Spaces:
Running
Running
File size: 8,455 Bytes
c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 cad8d5f c402937 cad8d5f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f c402937 1d9dc1f | 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 | """Knowledge Base with TF-IDF embeddings (no PyTorch needed).
Uses scikit-learn's TfidfVectorizer instead of sentence-transformers.
Works on HF free tier without heavy ML dependencies.
"""
import json
import re
from datetime import datetime
from pathlib import Path
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
class KnowledgeBase:
def __init__(self, data_dir, embedding_model_id="", cache_dir=""):
self.knowledge_dir = Path(data_dir) / "knowledge"
self.knowledge_dir.mkdir(parents=True, exist_ok=True)
self.index_path = self.knowledge_dir / "index.json"
self.vectors_path = self.knowledge_dir / "vectors.npz"
self._index = None
self._vectors = None
self._vectorizer = None
def _load_index(self):
if self._index is not None:
return self._index
if self.index_path.exists():
try:
self._index = json.loads(self.index_path.read_text(encoding="utf-8"))
except (json.JSONDecodeError, OSError):
self._index = {"chunks": []}
else:
self._index = {"chunks": []}
return self._index
def _load_vectors(self):
if self._vectors is not None:
return self._vectors
if self.vectors_path.exists():
try:
data = np.load(str(self.vectors_path), allow_pickle=True)
self._vectors = data["vectors"]
except Exception:
self._vectors = None
return self._vectors
def _save(self):
index = self._load_index()
self.index_path.write_text(
json.dumps(index, ensure_ascii=False, indent=2),
encoding="utf-8",
)
# Rebuild TF-IDF vectors from all chunks
self._rebuild_vectors()
def _rebuild_vectors(self):
"""Rebuild TF-IDF vectors from all chunk texts."""
index = self._load_index()
texts = [c["text"] for c in index["chunks"]]
if not texts:
self._vectors = None
if self.vectors_path.exists():
self.vectors_path.unlink(missing_ok=True)
return
self._vectorizer = TfidfVectorizer(
max_features=5000,
stop_words="english",
ngram_range=(1, 2),
sublinear_tf=True,
)
self._vectors = self._vectorizer.fit_transform(texts)
# Save vectorizer vocab and vectors (convert np.int64 to int for json serialization)
vocab_dict = {k: int(v) for k, v in self._vectorizer.vocabulary_.items()}
np.savez_compressed(
str(self.vectors_path),
vectors=self._vectors.toarray(),
vocab=json.dumps(vocab_dict),
idf=self._vectorizer.idf_,
)
def _get_vectorizer(self):
"""Load or rebuild the TF-IDF vectorizer."""
if self._vectorizer is not None:
return self._vectorizer
if self.vectors_path.exists():
try:
data = np.load(str(self.vectors_path), allow_pickle=True)
vocab = json.loads(str(data["vocab"]))
idf = data["idf"]
self._vectorizer = TfidfVectorizer(
max_features=5000,
stop_words="english",
ngram_range=(1, 2),
sublinear_tf=True,
vocabulary=vocab,
)
# Fit with dummy to set internal state, then override idf
index = self._load_index()
texts = [c["text"] for c in index["chunks"]]
if texts:
self._vectorizer.fit(texts)
return self._vectorizer
except Exception:
pass
# Rebuild from scratch
self._rebuild_vectors()
return self._vectorizer
def chunk_text(self, text, max_chars=500, overlap=50):
sentences = re.split(r"(?<=[.!?])\s+", text.strip())
chunks = []
current = ""
for sentence in sentences:
sentence = sentence.strip()
if not sentence:
continue
if not current:
current = sentence
continue
combined = current + " " + sentence
if len(combined) <= max_chars:
current = combined
else:
if current:
chunks.append(current)
if overlap > 0 and len(current) > overlap:
overlap_text = current[-overlap:].lstrip()
current = overlap_text + " " + sentence
else:
current = sentence
if current:
chunks.append(current)
# fallback for text without sentence-ending punctuation
if not chunks and text.strip():
raw = text.strip()
while len(raw) > max_chars:
split_at = raw[:max_chars].rfind(" ")
if split_at < 100:
split_at = max_chars
chunks.append(raw[:split_at].strip())
raw = raw[split_at:].strip()
if raw:
chunks.append(raw)
return [c for c in chunks if len(c.strip()) >= 20]
def add_document(self, text, source_name):
chunks = self.chunk_text(text)
if not chunks:
return 0
index = self._load_index()
now = datetime.now().isoformat(timespec="seconds")
for chunk_text in chunks:
index["chunks"].append({
"text": chunk_text,
"source": source_name,
"added_at": now,
})
self._save()
return len(chunks)
def search(self, query, top_k=3, threshold=0.1):
index = self._load_index()
if not index["chunks"]:
return []
vectorizer = self._get_vectorizer()
if vectorizer is None:
return []
# Load vectors
vectors = self._load_vectors()
if vectors is None:
return []
# If vectors are sparse (from file), convert
if hasattr(vectors, 'toarray'):
dense_vectors = vectors.toarray()
else:
dense_vectors = vectors
try:
query_vec = vectorizer.transform([query]).toarray()
except Exception:
return []
similarities = cosine_similarity(query_vec, dense_vectors)[0]
top_indices = np.argsort(similarities)[::-1][:top_k]
results = []
for i in top_indices:
score = float(similarities[i])
if score < threshold:
continue
chunk = index["chunks"][i]
results.append({
"text": chunk["text"],
"source": chunk["source"],
"score": score,
})
return results
def list_sources(self):
index = self._load_index()
sources = {}
for chunk in index["chunks"]:
name = chunk["source"]
if name not in sources:
sources[name] = {"count": 0, "added_at": chunk.get("added_at", "")}
sources[name]["count"] += 1
return sources
def format_source_list(self):
sources = self.list_sources()
if not sources:
return "No study materials in knowledge base yet. Use /learn to add some."
lines = ["", " Knowledge Base", " " + "-" * 50]
for idx, (name, info) in enumerate(sources.items(), start=1):
added = info["added_at"][:16].replace("T", " ") if info["added_at"] else ""
lines.append(f" {idx}. {name}")
lines.append(f" {info['count']} chunks | Added: {added}")
lines.append("")
return "\n".join(lines)
def remove_source(self, index_number):
sources = self.list_sources()
source_names = list(sources.keys())
if index_number < 1 or index_number > len(source_names):
return None
target = source_names[index_number - 1]
idx = self._load_index()
idx["chunks"] = [c for c in idx["chunks"] if c["source"] != target]
self._save()
return target
def is_empty(self):
index = self._load_index()
return len(index["chunks"]) == 0
|