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GeezerVison is showing github.com — feldera/python/felderize/felderize/docs.py at main · feldera/feldera Expand file tree/ Copy pathdocs.pyMore file actions 417 lines (348 loc) · 14.6 KB / Copy pathdocs.pyFile metadata and controls417 lines (348 loc) · 14.6 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 from __future__ import annotations import os import re import sys import urllib.request from pathlib import Path import yaml from felderize.constants import DEFAULT_DOCS_BASE_URL, HTTP_TIMEOUT # Map each category to its doc file. _DOC_FILES: dict[str, str] = { "types": "types.md", "string": "string.md", "datetime": "datetime.md", "json": "json.md", "aggregates": "aggregates.md", "array": "array.md", "map": "map.md", "decimal": "decimal.md", "float": "float.md", "casts": "casts.md", "comparisons": "comparisons.md", "boolean": "boolean.md", "integer": "integer.md", "binary": "binary.md", "operators": "operators.md", "streaming": "streaming.md", "unsupported": "unsupported-operations.md", "uuid": "uuid.md", } # SQL construct patterns that cannot be derived from the function index # (keywords, operators, syntax forms rather than named functions). # Keep these specific — broad patterns like \bDATE\b match almost every query. _EXTRA_PATTERNS: dict[str, list[str]] = { "datetime": [r"\bINTERVAL\b"], # DATE/TIMESTAMP covered by index function names "aggregates": [ r"\bGROUP\s+BY\b", r"\bHAVING\b", r"\bOVER\s*\(", r"\bFIRST_VALUE\b", r"\bLAST_VALUE\b", # in doc but not function index r"\bVARIANCE\b", r"\bVAR_POP\b", r"\bVAR_SAMP\b", r"\bPERCENT_RANK\b", r"\bCUME_DIST\b", r"\bNTILE\b", ], "array": [r"\bEXPLODE\b", r"\bUNNEST\b", r"\bsize\s*\("], "map": [r"\bMAP\s*<"], # MAP( covered by index; MAP< is type syntax "json": [r"\bVARIANT\b"], # JSON covered by index function names "casts": [r"::"], # CAST covered by index; :: is operator syntax "comparisons": [r"\bCASE\s+WHEN\b"], "boolean": [r"\bIS\s+(NOT\s+)?(?:TRUE|FALSE)\b", r"\bBOOLEAN\b", r"\bBOOL\b"], "decimal": [r"\bDECIMAL\b"], "integer": [ r"\bTINYINT\b", r"\bSMALLINT\b", r"\bBIGINT\b", r"\bDIV_NULL\b", r"\bSEQUENCE\b", ], "binary": [r"\bVARBINARY\b", r"\bBINARY\b", r"\bXXHASH\b"], "float": [r"\bFINITE_OR_NULL\b"], "operators": [r"\bBETWEEN\b", r"<=>", r"\bCONTAINS\b", r"\bOVERLAPS\b"], "streaming": [r"\bLATENESS\b", r"\bTUMBLE\b", r"\bHOP\b"], "uuid": [r"\bUUID\b"], } # Spark function names that appear in SQL but are not in the Feldera index. _SPARK_ALIASES: dict[str, list[str]] = { "json": [r"\bget_json_object\b", r"\bfrom_json\b", r"\bjson_tuple\b"], "array": [ r"\barray_contains\b", r"\bsort_array\b", r"\barray_distinct\b", r"\belement_at\b", ], "map": [r"\belement_at\b", r"\bstr_to_map\b", r"\bmap_from_arrays\b"], "datetime": [ r"\bto_date\b", r"\bto_timestamp\b", r"\bunix_timestamp\b", r"\bfrom_unixtime\b", r"\bdate_format\b", r"\badd_months\b", r"\bmonths_between\b", r"\blast_day\b", r"\bdayofweek\b", r"\bdayofmonth\b", r"\bdayofyear\b", r"\bweekofyear\b", ], "string": [ r"\bLPAD\b", r"\bRPAD\b", r"\bLTRIM\b", r"\bRTRIM\b", r"\bBTRIM\b", r"\bTRANSLATE\b", r"\bINSTR\b", r"\bLOCATE\b", r"\bSPACE\b", ], "decimal": [r"\bNUMERIC\b"], "float": [r"\bFLOAT\b", r"\bPOW\b", r"\bLOG2\b", r"\bHYPOT\b"], "comparisons": [r"\bNVL\b", r"\bNVL2\b", r"\bDECODE\b"], } # Regex to find HTML anchor IDs embedded in doc files: <a id="name"> _ANCHOR_ID_RE = re.compile(r'<a\s+id="([^"]+)"', re.IGNORECASE) # Regex to detect function calls in SQL: FUNC_NAME( _SQL_FUNC_RE = re.compile(r"\b([A-Z_][A-Z_0-9]*)\s*\(", re.IGNORECASE) # Regexes for parsing function-index.md lines. _INDEX_FUNC_RE = re.compile(r"^\* `([A-Z_][A-Z_0-9 ]*)`", re.IGNORECASE) _INDEX_LINK_RE = re.compile(r"\[([a-z]+)\]\(([^)#]+)(?:#([^)]+))?\)") # Categories that are always included — no trigger patterns needed. _ALWAYS_INCLUDED = {"types", "unsupported"} def _fetch_doc(filename: str) -> str: """Fetch a SQL doc file from the Feldera docs repo. The base URL is read from FELDERA_DOCS_BASE_URL (default: main branch on GitHub). """ base = os.environ.get("FELDERA_DOCS_BASE_URL", DEFAULT_DOCS_BASE_URL) url = f"{base}/{filename}" try: with urllib.request.urlopen(url, timeout=HTTP_TIMEOUT) as resp: return resp.read().decode("utf-8") except Exception as e: print(f"docs.py: failed to fetch {url}: {e}", file=sys.stderr) return "" def _build_categories_from_index() -> tuple[ dict[str, list[str]], dict[str, list[tuple[str, str]]] ]: """Fetch and parse function-index.md from the Feldera docs repo. Returns: categories: category → [\\bFUNC\\b, ...] trigger patterns func_anchors: FUNC_NAME_UPPER → [(doc_filename, anchor_id), ...] """ known = set(_DOC_FILES) - _ALWAYS_INCLUDED categories: dict[str, list[str]] = {cat: [] for cat in _DOC_FILES} func_anchors: dict[str, list[tuple[str, str]]] = {} text = _fetch_doc("function-index.md") if not text: return categories, func_anchors for line in text.splitlines(): m = _INDEX_FUNC_RE.match(line) if not m: continue func_name = m.group(1).strip() func_upper = func_name.upper() for link_m in _INDEX_LINK_RE.finditer(line): cat = link_m.group(1) doc_file = link_m.group(2) # e.g. "string.md" anchor = link_m.group(3) # e.g. "upper" (may be None) if cat in known: keyword = rf"\b{re.escape(func_name)}\b" if keyword not in categories[cat]: categories[cat].append(keyword) if anchor: func_anchors.setdefault(func_upper, []).append((doc_file, anchor)) return categories, func_anchors # Lazy cache — populated on first call to _get_categories_and_anchors(). _categories_cache: ( tuple[dict[str, list[str]], dict[str, list[tuple[str, str]]]] | None ) = None def _get_categories_and_anchors() -> tuple[ dict[str, list[str]], dict[str, list[tuple[str, str]]] ]: """Return (categories, func_anchors), fetching function-index.md on first call.""" global _categories_cache if _categories_cache is None: categories, func_anchors = _build_categories_from_index() for source in (_EXTRA_PATTERNS, _SPARK_ALIASES): for cat, patterns in source.items(): seen = set(categories.get(cat, [])) for p in patterns: if p not in seen: categories.setdefault(cat, []).append(p) seen.add(p) _categories_cache = (categories, func_anchors) return _categories_cache # Module-level categories for load_examples() — populated lazily on first use. def _get_categories() -> dict[str, list[str]]: return _get_categories_and_anchors()[0] # ── Section-level doc parsing ──────────────────────────────────────────────── # Cache: doc filename → (preamble, {heading: content}, {anchor_id: heading}) _section_cache: dict[str, tuple[str, dict[str, str], dict[str, str]]] = {} def _parse_doc_sections( content: str, ) -> tuple[str, dict[str, str], dict[str, str]]: """Split a doc file into (preamble, sections, anchor_map). preamble — text before the first ## heading sections — ordered dict: ## heading text → section content (includes heading line) anchor_map — <a id="x"> → ## heading text for every anchor in the file """ sections: dict[str, str] = {} anchor_map: dict[str, str] = {} preamble_lines: list[str] = [] current_heading: str | None = None current_lines: list[str] = [] for line in content.splitlines(keepends=True): if line.startswith("## "): if current_heading is not None: body = "".join(current_lines) sections[current_heading] = body for am in _ANCHOR_ID_RE.finditer(body): anchor_map[am.group(1)] = current_heading else: preamble_lines = current_lines[:] current_heading = line.rstrip() current_lines = [line] else: current_lines.append(line) if current_heading is not None: body = "".join(current_lines) sections[current_heading] = body for am in _ANCHOR_ID_RE.finditer(body): anchor_map[am.group(1)] = current_heading elif current_lines: preamble_lines = current_lines return "".join(preamble_lines), sections, anchor_map def _get_doc_sections( filename: str, ) -> tuple[str, dict[str, str], dict[str, str]]: """Return parsed sections for a doc file (fetched once, then cached).""" if filename not in _section_cache: text = _fetch_doc(filename) _section_cache[filename] = _parse_doc_sections(text) if text else ("", {}, {}) return _section_cache[filename] def _load_relevant_sections(filename: str, relevant_anchors: set[str]) -> str: """Return preamble + only the ## sections that contain a relevant anchor. Falls back to the full file content when no anchor information is available (e.g., the file has no <a id> tags) so that we never return empty docs for a matched category. """ preamble, sections, anchor_map = _get_doc_sections(filename) if not sections: # Plain file with no ## headings — return as-is. return preamble needed: set[str] = set() for anchor in relevant_anchors: if anchor in anchor_map: needed.add(anchor_map[anchor]) if not needed: # No specific functions detected or none matched → include everything. parts = [preamble] if preamble.strip() else [] parts.append("".join(sections.values())) return "".join(parts) parts = [preamble] if preamble.strip() else [] for heading, body in sections.items(): if heading in needed: parts.append(body) return "".join(parts) # ── Category detection ─────────────────────────────────────────────────────── def _detect_categories( sql: str, categories: dict[str, list[str]] | None = None ) -> set[str]: """Return set of category names whose trigger patterns match the SQL.""" matched = _ALWAYS_INCLUDED.copy() all_categories = categories if categories is not None else _get_categories() for category, patterns in all_categories.items(): if not patterns: continue for pattern in patterns: if re.search(pattern, sql, re.IGNORECASE): matched.add(category) break return matched def _detect_sql_functions(sql: str) -> set[str]: """Return uppercase names of all function calls found in the SQL.""" return {m.group(1).upper() for m in _SQL_FUNC_RE.finditer(sql)} # ── Public API ─────────────────────────────────────────────────────────────── def load_docs(sql: str) -> str: """Load relevant Feldera doc sections based on SQL content. Fetches docs from the Feldera GitHub repo on first call (cached per session). Only sections whose <a id> anchors correspond to functions present in the SQL are included. Falls back to full file content when no specific anchors match. """ categories, func_anchors = _get_categories_and_anchors() detected = _detect_categories(sql, categories) sql_funcs = _detect_sql_functions(sql) result_sections: list[str] = [] for category in sorted(detected): if category not in _DOC_FILES: continue doc_filename = _DOC_FILES[category] relevant_anchors: set[str] = set() for func in sql_funcs: for fname, anchor in func_anchors.get(func, []): if fname == doc_filename: relevant_anchors.add(anchor) content = _load_relevant_sections(doc_filename, relevant_anchors) if content.strip(): result_sections.append(f"### {category}\n\n{content}") return "\n\n---\n\n".join(result_sections) _example_cache: dict[Path, tuple[set[str], str] | None] = {} def _load_example_file(filepath: Path, detected: set[str]) -> str | None: """Return the body of one example file if it matches detected categories, else None. Returns None for files that are not valid examples (no frontmatter, etc.). """ if filepath not in _example_cache: raw = filepath.read_text() if not raw.startswith("---"): _example_cache[filepath] = None return None parts = raw.split("---", 2) if len(parts) < 3: _example_cache[filepath] = None return None file_categories: set[str] = set() try: meta = yaml.safe_load(parts[1]) if isinstance(meta, dict): file_categories = set(meta.get("categories", [])) except yaml.YAMLError: pass _example_cache[filepath] = (file_categories, parts[2].strip()) cached = _example_cache[filepath] if cached is None: return None file_categories, body = cached return body if (not file_categories or file_categories & detected) else None def _load_examples_from_dir(examples_dir: Path, detected: set[str]) -> list[str]: """Load matching example sections from a directory.""" if not examples_dir.is_dir(): return [] sections: list[str] = [] for filepath in sorted(examples_dir.glob("*.md")): body = _load_example_file(filepath, detected) if body is not None: sections.append(body) return sections def load_examples( sql: str, extra_examples_dirs: list[Path] | None = None, extra_examples_files: list[Path] | None = None, ) -> str: """Return validated translation examples relevant to the SQL input. Loads from user-provided directories and/or individual files. Directories are auto-discovered from ~/.felderize/examples/ and .felderize/examples/; individual files and extra directories are passed explicitly. """ detected = _detect_categories(sql) sections: list[str] = [] for d in extra_examples_dirs or []: sections += _load_examples_from_dir(d, detected) for f in extra_examples_files or []: body = _load_example_file(f, detected) if body is not None: sections.append(body) return "\n\n---\n\n".join(sections) |
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