langchain/docs/extras/integrations/text_embedding/ollama.ipynb
Ackermann Yuriy 5e50b89164
Added embeddings support for ollama (#10124)
- Description: Added support for Ollama embeddings
  - Issue: the issue # it fixes (if applicable),
  - Dependencies: N/A
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
  - Twitter handle: @herrjemand

cc  https://github.com/jmorganca/ollama/issues/436
2023-09-14 17:42:39 -07:00

229 lines
4.6 KiB
Plaintext

{
"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.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": [
{
"data": {
"text/plain": [
"[-0.09996652603149414,\n",
" 0.015568195842206478,\n",
" 0.17670190334320068,\n",
" 0.16521021723747253,\n",
" 0.21193109452724457]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_result = embeddings.embed_query(text)\n",
"query_result[:5]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-0.04242777079343796,\n",
" 0.016536075621843338,\n",
" 0.10052520781755447,\n",
" 0.18272875249385834,\n",
" 0.2079043835401535]"
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},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"doc_result = embeddings.embed_documents([text])\n",
"doc_result[0][:5]"
]
},
{
"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\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "14aefb64",
"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test document.\""
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3c39ed33",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2ee7ce9f-d506-4810-8897-e44334412714",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-0.09996627271175385,\n",
" 0.015567859634757042,\n",
" 0.17670205235481262,\n",
" 0.16521376371383667,\n",
" 0.21193283796310425]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_result[:5]"
]
},
{
"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": {},
"outputs": [
{
"data": {
"text/plain": [
"[-0.042427532374858856,\n",
" 0.01653730869293213,\n",
" 0.10052604228258133,\n",
" 0.18272635340690613,\n",
" 0.20790338516235352]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"doc_result[0][:5]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
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"vscode": {
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