diff --git a/fern/docs/pages/installation/installation.mdx b/fern/docs/pages/installation/installation.mdx index 67431ebdb..b47e2b2b7 100644 --- a/fern/docs/pages/installation/installation.mdx +++ b/fern/docs/pages/installation/installation.mdx @@ -30,8 +30,8 @@ pyenv local 3.11 PrivateGPT allows to customize the setup -from fully local to cloud based- by deciding the modules to use. Here are the different options available: -- LLM: "llama-cpp", "ollama", "sagemaker", "openai", "openailike" -- Embeddings: "huggingface", "openai", "sagemaker" +- LLM: "llama-cpp", "ollama", "sagemaker", "openai", "openailike", "azopenai" +- Embeddings: "huggingface", "openai", "sagemaker", "azopenai" - Vector stores: "qdrant", "chroma", "postgres" - UI: whether or not to enable UI (Gradio) or just go with the API @@ -49,10 +49,12 @@ Where `` can be any of the following: - llms-sagemaker: adds support for Amazon Sagemaker LLM, requires Sagemaker inference endpoints - llms-openai: adds support for OpenAI LLM, requires OpenAI API key - llms-openai-like: adds support for 3rd party LLM providers that are compatible with OpenAI's API +- llms-azopenai: adds support for Azure OpenAI LLM, requires Azure OpenAI inference endpoints - embeddings-ollama: adds support for Ollama Embeddings, requires Ollama running locally - embeddings-huggingface: adds support for local Embeddings using HuggingFace - embeddings-sagemaker: adds support for Amazon Sagemaker Embeddings, requires Sagemaker inference endpoints - embeddings-openai = adds support for OpenAI Embeddings, requires OpenAI API key +- embeddings-azopenai = adds support for Azure OpenAI Embeddings, requires Azure OpenAI inference endpoints - vector-stores-qdrant: adds support for Qdrant vector store - vector-stores-chroma: adds support for Chroma DB vector store - vector-stores-postgres: adds support for Postgres vector store @@ -160,6 +162,29 @@ PrivateGPT will use the already existing `settings-openai.yaml` settings file, w The UI will be available at http://localhost:8001 +### Non-Private, Azure OpenAI-powered test setup + +If you want to test PrivateGPT with Azure OpenAI's LLM and Embeddings -taking into account your data is going to Azure OpenAI!- you can run the following command: + +You need to have access to Azure OpenAI inference endpoints for the LLM and / or the embeddings, and have Azure OpenAI credentials properly configured. + +Edit the `settings-azopenai.yaml` file to include the correct Azure OpenAI endpoints. + +Then, install PrivateGPT with the following command: +```bash +poetry install --extras "ui llms-azopenai embeddings-azopenai vector-stores-qdrant" +``` + +Once installed, you can run PrivateGPT. + +```bash +PGPT_PROFILES=azopenai make run +``` + +PrivateGPT will use the already existing `settings-azopenai.yaml` settings file, which is already configured to use Azure OpenAI LLM and Embeddings endpoints, and Qdrant. + +The UI will be available at http://localhost:8001 + ### Local, Llama-CPP powered setup If you want to run PrivateGPT fully locally without relying on Ollama, you can run the following command: diff --git a/fern/docs/pages/manual/llms.mdx b/fern/docs/pages/manual/llms.mdx index 3013a0e62..8d9077eaa 100644 --- a/fern/docs/pages/manual/llms.mdx +++ b/fern/docs/pages/manual/llms.mdx @@ -98,6 +98,43 @@ to run an OpenAI compatible server. Then, you can run PrivateGPT using the `sett `PGPT_PROFILES=vllm make run` +### Using Azure OpenAI + +If you cannot run a local model (because you don't have a GPU, for example) or for testing purposes, you may +decide to run PrivateGPT using Azure OpenAI as the LLM and Embeddings model. + +In order to do so, create a profile `settings-azopenai.yaml` with the following contents: + +```yaml +llm: + mode: azopenai + +embedding: + mode: azopenai + +azopenai: + api_key: # You could skip this configuration and use the AZ_OPENAI_API_KEY env var instead + azure_endpoint: # You could skip this configuration and use the AZ_OPENAI_ENDPOINT env var instead + api_version: # The API version to use. Default is "2023_05_15" + embedding_deployment_name: # You could skip this configuration and use the AZ_OPENAI_EMBEDDING_DEPLOYMENT_NAME env var instead + embedding_model: # Optional model to use. Default is "text-embedding-ada-002" + llm_deployment_name: # You could skip this configuration and use the AZ_OPENAI_LLM_DEPLOYMENT_NAME env var instead + llm_model: # Optional model to use. Default is "gpt-35-turbo" +``` + +And run PrivateGPT loading that profile you just created: + +`PGPT_PROFILES=azopenai make run` + +or + +`PGPT_PROFILES=azopenai poetry run python -m private_gpt` + +When the server is started it will print a log *Application startup complete*. +Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API. +You'll notice the speed and quality of response is higher, given you are using Azure OpenAI's servers for the heavy +computations. + ### Using AWS Sagemaker For a fully private & performant setup, you can choose to have both your LLM and Embeddings model deployed using Sagemaker. diff --git a/poetry.lock b/poetry.lock index 6da365eb1..38c2374c0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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Concurrent data access will be coordinated by a file lock mechanism." +optional = true +python-versions = ">=3.7" +files = [ + {file = "msal-extensions-1.1.0.tar.gz", hash = "sha256:6ab357867062db7b253d0bd2df6d411c7891a0ee7308d54d1e4317c1d1c54252"}, + {file = "msal_extensions-1.1.0-py3-none-any.whl", hash = "sha256:01be9711b4c0b1a151450068eeb2c4f0997df3bba085ac299de3a66f585e382f"}, +] + +[package.dependencies] +msal = ">=0.4.1,<2.0.0" +packaging = "*" +portalocker = [ + {version = ">=1.0,<3", markers = "platform_system != \"Windows\""}, + {version = ">=1.6,<3", markers = "platform_system == \"Windows\""}, +] + [[package]] name = "multidict" version = "6.0.4" @@ -2948,6 +3173,7 @@ optional = true python-versions = ">=3" files = [ {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl", hash = "sha256:64335a8088e2b9d196ae8665430bc6a2b7e6ef2eb877a9c735c804bd4ff6467c"}, + {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux2014_aarch64.whl", hash = "sha256:211a63e7b30a9d62f1a853e19928fbb1a750e3f17a13a3d1f98ff0ced19478dd"}, {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-win_amd64.whl", hash = "sha256:1b2e317e437433753530792f13eece58f0aec21a2b05903be7bffe58a606cbd1"}, ] @@ -3708,6 +3934,17 @@ files = [ [package.dependencies] pyasn1 = ">=0.4.6,<0.6.0" +[[package]] +name = "pycparser" +version = "2.21" +description = "C parser in Python" +optional = true +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +files = [ + {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"}, + {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"}, +] + [[package]] name = "pydantic" version = "2.5.2" @@ -3902,6 +4139,26 @@ files = [ plugins = ["importlib-metadata"] windows-terminal = ["colorama (>=0.4.6)"] +[[package]] +name = "pyjwt" +version = "2.8.0" +description = "JSON Web Token implementation in Python" +optional = true +python-versions = ">=3.7" +files = [ + {file = "PyJWT-2.8.0-py3-none-any.whl", hash = "sha256:59127c392cc44c2da5bb3192169a91f429924e17aff6534d70fdc02ab3e04320"}, + {file = "PyJWT-2.8.0.tar.gz", hash = "sha256:57e28d156e3d5c10088e0c68abb90bfac3df82b40a71bd0daa20c65ccd5c23de"}, +] + +[package.dependencies] +cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""} + +[package.extras] +crypto = ["cryptography (>=3.4.0)"] +dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] +docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] +tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] + [[package]] name = "pymupdf" version = "1.23.25" @@ -5938,10 +6195,12 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] +embeddings-azopenai = ["llama-index-embeddings-azure-openai"] embeddings-huggingface = ["llama-index-embeddings-huggingface"] embeddings-ollama = ["llama-index-embeddings-ollama"] embeddings-openai = ["llama-index-embeddings-openai"] embeddings-sagemaker = ["boto3"] +llms-azopenai = ["llama-index-llms-azure-openai"] llms-llama-cpp = ["llama-index-llms-llama-cpp"] llms-ollama = ["llama-index-llms-ollama"] llms-openai = ["llama-index-llms-openai"] @@ -5956,4 +6215,4 @@ vector-stores-qdrant = ["llama-index-vector-stores-qdrant"] [metadata] lock-version = "2.0" python-versions = ">=3.11,<3.12" -content-hash = "689df29f4f2209e7ae6638563f4bb25700d1454098d0c728a164a708d42fa377" +content-hash = "3d5f21e5e41ea66d655891a6d9b01bcdd8348b275e27a54e90b65ac9d5719981" diff --git a/private_gpt/components/embedding/embedding_component.py b/private_gpt/components/embedding/embedding_component.py index f384262db..2967c38b9 100644 --- a/private_gpt/components/embedding/embedding_component.py +++ b/private_gpt/components/embedding/embedding_component.py @@ -72,6 +72,24 @@ def __init__(self, settings: Settings) -> None: model_name=ollama_settings.embedding_model, base_url=ollama_settings.api_base, ) + case "azopenai": + try: + from llama_index.embeddings.azure_openai import ( # type: ignore + AzureOpenAIEmbedding, + ) + except ImportError as e: + raise ImportError( + "Azure OpenAI dependencies not found, install with `poetry install --extras embeddings-azopenai`" + ) from e + + azopenai_settings = settings.azopenai + self.embedding_model = AzureOpenAIEmbedding( + model=azopenai_settings.embedding_model, + deployment_name=azopenai_settings.embedding_deployment_name, + api_key=azopenai_settings.api_key, + azure_endpoint=azopenai_settings.azure_endpoint, + api_version=azopenai_settings.api_version, + ) case "mock": # Not a random number, is the dimensionality used by # the default embedding model diff --git a/private_gpt/components/llm/llm_component.py b/private_gpt/components/llm/llm_component.py index d4e13a585..953209a86 100644 --- a/private_gpt/components/llm/llm_component.py +++ b/private_gpt/components/llm/llm_component.py @@ -132,5 +132,23 @@ def __init__(self, settings: Settings) -> None: context_window=settings.llm.context_window, additional_kwargs=settings_kwargs, ) + case "azopenai": + try: + from llama_index.llms.azure_openai import ( # type: ignore + AzureOpenAI, + ) + except ImportError as e: + raise ImportError( + "Azure OpenAI dependencies not found, install with `poetry install --extras llms-azopenai`" + ) from e + + azopenai_settings = settings.azopenai + self.llm = AzureOpenAI( + model=azopenai_settings.llm_model, + deployment_name=azopenai_settings.llm_deployment_name, + api_key=azopenai_settings.api_key, + azure_endpoint=azopenai_settings.azure_endpoint, + api_version=azopenai_settings.api_version, + ) case "mock": self.llm = MockLLM() diff --git a/private_gpt/settings/settings.py b/private_gpt/settings/settings.py index 868a3cde4..4c274384a 100644 --- a/private_gpt/settings/settings.py +++ b/private_gpt/settings/settings.py @@ -81,7 +81,9 @@ class DataSettings(BaseModel): class LLMSettings(BaseModel): - mode: Literal["llamacpp", "openai", "openailike", "sagemaker", "mock", "ollama"] + mode: Literal[ + "llamacpp", "openai", "openailike", "azopenai", "sagemaker", "mock", "ollama" + ] max_new_tokens: int = Field( 256, description="The maximum number of token that the LLM is authorized to generate in one completion.", @@ -152,7 +154,7 @@ class HuggingFaceSettings(BaseModel): class EmbeddingSettings(BaseModel): - mode: Literal["huggingface", "openai", "sagemaker", "ollama", "mock"] + mode: Literal["huggingface", "openai", "azopenai", "sagemaker", "ollama", "mock"] ingest_mode: Literal["simple", "batch", "parallel"] = Field( "simple", description=( @@ -239,6 +241,25 @@ class OllamaSettings(BaseModel): ) +class AzureOpenAISettings(BaseModel): + api_key: str + azure_endpoint: str + api_version: str = Field( + "2023_05_15", + description="The API version to use for this operation. This follows the YYYY-MM-DD format.", + ) + embedding_deployment_name: str + embedding_model: str = Field( + "text-embedding-ada-002", + description="OpenAI Model to use. Example: 'text-embedding-ada-002'.", + ) + llm_deployment_name: str + llm_model: str = Field( + "gpt-35-turbo", + description="OpenAI Model to use. Example: 'gpt-4'.", + ) + + class UISettings(BaseModel): enabled: bool path: str @@ -349,6 +370,7 @@ class Settings(BaseModel): sagemaker: SagemakerSettings openai: OpenAISettings ollama: OllamaSettings + azopenai: AzureOpenAISettings vectorstore: VectorstoreSettings nodestore: NodeStoreSettings qdrant: QdrantSettings | None = None diff --git a/pyproject.toml b/pyproject.toml index 1391fb214..d56899987 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,9 +21,11 @@ llama-index-llms-llama-cpp = {version = "^0.1.3", optional = true} llama-index-llms-openai = {version = "^0.1.6", optional = true} llama-index-llms-openai-like = {version ="^0.1.3", optional = true} llama-index-llms-ollama = {version ="^0.1.2", optional = true} +llama-index-llms-azure-openai = {version ="^0.1.5", optional = true} llama-index-embeddings-ollama = {version ="^0.1.2", optional = true} llama-index-embeddings-huggingface = {version ="^0.1.4", optional = true} llama-index-embeddings-openai = {version ="^0.1.6", optional = true} +llama-index-embeddings-azure-openai = {version ="^0.1.6", optional = true} llama-index-vector-stores-qdrant = {version ="^0.1.3", optional = true} llama-index-vector-stores-chroma = {version ="^0.1.4", optional = true} llama-index-vector-stores-postgres = {version ="^0.1.2", optional = true} @@ -45,10 +47,12 @@ llms-openai = ["llama-index-llms-openai"] llms-openai-like = ["llama-index-llms-openai-like"] llms-ollama = ["llama-index-llms-ollama"] llms-sagemaker = ["boto3"] +llms-azopenai = ["llama-index-llms-azure-openai"] embeddings-ollama = ["llama-index-embeddings-ollama"] embeddings-huggingface = ["llama-index-embeddings-huggingface"] embeddings-openai = ["llama-index-embeddings-openai"] embeddings-sagemaker = ["boto3"] +embeddings-azopenai = ["llama-index-embeddings-azure-openai"] vector-stores-qdrant = ["llama-index-vector-stores-qdrant"] vector-stores-chroma = ["llama-index-vector-stores-chroma"] vector-stores-postgres = ["llama-index-vector-stores-postgres"] diff --git a/settings-azopenai.yaml b/settings-azopenai.yaml new file mode 100644 index 000000000..7e4b47c1c --- /dev/null +++ b/settings-azopenai.yaml @@ -0,0 +1,17 @@ +server: + env_name: ${APP_ENV:azopenai} + +llm: + mode: azopenai + +embedding: + mode: azopenai + +azopenai: + api_key: ${AZ_OPENAI_API_KEY:} + azure_endpoint: ${AZ_OPENAI_ENDPOINT:} + embedding_deployment_name: ${AZ_OPENAI_EMBEDDING_DEPLOYMENT_NAME:} + llm_deployment_name: ${AZ_OPENAI_LLM_DEPLOYMENT_NAME:} + api_version: "2023-05-15" + embedding_model: text-embedding-ada-002 + llm_model: gpt-35-turbo \ No newline at end of file diff --git a/settings.yaml b/settings.yaml index 862844a19..0b4cb3414 100644 --- a/settings.yaml +++ b/settings.yaml @@ -89,3 +89,12 @@ ollama: llm_model: llama2 embedding_model: nomic-embed-text api_base: http://localhost:11434 + +azopenai: + api_key: ${AZ_OPENAI_API_KEY:} + azure_endpoint: ${AZ_OPENAI_ENDPOINT:} + embedding_deployment_name: ${AZ_OPENAI_EMBEDDING_DEPLOYMENT_NAME:} + llm_deployment_name: ${AZ_OPENAI_LLM_DEPLOYMENT_NAME:} + api_version: "2023-05-15" + embedding_model: text-embedding-ada-002 + llm_model: gpt-35-turbo