unique-search-proxy 0.2.0

Web Search Proxy implementation

Unique Search Proxy

A unified web search proxy API that provides a consistent interface for multiple search backends. Built with FastAPI and designed for seamless integration with AI applications.

Overview

This service acts as an abstraction layer over different search providers, allowing clients to switch between search engines without changing their integration code. Currently supports:

Engine	Description
Google Custom Search	Direct integration with Google's Custom Search JSON API
Vertex AI (Gemini)	AI-powered search using Google's Gemini models with grounding capabilities

Quick Start

Prerequisites

• Python 3.12+
• uv for dependency management
• Google Cloud credentials (for Vertex AI)
• Google Custom Search API key and Engine ID (for Google Search)

Installation

# Install dependencies
uv sync

# Copy and configure environment variables
cp .env.example .env

Environment Variables

# Google Custom Search
GOOGLE_SEARCH_API_KEY=your-api-key
GOOGLE_SEARCH_API_ENDPOINT=https://www.googleapis.com/customsearch/v1
GOOGLE_SEARCH_ENGINE_ID=your-engine-id

# Vertex AI
VERTEXAI_SERVICE_ACCOUNT_CREDENTIALS=path/to/credentials.json

Running the Service

Development:

uv run python -m unique_search_proxy.web.app

Docker (from published package — hash-verified):

CI generates a hash-pinned requirements.txt from uv.lock and passes it into the Docker build. Dependencies are installed with --require-hashes, then the package itself is installed with --no-deps. To reproduce locally:

uv export --locked --package unique-search-proxy --no-dev --no-emit-project \
  -o deploy/requirements.txt
docker build --build-arg PACKAGE_VERSION=0.2.0 -t search-proxy deploy/

Every transitive dependency is verified against its sha256 hash from the lockfile.

Docker (from local source — no registry required):

Build a wheel first, copy it into deploy/, then reference it:

uv build --wheel --out-dir deploy/
docker build \
  --build-arg LOCAL_WHEEL=unique_search_proxy-0.2.0-py3-none-any.whl \
  -t search-proxy deploy/

Running the container:

docker run --rm -p 8080:8080 search-proxy

# With custom environment variables
docker run --rm -p 8080:8080 -e WORKERS=8 -e LOG_LEVEL=debug search-proxy

API Documentation

FastAPI provides automatic interactive API documentation:

URL	Description
/docs	Swagger UI - interactive API explorer
/redoc	ReDoc - alternative documentation
/openapi.json	OpenAPI schema

API Reference

Health Check

GET /health

Response:

{
  "status": "healthy"
}

---

Search

POST /search
Content-Type: application/json

Request Body:

Field	Type	Required	Description
search_engine	string	No	"google" or "vertexai" (default: "google")
query	string	Yes	The search query
kwargs	object	No	Engine-specific parameters

Response:

{
  "results": [
    {
      "url": "https://example.com/article",
      "title": "Article Title",
      "snippet": "A brief description of the content...",
      "content": ""
    }
  ]
}

---

Search Engine Configuration

Google Custom Search

Uses Google's Custom Search JSON API for traditional web search results.

Parameters (kwargs):

Parameter	Type	Default	Description
cx	string	env default	Custom Search Engine ID (overrides env)
fetchSize	int	10	Number of results to fetch
timeout	int	10	Request timeout in seconds

Example:

{
  "search_engine": "google",
  "query": "latest AI developments",
  "kwargs": {
    "fetchSize": 20,
    "timeout": 15
  }
}

---

Vertex AI (Gemini)

Leverages Google's Gemini models with web grounding for AI-enhanced search results. This engine:

1. Uses Gemini to search and synthesize information from the web
2. Generates structured results with citations
3. Optionally resolves shortened/redirect URLs to final destinations

Parameters (kwargs):

Parameter	Type	Default	Description
modelName	string	"gemini-2.5-flash"	Gemini model to use
entrepriseSearch	bool	false	Use Enterprise Web Search
systemInstruction	string	(built-in)	Custom system prompt
resolveUrls	bool	true	Resolve redirect URLs

Example:

{
  "search_engine": "vertexai",
  "query": "Compare the top 3 cloud providers for ML workloads",
  "kwargs": {
    "modelName": "gemini-2.5-flash",
    "resolveUrls": true
  }
}

---

Project Structure

connectors/unique_search_proxy/
├── unique_search_proxy/          # Python package (published to PyPI)
│   ├── __init__.py
│   └── web/                      # Web search API sub-module
│       ├── __init__.py
│       ├── app.py                # FastAPI application
│       ├── settings.py           # Global settings
│       └── core/                 # Search engine implementations
│           ├── schema.py         # Shared schemas
│           ├── google_search/    # Google Custom Search backend
│           └── vertexai/         # Vertex AI (Gemini) backend
├── tests/                        # Test suite
├── deploy/                       # Container build artifacts
│   ├── Dockerfile                # Hash-verified install or local wheel
│   └── entrypoint.sh
└── pyproject.toml

The package uses a sub-module hierarchy (web/) to support future extensions (e.g. internal/ search) that can be deployed as separate containers from the same package.

Architecture

┌─────────────────────────────────────────────────────────────┐
│                       FastAPI App                           │
│                      /search endpoint                       │
└─────────────────────────┬───────────────────────────────────┘
                          │
                    ┌─────▼─────┐
                    │  Factory  │
                    └─────┬─────┘
                          │
          ┌───────────────┼───────────────┐
          │                               │
    ┌─────▼─────┐                   ┌─────▼─────┐
    │  Google   │                   │ Vertex AI │
    │  Search   │                   │  (Gemini) │
    └───────────┘                   └───────────┘

The service uses a factory pattern to register and resolve search engines, making it easy to add new backends.

Error Handling

All errors return a consistent format:

{
  "status": "failed",
  "error": "Error description"
}

Status Code	Description
400	Validation error (invalid request)
500	Internal server error

Production Deployment

The service includes a production-ready deploy/entrypoint.sh that uses Uvicorn:

Variable	Default	Description
HOST	0.0.0.0	Bind address
PORT	8080	Listen port
WORKERS	4	Uvicorn workers
TIMEOUT	120	Keep-alive timeout
LOG_LEVEL	info	Logging verbosity

Development

# Run with hot reload
uv run uvicorn unique_search_proxy.web.app:app --reload --port 2349

# Format code
uv run ruff format .

# Lint
uv run ruff check .

# Run tests
uv run pytest

# Type check
uv run basedpyright

License

Proprietary - Unique AG