mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-06 13:33:37 +00:00
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
This commit is contained in:
Ackermann Yuriy
GitHub
@@ -106,6 +106,25 @@
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"llm(\"Tell me about the history of AI\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Ollama supports embeddings via `OllamaEmbeddings`:\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import OllamaEmbeddings\n",
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"oembed = OllamaEmbeddings(base_url=\"http://localhost:11434\", model=\"llama2\")\n",
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"\n",
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"oembed.embed_query(\"Llamas are social animals and live with others as a herd.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@@ -121,7 +140,7 @@
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"ollama run llama2:13b \n",
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"```\n",
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"\n",
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"Let's also use local embeddings from `GPT4AllEmbeddings` and `Chroma`."
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"Let's also use local embeddings from `OllamaEmbeddings` and `Chroma`."
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]
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},
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{
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@@ -163,9 +182,9 @@
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],
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"source": [
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"from langchain.vectorstores import Chroma\n",
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"from langchain.embeddings import GPT4AllEmbeddings\n",
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"from langchain.embeddings import OllamaEmbeddings\n",
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"\n",
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"vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())"
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"vectorstore = Chroma.from_documents(documents=all_splits, embedding=OllamaEmbeddings())"
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]
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},
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{
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@@ -353,7 +372,7 @@
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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": "3.9.16"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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228
docs/extras/integrations/text_embedding/ollama.ipynb
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228
docs/extras/integrations/text_embedding/ollama.ipynb
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@@ -0,0 +1,228 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "278b6c63",
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"metadata": {},
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"source": [
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"# Ollama\n",
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"\n",
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"Let's load the Ollama Embeddings class."
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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": "0be1af71",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import OllamaEmbeddings"
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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": "2c66e5da",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = OllamaEmbeddings()"
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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": "01370375",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "a42e4035",
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"metadata": {},
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"source": [
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"To generate embeddings, you can either query an invidivual text, or you can query a list of texts."
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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": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
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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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"[-0.09996652603149414,\n",
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" 0.015568195842206478,\n",
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" 0.17670190334320068,\n",
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" 0.16521021723747253,\n",
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" 0.21193109452724457]"
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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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"query_result = embeddings.embed_query(text)\n",
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"query_result[:5]"
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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": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
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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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"[-0.04242777079343796,\n",
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" 0.016536075621843338,\n",
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" 0.10052520781755447,\n",
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" 0.18272875249385834,\n",
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" 0.2079043835401535]"
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]
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},
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"execution_count": 6,
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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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"doc_result = embeddings.embed_documents([text])\n",
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"doc_result[0][:5]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bb61bbeb",
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"metadata": {},
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"source": [
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"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)"
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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": 13,
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"id": "a56b70f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = OllamaEmbeddings(model=\"llama2:7b\")"
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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": 14,
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"id": "14aefb64",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": 15,
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"id": "3c39ed33",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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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": 17,
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"id": "2ee7ce9f-d506-4810-8897-e44334412714",
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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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"[-0.09996627271175385,\n",
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" 0.015567859634757042,\n",
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" 0.17670205235481262,\n",
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" 0.16521376371383667,\n",
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" 0.21193283796310425]"
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]
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},
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"execution_count": 17,
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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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"query_result[:5]"
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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": 18,
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"id": "e3221db6",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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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": 19,
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"id": "a0865409-3a6d-468f-939f-abde17c7cac3",
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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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"[-0.042427532374858856,\n",
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" 0.01653730869293213,\n",
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" 0.10052604228258133,\n",
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" 0.18272635340690613,\n",
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" 0.20790338516235352]"
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]
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},
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"execution_count": 19,
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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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"doc_result[0][:5]"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.11.5"
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},
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"vscode": {
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"interpreter": {
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"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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