`
* View the [Ollama documentation](https://github.com/ollama/ollama/tree/main/docs) 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](/langsmith/observability) API key:
```python theme={null}
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```
### Installation
The LangChain Ollama integration lives in the `langchain-ollama` package:
```python theme={null}
pip install -qU langchain-ollama
```
## Instantiation
Now we can instantiate our model object and generate embeddings:
```python theme={null}
from langchain_ollama import OllamaEmbeddings
embeddings = OllamaEmbeddings(
model="llama3",
)
```
If your model supports configurable output dimensions (e.g., `qwen3-embedding`), you can specify them via the `dimensions` parameter:
```python theme={null}
from langchain_ollama import OllamaEmbeddings
embeddings = OllamaEmbeddings(
model="qwen3-embedding:8b",
dimensions=1024,
)
```
## Indexing and retrieval
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](/oss/python/langchain/rag/).
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`.
```python theme={null}
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore
text = "LangChain is the framework for building context-aware reasoning applications"
vectorstore = InMemoryVectorStore.from_texts(
[text],
embedding=embeddings,
)
# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()
# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")
# Show the retrieved document's content
print(retrieved_documents[0].page_content)
```
```text theme={null}
LangChain is the framework for building context-aware reasoning applications
```
## Direct usage
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.
### Embed single texts
You can embed single texts or documents with `embed_query`:
```python theme={null}
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100]) # Show the first 100 characters of the vector
```
```text theme={null}
[-0.0039849705, 0.023019705, -0.001768838, -0.0058736936, 0.00040999008, 0.017861595, -0.011274585,
```
### Embed multiple texts
You can embed multiple texts with `embed_documents`:
```python theme={null}
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]) # Show the first 100 characters of the vector
```
```text theme={null}
[-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
```
***
## API reference
For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/python/langchain-ollama/embeddings/OllamaEmbeddings).
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/embeddings/ollama.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).