​

Optimizing your query

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1. Keep your query under 400 characters

For efficient processing, keep your query concise—under 400 characters. Think of it as a query for an agent performing web search, not long-form prompts. If your query exceeds the limit, you’ll see this error:

{
  "detail": {
    "error": "Query is too long. Max query length is 400 characters."
  }
}

​

2. Break your query into smaller sub-queries

If your query is complex or covers multiple topics, consider breaking it into smaller, more focused sub-queries and sending them as separate requests.

// Breaking the query into smaller, more focused sub-queries.
{
   "query":"Competitors of company ABC."
}
{
   "query":"Financial performance of company ABC."
}
{
   "query":"Recent developments of company ABC."
}
{
   "query":"Latest industry trends related to ABC."
}

​

Optimizing your request parameters

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max_results (Limiting the number of results)

• Limits the number of search results (default is 5).

// Customizing max_results based on your needs, limiting the results to 10 to improve relevance and focus on the most relevant sources.
{
  "query": "Info about renewable energy technologies",
  "max_results": 10
}

​

content (NLP-based snippet)

• Provides a summarized content snippet.
• Helps in quickly understanding the main context without extracting full content.
• When search_depth is set to advanced , it extracts content closely aligned with your query, surfacing the most valuable sections of a web page rather than a generic summary. Additionally, it uses chunks_per_source to determine the number of content chunks to return per source.

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search_depth=advanced (Ideal for higher relevance in search results)

• Retrieves the most relevant content snippets for your query.
• By setting include_raw_content to true, you can increase the likelihood of enhancing retrieval precision and retrieving the desired number of chunks_per_source.

// Using search_depth=advanced and chunks_per_source for a query to get the most relevant content, and enabling include_raw_content.
{
  "query": "How many countries use Monday.com?",
  "search_depth": "advanced",
  "chunks_per_source": 3,
  "include_raw_content": true
}

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time_range (Filtering by Date)

• Restricts search results to a specific time frame.

// Using time_range to filter sources from the past month.
{
  "query": "latest trends in machine learning",
  "time_range": "month"
}

​

include_raw_content (Extracted web content)

Set to true to return the full extracted content of the web page, useful for deeper content analysis. However, the most recommended approach for extracting web page content is using a two-step process:

1. Search: Retrieve relevant URLs.
2. Extract: Extract content from those URLs.

For more information on this two-step process, please refer to the Best Practices for the Extract API.

// Using include_raw_content to retrieve full content for comprehensive analysis.
{
    "query": "The impact of AI in healthcare",
    "include_raw_content": true
}

​

topic=news (Filtering news sources)

• Limits results to news-related sources.
• Includes published_date metadata.
• days specifies the number of days back from the current date to include in the results. The default is 3.
• Useful for getting real-time updates, particularly about politics, sports, and major current events covered by mainstream media sources.

// Using "topic=news" and "days=1" to get the latest updates from news sources.
{
  "query": "What happened today in NY?",
  "topic": "news",
  "days": 1
}

​

include_domains (Restricting searches to specific domains)

• Limits searches to predefined trusted domains.

// Using include_domains to restrict search for more domain-specific information.
{
  "query": "What is the professional background of the CEO at Google?",
  "include_domains": ["linkedin.com/in"]
}

• Minimize the number of domains in the include_domains list and make sure they are relevant to your search query.

// Using a concise list of 3 relevant domains to refine search results effectively.
{
    "query": "What are the latest funding rounds for AI startups?",
    "include_domains": [ "crunchbase.com", "techcrunch.com", "pitchbook.com" ]
 }

​

exclude_domains (Excluding specific domains)

• Filters out results from specific domains.

// Excluding unrelated domains to US economy trends, ensuring that irrelevant sources are filtered out.
{
   "query": "US economy trends in 2025",
   "exclude_domains": ["espn.com","vogue.com"]
}

• Minimize the number of domains in the exclude_domains list to ensure you only exclude domains that are truly irrelevant to your query.

// Using a concise list of 3 domains to exclude from the search results.
{
    "query": "US fashion trends in 2025",
    "exclude_domains": ["nytimes.com","forbes.com","bloomberg.com"]
}

​

Controlling search results by website region

Example: Limit to U.S.-based websites (.com domain):

{
    "query": "latest AI research",
    "include_domains": ["*.com"]
}

Example: Exclude Icelandic websites (.is domain):

{
    "query": "global economic trends",
    "exclude_domains": ["*.is"]
}

​

Combining include and exclude domains

Restrict search to .com but exclude example.com:

{
    "query": "AI industry news",
    "include_domains": ["*.com"],
    "exclude_domains": ["example.com"]
}

​

Asynchronous API calls with Tavily

• Use async/await to ensure non-blocking API requests.
• Initialize AsyncTavilyClient once and reuse it for multiple requests.
• Use asyncio.gather for handling multiple queries concurrently.
• Implement error handling to manage API failures gracefully.
• Limit concurrent requests to avoid hitting rate limits.

Example:

import asyncio
from tavily import AsyncTavilyClient

# Initialize Tavily client
tavily_client = AsyncTavilyClient("tvly-YOUR_API_KEY")

async def fetch_and_gather():
    queries = ["latest AI trends", "future of quantum computing"]

    # Perform search and continue even if one query fails (using return_exceptions=True)
    try:
        responses = await asyncio.gather(*(tavily_client.search(q) for q in queries), return_exceptions=True)

        # Handle responses and print
        for response in responses:
            if isinstance(response, Exception):
                print(f"Search query failed: {response}")
            else:
                print(response)

    except Exception as e:
        print(f"Error during search queries: {e}")

# Run the function
asyncio.run(fetch_and_gather())

​

Optimizing search results with post-processing techniques

When working with Tavily’s Search API, refining search results through post-processing techniques can significantly enhance the relevance of the retrieved information.

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Combining LLMs with Keyword Filtering

One of the most effective ways to refine search results is by using a combination of LLMs and deterministic keyword filtering.

• LLMs can analyze search results in a more contextual and semantic manner, understanding the deeper meaning of the text.
• Keyword filtering offers a rule-based approach to eliminate irrelevant results based on predefined terms, ensuring a balance between flexibility and precision.

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How it works

By applying keyword filters before or after processing results with an LLM, you can:

• Remove results that contain specific unwanted terms.
• Prioritize articles that contain high-value keywords relevant to your use case.
• Improve efficiency by reducing the number of search results requiring further LLM processing.

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Utilizing metadata for improved post-processing

Tavily’s Search API provides rich metadata that can be leveraged to refine and prioritize search results. By incorporating metadata into post-processing logic, you can improve precision in selecting the most relevant content.

​

Key metadata fields and their Functions

• title: Helps in identifying articles that are more likely to be relevant based on their headlines. Filtering results by keyword occurrences in the title can improve result relevancy.
• raw_content: Provides the extracted content from the web page, allowing deeper analysis. If the content does not provide enough information, raw content can be useful for further filtering and ranking. You can also use the Extract API with a two-step extraction process. For more information, see Best Practices for Extract API.
• score: Represents the relevancy between the query and the retrieved content snippet. Higher scores typically indicate better matches.
• content: Offers a general summary of the webpage, providing a quick way to gauge relevance without processing the full content. When search_depth is set to advanced, the content is more closely aligned with the query, offering valuable insights.

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Enhancing post-processing with metadata

By leveraging these metadata elements, you can:

• Sort results based on scores, prioritizing high-confidence matches.
• Perform additional filtering based on title or content to refine search results.

​

Understanding the score Parameter

Tavily assigns a score to each search result, indicating how well the content aligns with the query. This score helps in ranking and selecting the most relevant results.

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What does the score mean?

• The score is a numerical measure of relevance between the content and the query.
• A higher score generally indicates that the result is more relevant to the query.
• There is no fixed threshold that determines whether a result is useful. The ideal score cutoff depends on the specific use case.

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Best practices for using scores

• Set a minimum score threshold to exclude low-relevance results automatically.
• Analyze the distribution of scores within a search response to adjust thresholds dynamically.
• Combine similarity scores with other metadata fields (e.g., url, content) to improve ranking strategies.

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Using regex-based data extraction

In addition to leveraging LLMs and metadata for refining search results, Python’s re.search and re.findall methods can play a crucial role in post-processing by allowing you to parse and extract specific data from the raw_content. These methods enable pattern-based filtering and extraction, enhancing the precision and relevance of the processed results.

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Benefits of using re.search and re.findall

• Pattern Matching: Both methods are designed to search for specific patterns in text, which is ideal for structured data extraction.
• Efficiency: These methods help automate the extraction of specific elements from large datasets, improving post-processing efficiency.
• Flexibility: You can define custom patterns to match a variety of data types, from dates and addresses to keywords and job titles.

​

How they work

• re.search: Scans the content for the first occurrence of a specified pattern and returns a match object, which can be used to extract specific parts of the text.

Example:

import re
text = "Company: Tavily, Location: New York"
match = re.search(r"Location: (\w+)", text)
if match:
    print(match.group(1))  # Output: New York

• re.findall: Returns a list of all non-overlapping matches of a pattern in the content, making it suitable for extracting multiple instances of a pattern.

Example:

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)
print(emails)  # Output: ['john@example.com', 'support@tavily.com']

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Common use cases for post-processing

• Content Filtering: Use re.search to identify sections or specific patterns in content (e.g., dates, locations, company names).

• Data Extraction: Use re.findall to extract multiple instances of specific data points (e.g., phone numbers, emails).

• Improving Relevance: Apply regex patterns to remove irrelevant content, ensuring that only the most pertinent information remains.

By leveraging post-processing techniques such as LLM-assisted filtering, metadata analysis, and score-based ranking, along with regex-based data extraction, you can optimize Tavily’s Search API results for better relevance. Incorporating these methods into your workflow will help you extract high-quality insights tailored to your needs.