{ "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]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "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" }, "vscode": { "interpreter": { "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1" } } }, "nbformat": 4, "nbformat_minor": 5 }