feat(llm - embed): Add support for Azure OpenAI (#1698)

* Add support for Azure OpenAI

* fix: wrong default api_version

Should be dashes instead of underscores.
see: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference

* fix: code styling

applied "make check" changes

* refactor: extend documentation

* mention azopenai as available option and extras
* add recommended section
* include settings-azopenai.yaml configuration file

* fix: documentation

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 `<extra>` 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:

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: <your_azopenai_api_key>  # You could skip this configuration and use the AZ_OPENAI_API_KEY env var instead
+  azure_endpoint: <your_azopenai_endpoint> # You could skip this configuration and use the AZ_OPENAI_ENDPOINT env var instead
+  api_version: <api_version> # The API version to use. Default is "2023_05_15"
+  embedding_deployment_name: <your_embedding_deployment_name> # You could skip this configuration and use the AZ_OPENAI_EMBEDDING_DEPLOYMENT_NAME env var instead
+  embedding_model: <openai_embeddings_to_use> # Optional model to use. Default is "text-embedding-ada-002" 
+  llm_deployment_name: <your_model_deployment_name> # You could skip this configuration and use the AZ_OPENAI_LLM_DEPLOYMENT_NAME env var instead
+  llm_model: <openai_model_to_use> # 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.