nlweb-azure-models 0.7.0
Azure models provider for NLWeb - Azure OpenAI embedding and LLM interfaces
License: MIT
Requires Python: >=3.9

# nlweb-azure-models

Azure OpenAI LLM and embedding providers for NLWeb.

## Overview

This is a **blueprint package** demonstrating how to create individual model provider packages for NLWeb. It contains Azure OpenAI implementations for both LLM and embeddings.

Third-party developers can use this as a template for creating their own model provider packages.

## Installation

```bash
pip install nlweb-core nlweb-azure-models
```

For vector search, you'll also need a retrieval provider:
```bash
pip install nlweb-azure-vectordb
```

Or use the bundle packages:
```bash
pip install nlweb-core nlweb-retrieval nlweb-models
```

## Configuration

Create `config.yaml`:

```yaml
llm:
  provider: azure_openai
  import_path: nlweb_azure_models.llm.azure_oai
  class_name: provider
  endpoint_env: AZURE_OPENAI_ENDPOINT
  api_key_env: AZURE_OPENAI_KEY
  api_version: 2024-02-01
  auth_method: azure_ad  # or api_key
  models:
    high: gpt-4
    low: gpt-35-turbo

embedding:
  default:
    import_path: nlweb_azure_models.embedding.azure_oai_embedding
    class_name: AzureOpenAIEmbeddingProvider
    endpoint_env: AZURE_OPENAI_ENDPOINT
    auth_method: azure_ad
    model: text-embedding-ada-002

scoring_model:
  default:
    model: gpt-4.1-mini
    endpoint_env: AZURE_OPENAI_ENDPOINT
    api_key_env: AZURE_OPENAI_KEY
    api_version: "2024-02-01"
    auth_method: api_key
    import_path: nlweb_azure_models.llm.azure_oai
    class_name: AzureOpenAIScoringProvider
```

### Authentication Methods

#### API Key Authentication
```yaml
generative_model:
  high:
    import_path: nlweb_azure_models.llm.azure_oai
    class_name: AzureOpenAIProvider
    endpoint_env: AZURE_OPENAI_ENDPOINT
    api_key_env: AZURE_OPENAI_KEY
    api_version: 2024-02-01
    auth_method: api_key
    model: gpt-4
```

Set environment variables:
```bash
export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
export AZURE_OPENAI_KEY=your_key_here
```

#### Managed Identity (Azure AD) Authentication
```yaml
llm:
  provider: azure_openai
  import_path: nlweb_azure_models.llm.azure_oai
  class_name: provider
  endpoint_env: AZURE_OPENAI_ENDPOINT
  api_version: 2024-02-01
  auth_method: azure_ad
  models:
    high: gpt-4
    low: gpt-35-turbo
```

Set environment variable:
```bash
export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
```

## Usage

```python
from nlweb_core.config import get_config

# Get embedding provider by name
embedding_provider = get_config().get_embedding_provider("default")
vector = await embedding_provider.get_embedding("Text to embed")

# Batch embeddings
vectors = await embedding_provider.get_batch_embeddings(["Text 1", "Text 2"])
```

## Features

### LLM Provider (Generative)
- GPT-4, GPT-3.5-turbo, and other Azure OpenAI models
- Structured output with JSON schema
- Managed identity (Azure AD) authentication
- API key authentication
- Configurable API versions

### Scoring Provider (Ranking/Relevance)
- LLM-based scoring for search result ranking
- Scores items on relevance to user queries (0-100 scale)
- Supports item ranking, intent detection, and presence checking
- Same authentication methods as generative LLMs
- Optimized prompts for consistent scoring
- Batch processing support for efficient ranking

### Embedding Provider
- text-embedding-ada-002 and newer models
- Managed identity (Azure AD) authentication
- API key authentication
- Batch processing support

## Scoring Provider Configuration

The Azure OpenAI scoring provider uses LLMs to score search results for relevance. This is an alternative to specialized scoring models like Pi Labs.

### Scoring Configuration Options

**Option 1: Azure OpenAI (LLM-based scoring)**
```yaml
scoring-llm-model:
  llm_type: azure_openai
  model: gpt-4.1-mini  # Use mini models for cost efficiency
  endpoint_env: AZURE_OPENAI_ENDPOINT
  api_key_env: AZURE_OPENAI_KEY
  api_version: "2024-02-01"
  auth_method: api_key  # or azure_ad
  import_path: nlweb_azure_models.llm.azure_oai
  class_name: AzureOpenAIScoringProvider

ranking_config:
  scoring_questions:
    - "Is this item relevant to the query?"
```

**Option 2: Pi Labs (Specialized scoring model)**
```yaml
scoring-llm-model:
  llm_type: pilabs
  import_path: nlweb_pilabs_models.llm.pi_labs
  class_name: PiLabsScoringProvider
  endpoint_env: PI_LABS_ENDPOINT
  api_key_env: PI_LABS_KEY

ranking_config:
  scoring_questions:
    - "Is this item relevant to the query?"
```

### Scoring Use Cases

1. **Item Ranking**: Score search results based on relevance to user queries
   - Input: User query + item description
   - Output: Relevance score (0-100) + description
   - Uses NLWeb ranking prompt template

2. **Intent Detection**: Determine if a query matches a specific intent
   - Input: User query + intent to check
   - Output: Match score (0-100)

3. **Presence Checking**: Check if required information is present in a query
   - Input: User query + required information
   - Output: Presence score (0-100)

### Prompt Template Approach

Azure OpenAI scoring uses **direct prompt templates** (not question-based scoring):
- Item ranking uses the NLWeb ranking prompt template
- Focuses on relevance judgment and explanation generation
- The `scoring_questions` config field is ignored (used only by PI Labs)


## Complete Azure Stack Example

Use all three Azure packages together:

```bash
pip install nlweb-core nlweb-azure-vectordb nlweb-azure-models
```

```yaml
llm:
  provider: azure_openai
  import_path: nlweb_azure_models.llm.azure_oai
  class_name: provider
  endpoint_env: AZURE_OPENAI_ENDPOINT
  auth_method: azure_ad
  models:
    high: gpt-4
    low: gpt-35-turbo

embedding:
  default:
    import_path: nlweb_azure_models.embedding.azure_oai_embedding
    class_name: AzureOpenAIEmbeddingProvider
    endpoint_env: AZURE_OPENAI_ENDPOINT
    auth_method: azure_ad
    model: text-embedding-ada-002

retrieval:
  default:
    import_path: nlweb_azure_vectordb.azure_search_client
    class_name: AzureSearchClient
    api_endpoint_env: AZURE_SEARCH_ENDPOINT
    auth_method: azure_ad
    index_name: my-index

scoring_model:
  default:
    model: gpt-4.1-mini
    endpoint_env: AZURE_OPENAI_ENDPOINT
    api_key_env: AZURE_OPENAI_KEY
    api_version: "2024-02-01"
    auth_method: azure_ad
    import_path: nlweb_azure_models.llm.azure_oai
    class_name: AzureOpenAIScoringProvider

ranking_config:
  scoring_questions:
    - "Is this item relevant to the query?"
```

## Creating Your Own Model Provider Package

Use this package as a template:

1. **Create package structure**:
   ```
   nlweb-yourprovider/
   ├── pyproject.toml
   ├── README.md
   └── nlweb_yourprovider/
       ├── __init__.py
       ├── llm/
       │   └── your_llm.py
       └── embedding/
           └── your_embedding.py
   ```

2. **Implement provider interfaces** (inherit from ABCs in nlweb_core):
   ```python
   from nlweb_core.embedding import EmbeddingProvider

   class YourEmbeddingProvider(EmbeddingProvider):
       def __init__(self, api_key: str, model: str, **kwargs):
           self.api_key = api_key
           self.model = model

       async def get_embedding(self, text, timeout=30.0):
           # Your implementation
           return [0.1, 0.2, ...]

       async def close(self):
           pass
   ```

3. **Declare dependencies** in `pyproject.toml`
4. **Publish to PyPI**

## License

MIT License - Copyright (c) 2025 Microsoft Corporation