requests for each input text. By default, these requests run concurrently with a maximum of 5 parallel requests.

You can adjust this with the bedrock_max_concurrency setting:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.bedrock import BedrockEmbeddingSettings

# Increase concurrency for faster throughput
embedder = Embedder(
    'bedrock:amazon.titan-embed-text-v2:0',
    settings=BedrockEmbeddingSettings(bedrock_max_concurrency=10),
)

# Or reduce concurrency to avoid rate limits
embedder = Embedder(
    'bedrock:amazon.nova-2-multimodal-embeddings-v1:0',
    settings=BedrockEmbeddingSettings(bedrock_max_concurrency=2),
)

Regional Prefixes (Cross-Region Inference)

Bedrock supports cross-region inference using geographic prefixes like us., eu., or apac.:

from pydantic_ai import Embedder

embedder = Embedder('bedrock:us.amazon.titan-embed-text-v2:0')

Using AWS Application Inference Profiles

Set [bedrock_inference_profile][pydantic_ai.embeddings.bedrock.BedrockEmbeddingSettings.bedrock_inference_profile] to route requests through an inference profile while keeping the base model name for detecting model capabilities:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.bedrock import BedrockEmbeddingModel
from pydantic_ai.providers.bedrock import BedrockProvider

provider = BedrockProvider(region_name='us-east-1')

model = BedrockEmbeddingModel(
    'amazon.titan-embed-text-v2:0',
    provider=provider,
    settings={
        'bedrock_inference_profile': 'arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/my-embed-profile',
    },
)
embedder = Embedder(model)

Using a Custom Provider

For advanced configuration like explicit credentials or a custom boto3 client, you can create a [BedrockProvider][pydantic_ai.providers.bedrock.BedrockProvider] directly. See the Bedrock provider documentation for more details.

from pydantic_ai import Embedder
from pydantic_ai.embeddings.bedrock import BedrockEmbeddingModel
from pydantic_ai.providers.bedrock import BedrockProvider

provider = BedrockProvider(
    region_name='us-west-2',
    aws_access_key_id='your-access-key',
    aws_secret_access_key='your-secret-key',
)

model = BedrockEmbeddingModel('amazon.titan-embed-text-v2:0', provider=provider)
embedder = Embedder(model)

!!! note "Token Counting" Bedrock embedding models do not support the count_tokens() method because AWS Bedrock's token counting API only works with text generation models (Claude, Llama, etc.), not embedding models. Calling count_tokens() will raise NotImplementedError.

Sentence Transformers (Local)

[SentenceTransformerEmbeddingModel][pydantic_ai.embeddings.sentence_transformers.SentenceTransformerEmbeddingModel] runs embeddings locally using the Sentence Transformers library, giving you access to the thousands of embedding models on Hugging Face without any API calls. This is ideal for:

• Privacy — Data never leaves your infrastructure
• Cost — No API charges for high-volume workloads
• Offline use — No internet connection required after model download
• Specialized domains or languages - Pick models trained for code, multilingual, biomedical, legal, etc. from the MTEB leaderboard

Install

To use Sentence Transformers embedding models, you need to install pydantic-ai-slim with the sentence-transformers optional group:

pip/uv-add "pydantic-ai-slim[sentence-transformers]"

Usage

from pydantic_ai import Embedder

# Model is downloaded from Hugging Face on first use
embedder = Embedder('sentence-transformers:lightonai/DenseOn')


async def main():
    result = await embedder.embed_query('Hello world')
    print(len(result.embeddings[0]))
    #> 768

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

lightonai/DenseOn is a strong recent 149M-parameter general-purpose model that encodes queries and documents asymmetrically: [embed_query()][pydantic_ai.embeddings.Embedder.embed_query] and [embed_documents()][pydantic_ai.embeddings.Embedder.embed_documents] automatically apply the model's query: / document: prompts. See the Sentence Transformers pretrained models documentation and the MTEB leaderboard for more options; see also Choosing a model above.

Device Selection

Control which device to use for inference:

from pydantic_ai import Embedder
from pydantic_ai.embeddings.sentence_transformers import (
    SentenceTransformersEmbeddingSettings,
)

embedder = Embedder(
    'sentence-transformers:sentence-transformers/all-MiniLM-L6-v2',
    settings=SentenceTransformersEmbeddingSettings(
        sentence_transformers_device='cuda',  # Use GPU
        sentence_transformers_normalize_embeddings=True,  # L2 normalize
    ),
)

Using an Existing Model Instance

If you need more control over model initialization:

from sentence_transformers import SentenceTransformer

from pydantic_ai import Embedder
from pydantic_ai.embeddings.sentence_transformers import (
    SentenceTransformerEmbeddingModel,
)

# Create and configure the model yourself
st_model = SentenceTransformer('microsoft/harrier-oss-v1-270m', device='cpu')

# Wrap it for use with Pydantic AI
model = SentenceTransformerEmbeddingModel(st_model)
embedder = Embedder(model)

Settings

[EmbeddingSettings][pydantic_ai.embeddings.EmbeddingSettings] provides common configuration options that work across providers:

• dimensions: Reduce the output embedding dimensions (supported by OpenAI, Google, Cohere, Bedrock, VoyageAI)
• truncate: When True, truncate input text that exceeds the model's context length instead of raising an error (supported by Cohere, Bedrock, VoyageAI)

Settings can be specified at the embedder level (applied to all calls) or per-call:

from pydantic_ai import Embedder
from pydantic_ai.embeddings import EmbeddingSettings

# Default settings for all calls
embedder = Embedder(
    'openai:text-embedding-3-small',
    settings=EmbeddingSettings(dimensions=512),
)


async def main():
    # Override for a specific call
    result = await embedder.embed_query(
        'Hello world',
        settings=EmbeddingSettings(dimensions=256),
    )
    print(len(result.embeddings[0]))
    #> 256

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

Token Counting

You can check token counts before embedding to avoid exceeding model limits:

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')


async def main():
    text = 'Hello world, this is a test.'

    # Count tokens in text
    token_count = await embedder.count_tokens(text)
    print(f'Tokens: {token_count}')
    #> Tokens: 7

    # Check model's maximum input tokens (returns None if unknown)
    max_tokens = await embedder.max_input_tokens()
    print(f'Max tokens: {max_tokens}')
    #> Max tokens: 1024

(This example is complete, it can be run "as is" — you'll need to add asyncio.run(main()) to run main)

Testing

Use [TestEmbeddingModel][pydantic_ai.embeddings.TestEmbeddingModel] for testing without making API calls:

from pydantic_ai import Embedder
from pydantic_ai.embeddings import TestEmbeddingModel


async def test_my_rag_system():
    embedder = Embedder('openai:text-embedding-3-small')
    test_model = TestEmbeddingModel()

    with embedder.override(model=test_model):
        result = await embedder.embed_query('test query')

        # TestEmbeddingModel returns deterministic embeddings
        assert result.embeddings[0] == [1.0] * 8

        # Check what settings were used
        assert test_model.last_settings is not None

Instrumentation

Enable OpenTelemetry instrumentation for debugging and monitoring:

import logfire

from pydantic_ai import Embedder

logfire.configure()

# Instrument a specific embedder
embedder = Embedder('openai:text-embedding-3-small', instrument=True)

# Or instrument all embedders globally
Embedder.instrument_all()

See the Debugging and Monitoring guide for more details on using Logfire with Pydantic AI.

Two-stage retrieval with rerankers

For high-quality retrieval, a common pattern is two-stage: first use an embedding model to pull a broad shortlist of candidates cheaply, then use a cross-encoder reranker to score each candidate against the query more precisely. The cross-encoder reads the query and document together, so it's slower than an embedding lookup but dramatically more accurate, making it ideal for narrowing a top-100 recall list down to the top-5 results you actually hand to the LLM.

Pydantic AI does not ship a reranker provider class, so you bring your own. The most common local option is a CrossEncoder from sentence-transformers:

import asyncio
from functools import cache

from sentence_transformers import CrossEncoder


@cache
def get_reranker() -> CrossEncoder:
    # Loaded lazily on first call, then reused.
    return CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2')


async def rerank(query: str, candidates: list[str], top_k: int = 3) -> list[str]:
    """Rerank retrieval candidates by relevance to `query`."""
    reranker = get_reranker()
    # CrossEncoder.rank is blocking, so run it off the event loop.
    ranked = await asyncio.to_thread(
        reranker.rank, query, candidates, top_k=top_k, return_documents=True
    )
    return [item['text'] for item in ranked]

Call rerank() on the candidates returned by your vector search (for example, in the retrieve tool of the RAG example) before handing the results to the LLM.

!!! tip "Managed reranker alternatives" If you'd rather not run a reranker locally, several providers offer hosted rerankers, including Cohere Rerank, VoyageAI Rerank, and Jina Rerank. Call their HTTP clients or SDKs from a helper function with the same shape as rerank() above.

For more background on retrieve-and-rerank pipelines, see Hugging Face's advanced RAG cookbook. To serve open-source embedding and reranker models yourself, see Hugging Face Text Embeddings Inference and its supported rerankers.

Building Custom Embedding Models

To integrate a custom embedding provider, subclass [EmbeddingModel][pydantic_ai.embeddings.EmbeddingModel]:

from collections.abc import Sequence

from pydantic_ai.embeddings import EmbeddingModel, EmbeddingResult, EmbeddingSettings
from pydantic_ai.embeddings.result import EmbedInputType


class MyCustomEmbeddingModel(EmbeddingModel):
    @property
    def model_name(self) -> str:
        return 'my-custom-model'

    @property
    def system(self) -> str:
        return 'my-provider'

    async def embed(
        self,
        inputs: str | Sequence[str],
        *,
        input_type: EmbedInputType,
        settings: EmbeddingSettings | None = None,
    ) -> EmbeddingResult:
        inputs, settings = self.prepare_embed(inputs, settings)

        # Call your embedding API here
        embeddings = [[0.1, 0.2, 0.3] for _ in inputs]  # Placeholder

        return EmbeddingResult(
            embeddings=embeddings,
            inputs=inputs,
            input_type=input_type,
            model_name=self.model_name,
            provider_name=self.system,
        )

Use [WrapperEmbeddingModel][pydantic_ai.embeddings.WrapperEmbeddingModel] if you want to wrap an existing model to add custom behavior like caching or logging.