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upgraded code

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This commit is contained in:
Saifeddine ALOUI 2024-06-03 23:53:24 +02:00
parent 33e00ead57
commit 20c2e96351
2 changed files with 71 additions and 31 deletions
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5
lollms/app.py
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@ -1058,7 +1058,7 @@ class LollmsApplication(LoLLMsCom):
# If this is not a continue request, we add the AI prompt
if not is_continue:
message_tokenized = self.model.tokenize(
"\n" +self.personality.ai_message_prefix.strip()
self.personality.ai_message_prefix.strip()
)
full_message_list.append(message_tokenized)
# Update the cumulative number of tokens
@ -1172,7 +1172,8 @@ class LollmsApplication(LoLLMsCom):
"negative_boost":negative_boost,
"current_language":self.config.current_language,
"fun_mode":fun_mode,
"ai_prefix":ai_prefix
"ai_prefix":ai_prefix,
"extra":""
}
# Return the prepared query, original message content, and tokenized query

97
lollms/personality.py
View File

@ -2405,9 +2405,9 @@ class APScript(StateMachine):
f"{start_header_id_template}negative_boost{end_header_id_template}\n"+context_details["negative_boost"] if context_details["negative_boost"] else "",
f"{start_header_id_template}current_language{end_header_id_template}\n"+context_details["current_language"] if context_details["current_language"] else "",
f"{start_header_id_template}fun_mode{end_header_id_template}\n"+context_details["fun_mode"] if context_details["fun_mode"] else "",
f"{start_header_id_template}discussion_window{end_header_id_template}\n"+context_details["discussion_messages"] if context_details["discussion_messages"] else "",
f"{start_header_id_template}discussion_window{end_header_id_template}\n"+context_details["discussion_messages"] if context_details["discussion_messages"].strip()!="" else "",
custom_entries,
f"{start_header_id_template}"+context_details["ai_prefix"].replace(f"{start_header_id_template}","").replace(":","")+f"{end_header_id_template}"
f'{start_header_id_template}{context_details["ai_prefix"]}{end_header_id_template}'
],
8)
def build_prompt(self, prompt_parts:List[str], sacrifice_id:int=-1, context_size:int=None, minimum_spare_context_size:int=None):
@ -2433,11 +2433,13 @@ class APScript(StateMachine):
else:
part_tokens=[]
nb_tokens=0
for i,part in enumerate(prompt_parts):
tk = self.personality.model.tokenize(part)
for i, part in enumerate(prompt_parts):
part_s=part.strip()
tk = self.personality.model.tokenize(part_s)
part_tokens.append(tk)
if i != sacrifice_id:
nb_tokens += len(tk)
if len(part_tokens[sacrifice_id])>0:
sacrifice_tk = part_tokens[sacrifice_id]
sacrifice_tk= sacrifice_tk[-(context_size-nb_tokens-minimum_spare_context_size):]
@ -3291,12 +3293,12 @@ The AI should respond in this format using data from actions_list:
def generate_with_function_calls(self, prompt: str, functions: List[Dict[str, Any]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[str, Any]]:
def generate_with_function_calls(self, context_details: dict, functions: List[Dict[str, Any]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[str, Any]]:
"""
Performs text generation with function calls.
Args:
prompt (str): The full prompt (including conditioning, user discussion, extra data, and the user prompt).
context_details (dict): The full prompt (including conditioning, user discussion, extra data, and the user prompt).
functions (List[Dict[str, Any]]): A list of dictionaries describing functions that can be called.
max_answer_length (int, optional): Maximum string length allowed for the generated text.
@ -3304,8 +3306,9 @@ The AI should respond in this format using data from actions_list:
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)
upgraded_prompt = self._upgrade_prompt_with_function_info(context_details, functions)
# Generate the initial text based on the upgraded prompt.
generated_text = self.fast_gen(upgraded_prompt, max_answer_length, callback=callback)
@ -3319,7 +3322,7 @@ The AI should respond in this format using data from actions_list:
return generated_text, function_calls
def generate_with_function_calls_and_images(self, prompt: str, images:list, functions: List[Dict[str, Any]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[str, Any]]:
def generate_with_function_calls_and_images(self, context_details: dict, images:list, functions: List[Dict[str, Any]], max_answer_length: Optional[int] = None, callback = None) -> List[Dict[str, Any]]:
"""
Performs text generation with function calls.
@ -3332,7 +3335,7 @@ The AI should respond in this format using data from actions_list:
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)
upgraded_prompt = self._upgrade_prompt_with_function_info(context_details, functions)
# Generate the initial text based on the upgraded prompt.
generated_text = self.fast_gen_with_images(upgraded_prompt, images, max_answer_length, callback=callback)
@ -3381,12 +3384,40 @@ The AI should respond in this format using data from actions_list:
return results
def _upgrade_prompt_with_function_info(self, prompt: str, functions: List[Dict[str, Any]]) -> str:
def transform_functions(self, functions):
tools = []
for func in functions:
function_dict = {
"type": "function",
"function": {
"name": func["function_name"],
"description": func["function_description"],
"parameters": {
"type": "object",
"properties": {},
"required": [],
},
},
}
for param in func["function_parameters"]:
function_dict["function"]["parameters"]["properties"][param["name"]] = {
"type": "string" if param["type"] == "str" else param["type"],
"description": param.get("description", ""),
}
function_dict["function"]["parameters"]["required"].append(param["name"])
tools.append(function_dict)
return tools
def _upgrade_prompt_with_function_info(self, context_details: dict, functions: List[Dict[str, Any]]) -> str:
"""
Upgrades the prompt with information about function calls.
Args:
prompt (str): The original prompt.
context_details (dict): The original prompt.
functions (List[Dict[str, Any]]): A list of dictionaries describing functions that can be called.
Returns:
@ -3397,27 +3428,31 @@ The AI should respond in this format using data from actions_list:
system_message_template = self.config.system_message_template
separator_template = self.config.separator_template
function_descriptions = [f"{start_header_id_template}{system_message_template}{end_header_id_template}If you need to call a function to fulfill the user request, use a function markdown tag with the function call as the following json format:",
tools = self.transform_functions(functions)
import copy
cd = copy.deepcopy(context_details)
function_descriptions = [
f"{start_header_id_template}Available functions{end_header_id_template}\n",
f"{json.dumps(tools,indent=4).strip()}",
cd["conditionning"],
"Your objective is interact with the user and if you need to call a function, then use the available functions above and call them using the following json format inside a markdown tag:"
"```function",
"{",
'"function_name":the name of the function to be called,',
'"function_parameters": a list of parameter values',
"}",
"```",
"Only use available functions.",
"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:
description = f"{function['function_name']}: {function['function_description']}\nparameters:{function['function_parameters']}"
function_descriptions.append(description)
# Combine the function descriptions with the original prompt.
function_info = '\n'.join(function_descriptions)
upgraded_prompt = f"{function_info}\n{prompt}"
cd["conditionning"]=function_info
upgraded_prompt = self.build_prompt_from_context_details(cd)
return upgraded_prompt
def extract_function_calls_as_json(self, text: str) -> List[Dict[str, Any]]:
@ -3437,7 +3472,7 @@ The AI should respond in this format using data from actions_list:
# Filter out and parse JSON entries.
function_calls = []
for block in code_blocks:
if block["type"]=="function":
if block["type"]=="function" or block["type"]=="json":
content = block.get("content", "")
try:
# Attempt to parse the JSON content of the code block.
@ -3455,21 +3490,25 @@ The AI should respond in this format using data from actions_list:
def interact_with_function_call(
self,
prompt,
context_details,
function_definitions,
prompt_after_execution=True,
callback = None,
hide_function_call=False,
separate_output=False):
start_header_id_template = self.config.start_header_id_template
end_header_id_template = self.config.end_header_id_template
system_message_template = self.config.system_message_template
separator_template = self.config.separator_template
final_output = ""
if len(self.personality.image_files)>0:
out, function_calls = self.generate_with_function_calls_and_images(prompt, self.personality.image_files, function_definitions, callback=callback)
out, function_calls = self.generate_with_function_calls_and_images(context_details, self.personality.image_files, function_definitions, callback=callback)
else:
out, function_calls = self.generate_with_function_calls(prompt, function_definitions, callback=callback)
out, function_calls = self.generate_with_function_calls(context_details, function_definitions, callback=callback)
if len(function_calls)>0:
if hide_function_call:
self.full("") #Hide function call
@ -3480,17 +3519,17 @@ The AI should respond in this format using data from actions_list:
if separate_output:
self.full(final_output)
self.new_message("")
prompt += out +"\n"+ f"{start_header_id_template}"+self.personality.name+f"{end_header_id_template}"
context_details["discussion_messages"] +=out
if len(self.personality.image_files)>0:
out, function_calls = self.generate_with_function_calls_and_images(prompt, self.personality.image_files, function_definitions, callback=callback)
out, function_calls = self.generate_with_function_calls_and_images(context_details, self.personality.image_files, function_definitions, callback=callback)
else:
out, function_calls = self.generate_with_function_calls(prompt, function_definitions, callback=callback)
out, function_calls = self.generate_with_function_calls(context_details, function_definitions, callback=callback)
final_output += "\n" + out
if len(function_calls)>0:
outputs = self.execute_function_calls(function_calls,function_definitions)
final_output = "\n".join([str(o) if type(o)==str else str(o[0]) if (type(o)==tuple or type(0)==list) and len(o)>0 else "" for o in outputs])
out += f"{separator_template}{start_header_id_template}function calls results{end_header_id_template}\n" + final_output
prompt += out +"\n"+ f"{start_header_id_template}"+self.personality.name+f"{end_header_id_template}"
context_details["discussion_messages"] +=out
else:
final_output = out
return final_output