LlamafileEmbeddings#

class langchain_community.embeddings.llamafile.LlamafileEmbeddings[source]#

Bases: BaseModel, Embeddings

Llamafile lets you distribute and run large language models with a single file.

To get started, see: Mozilla-Ocho/llamafile

To use this class, you will need to first:

Download a llamafile.
Make the downloaded file executable: chmod +x path/to/model.llamafile
Start the llamafile in server mode with embeddings enabled:

./path/to/model.llamafile –server –nobrowser –embedding

Example

from langchain_community.embeddings import LlamafileEmbeddings
embedder = LlamafileEmbeddings()
doc_embeddings = embedder.embed_documents(
    [
        "Alpha is the first letter of the Greek alphabet",
        "Beta is the second letter of the Greek alphabet",
    ]
)
query_embedding = embedder.embed_query(
    "What is the second letter of the Greek alphabet"
)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param base_url: str = 'http://localhost:8080'#: Base url where the llamafile server is listening.

param request_timeout: int | None = None#: Timeout for server requests

async aembed_documents(texts: list[str]) → list[list[float]]#

Asynchronous Embed search docs.

Parameters:: texts (list[str]) – List of text to embed.
Returns:: List of embeddings.
Return type:: list[list[float]]

async aembed_query(text: str) → list[float]#

Asynchronous Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
Return type:: list[float]

embed_documents(texts: List[str]) → List[List[float]][source]#

Embed documents using a llamafile server running at self.base_url. llamafile server should be started in a separate process before invoking this method.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

embed_query(text: str) → List[float][source]#

Embed a query using a llamafile server running at self.base_url. llamafile server should be started in a separate process before invoking this method.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using LlamafileEmbeddings

llamafile