TXT RAG Search

Overview

We are still working on improving tools, so there might be unexpected behavior or changes in the future.

This tool is used to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file. It allows for semantic searching of a query within a specified text file’s content, making it an invaluable resource for quickly extracting information or finding specific sections of text based on the query provided.

Installation

To use the TXTSearchTool, you first need to install the crewai_tools package. This can be done using pip, a package manager for Python. Open your terminal or command prompt and enter the following command:

pip install 'crewai[tools]'

This command will download and install the TXTSearchTool along with any necessary dependencies.

Example

The following example demonstrates how to use the TXTSearchTool to search within a text file. This example shows both the initialization of the tool with a specific text file and the subsequent search within that file’s content.

Code

from crewai_tools import TXTSearchTool

# Initialize the tool to search within any text file's content 
# the agent learns about during its execution
tool = TXTSearchTool()

# OR

# Initialize the tool with a specific text file, 
# so the agent can search within the given text file's content
tool = TXTSearchTool(txt='path/to/text/file.txt')

Arguments

txt (str): Optional. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file.

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:

Code

from chromadb.config import Settings

tool = TXTSearchTool(
    config={
        # Required: embeddings provider + config
        "embedding_model": {
            "provider": "openai",  # or google-generativeai, cohere, ollama, ...
            "config": {
                "model": "text-embedding-3-small",
                # "api_key": "sk-...",  # optional if env var is set (e.g., OPENAI_API_KEY or EMBEDDINGS_OPENAI_API_KEY)
                # Provider examples:
                # Google → model_name: "gemini-embedding-001", task_type: "RETRIEVAL_DOCUMENT"
                # Cohere → model: "embed-english-v3.0"
                # Ollama → model: "nomic-embed-text"
            },
        },

        # Required: vector database config
        "vectordb": {
            "provider": "chromadb",  # or "qdrant"
            "config": {
                # Chroma settings (optional persistence)
                # "settings": Settings(
                #     persist_directory="/content/chroma",
                #     allow_reset=True,
                #     is_persistent=True,
                # ),

                # Qdrant vector params example:
                # 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").
            }
        },
    }
)

XML RAG Search

JSON RAG Search

⌘I

​Overview

​Installation

​Example

​Arguments

​Custom model and embeddings

Overview

Installation

Example

Arguments

Custom model and embeddings