Perplexity Context Embeddings via Locus x402

Name: Perplexity Context Embeddings via Locus x402
Price: 0.001 USD

x402

Perplexity Context Embeddings via Locus x402 is a paid API for AI agents from perplexity.x402.paywithlocus.com, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-15).

Generates context-aware embeddings for text chunks using a shared parent document as grounding context, via Perplexity's contextualized embedding models.

Price / call$0.001

Seller volume →—

Success—

StatusUnknown

How this service works

AI-powered search — Sonar chat with real-time web grounding, web search, and embeddings.

Output

Returns a JSON object with an embedding data payload (vectors for each chunk), a payment confirmation showing 0.001 USDC settled, and a request ID with a status URL for tracking the request.

Example use cases

RAG pipeline for legal documents

“I have a long contract document and I've split it into 20 sentence chunks — embed all those chunks using the 'pplx-embed-context-v1-4b' model with the full contract as context, so I can build a retrieval system that understands each clause in the context of the whole agreement.”

Semantic search over product manual

“Take these 50 sections I've extracted from our product manual and generate context-aware embeddings for each one using 'pplx-embed-context-v1-0.6b', with the entire manual text provided as the parent document so the embeddings reflect each section's role in the bigger picture.”

Academic paper chunk retrieval

“I need to embed 15 paragraph chunks from this academic paper using Perplexity's contextualized embedding model — use the 4B model and pass in the full paper as the document context so each chunk embedding reflects its meaning within the whole paper.”

Technical details

Endpoint

POSThttps://perplexity.x402.paywithlocus.com/perplexity/context-embed

When to prefer

Choose this endpoint when you need embeddings that are grounded in a shared parent document context — ideal for RAG pipelines where chunk meaning depends heavily on surrounding content. Prefer it over standard embedding APIs when document-level context significantly affects the semantics of individual chunks, such as legal documents, academic papers, or technical manuals. The pay-per-call x402 model makes it accessible without subscription commitments.

Side effects

Invalid model name returns an error — only 'pplx-embed-context-v1-0.6b' and 'pplx-embed-context-v1-4b' are valid
Empty or malformed chunks array may result in empty embeddings or a 400 error
Payment failure or insufficient USDC balance will block the request
Excessively long document or chunk arrays may exceed token limits
Missing required fields (model, chunks, or document) will cause a request error

Input

Schema · Request body

Field	Type	Description
model	string
chunks	array
document	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {},
    "payment": {
      "scheme": "exact",
      "settledUsdc": "0.001000",
      "authorizedMaxUsdc": "0.001000"
    },
    "request": {
      "id": "00000000-0000-4000-8000-000000000000",
      "statusUrl": "/requests/00000000-0000-4000-8000-000000000000"
    },
    "success": true
  }
}

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Example use cases

RAG pipeline for legal documents

Semantic search over product manual

Academic paper chunk retrieval

Technical details

Endpoint

POSThttps://perplexity.x402.paywithlocus.com/perplexity/context-embed

When to prefer

Side effects

Invalid model name returns an error — only 'pplx-embed-context-v1-0.6b' and 'pplx-embed-context-v1-4b' are valid
Empty or malformed chunks array may result in empty embeddings or a 400 error
Payment failure or insufficient USDC balance will block the request
Excessively long document or chunk arrays may exceed token limits
Missing required fields (model, chunks, or document) will cause a request error

Input

Schema · Request body

Field	Type	Description
model	string
chunks	array
document	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {},
    "payment": {
      "scheme": "exact",
      "settledUsdc": "0.001000",
      "authorizedMaxUsdc": "0.001000"
    },
    "request": {
      "id": "00000000-0000-4000-8000-000000000000",
      "statusUrl": "/requests/00000000-0000-4000-8000-000000000000"
    },
    "success": true
  }
}