Merge pull request #396 from MervinPraison/develop

v2.0.77

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.74 gunicorn markdown
+RUN pip install flask praisonai==2.0.77 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.74 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.77 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

praisonai.rb

@@ -3,7 +3,7 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.74.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.77.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/cli.py

@@ -240,7 +240,18 @@ class PraisonAI:
                 print("All packages installed.")
 
             train_args = sys.argv[2:]  # Get all arguments after 'train'
-            train_script_path = os.path.join(package_root, 'train.py')
+            
+            # Check if this is a vision model - handle all case variations
+            model_name = config.get("model_name", "").lower()
+            is_vision_model = any(x in model_name for x in ["-vl-", "-vl", "vl-", "-vision-", "-vision", "vision-", "visionmodel"])
+            
+            # Choose appropriate training script
+            if is_vision_model:
+                train_script_path = os.path.join(package_root, 'train_vision.py')
+                print("Using vision training script for VL model...")
+            else:
+                train_script_path = os.path.join(package_root, 'train.py')
+                print("Using standard training script...")
 
             # Set environment variables
             env = os.environ.copy()

praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.0.74 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.77 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

praisonai/setup/setup_conda_env.sh

@@ -96,7 +96,7 @@ PIP_FULL_PATH=$(conda run -n "$ENV_NAME" which pip)
 # Install other packages using pip
 $PIP_FULL_PATH install --upgrade pip 
 $PIP_FULL_PATH install "xformers==0.0.26.post1"
-$PIP_FULL_PATH install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@038e6d4c8d40207a87297ab3aaf787c19b1006d1"
+$PIP_FULL_PATH install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@53a773e4fbc53a1d96c7ba107e5fe75dab07027b"
 $PIP_FULL_PATH install --no-deps "trl<0.9.0" peft accelerate bitsandbytes 
 $PIP_FULL_PATH install unsloth_zoo
 $PIP_FULL_PATH install cut_cross_entropy

praisonai/train_vision.py

@@ -0,0 +1,306 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+This script finetunes a vision language model using Unsloth's fast training framework.
+It supports vision tasks by converting raw image-caption samples into a conversation format, 
+adding vision-specific LoRA adapters, and training using TRL's SFTTrainer with UnslothVisionDataCollator.
+"""
+
+import os
+import sys
+import yaml
+import torch
+import shutil
+import subprocess
+import gc  # For garbage collection
+
+from datasets import load_dataset, concatenate_datasets, Dataset
+from unsloth import FastVisionModel, is_bf16_supported
+from unsloth.trainer import UnslothVisionDataCollator
+from transformers import TrainingArguments
+from trl import SFTTrainer
+from tqdm import tqdm  # Add progress bar
+
+
+class TrainVisionModel:
+    def __init__(self, config_path="config.yaml"):
+        self.load_config(config_path)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = None
+        self.hf_tokenizer = None  # The underlying tokenizer
+
+    def load_config(self, path):
+        with open(path, "r") as file:
+            self.config = yaml.safe_load(file)
+        print("DEBUG: Loaded config:", self.config)
+
+    def print_system_info(self):
+        print("DEBUG: PyTorch version:", torch.__version__)
+        print("DEBUG: CUDA version:", torch.version.cuda)
+        if torch.cuda.is_available():
+            print("DEBUG: CUDA Device Capability:", torch.cuda.get_device_capability())
+        else:
+            print("DEBUG: CUDA is not available")
+        print("DEBUG: Python Version:", sys.version)
+        print("DEBUG: Python Path:", sys.executable)
+
+    def check_gpu(self):
+        gpu_stats = torch.cuda.get_device_properties(0)
+        print(f"DEBUG: GPU = {gpu_stats.name}. Max memory = {round(gpu_stats.total_memory/(1024**3),3)} GB.")
+
+    def check_ram(self):
+        from psutil import virtual_memory
+        ram_gb = virtual_memory().total / 1e9
+        print(f"DEBUG: Your runtime has {ram_gb:.1f} gigabytes of available RAM")
+        if ram_gb < 20:
+            print("DEBUG: Not using a high-RAM runtime")
+        else:
+            print("DEBUG: You are using a high-RAM runtime!")
+
+    def prepare_model(self):
+        print("DEBUG: Preparing vision model and tokenizer...")
+        self.model, original_tokenizer = FastVisionModel.from_pretrained(
+            model_name=self.config["model_name"],
+            load_in_4bit=self.config["load_in_4bit"],
+            use_gradient_checkpointing="unsloth"
+        )
+        print("DEBUG: Vision model and original tokenizer loaded.")
+        
+        # Use the full processor that supports image inputs.
+        self.hf_tokenizer = original_tokenizer
+        
+        # Set pad token if needed
+        if not hasattr(self.hf_tokenizer, 'pad_token') or self.hf_tokenizer.pad_token is None:
+            if hasattr(self.hf_tokenizer, 'eos_token'):
+                self.hf_tokenizer.pad_token = self.hf_tokenizer.eos_token
+            elif hasattr(self.hf_tokenizer, 'bos_token'):
+                self.hf_tokenizer.pad_token = self.hf_tokenizer.bos_token
+        
+        # Set max length
+        if hasattr(self.hf_tokenizer, 'model_max_length'):
+            self.hf_tokenizer.model_max_length = self.config.get("max_seq_length", 2048)
+        
+        # Add vision-specific LoRA adapters
+        self.model = FastVisionModel.get_peft_model(
+            self.model,
+            finetune_vision_layers=self.config.get("finetune_vision_layers", False),
+            finetune_language_layers=self.config.get("finetune_language_layers", True),
+            finetune_attention_modules=self.config.get("finetune_attention_modules", True),
+            finetune_mlp_modules=self.config.get("finetune_mlp_modules", True),
+            r=16,
+            lora_alpha=16,
+            lora_dropout=0,
+            bias="none",
+            random_state=3407,
+            use_rslora=False,
+            loftq_config=None
+        )
+        print("DEBUG: Vision LoRA adapters added.")
+
+    def convert_sample(self, sample):
+        
+        instruction = self.config.get(
+            "vision_instruction",
+            "You are an expert radiographer. Describe accurately what you see in this image."
+        )
+        conversation = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": instruction},
+                    {"type": "image", "image": sample["image"]}
+                ]
+            },
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": sample["caption"]}
+                ]
+            },
+        ]
+        
+        return {"messages": conversation}
+
+    def load_datasets(self):
+        all_converted = []
+        for dataset_info in self.config["dataset"]:
+            print("\nDEBUG: Loading vision dataset:", dataset_info)
+            ds = load_dataset(
+                dataset_info["name"], 
+                split=dataset_info.get("split_type", "train")
+            )
+            print("DEBUG: Dataset size:", len(ds))
+            print("DEBUG: First raw sample:", ds[0])
+            print("DEBUG: Dataset features:", ds.features)
+            
+            print("\nDEBUG: Converting dataset to vision conversation format...")
+            converted_ds = [self.convert_sample(sample) for sample in ds]
+            
+            # Debug first converted sample
+            print("\nDEBUG: First converted sample structure:")
+            first = converted_ds[0]
+            print("DEBUG: Message keys:", first["messages"][0]["content"][1].keys())
+            print("DEBUG: Image type in converted:", type(first["messages"][0]["content"][1].get("image")))
+            
+            all_converted.extend(converted_ds)
+        
+        print("\nDEBUG: Combined vision dataset has", len(all_converted), "examples.")
+        return all_converted
+
+    def train_model(self):
+        print("DEBUG: Starting vision training...")
+        raw_dataset = self.load_datasets()
+        
+        # Build training arguments using TrainingArguments
+        training_args = TrainingArguments(
+            per_device_train_batch_size=self.config.get("per_device_train_batch_size", 1),
+            gradient_accumulation_steps=self.config.get("gradient_accumulation_steps", 4),
+            warmup_steps=self.config.get("warmup_steps", 5),
+            max_steps=self.config.get("max_steps", 30),
+            learning_rate=self.config.get("learning_rate", 2e-4),
+            fp16=self.config.get("fp16", not is_bf16_supported()),
+            bf16=self.config.get("bf16", is_bf16_supported()),
+            logging_steps=self.config.get("logging_steps", 1),
+            optim=self.config.get("optim", "adamw_8bit"),
+            weight_decay=self.config.get("weight_decay", 0.01),
+            lr_scheduler_type=self.config.get("lr_scheduler_type", "linear"),
+            seed=self.config.get("seed", 3407),
+            output_dir=self.config.get("output_dir", "outputs"),
+            report_to="none" if not os.getenv("PRAISON_WANDB") else "wandb",
+            remove_unused_columns=False,
+            # Add memory optimization settings
+            gradient_checkpointing=True,
+            max_grad_norm=1.0,
+        )
+        
+        trainer = SFTTrainer(
+            model=self.model,
+            tokenizer=self.hf_tokenizer,
+            data_collator=UnslothVisionDataCollator(self.model, self.hf_tokenizer),
+            train_dataset=raw_dataset,
+            args=training_args,
+            max_seq_length=self.config.get("max_seq_length", 2048),
+            dataset_text_field="",  # Required for vision training
+            dataset_kwargs={"skip_prepare_dataset": True},  # Required for vision training
+            packing=False  # Explicitly set packing to False
+        )
+        print("DEBUG: Beginning vision trainer.train() ...")
+        trainer.train()
+        print("DEBUG: Vision training complete. Saving model and tokenizer locally...")
+        self.model.save_pretrained("lora_vision_model")
+        self.hf_tokenizer.save_pretrained("lora_vision_model")
+        print("DEBUG: Saved vision model and tokenizer to 'lora_vision_model'.")
+
+    def vision_inference(self, instruction, image):
+        FastVisionModel.for_inference(self.model)
+        messages = [
+            {"role": "user", "content": [
+                {"type": "image"},
+                {"type": "text", "text": instruction}
+            ]}
+        ]
+        input_text = self.hf_tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = self.hf_tokenizer(
+            image,
+            input_text,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to("cuda")
+        outputs = self.model.generate(
+            **inputs,
+            max_new_tokens=128,
+            use_cache=True,
+            temperature=1.5,
+            min_p=0.1
+        )
+        print("DEBUG: Vision inference output:", self.hf_tokenizer.batch_decode(outputs))
+
+    def save_model_merged(self):
+        if os.path.exists(self.config["hf_model_name"]):
+            shutil.rmtree(self.config["hf_model_name"])
+        self.model.push_to_hub_merged(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            save_method="merged_16bit",
+            token=os.getenv("HF_TOKEN")
+        )
+
+    def push_model_gguf(self):
+        self.model.push_to_hub_gguf(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            quantization_method=self.config.get("quantization_method", "q4_k_m"),
+            token=os.getenv("HF_TOKEN")
+        )
+
+    def save_model_gguf(self):
+        self.model.save_pretrained_gguf(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            quantization_method="q4_k_m"
+        )
+
+    def prepare_modelfile_content(self):
+        output_model = self.config["hf_model_name"]
+        
+        template = '''{{- range $index, $_ := .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
+
+{{ .Content }}
+{{- if gt (len (slice $.Messages $index)) 1 }}<|eot_id|>
+{{- else if ne .Role "assistant" }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}'''
+        
+        return f"""FROM {output_model}
+TEMPLATE {template}
+PARAMETER temperature 0.6
+PARAMETER top_p 0.9
+"""
+
+    def create_and_push_ollama_model(self):
+        modelfile_content = self.prepare_modelfile_content()
+        with open("Modelfile", "w") as file:
+            file.write(modelfile_content)
+        subprocess.run(["ollama", "serve"])
+        subprocess.run(["ollama", "create", f"{self.config['ollama_model']}:{self.config['model_parameters']}", "-f", "Modelfile"])
+        subprocess.run(["ollama", "push", f"{self.config['ollama_model']}:{self.config['model_parameters']}"])
+
+    def run(self):
+        self.print_system_info()
+        self.check_gpu()
+        self.check_ram()
+        if self.config.get("train", "true").lower() == "true":
+            self.prepare_model()
+            self.train_model()
+        if self.config.get("huggingface_save", "true").lower() == "true":
+            self.save_model_merged()
+        if self.config.get("huggingface_save_gguf", "true").lower() == "true":
+            self.push_model_gguf()
+        if self.config.get("ollama_save", "true").lower() == "true":
+            self.create_and_push_ollama_model()
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="PraisonAI Vision Training Script")
+    parser.add_argument("command", choices=["train", "inference"], help="Command to execute")
+    parser.add_argument("--config", default="config.yaml", help="Path to configuration file")
+    args = parser.parse_args()
+
+    trainer_obj = TrainVisionModel(config_path=args.config)
+    if args.command == "train":
+        trainer_obj.run()
+    elif args.command == "inference":
+        # For inference, we load a sample image from the first dataset
+        instr = trainer_obj.config.get("vision_instruction", "You are an expert radiographer. Describe accurately what you see in this image.")
+        ds_info = trainer_obj.config["dataset"][0]
+        ds = load_dataset(ds_info["name"], split=ds_info.get("split_type", "train"))
+        sample_image = ds[0]["image"]
+        if trainer_obj.model is None or trainer_obj.hf_tokenizer is None:
+            trainer_obj.prepare_model()
+        trainer_obj.vision_inference(instr, sample_image)
+
+
+if __name__ == "__main__":
+    main() 

praisonai/upload_vision.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+This script handles uploading trained vision models to Hugging Face and Ollama.
+It reads configuration from config.yaml and provides options to upload in different formats.
+"""
+
+import os
+import yaml
+import torch
+import shutil
+import subprocess
+from unsloth import FastVisionModel
+
+class UploadVisionModel:
+    def __init__(self, config_path="config.yaml"):
+        self.load_config(config_path)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = None
+        self.hf_tokenizer = None
+
+    def load_config(self, path):
+        """Load configuration from yaml file."""
+        with open(path, "r") as file:
+            self.config = yaml.safe_load(file)
+        print("DEBUG: Loaded config:", self.config)
+
+    def prepare_model(self):
+        """Load the trained model for uploading."""
+        print("DEBUG: Loading trained model and tokenizer...")
+        self.model, original_tokenizer = FastVisionModel.from_pretrained(
+            model_name=self.config.get("output_dir", "lora_model"),
+            load_in_4bit=self.config.get("load_in_4bit", True)
+        )
+        self.hf_tokenizer = original_tokenizer
+        print("DEBUG: Model and tokenizer loaded successfully.")
+
+    def save_model_merged(self):
+        """Save merged model to Hugging Face Hub."""
+        print(f"DEBUG: Saving merged model to Hugging Face Hub: {self.config['hf_model_name']}")
+        if os.path.exists(self.config["hf_model_name"]):
+            shutil.rmtree(self.config["hf_model_name"])
+        self.model.push_to_hub_merged(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            save_method="merged_16bit",
+            token=os.getenv("HF_TOKEN")
+        )
+        print("DEBUG: Model saved to Hugging Face Hub successfully.")
+
+    def push_model_gguf(self):
+        """Push model in GGUF format to Hugging Face Hub."""
+        print(f"DEBUG: Pushing GGUF model to Hugging Face Hub: {self.config['hf_model_name']}")
+        self.model.push_to_hub_gguf(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            quantization_method=self.config.get("quantization_method", "q4_k_m"),
+            token=os.getenv("HF_TOKEN")
+        )
+        print("DEBUG: GGUF model pushed to Hugging Face Hub successfully.")
+
+    def prepare_modelfile_content(self):
+        """Prepare Ollama modelfile content using Llama 3.2 vision template."""
+        output_model = self.config["hf_model_name"]
+        
+        # Using Llama 3.2 vision template format
+        template = """{{- range $index, $_ := .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
+
+{{ .Content }}
+{{- if gt (len (slice $.Messages $index)) 1 }}<|eot_id|>
+{{- else if ne .Role "assistant" }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}"""
+        
+        # Assemble the modelfile content with Llama 3.2 vision parameters
+        modelfile = f"FROM {output_model}\n"
+        modelfile += "TEMPLATE \"""" + template + "\"""\n"
+        modelfile += "PARAMETER temperature 0.6\n"
+        modelfile += "PARAMETER top_p 0.9\n"
+        return modelfile
+
+    def create_and_push_ollama_model(self):
+        """Create and push model to Ollama."""
+        print(f"DEBUG: Creating Ollama model: {self.config['ollama_model']}:{self.config['model_parameters']}")
+        modelfile_content = self.prepare_modelfile_content()
+        with open("Modelfile", "w") as file:
+            file.write(modelfile_content)
+        
+        print("DEBUG: Starting Ollama server...")
+        subprocess.run(["ollama", "serve"])
+        
+        print("DEBUG: Creating Ollama model...")
+        subprocess.run([
+            "ollama", "create", 
+            f"{self.config['ollama_model']}:{self.config['model_parameters']}", 
+            "-f", "Modelfile"
+        ])
+        
+        print("DEBUG: Pushing model to Ollama...")
+        subprocess.run([
+            "ollama", "push", 
+            f"{self.config['ollama_model']}:{self.config['model_parameters']}"
+        ])
+        print("DEBUG: Model pushed to Ollama successfully.")
+
+    def upload(self, target="all"):
+        """
+        Upload the model to specified targets.
+        Args:
+            target (str): One of 'all', 'huggingface', 'huggingface_gguf', or 'ollama'
+        """
+        self.prepare_model()
+        
+        if target in ["all", "huggingface"]:
+            self.save_model_merged()
+        
+        if target in ["all", "huggingface_gguf"]:
+            self.push_model_gguf()
+            
+        if target in ["all", "ollama"]:
+            self.create_and_push_ollama_model()
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="Upload Vision Model to Various Platforms")
+    parser.add_argument("--config", default="config.yaml", help="Path to configuration file")
+    parser.add_argument(
+        "--target", 
+        choices=["all", "huggingface", "huggingface_gguf", "ollama"],
+        default="all",
+        help="Target platform to upload to"
+    )
+    args = parser.parse_args()
+
+    uploader = UploadVisionModel(config_path=args.config)
+    uploader.upload(target=args.target)
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.74"
+version = "2.0.77"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.61",
+    "praisonaiagents>=0.0.62",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.74"
+version = "2.0.77"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -102,7 +102,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.61"
+praisonaiagents = ">=0.0.62"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai-agents/llm-tool-call.py

@@ -0,0 +1,18 @@
+from praisonaiagents import Agent
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent1 = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    llm="openai/gpt-4o-mini",
+    verbose=10
+)
+agent1.start("history of AI in 1 line")
+
+agent2 = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    llm="gpt-4o-mini",
+    verbose=10
+)
+agent2.start("history of AI in 1 line")

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -714,6 +714,22 @@ Your Goal: {self.goal}
             return None
 
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False):
+        # Log all parameter values when in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            param_info = {
+                "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                "temperature": temperature,
+                "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                "output_json": str(output_json.__class__.__name__) if output_json else None,
+                "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                "reasoning_steps": reasoning_steps,
+                "agent_name": self.name,
+                "agent_role": self.role,
+                "agent_goal": self.goal
+            }
+            logging.debug(f"Agent.chat parameters: {json.dumps(param_info, indent=2, default=str)}")
+        
+        start_time = time.time()
         reasoning_steps = reasoning_steps or self.reasoning_steps
         # Search for existing knowledge if any knowledge is provided
         if self.knowledge:
@@ -738,7 +754,7 @@ Your Goal: {self.goal}
                     system_prompt=f"{self.backstory}\n\nYour Role: {self.role}\n\nYour Goal: {self.goal}" if self.use_system_prompt else None,
                     chat_history=self.chat_history,
                     temperature=temperature,
-                    tools=tools,
+                    tools=self.tools if tools is None else tools,
                     output_json=output_json,
                     output_pydantic=output_pydantic,
                     verbose=self.verbose,
@@ -749,7 +765,7 @@ Your Goal: {self.goal}
                     console=self.console,
                     agent_name=self.name,
                     agent_role=self.role,
-                    agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools],
+                    agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in (tools if tools is not None else self.tools)],
                     execute_tool_fn=self.execute_tool,  # Pass tool execution function
                     reasoning_steps=reasoning_steps
                 )
@@ -757,6 +773,11 @@ Your Goal: {self.goal}
                 self.chat_history.append({"role": "user", "content": prompt})
                 self.chat_history.append({"role": "assistant", "content": response_text})
 
+                # Log completion time if in debug mode
+                if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                    total_time = time.time() - start_time
+                    logging.debug(f"Agent.chat completed in {total_time:.2f} seconds")
+
                 return response_text
             except Exception as e:
                 display_error(f"Error in LLM chat: {e}")
@@ -944,6 +965,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     display_error(f"Error in chat: {e}", console=self.console)
                     return None 
 
+        # Log completion time if in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            total_time = time.time() - start_time
+            logging.debug(f"Agent.chat completed in {total_time:.2f} seconds")
+            
+        return response_text
+
     def clean_json_output(self, output: str) -> str:
         """Clean and extract JSON from response text."""
         cleaned = output.strip()
@@ -958,6 +986,22 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
     async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False):
         """Async version of chat method. TODO: Requires Syncing with chat method.""" 
+        # Log all parameter values when in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            param_info = {
+                "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                "temperature": temperature,
+                "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                "output_json": str(output_json.__class__.__name__) if output_json else None,
+                "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                "reasoning_steps": reasoning_steps,
+                "agent_name": self.name,
+                "agent_role": self.role,
+                "agent_goal": self.goal
+            }
+            logging.debug(f"Agent.achat parameters: {json.dumps(param_info, indent=2, default=str)}")
+        
+        start_time = time.time()
         reasoning_steps = reasoning_steps or self.reasoning_steps
         try:
             # Search for existing knowledge if any knowledge is provided
@@ -996,9 +1040,15 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     self.chat_history.append({"role": "user", "content": prompt})
                     self.chat_history.append({"role": "assistant", "content": response_text})
 
+                    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                        total_time = time.time() - start_time
+                        logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                     return response_text
                 except Exception as e:
                     display_error(f"Error in LLM chat: {e}")
+                    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                        total_time = time.time() - start_time
+                        logging.debug(f"Agent.achat failed in {total_time:.2f} seconds: {str(e)}")
                     return None
 
             # For OpenAI client
@@ -1081,7 +1131,11 @@ Your Goal: {self.goal}
                             temperature=temperature,
                             tools=formatted_tools
                         )
-                        return await self._achat_completion(response, tools)
+                        result = await self._achat_completion(response, tools)
+                        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                            total_time = time.time() - start_time
+                            logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
+                        return result
                     elif output_json or output_pydantic:
                         response = await async_client.chat.completions.create(
                             model=self.llm,
@@ -1090,6 +1144,9 @@ Your Goal: {self.goal}
                             response_format={"type": "json_object"}
                         )
                         # Return the raw response
+                        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                            total_time = time.time() - start_time
+                            logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                         return response.choices[0].message.content
                     else:
                         response = await async_client.chat.completions.create(
@@ -1097,12 +1154,21 @@ Your Goal: {self.goal}
                             messages=messages,
                             temperature=temperature
                         )
+                        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                            total_time = time.time() - start_time
+                            logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                         return response.choices[0].message.content
                 except Exception as e:
                     display_error(f"Error in chat completion: {e}")
+                    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                        total_time = time.time() - start_time
+                        logging.debug(f"Agent.achat failed in {total_time:.2f} seconds: {str(e)}")
                     return None
         except Exception as e:
             display_error(f"Error in achat: {e}")
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                total_time = time.time() - start_time
+                logging.debug(f"Agent.achat failed in {total_time:.2f} seconds: {str(e)}")
             return None
 
     async def _achat_completion(self, response, tools, reasoning_steps=False):

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -172,6 +172,36 @@ class LLM:
         # Enable error dropping for cleaner output
         litellm.drop_params = True
         self._setup_event_tracking(events)
+        
+        # Log all initialization parameters when in debug mode
+        if not isinstance(verbose, bool) and verbose >= 10:
+            debug_info = {
+                "model": self.model,
+                "timeout": self.timeout,
+                "temperature": self.temperature,
+                "top_p": self.top_p,
+                "n": self.n,
+                "max_tokens": self.max_tokens,
+                "presence_penalty": self.presence_penalty,
+                "frequency_penalty": self.frequency_penalty,
+                "logit_bias": self.logit_bias,
+                "response_format": self.response_format,
+                "seed": self.seed,
+                "logprobs": self.logprobs,
+                "top_logprobs": self.top_logprobs,
+                "api_version": self.api_version,
+                "stop_phrases": self.stop_phrases,
+                "api_key": "***" if self.api_key else None,  # Mask API key for security
+                "base_url": self.base_url,
+                "verbose": self.verbose,
+                "markdown": self.markdown,
+                "self_reflect": self.self_reflect,
+                "max_reflect": self.max_reflect,
+                "min_reflect": self.min_reflect,
+                "reasoning_steps": self.reasoning_steps,
+                "extra_settings": {k: v for k, v in self.extra_settings.items() if k not in ["api_key"]}
+            }
+            logging.debug(f"LLM instance initialized with: {json.dumps(debug_info, indent=2, default=str)}")
 
     def get_response(
         self,
@@ -195,6 +225,56 @@ class LLM:
         **kwargs
     ) -> str:
         """Enhanced get_response with all OpenAI-like features"""
+        logging.info(f"Getting response from {self.model}")
+        # Log all self values when in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            debug_info = {
+                "model": self.model,
+                "timeout": self.timeout,
+                "temperature": self.temperature,
+                "top_p": self.top_p,
+                "n": self.n,
+                "max_tokens": self.max_tokens,
+                "presence_penalty": self.presence_penalty,
+                "frequency_penalty": self.frequency_penalty,
+                "logit_bias": self.logit_bias,
+                "response_format": self.response_format,
+                "seed": self.seed,
+                "logprobs": self.logprobs,
+                "top_logprobs": self.top_logprobs,
+                "api_version": self.api_version,
+                "stop_phrases": self.stop_phrases,
+                "api_key": "***" if self.api_key else None,  # Mask API key for security
+                "base_url": self.base_url,
+                "verbose": self.verbose,
+                "markdown": self.markdown,
+                "self_reflect": self.self_reflect,
+                "max_reflect": self.max_reflect,
+                "min_reflect": self.min_reflect,
+                "reasoning_steps": self.reasoning_steps
+            }
+            logging.debug(f"LLM instance configuration: {json.dumps(debug_info, indent=2, default=str)}")
+            
+            # Log the parameter values passed to get_response
+            param_info = {
+                "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                "system_prompt": system_prompt[:100] + "..." if system_prompt and len(system_prompt) > 100 else system_prompt,
+                "chat_history": f"[{len(chat_history)} messages]" if chat_history else None,
+                "temperature": temperature,
+                "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                "output_json": str(output_json.__class__.__name__) if output_json else None,
+                "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                "verbose": verbose,
+                "markdown": markdown,
+                "self_reflect": self_reflect,
+                "max_reflect": max_reflect,
+                "min_reflect": min_reflect,
+                "agent_name": agent_name,
+                "agent_role": agent_role,
+                "agent_tools": agent_tools,
+                "kwargs": str(kwargs)
+            }
+            logging.debug(f"get_response parameters: {json.dumps(param_info, indent=2, default=str)}")
         try:
             import litellm
             # This below **kwargs** is passed to .completion() directly. so reasoning_steps has to be popped. OR find alternate best way of handling this.
@@ -202,6 +282,23 @@ class LLM:
             # Disable litellm debug messages
             litellm.set_verbose = False
             
+            # Format tools if provided
+            formatted_tools = None
+            if tools:
+                formatted_tools = []
+                for tool in tools:
+                    if callable(tool):
+                        tool_def = self._generate_tool_definition(tool.__name__)
+                    elif isinstance(tool, str):
+                        tool_def = self._generate_tool_definition(tool)
+                    else:
+                        continue
+                        
+                    if tool_def:
+                        formatted_tools.append(tool_def)
+                if not formatted_tools:
+                    formatted_tools = None
+            
             # Build messages list
             messages = []
             if system_prompt:
@@ -260,6 +357,7 @@ class LLM:
                             messages=messages,
                             temperature=temperature,
                             stream=False,  # force non-streaming
+                            tools=formatted_tools,
                             **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                         )
                         reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
@@ -291,6 +389,7 @@ class LLM:
                                 for chunk in litellm.completion(
                                     model=self.model,
                                     messages=messages,
+                                    tools=formatted_tools,
                                     temperature=temperature,
                                     stream=True,
                                     **kwargs
@@ -305,6 +404,7 @@ class LLM:
                             for chunk in litellm.completion(
                                 model=self.model,
                                 messages=messages,
+                                tools=formatted_tools,
                                 temperature=temperature,
                                 stream=True,
                                 **kwargs
@@ -318,6 +418,7 @@ class LLM:
                     final_response = litellm.completion(
                         model=self.model,
                         messages=messages,
+                        tools=formatted_tools,
                         temperature=temperature,
                         stream=False,  # No streaming for tool call check
                         **kwargs
@@ -552,6 +653,11 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         except Exception as error:
             display_error(f"Error in get_response: {str(error)}")
             raise
+        
+        # Log completion time if in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            total_time = time.time() - start_time
+            logging.debug(f"get_response completed in {total_time:.2f} seconds")
 
     async def get_response_async(
         self,
@@ -577,6 +683,56 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         """Async version of get_response with identical functionality."""
         try:
             import litellm
+            logging.info(f"Getting async response from {self.model}")
+            # Log all self values when in debug mode
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                debug_info = {
+                    "model": self.model,
+                    "timeout": self.timeout,
+                    "temperature": self.temperature,
+                    "top_p": self.top_p,
+                    "n": self.n,
+                    "max_tokens": self.max_tokens,
+                    "presence_penalty": self.presence_penalty,
+                    "frequency_penalty": self.frequency_penalty,
+                    "logit_bias": self.logit_bias,
+                    "response_format": self.response_format,
+                    "seed": self.seed,
+                    "logprobs": self.logprobs,
+                    "top_logprobs": self.top_logprobs,
+                    "api_version": self.api_version,
+                    "stop_phrases": self.stop_phrases,
+                    "api_key": "***" if self.api_key else None,  # Mask API key for security
+                    "base_url": self.base_url,
+                    "verbose": self.verbose,
+                    "markdown": self.markdown,
+                    "self_reflect": self.self_reflect,
+                    "max_reflect": self.max_reflect,
+                    "min_reflect": self.min_reflect,
+                    "reasoning_steps": self.reasoning_steps
+                }
+                logging.debug(f"LLM async instance configuration: {json.dumps(debug_info, indent=2, default=str)}")
+                
+                # Log the parameter values passed to get_response_async
+                param_info = {
+                    "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                    "system_prompt": system_prompt[:100] + "..." if system_prompt and len(system_prompt) > 100 else system_prompt,
+                    "chat_history": f"[{len(chat_history)} messages]" if chat_history else None,
+                    "temperature": temperature,
+                    "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                    "output_json": str(output_json.__class__.__name__) if output_json else None,
+                    "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                    "verbose": verbose,
+                    "markdown": markdown,
+                    "self_reflect": self_reflect,
+                    "max_reflect": max_reflect,
+                    "min_reflect": min_reflect,
+                    "agent_name": agent_name,
+                    "agent_role": agent_role,
+                    "agent_tools": agent_tools,
+                    "kwargs": str(kwargs)
+                }
+                logging.debug(f"get_response_async parameters: {json.dumps(param_info, indent=2, default=str)}")
             reasoning_steps = kwargs.pop('reasoning_steps', self.reasoning_steps)
             litellm.set_verbose = False
 
@@ -983,6 +1139,11 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                 raise LLMContextLengthExceededException(str(error))
             display_error(f"Error in get_response_async: {str(error)}")
             raise
+            
+        # Log completion time if in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            total_time = time.time() - start_time
+            logging.debug(f"get_response_async completed in {total_time:.2f} seconds")
 
     def can_use_tools(self) -> bool:
         """Check if this model can use tool functions"""
@@ -1065,6 +1226,24 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             
             logger.debug("Using synchronous response function")
             
+            # Log all self values when in debug mode
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                debug_info = {
+                    "model": self.model,
+                    "timeout": self.timeout,
+                    "temperature": temperature,
+                    "top_p": self.top_p,
+                    "n": self.n,
+                    "max_tokens": self.max_tokens,
+                    "presence_penalty": self.presence_penalty,
+                    "frequency_penalty": self.frequency_penalty,
+                    "stream": stream,
+                    "verbose": verbose,
+                    "markdown": markdown,
+                    "kwargs": str(kwargs)
+                }
+                logger.debug(f"Response method configuration: {json.dumps(debug_info, indent=2, default=str)}")
+            
             # Build messages list
             messages = []
             if system_prompt:
@@ -1150,6 +1329,24 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             
             logger.debug("Using asynchronous response function")
             
+            # Log all self values when in debug mode
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                debug_info = {
+                    "model": self.model,
+                    "timeout": self.timeout,
+                    "temperature": temperature,
+                    "top_p": self.top_p,
+                    "n": self.n,
+                    "max_tokens": self.max_tokens,
+                    "presence_penalty": self.presence_penalty,
+                    "frequency_penalty": self.frequency_penalty,
+                    "stream": stream,
+                    "verbose": verbose,
+                    "markdown": markdown,
+                    "kwargs": str(kwargs)
+                }
+                logger.debug(f"Async response method configuration: {json.dumps(debug_info, indent=2, default=str)}")
+            
             # Build messages list
             messages = []
             if system_prompt:
@@ -1210,4 +1407,117 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
         except Exception as error:
             display_error(f"Error in response_async: {str(error)}")
-            raise
+            raise
+
+    def _generate_tool_definition(self, function_name: str) -> Optional[Dict]:
+        """Generate a tool definition from a function name."""
+        logging.debug(f"Attempting to generate tool definition for: {function_name}")
+        
+        # First try to get the tool definition if it exists
+        tool_def_name = f"{function_name}_definition"
+        tool_def = globals().get(tool_def_name)
+        logging.debug(f"Looking for {tool_def_name} in globals: {tool_def is not None}")
+        
+        if not tool_def:
+            import __main__
+            tool_def = getattr(__main__, tool_def_name, None)
+            logging.debug(f"Looking for {tool_def_name} in __main__: {tool_def is not None}")
+        
+        if tool_def:
+            logging.debug(f"Found tool definition: {tool_def}")
+            return tool_def
+
+        # Try to find the function
+        func = globals().get(function_name)
+        logging.debug(f"Looking for {function_name} in globals: {func is not None}")
+        
+        if not func:
+            import __main__
+            func = getattr(__main__, function_name, None)
+            logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
+        
+        if not func or not callable(func):
+            logging.debug(f"Function {function_name} not found or not callable")
+            return None
+
+        import inspect
+        # Handle Langchain and CrewAI tools
+        if inspect.isclass(func) and hasattr(func, 'run') and not hasattr(func, '_run'):
+            original_func = func
+            func = func.run
+            function_name = original_func.__name__
+        elif inspect.isclass(func) and hasattr(func, '_run'):
+            original_func = func
+            func = func._run
+            function_name = original_func.__name__
+
+        sig = inspect.signature(func)
+        logging.debug(f"Function signature: {sig}")
+        
+        # Skip self, *args, **kwargs
+        parameters_list = []
+        for name, param in sig.parameters.items():
+            if name == "self":
+                continue
+            if param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
+                continue
+            parameters_list.append((name, param))
+
+        parameters = {
+            "type": "object",
+            "properties": {},
+            "required": []
+        }
+        
+        # Parse docstring for parameter descriptions
+        docstring = inspect.getdoc(func)
+        logging.debug(f"Function docstring: {docstring}")
+        
+        param_descriptions = {}
+        if docstring:
+            import re
+            param_section = re.split(r'\s*Args:\s*', docstring)
+            logging.debug(f"Param section split: {param_section}")
+            if len(param_section) > 1:
+                param_lines = param_section[1].split('\n')
+                for line in param_lines:
+                    line = line.strip()
+                    if line and ':' in line:
+                        param_name, param_desc = line.split(':', 1)
+                        param_descriptions[param_name.strip()] = param_desc.strip()
+        
+        logging.debug(f"Parameter descriptions: {param_descriptions}")
+
+        for name, param in parameters_list:
+            param_type = "string"  # Default type
+            if param.annotation != inspect.Parameter.empty:
+                if param.annotation == int:
+                    param_type = "integer"
+                elif param.annotation == float:
+                    param_type = "number"
+                elif param.annotation == bool:
+                    param_type = "boolean"
+                elif param.annotation == list:
+                    param_type = "array"
+                elif param.annotation == dict:
+                    param_type = "object"
+            
+            parameters["properties"][name] = {
+                "type": param_type,
+                "description": param_descriptions.get(name, "Parameter description not available")
+            }
+            
+            if param.default == inspect.Parameter.empty:
+                parameters["required"].append(name)
+        
+        logging.debug(f"Generated parameters: {parameters}")
+        tool_def = {
+            "type": "function",
+            "function": {
+                "name": function_name,
+                "description": docstring.split('\n\n')[0] if docstring else "No description available",
+                "parameters": parameters
+            }
+        }
+        logging.debug(f"Generated tool definition: {tool_def}")
+        return tool_def

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.61"
+version = "0.0.62"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

src/praisonai-agents/uv.lock

@@ -1868,7 +1868,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
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uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
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@@ -3197,7 +3197,7 @@ requires-dist = [
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@@ -3250,16 +3250,16 @@ wheels = [
 
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