diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 75925037..4868c2e9 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -34,7 +34,7 @@ jobs: - name: Run checks run: make check - unit-test: + build: runs-on: ubuntu-latest steps: - name: Check out code @@ -45,6 +45,37 @@ jobs: with: python-version: '3.11' + - name: Build package + run: make build + + - name: Verify build artifacts + run: | + ls -la dist/ + # Verify both sdist and wheel are created + test -f dist/*.tar.gz || (echo "Source distribution not found" && exit 1) + test -f dist/*.whl || (echo "Wheel not found" && exit 1) + + - name: Upload build artifacts + uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + unit-test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ['3.11', '3.14'] + steps: + - name: Check out code + uses: actions/checkout@v4 + + - name: Set up environment + uses: ./.github/actions/setup-python-env + with: + python-version: ${{ matrix.python-version }} + - name: Install depencies for unit tests run: | uv run pip install openai sentence-transformers torch torchvision torchaudio boto3 litellm voyageai @@ -56,8 +87,9 @@ jobs: else log_level=INFO fi + set -o pipefail uv run pytest tests/unit_tests/ -v --log-cli-level=${log_level} | tee pytest.log - tail -n 1 pytest.log | grep -q 'failed' && exit 1 || exit 0 + tail -n 1 pytest.log | grep '=======' | grep 'passed' |grep -q 'failed' && exit 1 || exit 0 - name: Run integration tests without any test mode run: | @@ -66,8 +98,9 @@ jobs: else log_level=INFO fi + set -o pipefail uv run pytest tests/integration_tests/ -v --log-cli-level=${log_level} -k "not server and not embedded and not oceanbase" | tee pytest.log - tail -n 1 pytest.log | grep -q 'failed' && exit 1 || exit 0 + tail -n 1 pytest.log | grep '=======' | grep 'passed' | grep -q 'failed' && exit 1 || exit 0 integration-test: runs-on: ubuntu-latest @@ -128,5 +161,6 @@ jobs: else log_level=INFO fi + set -o pipefail uv run pytest tests/integration_tests/ -v --log-cli-level=${log_level} -k "${{ matrix.test_mode }}" | tee pytest.log - tail -n 1 pytest.log | grep -q 'failed' && exit 1 || exit 0 + tail -n 1 pytest.log | grep '=======' | grep 'passed' | grep -q 'failed' && exit 1 || exit 0 diff --git a/Makefile b/Makefile index 6b6e5ae4..0885a3c6 100644 --- a/Makefile +++ b/Makefile @@ -34,10 +34,20 @@ test-integration-embedded: ## Run embedded integration tests @$(UV) run pytest tests/integration_tests/ -v --log-cli-level=INFO -k embedded .PHONY: docs -docs: ## Build documentation +docs: ## Build documentation (single version) @echo ">> Building documentation" @$(UV) run sphinx-build -b html docs docs/_build/html +.PHONY: docs-multiversion +docs-multiversion: ## Build multi-version documentation + @echo ">> Building multi-version documentation" + @bash docs/build_multiversion.sh + +.PHONY: docs-serve +docs-serve: ## Serve documentation with auto-reload + @echo ">> Starting documentation server" + @$(UV) run sphinx-autobuild docs docs/_build/html --host 127.0.0.1 --port 8000 + .PHONY: build build: ## Build package @echo ">> Building package" diff --git a/README.md b/README.md index 5a2a6fb4..dc2edd6f 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,8 @@ To achieve the above design goals, this SDK follows the following design princip 6. [DQL Operations](#5-dql-operations) 7. [Embedding Functions](#6-embedding-functions) 8. [RAG Demo](#rag-demo) -9. [Testing](#testing) +9. [Development](#development) +10. [Testing](#testing) ## Installation @@ -1156,31 +1157,75 @@ The demo supports three embedding modes: For detailed instructions, see [demo/rag/README.md](demo/rag/README.md). -## Testing +## Development + +This project uses [uv](https://docs.astral.sh/uv/) as the package manager with [pdm-backend](https://pdm-backend.fming.dev/) as the build backend. All common development tasks are unified through the `Makefile`. + +### Prerequisites + +Install uv: ```bash -# Run all tests (unit + integration) -python3 -m pytest -v +# macOS/Linux +curl -LsSf https://astral.sh/uv/install.sh | sh + +# Windows +powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" -# Run tests with log output -python3 -m pytest -v -s +# Or via pip +pip install uv +``` + +### Setup Development Environment + +```bash +# Clone the repository +git clone https://github.com/oceanbase/pyseekdb.git +cd pyseekdb + +# Install dependencies and pre-commit hooks +make install +``` -# Run unit tests only -python3 -m pytest tests/unit_tests/ -v +### Make Targets + +Run `make help` to see all available targets: + +```bash +make help # Show all available targets +make install # Install dependencies and pre-commit hooks +make check # Run code quality tools (lint, format check) +make test # Run unit tests +make test-integration-embedded # Run embedded integration tests +make build # Build the package +make docs # Build documentation +make clean # Clean build artifacts +``` + +### Build Artifacts + +After running `make build`, the distribution files will be in the `dist/` directory: +- `pyseekdb-.tar.gz` - Source distribution +- `pyseekdb--py3-none-any.whl` - Wheel distribution + +## Testing + +```bash +# Run unit tests +make test -# Run integration tests only -python3 -m pytest tests/integration_tests/ -v +# Run embedded integration tests +make test-integration-embedded -# Run integration tests for specific mode -python3 -m pytest tests/integration_tests/ -v -k "embedded" # embedded mode -python3 -m pytest tests/integration_tests/ -v -k "server" # server mode (requires seekdb server) -python3 -m pytest tests/integration_tests/ -v -k "oceanbase" # oceanbase mode (requires OceanBase) +# Run specific tests with uv run +uv run pytest tests/integration_tests/ -v -k "server" # server mode (requires seekdb server) +uv run pytest tests/integration_tests/ -v -k "oceanbase" # oceanbase mode (requires OceanBase) # Run specific test file -python3 -m pytest tests/integration_tests/test_collection_query.py -v +uv run pytest tests/integration_tests/test_collection_query.py -v # Run specific test function -python3 -m pytest tests/integration_tests/test_collection_query.py::TestCollectionQuery::test_collection_query -v +uv run pytest tests/integration_tests/test_collection_query.py::TestCollectionQuery::test_collection_query -v ``` ## License diff --git a/docs/_static/custom.css b/docs/_static/custom.css index e69de29b..853148d2 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -0,0 +1,66 @@ +/* 版本选择器下拉框样式 - 放在左侧导航栏搜索框下方 */ +.version-selector-dropdown { + display: flex; + align-items: center; + gap: 8px; + margin-top: 8px; + border-radius: 50px; +} + +.version-selector-dropdown label { + font-size: 13px; + font-weight: 600; + color: #fff; + margin: 0; + white-space: nowrap; +} + +.version-selector-dropdown select { + flex: 1; + padding: 6px 10px; + font-size: 13px; + color: #404040; + background-color: #fff; + border: 1px solid #d0d0d0; + border-radius: 4px; + cursor: pointer; + outline: none; + transition: all 0.2s ease; +} + +.version-selector-dropdown select:hover { + border-color: #2980b9; +} + +.version-selector-dropdown select:focus { + border-color: #2980b9; + box-shadow: 0 0 0 2px rgba(41, 128, 185, 0.1); +} + +/* RTD 主题版本横幅样式 - 隐藏左下角的版本选择器 */ +.rst-versions { + display: none !important; +} + +/* 版本警告横幅 */ +.version-warning { + padding: 10px 15px; + margin-bottom: 20px; + background-color: #fff3cd; + border: 1px solid #ffeaa7; + border-radius: 4px; + color: #856404; +} + +.version-warning strong { + font-weight: bold; +} + +.version-warning a { + color: #856404; + text-decoration: underline; +} + +.version-warning a:hover { + color: #533f03; +} diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html new file mode 100644 index 00000000..2bdb8d8c --- /dev/null +++ b/docs/_templates/layout.html @@ -0,0 +1,18 @@ +{% extends "!layout.html" %} + +{# 在左侧导航栏的搜索框下方插入版本选择器 #} +{% block sidebartitle %} + {{ super() }} + {% if versions %} +
+ + +
+ {% endif %} +{% endblock %} diff --git a/docs/build_multiversion.sh b/docs/build_multiversion.sh new file mode 100755 index 00000000..9abd9c90 --- /dev/null +++ b/docs/build_multiversion.sh @@ -0,0 +1,50 @@ +#!/bin/bash +# 构建多版本文档的脚本 + +set -e + +# 颜色输出 +GREEN='\033[0;32m' +BLUE='\033[0;34m' +NC='\033[0m' # No Color + +echo -e "${BLUE}开始构建多版本文档...${NC}" + +# 确保在项目根目录 +cd "$(dirname "$0")/.." + +# 激活虚拟环境（如果存在） +if [ -d ".venv" ]; then + echo -e "${GREEN}激活虚拟环境...${NC}" + source .venv/bin/activate +fi + +# 清理旧的构建文件 +echo -e "${GREEN}清理旧的构建文件...${NC}" +rm -rf docs/_build/html + +# 使用 sphinx-multiversion 构建文档 +echo -e "${GREEN}使用 sphinx-multiversion 构建文档...${NC}" +sphinx-multiversion docs docs/_build/html + +# 创建重定向到最新版本的 index.html +echo -e "${GREEN}创建重定向页面...${NC}" +cat > docs/_build/html/index.html << 'EOF' + + + + Redirecting to latest version + + + + + +

Redirecting to latest version...

+ + +EOF + +echo -e "${BLUE}多版本文档构建完成！${NC}" +echo -e "${GREEN}文档位置: docs/_build/html${NC}" +echo -e "${GREEN}可以使用以下命令启动本地服务器查看:${NC}" +echo -e " cd docs/_build/html && python -m http.server 8000" diff --git a/docs/conf.py b/docs/conf.py index 1b1be534..75897341 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -26,6 +26,7 @@ "sphinx.ext.viewcode", "sphinx.ext.napoleon", "myst_parser", + "sphinx_multiversion", ] # Enable Markdown in docstrings @@ -59,3 +60,25 @@ # HTML theme html_theme = "sphinx_rtd_theme" html_static_path = ["_static"] +html_css_files = [ + "custom.css", +] + +# Sphinx-multiversion configuration +# 配置要包含的分支和标签 +smv_tag_whitelist = r"^v\d+\.\d+\.\d+$" # 匹配 v1.0.0 格式的标签 +smv_branch_whitelist = r"^(main|develop)$" # 包含 main 和 develop 分支 +smv_remote_whitelist = r"^origin$" # 只使用 origin 远程仓库 +smv_released_pattern = r"^refs/tags/.*$" # 标记已发布的版本 + +# 自定义模板路径 +templates_path = ["_templates"] + +# 版本横幅配置 +html_context = { + "display_github": True, + "github_user": "oceanbase", + "github_repo": "pyseekdb", + "github_version": "develop", + "conf_py_path": "/docs/", +} diff --git a/pyproject.toml b/pyproject.toml index 38406b6a..f519f93d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,17 +5,18 @@ description = "A unified Python client for seekdb that supports embedded, server readme = "README.md" license = { text = "Apache-2.0" } authors = [{ name = "OceanBase", email = "open_oceanbase@oceanbase.com" }] -requires-python = ">=3.11,<3.14" +requires-python = ">=3.11,<4" classifiers = [ "Development Status :: 4 - Beta", ] dependencies = [ "pymysql>=1.1.1", "pylibseekdb; sys_platform == \"linux\"", - "onnxruntime>=1.19.0", - "tokenizers>=0.15.0", - "httpx", - "tqdm", + "onnxruntime>=1.19.0; python_version < \"3.14\"", + "tokenizers>=0.15.0; python_version < \"3.14\"", + "httpx; python_version < \"3.14\"", + "tqdm; python_version < \"3.14\"", + "sentence-transformers; python_version >= \"3.14\"", "tenacity", "numpy>=1.26", ] @@ -100,6 +101,9 @@ ignore = [ [tool.ruff.format] preview = true +[project.scripts] +pyseekdb = "pyseekdb.cli:main" + [build-system] requires = ["pdm-backend"] build-backend = "pdm.backend" diff --git a/src/pyseekdb/__init__.py b/src/pyseekdb/__init__.py index fabcaf6b..86c10492 100644 --- a/src/pyseekdb/__init__.py +++ b/src/pyseekdb/__init__.py @@ -60,10 +60,11 @@ import importlib.metadata -# Note: pylibseekdb built with ABI=0 and onnxruntime built with ABI=1, so there's a conflict between the two libraries. +# Note: pylibseekdb built with ABI=0 and torch built with ABI=1, so there's a conflict between the two libraries. # pylibseekdb is built both with ABI=0 and the -Bsymbolic flag, so we can load libraries with ABI=1 first # and then pylibseekdb to avoid these conflicts. -import onnxruntime # noqa: F401 +if importlib.util.find_spec("torch"): + import torch # noqa: F401 from .client import ( AdminAPI, diff --git a/src/pyseekdb/cli/__init__.py b/src/pyseekdb/cli/__init__.py new file mode 100644 index 00000000..f9062630 --- /dev/null +++ b/src/pyseekdb/cli/__init__.py @@ -0,0 +1,14 @@ +""" +pyseekdb CLI - Debug and manage seekdb collections/databases. + +Usage: + pyseekdb [OPTIONS] db list|create|delete ... + pyseekdb [OPTIONS] collections list|create|delete|info ... + pyseekdb [OPTIONS] sql "SELECT ..." + pyseekdb [OPTIONS] query --text "..." [--n 5] + pyseekdb [OPTIONS] get [--limit 10] [--ids id1,id2] +""" + +from .main import main + +__all__ = ["main"] diff --git a/src/pyseekdb/cli/main.py b/src/pyseekdb/cli/main.py new file mode 100644 index 00000000..39a7b993 --- /dev/null +++ b/src/pyseekdb/cli/main.py @@ -0,0 +1,400 @@ +""" +CLI main: argument parsing, connection factory, and subcommand dispatch. +""" + +import argparse +import json +import os +import sys +from typing import Any + +from .. import AdminClient, Client +from ..client.admin_client import _AdminClientProxy, _ClientProxy +from ..client.configuration import HNSWConfiguration + + +def _add_connection_args(parser: argparse.ArgumentParser) -> None: + """Add global connection options (embedded vs server).""" + g = parser.add_argument_group("connection (choose one)") + g.add_argument( + "--path", + metavar="DIR", + default=None, + help="Embedded mode: path to seekdb data directory (default: seekdb.db in cwd)", + ) + g.add_argument("--host", default=None, help="Server mode: host (e.g. localhost)") + g.add_argument("--port", type=int, default=2881, help="Server mode: port (default: 2881)") + g.add_argument("--tenant", default="sys", help="Server mode: tenant (default: sys)") + g.add_argument("--database", "-d", default="test", help="Database name (default: test)") + g.add_argument("--user", default="root", help="Server mode: user (default: root)") + g.add_argument( + "--password", + "-p", + default=None, + help="Server mode: password (or set SEEKDB_PASSWORD)", + ) + parser.add_argument( + "-o", + "--output", + choices=["table", "json"], + default="table", + help="Output format (default: table)", + ) + + +def _make_client(args: argparse.Namespace) -> _ClientProxy: + """Build Client from parsed args (for collection operations).""" + password = args.password or os.environ.get("SEEKDB_PASSWORD", "") + return Client( + path=args.path, + host=args.host, + port=args.port, + tenant=args.tenant, + database=args.database, + user=args.user, + password=password, + ) + + +def _make_admin(args: argparse.Namespace) -> _AdminClientProxy: + """Build AdminClient from parsed args (for db operations).""" + password = args.password or os.environ.get("SEEKDB_PASSWORD", "") + return AdminClient( + path=args.path, + host=args.host, + port=args.port, + tenant=args.tenant, + user=args.user, + password=password, + ) + + +def _execute_sql(client: _ClientProxy, sql: str) -> Any: + """Run raw SQL using the underlying server (for debug).""" + server = client._server + return server._execute(sql) + + +def _print_table(rows: list[dict[str, Any]] | None, columns: list[str] | None = None) -> None: + """Print rows as a simple text table.""" + if not rows: + print("(0 rows)") + return + if columns is None: + columns = list(rows[0].keys()) if rows else [] + col_widths = [max(len(str(c)), 3) for c in columns] + for r in rows: + for i, c in enumerate(columns): + val = r.get(c, "") + if isinstance(val, (dict, list)): + val = json.dumps(val, ensure_ascii=False)[:40] + col_widths[i] = max(col_widths[i], min(len(str(val)), 50)) + fmt = " ".join(f"%-{w}s" for w in col_widths) + print(fmt % tuple(columns)) + print("-" * (sum(col_widths) + 2 * (len(columns) - 1))) + for r in rows: + cells = [] + for c in columns: + v = r.get(c, "") + if isinstance(v, (dict, list)): + v = json.dumps(v, ensure_ascii=False)[:40] + s = str(v) + if len(s) > 50: + s = s[:47] + "..." + cells.append(s) + print(fmt % tuple(cells)) + + +def _print_json(obj: Any) -> None: + """Print object as JSON.""" + if hasattr(obj, "__iter__") and not isinstance(obj, (str, dict)): + obj = list(obj) + print(json.dumps(obj, indent=2, ensure_ascii=False, default=str)) + + +# ---------- Commands ---------- + + +def cmd_db_list(args: argparse.Namespace) -> int: + """List databases.""" + admin = _make_admin(args) + try: + dbs = admin.list_databases() + rows = [{"name": d.name, "tenant": d.tenant or ""} for d in dbs] + if args.output == "json": + _print_json(rows) + else: + _print_table(rows, ["name", "tenant"]) + return 0 + finally: + admin._server._cleanup() + + +def cmd_db_create(args: argparse.Namespace) -> int: + """Create database.""" + admin = _make_admin(args) + try: + admin.create_database(args.name, tenant=args.tenant) + print(f"Created database: {args.name}") + return 0 + finally: + admin._server._cleanup() + + +def cmd_db_delete(args: argparse.Namespace) -> int: + """Delete database.""" + admin = _make_admin(args) + try: + admin.delete_database(args.name, tenant=args.tenant) + print(f"Deleted database: {args.name}") + return 0 + finally: + admin._server._cleanup() + + +def cmd_collections_list(args: argparse.Namespace) -> int: + """List collections.""" + client = _make_client(args) + try: + colls = client.list_collections() + rows = [ + { + "name": c.name, + "dimension": c.dimension or "", + "distance": c.distance or "", + } + for c in colls + ] + if args.output == "json": + _print_json(rows) + else: + _print_table(rows, ["name", "dimension", "distance"]) + return 0 + finally: + client._server._cleanup() + + +def cmd_collections_create(args: argparse.Namespace) -> int: + """Create collection.""" + client = _make_client(args) + try: + config = HNSWConfiguration(dimension=args.dimension) if args.dimension else None + client.create_collection(args.name, configuration=config) + print(f"Created collection: {args.name}") + return 0 + finally: + client._server._cleanup() + + +def cmd_collections_delete(args: argparse.Namespace) -> int: + """Delete collection.""" + client = _make_client(args) + try: + client.delete_collection(args.name) + print(f"Deleted collection: {args.name}") + return 0 + finally: + client._server._cleanup() + + +def cmd_collections_info(args: argparse.Namespace) -> int: + """Show collection info (schema, count, sample).""" + client = _make_client(args) + try: + if not client.has_collection(args.name): + print(f"Collection not found: {args.name}", file=sys.stderr) + return 1 + coll = client.get_collection(args.name) + info = { + "name": coll.name, + "dimension": coll.dimension, + "distance": coll.distance, + "metadata": coll.metadata, + "count": coll.count(), + } + if args.output == "json": + sample = coll.peek(limit=args.sample) if args.sample and info["count"] > 0 else {} + payload = {"info": info, "sample": sample} + _print_json(payload) + else: + print(f"Name: {info['name']}") + print(f"Dimension: {info['dimension']}") + print(f"Distance: {info['distance']}") + print(f"Count: {info['count']}") + if info.get("metadata"): + print(f"Metadata: {info['metadata']}") + if args.sample and info["count"] > 0: + sample = coll.peek(limit=args.sample) + print("\nSample (peek):") + for i in range(len(sample["ids"])): + print(f" id={sample['ids'][i]}") + if sample.get("documents"): + doc = sample["documents"][i] + print(f" document: {str(doc)[:80]}...") + if sample.get("metadatas"): + print(f" metadata: {sample['metadatas'][i]}") + return 0 + finally: + client._server._cleanup() + + +def cmd_sql(args: argparse.Namespace) -> int: + """Execute raw SQL (debug).""" + client = _make_client(args) + try: + result = _execute_sql(client, args.sql) + if result is None: + print("OK") + return 0 + if args.output == "json": + # result may be list of tuples or list of dicts + if not result: + _print_json([]) + elif hasattr(result[0], "keys"): + _print_json(result) + else: + _print_json([list(r) for r in result]) + else: + if not result: + print("(0 rows)") + elif hasattr(result[0], "keys"): + _print_table(result) + else: + cols = [f"col_{i}" for i in range(len(result[0]))] + _print_table([dict(zip(cols, r, strict=True)) for r in result], cols) + return 0 + finally: + client._server._cleanup() + + +def cmd_query(args: argparse.Namespace) -> int: + """Query collection by text or embedding.""" + client = _make_client(args) + try: + if not client.has_collection(args.collection): + print(f"Collection not found: {args.collection}", file=sys.stderr) + return 1 + coll = client.get_collection(args.collection) + if args.text: + res = coll.query( + query_texts=[args.text], n_results=args.n, include=args.include or ["documents", "metadatas"] + ) + else: + print("Specify --text for query by text (embedding not supported in CLI)", file=sys.stderr) + return 1 + if args.output == "json": + _print_json(res) + else: + ids = res.get("ids", [[]])[0] + docs = res.get("documents", [[]])[0] if res.get("documents") else [] + metas = res.get("metadatas", [[]])[0] if res.get("metadatas") else [] + dists = res.get("distances", [[]])[0] if res.get("distances") else [] + for i in range(len(ids)): + print(f"id={ids[i]} distance={dists[i] if i < len(dists) else ''}") + if i < len(docs): + print(f" document: {str(docs[i])[:80]}...") + if i < len(metas): + print(f" metadata: {metas[i]}") + return 0 + finally: + client._server._cleanup() + + +def cmd_get(args: argparse.Namespace) -> int: + """Get documents from collection by ids or limit.""" + client = _make_client(args) + try: + if not client.has_collection(args.collection): + print(f"Collection not found: {args.collection}", file=sys.stderr) + return 1 + coll = client.get_collection(args.collection) + ids = None + if args.ids: + ids = [x.strip() for x in args.ids.split(",") if x.strip()] + res = coll.get( + ids=ids, + limit=args.limit, + include=args.include or ["documents", "metadatas"], + ) + if args.output == "json": + _print_json(res) + else: + for i in range(len(res.get("ids", []))): + print(f"id={res['ids'][i]}") + if res.get("documents") and i < len(res["documents"]): + print(f" document: {str(res['documents'][i])[:80]}...") + if res.get("metadatas") and i < len(res["metadatas"]): + print(f" metadata: {res['metadatas'][i]}") + return 0 + finally: + client._server._cleanup() + + +def main(argv: list[str] | None = None) -> int: + """CLI entry point.""" + parser = argparse.ArgumentParser( + prog="pyseekdb", + description="seekdb CLI: debug and manage collections/databases (see spec.md).", + ) + _add_connection_args(parser) + sub = parser.add_subparsers(dest="command", required=True, help="command") + + # db + db = sub.add_parser("db", help="Database (admin) operations") + db_sub = db.add_subparsers(dest="db_command", required=True) + db_list = db_sub.add_parser("list", help="List databases") + db_list.set_defaults(func=cmd_db_list) + db_create = db_sub.add_parser("create", help="Create database") + db_create.add_argument("name", help="Database name") + db_create.set_defaults(func=cmd_db_create) + db_delete = db_sub.add_parser("delete", help="Delete database") + db_delete.add_argument("name", help="Database name") + db_delete.set_defaults(func=cmd_db_delete) + + # collections (and alias coll) + def _add_collection_subparsers(parent: argparse.ArgumentParser) -> None: + csub = parent.add_subparsers(dest="coll_command", required=True) + csub.add_parser("list", help="List collections").set_defaults(func=cmd_collections_list) + create = csub.add_parser("create", help="Create collection") + create.add_argument("name", help="Collection name") + create.add_argument("--dimension", type=int, default=None, help="Vector dimension") + create.set_defaults(func=cmd_collections_create) + delete = csub.add_parser("delete", help="Delete collection") + delete.add_argument("name", help="Collection name") + delete.set_defaults(func=cmd_collections_delete) + info = csub.add_parser("info", help="Show collection info and optional sample") + info.add_argument("name", help="Collection name") + info.add_argument("--sample", type=int, default=0, metavar="N", help="Peek first N rows") + info.set_defaults(func=cmd_collections_info) + + _add_collection_subparsers(sub.add_parser("collections", help="Collection operations")) + _add_collection_subparsers(sub.add_parser("coll", help="Alias for collections")) + + # sql + sql_p = sub.add_parser("sql", help="Execute raw SQL (debug)") + _add_connection_args(sql_p) + sql_p.add_argument("sql", help="SQL statement") + sql_p.set_defaults(func=cmd_sql) + + # query + query_p = sub.add_parser("query", help="Query collection by text") + _add_connection_args(query_p) + query_p.add_argument("collection", help="Collection name") + query_p.add_argument( + "--text", "-t", required=True, help="Query text (will be embedded if collection has embedding)" + ) + query_p.add_argument("--n", type=int, default=10, help="Number of results (default: 10)") + query_p.add_argument("--include", nargs="+", default=None, help="Include fields: documents, metadatas, embeddings") + query_p.set_defaults(func=cmd_query) + + # get + get_p = sub.add_parser("get", help="Get documents from collection") + _add_connection_args(get_p) + get_p.add_argument("collection", help="Collection name") + get_p.add_argument("--ids", default=None, help="Comma-separated IDs") + get_p.add_argument("--limit", type=int, default=10, help="Max rows (default: 10)") + get_p.add_argument("--include", nargs="+", default=None, help="Include fields") + get_p.set_defaults(func=cmd_get) + + args = parser.parse_args(argv) + return args.func(args) diff --git a/src/pyseekdb/client/client_base.py b/src/pyseekdb/client/client_base.py index bd0aa89d..18cd4166 100644 --- a/src/pyseekdb/client/client_base.py +++ b/src/pyseekdb/client/client_base.py @@ -210,14 +210,22 @@ def create_collection( @abstractmethod def get_collection(self, name: str, embedding_function: EmbeddingFunctionParam = _NOT_PROVIDED) -> "Collection": - """ - Get collection object + """Get an existing collection. Args: - name: Collection name - embedding_function: Embedding function to convert documents to embeddings. - Defaults to DefaultEmbeddingFunction. - If explicitly set to None, collection will not have an embedding function. + name: The name of the collection to retrieve. + embedding_function: The embedding function to use. If not provided, + it will try to load the function used when creating the collection. + If explicitly set to None, no embedding function will be used. + + Returns: + The ``Collection`` object. + + Raises: + ValueError: If the collection does not exist. + + Examples: + >>> collection = client.get_collection("my_collection") """ pass @@ -512,85 +520,48 @@ def create_collection( # noqa: C901 embedding_function: EmbeddingFunctionParam = _NOT_PROVIDED, **kwargs, ) -> "Collection": - """ - Create a collection (user-facing API) + """Create a new collection. Args: - name: Collection name - configuration: Index configuration (Configuration or HNSWConfiguration). - If not provided, uses default configuration (dimension=384, distance='cosine', analyzer='ik'). - If explicitly set to None, will try to calculate dimension from embedding_function. - If embedding_function is also None, will raise an error. - For backward compatibility, HNSWConfiguration is still accepted. - Configuration can include fulltext index configuration. - embedding_function: Embedding function to convert documents to embeddings. - Defaults to DefaultEmbeddingFunction. - If explicitly set to None, collection will not have an embedding function. - If provided, the actual dimension will be calculated by calling - embedding_function.__call__("seekdb"), and this dimension will be used - to create the table. If configuration.dimension is set and doesn't match - the calculated dimension, a ValueError will be raised. - **kwargs: Additional parameters + name: The name of the collection to create. Must contain only alphanumeric + characters or underscores. + configuration: Index configuration. Defaults to None (uses HNSW with + Cosine distance and dimension 384). Can be a ``Configuration`` or + ``HNSWConfiguration`` object. If set to None, the dimension will be + inferred from the embedding function. + embedding_function: The embedding function to use for this collection. + Defaults to ``DefaultEmbeddingFunction`` (all-MiniLM-L6-v2). If set to None, + no embedding function will be used (embeddings must be provided manually). + **kwargs: Additional parameters for collection creation. Returns: - Collection object + The created ``Collection`` object. Raises: - ValueError: If configuration is explicitly set to None and embedding_function is also None - (cannot determine dimension), or if embedding_function is provided and - configuration.dimension doesn't match the calculated dimension from embedding_function - TypeError: If configuration is not None, Configuration, or HNSWConfiguration + ValueError: If the collection name is invalid, already exists, or if the + configuration/embedding function combination is invalid (e.g., dimension mismatch). + TypeError: If the configuration object is of an invalid type. Examples: - .. code-block:: python - # Using default configuration and default embedding function (defaults to IK parser) - collection = client.create_collection('my_collection') - - .. code-block:: python - # Using custom embedding function (dimension will be calculated automatically) - from pyseekdb import DefaultEmbeddingFunction - ef = DefaultEmbeddingFunction(model_name='all-MiniLM-L6-v2') - config = HNSWConfiguration(dimension=384, distance='cosine') # Must match EF dimension - collection = client.create_collection( - 'my_collection', - configuration=config, - embedding_function=ef - ) + Create a collection with default settings: - .. code-block:: python - # Using Configuration wrapper with IK parser (default) - from pyseekdb import Configuration, HNSWConfiguration, FulltextIndexConfig - config = Configuration( - hnsw=HNSWConfiguration(dimension=384, distance='cosine'), - fulltext_config=FulltextIndexConfig(analyzer='ik') - ) - collection = client.create_collection('my_collection', configuration=config, embedding_function=ef) - - .. code-block:: python - # Using Space parser - config = Configuration( - hnsw=HNSWConfiguration(dimension=384, distance='cosine'), - fulltext_config=FulltextIndexConfig(analyzer='space') - ) - collection = client.create_collection('my_collection', configuration=config, embedding_function=ef) + >>> client.create_collection("my_collection") - .. code-block:: python - # Using Ngram parser with parameters - config = Configuration( - hnsw=HNSWConfiguration(dimension=384, distance='cosine'), - fulltext_config=FulltextIndexConfig(analyzer='ngram', properties={'size': 2}) - ) - collection = client.create_collection('my_collection', configuration=config, embedding_function=ef) + Create a collection with a custom embedding function: - .. code-block:: python - # Explicitly set configuration=None, use embedding function to determine dimension - collection = client.create_collection('my_collection', configuration=None, embedding_function=ef) + >>> from pyseekdb import DefaultEmbeddingFunction + >>> ef = DefaultEmbeddingFunction(model_name="all-MiniLM-L6-v2") + >>> collection = client.create_collection("my_docs", embedding_function=ef) - .. code-block:: python - # Explicitly disable embedding function (use configuration dimension) - config = HNSWConfiguration(dimension=128, distance='cosine') - collection = client.create_collection('my_collection', configuration=config, embedding_function=None) + Create a collection with specific configuration: + >>> from pyseekdb import HNSWConfiguration + >>> config = HNSWConfiguration(dimension=128, distance="l2") + >>> collection = client.create_collection( + ... "custom_config", + ... configuration=config, + ... embedding_function=None + ... ) """ _validate_collection_name(name) if self.has_collection(name): @@ -982,11 +953,16 @@ def _get_collection_v1(self, name: str, embedding_function: EmbeddingFunctionPar return Collection(client=self, name=name, embedding_function=embedding_function, **metadata) def delete_collection(self, name: str) -> None: - """ - Delete a collection (user-facing API) + """Delete a collection. Args: - name: Collection name + name: The name of the collection to delete. + + Raises: + ValueError: If the collection does not exist. + + Examples: + >>> client.delete_collection("my_collection") """ try: self._delete_collection_v2(name) @@ -1032,11 +1008,15 @@ def _delete_collection_v1(self, name: str) -> None: self._execute(f"DROP TABLE IF EXISTS `{table_name}`") def list_collections(self) -> list["Collection"]: - """ - List all collections (user-facing API) + """List all collections in the database. Returns: - List of Collection objects + A list of ``Collection`` objects. + + Examples: + >>> collections = client.list_collections() + >>> for col in collections: + ... print(col.name) """ collections = self._list_collections_v1() collections.extend(self._list_collections_v2()) @@ -1142,25 +1122,30 @@ def _list_collections_v1(self) -> list["Collection"]: return collections def count_collection(self) -> int: - """ - Count the number of collections in the current database + """Count the total number of collections. Returns: - Number of collections + The number of collections. Examples: - count = client.count_collection() - print(f"Database has {count} collections") + >>> count = client.count_collection() + >>> print(f"Database has {count} collections") """ collections = self.list_collections() return len(collections) def has_collection(self, name: str) -> bool: - """ - Check if a collection exists (user-facing API) + """Check if a collection exists. Args: - name: Collection name + name: The name of the collection to check. + + Returns: + True if the collection exists, False otherwise. + + Examples: + >>> if client.has_collection("my_collection"): + ... print("Collection exists!") """ return self._has_collection_v2(name) or self._has_collection_v1(name) @@ -1211,29 +1196,27 @@ def get_or_create_collection( embedding_function: EmbeddingFunctionParam = _NOT_PROVIDED, **kwargs, ) -> "Collection": - """ - Get an existing collection or create it if it doesn't exist (user-facing API) + """Get a collection if it exists, otherwise create it. Args: - name: Collection name - configuration: Configuration (HNSWConfiguration is accepted for backward compatibility) - Please refer to create_collection for more details. - embedding_function: Embedding function to convert documents to embeddings. - Defaults to DefaultEmbeddingFunction. - If explicitly set to None, collection will not have an embedding function. - If provided when creating a new collection, the actual dimension will be - calculated by calling embedding_function.__call__("seekdb"), and this - dimension will be used to create the table. If configuration.dimension is - set and doesn't match the calculated dimension, a ValueError will be raised. - **kwargs: Additional parameters for create_collection + name: The name of the collection. + configuration: Index configuration. Defaults to None (uses HNSW with + Cosine distance and dimension 384). Can be a ``Configuration`` or + ``HNSWConfiguration`` object. If set to None, the dimension will be + inferred from the embedding function. + embedding_function: The embedding function to use for this collection. + Defaults to ``DefaultEmbeddingFunction`` (all-MiniLM-L6-v2). If set to None, + no embedding function will be used (embeddings must be provided manually). + **kwargs: Additional parameters passed to ``create_collection`` if the collection is created. Returns: - Collection object + The existing or newly created ``Collection`` object. Raises: - ValueError: If creating a new collection and configuration is explicitly set to None and - embedding_function is also None (cannot determine dimension), or if embedding_function - is provided and configuration.dimension doesn't match the calculated dimension + ValueError: If the configuration/embedding function combination is invalid (e.g., dimension mismatch). + + Examples: + >>> collection = client.get_or_create_collection("my_collection") """ # Validate collection name before any database interaction _validate_collection_name(name) @@ -1393,10 +1376,10 @@ def _collection_add( # noqa: C901 elif documents: # embeddings not provided but documents are provided, check for embedding_function if embedding_function is not None: - logger.info(f"Generating embeddings for {len(documents)} documents using embedding function") + logger.debug(f"Generating embeddings for {len(documents)} documents using embedding function") try: embeddings = embedding_function(documents) - logger.info(f"✅ Successfully generated {len(embeddings)} embeddings") + logger.debug(f"✅ Successfully generated {len(embeddings)} embeddings") except Exception as e: logger.exception("Failed to generate embeddings") raise ValueError(f"Failed to generate embeddings from documents: {e}") from e @@ -1489,7 +1472,7 @@ def _collection_add( # noqa: C901 logger.debug(f"Executing SQL: {sql}") self._execute(sql) - logger.info(f"✅ Successfully added {num_items} item(s) to collection '{collection_name}'") + logger.debug(f"✅ Successfully added {num_items} item(s) to collection '{collection_name}'") def _collection_update( # noqa: C901 self, @@ -1550,10 +1533,10 @@ def _collection_update( # noqa: C901 elif documents: # embeddings not provided but documents are provided, check for embedding_function if embedding_function is not None: - logger.info(f"Generating embeddings for {len(documents)} documents using embedding function") + logger.debug(f"Generating embeddings for {len(documents)} documents using embedding function") try: embeddings = embedding_function(documents) - logger.info(f"✅ Successfully generated {len(embeddings)} embeddings") + logger.debug(f"✅ Successfully generated {len(embeddings)} embeddings") except Exception as e: logger.exception("Failed to generate embeddings") raise ValueError(f"Failed to generate embeddings from documents: {e}") from e @@ -1692,7 +1675,7 @@ def _collection_upsert( # noqa: C901 elif documents: # embeddings not provided but documents are provided, check for embedding_function if embedding_function is not None: - logger.info(f"Generating embeddings for {len(documents)} documents using embedding function") + logger.debug(f"Generating embeddings for {len(documents)} documents using embedding function") try: embeddings = embedding_function(documents) logger.info(f"✅ Successfully generated {len(embeddings)} embeddings") @@ -1835,7 +1818,7 @@ def _collection_delete( where_document: Filter condition on documents (optional) **kwargs: Additional parameters """ - logger.info(f"Deleting data from collection '{collection_name}'") + logger.debug(f"Deleting data from collection '{collection_name}'") # Validate that at least one filter is provided if not ids and not where and not where_document: @@ -1866,7 +1849,7 @@ def _collection_delete( use_context_manager = self._use_context_manager_for_cursor() self._execute_query_with_cursor(conn, sql, params, use_context_manager) - logger.info(f"✅ Successfully deleted data from collection '{collection_name}'") + logger.debug(f"✅ Successfully deleted data from collection '{collection_name}'") # -------------------- DQL Operations -------------------- # Note: _collection_query() and _collection_get() are implemented below with common SQL-based logic @@ -2631,7 +2614,15 @@ def _collection_hybrid_search( table_name = CollectionNames.table_name(collection_name) # Build search_parm JSON - search_parm = self._build_search_parm(query, knn, rank, n_results, dimension=dimension, **kwargs) + search_parm = self._build_search_parm( + query, + knn, + rank, + n_results, + include=include, + dimension=dimension, + **kwargs, + ) # Convert search_parm to JSON string search_parm_json = json.dumps(search_parm, ensure_ascii=False) @@ -2684,6 +2675,7 @@ def _build_search_parm( # noqa: C901 knn: dict[str, Any] | list[dict[str, Any]] | None, rank: dict[str, Any] | None, n_results: int, + include: list[str] | None = None, dimension: int | None = None, **kwargs, ) -> dict[str, Any]: @@ -2695,6 +2687,8 @@ def _build_search_parm( # noqa: C901 knn: Vector search configuration dict or list of dicts rank: Ranking configuration dict n_results: Final number of results to return + include: Fields requested by the SDK caller. Used to infer the minimal OceanBase GET_SQL + `_source` allowlist to avoid returning large unused columns (e.g. `embedding`). dimension: Collection dimension for validating query_embeddings (optional) **kwargs: Additional parameters, including: embedding_function: EmbeddingFunction instance to convert query_texts in knn to embeddings. @@ -2739,6 +2733,9 @@ def _build_search_parm( # noqa: C901 if rank: search_parm["rank"] = rank + # Always infer a minimal `_source` allowlist from include to reduce response payload. + search_parm["_source"] = self._build_source_fields(include) + return search_parm def _build_query_expression(self, query: dict[str, Any]) -> dict[str, Any] | None: @@ -3092,23 +3089,26 @@ def _normalize_vectors(raw_embeddings: Any) -> list[list[float]]: return knn_exprs if len(knn_exprs) > 1 else knn_exprs[0] def _build_source_fields(self, include: list[str] | None) -> list[str]: - """Build _source fields list from include parameter""" - if not include: - return ["document", "metadata", "embedding"] - - source_fields = [] - field_mapping = { - "documents": "document", - "metadatas": "metadata", - "embeddings": "embedding", - } + """ + Infer OceanBase GET_SQL `_source` allowlist from include. + """ + if include is None: + requested = {"documents", "metadatas"} + else: + if not isinstance(include, list) or not all(isinstance(item, str) for item in include): + raise TypeError("include must be a List[str] or None") + requested = {item.lower() for item in include} - for field in include: - mapped = field_mapping.get(field.lower(), field) - if mapped not in source_fields: - source_fields.append(mapped) + source = ["_id"] + + if {"documents", "document"} & requested: + source.append("document") + if {"metadatas", "metadata"} & requested: + source.append("metadata") + if {"embeddings", "embedding"} & requested: + source.append("embedding") - return source_fields if source_fields else ["document", "metadata", "embedding"] + return source def _transform_sql_result( # noqa: C901 self, result_rows: list[dict[str, Any]], include: list[str] | None @@ -3267,7 +3267,7 @@ def _collection_count(self, collection_id: str | None, collection_name: str) -> Returns: Item count """ - logger.info(f"Counting items in collection '{collection_name}'") + logger.debug(f"Counting items in collection '{collection_name}'") conn = self._ensure_connection() # Convert collection name to table name diff --git a/src/pyseekdb/client/client_seekdb_embedded.py b/src/pyseekdb/client/client_seekdb_embedded.py index 156656a6..48532580 100644 --- a/src/pyseekdb/client/client_seekdb_embedded.py +++ b/src/pyseekdb/client/client_seekdb_embedded.py @@ -93,7 +93,7 @@ def _cleanup(self): if self._connection is not None: self._connection.close() self._connection = None - logger.info("Connection closed") + logger.info(f"Connection closed: path={self.path}, database={self.database}") def is_connected(self) -> bool: """Check connection status""" diff --git a/src/pyseekdb/client/embedding_function.py b/src/pyseekdb/client/embedding_function.py index 58347fc3..0c7f249c 100644 --- a/src/pyseekdb/client/embedding_function.py +++ b/src/pyseekdb/client/embedding_function.py @@ -5,12 +5,10 @@ for converting text documents to vector embeddings. """ -import contextlib import logging -import os +import sys +import warnings from abc import abstractmethod -from functools import cached_property -from pathlib import Path from typing import ( Any, ClassVar, @@ -20,15 +18,6 @@ runtime_checkable, ) -import httpx -import numpy as np -import numpy.typing as npt - -# Set Hugging Face mirror endpoint for better download speed in China -# Users can override this by setting HF_ENDPOINT environment variable -if "HF_ENDPOINT" not in os.environ: - os.environ["HF_ENDPOINT"] = "https://hf-mirror.com" - logger = logging.getLogger(__name__) # Type variable for input types @@ -152,11 +141,8 @@ class DefaultEmbeddingFunction(EmbeddingFunction[Documents]): >>> print(len(embeddings[0])) # 384 """ - MODEL_NAME = "all-MiniLM-L6-v2" - HF_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2" # Hugging Face model ID - DOWNLOAD_PATH = Path.home() / ".cache" / "pyseekdb" / "onnx_models" / MODEL_NAME - EXTRACTED_FOLDER_NAME = "onnx" - ARCHIVE_FILENAME = "onnx.tar.gz" + _MODEL_NAME = "all-MiniLM-L6-v2" + _HF_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2" # Hugging Face model ID _DIMENSION = 384 # all-MiniLM-L6-v2 produces 384-dimensional embeddings def __init__( @@ -168,344 +154,43 @@ def __init__( Initialize the default embedding function. Args: - model_name: Name of the model (currently only 'all-MiniLM-L6-v2' is supported). - Default is 'all-MiniLM-L6-v2' (384 dimensions). - preferred_providers: The preferred ONNX runtime providers. - Defaults to None (uses available providers). + model_name: str = "all-MiniLM-L6-v2", # Deprecated. Will be removed in a future version. + preferred_providers: list[str] | None = None, # Deprecated. Will be removed in a future version. + # The preferred ONNX runtime providers. Defaults to None (uses available providers). """ - if model_name != "all-MiniLM-L6-v2": - raise ValueError(f"Currently only 'all-MiniLM-L6-v2' is supported, got '{model_name}'") - self.model_name = model_name - - # Validate preferred_providers - if preferred_providers and not all(isinstance(i, str) for i in preferred_providers): - raise ValueError("Preferred providers must be a list of strings") - if preferred_providers and len(preferred_providers) != len(set(preferred_providers)): - raise ValueError("Preferred providers must be unique") - - self._preferred_providers = preferred_providers + if model_name != self._MODEL_NAME: + raise ValueError(f"Currently only '{self._MODEL_NAME}' is supported, got '{model_name}'") + if preferred_providers: + warnings.warn( + "preferred_providers is deprecated and will be removed in a future version. " + "Use the preferred_providers argument of OnnxEmbeddingFunction instead.", + DeprecationWarning, + stacklevel=2, + ) + self.model_name = self._MODEL_NAME + if sys.version_info >= (3, 14): + from pyseekdb.utils.embedding_functions.sentence_transformer_embedding_function import ( + SentenceTransformerEmbeddingFunction, + ) - # Import required modules - import onnxruntime as ort_module - import tokenizers - import tqdm + self._backend = SentenceTransformerEmbeddingFunction(model_name=self._MODEL_NAME) + else: + from pyseekdb.utils.embedding_functions import OnnxEmbeddingFunction - self.ort = ort_module - self.tokenizers = tokenizers # Store the module - self.tqdm = tqdm.tqdm + self._backend = OnnxEmbeddingFunction( + model_name=self._MODEL_NAME, + hf_model_id=self._HF_MODEL_ID, + dimension=self._DIMENSION, + preferred_providers=preferred_providers, + ) @property def dimension(self) -> int: - """Get the dimension of embeddings produced by this function""" + """Get the dimension of embeddings produced by this function.""" return self._DIMENSION - def _download(self, url: str, fname: str, chunk_size: int = 8192) -> None: - """ - Download a file from the URL and save it to the file path. - - Args: - url: The URL to download the file from. - fname: The path to save the file to. - chunk_size: The chunk size to use when downloading (default: 8192 for better speed). - """ - logger.info(f"Downloading from {url}") - # Use Client to ensure correct handling of redirects - with httpx.Client(timeout=600.0, follow_redirects=True) as client, client.stream("GET", url) as resp: - resp.raise_for_status() - total = int(resp.headers.get("content-length", 0)) - with ( - open(fname, "wb") as file, - self.tqdm( - desc=os.path.basename(fname), - total=total, - unit="iB", - unit_scale=True, - unit_divisor=1024, - ) as bar, - ): - for data in resp.iter_bytes(chunk_size=chunk_size): - size = file.write(data) - bar.update(size) - - def _get_hf_endpoint(self) -> str: - """Get Hugging Face endpoint URL, using HF_ENDPOINT environment variable if set.""" - return os.environ.get("HF_ENDPOINT", "https://huggingface.co") - - def _download_from_huggingface(self) -> bool: # noqa: C901 - """ - Download model files from Hugging Face (supports mirror acceleration). - - Returns: - True if download successful, False otherwise. - """ - try: - hf_endpoint = self._get_hf_endpoint() - # Remove trailing slash - hf_endpoint = hf_endpoint.rstrip("/") - - # List of files to download - # ONNX model files are in the onnx/ subdirectory, other files in the root directory - files_to_download = { - "onnx/model.onnx": "model.onnx", # ONNX file in onnx subdirectory - "tokenizer.json": "tokenizer.json", - "config.json": "config.json", - "special_tokens_map.json": "special_tokens_map.json", - "tokenizer_config.json": "tokenizer_config.json", - "vocab.txt": "vocab.txt", - } - - extracted_folder = os.path.join(self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME) - os.makedirs(extracted_folder, exist_ok=True) - - logger.info(f"Downloading model from Hugging Face (endpoint: {hf_endpoint})") - - # Download each file - for hf_filename, local_filename in files_to_download.items(): - local_path = os.path.join(extracted_folder, local_filename) - - # Skip if file already exists - if os.path.exists(local_path): - continue - - # Construct Hugging Face download URL - # Format: https://hf-mirror.com/sentence-transformers/all-MiniLM-L6-v2/resolve/main/onnx/model.onnx - # Or: https://hf-mirror.com/sentence-transformers/all-MiniLM-L6-v2/resolve/main/tokenizer.json - url = f"{hf_endpoint}/{self.HF_MODEL_ID}/resolve/main/{hf_filename}" - - try: - # First check if file exists (HEAD request) - with contextlib.suppress(Exception): - head_resp = httpx.head(url, timeout=10.0, follow_redirects=True) - if head_resp.status_code == 404: - logger.warning(f"File {hf_filename} not found on Hugging Face (404), will try fallback") - return False - - self._download(url, local_path, chunk_size=8192) - logger.info(f"Successfully downloaded {local_filename}") - except httpx.HTTPStatusError as e: - if e.response.status_code == 404: - logger.warning(f"File {hf_filename} not found on Hugging Face (404), will try fallback") - return False - logger.warning(f"HTTP error downloading {hf_filename} from Hugging Face: {e}") - if os.path.exists(local_path): - os.remove(local_path) - return False - except Exception as e: - logger.warning(f"Failed to download {hf_filename} from Hugging Face: {e}") - # If download fails, try to delete partially downloaded file - if os.path.exists(local_path): - os.remove(local_path) - return False - - # Verify critical files exist - if not os.path.exists(os.path.join(extracted_folder, "model.onnx")): - logger.error("model.onnx not found after download") - return False - if not os.path.exists(os.path.join(extracted_folder, "tokenizer.json")): - logger.error("tokenizer.json not found after download") - return False - - logger.info("Successfully downloaded all model files from Hugging Face") - return True # noqa: TRY300 - - except Exception: - logger.exception("Error downloading from Hugging Face") - return False - - def _forward(self, documents: list[str], batch_size: int = 32) -> npt.NDArray[np.float32]: - """ - Generate embeddings for a list of documents. - - Args: - documents: The documents to generate embeddings for. - batch_size: The batch size to use when generating embeddings. - - Returns: - The embeddings for the documents. - """ - all_embeddings = [] - for i in range(0, len(documents), batch_size): - batch = documents[i : i + batch_size] - - # Encode each document separately - encoded = [self.tokenizer.encode(d) for d in batch] - - # Check if any document exceeds the max tokens - for doc_tokens in encoded: - if len(doc_tokens.ids) > self.max_tokens(): - raise ValueError( - f"Document length {len(doc_tokens.ids)} is greater than the max tokens {self.max_tokens()}" - ) - - # Create input arrays exactly like the working standalone script - # Create input arrays, ensuring int64 type - input_ids = np.array([e.ids for e in encoded], dtype=np.int64) - attention_mask = np.array([e.attention_mask for e in encoded], dtype=np.int64) - - # Ensure 2D arrays (batch_size, seq_length) - if input_ids.ndim == 1: - input_ids = input_ids.reshape(1, -1) - if attention_mask.ndim == 1: - attention_mask = attention_mask.reshape(1, -1) - - # Use zeros_like to create token_type_ids, ensuring exact shape match - token_type_ids = np.zeros_like(input_ids, dtype=np.int64) - - # Ensure all arrays are contiguous, which is important for onnxruntime 1.19.0 - input_ids = np.ascontiguousarray(input_ids, dtype=np.int64) - attention_mask = np.ascontiguousarray(attention_mask, dtype=np.int64) - token_type_ids = np.ascontiguousarray(token_type_ids, dtype=np.int64) - - onnx_input = { - "input_ids": input_ids, - "attention_mask": attention_mask, - "token_type_ids": token_type_ids, - } - - model_output = self.model.run(None, onnx_input) - last_hidden_state = model_output[0] - - # Mean pooling (exactly as in the code) - # Note: attention_mask needs to be converted to float type for floating point operations - attention_mask_float = attention_mask.astype(np.float32) - input_mask_expanded = np.broadcast_to(np.expand_dims(attention_mask_float, -1), last_hidden_state.shape) - embeddings = np.sum(last_hidden_state * input_mask_expanded, 1) / np.clip( - input_mask_expanded.sum(1), a_min=1e-9, a_max=None - ) - - embeddings = embeddings.astype(np.float32) - all_embeddings.append(embeddings) - - return np.concatenate(all_embeddings) - - @cached_property - def tokenizer(self) -> Any: - """ - Get the tokenizer for the model. - - Returns: - The tokenizer for the model. - """ - tokenizer = self.tokenizers.Tokenizer.from_file( - os.path.join(self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "tokenizer.json") - ) - # max_seq_length = 256, for some reason sentence-transformers uses 256 - # even though the HF config has a max length of 128 - tokenizer.enable_truncation(max_length=256) - tokenizer.enable_padding(pad_id=0, pad_token="[PAD]", length=256) # noqa: S106 - return tokenizer - - @cached_property - def model(self) -> Any: - """ - Get the model. - - Returns: - The model. - """ - if self._preferred_providers is None or len(self._preferred_providers) == 0: - if len(self.ort.get_available_providers()) > 0: - logger.debug( - f"WARNING: No ONNX providers provided, defaulting to available providers: " - f"{self.ort.get_available_providers()}" - ) - self._preferred_providers = self.ort.get_available_providers() - elif not set(self._preferred_providers).issubset(set(self.ort.get_available_providers())): - raise ValueError( - f"Preferred providers must be subset of available providers: {self.ort.get_available_providers()}" - ) - - # Create minimal session options to avoid issues - so = self.ort.SessionOptions() - so.log_severity_level = 3 - # Disable all optimizations that might cause issues - so.graph_optimization_level = self.ort.GraphOptimizationLevel.ORT_DISABLE_ALL - so.execution_mode = self.ort.ExecutionMode.ORT_SEQUENTIAL - so.inter_op_num_threads = 1 - so.intra_op_num_threads = 1 - - if self._preferred_providers and "CoreMLExecutionProvider" in self._preferred_providers: - # remove CoreMLExecutionProvider from the list, it is not as well optimized as CPU. - self._preferred_providers.remove("CoreMLExecutionProvider") - - return self.ort.InferenceSession( - os.path.join(self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "model.onnx"), - # Force CPU execution provider to avoid provider issues - providers=["CPUExecutionProvider"], - sess_options=so, - ) - - def _download_model_if_not_exists(self) -> None: - """ - Download from Hugging Face with image mirror if the model doesn't exist. - """ - onnx_files = [ - "config.json", - "model.onnx", - "special_tokens_map.json", - "tokenizer_config.json", - "tokenizer.json", - "vocab.txt", - ] - extracted_folder = os.path.join(self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME) - onnx_files_exist = True - for f in onnx_files: - if not os.path.exists(os.path.join(extracted_folder, f)): - onnx_files_exist = False - break - - # Model is not downloaded yet - if not onnx_files_exist: - os.makedirs(self.DOWNLOAD_PATH, exist_ok=True) - - logger.info("Attempting to download model from Hugging Face...") - hf_endpoint = self._get_hf_endpoint() - if not self._download_from_huggingface(): - raise RuntimeError( - f"Failed to download model from Hugging Face (endpoint: {hf_endpoint}). " - f"Please check your network connection or set HF_ENDPOINT environment variable " - f"to use a mirror site (e.g., export HF_ENDPOINT='https://hf-mirror.com'). " - f"Model ID: {self.HF_MODEL_ID}" - ) - logger.info("Model downloaded successfully from Hugging Face") - - def max_tokens(self) -> int: - """Get the maximum number of tokens supported by the model.""" - return 256 - def __call__(self, documents: Documents) -> Embeddings: - """ - Generate embeddings for the given documents. - - Args: - documents: Single document (str) or list of documents (List[str]) - - Returns: - List of embedding vectors - - Example: - >>> ef = DefaultEmbeddingFunction() - >>> # Single document - >>> embedding = ef("Hello world") - >>> # Multiple documents - >>> embeddings = ef(["Hello", "World"]) - """ - # Handle single string input - if isinstance(documents, str): - documents = [documents] - - # Handle empty input - if not documents: - return [] - - # Only download the model when it is actually used - self._download_model_if_not_exists() - - # Generate embeddings - embeddings = self._forward(documents) - - # Convert numpy arrays to lists - return [embedding.tolist() for embedding in embeddings] + return self._backend(documents) @staticmethod def name() -> str: @@ -634,6 +319,8 @@ def _initialize(cls) -> None: CohereEmbeddingFunction, GoogleVertexEmbeddingFunction, JinaEmbeddingFunction, + MistralEmbeddingFunction, + MorphEmbeddingFunction, OllamaEmbeddingFunction, OpenAIEmbeddingFunction, QwenEmbeddingFunction, @@ -646,6 +333,8 @@ def _initialize(cls) -> None: cls._registry["sentence_transformer"] = SentenceTransformerEmbeddingFunction cls._registry["openai"] = OpenAIEmbeddingFunction cls._registry["qwen"] = QwenEmbeddingFunction + cls._registry["mistral"] = MistralEmbeddingFunction + cls._registry["morph"] = MorphEmbeddingFunction cls._registry["siliconflow"] = SiliconflowEmbeddingFunction cls._registry["tencent_hunyuan"] = TencentHunyuanEmbeddingFunction cls._registry["ollama"] = OllamaEmbeddingFunction diff --git a/src/pyseekdb/utils/embedding_functions/__init__.py b/src/pyseekdb/utils/embedding_functions/__init__.py index f589b70a..de32a65b 100644 --- a/src/pyseekdb/utils/embedding_functions/__init__.py +++ b/src/pyseekdb/utils/embedding_functions/__init__.py @@ -10,7 +10,10 @@ from .google_vertex_embedding_function import GoogleVertexEmbeddingFunction from .jina_embedding_function import JinaEmbeddingFunction from .litellm_base_embedding_function import LiteLLMBaseEmbeddingFunction +from .mistral_embedding_function import MistralEmbeddingFunction +from .morph_embedding_function import MorphEmbeddingFunction from .ollama_embedding_function import OllamaEmbeddingFunction +from .onnx_embedding_function import OnnxEmbeddingFunction from .openai_base_embedding_function import OpenAIBaseEmbeddingFunction from .openai_embedding_function import OpenAIEmbeddingFunction from .qwen_embedding_function import QwenEmbeddingFunction @@ -27,7 +30,10 @@ "GoogleVertexEmbeddingFunction", "JinaEmbeddingFunction", "LiteLLMBaseEmbeddingFunction", + "MistralEmbeddingFunction", + "MorphEmbeddingFunction", "OllamaEmbeddingFunction", + "OnnxEmbeddingFunction", "OpenAIBaseEmbeddingFunction", "OpenAIEmbeddingFunction", "QwenEmbeddingFunction", diff --git a/src/pyseekdb/utils/embedding_functions/mistral_embedding_function.py b/src/pyseekdb/utils/embedding_functions/mistral_embedding_function.py new file mode 100644 index 00000000..e390ddde --- /dev/null +++ b/src/pyseekdb/utils/embedding_functions/mistral_embedding_function.py @@ -0,0 +1,153 @@ +import warnings +from typing import Any + +from pyseekdb.client.embedding_function import Documents, Embeddings +from pyseekdb.utils.embedding_functions.openai_base_embedding_function import ( + OpenAIBaseEmbeddingFunction, +) + +# Known Mistral embedding model dimensions +# Source: https://docs.mistral.ai/capabilities/embeddings/text_embeddings +_MISTRAL_MODEL_DIMENSIONS = { + "mistral-embed": 1024, +} + + +class MistralEmbeddingFunction(OpenAIBaseEmbeddingFunction): + """ + A convenient embedding function for Mistral text embedding models. + + This class provides a simplified interface to Mistral text embeddings using the + OpenAI-compatible API. + + Note: The embeddings API only accepts the model name and input texts. + + For more information about Mistral embeddings, see: + https://docs.mistral.ai/capabilities/embeddings/text_embeddings + + Example: + pip install pyseekdb openai + + .. code-block:: python + import pyseekdb + from pyseekdb.utils.embedding_functions import MistralEmbeddingFunction + + # Using Mistral text embedding model + # Set MISTRAL_API_KEY environment variable first + ef = MistralEmbeddingFunction(model_name="mistral-embed") + + # Using with additional parameters + ef = MistralEmbeddingFunction( + model_name="mistral-embed", + timeout=30, + max_retries=3 + ) + + db = pyseekdb.Client(path="./seekdb.db") + collection = db.create_collection(name="my_collection", embedding_function=ef) + # Add documents + collection.add(ids=["1", "2"], documents=["Hello world", "How are you?"], metadatas=[{"id": 1}, {"id": 2}]) + # Query using semantic search + results = collection.query("How are you?", n_results=1) + print(results) + + """ + + def __init__( + self, + model_name: str = "mistral-embed", + api_key_env: str | None = None, + api_base: str | None = None, + dimensions: int | None = None, + **kwargs: Any, + ): + """Initialize MistralEmbeddingFunction. + + Args: + model_name (str, optional): Name of the Mistral embedding model. + Defaults to "mistral-embed". + api_key_env (str, optional): Name of the environment variable containing the Mistral API key. + Defaults to "MISTRAL_API_KEY" if not provided. + api_base (str, optional): Base URL for the Mistral API endpoint. + Defaults to "https://api.mistral.ai/v1" if not provided. + dimensions (int, optional): This parameter is not supported by the Mistral embeddings API. + If provided, a warning will be issued and the parameter will be ignored. + **kwargs: Additional arguments to pass to the OpenAI client. + Common options include: + - timeout: Request timeout in seconds + - max_retries: Maximum number of retries + - See https://github.com/openai/openai-python for more options + """ + if dimensions is not None: + warnings.warn( + "The dimensions parameter is not supported by Mistral embeddings. " + "The provided dimensions parameter will be ignored.", + UserWarning, + stacklevel=2, + ) + + super().__init__( + model_name=model_name, + api_key_env=api_key_env, + api_base=api_base, + dimensions=None, + **kwargs, + ) + + def _get_default_api_base(self) -> str: + return "https://api.mistral.ai/v1" + + def _get_default_api_key_env(self) -> str: + return "MISTRAL_API_KEY" + + def _get_model_dimensions(self) -> dict[str, int]: + return _MISTRAL_MODEL_DIMENSIONS + + @staticmethod + def name() -> str: + return "mistral" + + def __call__(self, documents: Documents) -> Embeddings: + """Generate embeddings for the given documents using Mistral's input parameter.""" + if isinstance(documents, str): + documents = [documents] + + if not documents: + return [] + + request_params = { + "model": self.model_name, + "input": documents, + } + + response = self._client.embeddings.create(**request_params) + embeddings = [item.embedding for item in response.data] + + if len(embeddings) != len(documents): + raise ValueError(f"Expected {len(documents)} embeddings but got {len(embeddings)} from API") + + return embeddings + + def get_config(self) -> dict[str, Any]: + return super().get_config() + + @staticmethod + def build_from_config(config: dict[str, Any]) -> "MistralEmbeddingFunction": + model_name = config.get("model_name") + if model_name is None: + raise ValueError("Missing required field 'model_name' in configuration") + + api_key_env = config.get("api_key_env") + api_base = config.get("api_base") + dimensions = config.get("dimensions") + client_kwargs = config.get("client_kwargs", {}) + if not isinstance(client_kwargs, dict): + raise TypeError(f"client_kwargs must be a dictionary, but got {client_kwargs}") + + return MistralEmbeddingFunction( + model_name=model_name, + api_key_env=api_key_env, + api_base=api_base, + dimensions=dimensions, + **client_kwargs, + ) diff --git a/src/pyseekdb/utils/embedding_functions/morph_embedding_function.py b/src/pyseekdb/utils/embedding_functions/morph_embedding_function.py new file mode 100644 index 00000000..4d4661ef --- /dev/null +++ b/src/pyseekdb/utils/embedding_functions/morph_embedding_function.py @@ -0,0 +1,137 @@ +import logging +from typing import Any + +from pyseekdb.utils.embedding_functions.openai_base_embedding_function import ( + OpenAIBaseEmbeddingFunction, +) + +# Known Morph embedding model dimensions +# Source: https://docs.morphllm.com/api-reference/endpoint/embedding +_MORPH_MODEL_DIMENSIONS = { + "morph-embedding-v4": 1536, +} + +logger = logging.getLogger(__name__) + + +class MorphEmbeddingFunction(OpenAIBaseEmbeddingFunction): + """ + A convenient embedding function for Morph embedding models. + + This class provides a simplified interface to Morph embedding models using the + OpenAI-compatible API. + + Example: + pip install pyseekdb openai + + .. code-block:: python + import pyseekdb + from pyseekdb.utils.embedding_functions import MorphEmbeddingFunction + + # Using Morph embedding model + # Set MORPH_API_KEY environment variable first + ef = MorphEmbeddingFunction( + model_name="morph-embedding-v4" + ) + + # Using with custom api_key_env + ef = MorphEmbeddingFunction( + model_name="morph-embedding-v4", + api_key_env="MORPH_API_KEY" + ) + + db = pyseekdb.Client(path="./seekdb.db") + collection = db.create_collection(name="my_collection", embedding_function=ef) + # Add documents + collection.add(ids=["1", "2"], documents=["Hello world", "How are you?"], metadatas=[{"id": 1}, {"id": 2}]) + # Query using semantic search + results = collection.query("How are you?", n_results=1) + print(results) + """ + + def __init__( + self, + model_name: str, + api_key_env: str | None = None, + api_base: str | None = None, + **kwargs: Any, + ): + """Initialize MorphEmbeddingFunction. + + Args: + model_name (str): Name of the Morph embedding model. + api_key_env (str, optional): Name of the environment variable containing the Morph API key. + Defaults to "MORPH_API_KEY" if not provided. + api_base (str, optional): Base URL for the Morph API endpoint. + Defaults to "https://api.morphllm.com/v1" if not provided. + **kwargs: Additional arguments to pass to the OpenAI client. + Common options include: + - timeout: Request timeout in seconds + - max_retries: Maximum number of retries + - See https://github.com/openai/openai-python for more options + """ + super().__init__( + model_name=model_name, + api_key_env=api_key_env, + api_base=api_base, + dimensions=None, + **kwargs, + ) + + def _get_default_api_base(self) -> str: + """Get the default API base URL for Morph. + + Returns: + str: Default Morph API base URL + """ + return "https://api.morphllm.com/v1" + + def _get_default_api_key_env(self) -> str: + """Get the default API key environment variable name for Morph. + + Returns: + str: Default Morph API key environment variable name + """ + return "MORPH_API_KEY" + + def _get_model_dimensions(self) -> dict[str, int]: + """Get a dictionary mapping Morph model names to their default dimensions. + + Returns: + dict[str, int]: Dictionary mapping model names to dimensions + """ + return _MORPH_MODEL_DIMENSIONS + + @staticmethod + def name() -> str: + """Get the unique name identifier for MorphEmbeddingFunction. + + Returns: + The name identifier for this embedding function type + """ + return "morph" + + def get_config(self) -> dict[str, Any]: + return super().get_config() + + @staticmethod + def build_from_config(config: dict[str, Any]) -> "MorphEmbeddingFunction": + model_name = config.get("model_name") + if model_name is None: + raise ValueError("Missing required field 'model_name' in configuration") + + api_key_env = config.get("api_key_env") + api_base = config.get("api_base") + dimensions = config.get("dimensions") + if dimensions is not None: + logger.warning("Ignoring unsupported 'dimensions' for MorphEmbeddingFunction") + client_kwargs = config.get("client_kwargs", {}) + if not isinstance(client_kwargs, dict): + raise TypeError(f"client_kwargs must be a dictionary, but got {client_kwargs}") + + return MorphEmbeddingFunction( + model_name=model_name, + api_key_env=api_key_env, + api_base=api_base, + **client_kwargs, + ) diff --git a/src/pyseekdb/utils/embedding_functions/onnx_embedding_function.py b/src/pyseekdb/utils/embedding_functions/onnx_embedding_function.py new file mode 100644 index 00000000..6fad2f99 --- /dev/null +++ b/src/pyseekdb/utils/embedding_functions/onnx_embedding_function.py @@ -0,0 +1,394 @@ +""" +ONNX-based embedding function implementation. + +This module provides a generic ONNX embedding function that can run +sentence-transformer style models via onnxruntime. +""" + +import contextlib +import logging +import os +from functools import cached_property +from pathlib import Path +from typing import Any + +import numpy as np +import numpy.typing as npt + +logger = logging.getLogger(__name__) + +Documents = str | list[str] +Embeddings = list[list[float]] + + +class OnnxEmbeddingFunction: + """ + Generic ONNX runtime embedding function. + + This class handles model download, tokenizer/model loading, and embedding + generation using onnxruntime. + """ + + EXTRACTED_FOLDER_NAME = "onnx" + ARCHIVE_FILENAME = "onnx.tar.gz" + + def __init__( + self, + model_name: str, + hf_model_id: str, + dimension: int, + download_path: Path | None = None, + preferred_providers: list[str] | None = None, + ): + """ + Initialize an ONNX embedding function. + + Args: + model_name: Name of the model (used for cache directory naming). + hf_model_id: Hugging Face model ID. + dimension: Output embedding dimension. + download_path: Optional cache path override. + preferred_providers: Preferred ONNX runtime providers. + """ + if not model_name: + raise ValueError("model_name must be a non-empty string") + if not hf_model_id: + raise ValueError("hf_model_id must be a non-empty string") + if dimension <= 0: + raise ValueError("dimension must be a positive integer") + + self.model_name = model_name + self.hf_model_id = hf_model_id + self._dimension = dimension + self.download_path = ( + download_path + if download_path is not None + else Path.home() / ".cache" / "pyseekdb" / "onnx_models" / model_name + ) + + # Validate preferred_providers + if preferred_providers and not all(isinstance(i, str) for i in preferred_providers): + raise ValueError("Preferred providers must be a list of strings") + if preferred_providers and len(preferred_providers) != len(set(preferred_providers)): + raise ValueError("Preferred providers must be unique") + + self._preferred_providers = preferred_providers + + # Import required modules lazily to avoid hard dependencies at import time + import onnxruntime as ort_module + import tokenizers + import tqdm + + self.ort = ort_module + self.tokenizers = tokenizers # Store the module + self.tqdm = tqdm.tqdm + + @property + def dimension(self) -> int: + """Get the dimension of embeddings produced by this function.""" + return self._dimension + + def _download(self, url: str, fname: str, chunk_size: int = 8192) -> None: + """ + Download a file from the URL and save it to the file path. + + Args: + url: The URL to download the file from. + fname: The path to save the file to. + chunk_size: The chunk size to use when downloading. + """ + logger.info(f"Downloading from {url}") + # Use Client to ensure correct handling of redirects + import httpx + + with httpx.Client(timeout=600.0, follow_redirects=True) as client, client.stream("GET", url) as resp: + resp.raise_for_status() + total = int(resp.headers.get("content-length", 0)) + with ( + open(fname, "wb") as file, + self.tqdm( + desc=os.path.basename(fname), + total=total, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as bar, + ): + for data in resp.iter_bytes(chunk_size=chunk_size): + size = file.write(data) + bar.update(size) + + def _get_hf_endpoint(self) -> str: + """Get Hugging Face endpoint URL, using HF_ENDPOINT environment variable if set.""" + return os.environ.get("HF_ENDPOINT", "https://hf-mirror.com") + + def _download_from_huggingface(self) -> bool: # noqa: C901 + """ + Download model files from Hugging Face (supports mirror acceleration). + + Returns: + True if download successful, False otherwise. + """ + try: + hf_endpoint = self._get_hf_endpoint() + # Remove trailing slash + hf_endpoint = hf_endpoint.rstrip("/") + + # List of files to download + # ONNX model files are in the onnx/ subdirectory, other files in the root directory + files_to_download = { + "onnx/model.onnx": "model.onnx", # ONNX file in onnx subdirectory + "tokenizer.json": "tokenizer.json", + "config.json": "config.json", + "special_tokens_map.json": "special_tokens_map.json", + "tokenizer_config.json": "tokenizer_config.json", + "vocab.txt": "vocab.txt", + } + + extracted_folder = os.path.join(self.download_path, self.EXTRACTED_FOLDER_NAME) + os.makedirs(extracted_folder, exist_ok=True) + + logger.info(f"Downloading model from Hugging Face (endpoint: {hf_endpoint})") + import httpx + + # Download each file + for hf_filename, local_filename in files_to_download.items(): + local_path = os.path.join(extracted_folder, local_filename) + + # Skip if file already exists + if os.path.exists(local_path): + continue + + # Construct Hugging Face download URL + url = f"{hf_endpoint}/{self.hf_model_id}/resolve/main/{hf_filename}" + + try: + # First check if file exists (HEAD request) + with contextlib.suppress(Exception): + head_resp = httpx.head(url, timeout=10.0, follow_redirects=True) + if head_resp.status_code == 404: + logger.warning(f"File {hf_filename} not found on Hugging Face (404), will try fallback") + return False + + self._download(url, local_path, chunk_size=8192) + logger.info(f"Successfully downloaded {local_filename}") + except httpx.HTTPStatusError as e: + if e.response.status_code == 404: + logger.warning(f"File {hf_filename} not found on Hugging Face (404), will try fallback") + return False + logger.warning(f"HTTP error downloading {hf_filename} from Hugging Face: {e}") + if os.path.exists(local_path): + os.remove(local_path) + return False + except Exception as e: + logger.warning(f"Failed to download {hf_filename} from Hugging Face: {e}") + # If download fails, try to delete partially downloaded file + if os.path.exists(local_path): + os.remove(local_path) + return False + + # Verify critical files exist + if not os.path.exists(os.path.join(extracted_folder, "model.onnx")): + logger.error("model.onnx not found after download") + return False + if not os.path.exists(os.path.join(extracted_folder, "tokenizer.json")): + logger.error("tokenizer.json not found after download") + return False + + logger.info("Successfully downloaded all model files from Hugging Face") + return True # noqa: TRY300 + + except Exception: + logger.exception("Error downloading from Hugging Face") + return False + + def _forward(self, documents: list[str], batch_size: int = 32) -> npt.NDArray[np.float32]: + """ + Generate embeddings for a list of documents. + + Args: + documents: The documents to generate embeddings for. + batch_size: The batch size to use when generating embeddings. + + Returns: + The embeddings for the documents. + """ + all_embeddings = [] + for i in range(0, len(documents), batch_size): + batch = documents[i : i + batch_size] + + # Encode each document separately + encoded = [self.tokenizer.encode(d) for d in batch] + + # Check if any document exceeds the max tokens + for doc_tokens in encoded: + if len(doc_tokens.ids) > self.max_tokens(): + raise ValueError( + f"Document length {len(doc_tokens.ids)} is greater than the max tokens {self.max_tokens()}" + ) + + # Create input arrays exactly like the working standalone script + # Create input arrays, ensuring int64 type + input_ids = np.array([e.ids for e in encoded], dtype=np.int64) + attention_mask = np.array([e.attention_mask for e in encoded], dtype=np.int64) + + # Ensure 2D arrays (batch_size, seq_length) + if input_ids.ndim == 1: + input_ids = input_ids.reshape(1, -1) + if attention_mask.ndim == 1: + attention_mask = attention_mask.reshape(1, -1) + + # Use zeros_like to create token_type_ids, ensuring exact shape match + token_type_ids = np.zeros_like(input_ids, dtype=np.int64) + + # Ensure all arrays are contiguous, which is important for onnxruntime 1.19.0 + input_ids = np.ascontiguousarray(input_ids, dtype=np.int64) + attention_mask = np.ascontiguousarray(attention_mask, dtype=np.int64) + token_type_ids = np.ascontiguousarray(token_type_ids, dtype=np.int64) + + onnx_input = { + "input_ids": input_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + + model_output = self.model.run(None, onnx_input) + last_hidden_state = model_output[0] + + # Mean pooling (exactly as in the code) + # Note: attention_mask needs to be converted to float type for floating point operations + attention_mask_float = attention_mask.astype(np.float32) + input_mask_expanded = np.broadcast_to(np.expand_dims(attention_mask_float, -1), last_hidden_state.shape) + embeddings = np.sum(last_hidden_state * input_mask_expanded, 1) / np.clip( + input_mask_expanded.sum(1), a_min=1e-9, a_max=None + ) + + embeddings = embeddings.astype(np.float32) + all_embeddings.append(embeddings) + + return np.concatenate(all_embeddings) + + @cached_property + def tokenizer(self) -> Any: + """ + Get the tokenizer for the model. + + Returns: + The tokenizer for the model. + """ + tokenizer = self.tokenizers.Tokenizer.from_file( + os.path.join(self.download_path, self.EXTRACTED_FOLDER_NAME, "tokenizer.json") + ) + # max_seq_length = 256, for some reason sentence-transformers uses 256 + # even though the HF config has a max length of 128 + tokenizer.enable_truncation(max_length=256) + tokenizer.enable_padding(pad_id=0, pad_token="[PAD]", length=256) # noqa: S106 + return tokenizer + + @cached_property + def model(self) -> Any: + """ + Get the model. + + Returns: + The model. + """ + if self._preferred_providers is None or len(self._preferred_providers) == 0: + if len(self.ort.get_available_providers()) > 0: + logger.debug( + f"WARNING: No ONNX providers provided, defaulting to available providers: " + f"{self.ort.get_available_providers()}" + ) + self._preferred_providers = self.ort.get_available_providers() + elif not set(self._preferred_providers).issubset(set(self.ort.get_available_providers())): + raise ValueError( + f"Preferred providers must be subset of available providers: {self.ort.get_available_providers()}" + ) + + # Create minimal session options to avoid issues + so = self.ort.SessionOptions() + so.log_severity_level = 3 + # Disable all optimizations that might cause issues + so.graph_optimization_level = self.ort.GraphOptimizationLevel.ORT_DISABLE_ALL + so.execution_mode = self.ort.ExecutionMode.ORT_SEQUENTIAL + so.inter_op_num_threads = 1 + so.intra_op_num_threads = 1 + + if self._preferred_providers and "CoreMLExecutionProvider" in self._preferred_providers: + # remove CoreMLExecutionProvider from the list, it is not as well optimized as CPU. + self._preferred_providers.remove("CoreMLExecutionProvider") + + return self.ort.InferenceSession( + os.path.join(self.download_path, self.EXTRACTED_FOLDER_NAME, "model.onnx"), + # Force CPU execution provider to avoid provider issues + providers=["CPUExecutionProvider"], + sess_options=so, + ) + + def _download_model_if_not_exists(self) -> None: + """ + Download from Hugging Face with image mirror if the model doesn't exist. + """ + onnx_files = [ + "config.json", + "model.onnx", + "special_tokens_map.json", + "tokenizer_config.json", + "tokenizer.json", + "vocab.txt", + ] + extracted_folder = os.path.join(self.download_path, self.EXTRACTED_FOLDER_NAME) + onnx_files_exist = True + for f in onnx_files: + if not os.path.exists(os.path.join(extracted_folder, f)): + onnx_files_exist = False + break + + # Model is not downloaded yet + if not onnx_files_exist: + os.makedirs(self.download_path, exist_ok=True) + + logger.info("Attempting to download model from Hugging Face...") + hf_endpoint = self._get_hf_endpoint() + if not self._download_from_huggingface(): + raise RuntimeError( + f"Failed to download model from Hugging Face (endpoint: {hf_endpoint}). " + f"Please check your network connection or set HF_ENDPOINT environment variable " + f"to use a mirror site (e.g., export HF_ENDPOINT='https://hf-mirror.com'). " + f"Model ID: {self.hf_model_id}" + ) + logger.info("Model downloaded successfully from Hugging Face") + + def max_tokens(self) -> int: + """Get the maximum number of tokens supported by the model.""" + return 256 + + def __call__(self, documents: Documents) -> Embeddings: + """ + Generate embeddings for the given documents. + + Args: + documents: Single document (str) or list of documents (List[str]) + + Returns: + List of embedding vectors + """ + # Handle single string input + if isinstance(documents, str): + documents = [documents] + + # Handle empty input + if not documents: + return [] + + # Only download the model when it is actually used + self._download_model_if_not_exists() + + # Generate embeddings + embeddings = self._forward(documents) + + # Convert numpy arrays to lists + return [embedding.tolist() for embedding in embeddings] + + def __repr__(self) -> str: + return f"OnnxEmbeddingFunction(model_name='{self.model_name}')" diff --git a/tests/integration_tests/conftest.py b/tests/integration_tests/conftest.py index 3739e7dc..257c24df 100644 --- a/tests/integration_tests/conftest.py +++ b/tests/integration_tests/conftest.py @@ -10,15 +10,16 @@ import pytest -# Add project path -project_root = Path(__file__).parent.parent -sys.path.insert(0, str(project_root)) +# Add project path (repo root + src) +repo_root = Path(__file__).resolve().parents[2] +src_root = repo_root / "src" +sys.path.insert(0, str(src_root)) import pyseekdb # noqa: E402 # ==================== Environment Variable Configuration ==================== # Embedded mode -SEEKDB_PATH = os.environ.get("SEEKDB_PATH", os.path.join(project_root, "seekdb.db")) +SEEKDB_PATH = os.environ.get("SEEKDB_PATH", os.path.join(repo_root, "seekdb.db")) SEEKDB_DATABASE = os.environ.get("SEEKDB_DATABASE", "test") # Server mode diff --git a/tests/integration_tests/test_collection_hybrid_search_source_inference.py b/tests/integration_tests/test_collection_hybrid_search_source_inference.py new file mode 100644 index 00000000..8ee21afb --- /dev/null +++ b/tests/integration_tests/test_collection_hybrid_search_source_inference.py @@ -0,0 +1,133 @@ +""" +Integration tests for hybrid_search `_source` inference from include against a real database. + +These tests require a running OceanBase/MySQL-compatible endpoint that supports +`DBMS_HYBRID_SEARCH.GET_SQL`. +""" + +import contextlib +import json +import uuid + +from pymysql.converters import escape_string + +from pyseekdb import HNSWConfiguration + + +class TestCollectionHybridSearchSourceInferenceRealDB: + def _unique_collection_name(self, prefix: str) -> str: + # Keep names short to avoid MySQL/OceanBase identifier length limits after + # internal table-name prefixing (e.g. "c$v1$..."). + return f"{prefix}_{uuid.uuid4().hex[:8]}" + + def _create_test_collection(self, client, collection_name: str, dimension: int = 3): + config = HNSWConfiguration(dimension=dimension, distance="l2") + collection = client.create_collection(name=collection_name, configuration=config, embedding_function=None) + return collection, collection.dimension + + def _generate_query_vector(self, dimension: int) -> list[float]: + base = [1.0, 2.0, 3.0] + if dimension <= len(base): + return base[:dimension] + extended = base * ((dimension // len(base)) + 1) + return extended[:dimension] + + def _insert_test_data(self, collection, dimension: int): + test_data = [ + ( + "Machine learning is a subset of artificial intelligence", + [1.0, 2.0, 3.0], + {"category": "AI", "tag": "ml"}, + ), + ( + "Python programming language is widely used in data science", + [2.0, 3.0, 4.0], + {"category": "Programming", "tag": "python"}, + ), + ] + + ids: list[str] = [] + documents: list[str] = [] + embeddings: list[list[float]] = [] + metadatas: list[dict] = [] + + for document, base_vec, metadata in test_data: + record_id = str(uuid.uuid4()) + ids.append(record_id) + documents.append(document) + metadatas.append(metadata) + + if dimension <= len(base_vec): + embedding = base_vec[:dimension] + else: + embedding = (base_vec * ((dimension // len(base_vec)) + 1))[:dimension] + + embeddings.append(embedding) + + collection.add(ids=ids, documents=documents, embeddings=embeddings, metadatas=metadatas) + return ids + + def _get_sql_query(self, client, table_name: str, search_parm: dict) -> str: + search_parm_json = json.dumps(search_parm, ensure_ascii=False) + client._server._execute(f"SET @search_parm = '{escape_string(search_parm_json)}'") + get_sql_query = f"SELECT DBMS_HYBRID_SEARCH.GET_SQL('{table_name}', @search_parm) as query_sql FROM dual" # noqa: S608 + rows = client._server._execute(get_sql_query) + assert rows and rows[0].get("query_sql") + query_sql = rows[0]["query_sql"] + if isinstance(query_sql, str): + return query_sql.strip().strip("'\"") + return str(query_sql) + + def test_include_infers_source_result_shape_matrix(self, db_client): + """ + Verify `_source` inference end-to-end: + 1) GET_SQL result columns match requested include (avoid returning large unused columns like embedding) + 2) SDK return shape matches include + """ + collection_name = self._unique_collection_name("hs_include_matrix") + collection = None + try: + collection, dimension = self._create_test_collection(db_client, collection_name) + self._insert_test_data(collection, dimension=dimension) + + query_vector = self._generate_query_vector(dimension) + knn = {"query_embeddings": query_vector, "n_results": 2} + + # 1) include=None: default returns documents+metadatas; should not return embedding column + default_include = collection.hybrid_search(knn=knn, n_results=2) + assert set(default_include.keys()) == {"ids", "distances", "documents", "metadatas"} + assert all(isinstance(d, str) for d in default_include["documents"][0]) + assert all(isinstance(m, dict) and m for m in default_include["metadatas"][0]) + + # 2) include=[]: ids/distances only; should not return document/metadata/embedding columns + ids_only = collection.hybrid_search(knn=knn, n_results=2, include=[]) + assert set(ids_only.keys()) == {"ids", "distances"} + + # 3) include=["documents"]: only document column + docs_only = collection.hybrid_search(knn=knn, n_results=2, include=["documents"]) + assert set(docs_only.keys()) == {"ids", "distances", "documents"} + assert all(isinstance(d, str) for d in docs_only["documents"][0]) + + # 4) include=["metadatas"]: only metadata column + metadatas_only = collection.hybrid_search(knn=knn, n_results=2, include=["metadatas"]) + assert set(metadatas_only.keys()) == {"ids", "distances", "metadatas"} + assert all(isinstance(m, dict) and m for m in metadatas_only["metadatas"][0]) + + # 5) include=["embeddings"]: only embedding column + embeddings_only = collection.hybrid_search(knn=knn, n_results=2, include=["embeddings"]) + assert set(embeddings_only.keys()) == {"ids", "distances", "embeddings"} + first_embedding = embeddings_only["embeddings"][0][0] + assert isinstance(first_embedding, list) + assert len(first_embedding) == dimension + + # 6) include=["documents","embeddings"]: document+embedding columns + docs_and_embeddings = collection.hybrid_search(knn=knn, n_results=2, include=["documents", "embeddings"]) + assert set(docs_and_embeddings.keys()) == {"ids", "distances", "documents", "embeddings"} + assert all(isinstance(d, str) for d in docs_and_embeddings["documents"][0]) + assert all(isinstance(e, list) and len(e) == dimension for e in docs_and_embeddings["embeddings"][0]) + finally: + with contextlib.suppress(Exception): + db_client.delete_collection(name=collection_name) + + # NOTE: `HybridSearch` fluent builder was removed on `develop` (rollback enhanced hybrid search). + # Keep this file focused on verifying OceanBase GET_SQL `_source` inference and result shapes. diff --git a/tests/unit_tests/test_default_embedding_function.py b/tests/unit_tests/test_default_embedding_function.py index 37c3f410..0d0969e4 100644 --- a/tests/unit_tests/test_default_embedding_function.py +++ b/tests/unit_tests/test_default_embedding_function.py @@ -2,6 +2,7 @@ Unit tests for DefaultEmbeddingFunction. """ +import sys from typing import Any import pytest @@ -59,5 +60,21 @@ def test_persistence_roundtrip(self): assert restored_ef.model_name == original_ef.model_name +def test_default_embedding_function_on_py314(): + if sys.version_info < (3, 14): + pytest.skip("Python < 3.14") + embedding_function = DefaultEmbeddingFunction() + assert embedding_function.dimension == 384 + assert len(embedding_function("hello")[0]) == 384 + + +def test_default_embedding_function_uses_onnx_on_pre314(): + if sys.version_info >= (3, 14): + pytest.skip("Python >= 3.14") + embedding_function = DefaultEmbeddingFunction() + assert embedding_function.dimension == 384 + assert len(embedding_function("hello")[0]) == 384 + + if __name__ == "__main__": pytest.main([__file__, "-v", "-s"]) diff --git a/tests/unit_tests/test_hybrid_search_source_inference.py b/tests/unit_tests/test_hybrid_search_source_inference.py new file mode 100644 index 00000000..1ea019e3 --- /dev/null +++ b/tests/unit_tests/test_hybrid_search_source_inference.py @@ -0,0 +1,89 @@ +""" +Unit tests for hybrid_search `_source` inference from include. + +Public API exposes only `include`. The SDK infers a minimal OceanBase GET_SQL `_source` +allowlist from `include` to reduce response payload size. +""" + +from typing import Any + +from pyseekdb.client.client_base import BaseClient +from pyseekdb.client.collection import Collection + + +class _CapturingClient: + mode = "dummy" + + def __init__(self) -> None: + self.captured: dict[str, Any] | None = None + + def _collection_hybrid_search(self, **kwargs: Any) -> dict[str, Any]: + self.captured = kwargs + return {"captured": kwargs} + + +class _DummyClient: + def _build_source_fields(self, include: list[str] | None) -> list[str]: + return BaseClient._build_source_fields(self, include) + + +class TestHybridSearchPublicSurfaceUnit: + def test_collection_forwards_include_only(self) -> None: + client = _CapturingClient() + collection = Collection(client=client, name="test", dimension=3) + collection.hybrid_search(query={"where_document": {"$contains": "hi"}}, include=["documents"]) + assert client.captured is not None + assert client.captured["include"] == ["documents"] + assert "return_fields" not in client.captured + + +class TestBuildSearchParmSourceInferenceUnit: + def test_build_search_parm_sets_source_from_default_include(self) -> None: + dummy = _DummyClient() + result = BaseClient._build_search_parm( + dummy, + query=None, + knn=None, + rank=None, + n_results=10, + include=None, + ) + assert result["_source"] == ["_id", "document", "metadata"] + + def test_build_search_parm_sets_source_from_empty_include(self) -> None: + dummy = _DummyClient() + result = BaseClient._build_search_parm( + dummy, + query=None, + knn=None, + rank=None, + n_results=10, + include=[], + ) + assert result["_source"] == ["_id"] + + +class TestBuildSourceFieldsUnit: + def test_build_source_fields_defaults_to_documents_and_metadatas(self) -> None: + dummy = _DummyClient() + assert BaseClient._build_source_fields(dummy, include=None) == ["_id", "document", "metadata"] + + def test_build_source_fields_empty_include_is_id_only(self) -> None: + dummy = _DummyClient() + assert BaseClient._build_source_fields(dummy, include=[]) == ["_id"] + + def test_build_source_fields_includes_embedding_only_when_requested(self) -> None: + dummy = _DummyClient() + assert BaseClient._build_source_fields(dummy, include=["embeddings"]) == ["_id", "embedding"] + + def test_build_source_fields_accepts_singular_aliases(self) -> None: + dummy = _DummyClient() + assert BaseClient._build_source_fields(dummy, include=["document"]) == ["_id", "document"] + assert BaseClient._build_source_fields(dummy, include=["metadata"]) == ["_id", "metadata"] + assert BaseClient._build_source_fields(dummy, include=["embedding"]) == ["_id", "embedding"] + + def test_build_source_fields_ignores_non_source_include_items(self) -> None: + dummy = _DummyClient() + assert BaseClient._build_source_fields(dummy, include=["ids"]) == ["_id"] + assert BaseClient._build_source_fields(dummy, include=["distances"]) == ["_id"] + assert BaseClient._build_source_fields(dummy, include=["documents", "ids", "distances"]) == ["_id", "document"] diff --git a/tests/unit_tests/test_mistral_embedding_function.py b/tests/unit_tests/test_mistral_embedding_function.py new file mode 100644 index 00000000..9077f1fa --- /dev/null +++ b/tests/unit_tests/test_mistral_embedding_function.py @@ -0,0 +1,188 @@ +""" +Unit tests for MistralEmbeddingFunction. + +Tests Mistral embedding function initialization, embedding generation, and dimension detection. +Uses real API calls - requires MISTRAL_API_KEY environment variable to be set. + +To run this test manually: + pytest tests/unit_tests/test_mistral_embedding_function.py -v -s + # Or with environment variable: + MISTRAL_API_KEY=your-key pytest tests/unit_tests/test_mistral_embedding_function.py -v -s +""" + +import importlib.util +import os + +import pytest + +from pyseekdb.client.embedding_function import dimension_of +from pyseekdb.utils.embedding_functions import MistralEmbeddingFunction + +from .test_utils import env_guard + + +def is_openai_available() -> bool: + """ + Check if openai is available for testing. + + Returns: + True if openai is available, False otherwise. + """ + return importlib.util.find_spec("openai") is not None + + +# Skip this test by default - it requires external API access and API keys +@pytest.mark.skipif( + not os.environ.get("MISTRAL_API_KEY") or not is_openai_available(), + reason="MISTRAL_API_KEY environment variable must be set", +) +class TestMistralEmbeddingFunction: + """Test MistralEmbeddingFunction - skipped by default, requires manual execution""" + + def test_mistral_env(self): + """Test if openai package is installed and required environment variables are set.""" + if not is_openai_available(): + print("openai package is not installed") + raise AssertionError("openai package is not installed") + + if not os.environ.get("MISTRAL_API_KEY"): + print("MISTRAL_API_KEY environment variable is not set") + raise AssertionError("MISTRAL_API_KEY environment variable is not set") + + def test_initialization_with_defaults(self): + """Test MistralEmbeddingFunction initialization with default values""" + print("\nTesting MistralEmbeddingFunction initialization with defaults") + + # Check if openai is available and env vars are set + self.test_mistral_env() + + ef = MistralEmbeddingFunction() + + assert ef is not None + assert ef.model_name == "mistral-embed" + assert ef.api_key_env == "MISTRAL_API_KEY" + assert ef.api_base == "https://api.mistral.ai/v1" + assert ef._dimensions_param is None + print(f" Model name: {ef.model_name}") + print(f" API key env: {ef.api_key_env}") + print(f" API base: {ef.api_base}") + + def test_initialization_with_custom_api_key_env(self): + """Test MistralEmbeddingFunction initialization with custom API key env""" + print("\nTesting MistralEmbeddingFunction initialization with custom API key env") + + self.test_mistral_env() + + custom_key_env = "CUSTOM_MISTRAL_KEY" + if not os.environ.get(custom_key_env): + os.environ[custom_key_env] = "your-custom-key" + + ef = MistralEmbeddingFunction(model_name="mistral-embed", api_key_env=custom_key_env) + assert ef.api_key_env == custom_key_env + print(f" Custom API key env: {ef.api_key_env}") + + def test_initialization_with_custom_api_base(self): + """Test MistralEmbeddingFunction initialization with custom API base""" + print("\nTesting MistralEmbeddingFunction initialization with custom API base") + + self.test_mistral_env() + + custom_base = "https://api.mistral.ai/v1" + ef = MistralEmbeddingFunction(model_name="mistral-embed", api_base=custom_base) + assert ef.api_base == custom_base + print(f" Custom API base: {ef.api_base}") + + def test_initialization_with_additional_kwargs(self): + """Test MistralEmbeddingFunction initialization with additional kwargs""" + print("\nTesting MistralEmbeddingFunction initialization with kwargs") + + self.test_mistral_env() + + ef = MistralEmbeddingFunction(model_name="mistral-embed", timeout=30, max_retries=3) + assert ef._client_kwargs.get("timeout") == 30 + assert ef._client_kwargs.get("max_retries") == 3 + print(f" Client kwargs: {ef._client_kwargs}") + + def test_initialization_with_missing_api_key(self): + """Test MistralEmbeddingFunction initialization with missing API key""" + print("\nTesting MistralEmbeddingFunction initialization with missing API key") + + with env_guard(MISTRAL_API_KEY=None): + try: + MistralEmbeddingFunction(model_name="mistral-embed") + raise AssertionError("Expected ValueError for missing API key") + except ValueError as e: + print(f" Caught expected error: {e}") + + def test_dimension_property_for_known_model(self): + """Test MistralEmbeddingFunction dimension property for known model""" + print("\nTesting MistralEmbeddingFunction dimension property for known model") + + self.test_mistral_env() + + ef = MistralEmbeddingFunction(model_name="mistral-embed") + assert ef.dimension == 1024 + print(f" Model mistral-embed dimension: {ef.dimension}") + + def test_embedding_generation_single_document(self): + """Test MistralEmbeddingFunction embedding generation (single document)""" + print("\nTesting MistralEmbeddingFunction embedding generation (single document)") + + self.test_mistral_env() + + ef = MistralEmbeddingFunction(model_name="mistral-embed") + embeddings = ef("Hello world") + assert len(embeddings) == 1 + assert len(embeddings[0]) > 0 + print(f" Embedding dimension: {len(embeddings[0])}") + + def test_embedding_generation_multiple_documents(self): + """Test MistralEmbeddingFunction embedding generation (multiple documents)""" + print("\nTesting MistralEmbeddingFunction embedding generation (multiple documents)") + + self.test_mistral_env() + + ef = MistralEmbeddingFunction(model_name="mistral-embed") + embeddings = ef(["Hello world", "How are you?"]) + assert len(embeddings) == 2 + assert len(embeddings[0]) > 0 + print(f" Embedding dimension: {len(embeddings[0])}") + + def test_embedding_with_empty_input(self): + """Test MistralEmbeddingFunction with empty input""" + print("\nTesting MistralEmbeddingFunction with empty input") + + self.test_mistral_env() + + ef = MistralEmbeddingFunction(model_name="mistral-embed") + embeddings = ef([]) + assert embeddings == [] + print(" Empty input returned empty embeddings") + + def test_dimension_of_function(self): + """Test dimension_of function with MistralEmbeddingFunction""" + print("\nTesting dimension_of function with MistralEmbeddingFunction") + + self.test_mistral_env() + + ef = MistralEmbeddingFunction(model_name="mistral-embed") + dim = dimension_of(ef) + assert dim == 1024 + print(f" dimension_of returned: {dim}") + + def test_persistence(self): + """Test persistence for MistralEmbeddingFunction""" + print("\nTesting MistralEmbeddingFunction persistence") + + self.test_mistral_env() + + assert MistralEmbeddingFunction.name() == "mistral" + + ef = MistralEmbeddingFunction(model_name="mistral-embed") + config = ef.get_config() + + restored_ef = MistralEmbeddingFunction.build_from_config(config) + assert isinstance(restored_ef, MistralEmbeddingFunction) + assert restored_ef.model_name == ef.model_name + assert restored_ef.api_key_env == ef.api_key_env + assert restored_ef.api_base == ef.api_base diff --git a/tests/unit_tests/test_morph_embedding_function.py b/tests/unit_tests/test_morph_embedding_function.py new file mode 100644 index 00000000..402b025f --- /dev/null +++ b/tests/unit_tests/test_morph_embedding_function.py @@ -0,0 +1,343 @@ +""" +Unit tests for MorphEmbeddingFunction. + +Tests Morph embedding function initialization, embedding generation, and config handling. +Uses real API calls - requires MORPH_API_KEY environment variable to be set. + +To run this test manually: + pytest tests/unit_tests/test_morph_embedding_function.py -v -s + # Or with environment variable: + MORPH_API_KEY=your-key pytest tests/unit_tests/test_morph_embedding_function.py -v -s +""" + +import importlib.util +import os + +import pytest + +from pyseekdb.client.embedding_function import dimension_of +from pyseekdb.utils.embedding_functions import MorphEmbeddingFunction + +from .test_utils import env_guard + + +def is_openai_available() -> bool: + """ + Check if openai is available for testing. + + Returns: + True if openai is available, False otherwise. + """ + return importlib.util.find_spec("openai") is not None + + +# Skip this test by default - it requires external API access and API keys +@pytest.mark.skipif( + not os.environ.get("MORPH_API_KEY") or not is_openai_available(), + reason="MORPH_API_KEY environment variable must be set", +) +class TestMorphEmbeddingFunction: + """Test MorphEmbeddingFunction - skipped by default, requires manual execution""" + + def test_morph_env(self): + """Test if openai package is installed and required environment variables are set.""" + if not is_openai_available(): + print("openai package is not installed") + raise AssertionError("openai package is not installed") + + if not os.environ.get("MORPH_API_KEY"): + print("MORPH_API_KEY environment variable is not set") + raise AssertionError("MORPH_API_KEY environment variable is not set") + + def test_initialization_with_model_name(self): + """Test MorphEmbeddingFunction initialization with required model_name""" + print("\nTesting MorphEmbeddingFunction initialization with required model_name") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + + assert ef is not None + assert ef.model_name == "morph-embedding-v4" + assert ef.api_key_env == "MORPH_API_KEY" + assert ef.api_base == "https://api.morphllm.com/v1" + assert ef._dimensions_param is None + print(f" Model name: {ef.model_name}") + print(f" API key env: {ef.api_key_env}") + print(f" API base: {ef.api_base}") + + def test_initialization_with_custom_api_key_env(self): + """Test MorphEmbeddingFunction initialization with custom API key env""" + print("\nTesting MorphEmbeddingFunction initialization with custom API key env") + + self.test_morph_env() + + custom_key_env = "CUSTOM_MORPH_KEY" + if not os.environ.get(custom_key_env): + os.environ[custom_key_env] = "your-custom-key" + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4", api_key_env=custom_key_env) + assert ef.api_key_env == custom_key_env + print(f" Custom API key env: {ef.api_key_env}") + + def test_initialization_with_custom_api_base(self): + """Test MorphEmbeddingFunction initialization with custom API base""" + print("\nTesting MorphEmbeddingFunction initialization with custom API base") + + self.test_morph_env() + + custom_base = "https://api.morphllm.com/v1" + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4", api_base=custom_base) + assert ef.api_base == custom_base + print(f" Custom API base: {ef.api_base}") + + def test_initialization_with_kwargs(self): + """Test MorphEmbeddingFunction initialization with additional kwargs""" + print("\nTesting MorphEmbeddingFunction initialization with kwargs") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4", timeout=30, max_retries=3) + assert ef is not None + print(" Initialized with timeout and max_retries") + + def test_initialization_missing_api_key(self): + """Test that missing API key raises ValueError""" + print("\nTesting MorphEmbeddingFunction initialization with missing API key") + + original_key = os.environ.pop("MORPH_API_KEY", None) + try: + with pytest.raises(ValueError, match="API key environment variable"): + MorphEmbeddingFunction(model_name="morph-embedding-v4") + finally: + if original_key: + os.environ["MORPH_API_KEY"] = original_key + + def test_initialization_missing_model_name(self): + """Test that missing model_name raises TypeError""" + print("\nTesting MorphEmbeddingFunction initialization with missing model_name") + + self.test_morph_env() + + with pytest.raises(TypeError): + MorphEmbeddingFunction() + + def test_dimension_property_known_model(self): + """Test dimension property for known Morph model""" + print("\nTesting MorphEmbeddingFunction dimension property for known model") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + dim = ef.dimension + assert dim == 1536, f"Expected dimension 1536 for morph-embedding-v4, got {dim}" + print(f" morph-embedding-v4 dimension: {dim}") + + def test_call_single_document(self): + """Test __call__ with single document""" + print("\nTesting MorphEmbeddingFunction embedding generation (single document)") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + single_doc = "def add(a, b): return a + b" + embeddings = ef(single_doc) + + assert isinstance(embeddings, list) + assert len(embeddings) == 1 + assert isinstance(embeddings[0], list) + assert len(embeddings[0]) > 0 + print(f" Single document embedding dimension: {len(embeddings[0])}") + + def test_call_multiple_documents(self): + """Test __call__ with multiple documents""" + print("\nTesting MorphEmbeddingFunction embedding generation (multiple documents)") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + multiple_docs = [ + "def foo():\n return 1", + "class Bar:\n pass", + "print('hello')", + ] + embeddings = ef(multiple_docs) + + assert isinstance(embeddings, list) + assert len(embeddings) == len(multiple_docs) + for emb in embeddings: + assert isinstance(emb, list) + assert len(emb) == len(embeddings[0]), "All embeddings should have same dimension" + print(f" Multiple documents embedding dimension: {len(embeddings[0])}") + print(f" Number of embeddings: {len(embeddings)}") + + def test_call_empty_input(self): + """Test __call__ with empty input""" + print("\nTesting MorphEmbeddingFunction with empty input") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + empty_embeddings = ef([]) + assert empty_embeddings == [] + print(" Empty input correctly returns empty list") + + def test_dimension_of_function(self): + """Test dimension_of function with MorphEmbeddingFunction""" + print("\nTesting dimension_of function with MorphEmbeddingFunction") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + dim = dimension_of(ef) + assert dim == 1536 + print(f" dimension_of result: {dim}") + + def test_get_default_api_base(self): + """Test _get_default_api_base method""" + print("\nTesting _get_default_api_base method") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + api_base = ef._get_default_api_base() + assert api_base == "https://api.morphllm.com/v1" + print(f" Default API base: {api_base}") + + def test_get_default_api_key_env(self): + """Test _get_default_api_key_env method""" + print("\nTesting _get_default_api_key_env method") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + api_key_env = ef._get_default_api_key_env() + assert api_key_env == "MORPH_API_KEY" + print(f" Default API key env: {api_key_env}") + + def test_get_model_dimensions(self): + """Test _get_model_dimensions method""" + print("\nTesting _get_model_dimensions method") + + self.test_morph_env() + + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + dimensions = ef._get_model_dimensions() + + assert isinstance(dimensions, dict) + assert "morph-embedding-v4" in dimensions + assert dimensions["morph-embedding-v4"] == 1536 + print(f" Model dimensions: {dimensions}") + + +@pytest.mark.skipif(not is_openai_available(), reason="openai is not available on this system") +class TestMorphEmbeddingFunctionPersistence: + """Test persistence for MorphEmbeddingFunction""" + + def test_name(self): + """Test that name() returns the correct identifier""" + assert MorphEmbeddingFunction.name() == "morph" + + def test_get_config_with_defaults(self): + """Test that get_config() returns correct config with default values""" + with env_guard(MORPH_API_KEY="test-key"): + ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + config = ef.get_config() + + assert isinstance(config, dict) + assert config["model_name"] == "morph-embedding-v4" + assert config["api_key_env"] == "MORPH_API_KEY" + assert config["api_base"] == "https://api.morphllm.com/v1" + assert config["dimensions"] is None + assert isinstance(config["client_kwargs"], dict) + assert "name" not in config + + def test_get_config_with_custom_values(self): + """Test that get_config() returns correct config with custom values""" + with env_guard(CUSTOM_MORPH_KEY="test-key"): + ef = MorphEmbeddingFunction( + model_name="morph-embedding-v4", + api_key_env="CUSTOM_MORPH_KEY", + api_base="https://custom-morph.example.com/v1", + timeout=60, + ) + config = ef.get_config() + + assert config["model_name"] == "morph-embedding-v4" + assert config["api_key_env"] == "CUSTOM_MORPH_KEY" + assert config["api_base"] == "https://custom-morph.example.com/v1" + assert config["dimensions"] is None + assert config["client_kwargs"]["timeout"] == 60 + + def test_build_from_config_with_defaults(self): + """Test that build_from_config() restores instance with default values""" + config = { + "model_name": "morph-embedding-v4", + "api_key_env": "MORPH_API_KEY", + "api_base": "https://api.morphllm.com/v1", + "dimensions": None, + "client_kwargs": {}, + } + + with env_guard(MORPH_API_KEY="test-key"): + restored_ef = MorphEmbeddingFunction.build_from_config(config) + + assert isinstance(restored_ef, MorphEmbeddingFunction) + assert restored_ef.model_name == "morph-embedding-v4" + assert restored_ef.api_key_env == "MORPH_API_KEY" + assert restored_ef.api_base == "https://api.morphllm.com/v1" + assert restored_ef._dimensions_param is None + + def test_build_from_config_with_custom_values(self): + """Test that build_from_config() restores instance with custom values""" + config = { + "model_name": "morph-embedding-v4", + "api_key_env": "CUSTOM_MORPH_KEY", + "api_base": "https://custom-morph.example.com/v1", + "dimensions": None, + "client_kwargs": {"timeout": 60}, + } + + with env_guard(CUSTOM_MORPH_KEY="test-key"): + restored_ef = MorphEmbeddingFunction.build_from_config(config) + + assert isinstance(restored_ef, MorphEmbeddingFunction) + assert restored_ef.model_name == "morph-embedding-v4" + assert restored_ef.api_key_env == "CUSTOM_MORPH_KEY" + assert restored_ef.api_base == "https://custom-morph.example.com/v1" + assert restored_ef._dimensions_param is None + assert restored_ef._client_kwargs["timeout"] == 60 + + def test_build_from_config_with_dimensions_ignored(self, caplog): + """Test that build_from_config() ignores dimensions parameter""" + config = { + "model_name": "morph-embedding-v4", + "api_key_env": "MORPH_API_KEY", + "api_base": "https://api.morphllm.com/v1", + "dimensions": 1536, + "client_kwargs": {}, + } + + with env_guard(MORPH_API_KEY="test-key"): + restored_ef = MorphEmbeddingFunction.build_from_config(config) + + assert isinstance(restored_ef, MorphEmbeddingFunction) + assert restored_ef._dimensions_param is None + + def test_persistence_roundtrip(self): + """Test complete roundtrip: get_config -> build_from_config""" + with env_guard(MORPH_API_KEY="test-key"): + original_ef = MorphEmbeddingFunction(model_name="morph-embedding-v4") + + config = original_ef.get_config() + restored_ef = MorphEmbeddingFunction.build_from_config(config) + + assert isinstance(restored_ef, MorphEmbeddingFunction) + assert restored_ef.model_name == original_ef.model_name + assert restored_ef.api_key_env == original_ef.api_key_env + assert restored_ef.api_base == original_ef.api_base + assert restored_ef._dimensions_param == original_ef._dimensions_param + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-s"]) diff --git a/tests/unit_tests/test_onnx_embedding_function.py b/tests/unit_tests/test_onnx_embedding_function.py new file mode 100644 index 00000000..f1825e6e --- /dev/null +++ b/tests/unit_tests/test_onnx_embedding_function.py @@ -0,0 +1,78 @@ +""" +Unit tests for OnnxEmbeddingFunction. +""" + +from __future__ import annotations + +import importlib + +import pytest + +from pyseekdb.utils.embedding_functions.onnx_embedding_function import OnnxEmbeddingFunction + + +def is_onnx_available() -> bool: + """Check if onnxruntime is available for testing.""" + return importlib.util.find_spec("onnxruntime") is not None + + +# Skip this test by default - it requires external API access and API keys +@pytest.mark.skipif( + not is_onnx_available(), + reason="onnxruntime is not available", +) +class TestOnnxEmbeddingFunction: + def _make_onnx(self) -> OnnxEmbeddingFunction: + return OnnxEmbeddingFunction( + model_name="all-MiniLM-L6-v2", + hf_model_id="sentence-transformers/all-MiniLM-L6-v2", + dimension=384, + preferred_providers=None, + ) + + def test_init_validates_model_name(self) -> None: + with pytest.raises(ValueError, match="model_name must be a non-empty string"): + OnnxEmbeddingFunction(model_name="", hf_model_id="org/test", dimension=3) + + def test_init_validates_hf_model_id(self) -> None: + with pytest.raises(ValueError, match="hf_model_id must be a non-empty string"): + OnnxEmbeddingFunction(model_name="test", hf_model_id="", dimension=3) + + def test_init_validates_dimension(self) -> None: + with pytest.raises(ValueError, match="dimension must be a positive integer"): + OnnxEmbeddingFunction(model_name="test", hf_model_id="org/test", dimension=0) + + def test_init_validates_preferred_providers(self) -> None: + with pytest.raises(ValueError, match="Preferred providers must be a list of strings"): + OnnxEmbeddingFunction(model_name="test", hf_model_id="org/test", dimension=3, preferred_providers=[1]) # type: ignore[list-item] + with pytest.raises(ValueError, match="Preferred providers must be unique"): + OnnxEmbeddingFunction( + model_name="test", + hf_model_id="org/test", + dimension=3, + preferred_providers=["CPUExecutionProvider", "CPUExecutionProvider"], + ) + + def test_dimension_property(self) -> None: + ef = self._make_onnx() + assert ef.dimension == 384 + + def test_call_empty_returns_empty(self) -> None: + ef = self._make_onnx() + assert ef([]) == [] + + def test_call_generates_embeddings(self) -> None: + pytest.importorskip("onnxruntime") + pytest.importorskip("tokenizers") + pytest.importorskip("tqdm") + + ef = self._make_onnx() + embeddings = ef("hello world") + + assert isinstance(embeddings, list) + assert len(embeddings) == 1 + assert len(embeddings[0]) == ef.dimension + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-s"]) diff --git a/uv.lock b/uv.lock index dc335b1d..101dd5b0 100644 --- a/uv.lock +++ b/uv.lock @@ -1,8 +1,9 @@ version = 1 revision = 3 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