OllamaEmbeddings

This notebook covers how to get started with Ollama embedding models.

Installation

install package

%pip install langchain_ollama

Setup

First, follow these instructions to set up and run a local Ollama instance:

• Download and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)
• Fetch available LLM model via ollama pull <name-of-model>
  • View a list of available models via the model library
  • e.g., 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 download to ~/.ollama/models

On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models

• Specify the exact version of the model of interest as such ollama pull vicuna:13b-v1.5-16k-q4_0 (View the various tags for the Vicuna model in this instance)
• To view all pulled models, use ollama list
• To chat directly with a model from the command line, use ollama run <name-of-model>
• View the Ollama documentation for more commands. Run ollama help in the terminal to see available commands too.

Usage

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="llama3")

API Reference:OllamaEmbeddings

embeddings.embed_query("My query to look up")

[1.1588108539581299,
 -3.3943021297454834,
 0.8108075261116028,
 0.48006290197372437,
 -1.8064439296722412,
 -0.5782400965690613,
 1.8570188283920288,
 2.2842330932617188,
 -2.836144208908081,
 -0.6422690153121948,
 ...]

# async embed documents
await embeddings.aembed_documents(
    ["This is a content of the document", "This is another document"]
)

[[0.026717308908700943,
  -3.073253870010376,
  -0.983579158782959,
  -1.3976373672485352,
  0.3153868317604065,
  -0.9198529124259949,
  -0.5000395178794861,
  -2.8302183151245117,
  0.48412731289863586,
  -1.3201743364334106,
  ...]]

Related

• Embedding model conceptual guide
• Embedding model how-to guides