PDF RAG 搜索

PDFSearchTool 旨在搜索 PDF 文件并返回最相关的结果。

`PDFSearchTool`

NOTE

我们仍在改进这些工具，因此未来可能会出现意外行为或变更。

描述

PDFSearchTool 是一个 RAG 工具，专为在 PDF 内容中进行语义搜索而设计。它支持输入搜索查询和 PDF 文档，并借助先进的搜索技术高效地找到相关内容。这一能力使其特别适合从大型 PDF 文件中快速提取特定信息。

安装

要开始使用 PDFSearchTool，首先请通过以下命令确保已安装 crewai_tools 包：

shell

pip install 'crewai[tools]'

示例

以下是如何使用 PDFSearchTool 在 PDF 文档中进行搜索：

python

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：可选用于搜索的 PDF 路径。可以在初始化时提供，也可以作为 run 方法的参数提供。如果在初始化时提供，工具会将搜索范围限制在指定的文档内。

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：需要向量数据库，因为生成的嵌入必须存储到向量数据库并从其中查询。

python

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.
                    }
        },
    }
)

## Security

### Path Validation

File paths provided to this tool are validated against the current working directory. Paths that resolve outside the working directory are rejected with a `ValueError`.

To allow paths outside the working directory (for example, in tests or trusted pipelines), set the environment variable:

CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true


### URL Validation

URL inputs are validated: `file://` URIs and requests targeting private or reserved IP ranges are blocked to prevent server-side request forgery (SSRF) attacks.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.

PDF RAG 搜索

PDFSearchTool

描述

安装

示例

参数

自定义模型和嵌入

友情链接

`PDFSearchTool`