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crewai_tools/tools/txt_search_tool/README.md

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+# TXTSearchTool
+
+## Description
+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:
+
+```shell
+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.
+
+```python
+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): **Optinal**. 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:
+
+```python
+tool = TXTSearchTool(
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google",
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
+)
+```

crewai_tools/tools/txt_search_tool/txt_search_tool.py

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+from typing import Optional, Type
+
+from pydantic import BaseModel, Field
+
+from ..rag.rag_tool import RagTool
+
+
+class FixedTXTSearchToolSchema(BaseModel):
+    """Input for TXTSearchTool."""
+
+    search_query: str = Field(
+        ...,
+        description="Mandatory search query you want to use to search the txt's content",
+    )
+
+
+class TXTSearchToolSchema(FixedTXTSearchToolSchema):
+    """Input for TXTSearchTool."""
+
+    txt: str = Field(..., description="File path or URL of a TXT file to be searched")
+
+
+class TXTSearchTool(RagTool):
+    name: str = "Search a txt's content"
+    description: str = (
+        "A tool that can be used to semantic search a query from a txt's content."
+    )
+    args_schema: Type[BaseModel] = TXTSearchToolSchema
+
+    def __init__(self, txt: Optional[str] = None, **kwargs):
+        super().__init__(**kwargs)
+        if txt is not None:
+            self.add(txt)
+            self.description = f"A tool that can be used to semantic search a query the {txt} txt's content."
+            self.args_schema = FixedTXTSearchToolSchema
+            self._generate_description()
+
+    def _run(
+        self,
+        search_query: str,
+        txt: Optional[str] = None,
+    ) -> str:
+        if txt is not None:
+            self.add(txt)
+        return super()._run(query=search_query)