refactor: cleanup

README.md

@@ -40,12 +40,12 @@ graph TB
 - Creates a Streamlit playground where you can test the microservice.
 6. If it fails 10 times in a row, it moves on to the next approach.
 
-[//]: # ([![Watch the video](https://i.imgur.com/vKb2F1B.png)](https://user-images.githubusercontent.com/11627845/226220484-17810f7c-b184-4a03-9af2-3a977fbb014b.mov))
-
-
 # 🤏 limitations for now
 - stateless microservices only
 - deterministic microservices only to make sure input and output pairs can be used
 
 # 🔮 vision
 Use natural language interface to create, deploy and update your microservice infrastructure.
+
+
+[//]: # ([![Watch the video](https://i.imgur.com/vKb2F1B.png)](https://user-images.githubusercontent.com/11627845/226220484-17810f7c-b184-4a03-9af2-3a977fbb014b.mov))

main.py

@@ -1,5 +1,7 @@
 import random
 
+import click
+
 from src import gpt, jina_cloud
 from src.jina_cloud import push_executor, process_error_message
 from src.prompt_tasks import general_guidelines, executor_file_task, chain_of_thought_creation, test_executor_file_task, \
@@ -63,11 +65,12 @@ def wrap_content_in_code_block(executor_content, file_name, tag):
 def create_executor(
         executor_description,
         test_scenario,
+        output_path,
         executor_name,
         package,
         is_chain_of_thought=False,
 ):
-    EXECUTOR_FOLDER_v1 = get_executor_path(package, 1)
+    EXECUTOR_FOLDER_v1 = get_executor_path(output_path, package, 1)
     recreate_folder(EXECUTOR_FOLDER_v1)
     recreate_folder('flow')
 
@@ -163,16 +166,16 @@ print(response[0].text) # can also be blob in case of image/audio..., this shoul
     playground_content = extract_content_from_result(playground_content_raw, 'app.py')
     persist_file(playground_content, f'{executor_path}/app.py')
 
-def get_executor_path(package, version):
+def get_executor_path(output_path, package, version):
     package_path = '_'.join(package)
-    return f'executor/{package_path}/v{version}'
+    return f'{output_path}/{package_path}/v{version}'
 
-def debug_executor(package, executor_description, test_scenario):
+def debug_executor(output_path, package, executor_description, test_scenario):
     MAX_DEBUGGING_ITERATIONS = 10
     error_before = ''
     for i in range(1, MAX_DEBUGGING_ITERATIONS):
-        previous_executor_path = get_executor_path(package, i)
-        next_executor_path = get_executor_path(package, i + 1)
+        previous_executor_path = get_executor_path(output_path, package, i)
+        next_executor_path = get_executor_path(output_path, package, i + 1)
         log_hubble = push_executor(previous_executor_path)
         error = process_error_message(log_hubble)
         if error:
@@ -216,7 +219,7 @@ def debug_executor(package, executor_description, test_scenario):
             break
         if i == MAX_DEBUGGING_ITERATIONS - 1:
             raise MaxDebugTimeReachedException('Could not debug the executor.')
-    return get_executor_path(package, i)
+    return get_executor_path(output_path, package, i)
 
 class MaxDebugTimeReachedException(BaseException):
     pass
@@ -243,22 +246,27 @@ PDFParserExecutor
     name = extract_content_from_result(name_raw, 'name.txt')
     return name
 
-
+@click.command()
+@click.option('--executor-description', required=True, help='Description of the executor.')
+@click.option('--test-scenario', required=True, help='Test scenario for the executor.')
+@click.option('--num_approaches', default=3, type=int, help='Number of num_approaches to use to fulfill the task (default: 3).')
+@click.option('--output_path', default='executor', help='Path to the output folder (must be empty). ')
 def main(
         executor_description,
         test_scenario,
-        threads=3,
+        num_approaches=3,
+        output_path='executor',
 ):
     generated_name = generate_executor_name(executor_description)
     executor_name = f'{generated_name}{random.randint(0, 1000_000)}'
 
-    packages = get_possible_packages(executor_description, threads)
-    recreate_folder('executor')
+    packages = get_possible_packages(executor_description, num_approaches)
+    recreate_folder(output_path)
     for package in packages:
         try:
-            create_executor(executor_description, test_scenario, executor_name, package)
+            create_executor(executor_description, test_scenario, output_path, executor_name, package)
             # executor_name = 'MicroChainExecutor790050'
-            executor_path = debug_executor(package, executor_description, test_scenario)
+            executor_path = debug_executor(output_path, package, executor_description, test_scenario)
             # print('Executor can be built locally, now we will push it to the cloud.')
             # jina_cloud.push_executor(executor_path)
             print('Deploy a jina flow')

requirements.txt

@@ -1,6 +1,2 @@
 jina==3.14.1
-pyrender~=0.1.45
-trimesh~=3.10.0
-numpy~=1.22.3
-Pillow~=9.0.1
-requests~=2.27.1
+click==8.1.3

src/gpt.py

@@ -1,6 +1,6 @@
 import os
 from time import sleep
-from typing import Union, List, Tuple
+from typing import List, Tuple
 
 import openai
 from openai.error import RateLimitError, Timeout
@@ -9,13 +9,39 @@ from src.prompt_system import system_base_definition
 from src.utils.io import timeout_generator_wrapper, GenerationTimeoutError
 from src.utils.string_tools import print_colored
 
+PRICING_GPT4_PROMPT = 0.03
+PRICING_GPT4_GENERATION = 0.06
+PRICING_GPT3_5_TURBO_PROMPT = 0.002
+PRICING_GPT3_5_TURBO_GENERATION = 0.002
+
+if 'OPENAI_API_KEY' not in os.environ:
+    raise Exception('You need to set OPENAI_API_KEY in your environment')
 openai.api_key = os.environ['OPENAI_API_KEY']
 
+
+try:
+    openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{
+            "role": 'system',
+            "content": 'test'
+        }]
+    )
+    supported_model = 'gpt-4'
+    pricing_prompt = PRICING_GPT4_PROMPT
+    pricing_generation = PRICING_GPT4_GENERATION
+except openai.error.InvalidRequestError:
+    supported_model = 'gpt-3.5-turbo'
+    pricing_prompt = PRICING_GPT3_5_TURBO_PROMPT
+    pricing_generation = PRICING_GPT3_5_TURBO_GENERATION
+
 total_chars_prompt = 0
 total_chars_generation = 0
 
+
 class Conversation:
-    def __init__(self, prompt_list: List[Tuple[str, str]] = None):
+    def __init__(self, prompt_list: List[Tuple[str, str]] = None, model=supported_model):
+        self.model = model
         if prompt_list is None:
             prompt_list = [('system', system_base_definition)]
         self.prompt_list = prompt_list
@@ -24,49 +50,48 @@ class Conversation:
     def query(self, prompt: str):
         print_colored('user', prompt, 'blue')
         self.prompt_list.append(('user', prompt))
-        response = get_response(self.prompt_list)
+        response = self.get_response(self.prompt_list)
         self.prompt_list.append(('assistant', response))
         return response
 
-
-def get_response(prompt_list: List[Tuple[str, str]]):
-    global total_chars_prompt, total_chars_generation
-    for i in range(10):
-        try:
-            response_generator = openai.ChatCompletion.create(
-                temperature=0,
-                max_tokens=2_000,
-                model="gpt-4",
-                stream=True,
-                messages=[
-                    {
-                        "role": prompt[0],
-                        "content": prompt[1]
-                    }
-                    for prompt in prompt_list
-                ]
-            )
-            response_generator_with_timeout = timeout_generator_wrapper(response_generator, 10)
-            total_chars_prompt += sum(len(prompt[1]) for prompt in prompt_list)
-            complete_string = ''
-            for chunk in response_generator_with_timeout:
-                delta = chunk['choices'][0]['delta']
-                if 'content' in delta:
-                    content = delta['content']
-                    print_colored('' if complete_string else 'assistent', content, 'green', end='')
-                    complete_string += content
-                    total_chars_generation += len(content)
-            print('\n')
-            money_prompt = round(total_chars_prompt / 3.4 * 0.03 / 1000, 2)
-            money_generation = round(total_chars_generation / 3.4 * 0.06 / 1000, 2)
-            print('money prompt:', f'${money_prompt}')
-            print('money generation:', f'${money_generation}')
-            print('total money:', f'${money_prompt + money_generation}')
-            print('\n')
-            return complete_string
-        except (RateLimitError, Timeout, ConnectionError, GenerationTimeoutError) as e:
-            print(e)
-            print('retrying')
-            sleep(3)
-            continue
-    raise Exception('Failed to get response')
+    def get_response(self, prompt_list: List[Tuple[str, str]]):
+        global total_chars_prompt, total_chars_generation
+        for i in range(10):
+            try:
+                response_generator = openai.ChatCompletion.create(
+                    temperature=0,
+                    max_tokens=2_000,
+                    model=self.model,
+                    stream=True,
+                    messages=[
+                        {
+                            "role": prompt[0],
+                            "content": prompt[1]
+                        }
+                        for prompt in prompt_list
+                    ]
+                )
+                response_generator_with_timeout = timeout_generator_wrapper(response_generator, 10)
+                total_chars_prompt += sum(len(prompt[1]) for prompt in prompt_list)
+                complete_string = ''
+                for chunk in response_generator_with_timeout:
+                    delta = chunk['choices'][0]['delta']
+                    if 'content' in delta:
+                        content = delta['content']
+                        print_colored('' if complete_string else 'assistent', content, 'green', end='')
+                        complete_string += content
+                        total_chars_generation += len(content)
+                print('\n')
+                money_prompt = round(total_chars_prompt / 3.4 * pricing_prompt / 1000, 2)
+                money_generation = round(total_chars_generation / 3.4 * pricing_generation / 1000, 2)
+                print('money prompt:', f'${money_prompt}')
+                print('money generation:', f'${money_generation}')
+                print('total money:', f'${money_prompt + money_generation}')
+                print('\n')
+                return complete_string
+            except (RateLimitError, Timeout, ConnectionError, GenerationTimeoutError) as e:
+                print(e)
+                print('retrying')
+                sleep(3)
+                continue
+        raise Exception('Failed to get response')

src/server.py

@@ -7,7 +7,6 @@ from typing import Optional, Dict
 from starlette.middleware.cors import CORSMiddleware
 from starlette.requests import Request
 from starlette.responses import JSONResponse
-Flow.plot()
 from main import main
 
 app = FastAPI()

when_alignment_goes_wrong.txt

@@ -1,5 +0,0 @@
-# it mocked the executor function to fix the test
-executor.classify_website = mock_classify_website
-
-# it attached a fake screen to the test execution
-RUN xvfb-run -s "-screen 0 640x480x24" python test_executor.py