VESSL AI LLMProvider integration

docs/docs/examples/llm/vesslai_llm.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.llms.vesslai import VesslAILLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = VesslAILLM()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1 Serve with hf model name\n",
+    "llm.serve(\n",
+    "    service_name=\"llama-index-vesslai-test\",\n",
+    "    model_name=\"mistralai/Mistral-7B-Instruct-v0.3\",\n",
+    "    hf_token=\"HF_TOKEN\",\n",
+    "    api_key=\"OPENAI_API_KEY\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2 Serve with yaml file\n",
+    "llm.serve(\n",
+    "    service_name=\"llama-index-vesslai-test\",\n",
+    "    yaml_path=\"/your/own/servie_yaml_file.yaml\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3 Connect with pre-served endpoint\n",
+    "llm.connect(\n",
+    "    served_model_name=\"mistralai/Mistral-7B-Instruct-v0.3\",\n",
+    "    endpoint=\"https://model-service-gateway-abc.oregon.google-cluster.vessl.ai/v1\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resp = llm.complete(\"Who is Paul Graham?\")\n",
+    "print(resp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llama-index-V2OWx8vG-py3.12",
+   "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": 2
+}

llama-index-integrations/llms/llama-index-llms-vesslai/.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/llms/llama-index-llms-vesslai/BUILD

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

llama-index-integrations/llms/llama-index-llms-vesslai/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/

llama-index-integrations/llms/llama-index-llms-vesslai/README.md

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+# LlamaIndex Llms Integration: VESSL AI
+
+[VESSL AI](https://vessl.ai/) is an MLOps platform for professional AI & LLM teams. VESSL supports both Multi-Cloud and On-Premise systems, helping ML teams seamlessly manage their workloads and data.
+
+## LlamaIndex VESSL AI LLM Provider
+
+The VESSL AI LLM Provider helps AI Engineers serve their models efficiently using the `serve` method. If your team has already deployed an LLM model with VESSL and has an endpoint, you can easily connect to that endpoint and integrate it into your LlamaIndex workflow. The VESSL AI LLM Provider functions similarly to an OPENAI-like object.
+
+1. To serve a Hugging Face model, use the `serve` method:
+
+```python
+# 1 Serve with Hugging Face model name
+llm.serve(
+    service_name="llama-index-vesslai-test",
+    model_name="mistralai/Mistral-7B-Instruct-v0.3",
+    hf_token="HF_TOKEN",
+    api_key="OPENAI-API-KEY",
+)
+```
+
+2. If you have your own VESSL YAML file for serving, you can deploy the workload using the YAML file:
+
+```python
+# 2 Serve with YAML file
+llm.serve(
+    service_name="llama-index-vesslai-test",
+    yaml_path="/your/own/service_yaml_file.yaml",
+    api_key="OPENAI-API-KEY",
+)
+```
+
+3. If you have a pre-served LLM, you can simply connect to the endpoint:
+
+```python
+# 3 Connect with pre-served endpoint
+llm.connect(
+    served_model_name="mistralai/Mistral-7B-Instruct-v0.3",
+    endpoint="https://model-service-gateway-abc.oregon.google-cluster.vessl.ai/v1",
+)
+```

llama-index-integrations/llms/llama-index-llms-vesslai/llama_index/llms/vesslai/BUILD

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+python_sources()

llama-index-integrations/llms/llama-index-llms-vesslai/llama_index/llms/vesslai/__init__.py

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+from llama_index.llms.vesslai.base import VesslAILLM
+
+__all__ = ["VesslAILLM"]