PDF RAG Search ↗
noOriginal Documentation
Documentation Index#
Fetch the complete documentation index at: https://docs.crewai.com/llms.txt Use this file to discover all available pages before exploring further.
The
PDFSearchToolis designed to search PDF files and return the most relevant results.
PDFSearchTool#
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
Description#
The PDFSearchTool is a RAG tool designed for semantic searches within PDF content. It allows for inputting a search query and a PDF document, leveraging advanced search techniques to find relevant content efficiently. This capability makes it especially useful for extracting specific information from large PDF files quickly.
Installation#
To get started with the PDFSearchTool, first, ensure the crewai_tools package is installed with the following command:
pip install 'crewai[tools]'Example#
Here’s how to use the PDFSearchTool to search within a PDF document:
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')Arguments#
pdf: Optional The PDF path for the search. Can be provided at initialization or within therunmethod’s arguments. If provided at initialization, the tool confines its search to the specified document.
Custom model and embeddings#
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows. Note: a vector database is required because generated embeddings must be stored and queried from a vectordb.
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.
}
},
}
)