✨ docs: compliment example

README.md

@@ -98,10 +98,20 @@ jc list # get the microservice id
 jc delete <microservice id>
 ```
 
-
 ## Examples
 In this section you can get a feeling for the kind of microservices that can be generated with GPT Deploy.
 
+### Compliment Generator
+```bash
+gptdeploy generate \
+--description "The user writes something and gets a related deep compliment." \
+--test "Given the word test a deep compliment is generated" \
+--model gpt-4 \
+--path microservice
+```
+<img src="res/compliment_example.png" alt="Compliment Generator" width="400" />
+
+
 ### Extract and summarize news articles given a URL
 ```bash
 gptdeploy generate \
@@ -465,6 +475,8 @@ gptdeploy generate --description "Convert markdown to HTML" --test "# Hello, wel
 
 [//]: # (```)
 
+[//]: # (generate --description "Get a png as input and return a vectorized version as svg." --test "Make sure when you convert the image back, it looks similar." --path microservice --verbose)
+
 ## Technical Insights
 The graphic below illustrates the process of creating a microservice and deploying it to the cloud elaboration two different implementation strategies.
 
@@ -555,7 +567,7 @@ Make sure it is only printed twice in case it changed.
 - [ ] use gptdeploy list to show all deployments
 - [ ] gptdeploy delete to delete a deployment
 - [ ] gptdeploy update to update a deployment
-- [ ] test param optional - but how would you test the pdf extractor without a pdf?
+- [ ] test param optional - in case the test param is not there first ask gpt if more information is required to write a test - like access to pdf data
 - [ ] section for microservices built by the community
 - [ ] test feedback for playground generation (could be part of the debugging)
 - [ ] should we send everything via json in the text attribute for simplicity?

src/apis/gpt.py

@@ -11,6 +11,7 @@ from openai.error import RateLimitError
 from langchain.schema import HumanMessage, SystemMessage, BaseMessage
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from requests.exceptions import ConnectionError
+from urllib3.exceptions import InvalidChunkLength
 
 from src.constants import PRICING_GPT4_PROMPT, PRICING_GPT4_GENERATION, PRICING_GPT3_5_TURBO_PROMPT, \
     PRICING_GPT3_5_TURBO_GENERATION, CHARS_PER_TOKEN
@@ -119,7 +120,7 @@ class _GPTConversation:
             try:
                 response = self._chat([self.system_message] + self.messages)
                 break
-            except ConnectionError as e:
+            except (ConnectionError, InvalidChunkLength) as e:
                 print('There was a connection error. Retrying...')
                 if i == 9:
                     raise e