# retoor <retoor@molodetz.nl>
from __future__ import annotations
import logging
import math
import re
from collections import Counter
from dataclasses import dataclass, field
from devplacepy.config import DEEPSEARCH_CHROMA_DIR
logger = logging.getLogger(__name__)
TOKEN_PATTERN = re.compile(r"[a-z0-9]+")
BM25_K1 = 1.5
BM25_B = 0.75
HYBRID_VECTOR_WEIGHT = 0.6
HYBRID_KEYWORD_WEIGHT = 0.4
DEFAULT_TOP_K = 8
CANDIDATE_MULTIPLIER = 4
@dataclass
class Chunk:
uid: str
text: str
url: str
title: str
depth: int = 0
source: str = ""
position: int = 0
score: float = 0.0
metadata: dict = field(default_factory=dict)
def _tokenize(text: str) -> list[str]:
return TOKEN_PATTERN.findall((text or "").lower())
class VectorStore:
def __init__(self, collection_name: str) -> None:
self.collection_name = collection_name
self._client = None
self._collection = None
def _ensure(self):
if self._collection is not None:
return self._collection
import chromadb
DEEPSEARCH_CHROMA_DIR.mkdir(parents=True, exist_ok=True)
self._client = chromadb.PersistentClient(path=str(DEEPSEARCH_CHROMA_DIR))
self._collection = self._client.get_or_create_collection(
name=self.collection_name, metadata={"hnsw:space": "cosine"}
)
return self._collection
def add(self, chunks: list[Chunk], vectors: list[list[float]]) -> None:
if not chunks:
return
collection = self._ensure()
collection.add(
ids=[chunk.uid for chunk in chunks],
embeddings=vectors,
documents=[chunk.text for chunk in chunks],
metadatas=[
{
"url": chunk.url,
"title": chunk.title,
"depth": chunk.depth,
"source": chunk.source,
"position": chunk.position,
}
for chunk in chunks
],
)
def all_chunks(self) -> list[Chunk]:
collection = self._ensure()
data = collection.get(include=["documents", "metadatas"])
chunks: list[Chunk] = []
ids = data.get("ids") or []
documents = data.get("documents") or []
metadatas = data.get("metadatas") or []
for index, uid in enumerate(ids):
meta = metadatas[index] if index < len(metadatas) else {}
chunks.append(
Chunk(
uid=uid,
text=documents[index] if index < len(documents) else "",
url=str(meta.get("url", "")),
title=str(meta.get("title", "")),
depth=int(meta.get("depth", 0) or 0),
source=str(meta.get("source", "")),
position=int(meta.get("position", 0) or 0),
metadata=dict(meta),
)
)
return chunks
def count(self) -> int:
try:
return self._ensure().count()
except Exception:
return 0
def vector_search(
self, query_vector: list[float], top_k: int, where: dict | None = None
) -> list[Chunk]:
collection = self._ensure()
result = collection.query(
query_embeddings=[query_vector],
n_results=top_k,
where=where or None,
include=["documents", "metadatas", "distances"],
)
ids = (result.get("ids") or [[]])[0]
documents = (result.get("documents") or [[]])[0]
metadatas = (result.get("metadatas") or [[]])[0]
distances = (result.get("distances") or [[]])[0]
chunks: list[Chunk] = []
for index, uid in enumerate(ids):
meta = metadatas[index] if index < len(metadatas) else {}
distance = distances[index] if index < len(distances) else 1.0
chunks.append(
Chunk(
uid=uid,
text=documents[index] if index < len(documents) else "",
url=str(meta.get("url", "")),
title=str(meta.get("title", "")),
depth=int(meta.get("depth", 0) or 0),
source=str(meta.get("source", "")),
position=int(meta.get("position", 0) or 0),
score=1.0 - float(distance),
metadata=dict(meta),
)
)
return chunks
def keyword_scores(self, query: str, chunks: list[Chunk]) -> dict[str, float]:
terms = _tokenize(query)
if not terms or not chunks:
return {}
docs = [_tokenize(chunk.text) for chunk in chunks]
lengths = [len(doc) for doc in docs]
avg_len = (sum(lengths) / len(lengths)) if lengths else 0.0
doc_freq: Counter = Counter()
for doc in docs:
for term in set(doc):
if term in terms:
doc_freq[term] += 1
total_docs = len(docs)
scores: dict[str, float] = {}
for index, chunk in enumerate(chunks):
counts = Counter(docs[index])
length = lengths[index] or 1
score = 0.0
for term in terms:
freq = counts.get(term, 0)
if freq == 0:
continue
idf = math.log(
1 + (total_docs - doc_freq[term] + 0.5) / (doc_freq[term] + 0.5)
)
denom = freq + BM25_K1 * (
1 - BM25_B + BM25_B * (length / (avg_len or 1))
)
score += idf * (freq * (BM25_K1 + 1)) / (denom or 1)
scores[chunk.uid] = score
return scores
def hybrid_search(
self,
query: str,
query_vector: list[float],
top_k: int = DEFAULT_TOP_K,
where: dict | None = None,
) -> list[Chunk]:
candidates = self.vector_search(
query_vector, top_k * CANDIDATE_MULTIPLIER, where
)
if not candidates:
return []
keyword = self.keyword_scores(query, candidates)
vec_max = max((chunk.score for chunk in candidates), default=0.0) or 1.0
kw_max = max(keyword.values(), default=0.0) or 1.0
for chunk in candidates:
vec_norm = max(0.0, chunk.score) / vec_max
kw_norm = keyword.get(chunk.uid, 0.0) / kw_max
chunk.score = (
HYBRID_VECTOR_WEIGHT * vec_norm + HYBRID_KEYWORD_WEIGHT * kw_norm
)
candidates.sort(key=lambda chunk: chunk.score, reverse=True)
return candidates[:top_k]
def drop(self) -> None:
try:
import chromadb
DEEPSEARCH_CHROMA_DIR.mkdir(parents=True, exist_ok=True)
client = self._client or chromadb.PersistentClient(
path=str(DEEPSEARCH_CHROMA_DIR)
)
client.delete_collection(self.collection_name)
except Exception as exc:
logger.info("deepsearch collection drop skipped for %s: %s", self.collection_name, exc)
finally:
self._collection = None
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