"""Ollama embeddings models.""" from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings from ollama import AsyncClient, Client from pydantic import ( BaseModel, ConfigDict, PrivateAttr, model_validator, ) from typing_extensions import Self 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 ``. 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 = PrivateAttr(default=None) # type: ignore """ The client to use for making requests. """ _async_client: AsyncClient = PrivateAttr(default=None) # type: ignore """ The async client to use for making requests. """ mirostat: Optional[int] = None """Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)""" mirostat_eta: Optional[float] = None """Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)""" mirostat_tau: Optional[float] = None """Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)""" num_ctx: Optional[int] = None """Sets the size of the context window used to generate the next token. (Default: 2048) """ num_gpu: Optional[int] = None """The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.""" keep_alive: Optional[int] = None """controls how long the model will stay loaded into memory following the request (default: 5m) """ num_thread: Optional[int] = None """Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).""" repeat_last_n: Optional[int] = None """Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)""" repeat_penalty: Optional[float] = None """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)""" temperature: Optional[float] = None """The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)""" stop: Optional[List[str]] = None """Sets the stop tokens to use.""" tfs_z: Optional[float] = None """Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)""" top_k: Optional[int] = None """Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)""" top_p: Optional[float] = None """Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)""" model_config = ConfigDict( extra="forbid", ) @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Ollama.""" return { "mirostat": self.mirostat, "mirostat_eta": self.mirostat_eta, "mirostat_tau": self.mirostat_tau, "num_ctx": self.num_ctx, "num_gpu": self.num_gpu, "num_thread": self.num_thread, "repeat_last_n": self.repeat_last_n, "repeat_penalty": self.repeat_penalty, "temperature": self.temperature, "stop": self.stop, "tfs_z": self.tfs_z, "top_k": self.top_k, "top_p": self.top_p, } @model_validator(mode="after") def _set_clients(self) -> Self: """Set clients to use for ollama.""" client_kwargs = self.client_kwargs or {} self._client = Client(host=self.base_url, **client_kwargs) self._async_client = AsyncClient(host=self.base_url, **client_kwargs) return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" embedded_docs = self._client.embed( self.model, texts, options=self._default_params, keep_alive=self.keep_alive )["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, keep_alive=self.keep_alive ) )["embeddings"] return embedded_docs async def aembed_query(self, text: str) -> List[float]: """Embed query text.""" return (await self.aembed_documents([text]))[0]