Deepstream Generate Pipeline

DeepStream Pipeline Builder

Generate ready-to-run gst-launch-1.0 pipelines for NVIDIA DeepStream SDK by collecting pipeline requirements through an interactive questionnaire, then assembling the pipeline using a standalone BM25 retrieval backend with structural metadata boosting (similarity search over 270+ verified pipelines, zero external dependencies).

Prerequisites

• Python: 3.8+ (stdlib only — no pip packages required)
• DeepStream SDK: Installed at /opt/nvidia/deepstream/deepstream/ (for gst-inspect-1.0 validation and element verification)
• GStreamer: gst-launch-1.0 and gst-inspect-1.0 on PATH (installed with DeepStream)
• Platform: x86 dGPU (T4, A100, L40, RTX, etc.) or aarch64 — Jetson (Orin, Xavier, Nano) / SBSA (Grace, GH200)

Usage Examples

# Fully specified — skips most questions
detect and track on 4 rtsp streams and display on jetson

# Partially specified — asks remaining questions
give me a pipeline to infer on an image

# Minimal — asks all 7 questions
build a pipeline

Supported Configurations

Scripts

Workflow

Step 1 — Collect Pipeline Requirements

You MUST Read references/requirement-extraction.md before doing this step. It contains the query-inference table, compound-extraction examples, the full AskUserQuestion question bank (with the default-first ordering contract), the automatic-OSD and extras/flip-method rules, and the dynamic question-reduction examples that this step depends on. Apply them exactly.

Order of operations:

1. Infer everything you can from the query using the inference table in references/requirement-extraction.md. The goal is to identify which of the 7 parameters (input source, num sources, inference, tracker, sink, platform, extras) the user has already specified.
2. Ask the user about the unknowns via AskUserQuestion in a single call. Do not silently default tracker/sink/platform/extras — these are real choices the user should make explicitly (display vs save, no tracker vs NvDCF, x86 dGPU vs aarch64 Jetson/SBSA, etc.). Skip only the questions whose answer is already clear from the query.
3. Quote the inferred parameters back to the user in the lead-in to the question call so they can see what you already extracted. Example: "From your query I have: 3 mp4 videos, primary inference. Just need a few more details:"

Follow the inference table, question bank, and OSD/extras rules in references/requirement-extraction.md to decide which questions to ask and how to place transform elements, then proceed to Step 2.

Step 2 — Build the Natural Language Query

From the user's answers, construct a single descriptive query string. Follow this pattern:

Please provide a GStreamer pipeline that [operation] on [num_sources] [input_type] [input_detail] [tracker_detail] and [output_action] [platform_detail]

Examples of constructed queries:

Step 3 — Run the Pipeline Generator Script

Execute the backend script with the constructed query and user parameters:

python3 <skill-path>/scripts/generate_pipeline.py \
  --query "<constructed_query>" \
  --source-type "<Local video file|Local image file|RTSP stream|USB camera|Test pattern>" \
  --num-sources <N> \
  --inference "<None|primary|primary+secondary|primary+preprocess|primary+secondary+preprocess|primary-triton|primary+secondary-triton>" \
  --tracker "<none|NvDCF|IOU|NvSORT|DeepSORT>" \
  --sink "<display|display-jetson|save-jpg|save-png|save-mp4|save-h264|rtsp-out|fakesink>" \
  --platform "<dGPU|Jetson|SBSA>" \
  --extras "<none|resize|rotate|crop|color-convert|osd>" \
  --format compact

Always pass --format compact. The compact mode returns only confidence + the top retrieved pipeline (~25 lines), instead of dumping all 10 retrievals as ~150 lines of JSON in the chat. The json mode (default for backward compat) is only useful when debugging the retriever directly. A summary mode (single human-readable line) also exists for non-Claude callers.

The script will (zero external dependencies — pure Python stdlib):

1. Load the pipeline dataset (270+ verified DeepStream pipelines)
2. Extract structural metadata from each pipeline (platform, source type, sink type, inference mode, tracker, stream count)
3. Score with BM25 (document-length-normalized) + domain-specific synonym expansion on both queries and documents
4. Apply structural boosting — results matching the user's platform/source/sink/inference get boosted, mismatches get penalized
5. Return the top-K results as JSON with a confidence field (high/medium/low) based on the top score
6. Claude uses these retrieved examples + the assembly rules below to construct the final pipeline

When confidence is low, rely more heavily on the assembly rules below rather than the retrieved examples.

Step 4 — Validate the Pipeline

Before presenting, run the validation script to catch syntax errors, unknown elements, and linking issues:

python3 <skill-path>/scripts/validate_pipeline.py "<assembled_pipeline>" --format summary

Always pass --format summary. Summary prints a single status line (e.g. valid · 11 elements · 0 warnings · live-parse skipped (multi-stream)), with errors/warnings indented underneath only if present. The default json mode emits ~40 lines of structured output and is only useful for programmatic callers.

The validator performs 4 checks:

1. Syntax check — unbalanced quotes, empty pipe segments, missing source/sink
2. Element check — verifies each element exists via gst-inspect-1.0
3. Property check — validates known properties for DeepStream elements
4. Live parse check — uses gst-launch-1.0 itself to construct the pipeline graph (with fakesrc/fakesink substituted), catching linking errors and pad mismatches. Automatically skipped for multi-stream pipelines (those with named pad refs like m.sink_0) since fakesrc cannot negotiate caps through named pads.

If validation fails ("valid": false), fix the errors and re-validate before presenting. Limit validation retries to a maximum of 2 attempts — if the pipeline still fails after 2 fixes, present it as-is (the remaining checks already cover syntax, element, property, and structural correctness). If there are only warnings, present the pipeline but mention the warnings to the user.

Step 5 — Present the Pipeline

5.1 — Output format (THE ONLY ACCEPTED FORM)

Your response must be exactly five blocks, in this order:

1. One-line status badge (validation + confidence)
2. Single bash code block containing the full gst-launch-1.0 -e … command with concrete absolute paths, on one line (no \ continuations, no shell variables, no shell wrapper)
3. Breakdown table grouped by stage
4. Suggestions bullet list
5. (only if pre-flight failed) a ⚠ line above the status badge stating which default path is missing

That is the ONLY accepted output shape for this step. The Section 5.3 template in references/output-format.md is the literal template — match it.

5.2 — Pre-flight check (run before composing the response)

Run one Bash ls over the default paths the pipeline will reference (sample video, PGIE config, tracker lib/config). The result tells you whether to mark the badge with ⚠ default path not found: <path> and bump the matching "Use your own …" suggestion to the top.

ls /opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 \
   /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_infer_primary.txt \
   2>&1