From df1279633e508883220eca539f55c27a1eccfe31 Mon Sep 17 00:00:00 2001 From: along <1015042407@qq.com> Date: Sun, 11 Jan 2026 16:09:16 +0800 Subject: [PATCH] first commit --- .gitignore | 6 + .python-version | 1 + embeddings.py | 70 + ingest_pipeline.py | 147 ++ parsers.py | 137 ++ pyproject.toml | 17 + retriever.py | 151 ++ uv.lock | 3526 ++++++++++++++++++++++++++++++++++++++++++++ vector_stores.py | 64 + 9 files changed, 4119 insertions(+) create mode 100644 .python-version create mode 100644 embeddings.py create mode 100644 ingest_pipeline.py create mode 100644 parsers.py create mode 100644 pyproject.toml create mode 100644 retriever.py create mode 100644 uv.lock create mode 100644 vector_stores.py diff --git a/.gitignore b/.gitignore index 555e90e..fb4bbe7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,9 @@ +# custom .gitignore +.claude/ +.vscode/ +paper/ +papers_chroma_db/ + # ---> Python # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/embeddings.py b/embeddings.py new file mode 100644 index 0000000..23333f9 --- /dev/null +++ b/embeddings.py @@ -0,0 +1,70 @@ +""" +嵌入模型模块 + +提供多种文本嵌入模型的统一接口 +""" + +from abc import ABC, abstractmethod +from typing import Optional + +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_openai import OpenAIEmbeddings + + +class BaseEmbeddings(ABC): + """嵌入模型基类""" + + @abstractmethod + def get_embeddings(self): + """获取 LangChain 兼容的 embeddings 实例""" + pass + + +class HuggingFaceEmbeddingsProvider(BaseEmbeddings): + """HuggingFace 本地嵌入模型提供者""" + + def __init__(self, model_name: str): + """ + 初始化 HuggingFace 嵌入模型 + + Args: + model_name: 模型名称或路径 + """ + self.model_name = model_name + self._embeddings: Optional[HuggingFaceEmbeddings] = None + + def get_embeddings(self) -> HuggingFaceEmbeddings: + """获取 HuggingFace embeddings 实例（懒加载）""" + if self._embeddings is None: + print(f"[INFO] Initializing HuggingFace embeddings: {self.model_name}") + self._embeddings = HuggingFaceEmbeddings(model_name=self.model_name) + return self._embeddings + + +class OpenAIEmbeddingsProvider(BaseEmbeddings): + """OpenAI API 嵌入模型提供者""" + + def __init__(self, api_key: str, base_url: str, model: str = "text-embedding-3-large"): + """ + 初始化 OpenAI 嵌入模型 + + Args: + api_key: OpenAI API 密钥 + base_url: API 基础 URL + model: 嵌入模型名称 + """ + self.api_key = api_key + self.base_url = base_url + self.model = model + self._embeddings: Optional[OpenAIEmbeddings] = None + + def get_embeddings(self) -> OpenAIEmbeddings: + """获取 OpenAI embeddings 实例（懒加载）""" + if self._embeddings is None: + print(f"[INFO] Initializing OpenAI embeddings: {self.model}") + self._embeddings = OpenAIEmbeddings( + model=self.model, + api_key=self.api_key, + openai_api_base=self.base_url + ) + return self._embeddings diff --git a/ingest_pipeline.py b/ingest_pipeline.py new file mode 100644 index 0000000..e2f6975 --- /dev/null +++ b/ingest_pipeline.py @@ -0,0 +1,147 @@ +""" +学术论文向量化和存储系统 + +模块化、面向对象的论文检索系统，支持将论文文档向量化并持久化到 Chroma 向量数据库。 + +Usage: + # 基本用法 + python papers_embedding.py --input xxx.md + + # 自定义输出目录 + python papers_embedding.py --input xxx.md --output ./my_db + + # 程序化调用 + from papers_embedding import PaperIngestionPipeline + pipeline = PaperIngestionPipeline() + pipeline.ingest(Path("icde2025.md")) +""" + +import os +import argparse +from pathlib import Path +from typing import Optional + +from dotenv import load_dotenv + +from parsers import BaseParser, MarkdownPaperParser, PaperFileReader +from embeddings import BaseEmbeddings, HuggingFaceEmbeddingsProvider, OpenAIEmbeddingsProvider +from vector_stores import BaseVectorStore, ChromaVectorStore + + +# ============================================================================ +# Pipeline: 数据摄取管道 +# ============================================================================ + +class PaperIngestionPipeline: + """ + 论文摄取管道 + + 整合解析、嵌入、存储三个模块，提供完整的论文向量化流程 + """ + + def __init__( + self, + parser: Optional[BaseParser] = None, + embeddings_provider: Optional[BaseEmbeddings] = None, + vector_store: Optional[BaseVectorStore] = None, + ): + """ + 初始化摄取管道 + + Args: + parser: 论文解析器（默认 MarkdownPaperParser） + embeddings_provider: 嵌入模型提供者（需显式指定） + vector_store: 向量存储（需显式指定） + """ + self.parser = parser or MarkdownPaperParser() + self.embeddings_provider = embeddings_provider + self.vector_store = vector_store + + def ingest(self, input_file: Path) -> None: + """ + 执行论文摄取流程 + + Args: + input_file: 输入论文文件路径 + + Raises: + ValueError: 如果未配置 embeddings_provider 或 vector_store + """ + if self.embeddings_provider is None: + raise ValueError("embeddings_provider must be configured") + if self.vector_store is None: + raise ValueError("vector_store must be configured") + + # Step 1: 解析论文 + reader = PaperFileReader(self.parser) + texts, metadatas = reader.read(input_file) + + # Step 2 & 3: 向量化并存储 + self.vector_store.persist(texts, metadatas) + + +# ============================================================================ +# CLI Interface +# ============================================================================ + +def create_default_pipeline(output_dir: str) -> PaperIngestionPipeline: + """ + 创建默认配置的摄取管道 + + Args: + output_dir: 输出目录路径 + + Returns: + 配置好的 PaperIngestionPipeline 实例 + """ + load_dotenv() + + # 可选：使用 HuggingFace 本地模型 + # embeddings = HuggingFaceEmbeddingsProvider( + # model_name=r"E:\hf_models\all-mpnet-base-v2" + # ) + + # 使用 OpenAI API + embeddings = OpenAIEmbeddingsProvider( + api_key=os.getenv("OPENAI_API_KEY"), + base_url="https://api.chatanywhere.tech/v1", + model="text-embedding-3-large" + ) + + vector_store = ChromaVectorStore( + persist_directory=Path(output_dir), + embeddings_provider=embeddings + ) + + return PaperIngestionPipeline( + embeddings_provider=embeddings, + vector_store=vector_store + ) + + +def main() -> None: + """命令行入口""" + parser = argparse.ArgumentParser( + description="将论文 Markdown 文件向量化并存储到 Chroma 数据库" + ) + parser.add_argument( + "--input", "-i", + type=str, + required=True, + help="输入的 Markdown 论文文件路径" + ) + parser.add_argument( + "--output", "-o", + type=str, + default="papers_chroma_db", + help="Chroma 数据库输出目录（默认: papers_chroma_db）" + ) + args = parser.parse_args() + + # 创建并执行管道 + pipeline = create_default_pipeline(args.output) + pipeline.ingest(Path(args.input)) + + +if __name__ == "__main__": + main() diff --git a/parsers.py b/parsers.py new file mode 100644 index 0000000..964a10c --- /dev/null +++ b/parsers.py @@ -0,0 +1,137 @@ +""" +数据解析模块 + +提供论文文档的解析功能，支持多种格式（当前实现 Markdown 格式） +""" + +import re +from abc import ABC, abstractmethod +from pathlib import Path +from typing import List, Tuple + + +class BaseParser(ABC): + """解析器基类""" + + @abstractmethod + def parse(self, content: str) -> Tuple[List[str], List[dict]]: + """ + 解析内容文本 + + Args: + content: 待解析的文本内容 + + Returns: + (texts, metadatas): 文本列表和对应的元数据列表 + """ + pass + + +class MarkdownPaperParser(BaseParser): + """ + Markdown 格式论文解析器 + + 解析格式: + - 论文以 '---' 分隔 + - 每篇论文以 '## Title' 开头 + - 内容包含标题、摘要和其他部分 + """ + + def __init__(self, separator: str = r'\n---\s*\n', title_pattern: str = r'^##\s+(.+)$'): + """ + 初始化解析器 + + Args: + separator: 论文分隔符正则表达式 + title_pattern: 标题匹配正则表达式 + """ + self.separator = separator + self.title_pattern = title_pattern + + def parse(self, content: str) -> Tuple[List[str], List[dict]]: + """ + 解析 markdown 格式的论文内容 + + Args: + content: markdown 文件内容 + + Returns: + (texts, metadatas): 论文文本列表和元数据列表 + + Raises: + ValueError: 如果未找到有效论文 + """ + raw_chunks = re.split(self.separator, content) + + texts: List[str] = [] + metadatas: List[dict] = [] + + for chunk in raw_chunks: + chunk = chunk.strip() + if not chunk: + continue + + # 提取标题 + title_match = re.search(self.title_pattern, chunk, re.MULTILINE) + if not title_match: + self._handle_missing_title(chunk) + continue + + title = title_match.group(1).strip() + paper_content = chunk + + texts.append(paper_content) + metadatas.append({ + "title": title, + "content_length": len(paper_content), + }) + + if not texts: + raise ValueError("No valid papers were found in the content.") + + return texts, metadatas + + def _handle_missing_title(self, chunk: str) -> None: + """处理缺少标题的论文块""" + preview = chunk[:50].replace('\n', ' ') + print(f"[WARN] Skipping paper without ## title: {preview}...") + + +class PaperFileReader: + """论文文件读取器""" + + def __init__(self, parser: BaseParser): + """ + 初始化文件读取器 + + Args: + parser: 内容解析器实例 + """ + self.parser = parser + + def read(self, file_path: Path) -> Tuple[List[str], List[dict]]: + """ + 从文件读取并解析论文 + + Args: + file_path: 论文文件路径 + + Returns: + (texts, metadatas): 解析后的文本和元数据 + + Raises: + FileNotFoundError: 如果文件不存在 + """ + if not file_path.exists(): + raise FileNotFoundError(f"Paper file not found: {file_path}") + + print(f"[INFO] Reading papers from: {file_path}") + content = file_path.read_text(encoding="utf-8") + + texts, metadatas = self.parser.parse(content) + + # 添加源文件信息到元数据 + for meta in metadatas: + meta["source_file"] = file_path.name + + return texts, metadatas diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..33987e2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,17 @@ +[project] +name = "embedding" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "chromadb>=1.4.0", + "huggingface>=0.0.1", + "langchain>=1.2.3", + "langchain-community>=0.4.1", + "langchain-huggingface>=1.2.0", + "langchain-openai>=1.1.7", + "python-dotenv>=1.2.1", + "requests>=2.32.5", + "sentence-transformers>=5.2.0", +] diff --git a/retriever.py b/retriever.py new file mode 100644 index 0000000..50c8940 --- /dev/null +++ b/retriever.py @@ -0,0 +1,151 @@ +""" +学术论文检索工具 + +Usage: + from retriever import retrieve_papers + results = retrieve_papers("natural language to SQL") +""" + +from pathlib import Path +from typing import List +import os +from dotenv import load_dotenv + +from langchain_community.vectorstores import Chroma + +from embeddings import HuggingFaceEmbeddingsProvider, OpenAIEmbeddingsProvider +from vector_stores import BaseEmbeddings + + +# ============================================================================ +# Configuration +# ============================================================================ + +CHROMA_DIR = Path(r"E:\studio2\embedding\papers_chroma_db") +HUGGINGFACE_MODEL = r"E:\hf_models\all-mpnet-base-v2" + + +# ============================================================================ +# Retrieval Service +# ============================================================================ + +class PaperRetriever: + """ + 论文检索服务 + + 使用已有的 embeddings 和 vector_stores 模块进行论文检索 + """ + + def __init__( + self, + persist_directory: Path, + embeddings_provider: BaseEmbeddings, + ): + """ + 初始化检索器 + + Args: + persist_directory: Chroma 数据库目录 + embeddings_provider: 嵌入模型提供者 + """ + self.persist_directory = Path(persist_directory) + self.embeddings_provider = embeddings_provider + + def _validate_db_exists(self) -> None: + """验证向量数据库是否存在""" + if not self.persist_directory.exists(): + raise FileNotFoundError( + f"论文知识库目录不存在: {self.persist_directory}，" + f"请先运行 papers_embedding.py 生成数据库" + ) + + def retrieve(self, query: str, k: int = 10) -> List: + """ + 从论文向量数据库中检索相关论文 + + Args: + query: 检索查询文本 + k: 返回结果数量，默认10 + + Returns: + 检索到的文档列表 + """ + self._validate_db_exists() + + embeddings = self.embeddings_provider.get_embeddings() + vectorstore = Chroma( + persist_directory=str(self.persist_directory), + embedding_function=embeddings + ) + + retriever = vectorstore.as_retriever(search_kwargs={"k": k}) + docs = retriever.invoke(query) + + print(f"检索到 {len(docs)} 篇相关论文") + return docs + + +# ============================================================================ +# Convenience Functions +# ============================================================================ + +def create_default_retriever() -> PaperRetriever: + """ + 创建默认配置的检索器（使用 HuggingFace 本地模型） + + Returns: + 配置好的 PaperRetriever 实例 + """ + # embeddings = HuggingFaceEmbeddingsProvider(model_name=HUGGINGFACE_MODEL) + embeddings = OpenAIEmbeddingsProvider( + api_key=os.getenv("OPENAI_API_KEY"), + base_url="https://api.chatanywhere.tech/v1", + model="text-embedding-3-large" + ) + return PaperRetriever( + persist_directory=CHROMA_DIR, + embeddings_provider=embeddings + ) + + +def retrieve_papers(query: str, k: int = 10) -> List: + """ + 便捷函数：检索相关论文 + + Args: + query: 检索查询文本 + k: 返回结果数量，默认10 + + Returns: + 检索到的文档列表 + """ + retriever = create_default_retriever() + return retriever.retrieve(query, k) + + +# ============================================================================ +# CLI Interface +# ============================================================================ + +def main(): + """测试检索功能""" + import 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BaseVectorStore(ABC): + """向量存储基类""" + + @abstractmethod + def persist(self, texts: List[str], metadatas: List[dict]) -> None: + """ + 持久化文本到向量数据库 + + Args: + texts: 文本列表 + metadatas: 元数据列表 + """ + pass + + +class ChromaVectorStore(BaseVectorStore): + """Chroma 向量数据库实现""" + + def __init__(self, persist_directory: Path, embeddings_provider: BaseEmbeddings): + """ + 初始化 Chroma 向量存储 + + Args: + persist_directory: 持久化目录 + embeddings_provider: 嵌入模型提供者 + """ + self.persist_directory = Path(persist_directory) + self.embeddings_provider = embeddings_provider + + def persist(self, texts: List[str], metadatas: List[dict]) -> None: + """ + 将文本向量化并持久化到 Chroma + + Args: + texts: 文本列表 + metadatas: 元数据列表 + """ + self.persist_directory.mkdir(parents=True, exist_ok=True) + + embeddings = self.embeddings_provider.get_embeddings() + + print(f"[INFO] Writing {len(texts)} papers to Chroma: {self.persist_directory}") + vectorstore = Chroma.from_texts( + texts=texts, + metadatas=metadatas, + embedding=embeddings, + persist_directory=str(self.persist_directory), + ) + print("[OK] Chroma persistence complete.")