FEAT: Adding Harm Categories to Prompt Request Pieces

doc/_toc.yml

@@ -112,6 +112,7 @@ chapters:
       - file: code/memory/8_seed_prompt_database
       - file: code/memory/9_exporting_data
       - file: code/memory/10_schema_diagram.md
+      - file: code/memory/11_harm_categories
       - file: code/memory/azure_embeddings
       - file: code/memory/chat_message
     - file: code/auxiliary_attacks/0_auxiliary_attacks

doc/code/memory/11_harm_categories.py

@@ -0,0 +1,124 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.2
+# ---
+
+# %% [markdown]
+# # Querying by Harm Categories
+#
+# This notebook demonstrates how to retrieve attack results based on harm category. While harm category information is not duplicated into the `AttackResultEntries` table, PyRIT provides functions that perform the necessary SQL queries to filter `AttackResults` by harm category.
+
+# %% [markdown]
+# ## Import Seed Prompt Dataset
+#
+# First we import a dataset which has individual prompts with different harm categories as an example.
+
+# %%
+import pathlib
+
+from pyrit.common.initialization import initialize_pyrit
+from pyrit.common.path import DATASETS_PATH
+from pyrit.memory.central_memory import CentralMemory
+from pyrit.models import SeedPromptDataset
+
+initialize_pyrit(memory_db_type="InMemory")
+
+memory = CentralMemory.get_memory_instance()
+
+seed_prompts = SeedPromptDataset.from_yaml_file(pathlib.Path(DATASETS_PATH) / "seed_prompts" / "illegal.prompt")
+
+print(f"Dataset name: {seed_prompts.dataset_name}")
+print(f"Number of prompts in dataset: {len(seed_prompts.prompts)}")
+print()
+
+await memory.add_seed_prompts_to_memory_async(prompts=seed_prompts.prompts, added_by="bolor")  # type: ignore
+for i, prompt in enumerate(seed_prompts.prompts):
+    print(f"Prompt {i+1}: {prompt.value}, Harm Categories: {prompt.harm_categories}")
+
+# %% [markdown]
+# ## Send to target
+#
+# We use `PromptSendingAttack` to create our `AttackResults`
+
+# %%
+from pyrit.executor.attack import ConsoleAttackResultPrinter, PromptSendingAttack
+from pyrit.prompt_target import OpenAIChatTarget
+
+# Create a real OpenAI target
+target = OpenAIChatTarget()
+
+# Create the attack with the OpenAI target
+attack = PromptSendingAttack(objective_target=target)
+
+# Configure this to load the prompts loaded in the previous step.
+# In the last section, they were in the illegal.prompt file (which has a configured name of "2025_06_pyrit_illegal_example")
+prompt_groups = memory.get_seed_prompt_groups(dataset_name="2025_06_pyrit_illegal_example")
+print(f"Found {len(prompt_groups)} prompt groups for dataset")
+
+for i, group in enumerate(prompt_groups):
+    prompt_text = group.prompts[0].value
+
+    results = await attack.execute_async(objective=prompt_text, seed_prompt_group=group)  # type: ignore
+
+    print(f"Attack completed - Conversation ID: {results.conversation_id}")
+    await ConsoleAttackResultPrinter().print_conversation_async(result=results)  # type: ignore
+
+# %% [markdown]
+# ## Query by harm category
+# Now you can query your attack results by `targeted_harm_category`!
+
+# %% [markdown]
+# ### Single harm category:
+#
+# Here, we by a single harm category (eg shown below is querying for the harm category  `['illegal']`)
+
+# %%
+from pyrit.analytics.analyze_results import analyze_results
+
+all_attack_results = memory.get_attack_results()
+
+# Demonstrating how to query attack results by harm category
+print("=== Querying Attack Results by Harm Category ===")
+print()
+
+# First, let's see all attack results to understand what we have
+print(f"Overall attack analytics:")
+print(f"Total attack results in memory: {len(all_attack_results)}")
+
+overall_analytics = analyze_results(list(all_attack_results))
+
+print(f"  Success rate: {overall_analytics['Attack success rate']}")
+print(f"  Successes: {overall_analytics['Successes']}")
+print(f"  Failures: {overall_analytics['Failures']}")
+print(f"  Undetermined: {overall_analytics['Undetermined']}")
+print()
+
+# Example 1: Query for a single harm category
+print("1. Query for single harm category 'illegal':")
+illegal_attacks = memory.get_attack_results(targeted_harm_categories=["illegal"])
+print(f"\tFound {len(illegal_attacks)} attack results with 'illegal' category")
+
+if illegal_attacks:
+    for i, attack_result in enumerate(illegal_attacks):
+        print(f"Attack {i+1}: {attack_result.objective}")
+        print(f"Conversation ID: {attack_result.conversation_id}")
+        print(f"Outcome: {attack_result.outcome}")
+    print()
+
+# %% [markdown]
+# ### Multiple harm categories:
+
+# %%
+# Example 2: Query for multiple harm categories
+print("2. Query for multiple harm categories 'illegal' and 'violence':")
+multiple_groups = memory.get_attack_results(targeted_harm_categories=["illegal", "violence"])
+
+for i, attack_result in enumerate(multiple_groups):
+    print(f"Attack {i+1}: {attack_result.objective}...")
+    print(f"Conversation ID: {attack_result.conversation_id}")
+print()