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https://github.com/hwchase17/langchain
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docs: Update integration docs for OllamaEmbeddingsModel (#25314)
Issue: https://github.com/langchain-ai/langchain/issues/24856 --------- Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com> Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
This commit is contained in:
Eugene Yurtsev
GitHub
parent
a4ef830480
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8645a49f31
@ -12,34 +12,20 @@
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},
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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"id": "9a3d6f34",
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"metadata": {},
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"source": [
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"# OllamaEmbeddings\n",
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"\n",
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"This notebook covers how to get started with Ollama embedding models.\n",
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"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](https://api.python.langchain.com/en/latest/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"import { ItemTable } from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"text_embedding\" item=\"Ollama\" />\n",
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"\n",
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"## Installation"
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]
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},
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{
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"cell_type": "raw",
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"id": "57f50aa5",
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"metadata": {
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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"# install package\n",
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"%pip install langchain_ollama"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
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@ -60,86 +46,209 @@
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"* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
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"\n",
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"\n",
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"## Usage"
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"### Credentials\n",
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"\n",
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"There is no built-in auth mechanism for Ollama."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c84fb993",
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"metadata": {},
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"source": [
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"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "39a4953b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d9664366",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain Ollama integration lives in the `langchain-ollama` package:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "64853226",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install -qU langchain-ollama"
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]
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},
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{
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"cell_type": "markdown",
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"id": "45dd1724",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate embeddings:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9ea7a09b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_ollama import OllamaEmbeddings\n",
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"\n",
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"embeddings = OllamaEmbeddings(\n",
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" model=\"llama3\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "77d271b6",
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"metadata": {},
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"source": [
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"## Indexing and Retrieval\n",
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"\n",
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"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 under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
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"\n",
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"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`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"id": "d817716b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'LangChain is the framework for building context-aware reasoning applications'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_ollama import OllamaEmbeddings\n",
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"# Create a vector store with a sample text\n",
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"from langchain_core.vectorstores import InMemoryVectorStore\n",
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"\n",
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"embeddings = OllamaEmbeddings(model=\"llama3\")"
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"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
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"\n",
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"vectorstore = InMemoryVectorStore.from_texts(\n",
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" [text],\n",
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" embedding=embeddings,\n",
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")\n",
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"\n",
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"# Use the vectorstore as a retriever\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"# Retrieve the most similar text\n",
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"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
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"\n",
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"# show the retrieved document's content\n",
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"retrieved_documents[0].page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e02b9855",
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"metadata": {},
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"source": [
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"## Direct Usage\n",
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"\n",
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"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.\n",
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"\n",
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"You can directly call these methods to get embeddings for your own use cases.\n",
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"\n",
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"### Embed single texts\n",
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"\n",
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"You can embed single texts or documents with `embed_query`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "12fcfb4b",
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"id": "0d2befcd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[1.1588108539581299,\n",
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" -3.3943021297454834,\n",
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" 0.8108075261116028,\n",
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" 0.48006290197372437,\n",
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" -1.8064439296722412,\n",
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" -0.5782400965690613,\n",
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" 1.8570188283920288,\n",
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" 2.2842330932617188,\n",
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" -2.836144208908081,\n",
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" -0.6422690153121948,\n",
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" ...]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-0.001288981, 0.006547121, 0.018376578, 0.025603496, 0.009599175, -0.0042578303, -0.023250086, -0.0\n"
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]
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}
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],
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"source": [
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"embeddings.embed_query(\"My query to look up\")"
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"single_vector = embeddings.embed_query(text)\n",
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"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b5a7d03",
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"metadata": {},
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"source": [
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"### Embed multiple texts\n",
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"\n",
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"You can embed multiple texts with `embed_documents`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "1f2e6104",
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"id": "2f4d6e97",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[[0.026717308908700943,\n",
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" -3.073253870010376,\n",
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" -0.983579158782959,\n",
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" -1.3976373672485352,\n",
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" 0.3153868317604065,\n",
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" -0.9198529124259949,\n",
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" -0.5000395178794861,\n",
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" -2.8302183151245117,\n",
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" 0.48412731289863586,\n",
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" -1.3201743364334106,\n",
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" ...]]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-0.0013138362, 0.006438795, 0.018304596, 0.025530428, 0.009717592, -0.004225636, -0.023363983, -0.0\n",
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"[-0.010317663, 0.01632489, 0.0070348927, 0.017076202, 0.008924255, 0.007399284, -0.023064945, -0.003\n"
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]
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}
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],
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"source": [
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"# async embed documents\n",
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"await embeddings.aembed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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"text2 = (\n",
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" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
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")\n",
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"two_vectors = embeddings.embed_documents([text, text2])\n",
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"for vector in two_vectors:\n",
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" print(str(vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "98785c12",
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"metadata": {},
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"source": [
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"## API Reference\n",
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"\n",
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"For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n"
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]
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}
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],
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@ -159,7 +268,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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"version": "3.9.6"
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}
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},
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"nbformat": 4,
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