Refine getting started guide

build(deps): bump requests from 2.32.3 to 2.32.4

agentic_security/probe_data/data.py

@@ -245,7 +245,20 @@ def load_jailbreak_v28k() -> ProbeDataset:
         return create_probe_dataset("JailbreakV-28K/JailBreakV-28k", [])
 
 
-@cache_to_disk()
+@cache_to_disk(1)
+def file_dataset(file) -> list[str]:
+    prompts = []
+    try:
+        df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
+        if "prompt" in df.columns:
+            prompts = df["prompt"].tolist()
+        else:
+            logger.warning(f"File {file} lacks a suitable prompt column")
+    except Exception as e:
+        logger.error(f"Error reading {file}: {e}")
+    return prompts
+
+
 def load_local_csv() -> ProbeDataset:
     """Load prompts from local CSV files."""
     os.makedirs("./datasets", exist_ok=True)
@@ -254,35 +267,16 @@ def load_local_csv() -> ProbeDataset:
 
     prompts = []
     for file in csv_files:
-        try:
+        prompts.extend(file_dataset(file))
-            df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
-            if "prompt" in df.columns:
-                prompts.extend(df["prompt"].tolist())
-            else:
-                logger.warning(f"File {file} lacks a suitable prompt column")
-        except Exception as e:
-            logger.error(f"Error reading {file}: {e}")
-
     return create_probe_dataset("Local CSV", prompts, {"src": str(csv_files)})
 
 
-@cache_to_disk(1)
 def load_csv(file: str) -> ProbeDataset:
     """Load prompts from local CSV files."""
-    prompts = []
+    prompts = file_dataset(file)
-    try:
-        df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
-        prompts = df["prompt"].tolist()
-        if "prompt" in df.columns:
-            prompts.extend(df["prompt"].tolist())
-        else:
-            logger.warning(f"File {file} lacks a suitable prompt column")
-    except Exception as e:
-        logger.error(f"Error reading {file}: {e}")
     return create_probe_dataset(f"fs://{file}", prompts, {"src": str(file)})
 
 
-@cache_to_disk(1)
 def load_local_csv_files() -> list[ProbeDataset]:
     """Load prompts from local CSV files and return a list of ProbeDataset objects."""
     csv_files = [f for f in os.listdir("./datasets") if f.endswith(".csv")]
@@ -291,16 +285,7 @@ def load_local_csv_files() -> list[ProbeDataset]:
     datasets = []
 
     for file in csv_files:
-        try:
+        datasets.append(create_probe_dataset(file, file_dataset(file), {"src": file}))
-            df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
-            if "prompt" in df.columns:
-                prompts = df["prompt"].tolist()
-                datasets.append(create_probe_dataset(file, prompts, {"src": file}))
-            else:
-                logger.warning(f"File {file} lacks a suitable prompt column")
-        except Exception as e:
-            logger.error(f"Error reading {file}: {e}")
-
     return datasets
 
 

docs/getting_started.md

@@ -1,23 +1,54 @@
 # Getting Started
 
-Welcome to Agentic Security! This guide will help you get started with using the tool.
+Welcome to Agentic Security! This guide will help you orient yourself within the project and start using the tool quickly.
+
+## Project Overview
+
+Agentic Security is an open-source vulnerability scanner for Large Language Models (LLMs). It provides both a command line interface and a FastAPI server so you can probe models for weaknesses such as jailbreaks or refusal patterns. The tool supports multimodal attacks, multi-step scans and reinforcement-learning based probes.
+
+## Repository Layout
+
+```
+agentic_security/
+├── __main__.py    - CLI entry point
+├── app.py         - FastAPI app assembly
+├── lib.py         - SecurityScanner and utilities
+├── config.py      - Configuration handling
+├── core/          - app state and logging helpers
+├── probe_actor/   - scanning logic and RL modules
+├── probe_data/    - dataset registry and loaders
+├── routes/        - API endpoints
+└── ui/            - Web UI assets (Vue)
+```
+
+`tests/` contains unit tests, and `docs/` houses the project documentation.
 
 ## Quick Start
 
 1. Ensure you have completed the [installation](installation.md) steps.
-1. Run the following command to start the application:
+2. Run the following command to start the application:
    ```bash
    agentic_security
    ```
-1. Access the application at `http://localhost:8718`.
+   The server will start on `http://localhost:8718`.
+3. Explore available commands with:
+   ```bash
+   agentic_security --help
+   ```
 
 ## Basic Usage
 
-- To view available commands, use:
+- To view available commands, run:
   ```bash
   agentic_security --help
   ```
 
 ## Next Steps
 
-Explore the [Configuration](configuration.md) section to customize your setup.
+- Review the [Quickstart Guide](quickstart.md) for a fast setup walkthrough.
+- Check [http_spec.md](http_spec.md) to learn how LLM endpoints are described.
+- Browse the `probe_actor` and `probe_data` modules to understand how scanning works and how datasets are loaded.
+- Explore the [Configuration](configuration.md) section to customize your setup.
+- Run the tests in `tests/` to verify your environment once dependencies are installed.
+
+This guide should give you a solid foundation for exploring and extending Agentic Security. For more details, see the rest of the documentation.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "agentic_security"
-version = "0.7.3"
+version = "0.7.4"
 description = "Agentic LLM vulnerability scanner"
 authors = ["Alexander Miasoiedov <msoedov@gmail.com>"]
 maintainers = ["Alexander Miasoiedov <msoedov@gmail.com>"]