nvidia-nim-unified

---

name: nvidia-nim-unified description: Use when a task needs NVIDIA NIM OCR, page elements, table structure, graphic element detection, passage reranking, or text embedding through one shared routing layer. Selects the right capability or multi-step workflow from a free-form query, prepares requests, and can invoke the configured endpoint. Also supports RAG pipeline mode: file/URL → OCR → semantic chunking → structured output. short-description: Unified NVIDIA NIM router for OCR, layout, reranking, embedding, and RAG pipelines aliases: [nim, nvidia-nim] trigger_keywords: [extract text, OCR, document layout, page elements, table structure, table cells, chart labels, rerank passages, detect layout, image to text, pdf text extraction, passage ranking, search relevance, embed text, semantic search, vector embedding, semantic chunking, RAG pipeline] metadata: {openclaw: {emoji: "🟩", homepage: "https://github.com/JithendraNara/nvidia-nim-unified-skill", requires: {anyBins: [python3, python]}}}

NVIDIA NIM Unified

This skill wraps six NVIDIA NIM capabilities behind one routing layer:

• ocr
• page_elements
• table_structure
• graphic_elements
• rerank
• embed

Do not pretend these are one physical OpenAPI endpoint. They are different capabilities with different request shapes. Treat this skill as the abstraction layer that hides those differences from the agent.

Invocation Patterns

Claude Code

Explicit invocation:

/nvidia-nim
/nvidia-nim plan --task-query "extract text from invoice"
/nim invoke --capability ocr --image-url https://example.com/doc.png

Implicit trigger: Claude Code activates this skill when descriptions contain keywords like "extract text", "OCR", "image to text", "pdf text", "document layout", "table structure", "rerank", "passage ranking", "detect chart", "page elements".

Codex (OpenAI)

Explicit invocation:

$nvidia-nim
$nvidia-nim-unified
$nvidia-nim plan --task-query "extract text from invoice"
$nvidia-nim build-request --capability ocr --image-url https://example.com/doc.png

Implicit trigger: Codex activates this skill when queries mention OCR, layout detection, table extraction, chart understanding, or passage reranking.

OpenClaw

The skill loads automatically when the task description matches trigger keywords. For explicit invocation, reference the skill by name nvidia-nim-unified or nim.

Use This Skill When

• The user asks to extract text from images or documents.
• The user asks to detect document layout, page sections, charts, tables, or headers.
• The user asks to keep table or chart structure while processing an image.
• The user asks to rerank passages against a query.
• The user asks to embed text for semantic search or vector storage.
• The user asks to process files through a RAG pipeline (extract → chunk → format).
• The user wants one shared NVIDIA workflow that Codex, Claude, and OpenClaw can all call.

Files

• {baseDir}/references/nim-capabilities.json: normalized machine-readable catalog of capabilities, inputs, response shapes, routing hints, and source specs.
• {baseDir}/references/openapi/*.yaml: vendored source OpenAPI specs for all supported capabilities.
• {baseDir}/references/nvidia-nim-config.example.json: shared config template for endpoint URLs and auth env names.
• {baseDir}/scripts/nim_router.py: planner, request builder, and optional HTTP invoker.
• nvidia-nim-unified-skill.yaml: lightweight manifest for non-Codex consumers.

Core Workflow

1. Start with a natural-language task query.
2. Run the planner:

python3 {baseDir}/scripts/nim_router.py plan \
  --task-query "extract text from an invoice image and preserve table structure"

3. If the plan returns a single capability, build or invoke that request directly.
4. If the plan returns a workflow, execute the listed capabilities in sequence.
5. For exact payload or routing details, read {baseDir}/references/nim-capabilities.json.

Request Building

Build a request without sending it. For the four CV capabilities, the router accepts an https:// URL, a local image path, or an existing data:image/... URL and normalizes it to the base64 data-URL format that the managed NVIDIA endpoint expects.

python3 {baseDir}/scripts/nim_router.py build-request \
  --capability ocr \
  --image-url "https://example.com/invoice.png"

Rerank example:

python3 {baseDir}/scripts/nim_router.py build-request \
  --capability rerank \
  --query-text "Which chunk mentions H100 memory bandwidth?" \
  --passage "A100 reaches over 2 TB/s." \
  --passage "H100 offers 3 TB/s of memory bandwidth per GPU."

Invocation

If endpoints and auth are configured, the same script can invoke the request:

python3 {baseDir}/scripts/nim_router.py invoke \
  --capability page_elements \
  --image-url "https://example.com/page.png"

Use --config {baseDir}/references/nvidia-nim-config.example.json only after copying that file and replacing example values with real endpoints or env names. By default, the bundled config targets NVIDIA's managed ai.api.nvidia.com endpoints for all five capabilities.

Routing Rules

• Text extraction only: prefer ocr
• Page layout detection: prefer page_elements
• Table cell, row, column, or header detection: prefer table_structure
• Chart labels, axes, legends, or marks: prefer graphic_elements
• Search relevance or document chunk ranking: prefer rerank
• OCR plus ranking: use workflow ocr -> rerank
• OCR plus tables: use workflow page_elements -> table_structure -> ocr
• OCR plus chart understanding: use workflow page_elements -> graphic_elements -> ocr

Configuration

Default auth expectations:

• all five managed endpoints: NVIDIA_API_KEY
• self-hosted overrides: optional per-capability URL override plus bearer env if you are not using the managed API

Default endpoint env names:

• NVIDIA_NIM_OCR_URL
• NVIDIA_NIM_PAGE_ELEMENTS_URL
• NVIDIA_NIM_TABLE_STRUCTURE_URL
• NVIDIA_NIM_GRAPHIC_ELEMENTS_URL

These may be overridden in the shared config JSON.

Examples

Quick Start

Plan a task from a free-form query:

python3 {baseDir}/scripts/nim_router.py plan --task-query "extract text from invoice"

Build and invoke in one step (with API key configured):

python3 {baseDir}/scripts/nim_router.py invoke \
  --capability ocr \
  --image-url "https://example.com/invoice.png"

Capability Examples

OCR (Text Extraction)

Extract text from an image:

python3 {baseDir}/scripts/nim_router.py invoke \
  --capability ocr \
  --image-url "https://example.com/receipt.jpg"

Build request without invoking:

python3 {baseDir}/scripts/nim_router.py build-request \
  --capability ocr \
  --image-url "https://example.com/document.png"

Page Elements (Layout Detection)

Detect page layout, headers, paragraphs, sections:

python3 {baseDir}/scripts/nim_router.py invoke \
  --capability page_elements \
  --image-url "https://example.com/page.png"

Table Structure

Detect table cells, rows, columns, headers:

python3 {baseDir}/scripts/nim_router.py invoke \
  --capability table_structure \
  --image-url "https://example.com/spreadsheet.png"

Graphic Elements (Chart Detection)

Detect chart labels, axes, legends, titles:

python3 {baseDir}/scripts/nim_router.py invoke \
  --capability graphic_elements \
  --image-url "https://example.com/chart.png"

Rerank (Passage Ranking)

Rank passages by relevance to a query:

python3 {baseDir}/scripts/nim_router.py invoke \
  --capability rerank \
  --query-text "Which GPU has the fastest memory?" \
  --passage "A100: 2 TB/s memory bandwidth" \
  --passage "H100: 3.35 TB/s memory bandwidth" \
  --passage "RTX 4090: 1 TB/s memory bandwidth"

Embed (Text Vectorization)

Generate vector embeddings for text using NVIDIA's nv-embed-v1 model:

python3 {baseDir}/scripts/nim_router.py build-request \
  --capability embed \
  --text "H100 GPU specifications" \
  --input-type passage

python3 {baseDir}/scripts/nim_router.py build-request \
  --capability embed \
  --text "What are the H100 specs?" \
  --input-type query

Supported --input-type values:

• passage: For indexing documents (default)
• query: For search queries

RAG Pipeline

The pipeline command provides end-to-end RAG ingestion: file/URL → OCR → semantic chunking → structured output.

# Process an image file
python3 {baseDir}/scripts/nim_router.py pipeline \
  --input "document.pdf" \
  --format json-ld

# Process a URL
python3 {baseDir}/scripts/nim_router.py pipeline \
  --url "https://example.com/page.png" \
  --format markdown

# Process URL with browser automation (for login-gated pages)
python3 {baseDir}/scripts/nim_router.py pipeline \
  --url "https://example.com/login-protected" \
  --browser \
  --format json-ld

# Process with custom chunking
python3 {baseDir}/scripts/nim_router.py pipeline \
  --input "document.pdf" \
  --chunk-size 1024 \
  --overlap 128 \
  --format text

# Batch process a directory
python3 {baseDir}/scripts/nim_router.py pipeline \
  --input "/path/to/documents/" \
  --format json-ld

# Full RAG pipeline with embeddings (requires NVIDIA_API_KEY)
python3 {baseDir}/scripts/nim_router.py pipeline \
  --input "document.pdf" \
  --embed \
  --format json-ld

Pipeline Output Formats:

• json-ld: Schema.org ItemList format with chunk metadata (source, page_number, section_header, token_count)
• markdown: Human-readable format with chunk headers and metadata
• text: Plain text suitable for direct embedding pipelines

--embed Flag: When --embed is specified, each chunk is automatically vectorized using NVIDIA's nv-embed-v1 model (4096 dimensions). The embeddings are attached to each chunk in the output, ready for vector storage.

Workflow Examples

OCR + Rerank (ocr_then_rerank)

Use when task combines text extraction and relevance ranking:

# Step 1: Plan the workflow
python3 {baseDir}/scripts/nim_router.py plan \
  --task-query "extract text from image and rank passages by relevance" \
  --image-url "https://example.com/doc.png" \
  --query-text "H100 specifications"

# Step 2: Run OCR first
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability ocr \
  --image-url "https://example.com/doc.png"

# Step 3: Use extracted text as passages for reranking
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability rerank \
  --query-text "H100 specifications" \
  --passage "First passage from OCR result..." \
  --passage "Second passage from OCR result..."

Layout-Aware Table Extraction (page_elements → table_structure → ocr)

Use when extracting text while preserving table structure:

# Step 1: Detect page layout
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability page_elements \
  --image-url "https://example.com/report.png"

# Step 2: Detect table structure
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability table_structure \
  --image-url "https://example.com/report.png"

# Step 3: Extract text with layout context
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability ocr \
  --image-url "https://example.com/report.png"

Chart-Aware Extraction (page_elements → graphic_elements → ocr)

Use when understanding charts while reading surrounding content:

# Step 1: Detect page layout
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability page_elements \
  --image-url "https://example.com/dashboard.png"

# Step 2: Detect chart elements
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability graphic_elements \
  --image-url "https://example.com/dashboard.png"

# Step 3: Extract text with chart context
python3 {baseDir}/scripts/nim_router.py invoke \
  --capability ocr \
  --image-url "https://example.com/dashboard.png"

Embed + Rerank (Vector Search Pipeline)

Use for semantic search with NVIDIA embeddings:

# Step 1: Generate embeddings for documents
python3 {baseDir}/scripts/nim_router.py build-request \
  --capability embed \
  --text "H100 GPU specifications" \
  --input-type passage

# Step 2: Generate embedding for query
python3 {baseDir}/scripts/nim_router.py build-request \
  --capability embed \
  --text "Tell me about H100 memory" \
  --input-type query

RAG Pipeline (Full Ingestion)

End-to-end document processing with semantic chunking:

# Extract, chunk, and format for RAG
python3 {baseDir}/scripts/nim_router.py pipeline \
  --input "research_paper.pdf" \
  --chunk-size 512 \
  --overlap 64 \
  --format json-ld

Platform-Specific Invocation

Claude Code

Explicit invocation:

/nvidia-nim
/nvidia-nim invoke --capability ocr --image-url https://example.com/doc.png

Implicit trigger: Skill activates when descriptions contain "extract text", "OCR", "document layout", "table structure", "rerank", "chart labels".

Codex (OpenAI)

Explicit invocation:

$nvidia-nim
$nvidia-nim invoke --capability ocr --image-url https://example.com/doc.png

Implicit trigger: Skill activates for queries mentioning OCR, layout detection, table extraction, chart understanding, or passage reranking.

OpenClaw

The skill loads automatically when task description matches trigger keywords. For explicit invocation, reference nvidia-nim-unified or nim.

Image Source Formats

The router accepts three image source formats and converts to base64 data URLs:

# HTTPS URL (auto-downloaded and converted)
--image-url "https://example.com/image.png"

# Local file path (read and converted)
--image-url "/path/to/local/image.jpg"

# Data URL (passed through directly)
--image-url "data:image/png;base64,iVBORw0KG..."

Constraints

• The router is deterministic and rule-based. If the task is ambiguous, inspect the returned rationale instead of blindly invoking.
• Multi-step workflows are planned automatically, but the script invokes only one capability per call.
• the default bundle targets NVIDIA managed APIs for all five capabilities.
• managed CV endpoints rejected plain remote image URLs in live testing; the router converts image sources to base64 data URLs before invocation.