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": "current_title", "type": "like", "value": "Developer"},
{"column": "current_title", "type": "like", "value": "Software Engineer"},
{"column": "current_title", "type": "like", "value": "Développeur"}
]
},
{
"op": "or",
"conditions": [
{"column": "skill", "type": "=", "value": "Python (Programming Language)"},
{"column": "skill", "type": "=", "value": "Python"}
]
},
{
"op": "or",
"conditions": [
{"column": "current_job_location", "type": "like", "value": "Paris"},
{"column": "profile_location", "type": "like", "value": "Paris"}
]
}
]
},
"requested_count": null,
"simple_filters": {
"current_title": ["Developer", "Software Engineer", "Développeur"],
"skill": ["Python"],
"current_job_location": ["Paris"],
"profile_location": ["Paris"]
},
"total_results": null,
"total_results_is_capped": null,
"cached": false
}
AI Search
Text to Filters
Convert natural language queries into structured filters
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": "current_title", "type": "like", "value": "Developer"},
{"column": "current_title", "type": "like", "value": "Software Engineer"},
{"column": "current_title", "type": "like", "value": "Développeur"}
]
},
{
"op": "or",
"conditions": [
{"column": "skill", "type": "=", "value": "Python (Programming Language)"},
{"column": "skill", "type": "=", "value": "Python"}
]
},
{
"op": "or",
"conditions": [
{"column": "current_job_location", "type": "like", "value": "Paris"},
{"column": "profile_location", "type": "like", "value": "Paris"}
]
}
]
},
"requested_count": null,
"simple_filters": {
"current_title": ["Developer", "Software Engineer", "Développeur"],
"skill": ["Python"],
"current_job_location": ["Paris"],
"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
The response contains
/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
| Action | Credits |
|---|---|
| Filter generation | 1 credit |
Request Body
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"
string
required
Category to generate filters for:
"people" or "company" ("companies" is also accepted).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
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.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.object
The
simple_filters object returned by the previous call. Required alongside previous_query for incremental refinement.Response
string
The category for the filters:
"people" or "companies".object
integer | null
Count detected in the query if specified (e.g.,
"50 devs" → 50), otherwise null.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.integer | null
Number of matching records. Populated only when
with_total is true. Capped at 10,000.boolean | null
true when total_results reached the 10,000 cap — the real count is higher (display it as “10,000+”).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’squery 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"]
}
}
One-shot search
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:
integer
default:"25"
Number of results to return. Maximum: 100.
query_interpretation (how the AI read your query: original_query, the advanced_filters it applied, credits_used and credits_remaining), total, page, page_size, has_next_page 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
People filters
People filters
{
"query": "développeurs Python à Paris",
"category": "people"
}
Lookalike filters
Lookalike filters
{
"query": "lookalike https://www.linkedin.com/in/thomas-kurian-469b6219/",
"category": "people",
"lookalike_use_case": "recruiter"
}
Company filters with count
Company filters with count
{
"query": "50 fintech startups in London",
"category": "company"
}
With result count
With result count
{
"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": "current_title", "type": "like", "value": "Developer"},
{"column": "current_title", "type": "like", "value": "Software Engineer"},
{"column": "current_title", "type": "like", "value": "Développeur"}
]
},
{
"op": "or",
"conditions": [
{"column": "skill", "type": "=", "value": "Python (Programming Language)"},
{"column": "skill", "type": "=", "value": "Python"}
]
},
{
"op": "or",
"conditions": [
{"column": "current_job_location", "type": "like", "value": "Paris"},
{"column": "profile_location", "type": "like", "value": "Paris"}
]
}
]
},
"requested_count": null,
"simple_filters": {
"current_title": ["Developer", "Software Engineer", "Développeur"],
"skill": ["Python"],
"current_job_location": ["Paris"],
"profile_location": ["Paris"]
},
"total_results": null,
"total_results_is_capped": null,
"cached": false
}