Spaces:
Running
Running
File size: 8,543 Bytes
67be617 | 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 | """Lightweight RAG engine.
Scans a directory for ``.md`` and ``.txt`` files, performs basic semantic
chunking (paragraph-based with a soft character cap), and provides a simple
TF-IDF search over the resulting chunks.
Dependency-free so it can run in constrained environments. Can be swapped for
a vector store later without changing the public API (``ingest``, ``search``,
``stats``).
"""
from __future__ import annotations
import math
import re
import threading
from collections import Counter
from dataclasses import dataclass, field
from pathlib import Path
from typing import Iterable
from core.vector_store import DEFAULT_INDEX_DIR, FaissVectorStore
DEFAULT_HARVEST_DIR = Path("data") / "master_harvest"
_SUPPORTED_SUFFIXES = {".md", ".txt"}
_TOKEN_RE = re.compile(r"[A-Za-z0-9_]+")
_PARAGRAPH_SPLIT_RE = re.compile(r"\n\s*\n")
@dataclass
class Chunk:
"""A single searchable chunk of text."""
source: str
index: int
text: str
tokens: Counter = field(default_factory=Counter)
def to_dict(self) -> dict:
return {"source": self.source, "index": self.index, "text": self.text}
def _tokenize(text: str) -> list[str]:
return [t.lower() for t in _TOKEN_RE.findall(text)]
def _chunk_text(text: str, max_chars: int = 1200) -> list[str]:
"""Split text into semantic-ish chunks.
Paragraph boundaries are preferred. Paragraphs longer than ``max_chars``
are split on sentence boundaries, then on hard character boundaries.
"""
chunks: list[str] = []
for paragraph in _PARAGRAPH_SPLIT_RE.split(text):
paragraph = paragraph.strip()
if not paragraph:
continue
if len(paragraph) <= max_chars:
chunks.append(paragraph)
continue
sentences = re.split(r"(?<=[.!?])\s+", paragraph)
buf = ""
for sentence in sentences:
if len(buf) + len(sentence) + 1 > max_chars and buf:
chunks.append(buf.strip())
buf = ""
if len(sentence) > max_chars:
for i in range(0, len(sentence), max_chars):
chunks.append(sentence[i : i + max_chars].strip())
continue
buf = f"{buf} {sentence}".strip()
if buf:
chunks.append(buf.strip())
return [c for c in chunks if c]
class RAGEngine:
"""Simple, thread-safe RAG engine over a local directory of docs."""
def __init__(
self,
harvest_dir: str | Path = DEFAULT_HARVEST_DIR,
index_dir: str | Path | None = None,
prefer_faiss: bool = True,
) -> None:
self.harvest_dir = Path(harvest_dir)
self._chunks: list[Chunk] = []
self._doc_freq: Counter = Counter()
self._lock = threading.Lock()
self._index_dir = Path(index_dir) if index_dir else DEFAULT_INDEX_DIR
self._prefer_faiss = prefer_faiss and FaissVectorStore.is_available()
self._vector_store: FaissVectorStore | None = None
if self._prefer_faiss:
self._vector_store = FaissVectorStore(index_dir=self._index_dir)
# Best-effort load of any persisted index from a prior `build`.
self._vector_store.load()
# -- backend introspection -------------------------------------------
@property
def backend(self) -> str:
if self._vector_store is not None:
return "faiss"
return "tfidf"
def vector_store(self) -> FaissVectorStore | None:
return self._vector_store
# -- ingestion --------------------------------------------------------
def ingest(self) -> dict:
"""Re-scan the harvest directory and rebuild the in-memory index."""
with self._lock:
chunks: list[Chunk] = []
doc_freq: Counter = Counter()
files = self._discover_files()
for path in files:
try:
text = path.read_text(encoding="utf-8", errors="replace")
except OSError:
continue
try:
rel = str(path.relative_to(self.harvest_dir))
except ValueError:
rel = path.name
for idx, chunk_text in enumerate(_chunk_text(text)):
tokens = Counter(_tokenize(chunk_text))
if not tokens:
continue
chunks.append(
Chunk(source=rel, index=idx, text=chunk_text, tokens=tokens)
)
for token in tokens:
doc_freq[token] += 1
self._chunks = chunks
self._doc_freq = doc_freq
tfidf_stats = {
"files_ingested": len(files),
"chunks_indexed": len(chunks),
"harvest_dir": str(self.harvest_dir),
"backend": self.backend,
}
# Build the FAISS store from the same chunks so both backends stay
# consistent. Done outside the lock since FaissVectorStore has its
# own internal locking and embedding is slow.
if self._vector_store is not None:
try:
self._vector_store.build(
[
{"source": c.source, "index": c.index, "text": c.text}
for c in chunks
]
)
except Exception as exc: # pragma: no cover - defensive
tfidf_stats["faiss_error"] = str(exc)
return tfidf_stats
def _discover_files(self) -> list[Path]:
if not self.harvest_dir.exists():
return []
return sorted(
p
for p in self.harvest_dir.rglob("*")
if p.is_file() and p.suffix.lower() in _SUPPORTED_SUFFIXES
)
# -- search -----------------------------------------------------------
def search(self, query: str, top_k: int = 5) -> list[dict]:
"""Return the top ``k`` chunks matching ``query``.
Uses FAISS if available, otherwise the in-memory TF-IDF path.
"""
if top_k <= 0:
return []
if self._vector_store is not None:
hits = self._vector_store.search(query, top_k=top_k)
if hits:
return [hit.to_dict() for hit in hits]
# Fall through to TF-IDF if FAISS index is empty (e.g. no `ingest`
# has been run yet but TF-IDF was warmed via tests).
query_tokens = _tokenize(query)
if not query_tokens:
return []
with self._lock:
total_chunks = len(self._chunks)
if total_chunks == 0:
return []
scored: list[tuple[float, Chunk]] = []
for chunk in self._chunks:
score = 0.0
for token in query_tokens:
tf = chunk.tokens.get(token, 0)
if tf == 0:
continue
df = self._doc_freq.get(token, 0)
idf = math.log((total_chunks + 1) / (df + 1)) + 1.0
score += tf * idf
if score > 0:
scored.append((score, chunk))
scored.sort(key=lambda item: item[0], reverse=True)
results = []
for score, chunk in scored[:top_k]:
result = chunk.to_dict()
result["score"] = round(score, 4)
results.append(result)
return results
# -- introspection ----------------------------------------------------
def stats(self) -> dict:
with self._lock:
base = {
"chunks_indexed": len(self._chunks),
"unique_tokens": len(self._doc_freq),
"harvest_dir": str(self.harvest_dir),
"backend": self.backend,
}
if self._vector_store is not None:
base["vector_store"] = self._vector_store.stats()
return base
def iter_supported_files(root: Path) -> Iterable[Path]:
"""Public helper for callers that want to enumerate harvest files."""
if not root.exists():
return iter(())
return (
p for p in root.rglob("*") if p.is_file() and p.suffix.lower() in _SUPPORTED_SUFFIXES
)
|