New Pbd code, start of lldb w/ same way to do conversations.

src/chatdbg/assistant/assistant.py

@@ -0,0 +1,227 @@
+import atexit
+import inspect
+import json
+import textwrap
+import time
+
+import llm_utils
+from openai import *
+from pydantic import BaseModel
+
+
+class Assistant:
+    """
+    An Assistant is a wrapper around OpenAI's assistant API.  Example usage:
+
+        assistant = Assistant("Assistant Name", instructions, 
+                              model='gpt-4-1106-preview', debug=True)
+        assistant.add_function(my_func)
+        response = assistant.run(user_prompt)
+
+    Name can be any name you want.
+
+    If debug is True, it will create a log of all messages and JSON responses in
+    json.txt.
+    """
+
+    def __init__(self, name, instructions, model="gpt-3.5-turbo-1106", debug=True):
+
+        if debug:
+            self.json = open(f'json.txt', 'w')
+        else:
+            self.json = None
+
+        try:
+            self.client = OpenAI(timeout=30)
+        except OpenAIError:
+            print(textwrap.dedent("""\
+            You need an OpenAI key to use this tool.
+            You can get a key here: https://platform.openai.com/api-keys
+            Set the environment variable OPENAI_API_KEY to your key value.
+            """))
+            return
+
+
+        self.assistants = self.client.beta.assistants
+        self.threads = self.client.beta.threads
+        self.functions = dict()
+
+        self.assistant = self.assistants.create(name=name,
+                                                instructions=instructions,
+                                                model=model)        
+
+        self._log(self.assistant)
+
+        atexit.register(self._delete_assistant)
+
+        self.thread = self.threads.create()
+        self._log(self.thread)
+
+    def _delete_assistant(self):
+        if self.assistant != None:
+            try:
+                id = self.assistant.id
+                response = self.assistants.delete(id)
+                self._log(response)
+                assert response.deleted
+            except Exception as e:
+                print(f'Assistant {id} was not deleted ({e}).\nYou can do so at https://platform.openai.com/assistants.')
+
+    def add_function(self, function):
+        """
+        Add a new function to the list of function tools for the assistant.
+        The function should have the necessary json spec as is pydoc string.
+        """
+        function_json = json.loads(function.__doc__)
+        assert 'name' in function_json, "Bad JSON in pydoc for function tool."
+        try:
+            name = function_json['name']
+            self.functions[name] = function
+
+            tools = [ 
+                        {
+                            "type": "function", 
+                            "function": json.loads(function.__doc__)
+                        } for function in self.functions.values()
+                    ] 
+
+            assistant = self.assistants.update(self.assistant.id,   
+                                            tools=tools)
+            self._log(assistant)
+        except OpenAIError as e:
+            print(f"*** OpenAI Error: {e}")
+
+
+    def _make_call(self, tool_call):
+        name = tool_call.function.name
+        args = tool_call.function.arguments
+
+        # There is a sketchy case that happens occasionally because
+        # the API produces a bad call...
+        try:
+            args = json.loads(args)
+            function = self.functions[name]
+            result = function(**args)
+        except Exception as e:
+            result = f"Ill-formed function call ({e})\n"
+
+        return result
+
+    def _print_messages(self, messages, client_print):
+        client_print()        
+        for i,m in enumerate(messages):
+            message_text = m.content[0].text.value
+            if i == 0:
+                message_text = '(Message) ' + message_text
+            client_print(message_text)
+
+
+
+    def _wait_on_run(self, run, thread, client_print):
+        try:
+            while run.status == "queued" or run.status == "in_progress":
+                run = self.threads.runs.retrieve(
+                    thread_id=thread.id,
+                    run_id=run.id,
+                )
+                time.sleep(0.5)
+            return run
+        finally:
+            if run.status == 'in_progress':
+                client_print("Cancelling message that's in progress.")
+                self.threads.runs.cancel(thread_id=thread.id, run_id=run.id)
+
+    def run(self, prompt, client_print = print):
+        """
+        Give the prompt to the assistant and get the response, which may included
+        intermediate function calls.
+        All output is printed to the given file.
+        """
+        try:
+            if self.assistant == None:
+                return
+
+            assert len(prompt) <= 32768
+
+            message = self.threads.messages.create(thread_id=self.thread.id, 
+                                                role="user", 
+                                                content=prompt)
+            self._log(message)
+
+            last_printed_message_id = message.id
+
+
+            run = self.threads.runs.create(thread_id=self.thread.id, 
+                                        assistant_id=self.assistant.id)
+            self._log(run)
+
+            run = self._wait_on_run(run, self.thread, client_print)
+            self._log(run)
+
+            while run.status == "requires_action":
+
+                messages = self.threads.messages.list(thread_id=self.thread.id, 
+                                                    after=last_printed_message_id, 
+                                                    order='asc')
+
+                mlist = list(messages)
+                if len(mlist) > 0:
+                    self._print_messages(mlist, client_print)
+                    last_printed_message_id = mlist[-1].id
+                    client_print()
+
+
+                outputs = []
+                for tool_call in run.required_action.submit_tool_outputs.tool_calls:
+                    output = self._make_call(tool_call)
+                    self._log(output)
+                    outputs += [ { 'tool_call_id' : tool_call.id, 'output' : output } ]
+
+                try:
+                    run = self.threads.runs.submit_tool_outputs(thread_id=self.thread.id,
+                                                                run_id=run.id,
+                                                                tool_outputs=outputs)
+                    self._log(run)
+                except Exception as e:
+                    self._log(run, f'FAILED to submit tool call results: {e}')
+
+                run = self._wait_on_run(run, self.thread, client_print)
+                self._log(run)
+
+            if run.status == 'failed':
+                message = f"\n**Internal Failure ({run.last_error.code}):** {run.last_error.message}"
+                client_print(message)
+                return
+
+            messages = self.threads.messages.list(thread_id=self.thread.id, 
+                                                after=last_printed_message_id, 
+                                                order='asc')
+            self._print_messages(messages, client_print)
+
+            cost = llm_utils.calculate_cost(run.usage.prompt_tokens, 
+                                            run.usage.completion_tokens,
+                                            self.assistant.model)
+            client_print()
+            client_print(f'[Cost: ~${cost:.2f} USD]')
+        except OpenAIError as e:
+            client_print(f"*** OpenAI Error: {e}")
+
+
+
+
+    def _log(self, obj, title=''):
+        if self.json != None:
+            stack = inspect.stack()
+            caller_frame_record = stack[1]
+            lineno, function = caller_frame_record[2:4]
+            loc = f'{function}:{lineno}'
+
+            print('-' * 70, file=self.json)
+            print(f'{loc}  {title}', file=self.json)
+            if isinstance(obj, BaseModel):
+                json_obj = json.loads(obj.model_dump_json())
+            else:
+                json_obj = obj
+            print(f'\n{json.dumps(json_obj, indent=2)}\n', file=self.json)
+            self.json.flush()
+        return obj