➕ refactor: summarize error message without line number

dev_gpt/options/generate/chains/auto_refine_description.py

@@ -71,8 +71,14 @@ Note: If you are not sure about the details, then come up with the minimal numbe
 Note: If you can decide to return files as URLs or as base64 encoded strings, then choose the base64 encoded strings.'''
 
 summarize_description_and_schemas_prompt = '''{context_string}
-Write an updated microservice description by incorporating information about the request and response parameters in a concise way without losing any information.
-Note: You must not mention any details about algorithms or the technical implementation.
-Note: You must not mention that there is a request and response JSON schema
-Note: You must not use any formatting like triple backticks.
-Note: If an external API like google_custom_search or gpt_3_5_turbo is mentioned in the description, then you must mention the API in the updated description as well.'''
+Please write an updated microservice description by incorporating information about the request and response parameters in a concise manner, ensuring all information from the existing description is maintained.
+
+Constraints:
+
+- Do not mention any details about algorithms or the technical implementation.
+- Refrain from indicating there is a request and response JSON schema.
+- Avoid using any special formatting such as triple backticks.
+- If a specific tool or API (e.g. google_custom_search, gpt_3_5_turbo) is referred to in the original description, \
+include it in the updated description using the exact name given. \
+For instance, if the original description mentions 'gpt_3_5_turbo', \
+the updated description should also specify 'gpt_3_5_turbo'.'''

scripts/prompt_improvement.py

@@ -0,0 +1,55 @@
+from dev_gpt.options.generate.chains.auto_refine_description import better_description_prompt
+
+prompt_template = better_description_prompt
+
+d = {
+    'microservice_description': 'Input is a tweet that contains passive aggressive language. The output is the positive version of that tweet.'
+}
+
+task = '''\
+The main issue with GPT-3's output is that it violated the given prompt instructions. It used a non-specific formulation "such as GPT-3" to suggest a tool for natural language processing, which is explicitly prohibited in the prompt.
+
+How can I change the prompt template to make this more clear to GPT-3?'''
+
+generated_output = '''\
+Microservice Description:
+
+This microservice takes a tweet as input that contains passive aggressive language and returns the positive version of that tweet as output. The microservice performs the following tasks:
+
+1. Use a natural language processing (NLP) tool such as GPT-3 to analyze the input tweet and identify the passive aggressive language.
+2. Generate a list of positive words and phrases that can be used to replace the passive aggressive language in the tweet.
+3. Replace the passive aggressive language in the tweet with positive words and phrases.
+4. Return the positive version of the tweet as output.'''
+
+
+
+
+value_explanations = [
+    'This is the value of {k}: "{v}"' for k, v in d.items()
+]
+
+value_explanations_string = '\n'.join(value_explanations)
+
+
+fix_template = f'''\
+Here is the prompt template I used for GPT-3:
+# start of prompt
+
+{prompt_template}
+
+# end of prompt
+
+{value_explanations_string}
+
+
+Here is the generated output by GPT-3:
+# generated output start
+
+{generated_output}
+
+# generated output end
+
+
+###### your task #####
+{task}
+'''

test/conftest.py

@@ -3,6 +3,8 @@ from typing import List, Generator
 
 import pytest
 
+from dev_gpt.apis.gpt import GPTSession
+
 
 def input_generator(input_sequence: list) -> Generator[str, None, None]:
     """
@@ -34,3 +36,7 @@ def microservice_dir(tmpdir) -> str:
     """
     return os.path.join(str(tmpdir), "microservice")
 
+@pytest.fixture
+def init_gpt(tmpdir):
+    os.environ['VERBOSE'] = 'true'
+    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')

test/unit/test_response_parsing.py

@@ -48,9 +48,7 @@ def test_extract_content_from_result(plain_text, expected1, expected2):
     assert parsed_result2 == expected2
 
 
-def test_self_healing_json_parser(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_self_healing_json_parser(init_gpt):
     json_response = '''\
 ```json
 {

test/unit/test_self_refinement.py

@@ -1,15 +1,54 @@
-import os
-
-from dev_gpt.apis.gpt import GPTSession
-from dev_gpt.options.generate.chains.auto_refine_description import enhance_description
+from dev_gpt.apis.gpt import ask_gpt
+from dev_gpt.options.generate.chains.auto_refine_description import enhance_description, \
+    summarize_description_and_schemas_prompt
+from dev_gpt.options.generate.parser import identity_parser
+from dev_gpt.options.generate.prompt_factory import context_to_string
 
 
-def test_better_description(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_better_description(init_gpt):
 
     better_description = enhance_description({
         'microservice_description': 'Input is a tweet that contains passive aggressive language. The output is the positive version of that tweet.'
     })
     assert 'gpt_3_5_turbo' in better_description
     assert 'such as' not in better_description
+
+def test_update_description_based_on_schema(init_gpt):
+    updated_description = ask_gpt(
+        summarize_description_and_schemas_prompt,
+        identity_parser,
+        context_string=context_to_string({
+            'microservice_description': '''\
+Microservice Description:
+
+Given a tweet that contains passive aggressive language, the microservice will generate a positive version of that tweet. The microservice will perform the following tasks:
+
+1. Use a natural language processing tool such as gpt_3_5_turbo to analyze the sentiment of the input tweet and identify the passive aggressive language.
+2. Generate a positive version of the tweet using gpt_3_5_turbo or a similar text generation tool.
+3. Check the generated tweet for any grammatical errors or typos and correct them if necessary.
+4. Return the positive version of the tweet as output.''',
+            'request_schema': '''\
+{
+  "type": "object",
+  "properties": {
+    "tweet": {
+      "type": "string",
+      "description": "The input tweet that contains passive aggressive language."
+    }
+  },
+  "required": ["tweet"]
+}''',
+            'response_schema': '''\
+{
+  "type": "object",
+  "properties": {
+    "positive_tweet": {
+      "type": "string",
+      "description": "The positive version of the input tweet generated by the microservice."
+    }
+  },
+  "required": ["positive_tweet"]
+}'''
+        })
+    )
+    assert 'gpt_3_5_turbo' in updated_description

test/unit/test_tools.py

@@ -14,9 +14,7 @@ def test_no_search():
     tool_lines = get_available_tools().split('\n')
     assert len(tool_lines) == 1
 
-def test_get_used_apis(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_get_used_apis(init_gpt):
     used_apis = PM.get_used_apis('''\
 This microservice listens for incoming requests and generates a fixed output of "test" upon receiving a request. \
 The response sent back to the requester includes the output as a string parameter. \
@@ -24,9 +22,7 @@ No specific request parameters are required, and the response always follows a f
                                   )
     assert used_apis == []
 
-def test_get_used_apis_2(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_get_used_apis_2(init_gpt):
     description = '''\
 This microservice accepts a 1-minute WAV audio file of speech, encoded as a base64 binary string, and performs the following tasks:
 
@@ -39,9 +35,7 @@ The microservice returns the summarized text converted to audio and encoded as a
     used_apis = PM.get_used_apis(description)
     assert used_apis == ['Whisper API', 'gpt_3_5_turbo', 'text-to-speech (TTS) library']
 
-def test_get_used_apis_3(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_get_used_apis_3(init_gpt):
     description = '''\
 This microservice takes a PDF file as input and returns a summarized text output. \
 It uses PDF parsing and natural language processing tools to generate the summary, \
@@ -51,9 +45,7 @@ and the output parameter is the summarized text.'''
     used_apis = PM.get_used_apis(description)
     assert used_apis == []
 
-def test_get_used_apis_4(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_get_used_apis_4(init_gpt):
     description = '''\
 This microservice receives a tweet as input \
 and identifies passive aggressive language using natural language processing techniques. \

test/unit/test_yes_no_question.py

@@ -4,9 +4,7 @@ from dev_gpt.apis.gpt import GPTSession
 from dev_gpt.options.generate.chains.question_answering import answer_yes_no_question
 
 
-def test_answer_yes_no_question(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_answer_yes_no_question(init_gpt):
     assert answer_yes_no_question(
         '''\
 Microservice description:
@@ -20,9 +18,7 @@ The request parameter is "stock_symbol" and the response parameter is "summary".
 ''', 'Based on the microservice description, does the microservice interface with APIs?'
     )
 
-def test_answer_yes_no_question_2(tmpdir):
-    os.environ['VERBOSE'] = 'true'
-    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+def test_answer_yes_no_question_2(init_gpt):
     assert not answer_yes_no_question(
         '''\
 Microservice description: