> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tavily.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Best Practices for Search
> Learn how to optimize your queries, refine search filters, and leverage advanced parameters for better performance.
## Query Optimization
### Keep your query under 400 characters
Keep queries concise—under **400 characters**. Think of it as a query for an agent performing web search, not long-form prompts.
### Break complex queries into sub-queries
For complex or multi-topic queries, send separate focused requests:
```json theme={null}
// Instead of one massive query, break it down:
{ "query": "Competitors of company ABC." }
{ "query": "Financial performance of company ABC." }
{ "query": "Recent developments of company ABC." }
```
## Search Depth
The `search_depth` parameter controls the tradeoff between latency and relevance:
*This chart is a heuristic and is not to scale.*
| Depth | Latency | Relevance | Content Type |
| ------------ | ------- | --------- | ------------ |
| `ultra-fast` | Lowest | Lower | Content |
| `fast` | Low | Good | Chunks |
| `basic` | Medium | High | Content |
| `advanced` | Higher | Highest | Chunks |
### Content types
| Type | Description |
| ----------- | --------------------------------------------------------- |
| **Content** | NLP-based summary of the page, providing general context |
| **Chunks** | Short snippets reranked by relevance to your search query |
Use **chunks** when you need highly targeted information aligned with your query. Use **content** when a general page summary is sufficient.
### Fast + Ultra-Fast
| Depth | When to use |
| ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `ultra-fast` | When latency is absolutely crucial. Delivers near-instant results, prioritizing speed over relevance. Ideal for real-time applications where response time is critical. |
| `fast` | When latency is more important than relevance, but you want results in reranked chunks format. Good for applications that need quick, targeted snippets. |
| `basic` | A solid balance between relevance and latency. Best for general-purpose searches where you need quality results without the overhead of advanced processing. |
| `advanced` | When you need the highest relevance and are willing to trade off latency. Best for queries seeking specific, detailed information. |
### Using `search_depth=advanced`
Best for queries seeking specific information:
```json theme={null}
{
"query": "How many countries use Monday.com?",
"search_depth": "advanced",
"chunks_per_source": 3,
"include_raw_content": true
}
```
## Filtering Results
### By date
| Parameter | Description |
| ------------------------- | ------------------------------------------------------- |
| `time_range` | Filter by relative time: `day`, `week`, `month`, `year` |
| `start_date` / `end_date` | Filter by specific date range (format: `YYYY-MM-DD`) |
```json theme={null}
{ "query": "latest ML trends", "time_range": "month" }
{ "query": "AI news", "start_date": "2025-01-01", "end_date": "2025-02-01" }
```
### By topic
Use `topic` to filter by content type. Set to `news` for news sources (includes `published_date` metadata):
```json theme={null}
{ "query": "What happened today in NY?", "topic": "news" }
```
### By domain
| Parameter | Description |
| ----------------- | ------------------------------------- |
| `include_domains` | Limit to specific domains |
| `exclude_domains` | Filter out specific domains |
| `country` | Boost results from a specific country |
```json theme={null}
// Restrict to LinkedIn profiles
{ "query": "CEO background at Google", "include_domains": ["linkedin.com/in"] }
// Exclude irrelevant domains
{ "query": "US economy trends", "exclude_domains": ["espn.com", "vogue.com"] }
// Boost results from a country
{ "query": "tech startup funding", "country": "united states" }
// Wildcard: limit to .com, exclude specific site
{ "query": "AI news", "include_domains": ["*.com"], "exclude_domains": ["example.com"] }
```
Keep domain lists short and relevant for best results.
## Response Content
### `max_results`
Limits results returned (default: `5`). Setting too high may return lower-quality results.
### `include_raw_content`
Returns full extracted page content. For comprehensive extraction, consider a two-step process:
1. Search to retrieve relevant URLs
2. Use [Extract API](/documentation/best-practices/best-practices-extract#2-two-step-process-search-then-extract) to get content
### `auto_parameters`
Tavily automatically configures parameters based on query intent. Your explicit values override automatic ones.
```json theme={null}
{
"query": "impact of AI in education policy",
"auto_parameters": true,
"search_depth": "basic" // Override to control cost
}
```
`auto_parameters` may set `search_depth` to `advanced` (2 credits). Set it
manually to control cost.
## Exact Match
Use `exact_match` only when searching for a specific name or phrase that must appear verbatim in the source content. Wrap the phrase in quotes within your query:
```json theme={null}
{
"query": "\"John Smith\" CEO Acme Corp",
"exact_match": true
}
```
Because this narrows retrieval, it may return fewer results or empty result fields when no exact matches are found. Best suited for:
* **Due diligence** — finding information on a specific person or entity
* **Data enrichment** — retrieving details about a known company or individual
* **Legal/compliance** — locating exact names or phrases in public records
## Async & Performance
Use async calls for concurrent requests:
```python theme={null}
import asyncio
from tavily import AsyncTavilyClient
tavily_client = AsyncTavilyClient("tvly-YOUR_API_KEY")
async def fetch_and_gather():
queries = ["latest AI trends", "future of quantum computing"]
responses = await asyncio.gather(
*(tavily_client.search(q) for q in queries),
return_exceptions=True
)
for response in responses:
if isinstance(response, Exception):
print(f"Failed: {response}")
else:
print(response)
asyncio.run(fetch_and_gather())
```
## Post-Processing
### Using metadata
Leverage response metadata to refine results:
| Field | Use case |
| ------------- | ---------------------------------- |
| `score` | Filter/rank by relevance score |
| `title` | Keyword filtering on headlines |
| `content` | Quick relevance check |
| `raw_content` | Deep analysis and regex extraction |
### Score-based filtering
The `score` indicates relevance between query and content. Higher is better, but the ideal threshold depends on your use case.
```python theme={null}
# Filter results with score > 0.7
filtered = [r for r in results if r['score'] > 0.7]
```
### Regex extraction
Extract structured data from `raw_content`:
```python theme={null}
import re
# Extract location
text = "Company: Tavily, Location: New York"
match = re.search(r"Location: (\w+)", text)
location = match.group(1) if match else None # "New York"
# Extract all emails
text = "Contact: john@example.com, support@tavily.com"
emails = re.findall(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", text)
```