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feat(agentic_rag): add config-driven MLflow GenAI evaluation - #337
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- Config-driven scorer setup via evaluation/eval_config.yaml (7 core + 3 RAG scorers, 10 total) - evaluation/scorers.py for custom scorer definitions - evaluation/run_eval.py: sends golden queries to agent, reads traces, evaluates with dynamically resolved scorers - Content-based poll matching for data readiness - httpx.RequestError catch-all for invalid URLs - Empty-trace guard before evaluate() - Configurable EVAL_REQUEST_TIMEOUT and EVAL_POLL_INTERVAL - evaluation/eval_data.yaml: placeholder golden queries with expectations - RETRIEVER span logging in tools.py for RAG scorer compatibility - make eval target - EVAL_JUDGE_*, EVAL_ENABLE_PII, EVAL_REQUEST_TIMEOUT, EVAL_POLL_INTERVAL, AGENT_URL env vars in .env.example - README evaluation section with scorer docs and enablement link Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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📝 WalkthroughWalkthroughAdds optional MLflow tracing and a configuration-driven evaluation workflow to the Agentic RAG template. The workflow generates agent traces, matches them to golden queries, applies configured scorers, and reports evaluation metrics. ChangesAgentic RAG evaluation
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🟡 Moderate · up to The new evaluation workflow can terminate when given an invalid scorer definition or a negative request-timeout setting, preventing results from being produced. These are bounded configuration-handling issues, but they should be fixed or explicitly accepted before merging. Suggested reviewers: Sequence Diagram(s)sequenceDiagram
participant Makefile
participant run_eval
participant AgentChatCompletions
participant MLflow
Makefile->>run_eval: Run evaluation script
run_eval->>AgentChatCompletions: Submit golden queries
AgentChatCompletions->>MLflow: Record traces
run_eval->>MLflow: Match traces and execute scorers
MLflow-->>run_eval: Return evaluation metrics
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 52.78% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 36 functions across 4 files. (2 skipped: 2 unsupported.) ✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 4
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Inline comments:
In `@agents/langgraph/templates/agentic_rag/evaluation/run_eval.py`:
- Around line 237-239: Update the evaluation flow around the trace-matching loop
and request dispatch to generate a unique evaluation-run identifier, send it
with every evaluation request, and persist it in the resulting trace metadata.
Filter traces by this identifier before comparing _extract_question(t) against
golden_questions, so only traces from the current run contribute to matched and
expected_count.
- Line 1: Run Ruff formatting on the evaluation script containing the module
docstring, applying only the formatter’s changes and preserving the existing
behavior.
- Line 181: Update the judge endpoint handling around the env_var assignment to
prevent sending a configured judge key over non-loopback HTTP: require HTTPS for
remote endpoints, while allowing HTTP only for loopback hosts, or omit the
credential when that condition is not met.
In `@agents/langgraph/templates/agentic_rag/evaluation/scorers.py`:
- Around line 13-17: Update the my_domain_scorer example to use MLflow’s
custom-scorer signature with keyword-only inputs, outputs, expectations, or
trace parameters, and return a supported primitive score or Feedback result
instead of a dictionary. Keep the domain-quality scoring intent and rationale
behavior intact so the example is valid when enabled through eval_config.yaml.
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📒 Files selected for processing (10)
agents/langgraph/templates/agentic_rag/.env.exampleagents/langgraph/templates/agentic_rag/Makefileagents/langgraph/templates/agentic_rag/README.mdagents/langgraph/templates/agentic_rag/evaluation/__init__.pyagents/langgraph/templates/agentic_rag/evaluation/eval_config.yamlagents/langgraph/templates/agentic_rag/evaluation/eval_data.yamlagents/langgraph/templates/agentic_rag/evaluation/run_eval.pyagents/langgraph/templates/agentic_rag/evaluation/scorers.pyagents/langgraph/templates/agentic_rag/pyproject.tomlagents/langgraph/templates/agentic_rag/src/agentic_rag/tools.py
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- ruff format run_eval.py - ty.toml override for the optional mlflow import in tools.py (try/except None-fallback trips ty's module-vs-None check) - regenerate stale uv.lock
Ports react_agent's test_eval.py coverage (config/data loading, scorer resolution, trace-expectation matching) and adds a case for agentic_rag's RAG-specific use_case scorers, which are enabled by default unlike react_agent's. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The @scorer example used positional request/response/expected_facts params and returned a dict, neither of which MLflow's @scorer contract supports. Update to keyword-only inputs/outputs/expectations/trace and a Feedback return, per MLflow GenAI docs. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Dev-Team Review — 4-Dimension Analysis
Solid feature addition — config-driven evaluation with dynamic scorer resolution, timestamp-based trace isolation, and content-based matching is well-designed. The test coverage for run_eval.py utilities is good. A few issues to address before merge:
Findings
| # | Severity | Category | File | Description |
|---|---|---|---|---|
| 1 | Major | Consistency | README.md ~L190 vs scorers.py |
README custom scorer example shows old signature (request, response, expected_facts=None) returning dict, but scorers.py (fixed in 97bef41) now uses (*, inputs, outputs, expectations=None, trace=None) returning Feedback. Users following README will write broken scorers. |
| 2 | Major | Correctness | run_eval.py:133 |
resolve_scorer() blindly calls cls_or_obj(model=model) for all classes. Custom class-based scorers that don't accept model= throw TypeError. Should introspect the constructor or try/except. |
| 3 | Major | Robustness | run_eval.py:225 |
EVAL_POLL_INTERVAL=0 → ZeroDivisionError in max(trace_timeout // poll_interval, 1). Negative values also produce nonsensical behavior. |
| 4 | Major | Robustness | run_eval.py:127-131 |
Typo in eval_config.yaml (module: mlflow.genai.scorerz) crashes the entire eval run with ModuleNotFoundError. Since this is user-editable config, should catch and report "scorer X could not be loaded, skipping" instead of crashing. Same for AttributeError from getattr. |
| 5 | Minor | Robustness | run_eval.py:41,222-224 |
Non-numeric env var values for EVAL_REQUEST_TIMEOUT/EVAL_TRACE_TIMEOUT/EVAL_POLL_INTERVAL raise bare ValueError with no user-friendly message. |
| 6 | Minor | Consistency | pyproject.toml |
Tracing extra changed from mlflow>=3.10.0 to mlflow[kubernetes]>=3.11.0 — the [kubernetes] sub-extra pulls in the kubernetes Python package, which is unnecessary for tracing-only users. The >=3.11.0 bump is fine, but [kubernetes] should stay in the eval extra only. |
| 7 | Minor | Consistency | pyproject.toml |
httpx>=0.27 added to dev extras but test_eval.py doesn't use httpx directly (it mocks). Only the eval extra needs it. |
Verified Clean
Items reviewed and confirmed correct (click to expand)
- Security:
yaml.safe_load()used correctly throughout. No injection vectors.importlibcode execution is standard plugin pattern with developer-controlled config. Cleartext API key concern (CWE-319) is valid hardening suggestion but not blocking for a local dev tool — same risk model as any OpenAI SDK usage. - Correctness:
_extract_question()correctly readsmessages[0]["content"]matchinggenerate_traces()payload format. Polling loop withexpected_count=0correctly evaluates existing traces. Re-fetch afterattach_expectationscorrectly filtered bymatched_ids. Fail-fast ingenerate_traces()is intentional and correct. RETRIEVER span placement intools.pyis correct hierarchy for MLflow RAG scorers. - Consistency:
ty.tomloverride follows existing pattern.evaluation/__init__.pyneeded for test imports. Makefile target placement is acceptable.DEEPEVAL_TELEMETRY_OPT_OUT=YESuncommented by default is good practice.sys.path.insertin test matches other agent test patterns.
Suggested Fixes
#1 (README): Update the custom scorer example in the Evaluation section (~L190) to match the corrected scorers.py:
@scorer
def my_domain_scorer(*, inputs, outputs, expectations=None, trace=None):
"""Check domain-specific quality criteria."""
# Your scoring logic here
return Feedback(value=1.0, rationale="Meets criteria")#3 (poll_interval): Guard against zero/negative:
poll_interval = max(int(os.getenv("EVAL_POLL_INTERVAL", "4")), 1)#4 (scorer loading): Wrap resolution in try/except:
try:
s = resolve_scorer(entry, judge_model)
except (ModuleNotFoundError, AttributeError) as e:
print(f"WARNING: Could not load scorer '{entry.get('name')}': {e}. Skipping.")
continue#6 (pyproject.toml tracing extra): Revert to mlflow>=3.11.0 (without [kubernetes]).
Reviewed with 4-agent dev-team analysis (correctness, robustness, security, consistency). CodeRabbit's ruff-formatting and scorer-example findings confirmed addressed in follow-up commits. The trace-correlation-identifier suggestion from CodeRabbit is a nice-to-have but not blocking — the timestamp baseline + content matching provides sufficient isolation for a single-user eval tool.
- resolve_scorer: introspect __init__ signature before injecting model=, so custom @scorer functions and classes without a model param aren't called incorrectly - get_scorers: catch per-entry load errors and warn/skip instead of crashing the whole eval run - generate_traces/main: read EVAL_* timeout/interval env vars via a validated int helper, and guard poll_interval against zero/negative values - README: fix custom scorer example to match the actual @scorer contract (Feedback return type, inputs/outputs/expectations/trace kwargs) - pyproject.toml: drop unused httpx from dev extras, drop unnecessary kubernetes extra from tracing's mlflow dependency
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
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Inline comments:
In `@agents/langgraph/templates/agentic_rag/evaluation/run_eval.py`:
- Line 153: Update the scorer construction and warning handling around
cls_or_obj and the handler at line 161 to catch TypeError from invalid
constructor signatures, including positional-only or additional required
parameters, and skip those entries via the existing warning path. Safely derive
the scorer name without calling entry.get on non-mapping entries, using a
fallback representation or label for malformed values.
- Line 53: Validate the request_timeout value returned by _get_int_env for
EVAL_REQUEST_TIMEOUT before any HTTP requests are sent, rejecting negative
values while preserving valid timeout behavior. Ensure the validation occurs in
the evaluation flow that uses request_timeout so invalid configuration cannot
reach httpx.post.
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agents/langgraph/templates/agentic_rag/README.mdagents/langgraph/templates/agentic_rag/evaluation/run_eval.pyagents/langgraph/templates/agentic_rag/pyproject.toml
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Dev-Team Review — 4-Dimension Analysis (Round 2)
All 7 findings from the previous review have been addressed. Two additional issues from CodeRabbit's second pass (TypeError in scorer loading, negative timeout values) have been identified with fixes below. The PR is in good shape — no blocking issues remain.
New Findings
| # | Severity | Dimension | File | Description |
|---|---|---|---|---|
| 1 | Minor | Robustness | run_eval.py:161 |
_resolve_scorer_or_warn catches ModuleNotFoundError and AttributeError but not TypeError. A scorer class with an incompatible constructor (e.g., positional-only model param) throws TypeError and crashes the run. Fix: Add TypeError to the except tuple and use safe name extraction for non-dict entries. |
| 2 | Minor | Robustness | run_eval.py:53 |
_get_int_env allows negative integers. EVAL_REQUEST_TIMEOUT=-1 reaches httpx.post and raises ValueError under HTTPX 0.28.1. Same for EVAL_TRACE_TIMEOUT. Fix: Validate >= 0 after parsing. |
| 3 | Minor | Correctness | run_eval.py:253,261 |
expected_count = len(eval_data) (list length) vs golden_questions (set — deduplicates). Duplicate golden questions produce fewer traces than expected, causing the polling loop to run to exhaustion. Low risk — developer-authored data. |
| 4 | Info | Consistency | pyproject.toml |
react_agent tracing extra uses mlflow[kubernetes]>=3.11.0 while agentic_rag uses mlflow>=3.11.0. The agentic_rag version is more correct (kubernetes extra belongs in eval only), but creates cross-agent divergence. Sync in follow-up. |
Suggested Fixes for #1 and #2
#1 — TypeError catch:
- except (ModuleNotFoundError, AttributeError) as e:
- print(f"WARNING: Could not load scorer '{entry.get('name')}': {e}. Skipping.")
+ except (ModuleNotFoundError, AttributeError, TypeError) as e:
+ name = entry.get("name") if isinstance(entry, dict) else "<invalid entry>"
+ print(f"WARNING: Could not load scorer '{name}': {e}. Skipping.")#2 — Negative timeout validation (in generate_traces and main):
request_timeout = _get_int_env("EVAL_REQUEST_TIMEOUT", 60)
if request_timeout < 0:
print(f"ERROR: EVAL_REQUEST_TIMEOUT={request_timeout} is negative.")
raise SystemExit(1)Same pattern for EVAL_TRACE_TIMEOUT.
Verified Clean (across all dimensions)
Items reviewed and confirmed correct (click to expand)
Correctness:
_extract_questioncorrectly readsmessages[0]["content"]matchinggenerate_tracespayload format- Timestamp isolation via
baseline_mscorrectly excludes pre-existing and includes new traces attach_expectations1:1 matching withmatched_idsskip prevents double-matchingresolve_scorerconstructor introspection correctly detectsmodelparam and**kwargsget_scorers/eval_config.yamlschema alignment verifiedexpected_count=0path evaluates pre-existing traces without unnecessary delayseval_data.yamlformat matchesload_eval_dataandgenerate_tracesexpectations
Robustness:
- All httpx exceptions covered:
ConnectError,TimeoutException,HTTPStatusError,RequestError - Empty eval_data.yaml →
expected_count=0, skipsgenerate_traces/attach_expectations load_eval_configreturns{}on empty YAML;get_scorershandles missing keys- MLflow import failure in tools.py handled via try/except
poll_intervalfloored to 1 (ZeroDivisionError fix from round 1 confirmed)- Polling race condition is benign — worst case is one extra sleep iteration
Security:
yaml.safe_load()used correctly everywhere. Noyaml.load().- importlib pattern acceptable for developer-controlled config
- API keys never logged. Error messages print URLs/status only.
- CWE-319 (HTTP cleartext) is same risk model as OpenAI SDK local dev — non-blocking
- No SSRF risk —
AGENT_URLis developer-set in.env .env.examplehas no real credentials;DEEPEVAL_TELEMETRY_OPT_OUT=YESis a good default
Consistency:
- Makefile
evaltarget matches react_agent pattern exactly .env.examplecomment style matches existing pattern- pyproject.toml extras structure (auth/dev/eval/tracing) identical across agents
evaluation/correctly excluded from Dockerfile container build- Code style (f-strings, print-based output, SystemExit) consistent with codebase
- EVAL_ prefix naming convention consistent
- README evaluation section follows existing style
Previous Review Findings — All Addressed
| # | Finding | Status |
|---|---|---|
| 1 | README custom scorer example mismatch | ✅ Fixed |
| 2 | resolve_scorer() blindly calls cls_or_obj(model=model) |
✅ Fixed (constructor introspection added) |
| 3 | EVAL_POLL_INTERVAL=0 → ZeroDivisionError |
✅ Fixed (max(..., 1)) |
| 4 | Scorer loading crashes entire eval run | ✅ Fixed (_resolve_scorer_or_warn wrapper) |
| 5 | Non-numeric env vars raise bare ValueError |
✅ Fixed (_get_int_env with clear message) |
| 6 | tracing extra has [kubernetes] |
✅ Fixed (removed from tracing, kept in eval) |
| 7 | httpx in dev extras | ✅ Fixed (only in eval extra) |
Reviewed with 4-agent dev-team analysis (correctness, robustness, security, consistency). All previous major findings confirmed addressed. Two quick fixes suggested above — once applied, PR is ready to merge.
…ve int env vars Addresses two findings from shricharan-ks's round-2 review: scorer loading only caught ModuleNotFoundError/AttributeError, letting a TypeError from an incompatible scorer constructor crash the whole eval run; _get_int_env accepted negative values, which reached httpx.post(timeout=-1) as a raw ValueError instead of a clear config error.
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Dev-Team PR Review Summary
Classification: pr-review
Team: Checker (correctness, robustness, consistency) + Security Reviewer (credentials, SSRF, dynamic imports)
Scope: 13 files, +1171/−8 — config-driven MLflow GenAI evaluation for agentic_rag
CI: All checks passing (lint, type-check, unit tests, secret-scanning)
This is a well-designed feature addition. The eval workflow improves meaningfully on the existing react_agent template: golden-query-aware trace polling, resilient scorer loading (_resolve_scorer_or_warn), constructor introspection for custom @scorer functions, empty-match guard before mlflow.genai.evaluate, and RAG-specific scorers backed by RETRIEVER spans in tools.py. Unit tests (tests/test_eval.py, 21 cases) cover config loading, scorer resolution, and expectation attachment. End-to-end validation on OpenShift MLflow is documented in the PR description.
Previous review rounds addressed the major blockers (README/scorer signature mismatch, scorer crash on bad config, poll-interval division, pyproject extras). Those fixes are confirmed in the latest commits.
Critical (0)
None.
Important (2)
-
[Important] Partial golden-query matching proceeds silently -> After the poll loop, warn when
len(matched_ids) < len(eval_data)so developers know evaluation ran on a subset [evaluation/run_eval.py:277-303]- Today a warning fires only when total trace count is low (
len(traces) < expected_count), not when enough traces exist but content matching is incomplete. Eval can proceed on partial data without an explicit "N/M golden queries matched" message. - Fix: After
attach_expectations, comparelen(matched_ids)tolen(eval_data)and print e.g.WARNING: Matched 2/5 golden queries; unmatched queries will not be evaluated.
- Today a warning fires only when total trace count is low (
-
[Important] No RETRIEVER span when retrieval returns zero usable chunks -> Always emit a RETRIEVER span (with empty outputs) when the retriever tool runs [
src/agentic_rag/tools.py:109-110,143-147]- Span logging is gated on
if mlflow and retriever_docs, so paths whereresponse.chunksis empty (L109) or all chunks are filtered out (L150) produce no span. MLflow RAG scorers expect a RETRIEVER span even for empty retrieval; without it,RetrievalRelevance/RetrievalSufficiencymay error instead of scoring "no documents retrieved." - Fix: Log the span whenever retrieval is attempted; call
span.set_outputs([])on zero-result paths.
- Span logging is gated on
Suggestions (5)
-
[Suggestion] Eval against auth-enabled deployments -> Add optional
EVAL_AGENT_TOKEN/ auth header support ingenerate_traces()[evaluation/run_eval.py:61-68]- Agent supports
Authorization: BearerandX-Api-Keyviaauth_wrapper.py, but eval sends unauthenticated POSTs. Blocks eval on deployed agents withAUTH_ENABLED=true.
- Agent supports
-
[Suggestion] Module allowlist for dynamic scorer imports -> Restrict
importlib.import_module(entry["module"])to known prefixes [evaluation/run_eval.py:145-146]- Acceptable for developer-controlled config today; an allowlist (
mlflow.genai.scorers*, localscorers) reduces risk if config is tampered with in shared CI.
- Acceptable for developer-controlled config today; an allowlist (
-
[Suggestion] Document timeout tuning env vars in README -> Mention
EVAL_REQUEST_TIMEOUT,EVAL_TRACE_TIMEOUT,EVAL_POLL_INTERVAL[README.mdevaluation section]- Already in
.env.examplebut not documented in the README evaluation section.
- Already in
-
[Suggestion] Duplicate golden questions skew polling -> Align
expected_countwith deduplicated question set [evaluation/run_eval.py:253,260-261]expected_count = len(eval_data)vsgolden_questionsas a set. Duplicate questions in YAML can cause polling to run to exhaustion. Low risk for developer-authored data.
-
[Suggestion] Golden query text logged to stdout -> Gate full question text behind a verbose flag [
evaluation/run_eval.py:89]- Eval questions may contain sensitive content in CI logs.
Strengths
- Architecture: Config-driven scorer resolution via YAML +
importlibis the right pattern for a template — no Python edits needed to customize scorers. - Trace isolation: Timestamp baseline + content-based matching handles concurrent agent traffic better than count-only polling in
react_agent. - Resilience:
_resolve_scorer_or_warn,_get_int_env, and negative timeout guards make misconfiguration survivable instead of crash-prone. - RAG integration: RETRIEVER span instrumentation in
tools.pywith graceful optionalmlflowimport; RAG scorers pre-configured ineval_config.yaml. - Testing: Solid unit test coverage including regressions for
@scorerfunctions, empty YAML, and content-based trace matching. - Security basics:
yaml.safe_load()throughout; API keys set in env but never logged;.env.exampleuses placeholders only. - Docs: README evaluation section is thorough — scorer sources, PIILeakage opt-in, RAG scorer caveats, and link to enablement material.
Verdict: Approve
No blocking issues remain. The two Important items are edge-case correctness/UX improvements worth a fast-follow commit but should not hold up merge of this template feature. Security hardening (auth headers, URL validation, module allowlist) aligns with follow-up work already noted for react_agent parity.
Reviewed with dev-team workflow: Checker + Security Reviewer agents, cross-checked against react_agent/evaluation/run_eval.py and project conventions (AGENTS.md).
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…uation Integrates the eval framework (evaluation/ dir, eval extras, make eval target, README section) built and validated in the demo, including the scorer-loading and env-var-validation fixes from shricharan-ks's PR red-hat-data-services#337 review. Verified with unit tests and a live make eval run against both a local MLflow server and the RHOAI-hosted instance.
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…uation (#343) Adds a config-driven MLflow GenAI evaluation setup to the react_with_database_memory template. Developers can evaluate their agent's traces using core scorers (Safety, RelevanceToQuery, Completeness, Correctness, ToolCallCorrectness, ToolCallEfficiency, PIILeakage) plus custom scorers — all configured via YAML, no Python edits needed. **Key changes:** - `evaluation/eval_config.yaml` — scorer configuration with core, use-case, and guidelines sections. Scorers can come from MLflow, any MLflow-integrated third-party framework (e.g., DeepEval, Phoenix), or custom definitions in `scorers.py` - `evaluation/scorers.py` — template for custom scorer definitions - `evaluation/run_eval.py` — sends golden queries to the running agent, reads traces from MLflow, resolves scorers dynamically via `importlib`. Isolates traces from the current run via a timestamp baseline, then matches traces to golden queries by exact question-content comparison (immune to interleaved traces from concurrent agent traffic), with polling/retry for async trace flush - `evaluation/eval_data.yaml` — placeholder golden queries (commented out) for developers to fill in - `make eval` target added - `EVAL_JUDGE_*`, `EVAL_ENABLE_PII`, `EVAL_REQUEST_TIMEOUT`, `EVAL_POLL_INTERVAL`, `AGENT_URL` env vars in `.env.example` - README evaluation section with scorer configuration docs - Includes the scorer-loading (`TypeError` handling) and env-var-validation (negative integer rejection) fixes from shricharan-ks's review on the agentic_rag PR (#337), applied here since this template's `run_eval.py` shares the same base **How it works:** 1. Developer runs agent with tracing enabled 2. Developer adds golden queries to `evaluation/eval_data.yaml` 3. Developer customizes guidelines and scorers in `evaluation/eval_config.yaml` 4. Developer runs `make eval` → sends queries to agent, reads traces, attaches expectations, scores them 5. Results logged to MLflow ## Testing - [x] `make test` passes (run from the affected agent directory) - [x] Manual testing performed (describe steps below) - [ ] No testing required (documentation/config change only) Tested end-to-end against both a local MLflow server and OpenShift-hosted MLflow (agen-e2e-rhoai2 cluster): - Agent running with tracing enabled, traces recorded in MLflow - `make eval` sends golden queries, reads traces, attaches expectations - All scorers produce results (Safety, RelevanceToQuery, Completeness, Correctness, ToolCallCorrectness, ToolCallEfficiency, Guidelines) - Timestamp-based trace isolation verified — only current run's traces evaluated - Content-based matching confirmed immune to interleaved traces - Retry loop confirmed working for async trace flush delays - Full unit test suite passing (41/41) ## Checklist - [x] I have read CONTRIBUTING.md - [x] No .env or secret files are included in this PR - [x] All changes are within scope of the eval integration work ## Review Guidance - `evaluation/run_eval.py` is the core logic — config-driven scorer resolution via `resolve_scorer()`/`_add_scorer()` and `get_scorers()`, plus timestamp + content-based trace matching in `main()` - `evaluation/eval_config.yaml` is the user-facing config — three sections (`core`, `use_case`, `guidelines`) - `pyproject.toml` eval deps: `mlflow[kubernetes]`, `pyyaml`, `httpx`, `deepeval` - README Evaluation section documents the scorer architecture 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
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Adds a config-driven MLflow GenAI evaluation setup to the agentic_rag template. Developers can evaluate their agent's traces using core scorers (Safety, RelevanceToQuery, Completeness, Correctness, ToolCallCorrectness, ToolCallEfficiency, PIILeakage), RAG-specific retrieval scorers (RetrievalGroundedness, RetrievalRelevance, RetrievalSufficiency), plus custom scorers — all configured via YAML, no Python edits needed.
Key changes:
evaluation/eval_config.yaml— scorer configuration with core, use-case (RAG), and guidelines sections. Scorers can come from MLflow, any MLflow-integrated third-party framework (e.g., DeepEval, Phoenix), or custom definitions inscorers.pyevaluation/scorers.py— template for custom scorer definitionsevaluation/run_eval.py— sends golden queries to the running agent, reads traces from MLflow, resolves scorers dynamically viaimportlib. Isolates traces from the current run via a timestamp baseline, then matches traces to golden queries by exact question-content comparison (immune to interleaved traces from concurrent agent traffic), with polling/retry for async trace flushevaluation/eval_data.yaml— placeholder golden queries with examplessrc/agentic_rag/tools.py— RETRIEVER span logging in the retriever tool so RAG scorers can evaluate retrieval qualityEVAL_ENABLE_PIIenv var (no code commenting/uncommenting)make evaltarget addedEVAL_JUDGE_*,EVAL_ENABLE_PII,EVAL_REQUEST_TIMEOUT,EVAL_POLL_INTERVAL,AGENT_URLenv vars in.env.exampleHow it works:
evaluation/eval_data.yamlevaluation/eval_config.yamlmake eval→ sends queries to agent, reads traces, attaches expectations, scores themJira Ticket
RHAIENG-7143
Testing
make testpasses (run from the affected agent directory)Tested end-to-end on OpenShift MLflow (agen-e2e-rhoai2 cluster):
make evalsends golden queries, reads traces, attaches expectationsChecklist
Review Guidance
evaluation/run_eval.pyis the core logic — config-driven scorer resolution viaresolve_scorer()andget_scorers(), plus timestamp + content-based trace matching inmain()evaluation/eval_config.yamlis the user-facing config — three sections (core,use_case,guidelines)src/agentic_rag/tools.py— RETRIEVER span logging needed for the RAG use-case scorerspyproject.tomleval deps:mlflow[kubernetes],pyyaml,httpx,deepeval