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# `langchain_community.embeddings.ollama`.OllamaEmbeddings

class langchain_community.embeddings.ollama.OllamaEmbeddings

  Bases: `BaseModel`, `Embeddings`

  Ollama locally runs large language models.

  To use, follow the instructions at https://ollama.ai/.

  Example

      from langchain_community.embeddings import OllamaEmbeddings
      ollama_emb = OllamaEmbeddings(
          model="llama:7b",
      )
      r1 = ollama_emb.embed_documents(
          [
              "Alpha is the first letter of Greek alphabet",
              "Beta is the second letter of Greek alphabet",
          ]
      )
      r2 = ollama_emb.embed_query(
          "What is the second letter of Greek alphabet"
      )

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

  Raises ValidationError if the input data cannot be parsed to form a valid model.

  param base_url: str = 'http://localhost:11434'

    Base url the model is hosted under.

  param embed_instruction: str = 'passage: '

    Instruction used to embed documents.

  param headers: Optional[dict] = None

    Additional headers to pass to endpoint (e.g. Authorization, Referer). This is useful when Ollama is hosted on cloud services that require tokens for authentication.

  param mirostat: Optional[int] = None

    Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)

  param 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)

  param 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)

  param model: str = 'llama2'

    Model name to use.

  param model_kwargs: Optional[dict] = None

    Other model keyword args

  param num_ctx: Optional[int] = None

    Sets the size of the context window used to generate the next token. (Default: 2048)

  param num_gpu: Optional[int] = None

    The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.

  param 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).

  param query_instruction: str = 'query: '

    Instruction used to embed the query.

  param 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)

  param 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)

  param show_progress: bool = False

    Whether to show a tqdm progress bar. Must have tqdm installed.

  param stop: Optional[List[str]] = None

    Sets the stop tokens to use.

  param temperature: Optional[float] = None

    The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)

  param 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)

  param 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)

  param 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)

  async aembed_documents(texts: List[str]) → List[List[float]]

    Asynchronous Embed search docs.

    Parameters

      texts (List[str]) –

    Return type

      List[List[float]]

  async aembed_query(text: str) → List[float]

    Asynchronous Embed query text.

    Parameters

      text (str) –

    Return type

      List[float]

  classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

    Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

    Parameters

      - _fields_set (Optional[SetStr]) –

      - values (Any) –

    Return type

      Model

  copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

    Duplicate a model, optionally choose which fields to include, exclude and change.

    Parameters

      - include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

      - exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

      - update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

      - deep (bool) – set to True to make a deep copy of the model

      - self (Model) –

    Returns

      new model instance

    Return type

      Model

  dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

    Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

    Parameters

      - include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - by_alias (bool) –

      - skip_defaults (Optional[bool]) –

      - exclude_unset (bool) –

      - exclude_defaults (bool) –

      - exclude_none (bool) –

    Return type

      DictStrAny

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

    Embed documents using an Ollama deployed embedding model.

    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]

    Embed a query using a Ollama deployed embedding model.

    Parameters

      text (str) – The text to embed.

    Returns

      Embeddings for the text.

    Return type

      List[float]

  classmethod from_orm(obj: Any) → Model
    Parameters

      obj (Any) –

    Return type

      Model

  json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

    Generate a JSON representation of the model, include and exclude arguments as per dict().

    encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

    Parameters

      - include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - by_alias (bool) –

      - skip_defaults (Optional[bool]) –

      - exclude_unset (bool) –

      - exclude_defaults (bool) –

      - exclude_none (bool) –

      - encoder (Optional[Callable[[Any], Any]]) –

      - models_as_dict (bool) –

      - dumps_kwargs (Any) –

    Return type

      unicode

  classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
    Parameters

      - path (Union[str, Path]) –

      - content_type (unicode) –

      - encoding (unicode) –

      - proto (Protocol) –

      - allow_pickle (bool) –

    Return type

      Model

  classmethod parse_obj(obj: Any) → Model
    Parameters

      obj (Any) –

    Return type

      Model

  classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
    Parameters

      - b (Union[str, bytes]) –

      - content_type (unicode) –

      - encoding (unicode) –

      - proto (Protocol) –

      - allow_pickle (bool) –

    Return type

      Model

  classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny
    Parameters

      - by_alias (bool) –

      - ref_template (unicode) –

    Return type

      DictStrAny

  classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode
    Parameters

      - by_alias (bool) –

      - ref_template (unicode) –

      - dumps_kwargs (Any) –

    Return type

      unicode

  classmethod update_forward_refs(**localns: Any) → None

    Try to update ForwardRefs on fields based on this Model, globalns and localns.

    Parameters

      localns (Any) –

    Return type

      None

  classmethod validate(value: Any) → Model
    Parameters

      value (Any) –

    Return type

      Model

# Examples using OllamaEmbeddings

- Ollama

- Quickstart

- ollama.md