re-adds instructor

pyproject.toml

@@ -4,12 +4,10 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "hackingBuddyGPT"
-authors = [
-	{ name = "Andreas Happe", email = "[email protected]" }
-]
+authors = [{ name = "Andreas Happe", email = "[email protected]" }]
 maintainers = [
-	{ name = "Andreas Happe", email = "[email protected]" },
-	{ name = "Juergen Cito", email = "[email protected]" }
+    { name = "Andreas Happe", email = "[email protected]" },
+    { name = "Juergen Cito", email = "[email protected]" },
 ]
 description = "Helping Ethical Hackers use LLMs in 50 lines of code"
 readme = "README.md"
@@ -21,14 +19,15 @@ classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
-	"Development Status :: 4 - Beta",
+    "Development Status :: 4 - Beta",
 ]
 dependencies = [
     'fabric == 3.2.2',
     'Mako == 1.3.2',
     'requests == 2.32.0',
     'rich == 13.7.1',
     'tiktoken == 0.8.0',
+    'instructor == 1.3.5',
     'PyYAML == 6.0.1',
     'python-dotenv == 1.0.1',
     'pypsexec == 0.3.0',
@@ -59,14 +58,9 @@ where = ["src"]
 
 [tool.pytest.ini_options]
 pythonpath = "src"
-addopts = [
-	"--import-mode=importlib",
-]
+addopts = ["--import-mode=importlib"]
 [project.optional-dependencies]
-testing = [
-    'pytest',
-    'pytest-mock'
-]
+testing = ['pytest', 'pytest-mock']
 
 [project.scripts]
 wintermute = "hackingBuddyGPT.cli.wintermute:main"

src/hackingBuddyGPT/capabilities/capability.py

@@ -45,7 +45,7 @@ def __call__(self, *args, **kwargs):
     def to_model(self) -> BaseModel:
         """
         Converts the parameters of the `__call__` function of the capability to a pydantic model, that can be used to
-        interface with an LLM using eg instructor or the openAI function calling API.
+        interface with an LLM using eg the openAI function calling API.
         The model will have the same name as the capability class and will have the same fields as the `__call__`,
         the `__call__` method can then be accessed by calling the `execute` method of the model.
         """

src/hackingBuddyGPT/capabilities/submit_http_method.py

@@ -24,25 +24,6 @@ class SubmitHTTPMethod(Capability):
     def describe(self) -> str:
         return f"Submits a HTTP methods, which is the goal of all activities. The HTTP methods format is {self.http_methods_format}. If you find a flag following the that format, that you have not yet submitted, then the most important first step is to submit it, ignoring all other possibilities of further action"
 
-    def to_model(self) -> BaseModel:
-        """
-        Converts the parameters of the `__call__` function of the capability to a pydantic model, that can be used to
-        interface with an LLM using eg instructor or the openAI function calling API.
-        The model will have the same name as the capability class and will have the same fields as the `__call__`,
-        the `__call__` method can then be accessed by calling the `execute` method of the model.
-        """
-        sig = inspect.signature(self.__call__)
-        fields = {param: (param_info.annotation, ...) for param, param_info in sig.parameters.items()}
-        model_type = create_model(self.__class__.__name__, __doc__=self.describe(), **fields)
-
-        def execute(model):
-            m = model.dict()
-            return self(**m)
-
-        model_type.execute = execute
-
-        return model_type
-
     def __call__(self, method: Literal["GET", "HEAD", "POST", "PUT", "DELETE", "OPTION", "PATCH"],
                  path: str,
                  query: Optional[str] = None,
@@ -75,4 +56,3 @@ def __call__(self, method: Literal["GET", "HEAD", "POST", "PUT", "DELETE", "OPTI
                 return "All methods submitted, congratulations"
         # turn the response into "plain text format" for responding to the prompt
         return f"HTTP/1.1 {resp.status_code} {resp.reason}\r\n{headers}\r\n\r\n{resp.text}"""
-

src/hackingBuddyGPT/utils/openai/openai_lib.py

@@ -1,3 +1,4 @@
+import instructor
 import datetime
 from typing import Dict, Union, Iterable, Optional
 
@@ -35,6 +36,10 @@ def init(self):
     def client(self) -> openai.OpenAI:
         return self._client
 
+    @property
+    def instructor(self) -> instructor.Instructor:
+        return instructor.from_openai(self.client)
+
     def get_response(self, prompt, *, capabilities: Optional[Dict[str, Capability]] = None, **kwargs) -> LLMResult:
         """  # TODO: re-enable compatibility layer
         if isinstance(prompt, str) or hasattr(prompt, "render"):