File size: 7,540 Bytes
582520a a6e51ba 582520a a6e51ba 582520a a6e51ba 582520a | 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 | """RAG knowledge base: ingest, search, no hallucination."""
from __future__ import annotations
import json
import logging
import uuid
from pathlib import Path
from typing import Any
from bs4 import BeautifulSoup
from pypdf import PdfReader
from app.config import get_settings
from app.services.embeddings import (
chunk_text,
cosine_similarity,
embed_texts,
token_overlap_score,
)
logger = logging.getLogger(__name__)
class KnowledgeStore:
"""File-backed vector store (Chroma when available)."""
def __init__(self) -> None:
self.settings = get_settings()
self._collection = None
self._fallback_path = (
Path(self.settings.chroma_path) / "fallback.json"
)
self._fallback: list[dict[str, Any]] = []
self._init_store()
def _init_store(self) -> None:
path = Path(self.settings.chroma_path)
path.mkdir(parents=True, exist_ok=True)
if self.settings.use_json_store:
self._collection = None
self._load_fallback()
logger.info("JSON knowledge store ready")
return
try:
import chromadb
client = chromadb.PersistentClient(path=str(path))
self._collection = client.get_or_create_collection(
name="replyflow_kb",
metadata={"hnsw:space": "cosine"},
)
logger.info("Chroma knowledge store ready")
except Exception as exc: # noqa: BLE001
logger.warning(
"Chroma unavailable (%s); using JSON fallback",
exc,
)
self._collection = None
self._load_fallback()
def _load_fallback(self) -> None:
if self._fallback_path.exists():
self._fallback = json.loads(
self._fallback_path.read_text(encoding="utf-8")
)
else:
self._fallback = []
def _save_fallback(self) -> None:
self._fallback_path.parent.mkdir(
parents=True, exist_ok=True
)
self._fallback_path.write_text(
json.dumps(self._fallback, ensure_ascii=False),
encoding="utf-8",
)
@property
def chunk_count(self) -> int:
if self._collection is not None:
return int(self._collection.count())
return len(self._fallback)
async def add_chunks(
self,
chunks: list[str],
metadata: dict[str, Any],
) -> int:
"""Embed and store text chunks."""
if not chunks:
return 0
embeddings = await embed_texts(
chunks,
force_local=self.settings.seed_local_embeddings,
)
ids = [str(uuid.uuid4()) for _ in chunks]
metadatas = [
{**metadata, "chunk_index": i}
for i in range(len(chunks))
]
if self._collection is not None:
self._collection.add(
ids=ids,
documents=chunks,
embeddings=embeddings,
metadatas=metadatas,
)
else:
for i, chunk in enumerate(chunks):
self._fallback.append(
{
"id": ids[i],
"document": chunk,
"embedding": embeddings[i],
"metadata": metadatas[i],
}
)
self._save_fallback()
return len(chunks)
async def search(
self,
query: str,
top_k: int = 4,
min_score: float = 0.12,
) -> list[dict[str, Any]]:
"""Return relevant chunks above similarity threshold."""
if self.chunk_count == 0:
return []
query_emb = (
await embed_texts(
[query],
force_local=self.settings.seed_local_embeddings,
)
)[0]
use_openai = (
get_settings().has_openai
and not self.settings.seed_local_embeddings
)
if self._collection is not None:
result = self._collection.query(
query_embeddings=[query_emb],
n_results=min(top_k, max(1, self.chunk_count)),
)
docs = result.get("documents", [[]])[0]
metas = result.get("metadatas", [[]])[0]
dists = result.get("distances", [[]])[0]
hits: list[dict[str, Any]] = []
for doc, meta, dist in zip(docs, metas, dists):
vector_score = 1.0 - float(dist)
keyword = token_overlap_score(query, doc)
score = (
vector_score
if use_openai
else 0.45 * vector_score + 0.55 * keyword
)
if score >= min_score:
hits.append(
{
"text": doc,
"metadata": meta or {},
"score": score,
}
)
hits.sort(key=lambda x: x["score"], reverse=True)
return hits[:top_k]
scored = []
for item in self._fallback:
vector_score = cosine_similarity(
query_emb, item["embedding"]
)
keyword = token_overlap_score(query, item["document"])
score = 0.35 * vector_score + 0.65 * keyword
if score >= min_score:
scored.append(
{
"text": item["document"],
"metadata": item["metadata"],
"score": score,
}
)
scored.sort(key=lambda x: x["score"], reverse=True)
return scored[:top_k]
_store: KnowledgeStore | None = None
def get_knowledge_store() -> KnowledgeStore:
"""Singleton knowledge store."""
global _store
if _store is None:
_store = KnowledgeStore()
return _store
def extract_text_from_file(path: Path) -> str:
"""Extract text from pdf, html, md, or txt."""
suffix = path.suffix.lower()
raw = path.read_bytes()
if suffix == ".pdf":
reader = PdfReader(str(path))
parts = []
for page in reader.pages:
parts.append(page.extract_text() or "")
return "\n".join(parts)
text = raw.decode("utf-8", errors="ignore")
if suffix in {".html", ".htm"}:
soup = BeautifulSoup(text, "html.parser")
return soup.get_text(separator="\n")
return text
async def ingest_path(path: Path, source_type: str) -> int:
"""Ingest a single file into the knowledge store."""
text = extract_text_from_file(path)
chunks = chunk_text(text)
store = get_knowledge_store()
return await store.add_chunks(
chunks,
{
"filename": path.name,
"source_type": source_type,
},
)
async def ingest_directory(directory: Path) -> int:
"""Ingest all supported files from a directory."""
total = 0
patterns = ("*.md", "*.txt", "*.pdf", "*.html", "*.htm")
files: list[Path] = []
for pattern in patterns:
files.extend(directory.glob(pattern))
for path in sorted(set(files)):
source = path.suffix.lower().lstrip(".") or "text"
count = await ingest_path(path, source)
total += count
logger.info("Ingested %s (%s chunks)", path.name, count)
return total
|