Remove NRL docs and Perflab baseline reporting

README.md

@@ -141,7 +141,6 @@ Cat is the animal whose activity (jumping onto a laptop) matches the location of
 
 - **[Official Documentation](https://docs.nvidia.com/nemo/retriever/extraction/)** - Complete user guides, API references, and deployment instructions
 - **[Getting Started Guide](https://docs.nvidia.com/nemo/retriever/extraction/overview/)** - Overview and prerequisites for production deployments
-- **[Retriever Harness](nemo_retriever/harness/README.md)** - Repeatable end-to-end ingest and retrieval benchmarks
 - **[Stage Benchmarking](nemo_retriever/docs/cli/benchmarking.md)** - Internal per-stage throughput measurements
 - **[MIG Deployment](nemo_retriever/helm/README.md)** - Multi-Instance GPU configurations for Kubernetes
 - **[API Documentation](docs/docs/extraction/nemo-retriever-api-reference.md)** - Python client and API reference

docs/docs/extraction/prerequisites-support-matrix.md

@@ -181,7 +181,7 @@ When you call [NVIDIA-hosted NIMs](deployment-options.md#when-to-use-nvidia-host
 | nemotron-3-nano-omni-30b-a3b-reasoning | `https://integrate.api.nvidia.com/v1/chat/completions` with model ID `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning` | Optional image captioning. Also a supported configurable `/v1/answer` VLM backend when you point `serviceConfig.llm` at this endpoint. Enabling the Omni caption Helm key does not enable `/v1/answer`. |
 | llama-3.3-nemotron-super-49b-v1.5 | `https://integrate.api.nvidia.com/v1/chat/completions` with model ID `nvidia/llama-3.3-nemotron-super-49b-v1.5` | Default optional `/v1/answer` LLM (Helm `answer_llm`) and OpenAI-compatible agentic RAG endpoint mode. Not part of the default extraction pipeline. Agentic query and harness runs default to local in-process vLLM instead. Helm auto-wires to the in-cluster NIM when `nimOperator.answer_llm` is enabled. Refer to [Answer generation](#answer-generation). |
 | nemotron-3-nano-omni-30b-a3b-reasoning | `https://integrate.api.nvidia.com/v1/chat/completions` with model ID `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning` | Optional image captioning |
-| llama-3.3-nemotron-super-49b-v1.5 | `https://integrate.api.nvidia.com/v1/chat/completions` with model ID `nvidia/llama-3.3-nemotron-super-49b-v1.5` | Optional `/v1/answer` (Helm `answer_llm`) and OpenAI-compatible agentic RAG endpoint mode; not part of the default extraction pipeline. Agentic query/harness runs default to local in-process vLLM instead. Helm auto-wires to the in-cluster NIM when `nimOperator.answer_llm` is enabled. Refer to [Workflow: Agentic retrieval](workflow-agentic-retrieval.md). |
+| llama-3.3-nemotron-super-49b-v1.5 | `https://integrate.api.nvidia.com/v1/chat/completions` with model ID `nvidia/llama-3.3-nemotron-super-49b-v1.5` | Optional `/v1/answer` (Helm `answer_llm`) and OpenAI-compatible agentic RAG endpoint mode; not part of the default extraction pipeline. Agentic query and NRB benchmark runs default to local in-process vLLM instead. Helm auto-wires to the in-cluster NIM when `nimOperator.answer_llm` is enabled. Refer to [Workflow: Agentic retrieval](workflow-agentic-retrieval.md). |
 | parakeet-1-1b-ctc-en-us | `grpc.nvcf.nvidia.com:443` (function ID from [build.nvidia.com](https://build.nvidia.com/)) | Optional ASR; refer to [Parakeet hosted inference](audio-video.md#parakeet-hosted-inference-build-nvidia) |
 
 <a id="nemotron-parse-hosted-vs-self-hosted"></a>

docs/docs/extraction/releasenotes.md

@@ -67,8 +67,9 @@ The following sections summarize user-visible changes introduced in 26.08. Capab
 
 ### CLI { #cli }
 
-- `retriever ingest` and `retriever query` replace the retired compatibility pipeline command. Other top-level subcommands, including `eval`, `benchmark`, `harness`, and `skill-eval`, are development and experimental.
-- `retriever query --agentic` is the CLI entry point for agentic retrieval. Refer to [Workflow: Agentic retrieval](workflow-agentic-retrieval.md).
+- Root CLI adds first-class `retriever ingest` and `retriever query` commands with NIM URL flags, batch tuning, and LanceDB overwrite/append controls
+- `retriever query --agentic` runs an LLM-driven ReAct retrieval loop over the same LanceDB table as one-pass retrieval. Local CLI and NRB benchmark runs default to in-process vLLM (`nemotron-8b`). Remote OpenAI-compatible NIM or NVIDIA-hosted endpoints use `--agentic-invoke-url`. Refer to [Workflow: Agentic retrieval](workflow-agentic-retrieval.md).
+- `retriever ingest` and `retriever query` replace the retired compatibility pipeline command. Other top-level subcommands—including `eval`, `benchmark`, and `skill-eval`—are development and experimental.
 
 ### Retriever Service and deployment { #retriever-service-and-deployment }
 

docs/docs/extraction/workflow-agentic-retrieval.md

@@ -10,7 +10,10 @@ Use this workflow after you have ingested documents into a LanceDB table. Agenti
 
 ### Local in-process vLLM { #local-in-process-vllm }
 
-The CLI and harness BEIR agentic paths default to an in-process local vLLM agent LLM. If you omit `--agentic-llm-model` and `--agentic-invoke-url`, the library loads `nemotron-8b` (`nvidia/Llama-3.1-Nemotron-Nano-8B-v1`) on the local CUDA host. This requires a Linux CUDA GPU and the `[local]` extra.
+The CLI and NRB agentic benchmark paths default to an in-process local vLLM agent
+LLM. If you omit `--agentic-llm-model` and `--agentic-invoke-url`, the library
+loads `nemotron-8b` (`nvidia/Llama-3.1-Nemotron-Nano-8B-v1`) on the local CUDA
+host. This requires a Linux CUDA GPU and the `[local]` extra.
 
 GPU placement follows process-level vLLM behavior. Set `CUDA_VISIBLE_DEVICES` before you start the command.
 

examples/README.md

@@ -18,6 +18,5 @@ For advanced scenarios, use these guides and notebooks:
 
 - [Build a Custom Vector Database Operator](building_vdb_operator.ipynb)
 - [Try Enterprise RAG Blueprint](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag)
-- [Evaluate BO767 retrieval with the Retriever harness](https://github.com/NVIDIA/NeMo-Retriever/blob/main/evaluation/bo767_recall.md)
 - [Multimodal RAG with LangChain](langchain_multimodal_rag.ipynb)
 - [Multimodal RAG with LlamaIndex](llama_index_multimodal_rag.ipynb)

nemo_retriever/README.md

@@ -829,14 +829,12 @@ sudo apt install python3.12-dev
 
 After installing the headers, restart the pipeline.
 
-## Retriever Harness
-
-The developer harness runs registered ingest and retrieval benchmarks through
-`retriever harness`. Start with the
-[harness guide](harness/README.md), then choose
-[library execution](harness/docs/library.md) or
-[service execution](harness/docs/service.md). Recurring workstation runs use the
-[nightly launcher](../ops/retriever-nightly/README.md).
+## Benchmarking
+
+End-to-end Retriever experiments and benchmark orchestration are maintained in
+the [NeMo Retriever Benchmark (NRB) repository](https://gitlab-master.nvidia.com/charlesb/nemo-retriever-benchmark/).
+This repository continues to provide the library, CLI workflows, service
+implementation, and Helm chart that NRB benchmarks.
 
 ### Ingest image storage
 

nemo_retriever/developer_docs/README.md

@@ -9,6 +9,4 @@ architecture, subsystems, and developer-facing tools.
 |-------|-------------|
 | [Graph Pipeline Registry](graph_pipeline_registry.md) | Central registry for managing, inspecting, comparing, and serializing golden pipeline graphs. |
 | [NimClient and Custom NIM Endpoints](nimclient.md) | Developer guide for custom NIM integrations with `NimClient`, `ModelInterface`, and UDFs. |
-| [Retriever Harness](../harness/README.md) | Current user and agent guide for registered Retriever benchmarks. |
-| [Retriever Harness Design History](harness_retriever_ingest_query_prd.md) | Implemented design decisions and product boundaries. |
 | [Root Ingest CLI Design](root_ingest_cli_design.md) | Reviewer guide for the `retriever ingest` local, batch, and service CLI ownership split. |

nemo_retriever/developer_docs/root_ingest_cli_design.md

@@ -2,7 +2,7 @@
 
 This note explains the `retriever ingest` redesign for reviewers and future CLI
 maintainers. It is intentionally scoped to the root ingest CLI. It does not
-change `GraphIngestor`, `Retriever.query`, eval, harness logic, BEIR/QA/audio
+change `GraphIngestor`, `Retriever.query`, eval, benchmark logic, BEIR/QA/audio
 recall, or pipeline reporting.
 
 ## Summary

nemo_retriever/docs/cli/README.md

@@ -8,7 +8,6 @@ For product-facing examples, prefer these commands:
 - `retriever ingest` - ingest supported documents and media into a Retriever index.
 - `retriever query` - query a local LanceDB table written by local or batch ingest.
 - `retriever query service` - query a Retriever service deployment.
-- `retriever harness run` - run a named, code-owned benchmark.
 - `retriever service` - operate a Retriever service deployment.
 
 Format names and internal stages are not root commands. Use `retriever ingest`
@@ -48,7 +47,6 @@ configuration, local embed backend selection, or local media controls.
 <!-- --8<-- [start:quickstart] -->
 
 > Use `retriever ingest` and `retriever query` for product-facing workflows.
-> Use `retriever harness run` only for benchmark execution.
 
 ## Quick start
 

nemo_retriever/docs/cli/benchmarking.md

@@ -1,33 +1,10 @@
 # Benchmarking with the Retriever CLI
 
-Retriever has two development benchmarking surfaces:
-
-| Goal | Command | Documentation |
-| --- | --- | --- |
-| End-to-end ingest, query, and retrieval evaluation | `retriever harness` | [Retriever Harness](../../harness/README.md) |
-| Throughput for one internal pipeline stage | `retriever benchmark` | [Stage micro-benchmarks](#stage-micro-benchmarks) |
-
-For product workflows on your own inputs, use `retriever ingest` and
-`retriever query` instead.
-
-## End-to-End Benchmarks
-
-The harness owns registered datasets, repeatable runfiles, metric gates, and
-stable artifacts. Start with:
-
-```bash
-retriever harness list --runsets
-retriever harness show jp20_beir
-```
-
-Then choose the execution guide:
-
-- [Local and batch library runs](../../harness/docs/library.md)
-- [Existing-service and managed-Helm runs](../../harness/docs/service.md)
-- [Recurring workstation nightly](../../../ops/retriever-nightly/README.md)
-
-Agentic BEIR evaluation is documented under
-[library harness runs](../../harness/docs/library.md#evaluate-agentic-retrieval).
+End-to-end experiments are maintained in the [NeMo Retriever Benchmark (NRB)
+repository](https://gitlab-master.nvidia.com/charlesb/nemo-retriever-benchmark/).
+The product CLI retains internal stage micro-benchmarks for focused development
+measurements. For product workflows on your own inputs, use
+`retriever ingest` and `retriever query`.
 
 ## Stage Micro-Benchmarks
 
@@ -54,5 +31,5 @@ retriever benchmark extract ./data/pdf_corpus \
 ```
 
 Stage commands report rows per second, or chunk rows per second for audio. They
-do not produce the harness artifact contract and should not be used as retrieval
+do not produce the NRB artifact contract and should not be used as retrieval
 quality evidence.