跳转到主要内容

​
PDFSearchTool

我们仍在努力改进工具，因此未来可能会出现意外行为或变更。

​
描述

PDFSearchTool 是一个为 PDF 内容中的语义搜索而设计的 RAG 工具。它允许输入搜索查询和 PDF 文档，利用高级搜索技术有效地查找相关内容。此功能使其在从大型 PDF 文件中快速提取特定信息方面特别有用。

​
安装

要开始使用 PDFSearchTool，首先请确保已安装 crewai_tools 包，使用以下命令： 
pip install 'crewai[tools]'

​
示例

以下是如何使用 PDFSearchTool 在 PDF 文档中进行搜索：
代码
from crewai_tools import PDFSearchTool

# Initialize the tool allowing for any PDF content search if the path is provided during execution
tool = PDFSearchTool()

# OR

# Initialize the tool with a specific PDF path for exclusive search within that document
tool = PDFSearchTool(pdf='path/to/your/document.pdf')

​
参数

  • pdf：可选 搜索的 PDF 路径。可以在初始化时提供，也可以在 run 方法的参数中提供。如果在初始化时提供，则工具将搜索范围限制为指定文档。

​
自定义模型和嵌入

默认情况下，该工具使用 OpenAI 进行嵌入和摘要。要自定义模型，可以使用如下配置字典。注意：需要一个向量数据库，因为生成的嵌入必须存储在向量数据库中并从中查询。
代码
from crewai_tools import PDFSearchTool

# - embedding_model (required): choose provider + provider-specific config
# - vectordb (required): choose vector DB and pass its config

tool = PDFSearchTool(
    config={
        "embedding_model": {
            # Supported providers: "openai", "azure", "google-generativeai", "google-vertex",
            # "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
            # "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
            "provider": "openai",  # or: "google-generativeai", "cohere", "ollama", ...
            "config": {
                # Model identifier for the chosen provider. "model" will be auto-mapped to "model_name" internally.
                "model": "text-embedding-3-small",
                # Optional: API key. If omitted, the tool will use provider-specific env vars
                # (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY for OpenAI).
                # "api_key": "sk-...",

                # Provider-specific examples:
                # --- Google Generative AI ---
                # (Set provider="google-generativeai" above)
                # "model_name": "gemini-embedding-001",
                # "task_type": "RETRIEVAL_DOCUMENT",
                # "title": "Embeddings",

                # --- Cohere ---
                # (Set provider="cohere" above)
                # "model": "embed-english-v3.0",

                # --- Ollama (local) ---
                # (Set provider="ollama" above)
                # "model": "nomic-embed-text",
            },
        },
        "vectordb": {
                    "provider": "chromadb",  # or "qdrant"
                    "config": {
                        # For ChromaDB: pass "settings" (chromadb.config.Settings) or rely on defaults.
                        # Example (uncomment and import):
                        # from chromadb.config import Settings
                        # "settings": Settings(
                        #     persist_directory="/content/chroma",
                        #     allow_reset=True,
                        #     is_persistent=True,
                        # ),

                        # For Qdrant: pass "vectors_config" (qdrant_client.models.VectorParams).
                        # Example (uncomment and import):
                        # from qdrant_client.models import VectorParams, Distance
                        # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),

                        # Note: collection name is controlled by the tool (default: "rag_tool_collection"), not set here.
                    }
        },
    }
)