mirror of
https://github.com/BerriAI/litellm.git
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[Feat] Add cost tracking for Search API requests - Google PSE, Tavily, Parallel AI, Exa AI (#15821)
* add search cost tracking * add cost tracking for tavily tiers * add search to call types * add search_provider_cost_per_query * add cost tracking for search APIs * add cost tracking search APIs * docs cost tracking search * docs search * fix linting
This commit is contained in:
Ishaan Jaff
@@ -1,753 +0,0 @@
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# /search
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| Feature | Supported |
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|---------|-----------|
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| Supported Providers | `perplexity`, `tavily`, `parallel_ai`, `exa_ai`, `google_pse`, `dataforseo` |
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| Cost Tracking | ❌ |
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| Logging | ✅ |
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| Load Balancing | ❌ |
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:::tip
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LiteLLM follows the [Perplexity API request/response for the Search API](https://docs.perplexity.ai/api-reference/search-post)
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:::
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:::info
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Supported from LiteLLM v1.78.7+
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:::
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## **LiteLLM Python SDK Usage**
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### Quick Start
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```python showLineNumbers title="Basic Search"
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from litellm import search
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import os
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os.environ["PERPLEXITYAI_API_KEY"] = "pplx-..."
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response = search(
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query="latest AI developments in 2024",
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search_provider="perplexity",
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max_results=5
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)
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# Access search results
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for result in response.results:
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print(f"{result.title}: {result.url}")
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print(f"Snippet: {result.snippet}\n")
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```
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### Async Usage
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```python showLineNumbers title="Async Search"
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from litellm import asearch
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import os, asyncio
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os.environ["PERPLEXITYAI_API_KEY"] = "pplx-..."
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async def search_async():
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response = await asearch(
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query="machine learning research papers",
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search_provider="perplexity",
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max_results=10,
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search_domain_filter=["arxiv.org", "nature.com"]
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)
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# Access search results
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for result in response.results:
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print(f"{result.title}: {result.url}")
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print(f"Snippet: {result.snippet}")
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asyncio.run(search_async())
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```
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### Optional Parameters
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```python showLineNumbers title="Search with Options"
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response = search(
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query="AI developments",
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search_provider="perplexity",
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# Unified parameters (work across all providers)
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max_results=10, # Maximum number of results (1-20)
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search_domain_filter=["arxiv.org"], # Filter to specific domains
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country="US", # Country code filter
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max_tokens_per_page=1024 # Max tokens per page
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)
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```
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## **LiteLLM AI Gateway Usage**
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LiteLLM provides a Perplexity API compatible `/search` endpoint for search calls.
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**Setup**
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Add this to your litellm proxy config.yaml
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```yaml showLineNumbers title="config.yaml"
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model_list:
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- model_name: gpt-4
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litellm_params:
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model: gpt-4
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api_key: os.environ/OPENAI_API_KEY
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search_tools:
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- search_tool_name: perplexity-search
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litellm_params:
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search_provider: perplexity
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api_key: os.environ/PERPLEXITYAI_API_KEY
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- search_tool_name: tavily-search
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litellm_params:
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search_provider: tavily
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api_key: os.environ/TAVILY_API_KEY
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```
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Start litellm
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```bash
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litellm --config /path/to/config.yaml
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# RUNNING on http://0.0.0.0:4000
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```
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### Test Request
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**Option 1: Search tool name in URL (Recommended - keeps body Perplexity-compatible)**
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```bash showLineNumbers title="cURL Request"
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curl http://0.0.0.0:4000/v1/search/perplexity-search \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "latest AI developments 2024",
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"max_results": 5,
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"search_domain_filter": ["arxiv.org", "nature.com"],
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"country": "US"
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}'
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```
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**Option 2: Search tool name in body**
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```bash showLineNumbers title="cURL Request with search_tool_name in body"
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curl http://0.0.0.0:4000/v1/search \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"search_tool_name": "perplexity-search",
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"query": "latest AI developments 2024",
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"max_results": 5
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}'
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```
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### Load Balancing
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Configure multiple search providers for automatic load balancing and fallbacks:
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```yaml showLineNumbers title="config.yaml with load balancing"
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search_tools:
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- search_tool_name: my-search
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litellm_params:
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search_provider: perplexity
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api_key: os.environ/PERPLEXITYAI_API_KEY
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- search_tool_name: my-search
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litellm_params:
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search_provider: tavily
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api_key: os.environ/TAVILY_API_KEY
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- search_tool_name: my-search
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litellm_params:
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search_provider: exa_ai
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api_key: os.environ/EXA_API_KEY
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router_settings:
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routing_strategy: simple-shuffle # or 'least-busy', 'latency-based-routing'
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```
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Test with load balancing:
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```bash
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curl http://0.0.0.0:4000/v1/search/my-search \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "AI developments",
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"max_results": 10
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}'
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```
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## **Request/Response Format**
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:::info
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LiteLLM follows the **Perplexity Search API specification**.
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See the [official Perplexity Search documentation](https://docs.perplexity.ai/api-reference/search-post) for complete details.
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:::
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### Example Request
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```json showLineNumbers title="Search Request"
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{
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"query": "latest AI developments 2024",
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"max_results": 10,
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"search_domain_filter": ["arxiv.org", "nature.com"],
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"country": "US",
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"max_tokens_per_page": 1024
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}
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```
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### Request Parameters
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| Parameter | Type | Required | Description |
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|-----------|------|----------|-------------|
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| `query` | string or array | Yes | Search query. Can be a single string or array of strings |
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| `search_provider` | string | Yes (SDK) | The search provider to use: `"perplexity"`, `"tavily"`, `"parallel_ai"`, `"exa_ai"`, or `"google_pse"` |
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| `search_tool_name` | string | Yes (Proxy) | Name of the search tool configured in `config.yaml` |
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| `max_results` | integer | No | Maximum number of results to return (1-20). Default: 10 |
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| `search_domain_filter` | array | No | List of domains to filter results (max 20 domains) |
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| `max_tokens_per_page` | integer | No | Maximum tokens per page to process. Default: 1024 |
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| `country` | string | No | Country code filter (e.g., `"US"`, `"GB"`, `"DE"`) |
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**Query Format Examples:**
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```python
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# Single query
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query = "AI developments"
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# Multiple queries
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query = ["AI developments", "machine learning trends"]
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```
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### Response Format
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The response follows Perplexity's search format with the following structure:
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```json showLineNumbers title="Search Response"
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{
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"object": "search",
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"results": [
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{
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"title": "Latest Advances in Artificial Intelligence",
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"url": "https://arxiv.org/paper/example",
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"snippet": "This paper discusses recent developments in AI...",
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"date": "2024-01-15"
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},
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{
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"title": "Machine Learning Breakthroughs",
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"url": "https://nature.com/articles/ml-breakthrough",
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"snippet": "Researchers have achieved new milestones...",
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"date": "2024-01-10"
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}
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]
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}
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```
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#### Response Fields
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| Field | Type | Description |
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|-------|------|-------------|
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| `object` | string | Always `"search"` for search responses |
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| `results` | array | List of search results |
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| `results[].title` | string | Title of the search result |
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| `results[].url` | string | URL of the search result |
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| `results[].snippet` | string | Text snippet from the result |
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| `results[].date` | string | Optional publication or last updated date |
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## **Supported Providers**
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| Provider | Environment Variable | `search_provider` Value |
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|----------|---------------------|------------------------|
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| Perplexity AI | `PERPLEXITYAI_API_KEY` | `perplexity` |
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| Tavily | `TAVILY_API_KEY` | `tavily` |
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| Exa AI | `EXA_API_KEY` | `exa_ai` |
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| Parallel AI | `PARALLEL_AI_API_KEY` | `parallel_ai` |
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| Google PSE | `GOOGLE_PSE_API_KEY`, `GOOGLE_PSE_ENGINE_ID` | `google_pse` |
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| DataForSEO | `DATAFORSEO_LOGIN`, `DATAFORSEO_PASSWORD` | `dataforseo` |
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### Perplexity AI
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**Get API Key:** [https://www.perplexity.ai/settings/api](https://www.perplexity.ai/settings/api)
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#### LiteLLM Python SDK
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```python showLineNumbers title="Perplexity Search"
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import os
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from litellm import search
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os.environ["PERPLEXITYAI_API_KEY"] = "pplx-..."
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response = search(
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query="latest AI developments",
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search_provider="perplexity",
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max_results=5
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)
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```
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#### LiteLLM AI Gateway
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**1. Setup config.yaml**
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```yaml showLineNumbers title="config.yaml"
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model_list:
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- model_name: gpt-4
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litellm_params:
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model: gpt-4
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api_key: os.environ/OPENAI_API_KEY
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search_tools:
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- search_tool_name: perplexity-search
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litellm_params:
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search_provider: perplexity
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api_key: os.environ/PERPLEXITYAI_API_KEY
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```
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**2. Start the proxy**
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```bash
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litellm --config /path/to/config.yaml
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# RUNNING on http://0.0.0.0:4000
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```
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**3. Test the search endpoint**
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```bash showLineNumbers title="Test Request"
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curl http://0.0.0.0:4000/v1/search/perplexity-search \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "latest AI developments",
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"max_results": 5
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}'
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```
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### Tavily
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**Get API Key:** [https://tavily.com](https://tavily.com)
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#### LiteLLM Python SDK
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```python showLineNumbers title="Tavily Search"
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import os
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from litellm import search
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os.environ["TAVILY_API_KEY"] = "tvly-..."
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response = search(
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query="latest AI developments",
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search_provider="tavily",
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max_results=5
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)
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```
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#### LiteLLM AI Gateway
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**1. Setup config.yaml**
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```yaml showLineNumbers title="config.yaml"
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model_list:
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- model_name: gpt-4
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litellm_params:
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model: gpt-4
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api_key: os.environ/OPENAI_API_KEY
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search_tools:
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- search_tool_name: tavily-search
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litellm_params:
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search_provider: tavily
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api_key: os.environ/TAVILY_API_KEY
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```
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**2. Start the proxy**
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```bash
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litellm --config /path/to/config.yaml
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# RUNNING on http://0.0.0.0:4000
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```
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**3. Test the search endpoint**
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```bash showLineNumbers title="Test Request"
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curl http://0.0.0.0:4000/v1/search/tavily-search \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "latest AI developments",
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"max_results": 5
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}'
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```
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### Exa AI
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**Get API Key:** [https://exa.ai](https://exa.ai)
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#### LiteLLM Python SDK
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```python showLineNumbers title="Exa AI Search"
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import os
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from litellm import search
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os.environ["EXA_API_KEY"] = "exa-..."
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response = search(
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query="latest AI developments",
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search_provider="exa_ai",
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max_results=5
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)
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```
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#### LiteLLM AI Gateway
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**1. Setup config.yaml**
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```yaml showLineNumbers title="config.yaml"
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model_list:
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- model_name: gpt-4
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litellm_params:
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model: gpt-4
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api_key: os.environ/OPENAI_API_KEY
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search_tools:
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- search_tool_name: exa-search
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litellm_params:
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search_provider: exa_ai
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api_key: os.environ/EXA_API_KEY
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```
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**2. Start the proxy**
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```bash
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litellm --config /path/to/config.yaml
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# RUNNING on http://0.0.0.0:4000
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```
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**3. Test the search endpoint**
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```bash showLineNumbers title="Test Request"
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curl http://0.0.0.0:4000/v1/search/exa-search \
|
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "latest AI developments",
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"max_results": 5
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}'
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```
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### Parallel AI
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|
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**Get API Key:** [https://www.parallel.ai](https://www.parallel.ai)
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|
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#### LiteLLM Python SDK
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```python showLineNumbers title="Parallel AI Search"
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import os
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from litellm import search
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|
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os.environ["PARALLEL_AI_API_KEY"] = "..."
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|
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response = search(
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query="latest AI developments",
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search_provider="parallel_ai",
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max_results=5
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)
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```
|
||||
|
||||
#### LiteLLM AI Gateway
|
||||
|
||||
**1. Setup config.yaml**
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
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model_list:
|
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- model_name: gpt-4
|
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litellm_params:
|
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model: gpt-4
|
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api_key: os.environ/OPENAI_API_KEY
|
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|
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search_tools:
|
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- search_tool_name: parallel-search
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litellm_params:
|
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search_provider: parallel_ai
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api_key: os.environ/PARALLEL_AI_API_KEY
|
||||
```
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||||
|
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**2. Start the proxy**
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||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
**3. Test the search endpoint**
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/parallel-search \
|
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-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
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||||
"query": "latest AI developments",
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"max_results": 5
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}'
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```
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### Google Programmable Search Engine (PSE)
|
||||
|
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**Get API Key:** [Google Cloud Console](https://console.cloud.google.com/apis/credentials)
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||||
**Create Search Engine:** [Programmable Search Engine](https://programmablesearchengine.google.com/)
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|
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#### Setup
|
||||
|
||||
1. Go to [Google Developers Programmable Search Engine](https://programmablesearchengine.google.com/) and log in or create an account
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2. Click the **Add** button in the control panel
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3. Enter a search engine name and configure properties:
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- Choose which sites to search (entire web or specific sites)
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- Set language and other preferences
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- Verify you're not a robot
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4. Click **Create** button
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5. Once created, you'll see:
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||||
- **Search engine ID (cx)** - Copy this for `GOOGLE_PSE_ENGINE_ID`
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- Instructions to get your API key
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||||
6. Generate API key:
|
||||
- Go to [Google Cloud Console - Credentials](https://console.cloud.google.com/apis/credentials)
|
||||
- Create a new API key or use existing one
|
||||
- Enable **Custom Search API** for your project
|
||||
- Copy the API key for `GOOGLE_PSE_API_KEY`
|
||||
|
||||
#### LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="Google PSE Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["GOOGLE_PSE_API_KEY"] = "AIza..."
|
||||
os.environ["GOOGLE_PSE_ENGINE_ID"] = "your-search-engine-id"
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="google_pse",
|
||||
max_results=10
|
||||
)
|
||||
```
|
||||
|
||||
#### LiteLLM AI Gateway
|
||||
|
||||
**1. Setup config.yaml**
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: google-search
|
||||
litellm_params:
|
||||
search_provider: google_pse
|
||||
api_key: os.environ/GOOGLE_PSE_API_KEY
|
||||
search_engine_id: os.environ/GOOGLE_PSE_ENGINE_ID
|
||||
```
|
||||
|
||||
**2. Start the proxy**
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
**3. Test the search endpoint**
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/google-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 10
|
||||
}'
|
||||
```
|
||||
|
||||
### DataForSEO
|
||||
|
||||
**Get API Access:** [DataForSEO](https://dataforseo.com/)
|
||||
|
||||
#### Setup
|
||||
|
||||
1. Go to [DataForSEO](https://dataforseo.com/) and create an account
|
||||
2. Navigate to your account dashboard
|
||||
3. Generate API credentials:
|
||||
- You'll receive a **login** (username)
|
||||
- You'll receive a **password**
|
||||
4. Set up your environment variables:
|
||||
- `DATAFORSEO_LOGIN` - Your DataForSEO login/username
|
||||
- `DATAFORSEO_PASSWORD` - Your DataForSEO password
|
||||
|
||||
#### LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="DataForSEO Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["DATAFORSEO_LOGIN"] = "your-login"
|
||||
os.environ["DATAFORSEO_PASSWORD"] = "your-password"
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="dataforseo",
|
||||
max_results=10
|
||||
)
|
||||
```
|
||||
|
||||
#### LiteLLM AI Gateway
|
||||
|
||||
**1. Setup config.yaml**
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: dataforseo-search
|
||||
litellm_params:
|
||||
search_provider: dataforseo
|
||||
api_key: "os.environ/DATAFORSEO_LOGIN:os.environ/DATAFORSEO_PASSWORD"
|
||||
```
|
||||
|
||||
**2. Start the proxy**
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
**3. Test the search endpoint**
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/dataforseo-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 10
|
||||
}'
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Provider-specific parameters
|
||||
|
||||
Sending provider-specific parameters is supported for all providers, you just need to pass them in the request body.
|
||||
|
||||
#### Tavily Search
|
||||
|
||||
```python showLineNumbers title="Tavily Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["TAVILY_API_KEY"] = "tvly-..."
|
||||
|
||||
response = search(
|
||||
query="latest tech news",
|
||||
search_provider="tavily",
|
||||
max_results=5,
|
||||
# Tavily-specific parameters
|
||||
topic="news", # 'general', 'news', 'finance'
|
||||
search_depth="advanced", # 'basic', 'advanced'
|
||||
include_answer=True, # Include AI-generated answer
|
||||
include_raw_content=True # Include raw HTML content
|
||||
)
|
||||
```
|
||||
|
||||
#### Exa AI Search
|
||||
|
||||
```python showLineNumbers title="Exa AI Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["EXA_API_KEY"] = "exa-..."
|
||||
|
||||
response = search(
|
||||
query="AI research papers",
|
||||
search_provider="exa_ai",
|
||||
max_results=10,
|
||||
search_domain_filter=["arxiv.org"],
|
||||
# Exa-specific parameters
|
||||
type="neural", # 'neural', 'keyword', or 'auto'
|
||||
contents={"text": True}, # Request text content
|
||||
use_autoprompt=True # Enable Exa's autoprompt
|
||||
)
|
||||
```
|
||||
|
||||
#### Parallel AI Search
|
||||
|
||||
```python showLineNumbers title="Parallel AI Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["PARALLEL_AI_API_KEY"] = "..."
|
||||
|
||||
response = search(
|
||||
query="latest developments in quantum computing",
|
||||
search_provider="parallel_ai",
|
||||
max_results=5,
|
||||
# Parallel AI-specific parameters
|
||||
processor="pro", # 'base' or 'pro'
|
||||
max_chars_per_result=500 # Max characters per result
|
||||
)
|
||||
```
|
||||
|
||||
#### Google PSE Search
|
||||
|
||||
```python showLineNumbers title="Google PSE Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["GOOGLE_PSE_API_KEY"] = "AIza..."
|
||||
os.environ["GOOGLE_PSE_ENGINE_ID"] = "your-search-engine-id"
|
||||
|
||||
response = search(
|
||||
query="latest AI research papers",
|
||||
search_provider="google_pse",
|
||||
max_results=10,
|
||||
search_domain_filter=["arxiv.org"],
|
||||
# Google PSE-specific parameters (use actual Google PSE API parameter names)
|
||||
dateRestrict="m6", # 'm6' = last 6 months, 'd7' = last 7 days
|
||||
lr="lang_en", # Language restriction (e.g., 'lang_en', 'lang_es')
|
||||
safe="active", # Search safety level ('active' or 'off')
|
||||
exactTerms="machine learning", # Phrase that all documents must contain
|
||||
fileType="pdf" # File type to restrict results to
|
||||
)
|
||||
```
|
||||
|
||||
#### DataForSEO Search
|
||||
|
||||
```python showLineNumbers title="DataForSEO Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["DATAFORSEO_LOGIN"] = "your-login"
|
||||
os.environ["DATAFORSEO_PASSWORD"] = "your-password"
|
||||
|
||||
response = search(
|
||||
query="AI developments",
|
||||
search_provider="dataforseo",
|
||||
max_results=10,
|
||||
# DataForSEO-specific parameters
|
||||
country="United States", # Country name for location_name
|
||||
language_code="en", # Language code
|
||||
depth=20, # Number of results (max 700)
|
||||
device="desktop", # Device type ('desktop', 'mobile', 'tablet')
|
||||
os="windows" # Operating system
|
||||
)
|
||||
```
|
||||
91
docs/my-website/docs/search/dataforseo.md
Normal file
91
docs/my-website/docs/search/dataforseo.md
Normal file
@@ -0,0 +1,91 @@
|
||||
# DataForSEO Search
|
||||
|
||||
**Get API Access:** [DataForSEO](https://dataforseo.com/)
|
||||
|
||||
## Setup
|
||||
|
||||
1. Go to [DataForSEO](https://dataforseo.com/) and create an account
|
||||
2. Navigate to your account dashboard
|
||||
3. Generate API credentials:
|
||||
- You'll receive a **login** (username)
|
||||
- You'll receive a **password**
|
||||
4. Set up your environment variables:
|
||||
- `DATAFORSEO_LOGIN` - Your DataForSEO login/username
|
||||
- `DATAFORSEO_PASSWORD` - Your DataForSEO password
|
||||
|
||||
## LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="DataForSEO Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["DATAFORSEO_LOGIN"] = "your-login"
|
||||
os.environ["DATAFORSEO_PASSWORD"] = "your-password"
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="dataforseo",
|
||||
max_results=10
|
||||
)
|
||||
```
|
||||
|
||||
## LiteLLM AI Gateway
|
||||
|
||||
### 1. Setup config.yaml
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: dataforseo-search
|
||||
litellm_params:
|
||||
search_provider: dataforseo
|
||||
api_key: "os.environ/DATAFORSEO_LOGIN:os.environ/DATAFORSEO_PASSWORD"
|
||||
```
|
||||
|
||||
### 2. Start the proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
### 3. Test the search endpoint
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/dataforseo-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 10
|
||||
}'
|
||||
```
|
||||
|
||||
## Provider-specific Parameters
|
||||
|
||||
```python showLineNumbers title="DataForSEO Search with Provider-specific Parameters"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["DATAFORSEO_LOGIN"] = "your-login"
|
||||
os.environ["DATAFORSEO_PASSWORD"] = "your-password"
|
||||
|
||||
response = search(
|
||||
query="AI developments",
|
||||
search_provider="dataforseo",
|
||||
max_results=10,
|
||||
# DataForSEO-specific parameters
|
||||
country="United States", # Country name for location_name
|
||||
language_code="en", # Language code
|
||||
depth=20, # Number of results (max 700)
|
||||
device="desktop", # Device type ('desktop', 'mobile', 'tablet')
|
||||
os="windows" # Operating system
|
||||
)
|
||||
```
|
||||
|
||||
77
docs/my-website/docs/search/exa_ai.md
Normal file
77
docs/my-website/docs/search/exa_ai.md
Normal file
@@ -0,0 +1,77 @@
|
||||
# Exa AI Search
|
||||
|
||||
**Get API Key:** [https://exa.ai](https://exa.ai)
|
||||
|
||||
## LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="Exa AI Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["EXA_API_KEY"] = "exa-..."
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="exa_ai",
|
||||
max_results=5
|
||||
)
|
||||
```
|
||||
|
||||
## LiteLLM AI Gateway
|
||||
|
||||
### 1. Setup config.yaml
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: exa-search
|
||||
litellm_params:
|
||||
search_provider: exa_ai
|
||||
api_key: os.environ/EXA_API_KEY
|
||||
```
|
||||
|
||||
### 2. Start the proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
### 3. Test the search endpoint
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/exa-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 5
|
||||
}'
|
||||
```
|
||||
|
||||
## Provider-specific Parameters
|
||||
|
||||
```python showLineNumbers title="Exa AI Search with Provider-specific Parameters"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["EXA_API_KEY"] = "exa-..."
|
||||
|
||||
response = search(
|
||||
query="AI research papers",
|
||||
search_provider="exa_ai",
|
||||
max_results=10,
|
||||
search_domain_filter=["arxiv.org"],
|
||||
# Exa-specific parameters
|
||||
type="neural", # 'neural', 'keyword', or 'auto'
|
||||
contents={"text": True}, # Request text content
|
||||
use_autoprompt=True # Enable Exa's autoprompt
|
||||
)
|
||||
```
|
||||
|
||||
101
docs/my-website/docs/search/google_pse.md
Normal file
101
docs/my-website/docs/search/google_pse.md
Normal file
@@ -0,0 +1,101 @@
|
||||
# Google Programmable Search Engine (PSE)
|
||||
|
||||
**Get API Key:** [Google Cloud Console](https://console.cloud.google.com/apis/credentials)
|
||||
**Create Search Engine:** [Programmable Search Engine](https://programmablesearchengine.google.com/)
|
||||
|
||||
## Setup
|
||||
|
||||
1. Go to [Google Developers Programmable Search Engine](https://programmablesearchengine.google.com/) and log in or create an account
|
||||
2. Click the **Add** button in the control panel
|
||||
3. Enter a search engine name and configure properties:
|
||||
- Choose which sites to search (entire web or specific sites)
|
||||
- Set language and other preferences
|
||||
- Verify you're not a robot
|
||||
4. Click **Create** button
|
||||
5. Once created, you'll see:
|
||||
- **Search engine ID (cx)** - Copy this for `GOOGLE_PSE_ENGINE_ID`
|
||||
- Instructions to get your API key
|
||||
6. Generate API key:
|
||||
- Go to [Google Cloud Console - Credentials](https://console.cloud.google.com/apis/credentials)
|
||||
- Create a new API key or use existing one
|
||||
- Enable **Custom Search API** for your project
|
||||
- Copy the API key for `GOOGLE_PSE_API_KEY`
|
||||
|
||||
## LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="Google PSE Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["GOOGLE_PSE_API_KEY"] = "AIza..."
|
||||
os.environ["GOOGLE_PSE_ENGINE_ID"] = "your-search-engine-id"
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="google_pse",
|
||||
max_results=10
|
||||
)
|
||||
```
|
||||
|
||||
## LiteLLM AI Gateway
|
||||
|
||||
### 1. Setup config.yaml
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: google-search
|
||||
litellm_params:
|
||||
search_provider: google_pse
|
||||
api_key: os.environ/GOOGLE_PSE_API_KEY
|
||||
search_engine_id: os.environ/GOOGLE_PSE_ENGINE_ID
|
||||
```
|
||||
|
||||
### 2. Start the proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
### 3. Test the search endpoint
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/google-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 10
|
||||
}'
|
||||
```
|
||||
|
||||
## Provider-specific Parameters
|
||||
|
||||
```python showLineNumbers title="Google PSE Search with Provider-specific Parameters"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["GOOGLE_PSE_API_KEY"] = "AIza..."
|
||||
os.environ["GOOGLE_PSE_ENGINE_ID"] = "your-search-engine-id"
|
||||
|
||||
response = search(
|
||||
query="latest AI research papers",
|
||||
search_provider="google_pse",
|
||||
max_results=10,
|
||||
search_domain_filter=["arxiv.org"],
|
||||
# Google PSE-specific parameters (use actual Google PSE API parameter names)
|
||||
dateRestrict="m6", # 'm6' = last 6 months, 'd7' = last 7 days
|
||||
lr="lang_en", # Language restriction (e.g., 'lang_en', 'lang_es')
|
||||
safe="active", # Search safety level ('active' or 'off')
|
||||
exactTerms="machine learning", # Phrase that all documents must contain
|
||||
fileType="pdf" # File type to restrict results to
|
||||
)
|
||||
```
|
||||
|
||||
272
docs/my-website/docs/search/index.md
Normal file
272
docs/my-website/docs/search/index.md
Normal file
@@ -0,0 +1,272 @@
|
||||
# Overview
|
||||
|
||||
| Feature | Supported |
|
||||
|---------|-----------|
|
||||
| Supported Providers | `perplexity`, `tavily`, `parallel_ai`, `exa_ai`, `google_pse`, `dataforseo` |
|
||||
| Cost Tracking | ✅ |
|
||||
| Logging | ✅ |
|
||||
| Load Balancing | ❌ |
|
||||
|
||||
:::tip
|
||||
|
||||
LiteLLM follows the [Perplexity API request/response for the Search API](https://docs.perplexity.ai/api-reference/search-post)
|
||||
|
||||
:::
|
||||
|
||||
:::info
|
||||
|
||||
Supported from LiteLLM v1.78.7+
|
||||
:::
|
||||
|
||||
## **LiteLLM Python SDK Usage**
|
||||
### Quick Start
|
||||
|
||||
```python showLineNumbers title="Basic Search"
|
||||
from litellm import search
|
||||
import os
|
||||
|
||||
os.environ["PERPLEXITYAI_API_KEY"] = "pplx-..."
|
||||
|
||||
response = search(
|
||||
query="latest AI developments in 2024",
|
||||
search_provider="perplexity",
|
||||
max_results=5
|
||||
)
|
||||
|
||||
# Access search results
|
||||
for result in response.results:
|
||||
print(f"{result.title}: {result.url}")
|
||||
print(f"Snippet: {result.snippet}\n")
|
||||
```
|
||||
|
||||
### Async Usage
|
||||
|
||||
```python showLineNumbers title="Async Search"
|
||||
from litellm import asearch
|
||||
import os, asyncio
|
||||
|
||||
os.environ["PERPLEXITYAI_API_KEY"] = "pplx-..."
|
||||
|
||||
async def search_async():
|
||||
response = await asearch(
|
||||
query="machine learning research papers",
|
||||
search_provider="perplexity",
|
||||
max_results=10,
|
||||
search_domain_filter=["arxiv.org", "nature.com"]
|
||||
)
|
||||
|
||||
# Access search results
|
||||
for result in response.results:
|
||||
print(f"{result.title}: {result.url}")
|
||||
print(f"Snippet: {result.snippet}")
|
||||
|
||||
asyncio.run(search_async())
|
||||
```
|
||||
|
||||
### Optional Parameters
|
||||
|
||||
```python showLineNumbers title="Search with Options"
|
||||
response = search(
|
||||
query="AI developments",
|
||||
search_provider="perplexity",
|
||||
# Unified parameters (work across all providers)
|
||||
max_results=10, # Maximum number of results (1-20)
|
||||
search_domain_filter=["arxiv.org"], # Filter to specific domains
|
||||
country="US", # Country code filter
|
||||
max_tokens_per_page=1024 # Max tokens per page
|
||||
)
|
||||
```
|
||||
|
||||
## **LiteLLM AI Gateway Usage**
|
||||
|
||||
LiteLLM provides a Perplexity API compatible `/search` endpoint for search calls.
|
||||
|
||||
**Setup**
|
||||
|
||||
Add this to your litellm proxy config.yaml
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: perplexity-search
|
||||
litellm_params:
|
||||
search_provider: perplexity
|
||||
api_key: os.environ/PERPLEXITYAI_API_KEY
|
||||
|
||||
- search_tool_name: tavily-search
|
||||
litellm_params:
|
||||
search_provider: tavily
|
||||
api_key: os.environ/TAVILY_API_KEY
|
||||
```
|
||||
|
||||
Start litellm
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
### Test Request
|
||||
|
||||
**Option 1: Search tool name in URL (Recommended - keeps body Perplexity-compatible)**
|
||||
|
||||
```bash showLineNumbers title="cURL Request"
|
||||
curl http://0.0.0.0:4000/v1/search/perplexity-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments 2024",
|
||||
"max_results": 5,
|
||||
"search_domain_filter": ["arxiv.org", "nature.com"],
|
||||
"country": "US"
|
||||
}'
|
||||
```
|
||||
|
||||
**Option 2: Search tool name in body**
|
||||
|
||||
```bash showLineNumbers title="cURL Request with search_tool_name in body"
|
||||
curl http://0.0.0.0:4000/v1/search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"search_tool_name": "perplexity-search",
|
||||
"query": "latest AI developments 2024",
|
||||
"max_results": 5
|
||||
}'
|
||||
```
|
||||
|
||||
### Load Balancing
|
||||
|
||||
Configure multiple search providers for automatic load balancing and fallbacks:
|
||||
|
||||
```yaml showLineNumbers title="config.yaml with load balancing"
|
||||
search_tools:
|
||||
- search_tool_name: my-search
|
||||
litellm_params:
|
||||
search_provider: perplexity
|
||||
api_key: os.environ/PERPLEXITYAI_API_KEY
|
||||
|
||||
- search_tool_name: my-search
|
||||
litellm_params:
|
||||
search_provider: tavily
|
||||
api_key: os.environ/TAVILY_API_KEY
|
||||
|
||||
- search_tool_name: my-search
|
||||
litellm_params:
|
||||
search_provider: exa_ai
|
||||
api_key: os.environ/EXA_API_KEY
|
||||
|
||||
router_settings:
|
||||
routing_strategy: simple-shuffle # or 'least-busy', 'latency-based-routing'
|
||||
```
|
||||
|
||||
Test with load balancing:
|
||||
|
||||
```bash
|
||||
curl http://0.0.0.0:4000/v1/search/my-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "AI developments",
|
||||
"max_results": 10
|
||||
}'
|
||||
```
|
||||
|
||||
## **Request/Response Format**
|
||||
|
||||
:::info
|
||||
|
||||
LiteLLM follows the **Perplexity Search API specification**.
|
||||
|
||||
See the [official Perplexity Search documentation](https://docs.perplexity.ai/api-reference/search-post) for complete details.
|
||||
|
||||
:::
|
||||
|
||||
### Example Request
|
||||
|
||||
```json showLineNumbers title="Search Request"
|
||||
{
|
||||
"query": "latest AI developments 2024",
|
||||
"max_results": 10,
|
||||
"search_domain_filter": ["arxiv.org", "nature.com"],
|
||||
"country": "US",
|
||||
"max_tokens_per_page": 1024
|
||||
}
|
||||
```
|
||||
|
||||
### Request Parameters
|
||||
|
||||
| Parameter | Type | Required | Description |
|
||||
|-----------|------|----------|-------------|
|
||||
| `query` | string or array | Yes | Search query. Can be a single string or array of strings |
|
||||
| `search_provider` | string | Yes (SDK) | The search provider to use: `"perplexity"`, `"tavily"`, `"parallel_ai"`, `"exa_ai"`, or `"google_pse"` |
|
||||
| `search_tool_name` | string | Yes (Proxy) | Name of the search tool configured in `config.yaml` |
|
||||
| `max_results` | integer | No | Maximum number of results to return (1-20). Default: 10 |
|
||||
| `search_domain_filter` | array | No | List of domains to filter results (max 20 domains) |
|
||||
| `max_tokens_per_page` | integer | No | Maximum tokens per page to process. Default: 1024 |
|
||||
| `country` | string | No | Country code filter (e.g., `"US"`, `"GB"`, `"DE"`) |
|
||||
|
||||
**Query Format Examples:**
|
||||
|
||||
```python
|
||||
# Single query
|
||||
query = "AI developments"
|
||||
|
||||
# Multiple queries
|
||||
query = ["AI developments", "machine learning trends"]
|
||||
```
|
||||
|
||||
### Response Format
|
||||
|
||||
The response follows Perplexity's search format with the following structure:
|
||||
|
||||
```json showLineNumbers title="Search Response"
|
||||
{
|
||||
"object": "search",
|
||||
"results": [
|
||||
{
|
||||
"title": "Latest Advances in Artificial Intelligence",
|
||||
"url": "https://arxiv.org/paper/example",
|
||||
"snippet": "This paper discusses recent developments in AI...",
|
||||
"date": "2024-01-15"
|
||||
},
|
||||
{
|
||||
"title": "Machine Learning Breakthroughs",
|
||||
"url": "https://nature.com/articles/ml-breakthrough",
|
||||
"snippet": "Researchers have achieved new milestones...",
|
||||
"date": "2024-01-10"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Response Fields
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `object` | string | Always `"search"` for search responses |
|
||||
| `results` | array | List of search results |
|
||||
| `results[].title` | string | Title of the search result |
|
||||
| `results[].url` | string | URL of the search result |
|
||||
| `results[].snippet` | string | Text snippet from the result |
|
||||
| `results[].date` | string | Optional publication or last updated date |
|
||||
|
||||
## **Supported Providers**
|
||||
|
||||
| Provider | Environment Variable | `search_provider` Value |
|
||||
|----------|---------------------|------------------------|
|
||||
| Perplexity AI | `PERPLEXITYAI_API_KEY` | `perplexity` |
|
||||
| Tavily | `TAVILY_API_KEY` | `tavily` |
|
||||
| Exa AI | `EXA_API_KEY` | `exa_ai` |
|
||||
| Parallel AI | `PARALLEL_AI_API_KEY` | `parallel_ai` |
|
||||
| Google PSE | `GOOGLE_PSE_API_KEY`, `GOOGLE_PSE_ENGINE_ID` | `google_pse` |
|
||||
| DataForSEO | `DATAFORSEO_LOGIN`, `DATAFORSEO_PASSWORD` | `dataforseo` |
|
||||
|
||||
See the individual provider documentation for detailed setup instructions and provider-specific parameters.
|
||||
|
||||
75
docs/my-website/docs/search/parallel_ai.md
Normal file
75
docs/my-website/docs/search/parallel_ai.md
Normal file
@@ -0,0 +1,75 @@
|
||||
# Parallel AI Search
|
||||
|
||||
**Get API Key:** [https://www.parallel.ai](https://www.parallel.ai)
|
||||
|
||||
## LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="Parallel AI Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["PARALLEL_AI_API_KEY"] = "..."
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="parallel_ai",
|
||||
max_results=5
|
||||
)
|
||||
```
|
||||
|
||||
## LiteLLM AI Gateway
|
||||
|
||||
### 1. Setup config.yaml
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: parallel-search
|
||||
litellm_params:
|
||||
search_provider: parallel_ai
|
||||
api_key: os.environ/PARALLEL_AI_API_KEY
|
||||
```
|
||||
|
||||
### 2. Start the proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
### 3. Test the search endpoint
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/parallel-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 5
|
||||
}'
|
||||
```
|
||||
|
||||
## Provider-specific Parameters
|
||||
|
||||
```python showLineNumbers title="Parallel AI Search with Provider-specific Parameters"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["PARALLEL_AI_API_KEY"] = "..."
|
||||
|
||||
response = search(
|
||||
query="latest developments in quantum computing",
|
||||
search_provider="parallel_ai",
|
||||
max_results=5,
|
||||
# Parallel AI-specific parameters
|
||||
processor="pro", # 'base' or 'pro'
|
||||
max_chars_per_result=500 # Max characters per result
|
||||
)
|
||||
```
|
||||
|
||||
57
docs/my-website/docs/search/perplexity.md
Normal file
57
docs/my-website/docs/search/perplexity.md
Normal file
@@ -0,0 +1,57 @@
|
||||
# Perplexity AI Search
|
||||
|
||||
**Get API Key:** [https://www.perplexity.ai/settings/api](https://www.perplexity.ai/settings/api)
|
||||
|
||||
## LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="Perplexity Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["PERPLEXITYAI_API_KEY"] = "pplx-..."
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="perplexity",
|
||||
max_results=5
|
||||
)
|
||||
```
|
||||
|
||||
## LiteLLM AI Gateway
|
||||
|
||||
### 1. Setup config.yaml
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: perplexity-search
|
||||
litellm_params:
|
||||
search_provider: perplexity
|
||||
api_key: os.environ/PERPLEXITYAI_API_KEY
|
||||
```
|
||||
|
||||
### 2. Start the proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
### 3. Test the search endpoint
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/perplexity-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 5
|
||||
}'
|
||||
```
|
||||
|
||||
77
docs/my-website/docs/search/tavily.md
Normal file
77
docs/my-website/docs/search/tavily.md
Normal file
@@ -0,0 +1,77 @@
|
||||
# Tavily Search
|
||||
|
||||
**Get API Key:** [https://tavily.com](https://tavily.com)
|
||||
|
||||
## LiteLLM Python SDK
|
||||
|
||||
```python showLineNumbers title="Tavily Search"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["TAVILY_API_KEY"] = "tvly-..."
|
||||
|
||||
response = search(
|
||||
query="latest AI developments",
|
||||
search_provider="tavily",
|
||||
max_results=5
|
||||
)
|
||||
```
|
||||
|
||||
## LiteLLM AI Gateway
|
||||
|
||||
### 1. Setup config.yaml
|
||||
|
||||
```yaml showLineNumbers title="config.yaml"
|
||||
model_list:
|
||||
- model_name: gpt-4
|
||||
litellm_params:
|
||||
model: gpt-4
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
|
||||
search_tools:
|
||||
- search_tool_name: tavily-search
|
||||
litellm_params:
|
||||
search_provider: tavily
|
||||
api_key: os.environ/TAVILY_API_KEY
|
||||
```
|
||||
|
||||
### 2. Start the proxy
|
||||
|
||||
```bash
|
||||
litellm --config /path/to/config.yaml
|
||||
|
||||
# RUNNING on http://0.0.0.0:4000
|
||||
```
|
||||
|
||||
### 3. Test the search endpoint
|
||||
|
||||
```bash showLineNumbers title="Test Request"
|
||||
curl http://0.0.0.0:4000/v1/search/tavily-search \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "latest AI developments",
|
||||
"max_results": 5
|
||||
}'
|
||||
```
|
||||
|
||||
## Provider-specific Parameters
|
||||
|
||||
```python showLineNumbers title="Tavily Search with Provider-specific Parameters"
|
||||
import os
|
||||
from litellm import search
|
||||
|
||||
os.environ["TAVILY_API_KEY"] = "tvly-..."
|
||||
|
||||
response = search(
|
||||
query="latest tech news",
|
||||
search_provider="tavily",
|
||||
max_results=5,
|
||||
# Tavily-specific parameters
|
||||
topic="news", # 'general', 'news', 'finance'
|
||||
search_depth="advanced", # 'basic', 'advanced'
|
||||
include_answer=True, # Include AI-generated answer
|
||||
include_raw_content=True # Include raw HTML content
|
||||
)
|
||||
```
|
||||
|
||||
@@ -381,7 +381,19 @@ const sidebars = {
|
||||
"realtime",
|
||||
"rerank",
|
||||
"response_api",
|
||||
"search",
|
||||
{
|
||||
type: "category",
|
||||
label: "/search",
|
||||
items: [
|
||||
"search/index",
|
||||
"search/perplexity",
|
||||
"search/tavily",
|
||||
"search/exa_ai",
|
||||
"search/parallel_ai",
|
||||
"search/google_pse",
|
||||
"search/dataforseo",
|
||||
]
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "/vector_stores",
|
||||
|
||||
@@ -29,6 +29,7 @@ from litellm.llms.anthropic.cost_calculation import (
|
||||
from litellm.llms.azure.cost_calculation import (
|
||||
cost_per_token as azure_openai_cost_per_token,
|
||||
)
|
||||
from litellm.llms.base_llm.search.transformation import SearchResponse
|
||||
from litellm.llms.bedrock.cost_calculation import (
|
||||
cost_per_token as bedrock_cost_per_token,
|
||||
)
|
||||
@@ -315,6 +316,16 @@ def cost_per_token( # noqa: PLR0915
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
duration=audio_transcription_file_duration,
|
||||
)
|
||||
elif call_type == "search" or call_type == "asearch":
|
||||
# Search providers use per-query pricing
|
||||
from litellm.search import search_provider_cost_per_query
|
||||
|
||||
return search_provider_cost_per_query(
|
||||
model=model,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
number_of_queries=number_of_queries or 1,
|
||||
optional_params=response._hidden_params if response and hasattr(response, "_hidden_params") else None
|
||||
)
|
||||
elif custom_llm_provider == "vertex_ai":
|
||||
cost_router = google_cost_router(
|
||||
model=model_without_prefix,
|
||||
@@ -1094,6 +1105,7 @@ def response_cost_calculator(
|
||||
LiteLLMRealtimeStreamLoggingObject,
|
||||
OpenAIModerationResponse,
|
||||
Response,
|
||||
SearchResponse,
|
||||
],
|
||||
model: str,
|
||||
custom_llm_provider: Optional[str],
|
||||
@@ -1114,6 +1126,8 @@ def response_cost_calculator(
|
||||
"speech",
|
||||
"rerank",
|
||||
"arerank",
|
||||
"search",
|
||||
"asearch",
|
||||
],
|
||||
optional_params: dict,
|
||||
cache_hit: Optional[bool] = None,
|
||||
|
||||
@@ -67,7 +67,7 @@ class ExaAISearchConfig(BaseSearchConfig):
|
||||
self,
|
||||
api_base: Optional[str],
|
||||
optional_params: dict,
|
||||
data: Optional[dict] = None,
|
||||
data: Optional[Union[Dict, List[Dict]]] = None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
"""
|
||||
|
||||
@@ -90,7 +90,7 @@ class GooglePSESearchConfig(BaseSearchConfig):
|
||||
self,
|
||||
api_base: Optional[str],
|
||||
optional_params: dict,
|
||||
data: Optional[dict] = None,
|
||||
data: Optional[Union[Dict, List[Dict]]] = None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
"""
|
||||
@@ -104,7 +104,7 @@ class GooglePSESearchConfig(BaseSearchConfig):
|
||||
api_base = api_base or get_secret_str("GOOGLE_PSE_API_BASE") or self.GOOGLE_PSE_API_BASE
|
||||
|
||||
# Build query parameters from the transformed request body
|
||||
if data and "_google_pse_params" in data:
|
||||
if data and isinstance(data, dict) and "_google_pse_params" in data:
|
||||
params = data["_google_pse_params"]
|
||||
query_string = urlencode(params)
|
||||
return f"{api_base}?{query_string}"
|
||||
|
||||
@@ -66,7 +66,7 @@ class TavilySearchConfig(BaseSearchConfig):
|
||||
self,
|
||||
api_base: Optional[str],
|
||||
optional_params: dict,
|
||||
data: Optional[dict] = None,
|
||||
data: Optional[Union[Dict, List[Dict]]] = None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
"""
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
"max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens",
|
||||
"max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens",
|
||||
"max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.",
|
||||
"mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank",
|
||||
"mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank, search",
|
||||
"output_cost_per_reasoning_token": 0.0,
|
||||
"output_cost_per_token": 0.0,
|
||||
"search_context_cost_per_query": {
|
||||
@@ -6460,6 +6460,11 @@
|
||||
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
||||
"supports_tool_choice": true
|
||||
},
|
||||
"dataforseo/search": {
|
||||
"input_cost_per_query": 0.003,
|
||||
"litellm_provider": "dataforseo",
|
||||
"mode": "search"
|
||||
},
|
||||
"davinci-002": {
|
||||
"input_cost_per_token": 2e-06,
|
||||
"litellm_provider": "text-completion-openai",
|
||||
@@ -7800,6 +7805,31 @@
|
||||
"output_cost_per_token": 0.0,
|
||||
"output_vector_size": 2560
|
||||
},
|
||||
"exa_ai/search": {
|
||||
"litellm_provider": "exa_ai",
|
||||
"mode": "search",
|
||||
"tiered_pricing": [
|
||||
{
|
||||
"input_cost_per_query": 5e-03,
|
||||
"max_results_range": [
|
||||
0,
|
||||
25
|
||||
]
|
||||
},
|
||||
{
|
||||
"input_cost_per_query": 25e-03,
|
||||
"max_results_range": [
|
||||
26,
|
||||
100
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
"perplexity/search": {
|
||||
"input_cost_per_query": 5e-03,
|
||||
"litellm_provider": "perplexity",
|
||||
"mode": "search"
|
||||
},
|
||||
"elevenlabs/scribe_v1": {
|
||||
"input_cost_per_second": 6.11e-05,
|
||||
"litellm_provider": "elevenlabs",
|
||||
@@ -12211,6 +12241,11 @@
|
||||
"video"
|
||||
]
|
||||
},
|
||||
"google_pse/search": {
|
||||
"input_cost_per_query": 0.005,
|
||||
"litellm_provider": "google_pse",
|
||||
"mode": "search"
|
||||
},
|
||||
"global.anthropic.claude-sonnet-4-5-20250929-v1:0": {
|
||||
"cache_creation_input_token_cost": 3.75e-06,
|
||||
"cache_read_input_token_cost": 3e-07,
|
||||
@@ -18802,6 +18837,16 @@
|
||||
"output_cost_per_token": 1.25e-07,
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
|
||||
},
|
||||
"parallel_ai/search": {
|
||||
"input_cost_per_query": 0.004,
|
||||
"litellm_provider": "parallel_ai",
|
||||
"mode": "search"
|
||||
},
|
||||
"parallel_ai/search-pro": {
|
||||
"input_cost_per_query": 0.009,
|
||||
"litellm_provider": "parallel_ai",
|
||||
"mode": "search"
|
||||
},
|
||||
"perplexity/codellama-34b-instruct": {
|
||||
"input_cost_per_token": 3.5e-07,
|
||||
"litellm_provider": "perplexity",
|
||||
@@ -19812,6 +19857,16 @@
|
||||
"mode": "image_generation",
|
||||
"output_cost_per_pixel": 0.0
|
||||
},
|
||||
"tavily/search": {
|
||||
"input_cost_per_query": 0.008,
|
||||
"litellm_provider": "tavily",
|
||||
"mode": "search"
|
||||
},
|
||||
"tavily/search-advanced": {
|
||||
"input_cost_per_query": 0.016,
|
||||
"litellm_provider": "tavily",
|
||||
"mode": "search"
|
||||
},
|
||||
"text-bison": {
|
||||
"input_cost_per_character": 2.5e-07,
|
||||
"litellm_provider": "vertex_ai-text-models",
|
||||
|
||||
@@ -1,7 +1,8 @@
|
||||
"""
|
||||
LiteLLM Search API module.
|
||||
"""
|
||||
from litellm.search.cost_calculator import search_provider_cost_per_query
|
||||
from litellm.search.main import asearch, search
|
||||
|
||||
__all__ = ["search", "asearch"]
|
||||
__all__ = ["search", "asearch", "search_provider_cost_per_query"]
|
||||
|
||||
|
||||
52
litellm/search/cost_calculator.py
Normal file
52
litellm/search/cost_calculator.py
Normal file
@@ -0,0 +1,52 @@
|
||||
"""
|
||||
Cost calculation for search providers.
|
||||
"""
|
||||
from typing import Optional, Tuple
|
||||
|
||||
from litellm.utils import get_model_info
|
||||
|
||||
|
||||
def search_provider_cost_per_query(
|
||||
model: str,
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
number_of_queries: int = 1,
|
||||
optional_params: Optional[dict] = None,
|
||||
) -> Tuple[float, float]:
|
||||
"""
|
||||
Calculate cost for search-only providers.
|
||||
|
||||
Returns (input_cost, output_cost) where input_cost = queries * cost_per_query
|
||||
Supports tiered pricing based on max_results parameter.
|
||||
|
||||
Args:
|
||||
model: Model name (e.g., "exa_ai/search", "tavily/search")
|
||||
custom_llm_provider: Provider name (e.g., "exa_ai", "tavily")
|
||||
number_of_queries: Number of search queries performed (default: 1)
|
||||
optional_params: Optional parameters including max_results for tiered pricing
|
||||
|
||||
Returns:
|
||||
Tuple of (input_cost, output_cost) where output_cost is always 0.0
|
||||
"""
|
||||
model_info = get_model_info(model=model, custom_llm_provider=custom_llm_provider)
|
||||
|
||||
# Check for tiered pricing (e.g., Exa AI based on max_results)
|
||||
tiered_pricing = model_info.get("tiered_pricing")
|
||||
if tiered_pricing and isinstance(tiered_pricing, list):
|
||||
max_results = (optional_params or {}).get("max_results", 10) # default 10 results
|
||||
cost_per_query = 0.0
|
||||
|
||||
for tier in tiered_pricing:
|
||||
range_min, range_max = tier["max_results_range"]
|
||||
if range_min <= max_results <= range_max:
|
||||
cost_per_query = tier["input_cost_per_query"]
|
||||
break
|
||||
else:
|
||||
# Fallback to highest tier if out of range
|
||||
cost_per_query = tiered_pricing[-1]["input_cost_per_query"]
|
||||
else:
|
||||
# Simple flat rate
|
||||
cost_per_query = float(model_info.get("input_cost_per_query") or 0.0)
|
||||
|
||||
total_cost = number_of_queries * cost_per_query
|
||||
return (total_cost, 0.0) # (input_cost, output_cost)
|
||||
|
||||
@@ -149,8 +149,9 @@ async def asearch(
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
model_name = f"{search_provider}/search"
|
||||
raise litellm.exception_type(
|
||||
model="",
|
||||
model=model_name,
|
||||
custom_llm_provider=search_provider,
|
||||
original_exception=e,
|
||||
completion_kwargs=local_vars,
|
||||
@@ -287,8 +288,9 @@ def search(
|
||||
)
|
||||
|
||||
# Pre Call logging
|
||||
model_name = f"{search_provider}/search"
|
||||
litellm_logging_obj.update_environment_variables(
|
||||
model="",
|
||||
model=model_name,
|
||||
optional_params=optional_params,
|
||||
litellm_params={
|
||||
"litellm_call_id": litellm_call_id,
|
||||
@@ -313,8 +315,9 @@ def search(
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
model_name = f"{search_provider}/search"
|
||||
raise litellm.exception_type(
|
||||
model="",
|
||||
model=model_name,
|
||||
custom_llm_provider=search_provider,
|
||||
original_exception=e,
|
||||
completion_kwargs=local_vars,
|
||||
|
||||
@@ -239,6 +239,8 @@ class CallTypes(str, Enum):
|
||||
speech = "speech"
|
||||
rerank = "rerank"
|
||||
arerank = "arerank"
|
||||
search = "search"
|
||||
asearch = "asearch"
|
||||
arealtime = "_arealtime"
|
||||
create_batch = "create_batch"
|
||||
acreate_batch = "acreate_batch"
|
||||
@@ -321,6 +323,8 @@ CallTypesLiteral = Literal[
|
||||
"speech",
|
||||
"rerank",
|
||||
"arerank",
|
||||
"search",
|
||||
"asearch",
|
||||
"_arealtime",
|
||||
"create_batch",
|
||||
"acreate_batch",
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
"max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens",
|
||||
"max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens",
|
||||
"max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.",
|
||||
"mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank",
|
||||
"mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank, search",
|
||||
"output_cost_per_reasoning_token": 0.0,
|
||||
"output_cost_per_token": 0.0,
|
||||
"search_context_cost_per_query": {
|
||||
@@ -6460,6 +6460,11 @@
|
||||
"source": "https://www.databricks.com/product/pricing/foundation-model-serving",
|
||||
"supports_tool_choice": true
|
||||
},
|
||||
"dataforseo/search": {
|
||||
"input_cost_per_query": 0.003,
|
||||
"litellm_provider": "dataforseo",
|
||||
"mode": "search"
|
||||
},
|
||||
"davinci-002": {
|
||||
"input_cost_per_token": 2e-06,
|
||||
"litellm_provider": "text-completion-openai",
|
||||
@@ -7800,6 +7805,31 @@
|
||||
"output_cost_per_token": 0.0,
|
||||
"output_vector_size": 2560
|
||||
},
|
||||
"exa_ai/search": {
|
||||
"litellm_provider": "exa_ai",
|
||||
"mode": "search",
|
||||
"tiered_pricing": [
|
||||
{
|
||||
"input_cost_per_query": 5e-03,
|
||||
"max_results_range": [
|
||||
0,
|
||||
25
|
||||
]
|
||||
},
|
||||
{
|
||||
"input_cost_per_query": 25e-03,
|
||||
"max_results_range": [
|
||||
26,
|
||||
100
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
"perplexity/search": {
|
||||
"input_cost_per_query": 5e-03,
|
||||
"litellm_provider": "perplexity",
|
||||
"mode": "search"
|
||||
},
|
||||
"elevenlabs/scribe_v1": {
|
||||
"input_cost_per_second": 6.11e-05,
|
||||
"litellm_provider": "elevenlabs",
|
||||
@@ -12211,6 +12241,11 @@
|
||||
"video"
|
||||
]
|
||||
},
|
||||
"google_pse/search": {
|
||||
"input_cost_per_query": 0.005,
|
||||
"litellm_provider": "google_pse",
|
||||
"mode": "search"
|
||||
},
|
||||
"global.anthropic.claude-sonnet-4-5-20250929-v1:0": {
|
||||
"cache_creation_input_token_cost": 3.75e-06,
|
||||
"cache_read_input_token_cost": 3e-07,
|
||||
@@ -18802,6 +18837,16 @@
|
||||
"output_cost_per_token": 1.25e-07,
|
||||
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
|
||||
},
|
||||
"parallel_ai/search": {
|
||||
"input_cost_per_query": 0.004,
|
||||
"litellm_provider": "parallel_ai",
|
||||
"mode": "search"
|
||||
},
|
||||
"parallel_ai/search-pro": {
|
||||
"input_cost_per_query": 0.009,
|
||||
"litellm_provider": "parallel_ai",
|
||||
"mode": "search"
|
||||
},
|
||||
"perplexity/codellama-34b-instruct": {
|
||||
"input_cost_per_token": 3.5e-07,
|
||||
"litellm_provider": "perplexity",
|
||||
@@ -19812,6 +19857,16 @@
|
||||
"mode": "image_generation",
|
||||
"output_cost_per_pixel": 0.0
|
||||
},
|
||||
"tavily/search": {
|
||||
"input_cost_per_query": 0.008,
|
||||
"litellm_provider": "tavily",
|
||||
"mode": "search"
|
||||
},
|
||||
"tavily/search-advanced": {
|
||||
"input_cost_per_query": 0.016,
|
||||
"litellm_provider": "tavily",
|
||||
"mode": "search"
|
||||
},
|
||||
"text-bison": {
|
||||
"input_cost_per_character": 2.5e-07,
|
||||
"litellm_provider": "vertex_ai-text-models",
|
||||
|
||||
@@ -6,6 +6,7 @@ This follows the same pattern as BaseOCRTest in tests/ocr_tests/base_ocr_unit_te
|
||||
import pytest
|
||||
import litellm
|
||||
from abc import ABC, abstractmethod
|
||||
import os
|
||||
import json
|
||||
|
||||
|
||||
@@ -37,6 +38,8 @@ class BaseSearchTest(ABC):
|
||||
"""
|
||||
Test basic search functionality with a simple query.
|
||||
"""
|
||||
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
||||
litellm.model_cost = litellm.get_model_cost_map(url="")
|
||||
litellm._turn_on_debug()
|
||||
search_provider = self.get_search_provider()
|
||||
print("Search Provider=", search_provider)
|
||||
@@ -77,6 +80,18 @@ class BaseSearchTest(ABC):
|
||||
assert len(first_result.url) > 0, "URL should not be empty"
|
||||
assert len(first_result.snippet) > 0, "Snippet should not be empty"
|
||||
|
||||
# Validate cost tracking in _hidden_params
|
||||
assert hasattr(response, "_hidden_params"), "Response should have '_hidden_params' attribute"
|
||||
hidden_params = response._hidden_params
|
||||
assert "response_cost" in hidden_params, "_hidden_params should contain 'response_cost'"
|
||||
|
||||
response_cost = hidden_params["response_cost"]
|
||||
assert response_cost is not None, "response_cost should not be None"
|
||||
assert isinstance(response_cost, (int, float)), "response_cost should be a number"
|
||||
assert response_cost >= 0, "response_cost should be non-negative"
|
||||
|
||||
print(f"Cost tracking: ${response_cost:.6f}")
|
||||
|
||||
except Exception as e:
|
||||
pytest.fail(f"Search call failed: {str(e)}")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user