Hi @earnest jay. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
#Assistant cant cite courses the way I want.
hardy moatBOT
The strange characters you're seeing, such as 【4:1†source】, are part of the annotation system used by OpenAI's Assistants. These annotations provide information about how you should annotate the text in the message.
There are two types of annotations: file_citation and file_path. The file_citation annotations are created by the file_search tool and define references to a specific file that was uploaded and used by the Assistant to generate the response. The file_path annotations are created by the code_interpreter tool and contain references to the files generated by the tool.
When these annotations are present in the Message object, you'll see these model-generated substrings in the text. You should replace these substrings with the information present in the annotations.
Here's an example Python code snippet that replaces these strings with information present in the annotations:
# Retrieve the message object
message = client.beta.threads.messages.retrieve(
thread_id="...",
message_id="..."
)
# Extract the message content
message_content = message.content[0].text
annotations = message_content.annotations
citations = []
# Iterate over the annotations and add footnotes
for index, annotation in enumerate(annotations):
# Replace the text with a footnote
message_content.value = message_content.value.replace(annotation.text, f' [{index}]')
# Gather citations based on annotation attributes
if (file_citation := getattr(annotation, 'file_citation', None)):
cited_file = client.files.retrieve(file_citation.file_id)
citations.append(f'[{index}] {file_citation.quote} from {cited_file.filename}')
elif (file_path := getattr(annotation, 'file_path', None)):
# Handle file_path annotations
This code will replace the model-generated substrings with footnotes and gather citations based on the annotation attributes. You can modify this code to suit your specific needs.
For more information, you can refer to the OpenAI documentation on message annotations (https://platform.openai.com/docs/assistants/deep-dive/message-annotations).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
earnest jay
@hardy moat Please integrate it into the relevent methods
hardy moatBOT
Let me check the knowledge base for you again. One moment! :books:
I'm sorry for any confusion, but the knowledge sources provided do not contain specific methods or code snippets related to integrating the annotation replacement functionality into an existing system or application.
However, I can provide a general approach on how you might integrate this into your application:
-
Identify where the response is generated: Find the part of your code where the assistant's response is generated and received.
-
Apply the annotation replacement: After receiving the response, apply the annotation replacement code to the response text. This will replace the model-generated substrings with the information present in the annotations.
Here's a simplified example of how you might do this:
def process_response(response):
# Extract the message content
message_content = response.content[0].text
annotations = response.annotations
citations = []
# Iterate over the annotations and add footnotes
for index, annotation in enumerate(annotations):
# Replace the text with a footnote
message_content.value = message_content.value.replace(annotation.text, f' [{index}]')
# Gather citations based on annotation attributes
if (file_citation := getattr(annotation, 'file_citation', None)):
cited_file = client.files.retrieve(file_citation.file_id)
citations.append(f'[{index}] {file_citation.quote} from {cited_file.filename}')
elif (file_path := getattr(annotation, 'file_path', None)):
# Handle file_path annotations
return message_content, citations
# When you receive a response from the assistant
response = client.beta.threads.messages.retrieve(thread_id="...", message_id="...")
processed_response, citations = process_response(response)
Please note that this is a simplified example and you may need to adjust it to fit your specific application and use case. For more detailed information, you can refer to the OpenAI documentation on message annotations (https://platform.openai.com/docs/assistants/deep-dive/message-annotations).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
earnest jay
@hardy moat please update my code to make it work:
def get_response(self, message: str, max_tokens: int = 150) -> str:
"""
Generates a response from the AI based on the provided message.
If the message is "/dump", it fetches all messages in the thread.
Otherwise, it sends the user message to the assistant and streams the response.
Args:
message (str): The user message to send to the assistant.
max_tokens (int): The maximum number of tokens for the response (default is 150).
Returns:
str: The formatted response from the assistant or an error message.
"""
try:
if message == "/dump":
# Fetch all messages in the thread
messages = self.client.beta.threads.messages.list(thread_id=self.thread.id)
formatted_response = json.dumps([msg.to_dict() for msg in messages], indent=2)
else:
self.client.beta.threads.messages.create(
thread_id=self.thread.id,
role="user",
content=message
)
# Create an EventHandler instance
event_handler = EventHandler()
# Start streaming the assistant's response using event_handler
with self.client.beta.threads.runs.stream(
thread_id=self.thread.id,
assistant_id=self.assistant_id,
instructions = ' '.join(self.json_data["prompts"]["system_prompt"]),
event_handler=event_handler,
) as stream:
stream.until_done()
# Get the full response from the event_handler
formatted_response = event_handler.formatted_response
return formatted_response
except Exception as e:
return f"An error occurred: {str(e)}"
Let me check the knowledge base for you again. One moment! :books:
To modify your code to handle the annotation replacement, you can add a new method to process the response and call this method before returning the response. Here's how you can do it:
def process_response(self, response):
# Extract the message content
message_content = response.content[0].text
annotations = response.annotations
citations = []
# Iterate over the annotations and add footnotes
for index, annotation in enumerate(annotations):
# Replace the text with a footnote
message_content.value = message_content.value.replace(annotation.text, f' [{index}]')
# Gather citations based on annotation attributes
if (file_citation := getattr(annotation, 'file_citation', None)):
cited_file = self.client.files.retrieve(file_citation.file_id)
citations.append(f'[{index}] {file_citation.quote} from {cited_file.filename}')
elif (file_path := getattr(annotation, 'file_path', None)):
# Handle file_path annotations
return message_content, citations
def get_response(self, message: str, max_tokens: int = 150) -> str:
"""
Generates a response from the AI based on the provided message.
If the message is "/dump", it fetches all messages in the thread.
Otherwise, it sends the user message to the assistant and streams the response.
Args:
message (str): The user message to send to the assistant.
max_tokens (int): The maximum number of tokens for the response (default is 150).Returns:
str: The formatted response from the assistant or an error message.
"""
try:
if message == "/dump":
# Fetch all messages in the thread
messages = self.client.beta.threads.messages.list(thread_id=self.thread.id)
formatted_response = json.dumps([msg.to_dict() for msg in messages], indent=2)
else:
self.client.beta.threads.messages.create(
thread_id=self.thread.id,
role="user",
content=message
)
# Create an EventHandler instance
event_handler = EventHandler()
# Start streaming the assistant's response using event_handler
with self.client.beta.threads.runs.stream(
thread_id=self.thread.id,
assistant_id=self.assistant_id,
instructions = ' '.join(self.json_data["prompts"]["system_prompt"]),
event_handler=event_handler,
) as stream:
stream.until_done()
# Get the full response from the event_handler
response = event_handler.formatted_response
# Process the response to handle annotations
formatted_response, citations = self.process_response(response)
return formatted_response
except Exception as e:
return f"An error occurred: {str(e)}"
Please note that this is a simplified example and you may need to adjust it to fit your specific application and use case. For more detailed information, you can refer to the **OpenAI documentation on message annotations** (<https://platform.openai.com/docs/assistants/deep-dive/message-annotations>).
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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