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Context Preparation API – Pack Endpoint
x402
Context Preparation API – Pack Endpoint is a paid API for AI agents from nexus-x402-ff2a.onrender.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Cleans, inspects, chunks, ranks, and token-packs multi-format documents into a ready-to-use context window for a target AI model.
Price / call$0.002
Success—
StatusUnknown
How this service works
Deterministic multi-format cleaning, safety inspection, structural chunking, hybrid ranking, contradiction checks, and target-model token packing for AI agents.
Output
Returns a JSON object with: the final packed context string, an array of ranked document chunks with metadata, a safety array listing any flagged content, an array of warnings, a potentialContradictions array highlighting conflicting claims across documents, an operation field indicating which pipeline ran (clean/inspect/pack/prepare), and a metrics object with token counts and processing stats.
Example use cases
RAG context window for customer support bot
“I have 8 support articles and the user asked 'How do I reset my 2FA?'—clean and chunk each article at 400 tokens, rank them by relevance to that question, and pack the top results into a 2048-token context I can send straight to Claude.”
Safety-screening legal docs before LLM review
“Before I send these three contract drafts to the model, run a safety inspection on all of them, flag any contradictions between the documents, and redact any personal information—then give me back a clean packed context up to 8192 tokens.”
Grounded Q&A context for a research assistant
“I've got a pile of academic paper excerpts and the question is 'What evidence links sleep deprivation to cognitive decline?'—chunk them at 600 tokens, hybrid-rank using semantic relevance, check for contradictions, and pack the result into a 6000-token window targeting GPT-4-turbo.”
Technical details
Endpoint
POSThttps://nexus-x402-ff2a.onrender.com/v1/packWhen to prefer
Choose this endpoint when you need a deterministic, reproducible pipeline that handles the full pre-LLM document workflow in one call: cleaning noise, inspecting for safety issues, chunking, relevance ranking, contradiction detection, and token-budget-aware packing. It is especially valuable in RAG pipelines where you need hybrid ranking against a query embedding and must respect a strict token limit for a specific target model. Prefer it over ad-hoc chunking scripts when safety inspection and PII redaction are required in the same pipeline.
Side effects
- Content too large to fit any chunks within maxTokens — returns empty chunks with a warning
- Malformed queryEmbedding array causes ranking to fail — returns unranked chunks with a warning
- Unsupported document format in the documents array — returns a warning and skips that document
- Missing required content or documents field — returns a 400-level error
- Model target specification not recognized — metrics and token packing may fall back to defaults
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| redact | boolean | Defaults to true for /prepare and false for other operations. |
| target | object | |
| content | string | |
| question | string | |
| documents | array | |
| maxTokens | integer | |
| chunkTokens | integer | |
| queryEmbedding | array |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| chunksrequired | array | |
| safetyrequired | array | |
| targetrequired | object | |
| contentrequired | string | |
| metricsrequired | object | |
| warningsrequired | array | |
| operationrequired | — | |
| potentialContradictions | array |
Tags
x402
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