Construct Annoy wrapper from embeddings.
from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
metric: str = DEFAULT_METRIC,
trees: int = 100,
n_jobs: int = 1,
**kwargs: Any = {}
) -> AnnoyThis is a user friendly interface that:
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_community.vectorstores import Annoy from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() text_embeddings = embeddings.embed_documents(texts) text_embedding_pairs = list(zip(texts, text_embeddings)) db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
| Name | Type | Description |
|---|---|---|
text_embeddings* | List[Tuple[str, List[float]]] | List of tuples of (text, embedding) |
embedding* | Embeddings | Embedding function to use. |
metadatas | Optional[List[dict]] | Default: NoneList of metadata dictionaries to associate with documents. |
metric | str | Default: DEFAULT_METRICMetric to use for indexing. Defaults to "angular". |
trees | int | Default: 100Number of trees to use for indexing. Defaults to 100. |
n_jobs | int | Default: -1Number of jobs to use for indexing. Defaults to -1 |