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2fb27b63f5
- docs: for the Ollama notebooks, improve the specificity of some links, add `homebrew` install info, update some wording - tests: reduce number of local models needed to run in half from 4 → 2 (shedding 8gb of required installs) - bump deps (non-breaking) in anticipation of upcoming "thinking" PR
56 lines
1.5 KiB
Markdown
56 lines
1.5 KiB
Markdown
# langchain-ollama
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This package contains the LangChain integration with Ollama
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## Installation
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```bash
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pip install -U langchain-ollama
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```
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For the package to work, you will need to install and run the Ollama server locally ([download](https://ollama.com/download)).
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To run integration tests (`make integration_tests`), you will need the following models installed in your Ollama server:
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- `llama3.1`
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- `deepseek-r1:1.5b`
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Install these models by running:
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```bash
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ollama pull <name-of-model>
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```
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## [Chat Models](https://python.langchain.com/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html#chatollama)
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`ChatOllama` class exposes chat models from Ollama.
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```python
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from langchain_ollama import ChatOllama
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llm = ChatOllama(model="llama3.1")
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llm.invoke("Sing a ballad of LangChain.")
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```
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## [Embeddings](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html#ollamaembeddings)
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`OllamaEmbeddings` class exposes embeddings from Ollama.
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```python
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from langchain_ollama import OllamaEmbeddings
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embeddings = OllamaEmbeddings(model="llama3.1")
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embeddings.embed_query("What is the meaning of life?")
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```
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## [LLMs](https://python.langchain.com/api_reference/ollama/llms/langchain_ollama.llms.OllamaLLM.html#ollamallm)
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`OllamaLLM` class exposes traditional LLMs from Ollama.
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```python
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from langchain_ollama import OllamaLLM
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llm = OllamaLLM(model="llama3.1")
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llm.invoke("The meaning of life is")
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```
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