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langchain/docs/versioned_docs/version-0.2.x/integrations/text_embedding/ollama.ipynb
Jacob Lee aff771923a Jacob/new docs (#20570)
Use docusaurus versioning with a callout, merged master as well

@hwchase17 @baskaryan

---------

Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Leonid Ganeline <leo.gan.57@gmail.com>
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Co-authored-by: Christophe Bornet <cbornet@hotmail.com>
2024-04-18 11:10:55 -07:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "278b6c63",
"metadata": {},
"source": [
"# Ollama\n",
"\n",
"Let's load the Ollama Embeddings class."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0be1af71",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.embeddings import OllamaEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2c66e5da",
"metadata": {},
"outputs": [],
"source": [
"embeddings = OllamaEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "01370375",
"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test document.\""
]
},
{
"cell_type": "markdown",
"id": "a42e4035",
"metadata": {},
"source": [
"To generate embeddings, you can either query an invidivual text, or you can query a list of texts."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
"metadata": {},
"outputs": [
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"query_result = embeddings.embed_query(text)\n",
"query_result[:5]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
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"doc_result[0][:5]"
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{
"cell_type": "markdown",
"id": "bb61bbeb",
"metadata": {},
"source": [
"Let's load the Ollama Embeddings class with smaller model (e.g. llama:7b). Note: See other supported models [https://ollama.ai/library](https://ollama.ai/library)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a56b70f5",
"metadata": {},
"outputs": [],
"source": [
"embeddings = OllamaEmbeddings(model=\"llama2:7b\")"
]
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{
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"id": "14aefb64",
"metadata": {},
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{
"cell_type": "code",
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"id": "3c39ed33",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
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{
"cell_type": "code",
"execution_count": 18,
"id": "e3221db6",
"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([text])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a0865409-3a6d-468f-939f-abde17c7cac3",
"metadata": {},
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"[-0.042427532374858856,\n",
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