Search then Enrich

The point of this pattern is that a search response is already a valid pipe input — you don't have to reshape anything in between. Search for a list of people, then hand search.results directly to a pipes request to enrich every row.

This example finds senior software-development people, then resolves a work email and mobile number for each of them.

import { Pipe0 } from "@pipe0/client";

const pipe0 = new Pipe0({ apiKey: process.env.PIPE0_API_KEY });

// 1. Search for records.
const search = await pipe0.searches.search({
  config: {
    environment: "production",
  },
  search: {
    search_id: "people:profiles:amplemarket@2",
    config: {
      limit: 10,
      filters: {
        current_employer_linkedin_industries: [
          "Software Development",
        ],
        current_seniority_levels: [
          "C-Suite",
          "VP",
          "Head",
          "Director",
        ],
      },
    },
  },
});

// 2. Feed the search results straight into the enrichment request.
const enriched = await pipe0.pipes.pipe({
  config: {
    environment: "production",
  },
  pipes: [
    {
      pipe_id: "person:workemail:profileurl:waterfall@1",
    },
    {
      pipe_id: "person:mobile:profileurl:waterfall@1",
    },
  ],
  input: search.results,
});
console.log(enriched);

import { Pipe0 } from "@pipe0/client";

const pipe0 = new Pipe0({ apiKey: process.env.PIPE0_API_KEY });

// 1. Search for records.
const search = await pipe0.searches.search({
  config: {
    environment: "production",
  },
  search: {
    search_id: "people:profiles:amplemarket@2",
    config: {
      limit: 10,
      filters: {
        current_employer_linkedin_industries: [
          "Software Development",
        ],
        current_seniority_levels: [
          "C-Suite",
          "VP",
          "Head",
          "Director",
        ],
      },
    },
  },
});

// 2. Feed the search results straight into the enrichment request.
const enriched = await pipe0.pipes.pipe({
  config: {
    environment: "production",
  },
  pipes: [
    {
      pipe_id: "person:workemail:profileurl:waterfall@1",
    },
    {
      pipe_id: "person:mobile:profileurl:waterfall@1",
    },
  ],
  input: search.results,
});
console.log(enriched);

import requests

headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

# 1. Search for records.
search = requests.post(
    "https://api.pipe0.com/v1/search/run/sync",
    headers=headers,
    json={
        "config": {
            "environment": "production",
        },
        "search": {
            "search_id": "people:profiles:amplemarket@2",
            "config": {
                "limit": 10,
                "filters": {
                    "current_employer_linkedin_industries": [
                        "Software Development",
                    ],
                    "current_seniority_levels": [
                        "C-Suite",
                        "VP",
                        "Head",
                        "Director",
                    ],
                },
            },
        },
    },
).json()

# 2. Feed the search results straight into the enrichment request.
enriched = requests.post(
    "https://api.pipe0.com/v1/pipes/run/sync",
    headers=headers,
    json={
        "config": {
            "environment": "production",
        },
        "pipes": [
            {
                "pipe_id": "person:workemail:profileurl:waterfall@1",
            },
            {
                "pipe_id": "person:mobile:profileurl:waterfall@1",
            },
        ],
        "input": search["results"],
    },
).json()
print(enriched)

import requests

headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

# 1. Search for records.
search = requests.post(
    "https://api.pipe0.com/v1/search/run/sync",
    headers=headers,
    json={
        "config": {
            "environment": "production",
        },
        "search": {
            "search_id": "people:profiles:amplemarket@2",
            "config": {
                "limit": 10,
                "filters": {
                    "current_employer_linkedin_industries": [
                        "Software Development",
                    ],
                    "current_seniority_levels": [
                        "C-Suite",
                        "VP",
                        "Head",
                        "Director",
                    ],
                },
            },
        },
    },
).json()

# 2. Feed the search results straight into the enrichment request.
enriched = requests.post(
    "https://api.pipe0.com/v1/pipes/run/sync",
    headers=headers,
    json={
        "config": {
            "environment": "production",
        },
        "pipes": [
            {
                "pipe_id": "person:workemail:profileurl:waterfall@1",
            },
            {
                "pipe_id": "person:mobile:profileurl:waterfall@1",
            },
        ],
        "input": search["results"],
    },
).json()
print(enriched)

# 1. Search and capture the results array.
RESULTS=$(curl -s -X POST "https://api.pipe0.com/v1/search/run/sync" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "config": {
    "environment": "production"
  },
  "search": {
    "search_id": "people:profiles:amplemarket@2",
    "config": {
      "limit": 10,
      "filters": {
        "current_employer_linkedin_industries": [
          "Software Development"
        ],
        "current_seniority_levels": [
          "C-Suite",
          "VP",
          "Head",
          "Director"
        ]
      }
    }
  }
}' | jq '.results')

# 2. Enrich, feeding the search results in as the input array.
curl -X POST "https://api.pipe0.com/v1/pipes/run/sync" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d "$(jq -n --argjson rows "$RESULTS" '{
  "config": {
    "environment": "production"
  },
  "pipes": [
    {
      "pipe_id": "person:workemail:profileurl:waterfall@1"
    },
    {
      "pipe_id": "person:mobile:profileurl:waterfall@1"
    }
  ]
} + { input: $rows }')"

# 1. Search and capture the results array.
RESULTS=$(curl -s -X POST "https://api.pipe0.com/v1/search/run/sync" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "config": {
    "environment": "production"
  },
  "search": {
    "search_id": "people:profiles:amplemarket@2",
    "config": {
      "limit": 10,
      "filters": {
        "current_employer_linkedin_industries": [
          "Software Development"
        ],
        "current_seniority_levels": [
          "C-Suite",
          "VP",
          "Head",
          "Director"
        ]
      }
    }
  }
}' | jq '.results')

# 2. Enrich, feeding the search results in as the input array.
curl -X POST "https://api.pipe0.com/v1/pipes/run/sync" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d "$(jq -n --argjson rows "$RESULTS" '{
  "config": {
    "environment": "production"
  },
  "pipes": [
    {
      "pipe_id": "person:workemail:profileurl:waterfall@1"
    },
    {
      "pipe_id": "person:mobile:profileurl:waterfall@1"
    }
  ]
} + { input: $rows }')"

How it fits together

The search outputs name, job_title, profile_url and company_domain for each person.
Passing search.results as the pipe input works because search results are returned as fully expanded field values — the API accepts them directly.
The two waterfall pipes read each row's profile_url and add work_email and mobile.

For large result sets, swap pipes.pipe for pipeInBatches — it splits search.results into chunks and runs them with bounded concurrency, so the same code handles 10 rows or 10,000.