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Showing content from https://python.langchain.com/docs/integrations/text_embedding/ollama below:

OllamaEmbeddings | 🦜️🔗 LangChain

This will help you get started with Ollama embedding models using LangChain. For detailed documentation on OllamaEmbeddings features and configuration options, please refer to the API reference.

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 aka WSL, macOS, and Linux)
  • macOS users can install via Homebrew with brew install ollama and start with brew services start ollama
• 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. You can run ollama help in the terminal to see available commands.

To enable automated tracing of your model calls, set your LangSmith API key:

The LangChain Ollama integration lives in the langchain-ollama package:

%pip install -qU langchain-ollama

Note: you may need to restart the kernel to use updated packages.

Now we can instantiate our model object and generate embeddings:

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(
    model="llama3",
)

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials.

Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore.

from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)


retriever = vectorstore.as_retriever()


retrieved_documents = retriever.invoke("What is LangChain?")


print(retrieved_documents[0].page_content)

LangChain is the framework for building context-aware reasoning applications

Under the hood, the vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings for the text(s) used in from_texts and retrieval invoke operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  

[-0.0039849705, 0.023019705, -0.001768838, -0.0058736936, 0.00040999008, 0.017861595, -0.011274585,

You can embed multiple texts with embed_documents:

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  

[-0.0039849705, 0.023019705, -0.001768838, -0.0058736936, 0.00040999008, 0.017861595, -0.011274585, 
[-0.0066985516, 0.009878328, 0.008019467, -0.009384944, -0.029560851, 0.025744654, 0.004872892, -0.0

For detailed documentation on OllamaEmbeddings features and configuration options, please refer to the API reference.

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

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