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from __future__ import annotations
import hashlib
import json
import re
import sqlite3
from contextlib import closing
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable
import numpy as np
from .records import FileIndex
from .synthetic_queries import build_synthetic_questions
DEFAULT_DIMENSIONS = 384
SCHEMA_VERSION = "coderag-sqlite-vector-db-v1"
TOKEN_RE = re.compile(r"[A-Za-z_][A-Za-z0-9_]*|\d+(?:\.\d+)?")
@dataclass(frozen=True)
class SearchHit:
key: str
file_path: str
qualified_name: str
symbol_type: str
start_line: int
end_line: int
raw_code: str
own_sketch: str
propagated_text: str
source_scores: dict[str, float]
best_source: str
best_score: float
matched_text: str
matched_scope: str | None
matched_evidence: list[str]
def to_dict(self) -> dict:
return {
"key": self.key,
"file_path": self.file_path,
"qualified_name": self.qualified_name,
"symbol_type": self.symbol_type,
"start_line": self.start_line,
"end_line": self.end_line,
"raw_code": self.raw_code,
"own_sketch": self.own_sketch,
"propagated_text": self.propagated_text,
"source_scores": self.source_scores,
"best_source": self.best_source,
"best_score": self.best_score,
"matched_text": self.matched_text,
"matched_scope": self.matched_scope,
"matched_evidence": self.matched_evidence,
}
def _tokens(text: str) -> list[str]:
words = [match.group(0).lower() for match in TOKEN_RE.finditer(text)]
bigrams = [
f"{left}_{right}"
for left, right in zip(words, words[1:])
if left != right
]
return [*words, *bigrams]
def embed_text(text: str, dimensions: int = DEFAULT_DIMENSIONS) -> np.ndarray:
"""Create a deterministic normalized hashing vector for local search."""
vector = np.zeros(dimensions, dtype=np.float32)
for token in _tokens(text):
digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest()
bucket = int.from_bytes(digest[:4], "little") % dimensions
sign = 1.0 if digest[4] & 1 else -1.0
vector[bucket] += sign
norm = float(np.linalg.norm(vector))
if norm > 0:
vector /= norm
return vector
def _blob(vector: np.ndarray) -> bytes:
return np.asarray(vector, dtype=np.float32).tobytes()
def _vector(blob: bytes) -> np.ndarray:
return np.frombuffer(blob, dtype=np.float32)
def _record_key(record: dict, root_path: str | None = None) -> str:
path = Path(record["file_path"])
if root_path:
try:
file_path = path.resolve().relative_to(Path(root_path).resolve()).as_posix()
except ValueError:
file_path = path.as_posix()
else:
file_path = path.as_posix()
return f"{file_path}::{record['qualified_name']}"
class CodeRagVectorDB:
"""Small vector database used by the CodeRAG React demo."""
def __init__(self, path: str | Path, *, dimensions: int = DEFAULT_DIMENSIONS):
self.path = Path(path)
self.dimensions = dimensions
def connect(self) -> sqlite3.Connection:
self.path.parent.mkdir(parents=True, exist_ok=True)
connection = sqlite3.connect(self.path)
connection.row_factory = sqlite3.Row
return connection
def initialize(self) -> None:
with closing(self.connect()) as connection, connection:
connection.executescript(
"""
CREATE TABLE IF NOT EXISTS metadata (
key TEXT PRIMARY KEY,
value TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS functions (
key TEXT PRIMARY KEY,
file_path TEXT NOT NULL,
qualified_name TEXT NOT NULL,
symbol_type TEXT NOT NULL,
start_line INTEGER NOT NULL DEFAULT 0,
end_line INTEGER NOT NULL DEFAULT 0,
raw_code TEXT NOT NULL DEFAULT '',
own_sketch TEXT NOT NULL DEFAULT '',
propagated_text TEXT NOT NULL DEFAULT ''
);
CREATE TABLE IF NOT EXISTS vectors (
id INTEGER PRIMARY KEY AUTOINCREMENT,
function_key TEXT NOT NULL,
source TEXT NOT NULL,
scope TEXT,
text TEXT NOT NULL,
evidence_json TEXT NOT NULL DEFAULT '[]',
vector BLOB NOT NULL,
FOREIGN KEY(function_key) REFERENCES functions(key)
);
CREATE INDEX IF NOT EXISTS idx_vectors_source
ON vectors(source);
CREATE INDEX IF NOT EXISTS idx_vectors_function
ON vectors(function_key);
"""
)
connection.execute(
"""
INSERT OR REPLACE INTO metadata(key, value)
VALUES ('schema_version', ?), ('dimensions', ?)
""",
(SCHEMA_VERSION, str(self.dimensions)),
)
def clear(self) -> None:
with closing(self.connect()) as connection, connection:
connection.execute("DELETE FROM vectors")
connection.execute("DELETE FROM functions")
def build(
self,
*,
repository_index_path: str | Path,
synthetic_questions_path: str | Path | None = None,
) -> dict:
self.initialize()
self.clear()
repository_index = json.loads(
Path(repository_index_path).read_text(encoding="utf-8")
)
root_path = repository_index.get("root_path")
function_count = 0
vector_count = 0
with closing(self.connect()) as connection, connection:
for record in repository_index["functions"]:
key = _record_key(record, root_path)
self._upsert_function(connection, key, record)
for source, field in (
("raw_code", "raw_code"),
("cross_file_propagated", "propagated_text"),
):
text = record.get(field, "")
if text.strip():
self._insert_vector(
connection,
function_key=key,
source=source,
text=text,
scope=None,
evidence=[],
)
vector_count += 1
function_count += 1
synthetic_count = 0
if synthetic_questions_path is not None:
synthetic_path = Path(synthetic_questions_path)
if synthetic_path.exists():
synthetic = json.loads(
synthetic_path.read_text(encoding="utf-8")
)
for payload in synthetic["repositories"].values():
for key, questions in payload["functions"].items():
self._upsert_placeholder(connection, key)
for question in questions:
self._insert_vector(
connection,
function_key=key,
source="semantic_synthetic",
text=question["text"],
scope=question.get("scope"),
evidence=question.get("evidence", []),
)
vector_count += 1
synthetic_count += 1
connection.execute(
"""
INSERT OR REPLACE INTO metadata(key, value)
VALUES
('repository_index_path', ?),
('synthetic_questions_path', ?),
('function_count', ?),
('vector_count', ?),
('synthetic_question_count', ?)
""",
(
str(repository_index_path),
str(synthetic_questions_path or ""),
str(function_count),
str(vector_count),
str(synthetic_count),
),
)
return self.stats()
def build_from_file_index(self, index: FileIndex) -> dict:
"""Replace the vector database with records from one pasted-code index."""
self.initialize()
self.clear()
records_by_key = {
f"{Path(record.file_path).as_posix()}::{record.qualified_name}": record
for record in index.functions
}
graph = {}
for key, record in records_by_key.items():
graph[key] = [
f"{Path(record.file_path).as_posix()}::{callee}"
for callee in record.direct_callees
if f"{Path(record.file_path).as_posix()}::{callee}" in records_by_key
]
synthetic = build_synthetic_questions(records_by_key, graph)
vector_count = 0
with closing(self.connect()) as connection, connection:
for key, record in records_by_key.items():
self._upsert_function(connection, key, record.to_dict())
for source, text in (
("raw_code", record.raw_code),
("cross_file_propagated", record.propagated_text),
):
if text.strip():
self._insert_vector(
connection,
function_key=key,
source=source,
text=text,
scope=None,
evidence=[],
)
vector_count += 1
for question in synthetic.get(key, []):
self._insert_vector(
connection,
function_key=key,
source="semantic_synthetic",
text=question.text,
scope=question.scope,
evidence=list(question.evidence),
)
vector_count += 1
connection.execute(
"""
INSERT OR REPLACE INTO metadata(key, value)
VALUES
('repository_index_path', ?),
('synthetic_questions_path', ?),
('function_count', ?),
('vector_count', ?),
('synthetic_question_count', ?),
('active_index_mode', ?),
('active_file_path', ?)
""",
(
str(index.file_path),
"generated-from-pasted-code",
str(len(records_by_key)),
str(vector_count),
str(sum(len(items) for items in synthetic.values())),
"pasted_code",
str(index.file_path),
),
)
stats = self.stats()
stats["indexing_order"] = index.indexing_order
stats["call_edge_count"] = len(index.call_edges)
return stats
def stats(self) -> dict:
self.initialize()
with closing(self.connect()) as connection:
metadata = {
row["key"]: row["value"]
for row in connection.execute("SELECT key, value FROM metadata")
}
sources = {
row["source"]: row["count"]
for row in connection.execute(
"""
SELECT source, COUNT(*) AS count
FROM vectors
GROUP BY source
ORDER BY source
"""
)
}
function_count = connection.execute(
"SELECT COUNT(*) AS count FROM functions"
).fetchone()["count"]
vector_count = connection.execute(
"SELECT COUNT(*) AS count FROM vectors"
).fetchone()["count"]
return {
"schema_version": metadata.get("schema_version", SCHEMA_VERSION),
"database_path": str(self.path),
"dimensions": int(metadata.get("dimensions", self.dimensions)),
"function_count": function_count,
"vector_count": vector_count,
"sources": sources,
"repository_index_path": metadata.get("repository_index_path", ""),
"synthetic_questions_path": metadata.get(
"synthetic_questions_path",
"",
),
"active_index_mode": metadata.get("active_index_mode", "repository"),
"active_file_path": metadata.get("active_file_path", ""),
}
def search(
self,
query: str,
*,
top_k: int = 8,
per_source: int = 5,
sources: Iterable[str] | None = None,
) -> dict:
self.initialize()
requested_sources = list(sources or (
"raw_code",
"cross_file_propagated",
"semantic_synthetic",
))
query_vector = embed_text(query, self.dimensions)
if not np.any(query_vector):
return {
"query": query,
"hits": [],
"candidate_count": 0,
"per_source": per_source,
"sources": requested_sources,
"stats": self.stats(),
}
with closing(self.connect()) as connection:
rows = connection.execute(
"""
SELECT
vectors.id,
vectors.function_key,
vectors.source,
vectors.scope,
vectors.text,
vectors.evidence_json,
vectors.vector,
functions.file_path,
functions.qualified_name,
functions.symbol_type,
functions.start_line,
functions.end_line,
functions.raw_code,
functions.own_sketch,
functions.propagated_text
FROM vectors
JOIN functions ON functions.key = vectors.function_key
WHERE vectors.source IN ({})
""".format(",".join("?" for _ in requested_sources)),
requested_sources,
).fetchall()
ranked_by_source: dict[str, list[tuple[float, sqlite3.Row]]] = {
source: [] for source in requested_sources
}
for row in rows:
score = float(np.dot(_vector(row["vector"]), query_vector))
ranked_by_source[row["source"]].append((score, row))
candidate_rows: dict[str, dict] = {}
for source, scored_rows in ranked_by_source.items():
for score, row in sorted(
scored_rows,
key=lambda item: item[0],
reverse=True,
)[:per_source]:
key = row["function_key"]
candidate = candidate_rows.setdefault(
key,
{
"row": row,
"source_scores": {},
"best_score": score,
"best_source": source,
"matched_text": row["text"],
"matched_scope": row["scope"],
"matched_evidence": json.loads(row["evidence_json"]),
},
)
candidate["source_scores"][source] = round(score, 6)
if score > candidate["best_score"]:
candidate["best_score"] = score
candidate["best_source"] = source
candidate["matched_text"] = row["text"]
candidate["matched_scope"] = row["scope"]
candidate["matched_evidence"] = json.loads(
row["evidence_json"]
)
hits = []
for key, candidate in candidate_rows.items():
row = candidate["row"]
hits.append(SearchHit(
key=key,
file_path=row["file_path"],
qualified_name=row["qualified_name"],
symbol_type=row["symbol_type"],
start_line=row["start_line"],
end_line=row["end_line"],
raw_code=row["raw_code"],
own_sketch=row["own_sketch"],
propagated_text=row["propagated_text"],
source_scores=candidate["source_scores"],
best_source=candidate["best_source"],
best_score=round(candidate["best_score"], 6),
matched_text=candidate["matched_text"],
matched_scope=candidate["matched_scope"],
matched_evidence=candidate["matched_evidence"],
))
hits.sort(key=lambda hit: hit.best_score, reverse=True)
return {
"query": query,
"hits": [hit.to_dict() for hit in hits[:top_k]],
"candidate_count": len(candidate_rows),
"per_source": per_source,
"sources": requested_sources,
"stats": self.stats(),
}
def _upsert_function(
self,
connection: sqlite3.Connection,
key: str,
record: dict,
) -> None:
connection.execute(
"""
INSERT OR REPLACE INTO functions(
key,
file_path,
qualified_name,
symbol_type,
start_line,
end_line,
raw_code,
own_sketch,
propagated_text
)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
key,
record.get("file_path", key.split("::", 1)[0]),
record.get("qualified_name", key.split("::", 1)[-1]),
record.get("symbol_type", "function"),
int(record.get("start_line", 0) or 0),
int(record.get("end_line", 0) or 0),
record.get("raw_code", ""),
record.get("own_sketch", ""),
record.get("propagated_text", ""),
),
)
def _upsert_placeholder(
self,
connection: sqlite3.Connection,
key: str,
) -> None:
path, qualified = key.split("::", 1)
connection.execute(
"""
INSERT OR IGNORE INTO functions(
key,
file_path,
qualified_name,
symbol_type
)
VALUES (?, ?, ?, ?)
""",
(key, path, qualified, "function"),
)
def _insert_vector(
self,
connection: sqlite3.Connection,
*,
function_key: str,
source: str,
text: str,
scope: str | None,
evidence: list[str],
) -> None:
connection.execute(
"""
INSERT INTO vectors(
function_key,
source,
scope,
text,
evidence_json,
vector
)
VALUES (?, ?, ?, ?, ?, ?)
""",
(
function_key,
source,
scope,
text,
json.dumps(evidence),
_blob(embed_text(text, self.dimensions)),
),
)
|