WebPOSTlive
Search-grounded answer
Get a search-grounded answer for a question.
$0.01
per call
Success
100%
p50 latency
18.95s
p95 latency
20.09s
Calls (30d)
3
Provider contract
- Call path
- /routes//web/answer
stableenrich-exadefault
- Method
- POST
- Selected provider
- stableenrich-exadefault
- Provider price
- $0.01
- Settlement
- Base USDC · x402 · 5% h402 fee
Quickstart
Both commands call the selected provider directly.
cURL
curl -X POST '/routes/stableenrich-exa/web/answer' \
-H 'content-type: application/json' \
-d '{"query":"What is retrieval augmented generation?"}'CLI
h402 call 'web/answer' --provider 'stableenrich-exa' --method POST --json '{"query":"What is retrieval augmented generation?"}'Parameters & live test
pays real USDCstableenrich-exadefault$0.010/call
Fill the request fields, quote the exact per-call total, then choose x402 or eligible bonus credit explicitly before sending. Only the selected provider's listed parameters are accepted.
querystringrequired1–1000 charsQuestion to answer using web search context.
POST/routes/stableenrich-exa/web/answer
Request body
{
"query": "What is retrieval augmented generation?"
}ResponseExample response
{ "data": { "requestId": "d681c6731fdca295e3d56a55e3ed1e3f", "answer": "Retrieval-augmented generation (RAG) is an AI framework that improves large language model (LLM) responses by connecting them to external, private, or up-to-date data sources [1][2][3]. Instead of relying solely on static training data, RAG retrieves relevant information from outside sources—such as internal databases, documents, or the internet—and provides it to the model alongside the user's prompt [1][4][5]. This grounds the LLM in specific facts, increasing accuracy, reducing hallucinations, and allowing access to information that was not available during the model's original training [1][4][6][7].\n\nThe process generally involves three key stages: \n\n1. Ingestion: Data is converted into numerical vector embeddings and stored in a database to enable efficient semantic search [6][3][7].\n2. Retrieval: When a user submits a query, the system searches the database to find the most relevant snippets of information [4][6][3].\n3. Generation: These retrieved snippets are appended to the original prompt, allowing the LLM to synthesize a context-aware, grounded response [4][8][3].\n\nRAG is widely used to keep AI tools current and domain-specific without the high cost and complexity of retraining models [4][5][7].", "citations": [ { "id": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation", "title": "Retrieval-augmented generation - Wikipedia", "url": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation" }, { "id": "https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation", "title": "What is Retrieval-Augmented Generation (RAG) in AI? | Definition from TechTarget", "url": "https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation", "publishedDate": "2024-12-30T12:00:00.000Z", "author": "By: George Lawton" }, { "id": "https://www.elastic.co/what-is/retrieval-augmented-generation", "title": "What is Retrieval Augmented Generation (RAG)? | A Comprehensive RAG Guide | Elastic", "url": "https://www.elastic.co/what-is/retrieval-augmented-generation", "publishedDate": "2023-07-12T17:40:07.000Z" } ], "costDollars": { "total": 0.005 } }, "h402": { "routeId": "web/answer", "provider": "stableenrich-exa", "selectedCandidateId": "web/answer/stableenrich-exa", "routing": "manual", "paidBy": "x402-exact" } }
Providers
Choose a provider for this task. The catalog recommends the current default from quality, reliability, and price; your selected provider stays pinned. Listed prices are provider prices; settlement adds a 5% h402 fee.
stableenrich-exadefaultcheapest
x402mpp
100
- Success
- 100%
- p50
- 2.58s
- Calls
- 1
100%
$0.01$0.010/call
blockrun-grok
x402
100
- Success
- 100%
- p50
- 18.95s
- Calls
- 2
100%
Dynamic $0.025-$1.25; quote required$0.263/call
Last successful call: 27d ago· Aggregated by h402 — not the source provider