Skip to main content
POST
/
search
/
llm
/
filters
curl -X POST https://api.dataforb2b.ai/search/llm/filters \
  -H "api_key: YOUR_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "développeurs Python à Paris",
    "category": "people"
  }'

import requests

response = requests.post(
    "https://api.dataforb2b.ai/search/llm/filters",
    headers={
        "api_key": "YOUR_api_key",
        "Content-Type": "application/json"
    },
    json={
        "query": "développeurs Python à Paris",
        "category": "people"
    }
)

data = response.json()
# Use filters with /search/people
search_response = requests.post(
    "https://api.dataforb2b.ai/search/people",
    headers={
        "api_key": "YOUR_api_key",
        "Content-Type": "application/json"
    },
    json={
        "filters": data["filters"],
        "count": data["requested_count"] or 25
    }
)

const response = await fetch('https://api.dataforb2b.ai/search/llm/filters', {
  method: 'POST',
  headers: {
    'api_key': 'YOUR_api_key',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    query: 'développeurs Python à Paris',
    category: 'people'
  })
});

const data = await response.json();
// Use filters with /search/people
const searchResponse = await fetch('https://api.dataforb2b.ai/search/people', {
  method: 'POST',
  headers: {
    'api_key': 'YOUR_api_key',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    filters: data.filters,
    count: data.requested_count || 25
  })
});

{
  "category": "people",
  "filters": {
    "op": "and",
    "conditions": [
      {
        "op": "or",
        "conditions": [
          {"column": "skills", "type": "like", "value": "Python"},
          {"column": "headline", "type": "like", "value": "Python"},
          {"column": "current_title", "type": "like", "value": "Python Developer"}
        ]
      },
      {
        "op": "or",
        "conditions": [
          {"column": "location", "type": "like", "value": "Paris"},
          {"column": "country", "type": "eq", "value": "FR"}
        ]
      }
    ]
  },
  "requested_count": null,
  "simple_filters": {
    "skill": ["Python"],
    "profile_location": ["Paris"]
  },
  "total_results": null,
  "total_results_is_capped": null,
  "cached": false
}
Convert a natural language query into structured filters that can be used with /search/people or /search/companies endpoints.
For the most relevant results from a natural language query, use Reasoning Search: an AI agent probes the database, asks clarifying questions when needed, and builds the best possible search. Use Text to Filters when you only need the filters, or the one-shot search below for a single fast query-to-results call.

​
Credit Cost

ActionCredits
Filter generation1 credit

​
Request Body

​
query
string
required
Natural language search query (minimum 3 characters).Examples:
  • "développeurs Python à Paris"
  • "CEOs of fintech startups in London"
  • "50 data scientists with 5+ years experience"
​
category
string
required
Category to generate filters for: "people" or "company".
​
lookalike_use_case
string
default:"sales"
Use case for lookalike search. Only applies when the query contains a LinkedIn profile URL (e.g., linkedin.com/in/...), which is automatically detected.
  • "sales" — Generate filters similar from a sales/prospecting perspective
  • "recruiter" — Generate filters similar from a recruiting perspective
​
with_total
boolean
default:"false"
When true, runs a count on the generated filters and returns total_results — the number of matching records. The count is capped at 10,000.
​
previous_query
string
The query from a previous call, for incremental refinement. Provide it together with previous_filters: the model re-extracts the full filter set anchored on what was already detected, so the output stays stable when the user edits their query. Filters whose intent is unchanged are kept identical; filters the new query no longer mentions are dropped.
​
previous_filters
object
The simple_filters object returned by the previous call. Required alongside previous_query for incremental refinement.

​
Response

​
category
string
The category for the filters ("people" or "company").
​
filters
object
Structured filters in FilterGroup format, directly usable with /search/people or /search/companies.
​
requested_count
integer | null
Count detected in the query if specified (e.g., "50 devs" → 50), otherwise null.
​
simple_filters
object | null
The flat key/value filters detected by the model, before expansion into the nested FilterGroup. Pass this object back as previous_filters on the next call to enable incremental refinement.
​
total_results
integer | null
Number of matching records. Populated only when with_total is true. Capped at 10,000.
​
total_results_is_capped
boolean | null
true when total_results reached the 10,000 cap — the real count is higher (display it as “10,000+”).
​
cached
boolean
true when the filters were served from cache (no LLM call was made for this request).

​
Incremental refinement

When a user edits a query, pass the previous call’s query and simple_filters back as previous_query and previous_filters. The model refines the existing filters instead of recomputing from scratch — keeping unchanged criteria stable and only applying the delta. Both parameters must be provided together. 
{
  "query": "VP of Sales at SaaS companies",
  "category": "people",
  "with_total": true
}

{
  "query": "VP of Sales at SaaS companies in New York",
  "category": "people",
  "with_total": true,
  "previous_query": "VP of Sales at SaaS companies",
  "previous_filters": {
    "current_title": ["VP Sales", "Vice President of Sales"],
    "current_company_category": ["SaaS"]
  }
}
POST /search/llm takes the same query, category and lookalike_use_case parameters but runs the search immediately and returns results in a single call — no separate /search/people request needed. Two extra parameters:
​
count
integer
default:"25"
Number of results to return. Maximum: 100.
​
enrich_live
boolean
default:"true"
Whether to enrich results with live data.
  • true — Returns fresh, up-to-date data (1.5 credits per result)
  • false — Returns cached data (0.75 credits per result)
The response contains query_interpretation (how the AI read your query, including the advanced_filters it applied), total, count and results — profiles or companies in the same structure as /search/people / /search/companies. 
curl -X POST https://api.dataforb2b.ai/search/llm \
  -H "api_key: YOUR_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "développeurs Python à Paris",
    "category": "people",
    "count": 10
  }'
Pagination is not available on this endpoint. To paginate, reuse query_interpretation.advanced_filters with /search/people or /search/companies and set an offset. For multi-turn refinement and clarifying questions, use Reasoning Search instead.

​
Examples

{
  "query": "développeurs Python à Paris",
  "category": "people"
}

{
  "query": "lookalike https://www.linkedin.com/in/thomas-kurian-469b6219/",
  "category": "people",
  "lookalike_use_case": "recruiter"
}

{
  "query": "50 fintech startups in London",
  "category": "company"
}

{
  "query": "data scientists at Google",
  "category": "people",
  "with_total": true
}

curl -X POST https://api.dataforb2b.ai/search/llm/filters \
  -H "api_key: YOUR_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "développeurs Python à Paris",
    "category": "people"
  }'

import requests

response = requests.post(
    "https://api.dataforb2b.ai/search/llm/filters",
    headers={
        "api_key": "YOUR_api_key",
        "Content-Type": "application/json"
    },
    json={
        "query": "développeurs Python à Paris",
        "category": "people"
    }
)

data = response.json()
# Use filters with /search/people
search_response = requests.post(
    "https://api.dataforb2b.ai/search/people",
    headers={
        "api_key": "YOUR_api_key",
        "Content-Type": "application/json"
    },
    json={
        "filters": data["filters"],
        "count": data["requested_count"] or 25
    }
)

const response = await fetch('https://api.dataforb2b.ai/search/llm/filters', {
  method: 'POST',
  headers: {
    'api_key': 'YOUR_api_key',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    query: 'développeurs Python à Paris',
    category: 'people'
  })
});

const data = await response.json();
// Use filters with /search/people
const searchResponse = await fetch('https://api.dataforb2b.ai/search/people', {
  method: 'POST',
  headers: {
    'api_key': 'YOUR_api_key',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    filters: data.filters,
    count: data.requested_count || 25
  })
});

{
  "category": "people",
  "filters": {
    "op": "and",
    "conditions": [
      {
        "op": "or",
        "conditions": [
          {"column": "skills", "type": "like", "value": "Python"},
          {"column": "headline", "type": "like", "value": "Python"},
          {"column": "current_title", "type": "like", "value": "Python Developer"}
        ]
      },
      {
        "op": "or",
        "conditions": [
          {"column": "location", "type": "like", "value": "Paris"},
          {"column": "country", "type": "eq", "value": "FR"}
        ]
      }
    ]
  },
  "requested_count": null,
  "simple_filters": {
    "skill": ["Python"],
    "profile_location": ["Paris"]
  },
  "total_results": null,
  "total_results_is_capped": null,
  "cached": false
}