Added reprompting loop workflow for new tools.

client/tool_workflows.py

@@ -0,0 +1,215 @@
+'''Functions to handle re-prompting and final reply generation
+downstream of LLM tool calls.'''
+
+import json
+import logging
+import queue
+from anthropic.types import text_block
+from client import prompts
+from client.anthropic_bridge import AnthropicBridge
+
+INTERMEDIATE_REPLY_HINTS = {
+    'context_search': 'Let me find some additional context before I generate a final answer.',
+    'find_article': 'I will find the title of that article.',
+    'get_summary': 'I will summarize that article',
+    'get_link': 'I will get the link to that article'
+}
+
+async def tool_loop(
+        user_query: str,
+        prior_reply: str,
+        result: list,
+        bridge: AnthropicBridge,
+        output_queue: queue.Queue,
+        dialog: logging.Logger
+) -> None:
+
+    '''Re-prompts the LLM in a loop until it generates a final reply based on tool output.
+
+    Args:
+        user_query: the original user input that provoked the tool call
+        result: the complete model reply containing the tool call
+        bridge: AnthropicBridge class instance
+        output_queue: queue to send results back to Gradio UI
+        dialog: logger instance to record intermediate responses and internal dialog
+    '''
+
+    tool_call = result['tool_call']
+    tool_name = tool_call['name']
+
+    if tool_name == 'get_feed':
+        reply = await get_feed_call(
+            user_query,
+            result,
+            bridge,
+            output_queue,
+            dialog
+        )
+
+        output_queue.put(reply)
+
+    else:
+        tool_call = result['tool_call']
+        tool_name = tool_call['name']
+        tool_parameters = tool_call['parameters']
+        response_content = result['llm_response'].content[0]
+
+        if isinstance(response_content, text_block.TextBlock):
+            intermediate_reply = response_content.text
+        else:
+            intermediate_reply = INTERMEDIATE_REPLY_HINTS[tool_name]
+
+        dialog.info('LLM intermediate reply: %s', intermediate_reply)
+        dialog.info('MCP: called %s', tool_name)
+
+        tool_result = json.loads(result['tool_result'].content)['text']
+
+        prompt = prompts.OTHER_TOOL_PROMPT.substitute(
+            user_query=user_query,
+            prior_reply=prior_reply,
+            intermediate_reply=intermediate_reply,
+            tool_name=tool_name,
+            tool_parameters=tool_parameters,
+            tool_result=tool_result
+        )
+
+        dialog.info('System: re-prompting LLM with return from %s call', tool_name)
+
+        while True:
+
+            reply = await other_call(
+                prompt,
+                bridge,
+                dialog
+            )
+
+            if 'final reply' in reply:
+                final_reply = reply['final reply']
+                dialog.info('LLM final reply: %s ...', final_reply[:50])
+                output_queue.put(final_reply)
+                break
+
+            else:
+                prompt = reply['new_prompt']
+
+
+async def get_feed_call(
+        user_query: str,
+        result: list,
+        bridge: AnthropicBridge,
+        output_queue: queue.Queue,
+        dialog: logging.Logger
+) -> str:
+
+    '''Re-prompts LLM after a call to get_feed().
+    
+    Args:
+        user_query: the original user input that provoked the tool call
+        result: the complete model reply containing the tool call
+        bridge: AnthropicBridge class instance
+        output_queue: queue to send results back to Gradio UI
+        dialog: logger instance to record intermediate responses and internal dialog
+    '''
+
+    tool_call = result['tool_call']
+    tool_name = tool_call['name']
+    tool_parameters = tool_call['parameters']
+    website = tool_parameters['website']
+    response_content = result['llm_response'].content[0]
+
+    if isinstance(response_content, text_block.TextBlock):
+        intermediate_reply = response_content.text
+    else:
+        intermediate_reply = f'I Will check the {website} RSS feed for you'
+
+    dialog.info('LLM intermediate reply: %s', intermediate_reply)
+    dialog.info('MCP: called %s on %s', tool_name, website)
+
+    articles = json.loads(result['tool_result'].content)['text']
+
+    prompt = prompts.GET_FEED_PROMPT.substitute(
+        website=website,
+        user_query=user_query,
+        intermediate_reply=intermediate_reply,
+        articles=articles
+    )
+
+    input_message =[{
+        'role': 'user',
+        'content': prompt
+    }]
+
+    dialog.info('System: re-prompting LLM with return from %s call', tool_name)
+
+    result = await bridge.process_query(
+        prompts.REPROMPTING_SYSTEM_PROMPT,
+        input_message
+    )
+
+    try:
+
+        reply = result['llm_response'].content[0].text
+
+    except (IndexError, AttributeError):
+        reply = 'No final reply from model'
+
+    dialog.info('LLM final reply: %s ...', reply[:50])
+
+    output_queue.put(reply)
+
+
+async def other_call(
+        prompt: list[dict],
+        bridge: AnthropicBridge,
+        dialog: logging.Logger
+) -> dict:
+
+    '''Re-prompts LLM after a call to get_feed().
+    
+    Args:
+        prompt: prompt to to send the LLM
+        result: the complete model reply containing the tool call
+        bridge: AnthropicBridge class instance
+        output_queue: queue to send results back to Gradio UI
+        dialog: logger instance to record intermediate responses and internal dialog
+    '''
+
+    input_message =[{
+        'role': 'user',
+        'content': prompt
+    }]
+
+    result = await bridge.process_query(
+        prompts.REPROMPTING_SYSTEM_PROMPT,
+        input_message
+    )
+
+    if result['tool_result']:
+
+        tool_call = result['tool_call']
+        tool_name = tool_call['name']
+        tool_parameters = tool_call['parameters']
+        response_content = result['llm_response'].content[0]
+
+        if isinstance(response_content, text_block.TextBlock):
+            intermediate_reply = response_content.text
+        else:
+            intermediate_reply = INTERMEDIATE_REPLY_HINTS[tool_name]
+
+        dialog.info('LLM intermediate reply: %s', intermediate_reply)
+        dialog.info('MCP: called %s', tool_name)
+
+        tool_result = json.loads(result['tool_result'].content)['text']
+
+        prompt += f'agent: {intermediate_reply}\n'
+        prompt += f'function call: {tool_name}("{tool_parameters}")'
+        prompt += f'function return: {tool_result}'
+
+        dialog.info('System: re-prompting LLM with return from %s call', tool_name)
+
+        return {'new_prompt': prompt}
+
+    else:
+
+        reply = result['llm_response'].content[0].text
+        return {'final reply': reply}