upgraded function calls

lollms/functions/bibliography.py

@@ -343,8 +343,8 @@ Local PDF: {local_url}
 # Metadata function
 def search_and_rank_function(llm, score_threshold:float, client: Optional[Any] = None):
     return {
-        "function_name": "arxiv_pdf_search",  # The function name in string
-        "function": partial(arxiv_pdf_search, llm=llm, client=client, score_threshold= score_threshold),  # The function to be called with partial to preset client
+        "function_name": "search_and_rank",  # The function name in string
+        "function": partial(search_and_rank, llm=llm, client=client, score_threshold= score_threshold),  # The function to be called with partial to preset client
         "function_description": "Searches for PDFs on arXiv based on a query, downloads them to a specified directory, rates them, sort them by rating and returns a HTML string containing article details and links, along with a dictionary containing detailed information about each PDF.",  # Description of the function
         "function_parameters": [  # The set of parameters
             {"name": "search_subject", "type": "str", "description": "The search subject."},

lollms/functions/summary.py

@@ -0,0 +1,56 @@
+# Lollms function call definition file
+
+# Import necessary libraries
+import requests
+from pathlib import Path
+
+# Partial is useful if we need to preset some parameters
+from functools import partial
+
+# It is advised to import typing elements
+from typing import List, Optional, Any, Tuple, Dict
+
+# Import PackageManager if there are potential libraries that need to be installed 
+from lollms.utilities import PackageManager, find_first_available_file_index, discussion_path_to_url
+
+# ascii_colors offers advanced console coloring and bug tracing
+from ascii_colors import trace_exception
+
+# Import Client from lollms.client_session
+from lollms.client_session import Client
+
+# Here is an example of how we install a non-installed library using PackageManager
+if not PackageManager.check_package_installed("bs4"):
+    PackageManager.install_package("beautifulsoup4")
+
+# Now we can import the library
+from bs4 import BeautifulSoup
+
+
+from lollms.databases.discussions_database import Discussion
+
+# Core function to search for PDFs on arXiv and download them to a specified directory
+def summerize_discussion(summary_request:str,llm, discussion:Discussion) -> str:
+    messages = discussion.get_messages()
+    text = ""
+    for message in messages:
+        text += message.content
+
+
+    summary  = llm.summerize_text(
+            text, 
+            summary_request,
+            doc_name="discussion"
+            )
+    return summary
+
+# Metadata function
+def summerize_discussion_function(llm, discussion:Discussion):
+    return {
+        "function_name": "summerize_discussion",  # The function name in string
+        "function": partial(summerize_discussion, llm=llm, discussion=discussion),  # The function to be called with partial to preset client
+        "function_description": "Summerizes the discussion while keeping some key information as requested by the summary_request parameter",  # Description of the function
+        "function_parameters": [  # The set of parameters
+            {"name": "summary_request", "type": "str", "description": "The desired information to recover while summerizing."},
+        ]
+    }

lollms/personality.py

@@ -3313,12 +3313,16 @@ The AI should respond in this format using data from actions_list:
         Returns:
             List[Dict[str, Any]]: A list of dictionaries with the function names and parameters to execute.
         """
+
         # Upgrade the prompt with information about the function calls.
         upgraded_prompt = self._upgrade_prompt_with_function_info(prompt, functions)
 
         # Generate the initial text based on the upgraded prompt.
         generated_text = self.fast_gen(upgraded_prompt, max_answer_length, callback=callback)
 
+        if self.config.debug:
+            self.print_prompt("Generrated", generated_text)
+
         # Extract the function calls from the generated text.
         function_calls = self.extract_function_calls_as_json(generated_text)
 
@@ -3411,9 +3415,9 @@ The AI should respond in this format using data from actions_list:
                                  "}",
                                  "```",
                                  "Only use available functions.",
-                                 "You can call multiple functions in one generation.",
-                                 "Each function call needs to be in a separate function markdown tag.",
-                                 f"Do not add status of the execution as it will be added automatically by the system.{separator_template}"
+                                 "If you choose to use a function call, do not write anything else. Just the function call.",
+                                 f"Do not add status of the execution as it will be added automatically by the system.",
+                                 f"A function call must not be followed by any text{separator_template}"
                                  f"{start_header_id_template}Available functions{end_header_id_template}\n"]
 
         for function in functions: