Principle:Langchain ai Langchain Embedding Model Initialization
| Knowledge Sources | |
|---|---|
| Domains | NLP, Embeddings, Vector_Search |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
A configuration step that creates a ready-to-use embedding model instance for converting text into dense vector representations.
Description
Embedding model initialization configures a model that maps text strings to fixed-dimensional dense vectors. These vectors capture semantic meaning, enabling similarity-based operations like nearest-neighbor search. LangChain defines the Embeddings abstract base class with two key methods: embed_documents() (batch embedding for storage) and embed_query() (single embedding for search queries).
Different providers offer different embedding models with varying dimensions, context lengths, and performance characteristics.
Usage
Initialize an embedding model at the start of any vector-based workflow (RAG, semantic search, clustering). Choose the provider based on quality, cost, and latency requirements.
Theoretical Basis
Embedding models map text to points in a high-dimensional vector space where semantic similarity corresponds to geometric proximity:
The Embeddings interface provides:
# Abstract interface (not real code)
class Embeddings(ABC):
def embed_documents(texts: list[str]) -> list[list[float]]: ...
def embed_query(text: str) -> list[float]: ...