Source code for langchain_ollama.embeddings

from typing import (
    List,
    Optional,
)

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from ollama import AsyncClient, Client


class OllamaEmbeddings(BaseModel, Embeddings):
    """Ollama embedding model integration.

    Set up a local Ollama instance:
        Install the Ollama package and set up a local Ollama instance
        using the instructions here: https://github.com/ollama/ollama .

        You will need to choose a model to serve.

        You can view a list of available models via the model library (https://ollama.com/library).

        To fetch a model from the Ollama model library use ``ollama pull <name-of-model>``.

        For example, to pull the llama3 model:

        .. code-block:: bash

            ollama pull llama3

        This will download the default tagged version of the model.
        Typically, the default points to the latest, smallest sized-parameter model.

        * On Mac, the models will be downloaded to ~/.ollama/models
        * On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models

        You can specify the exact version of the model of interest
        as such ``ollama pull vicuna:13b-v1.5-16k-q4_0``.

        To view pulled models:

        .. code-block:: bash

            ollama list

        To start serving:

        .. code-block:: bash

            ollama serve

        View the Ollama documentation for more commands.

        .. code-block:: bash

            ollama help

    Install the langchain-ollama integration package:
        .. code-block:: bash

            pip install -U langchain_ollama

    Key init args — completion params:
        model: str
            Name of Ollama model to use.
        base_url: Optional[str]
            Base url the model is hosted under.

    See full list of supported init args and their descriptions in the params section.

    Instantiate:
        .. code-block:: python

            from langchain_ollama import OllamaEmbeddings

            embed = OllamaEmbeddings(
                model="llama3"
            )

    Embed single text:
        .. code-block:: python

            input_text = "The meaning of life is 42"
            vector = embed.embed_query(input_text)
            print(vector[:3])

        .. code-block:: python

            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

    Embed multiple texts:
        .. code-block:: python

             input_texts = ["Document 1...", "Document 2..."]
            vectors = embed.embed_documents(input_texts)
            print(len(vectors))
            # The first 3 coordinates for the first vector
            print(vectors[0][:3])

        .. code-block:: python

            2
            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

    Async:
        .. code-block:: python

            vector = await embed.aembed_query(input_text)
           print(vector[:3])

            # multiple:
            # await embed.aembed_documents(input_texts)

        .. code-block:: python

            [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
    """  # noqa: E501

    model: str
    """Model name to use."""

    base_url: Optional[str] = None
    """Base url the model is hosted under."""

    client_kwargs: Optional[dict] = {}
    """Additional kwargs to pass to the httpx Client. 
    For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html)
    """

    _client: Client = Field(default=None)
    """
    The client to use for making requests.
    """

    _async_client: AsyncClient = Field(default=None)
    """
    The async client to use for making requests.
    """

    class Config:
        """Configuration for this pydantic object."""

        extra = "forbid"

    @root_validator(pre=False, skip_on_failure=True)
    def _set_clients(cls, values: dict) -> dict:
        """Set clients to use for ollama."""
        values["_client"] = Client(host=values["base_url"], **values["client_kwargs"])
        values["_async_client"] = AsyncClient(
            host=values["base_url"], **values["client_kwargs"]
        )
        return values

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed search docs."""
        embedded_docs = self._client.embed(self.model, texts)["embeddings"]
        return embedded_docs

    def embed_query(self, text: str) -> List[float]:
        """Embed query text."""
        return self.embed_documents([text])[0]

    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed search docs."""
        embedded_docs = (await self._async_client.embed(self.model, texts))[
            "embeddings"
        ]
        return embedded_docs

    async def aembed_query(self, text: str) -> List[float]:
        """Embed query text."""
        return (await self.aembed_documents([text]))[0]