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: dict | None = 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: int | None = None

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

param mirostat_eta: float | None = 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: float | None = 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: dict | None = None

Other model keyword args

param num_ctx: int | None = None

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

param num_gpu: int | None = None

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

param num_thread: int | None = 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: int | None = None

Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)

param repeat_penalty: float | None = 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: List[str] | None = None

Sets the stop tokens to use.

param temperature: float | None = None

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

param tfs_z: float | None = 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: int | None = 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: float | None = 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]) – 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]]

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]

Examples using OllamaEmbeddings

• ApertureDB

• Ollama