nlweb-core 0.5.5

NLWeb Core library for building natural language web applications with vector database retrieval and LLM-based ranking

NLWeb Core

A modular Python framework for building natural language web applications with vector database retrieval, LLM-based ranking, and multiple protocol support (HTTP, MCP, A2A).

Features

• Modular Package Architecture: Install only what you need - core, network, data loading, and provider packages
• Vector Database Support: Azure AI Search, Qdrant, Elasticsearch, OpenSearch, PostgreSQL, Milvus, Snowflake, and more
• Multiple LLM Providers: OpenAI, Azure OpenAI, Anthropic, Google Gemini, Hugging Face, and more
• Multiple Embedding Providers: OpenAI, Azure OpenAI, Google Gemini, Ollama, Snowflake
• Flexible Authentication: API key and Azure Managed Identity support for Azure services
• Multiple Protocol Support:
  • HTTP: REST API with JSON and Server-Sent Events (SSE) streaming
  • MCP (Model Context Protocol): JSON-RPC 2.0 for AI model integration
  • A2A (Agent-to-Agent): JSON-RPC 2.0 for multi-agent communication
• NLWeb Protocol v0.5: Standardized response format with metadata and content

Installation

From PyPI

# Core packages
pip install nlweb-dataload  # Standalone data loading tools
pip install nlweb-core      # Core framework
pip install nlweb-network   # Network interfaces (HTTP/MCP/A2A)

# Provider packages (optional)
pip install nlweb-azure-vectordb  # Azure AI Search
pip install nlweb-azure-models    # Azure OpenAI

# Or install bundles
pip install nlweb-retrieval  # All vector database providers
pip install nlweb-models     # All LLM/embedding providers

From Source

git clone https://github.com/nlweb-ai/NLWeb_Core.git
cd NLWeb_Core
pip install -e packages/dataload
pip install -e packages/core
pip install -e packages/network

Quick Start

1. Create a configuration file

Create config.yaml:

# Vector database
retrieval:
  nlweb_azure:
    enabled: true
    api_key_env: AZURE_VECTOR_SEARCH_API_KEY
    api_endpoint_env: AZURE_VECTOR_SEARCH_ENDPOINT
    index_name: your-index-name
    db_type: azure_ai_search

# LLM provider
llm:
  azure_openai:
    llm_type: azure_openai
    api_key_env: AZURE_OPENAI_API_KEY
    endpoint_env: AZURE_OPENAI_ENDPOINT
    api_version: "2024-10-21"
    models:
      high: gpt-4o
      low: gpt-4o-mini

# Embedding provider
embedding:
  azure_openai:
    embedding_type: azure_openai
    api_key_env: AZURE_OPENAI_API_KEY
    endpoint_env: AZURE_OPENAI_ENDPOINT
    api_version: "2024-10-21"
    deployment_name: text-embedding-3-large

# Server
server:
  host: localhost
  port: 8080
  enable_cors: true

2. Set environment variables

export AZURE_VECTOR_SEARCH_API_KEY=your_key
export AZURE_VECTOR_SEARCH_ENDPOINT=https://your-search.search.windows.net
export AZURE_OPENAI_API_KEY=your_key
export AZURE_OPENAI_ENDPOINT=https://your-openai.openai.azure.com

3. Start the server

# Using the network package
python -m nlweb_network.server.main config.yaml

# Or programmatically
from nlweb_core import init
from nlweb_network.server import main

init('config.yaml')
main()

4. Query via HTTP

# Health check
curl http://localhost:8080/health

# Non-streaming query
curl "http://localhost:8080/ask?query=pasta+recipes&streaming=false&num_results=5"

# Streaming query (SSE)
curl "http://localhost:8080/ask?query=pasta+recipes&streaming=true"

# With site filter
curl "http://localhost:8080/ask?query=spicy+snacks&site=seriouseats"

# POST request
curl -X POST http://localhost:8080/ask \
  -H 'Content-Type: application/json' \
  -d '{"query": "best pizza recipes", "num_results": 10}'

5. Query via MCP (Model Context Protocol)

# List available tools
curl -X POST http://localhost:8080/mcp \
  -H 'Content-Type: application/json' \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/list",
    "id": 1
  }'

# Call a tool
curl -X POST http://localhost:8080/mcp \
  -H 'Content-Type: application/json' \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "ask",
      "arguments": {
        "query": "healthy breakfast recipes",
        "num_results": 5
      }
    },
    "id": 2
  }'

6. Query via A2A (Agent-to-Agent Protocol)

# Get agent card
curl -X POST http://localhost:8080/a2a \
  -H 'Content-Type: application/json' \
  -d '{
    "jsonrpc": "2.0",
    "method": "agent/card",
    "id": 1
  }'

# Send message to agent
curl -X POST http://localhost:8080/a2a \
  -H 'Content-Type: application/json' \
  -d '{
    "jsonrpc": "2.0",
    "method": "message/send",
    "params": {
      "message": {
        "role": "user",
        "parts": [{"kind": "text", "text": "vegan desserts"}]
      }
    },
    "id": 2
  }'

Data Loading

Use nlweb-dataload to index content into your vector database:

# Load from JSON file
python -m nlweb_dataload.load_from_json \
  --json-file recipes.json \
  --config config.yaml

# Load from sitemap
python -m nlweb_dataload.load_from_sitemap \
  --sitemap-url https://example.com/sitemap.xml \
  --config config.yaml

See the dataload README for more details.

Authentication Options

Azure AI Search with API Key

nlweb_azure:
  enabled: true
  api_key_env: AZURE_VECTOR_SEARCH_API_KEY
  api_endpoint_env: AZURE_VECTOR_SEARCH_ENDPOINT
  index_name: embeddings
  db_type: azure_ai_search

Azure AI Search with Managed Identity

nlweb_azure:
  enabled: true
  api_endpoint_env: AZURE_VECTOR_SEARCH_ENDPOINT
  index_name: embeddings
  db_type: azure_ai_search
  auth_method: azure_ad  # Uses DefaultAzureCredential

Azure OpenAI with Managed Identity

azure_openai:
  llm_type: azure_openai
  endpoint_env: AZURE_OPENAI_ENDPOINT
  api_version: "2024-10-21"
  auth_method: azure_ad
  models:
    high: gpt-4o
    low: gpt-4o-mini

Package Structure

• nlweb-dataload: Standalone data loading tools (no dependencies on other NLWeb packages)
• nlweb-core: Core framework and abstractions
• nlweb-network: HTTP/MCP/A2A server and interfaces
• nlweb-azure-vectordb: Azure AI Search provider
• nlweb-azure-models: Azure OpenAI LLM and embedding providers
• nlweb-retrieval: Bundle of all retrieval providers
• nlweb-models: Bundle of all LLM and embedding providers

API Endpoints

HTTP Endpoints

• GET/POST /ask - Query endpoint
  • Parameters: query (required), site, num_results, streaming, db
• GET /health - Health check

MCP Endpoints (JSON-RPC 2.0)

• tools/list - List available tools
• tools/call - Call a tool with arguments

A2A Endpoints (JSON-RPC 2.0)

• agent/card - Get agent capabilities
• message/send - Send message to agent

Supported Vector Databases

• Azure AI Search
• Qdrant (local or cloud)
• Elasticsearch
• OpenSearch
• PostgreSQL with pgvector
• Milvus
• Snowflake Cortex Search
• Cloudflare AutoRAG
• Bing Search API

Supported LLM Providers

• OpenAI
• Azure OpenAI
• Anthropic (Claude)
• Google Gemini
• Hugging Face
• Azure Llama
• Azure DeepSeek
• Snowflake Cortex
• Inception

Testing

We provide comprehensive test scripts to verify end-to-end functionality:

# Test all protocols (HTTP, MCP, A2A) from PyPI packages
./scripts/test_packages.sh

# Test from TestPyPI
./scripts/test_from_testpypi.sh

# Test from production PyPI
./scripts/test_from_pypi.sh

See scripts/ for all available test scripts.

Examples

Complete examples are available in the examples/ directory:

• azure_hello_world: Minimal Azure setup with API key auth
• azure_managed_identity: Using Azure Managed Identity
• multi_provider: Multiple LLM and vector database providers

Development

Running Tests

pip install -e "packages/core[dev]"
pytest

Code Formatting

black packages/
flake8 packages/

Publishing to PyPI

See scripts/setup_pypi.md for detailed instructions.

# Upload to TestPyPI
./scripts/upload_to_pypi.sh test

# Upload to production PyPI
./scripts/upload_to_pypi.sh prod

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on GitHub.