fix(ci): pin ruff and make the lint rule set explicit

CI installs ruff unpinned, so it drifted to a newer default rule set (BLE001,
S110, B008, ...) and failed a build that passed the day before. Pin ruff to
0.16.0 and declare [tool.ruff.lint] select/ignore explicitly, so linting is
deterministic regardless of the ruff version installed.

Intentional patterns are ignored with a documented reason: broad except (Atlas
integrations degrade gracefully by design), FastAPI's File() default idiom, the
JSON-serializable str-Enum, and em/en dashes in prose. The genuine findings are
fixed: import order, datetime.UTC, enumerate, contextlib.suppress, sorted
__all__, explicit zip(strict=), and wrapped long lines.

Author: hari9618

.gitignore

@@ -43,3 +43,4 @@ Thumbs.db
 .coverage.*
 htmlcov/
 coverage.xml
+How Top lead generation systems generate Quotes and how we can generate.md

apps/api/atlas/core/agents/synthesizer.py

@@ -64,8 +64,9 @@ def synthesize_node(state: ResearchState) -> dict:
     )
 
     meta = chat(
-        f"Given this brief, output ONLY JSON with an overall confidence (0..1) and a list "
-        f'of uncertainties: {{"confidence": 0.0, "uncertainties": ["..."]}}\n\nBrief:\n{report[:3000]}',
+        "Given this brief, output ONLY JSON with an overall confidence (0..1) and a list "
+        'of uncertainties: {"confidence": 0.0, "uncertainties": ["..."]}\n\n'
+        f"Brief:\n{report[:3000]}",
         temperature=0.0, run_name="synthesizer.meta",
     )
     parsed = extract_json(meta, default={})

apps/api/atlas/core/llmops/__init__.py

@@ -14,6 +14,12 @@
 from .registry import PromptRegistry, get_registry
 
 __all__ = [
-    "PromptRegistry", "get_registry", "run_gate", "GateResult", "optimize",
-    "evaluate_agents", "score_agent", "weakest_agent",
+    "GateResult",
+    "PromptRegistry",
+    "evaluate_agents",
+    "get_registry",
+    "optimize",
+    "run_gate",
+    "score_agent",
+    "weakest_agent",
 ]

apps/api/atlas/core/llmops/optimizer.py

@@ -75,7 +75,9 @@ def optimize(query: str, *, max_iters: int = 1) -> dict:
 
     for i in range(1, max_iters + 1):
         diagnosis = "; ".join(g.reasons) or "low overall score"
-        candidate = propose_improved_prompt(reg.effective(_PROMPT_NAME), diagnosis, result.get("report", ""))
+        candidate = propose_improved_prompt(
+            reg.effective(_PROMPT_NAME), diagnosis, result.get("report", "")
+        )
         reg.set_candidate(_PROMPT_NAME, candidate)
 
         result_i = research(query, thread_id=f"ops-{i}")
@@ -89,7 +91,9 @@ def optimize(query: str, *, max_iters: int = 1) -> dict:
         if scores_i["overall"] > baseline_overall:
             best_result = result_i
         if g_i.passed and scores_i["overall"] >= baseline_overall:
-            new_version = reg.release_candidate(_PROMPT_NAME, scores_i, notes=f"auto-improve: {diagnosis}")
+            new_version = reg.release_candidate(
+                _PROMPT_NAME, scores_i, notes=f"auto-improve: {diagnosis}"
+            )
             released = True
             best_result = result_i
             break

apps/api/atlas/core/llmops/registry.py

@@ -12,7 +12,7 @@
 import json
 import logging
 from dataclasses import asdict, dataclass, field
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from pathlib import Path
 
 from ...paths import cache_dir
@@ -135,7 +135,7 @@ def release_candidate(self, name: str, scores: dict, notes: str = "") -> int:
 
 
 def _now() -> str:
-    return datetime.now(timezone.utc).isoformat()
+    return datetime.now(UTC).isoformat()
 
 
 _registry: PromptRegistry | None = None

apps/api/atlas/core/memory/__init__.py

@@ -8,7 +8,7 @@
 A Summarizer agent periodically distills episodic memory into semantic facts.
 """
 
-from .episodic import EpisodicMemory, Episode
+from .episodic import Episode, EpisodicMemory
 from .procedural import ProceduralMemory
 
-__all__ = ["EpisodicMemory", "Episode", "ProceduralMemory"]
+__all__ = ["Episode", "EpisodicMemory", "ProceduralMemory"]

apps/api/atlas/core/memory/episodic.py

@@ -23,7 +23,7 @@
 import sqlite3
 from contextlib import closing
 from dataclasses import dataclass, field
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from pathlib import Path
 
 import numpy as np
@@ -98,7 +98,7 @@ def _unpack(blob: bytes | None):
     # ---- write ----
     def save(self, query: str, report: str, confidence: float | None,
              findings: list[dict], target: str = "") -> int:
-        created_at = datetime.now(timezone.utc).isoformat()
+        created_at = datetime.now(UTC).isoformat()
         # Embed once, here — recall then reads the vector instead of recomputing it.
         try:
             blob = self._pack(self._embed().embed_query(query))
@@ -139,7 +139,7 @@ def relevant(self, query: str, limit: int = 3) -> list[Episode]:
         if missing:
             emb = self._embed()
             fresh = emb.embed_documents([ep.query for ep, _ in missing])
-            for (ep, idx), vec in zip((m for m in missing), fresh):
+            for (ep, idx), vec in zip(missing, fresh, strict=False):
                 rows[idx] = (ep, np.asarray(vec, dtype=np.float32))
             self._backfill({ep.id: rows[idx][1] for ep, idx in missing})
 

apps/api/atlas/core/rag/__init__.py

@@ -12,13 +12,13 @@
 from .types import Chunk, Document, RetrievedChunk
 
 __all__ = [
-    "HybridIndex",
-    "build_index",
-    "ingest_documents",
-    "corrective_retrieve",
-    "CragResult",
-    "Grade",
     "Chunk",
+    "CragResult",
     "Document",
+    "Grade",
+    "HybridIndex",
     "RetrievedChunk",
+    "build_index",
+    "corrective_retrieve",
+    "ingest_documents",
 ]

apps/api/atlas/core/rag/chunking.py

@@ -31,8 +31,7 @@ def chunk_document(
 
     step = max(1, chunk_words - overlap_words)
     chunks: list[Chunk] = []
-    ordinal = 0
-    for start in range(0, len(words), step):
+    for ordinal, start in enumerate(range(0, len(words), step)):
         window = words[start : start + chunk_words]
         if not window:
             break
@@ -50,7 +49,6 @@ def chunk_document(
                 metadata=dict(doc.metadata),
             )
         )
-        ordinal += 1
         if start + chunk_words >= len(words):
             break
     return chunks

apps/api/atlas/core/rag/index.py

@@ -174,7 +174,7 @@ def save(self, base: Path = DEFAULT_CACHE) -> None:
         log.info("Saved index (%d chunks) → %s.json", len(self.chunks), base)
 
     @classmethod
-    def load(cls, base: Path = DEFAULT_CACHE, offline: bool = False) -> "HybridIndex":
+    def load(cls, base: Path = DEFAULT_CACHE, offline: bool = False) -> HybridIndex:
         idx = build_index(offline=offline)
         payload = json.loads(base.with_suffix(".json").read_text(encoding="utf-8"))
         idx.add_chunks([Chunk(**c) for c in payload["chunks"].values()])