Embeddings
Embeddings models.
Reference docs
This page contains reference documentation for Embeddings. See the docs for conceptual guides, tutorials, and examples on using Embeddings.
Modules moved
With the release of langchain 1.0.0, several embeddings modules were moved to langchain-classic, such as CacheBackedEmbeddings and all community embeddings. See list of moved modules to inform your migration.
init_embeddings( model: str, *, provider: str | None = None, **kwargs: Any ) -> Embeddings Initialize an embedding model from a model name and optional provider.
Note
Requires the integration package for the chosen model provider to be installed.
See the model_provider parameter below for specific package names (e.g., pip install langchain-openai).
Refer to the provider integration's API reference for supported model parameters to use as **kwargs.
| PARAMETER | DESCRIPTION |
|---|---|
model | The name of the model, e.g. You can also specify model and model provider in a single argument using TYPE: |
provider | The model provider if not specified as part of the model arg (see above). Supported
TYPE: |
**kwargs | Additional model-specific parameters passed to the embedding model. These vary by provider. Refer to the specific model provider's integration reference for all available parameters. TYPE: |
| RETURNS | DESCRIPTION |
|---|---|
Embeddings | An |
| RAISES | DESCRIPTION |
|---|---|
ValueError | If the model provider is not supported or cannot be determined |
ImportError | If the required provider package is not installed |
Example
# pip install langchain langchain-openai # Using a model string model = init_embeddings("openai:text-embedding-3-small") model.embed_query("Hello, world!") # Using explicit provider model = init_embeddings(model="text-embedding-3-small", provider="openai") model.embed_documents(["Hello, world!", "Goodbye, world!"]) # With additional parameters model = init_embeddings("openai:text-embedding-3-small", api_key="sk-...") Added in version 0.3.9
Bases: ABC
Interface for embedding models.
This is an interface meant for implementing text embedding models.
Text embedding models are used to map text to a vector (a point in n-dimensional space).
Texts that are similar will usually be mapped to points that are close to each other in this space. The exact details of what's considered "similar" and how "distance" is measured in this space are dependent on the specific embedding model.
This abstraction contains a method for embedding a list of documents and a method for embedding a query text. The embedding of a query text is expected to be a single vector, while the embedding of a list of documents is expected to be a list of vectors.
Usually the query embedding is identical to the document embedding, but the abstraction allows treating them independently.
In addition to the synchronous methods, this interface also provides asynchronous versions of the methods.
By default, the asynchronous methods are implemented using the synchronous methods; however, implementations may choose to override the asynchronous methods with an async native implementation for performance reasons.