🧪3️⃣ test: level 3 refinement

src/apis/gpt.py

@@ -11,7 +11,7 @@ from langchain.chat_models import ChatOpenAI
 from openai.error import RateLimitError
 from langchain.schema import HumanMessage, SystemMessage, BaseMessage, AIMessage
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from requests.exceptions import ConnectionError
+from requests.exceptions import ConnectionError, ChunkedEncodingError
 from urllib3.exceptions import InvalidChunkLength
 
 from src.constants import PRICING_GPT4_PROMPT, PRICING_GPT4_GENERATION, PRICING_GPT3_5_TURBO_PROMPT, \
@@ -132,7 +132,7 @@ class _GPTConversation:
             try:
                 response = self._chat(self.messages)
                 break
-            except (ConnectionError, InvalidChunkLength) as e:
+            except (ConnectionError, InvalidChunkLength, ChunkedEncodingError) as e:
                 print('There was a connection error. Retrying...')
                 if i == 9:
                     raise e

src/options/generate/templates_system.py

@@ -48,15 +48,16 @@ Your response must exactly match the following block code format (double asteris
 ```
 
 b)
-Otherwise you respond with the summarized description.
-The summarized description must contain all the information mentioned by the client.
+Otherwise you respond with the detailed description.
+The detailed description must contain all the information mentioned by the client.
 Your response must exactly match the following block code format (double asterisks for the file name and triple backticks for the file block):
 
 **final.json**
 ```json
 {{
     "description": "<microservice description here>",
-    "implementation_details": "<additional implementation details here>",
+    "code_samples": "<code samples from the client here>",
+    "documentation_info": "<documentation info here>",
     "credentials: "<credentials here>"
 }}
 ```
@@ -87,7 +88,8 @@ database access: n/a
 ```json
 {{
     "description": "The user inserts a png and gets an svg as response.",
-    "implementation_details": "n/a",
+    "code_samples": "n/a",
+    "documentation_info": "n/a",
     "credentials: "n/a"
 }}
 ```

src/options/generate/templates_user.py

@@ -383,12 +383,14 @@ Either ask for clarification like this:
 }}
 ```
 
-Or write the summarized microservice description and additional implementation details like this:
+Or write the detailed microservice description all mentioned code samples, documentation info and credentials like this:
 **final.json**
 ```json
 {{
     "description": "<microservice description here>",
-    "implementation_details": "<additional implementation details here>",
+    "example_input_file": "<example input file here if mentioned before otherwise n/a>",
+    "code_samples": "<code samples from the client here>",
+    "documentation_info": "<documentation info here>",
     "credentials: "<credentials here>"
 }}
 ``` 

test/test_generator.py

@@ -85,8 +85,9 @@ def test_generation_level_3(tmpdir):
     """
     os.environ['VERBOSE'] = 'true'
     generator = Generator(
-        f'''Given an audio file (1min) of speech like https://www.signalogic.com/melp/EngSamples/Orig/ENG_M.wav, 
-get convert it to text using the following api:
+        f'''Given an audio file (1min wav) of speech, 
+1. convert it to text using the Whisper API.
+Here is the documentation on how to use the API:
 import requests
 url = "https://transcribe.whisperapi.com"
 headers = {{
@@ -97,8 +98,9 @@ data = {{
 }}
 response = requests.post(url, headers=headers, files=file, data=data)
 print(response.text)
-Summarize the text (50 words).
-Create an audio file of the summarized text.
+2. Summarize the text (~50 words) while still maintaining the key facts.
+3. Create an audio file of the summarized text using a tts library.
+Example input file: https://www.signalogic.com/melp/EngSamples/Orig/ENG_M.wav
 ''',
         str(tmpdir) + 'microservice',
         'gpt-3.5-turbo'