Edited summarizer prompt and web search agent prompt

examples/03_Tools_And_Function_Calling/web_search_agent.py

@@ -1,29 +1,28 @@
 from llama_cpp_agent import MessagesFormatterType, LlamaCppAgent
 from llama_cpp_agent.chat_history.messages import Roles
 from llama_cpp_agent.llm_output_settings import LlmStructuredOutputSettings
+from llama_cpp_agent.prompt_templates import web_search_system_prompt
 from llama_cpp_agent.providers import LlamaCppServerProvider
 from llama_cpp_agent.tools import WebSearchTool
 
-
-def send_message_to_user(message: str):
-    """
-    Send a message to user.
-    Args:
-        message (str): Message to send.
-    """
-    print(message)
-
-
-provider = LlamaCppServerProvider("http://localhost:8080")
+provider = LlamaCppServerProvider("http://hades.hq.solidrust.net:8084")
 agent = LlamaCppAgent(
     provider,
     debug_output=True,
-    system_prompt="You are a helpful assistant. Use additional available information you have access to when giving a response. Always give detailed and long responses. Format your response, well structured in markdown format.",
-    predefined_messages_formatter_type=MessagesFormatterType.CHATML,
+    system_prompt=web_search_system_prompt,
+    predefined_messages_formatter_type=MessagesFormatterType.MISTRAL,
     add_tools_and_structures_documentation_to_system_prompt=True,
 )
 
-search_tool = WebSearchTool(provider, MessagesFormatterType.CHATML, max_tokens_search_results=20000)
+
+def write_message_to_user():
+    """
+    Let you write a message to the user.
+    """
+    return "Please write the message to the user."
+
+
+search_tool = WebSearchTool(provider, MessagesFormatterType.MISTRAL, max_tokens_search_results=20000)
 
 settings = provider.get_provider_default_settings()
 
@@ -33,15 +32,26 @@ def send_message_to_user(message: str):
 # settings.tfs_z = 0.95
 settings.max_tokens = 2048
 output_settings = LlmStructuredOutputSettings.from_functions(
-    [search_tool.get_tool(), send_message_to_user])
-user = input(">")
-result = agent.get_chat_response(user, prompt_suffix="\n```json\n",
-                                 llm_sampling_settings=settings, structured_output_settings=output_settings)
-while True:
-    if result[0]["function"] == "send_message_to_user":
-        user = input(">")
-        result = agent.get_chat_response(user, prompt_suffix="\n```json\n", structured_output_settings=output_settings,
-                                         llm_sampling_settings=settings)
-    else:
-        result = agent.get_chat_response(result[0]["return_value"], role=Roles.tool, prompt_suffix="\n```json\n",
-                                         structured_output_settings=output_settings, llm_sampling_settings=settings)
+    [search_tool.get_tool(), write_message_to_user])
+
+
+def run_web_search_agent():
+    user = input(">")
+    if user == "exit":
+        return
+    result = agent.get_chat_response(user,
+                                     llm_sampling_settings=settings, structured_output_settings=output_settings)
+    while True:
+        if result[0]["function"] == "write_message_to_user":
+            break
+        else:
+            result = agent.get_chat_response(result[0]["return_value"], role=Roles.tool,
+                                             structured_output_settings=output_settings, llm_sampling_settings=settings)
+
+    result = agent.get_chat_response(result[0]["return_value"], role=Roles.tool,
+                                     llm_sampling_settings=settings)
+
+    print(result)
+    run_web_search_agent()
+
+run_web_search_agent()

src/llama_cpp_agent/prompt_templates.py

@@ -84,3 +84,21 @@
 ## Output Models
 
 '''
+
+
+summarizing_system_prompt = """You are a text summarization and information extraction specialist and you are able to summarize and filter out information of websites relevant to a specific query.
+Provide all the relevant information of the website in a structured markdown document following the format below:
+
+---
+Website Title: {Website Title}
+Website URL: {Website URL}
+
+Content: 
+{Relevant Information}
+---
+
+Write only the markdown document in your response and begin and end your response with '---'.
+"""
+
+web_search_system_prompt = """You are a web search specialist and you are able to give detailed answers to user queries based on information extracted from the web.
+Write your response to the user in a structured markdown document."""

src/llama_cpp_agent/tools/web_search/tool.py

@@ -3,6 +3,7 @@
 from .web_search_interfaces import WebCrawler, WebSearchProvider
 from .default_web_crawlers import TrafilaturaWebCrawler
 from .default_web_search_providers import DDGWebSearchProvider
+from ...prompt_templates import summarizing_system_prompt
 
 
 class WebSearchTool:
@@ -14,7 +15,7 @@ def __init__(self, llm_provider: LlmProvider, message_formatter_type: MessagesFo
                  max_tokens_per_summary: int = 750):
         self.llm_provider = llm_provider
         self.summarising_agent = LlamaCppAgent(llm_provider, debug_output=True,
-                                               system_prompt="You are a text summarization and information extraction specialist and you are able to summarize and filter out information relevant to a specific query.",
+                                               system_prompt=summarizing_system_prompt,
                                                predefined_messages_formatter_type=message_formatter_type)
         if web_crawler is None:
             self.web_crawler = TrafilaturaWebCrawler()
@@ -56,7 +57,7 @@ def search_web(self, search_query: str):
                 web_info = self.summarising_agent.get_chat_response(
                     f"Please summarize the following Website content and extract relevant information to this query:'{search_query}'.\n\n" + web_info,
                     add_response_to_chat_history=False, add_message_to_chat_history=False, llm_sampling_settings=self.settings)
-                result_string += web_info
+                result_string += f"\n\n{web_info.strip()}"
 
         res = result_string.strip()
         tokens = self.llm_provider.tokenize(res)
@@ -69,7 +70,7 @@ def search_web(self, search_query: str):
                 else:
                     remove_chars += 100
 
-        return "Based on the following results, answer the previous user query:\nResults:\n\n" + res[:self.max_tokens_search_results]
+        return "\nResults of searching the web:\n\n" + res[:self.max_tokens_search_results]
 
     def get_tool(self):
         return self.search_web