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


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