added generate with images as well as new function call livbrary

lollms/functions.py

@@ -0,0 +1,70 @@
+from functools import partial
+from lollms.tasks import TasksLibrary
+from typing import Dict, Any, List
+class FunctionCalling_Library:
+    def __init__(self, tasks_library:TasksLibrary):
+        self.tl = tasks_library
+        self.function_definitions = []
+
+    def register_function(self, function_name, function_callable, function_description, function_parameters):
+        self.function_definitions.append({
+            "function_name": function_name,
+            "function": function_callable,
+            "function_description": function_description,
+            "function_parameters": function_parameters
+        })
+
+    def unregister_function(self, function_name):
+        self.function_definitions = [func for func in self.function_definitions if func["function_name"] != function_name]
+
+
+    def execute_function_calls(self, function_calls: List[Dict[str, Any]]) -> List[Any]:
+        """
+        Executes the function calls with the parameters extracted from the generated text,
+        using the original functions list to find the right function to execute.
+
+        Args:
+            function_calls (List[Dict[str, Any]]): A list of dictionaries representing the function calls.
+            function_definitions (List[Dict[str, Any]]): The original list of functions with their descriptions and callable objects.
+
+        Returns:
+            List[Any]: A list of results from executing the function calls.
+        """
+        results = []
+        # Convert function_definitions to a dict for easier lookup
+        functions_dict = {func['function_name']: func['function'] for func in self.function_definitions}
+
+        for call in function_calls:
+            function_name = call.get("function_name")
+            parameters = call.get("function_parameters", [])
+            function = functions_dict.get(function_name)
+
+            if function:
+                try:
+                    # Assuming parameters is a dictionary that maps directly to the function's arguments.
+                    if type(parameters)==list:
+                        result = function(*parameters)
+                    elif type(parameters)==dict:
+                        result = function(**parameters)
+                    results.append(result)
+                except TypeError as e:
+                    # Handle cases where the function call fails due to incorrect parameters, etc.
+                    results.append(f"Error calling {function_name}: {e}")
+            else:
+                results.append(f"Function {function_name} not found.")
+
+        return results
+    
+    def generate_with_functions(self, prompt):
+        # Assuming generate_with_function_calls is a method from TasksLibrary
+        ai_response, function_calls = self.tl.generate_with_function_calls(prompt, self.function_definitions)
+        return ai_response, function_calls
+
+    def generate_with_functions_with_images(self, prompt, image_files):
+        # Assuming generate_with_function_calls_and_images is a method from TasksLibrary
+        if len(image_files) > 0:
+            ai_response, function_calls = self.tl.generate_with_function_calls_and_images(prompt, image_files, self.function_definitions)
+        else:
+            ai_response, function_calls = self.tl.generate_with_function_calls(prompt, self.function_definitions)
+
+        return ai_response, function_calls

lollms/personality.py

@@ -3384,7 +3384,7 @@ The AI should respond in this format using data from actions_list:
 
         # Combine the function descriptions with the original prompt.
         function_info = ' '.join(function_descriptions)
-        upgraded_prompt = f"{function_info} {prompt}"
+        upgraded_prompt = f"{function_info}\n{prompt}"
 
         return upgraded_prompt
 

lollms/server/endpoints/lollms_generator.py

@@ -9,6 +9,7 @@ description:
 """
 
 from fastapi import APIRouter, Request, Body, Response
+from fastapi.responses import PlainTextResponse
 from lollms.server.elf_server import LOLLMSElfServer
 from pydantic import BaseModel
 from starlette.responses import StreamingResponse
@@ -23,7 +24,7 @@ import random
 import string
 import json
 from enum import Enum
-import asyncio
+import base64
 from datetime import datetime
 
 
@@ -192,7 +193,160 @@ async def lollms_generate(request: LollmsGenerateRequest):
                                                 temperature=request.temperature or elf_server.config.temperature
                                             )
                 completion_tokens = len(elf_server.binding.tokenize(reception_manager.reception_buffer))
-                return reception_manager.reception_buffer
+                return PlainTextResponse(reception_manager.reception_buffer)
+        else:
+            return None
+    except Exception as ex:
+        trace_exception(ex)
+        elf_server.error(ex)
+        return {"status":False,"error":str(ex)}
+
+
+
+class LollmsGenerateRequest(BaseModel):
+    prompt: str
+    images: List[str]
+    model_name: Optional[str] = None
+    personality: Optional[int] = -1
+    n_predict: Optional[int] = 1024
+    stream: bool = False
+    temperature: float = 0.1
+    top_k: Optional[int] = 50
+    top_p: Optional[float] = 0.95
+    repeat_penalty: Optional[float] = 0.8
+    repeat_last_n: Optional[int] = 40
+    seed: Optional[int] = None
+    n_threads: Optional[int] = 8
+
+@router.post("/lollms_generate_with_images")
+async def lollms_generate_with_images(request: LollmsGenerateRequest):
+    """ Endpoint for generating text from prompts using the LoLLMs fastAPI server.
+
+    Args:
+    Data model for the Generate with images Request.
+    Attributes:
+    - prompt: str : representing the input text prompt for text generation.
+    - images: str : a list of 64 bits encoded images
+    - model_name: Optional[str] = None : The name of the model to be used (it should be one of the current models)
+    - personality : Optional[int] = None : The name of the mounted personality to be used (if a personality is None, the endpoint will just return a completion text). To get the list of mounted personalities, just use /list_mounted_personalities
+    - n_predict: int representing the number of predictions to generate.
+    - stream: bool indicating whether to stream the generated text or not.
+    - temperature: float representing the temperature parameter for text generation.
+    - top_k: int representing the top_k parameter for text generation.
+    - top_p: float representing the top_p parameter for text generation.
+    - repeat_penalty: float representing the repeat_penalty parameter for text generation.
+    - repeat_last_n: int representing the repeat_last_n parameter for text generation.
+    - seed: int representing the seed for text generation.
+    - n_threads: int representing the number of threads for text generation.
+
+    Returns:
+    - If the elf_server binding is not None:
+    - If stream is True, returns a StreamingResponse of generated text chunks.
+    - If stream is False, returns the generated text as a string.
+    - If the elf_server binding is None, returns None.
+    """
+
+    try:
+        headers = { 'Content-Type': 'text/event-stream', 'Cache-Control': 'no-cache', 'Connection': 'keep-alive',}
+        reception_manager=RECEPTION_MANAGER()
+        prompt = request.prompt
+        encoded_images = request.images
+        n_predict = request.n_predict if request.n_predict>0 else 1024
+        stream = request.stream
+        prompt_tokens = len(elf_server.binding.tokenize(prompt))
+        if elf_server.binding is not None:
+            image_files = []
+            images_path = elf_server.lollms_paths.personal_outputs_path / "tmp_images"
+            images_path.mkdir(parents=True, exist_ok=True)
+            for i, encoded_image in enumerate(encoded_images):
+                image_path = images_path/ f'image_{i}.png'
+                with open(image_path, 'wb') as image_file:
+                    image_file.write(base64.b64decode(encoded_image))
+                image_files.append(image_path)            
+            if stream:
+                new_output={"new_values":[]}
+                async def generate_chunks():
+                    lk = threading.Lock()
+
+                    def callback(chunk, chunk_type:MSG_TYPE=MSG_TYPE.MSG_TYPE_CHUNK):
+                        if elf_server.cancel_gen:
+                            return False
+                        
+                        if chunk is None:
+                            return
+
+                        rx = reception_manager.new_chunk(chunk)
+                        if rx.status!=ROLE_CHANGE_DECISION.MOVE_ON:
+                            if rx.status==ROLE_CHANGE_DECISION.PROGRESSING:
+                                return True
+                            elif rx.status==ROLE_CHANGE_DECISION.ROLE_CHANGED:
+                                return False
+                            else:
+                                chunk = chunk + rx.value
+
+                        # Yield each chunk of data
+                        lk.acquire()
+                        try:
+                            new_output["new_values"].append(reception_manager.chunk)
+                            lk.release()
+                            return True
+                        except Exception as ex:
+                            trace_exception(ex)
+                            lk.release()
+                            return False
+                        
+                    def chunks_builder():
+                        if request.model_name in elf_server.binding.list_models() and elf_server.binding.model_name!=request.model_name:
+                            elf_server.binding.build_model(request.model_name)    
+
+                        elf_server.binding.generate_with_images(
+                                                prompt,
+                                                image_files,
+                                                n_predict, 
+                                                callback=callback, 
+                                                temperature=request.temperature or elf_server.config.temperature
+                                            )
+                        reception_manager.done = True
+                    thread = threading.Thread(target=chunks_builder)
+                    thread.start()
+                    current_index = 0
+                    while (not reception_manager.done and elf_server.cancel_gen == False):
+                        while (not reception_manager.done and len(new_output["new_values"])==0):
+                            time.sleep(0.001)
+                        lk.acquire()
+                        for i in range(len(new_output["new_values"])):
+                            current_index += 1                        
+                            yield (new_output["new_values"][i])
+                        new_output["new_values"]=[]
+                        lk.release()
+                    elf_server.cancel_gen = False         
+                return StreamingResponse(generate_chunks(), media_type="text/plain", headers=headers)
+            else:
+                def callback(chunk, chunk_type:MSG_TYPE=MSG_TYPE.MSG_TYPE_CHUNK):
+                    # Yield each chunk of data
+                    if chunk is None:
+                        return True
+
+                    rx = reception_manager.new_chunk(chunk)
+                    if rx.status!=ROLE_CHANGE_DECISION.MOVE_ON:
+                        if rx.status==ROLE_CHANGE_DECISION.PROGRESSING:
+                            return True
+                        elif rx.status==ROLE_CHANGE_DECISION.ROLE_CHANGED:
+                            return False
+                        else:
+                            chunk = chunk + rx.value
+
+
+                    return True
+                elf_server.binding.generate_with_images(
+                                                prompt,
+                                                image_files,
+                                                n_predict, 
+                                                callback=callback,
+                                                temperature=request.temperature or elf_server.config.temperature
+                                            )
+                completion_tokens = len(elf_server.binding.tokenize(reception_manager.reception_buffer))
+                return PlainTextResponse(reception_manager.reception_buffer)
         else:
             return None
     except Exception as ex:

lollms/tasks.py

@@ -1,14 +1,14 @@
 
 import sys
-from typing import Callable, List
+from typing import Callable, List, Dict, Any, Optional
 from functools import partial
 from datetime import datetime
 from ascii_colors import ASCIIColors
 from lollms.types import MSG_TYPE, SUMMARY_MODE
 from lollms.com import LoLLMsCom
-from lollms.utilities import PromptReshaper, remove_text_from_string
+from lollms.utilities import PromptReshaper, remove_text_from_string, process_ai_output
 from safe_store import DocumentDecomposer
-
+import json
 class TasksLibrary:
     def __init__(self, lollms:LoLLMsCom, callback: Callable[[str, MSG_TYPE, dict, list], bool]=None) -> None:
         self.lollms = lollms
@@ -93,6 +93,26 @@ class TasksLibrary:
                                 ).strip()
         return self.bot_says
 
+    def generate_with_images(self, prompt, images, max_size, temperature = None, top_k = None, top_p=None, repeat_penalty=None, repeat_last_n=None, callback=None, debug=False, show_progress=False ):
+        ASCIIColors.info("Text generation started: Warming up")
+        self.nb_received_tokens = 0
+        self.bot_says = ""
+        if debug:
+            self.print_prompt("gen",prompt)
+
+        self.lollms.model.generate_with_images(
+                                prompt,
+                                images,
+                                max_size,
+                                partial(self.process, callback=callback, show_progress=show_progress),
+                                temperature=self.lollms.config.model_temperature if temperature is None else temperature,
+                                top_k=self.lollms.config.model_top_k if top_k is None else top_k,
+                                top_p=self.lollms.config.model_top_p if top_p is None else top_p,
+                                repeat_penalty=self.lollms.config.model_repeat_penalty if repeat_penalty is None else repeat_penalty,
+                                repeat_last_n = self.lollms.config.model_repeat_last_n if repeat_last_n is None else repeat_last_n
+                                ).strip()
+        return self.bot_says
+
     def fast_gen(
                     self, 
                     prompt: str, 
@@ -145,7 +165,62 @@ class TasksLibrary:
 
         return gen
     
+    def fast_gen_with_images(self, prompt: str, images:list, max_generation_size: int=None, placeholders: dict = {}, sacrifice: list = ["previous_discussion"], debug: bool  = False, callback=None, show_progress=False) -> str:
+        """
+        Fast way to generate text from text and images
 
+        This method takes in a prompt, maximum generation size, optional placeholders, sacrifice list, and debug flag.
+        It reshapes the context before performing text generation by adjusting and cropping the number of tokens.
+
+        Parameters:
+        - prompt (str): The input prompt for text generation.
+        - max_generation_size (int): The maximum number of tokens to generate.
+        - placeholders (dict, optional): A dictionary of placeholders to be replaced in the prompt. Defaults to an empty dictionary.
+        - sacrifice (list, optional): A list of placeholders to sacrifice if the window is bigger than the context size minus the number of tokens to generate. Defaults to ["previous_discussion"].
+        - debug (bool, optional): Flag to enable/disable debug mode. Defaults to False.
+
+        Returns:
+        - str: The generated text after removing special tokens ("<s>" and "</s>") and stripping any leading/trailing whitespace.
+        """
+        prompt = "\n".join([
+            "!@>system: I am an AI assistant that can converse and analyze images. When asked to locate something in an image you send, I will reply with:",
+            "boundingbox(image_index, label, left, top, width, height)",
+            "Where:",
+            "image_index: 0-based index of the image",
+            "label: brief description of what is located",
+            "left, top: x,y coordinates of top-left box corner (0-1 scale)",
+            "width, height: box dimensions as fraction of image size",
+            "Coordinates have origin (0,0) at top-left, (1,1) at bottom-right.",
+            "For other queries, I will respond conversationally to the best of my abilities.",
+            prompt
+        ])
+        if debug == False:
+            debug = self.lollms.config.debug
+
+        if max_generation_size is None:
+            prompt_size = self.lollms.model.tokenize(prompt)
+            max_generation_size = self.lollms.model.config.ctx_size - len(prompt_size)
+
+        pr = PromptReshaper(prompt)
+        prompt = pr.build(placeholders,
+                        self.lollms.model.tokenize,
+                        self.lollms.model.detokenize,
+                        self.lollms.model.config.ctx_size - max_generation_size,
+                        sacrifice
+                        )
+        ntk = len(self.lollms.model.tokenize(prompt))
+        max_generation_size = min(self.lollms.model.config.ctx_size - ntk, max_generation_size)
+        # TODO : add show progress
+
+        gen = self.generate_with_images(prompt, images, max_generation_size, callback=callback, show_progress=show_progress).strip().replace("</s>", "").replace("<s>", "")
+        try:
+            gen = process_ai_output(gen, images, "/discussions/")
+        except Exception as ex:
+            pass
+        if debug:
+            self.print_prompt("prompt", prompt+gen)
+
+        return gen    
     
     # Communications with the user
     def step_start(self, step_text, callback: Callable[[str, MSG_TYPE, dict, list], bool]=None):
@@ -653,3 +728,147 @@ class TasksLibrary:
             self.step_end(f" Summary of {doc_name} - Processing chunk : {i+1}/{len(chunks)}")
         return "\n".join(summeries)
 
+    #======================= Function calls
+    def _upgrade_prompt_with_function_info(self, prompt: str, functions: List[Dict[str, Any]]) -> str:
+        """
+        Upgrades the prompt with information about function calls.
+
+        Args:
+            prompt (str): The original prompt.
+            functions (List[Dict[str, Any]]): A list of dictionaries describing functions that can be called.
+
+        Returns:
+            str: The upgraded prompt that includes information about the function calls.
+        """
+        function_descriptions = ["!@>information: If you need to call a function to fulfull the user request, use a function markdown tag with the function call as the following json format:",
+                                 "```function",
+                                 "{",
+                                 '"function_name":the name of the function to be called,',
+                                 '"function_parameters": a list of  parameter values',
+                                 "}",
+                                 "```",
+                                 "You can call multiple functions in one generation.",
+                                 "Each function call needs to be in a separate function markdown tag.",
+                                 "Do not add status of the execution as it will be added automatically by the system.",
+                                 "If you want to get the output of the function before answering the user, then use the keyword @<NEXT>@ at the end of your message.",
+                                 "!@>List of possible functions to be called:\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 = ' '.join(function_descriptions)
+        upgraded_prompt = f"{function_info}\n{prompt}"
+
+        return upgraded_prompt
+    def extract_function_calls_as_json(self, text: str) -> List[Dict[str, Any]]:
+        """
+        Extracts function calls formatted as JSON inside markdown code blocks.
+
+        Args:
+            text (str): The generated text containing JSON markdown entries for function calls.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries representing the function calls.
+        """
+        # Extract markdown code blocks that contain JSON.
+        code_blocks = self.extract_code_blocks(text)
+
+        # Filter out and parse JSON entries.
+        function_calls = []
+        for block in code_blocks:
+            if block["type"]=="function":
+                content = block.get("content", "")
+                try:
+                    # Attempt to parse the JSON content of the code block.
+                    function_call = json.loads(content)
+                    if type(function_call)==dict:
+                        function_calls.append(function_call)
+                    elif type(function_call)==list:
+                        function_calls+=function_call
+                except json.JSONDecodeError:
+                    # If the content is not valid JSON, skip it.
+                    continue
+
+        return function_calls    
+    def execute_function_calls(self, function_calls: List[Dict[str, Any]], function_definitions: List[Dict[str, Any]]) -> List[Any]:
+        """
+        Executes the function calls with the parameters extracted from the generated text,
+        using the original functions list to find the right function to execute.
+
+        Args:
+            function_calls (List[Dict[str, Any]]): A list of dictionaries representing the function calls.
+            function_definitions (List[Dict[str, Any]]): The original list of functions with their descriptions and callable objects.
+
+        Returns:
+            List[Any]: A list of results from executing the function calls.
+        """
+        results = []
+        # Convert function_definitions to a dict for easier lookup
+        functions_dict = {func['function_name']: func['function'] for func in function_definitions}
+
+        for call in function_calls:
+            function_name = call.get("function_name")
+            parameters = call.get("function_parameters", [])
+            function = functions_dict.get(function_name)
+
+            if function:
+                try:
+                    # Assuming parameters is a dictionary that maps directly to the function's arguments.
+                    if type(parameters)==list:
+                        result = function(*parameters)
+                    elif type(parameters)==dict:
+                        result = function(**parameters)
+                    results.append(result)
+                except TypeError as e:
+                    # Handle cases where the function call fails due to incorrect parameters, etc.
+                    results.append(f"Error calling {function_name}: {e}")
+            else:
+                results.append(f"Function {function_name} not found.")
+
+        return results
+    def generate_with_function_calls(self, prompt: str, functions: List[Dict[str, Any]], max_answer_length: Optional[int] = 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).
+            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.
+
+        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)
+
+        # Extract the function calls from the generated text.
+        function_calls = self.extract_function_calls_as_json(generated_text)
+
+        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) -> 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).
+            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.
+
+        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_with_images(upgraded_prompt, images, max_answer_length)
+
+        # Extract the function calls from the generated text.
+        function_calls = self.extract_function_calls_as_json(generated_text)
+
+        return generated_text, function_calls