Add OCI Data Science Model Deployment Embedding Integration (#17243)

docs/docs/examples/embeddings/oci_data_science.ipynb

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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "6d1ca9ac",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/embeddings/oci_data_science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e3a8796-edc8-43f2-94ad-fe4fb20d70ed",
+   "metadata": {},
+   "source": [
+    "# Oracle Cloud Infrastructure (OCI) Data Science Service\n",
+    "\n",
+    "Oracle Cloud Infrastructure (OCI) [Data Science](https://www.oracle.com/artificial-intelligence/data-science) is a fully managed, serverless platform for data science teams to build, train, and manage machine learning models in Oracle Cloud Infrastructure.\n",
+    "\n",
+    "It offers [AI Quick Actions](https://docs.oracle.com/en-us/iaas/data-science/using/ai-quick-actions.htm), which can be used to deploy embedding models in OCI Data Science. AI Quick Actions target users who want to quickly leverage the capabilities of AI. They aim to expand the reach of foundation models to a broader set of users by providing a streamlined, code-free, and efficient environment for working with foundation models. AI Quick Actions can be accessed from the Data Science Notebook.\n",
+    "\n",
+    "Detailed documentation on how to deploy embedding models in OCI Data Science using AI Quick Actions is available [here](https://github.com/oracle-samples/oci-data-science-ai-samples/blob/main/ai-quick-actions/model-deployment-tips.md) and [here](https://docs.oracle.com/en-us/iaas/data-science/using/ai-quick-actions-model-deploy.htm).\n",
+    "\n",
+    "This notebook explains how to use OCI's Data Science embedding models with LlamaIndex."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3802e8c4",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb0dd8c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install llama-index-embeddings-oci-data-science"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "544d49f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2921307",
+   "metadata": {},
+   "source": [
+    "You will also need to install the [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/index.html) SDK."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "378d5179",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -U oracle-ads"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "423605c6",
+   "metadata": {},
+   "source": [
+    "## Authentication\n",
+    "\n",
+    "The authentication methods supported for LlamaIndex are equivalent to those used with other OCI services and follow the standard SDK authentication methods, specifically API Key, session token, instance principal, and resource principal. More details can be found [here](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html). Make sure to have the required [policies](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-policies-auth.htm) to access the OCI Data Science Model Deployment endpoint. The [oracle-ads](https://accelerated-data-science.readthedocs.io/en/latest/index.html) helps to simplify the authentication within OCI Data Science."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d4024a",
+   "metadata": {},
+   "source": [
+    "## Basic Usage\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60be18ae-c957-4ac2-a58a-0652e18ee6d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ads\n",
+    "from llama_index.embeddings.oci_data_science import OCIDataScienceEmbedding\n",
+    "\n",
+    "ads.set_auth(auth=\"security_token\", profile=\"<replace-with-your-profile>\")\n",
+    "\n",
+    "embedding = OCIDataScienceEmbedding(\n",
+    "    endpoint=\"https://<MD_OCID>/predict\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "e1 = embeddings.get_text_embedding(\"This is a test document\")\n",
+    "print(e1)\n",
+    "\n",
+    "e2 = embeddings.get_text_embedding_batch(\n",
+    "    [\"This is a test document\", \"This is another test document\"]\n",
+    ")\n",
+    "print(e2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "170e1ad7",
+   "metadata": {},
+   "source": [
+    "## Async"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbc3cba9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ads\n",
+    "from llama_index.embeddings.oci_data_science import OCIDataScienceEmbedding\n",
+    "\n",
+    "ads.set_auth(auth=\"security_token\", profile=\"<replace-with-your-profile>\")\n",
+    "\n",
+    "embedding = OCIDataScienceEmbedding(\n",
+    "    endpoint=\"https://<MD_OCID>/predict\",\n",
+    ")\n",
+    "\n",
+    "e1 = await embeddings.aget_text_embedding(\"This is a test document\")\n",
+    "print(e1)\n",
+    "\n",
+    "e2 = await embeddings.aget_text_embedding_batch(\n",
+    "    [\"This is a test document\", \"This is another test document\"]\n",
+    ")\n",
+    "print(e2)"
+   ]
+  }
+ ],
+ "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

llama-index-integrations/embeddings/llama-index-embeddings-oci-data-science/.gitignore

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+llama_index/_static
+.DS_Store
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+bin/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+etc/
+include/
+lib/
+lib64/
+parts/
+sdist/
+share/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+.ruff_cache
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+notebooks/
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pyvenv.cfg
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Jetbrains
+.idea
+modules/
+*.swp
+
+# VsCode
+.vscode
+
+# pipenv
+Pipfile
+Pipfile.lock
+
+# pyright
+pyrightconfig.json

llama-index-integrations/embeddings/llama-index-embeddings-oci-data-science/BUILD

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+poetry_requirements(
+    name="poetry",
+)

llama-index-integrations/embeddings/llama-index-embeddings-oci-data-science/Makefile

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+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help:	## Show all Makefile targets.
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format:	## Run code autoformatters (black).
+	pre-commit install
+	git ls-files | xargs pre-commit run black --files
+
+lint:	## Run linters: pre-commit (black, ruff, codespell) and mypy
+	pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test:	## Run tests via pytest.
+	pytest tests
+
+watch-docs:	## Build and watch documentation.
+	sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/