Integrate mistral.rs LLM

llama-index-integrations/llms/llama-index-llms-mistral-rs/.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
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+# Rope project settings
+.ropeproject
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+# mkdocs documentation
+/site
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+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Jetbrains
+.idea
+modules/
+*.swp
+
+# VsCode
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+
+# pipenv
+Pipfile
+Pipfile.lock
+
+# pyright
+pyrightconfig.json

llama-index-integrations/llms/llama-index-llms-mistral-rs/BUILD

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

llama-index-integrations/llms/llama-index-llms-mistral-rs/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-mistral-rs/README.md

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+# LlamaIndex Llms Integration: `mistral.rs`
+
+To use this integration, please install the Python `mistralrs` package:
+
+## Installation of `mistralrs` from PyPi
+
+0. Install Rust: https://rustup.rs/
+
+   ```bash
+   curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
+   source $HOME/.cargo/env
+   ```
+
+1. `mistralrs` depends on the `openssl` library.
+
+To install it on Ubuntu:
+
+```
+sudo apt install libssl-dev
+sudo apt install pkg-config
+```
+
+2. Install it!
+
+- CUDA
+
+  `pip install mistralrs-cuda`
+
+- Metal
+
+  `pip install mistralrs-metal`
+
+- Apple Accelerate
+
+  `pip install mistralrs-accelerate`
+
+- Intel MKL
+
+  `pip install mistralrs-mkl`
+
+- Without accelerators
+
+  `pip install mistralrs`
+
+All installations will install the `mistralrs` package. The suffix on the package installed by `pip` only controls the feature activation.
+
+## Installation from source
+
+Please follow the instructions [here](https://github.com/EricLBuehler/mistral.rs/blob/master/mistralrs-pyo3/README.md).
+
+## Usage
+
+```python
+from llama_index.llms.mistral_rs import MistralRS
+from mistralrs import Which
+
+llm = MistralRS(
+    which=Which.GGUF(
+        tok_model_id="mistralai/Mistral-7B-Instruct-v0.1",
+        quantized_model_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
+        quantized_filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
+        tokenizer_json=None,
+        repeat_last_n=64,
+    ),
+    max_new_tokens=4096,
+    context_window=1024 * 5,
+)
+```

llama-index-integrations/llms/llama-index-llms-mistral-rs/examples/plain.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings\n",
+    "from llama_index.core.embeddings import resolve_embed_model\n",
+    "from llama_index.llms.mistral_rs import MistralRS\n",
+    "from mistralrs import Which, Architecture\n",
+    "import sys\n",
+    "\n",
+    "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+    "\n",
+    "# bge embedding model\n",
+    "Settings.embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Settings.llm = MistralRS(\n",
+    "    which=Which.Plain(\n",
+    "        model_id=\"mistralai/Mistral-7B-Instruct-v0.1\",\n",
+    "        arch=Architecture.Mistral,\n",
+    "        tokenizer_json=None,\n",
+    "        repeat_last_n=64,\n",
+    "    ),\n",
+    "    max_new_tokens=4096,\n",
+    "    context_window=1024 * 5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = VectorStoreIndex.from_documents(\n",
+    "    documents,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_engine = index.as_query_engine()\n",
+    "response = query_engine.query(\"How do I pronounce graphene?\")\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

llama-index-integrations/llms/llama-index-llms-mistral-rs/examples/streaming.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings\n",
+    "from llama_index.core.embeddings import resolve_embed_model\n",
+    "from llama_index.llms.mistral_rs import MistralRS\n",
+    "from mistralrs import Which\n",
+    "import sys\n",
+    "\n",
+    "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+    "\n",
+    "# bge embedding model\n",
+    "Settings.embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Settings.llm = MistralRS(\n",
+    "    which=Which.GGUF(\n",
+    "        tok_model_id=\"mistralai/Mistral-7B-Instruct-v0.1\",\n",
+    "        quantized_model_id=\"TheBloke/Mistral-7B-Instruct-v0.1-GGUF\",\n",
+    "        quantized_filename=\"mistral-7b-instruct-v0.1.Q4_K_M.gguf\",\n",
+    "        tokenizer_json=None,\n",
+    "        repeat_last_n=64,\n",
+    "    ),\n",
+    "    max_new_tokens=4096,\n",
+    "    context_window=1024 * 5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = VectorStoreIndex.from_documents(\n",
+    "    documents,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_engine = index.as_query_engine(streaming=True)\n",
+    "response = query_engine.query(\"What are non-granular scalings?\")\n",
+    "for text in response.response_gen:\n",
+    "    print(text, end=\"\")\n",
+    "    sys.stdout.flush()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}