fixes to prompt message

RBotReloaded.py

@@ -33,14 +33,40 @@ from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline #
 from typing import Any, Dict, List  
 import torch
 from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
+import inspect
 
 # Config  
 EMBD_CHUNK_SIZE = 512  
 AI_NAME = "Agent Llama"
 USER_NAME = "Buddy"
+MODELS_DIR = "models"
 
+def validate_and_fix_params(tool_name, params_list):
+    try:
+        # Create a list to store the validated and fixed parameters
+        validated_params = []
+        n_param = 0
+        while n_param < len(params_list) and len(validated_params) < 1:        
+          val = str(params_list[n_param]).replace("(", "").replace(")", "").replace("\"", "").replace("#", "").strip()
+          n_param = n_param + 1
+          if len(val) > 2:
+            validated_params.append(val)
+
+        if (n_param < len(params_list) and tool_name == "ImageGenerator" and len(validated_params) < 3):
+          while n_param < len(params_list):        
+            val = int(str(params_list[n_param]).replace("(", "").replace(")", "").replace("\"", "").replace("#", "").strip())
+            n_param = n_param + 1
+            if val > 0:
+              validated_params.append(val)
+          
+        return validated_params
+    except Exception as e:
+        # Handle any exceptions that may occur during parameter validation
+        print(f"Error parsing params: {str(e)}")
+        return []
+      
 # Helper to load LM  
-def create_llm(model_id="./models/mistral-7b-instruct-v0.1.Q4_K_M.gguf", load_4bit=False, load_8bit=False, ctx_len = 8192, temperature=0.5, top_p=0.95):
+def create_llm(model_id=f"{MODELS_DIR}/mistral-7b-instruct-v0.1.Q4_K_M.gguf", load_4bit=False, load_8bit=False, ctx_len = 8192, temperature=0.5, top_p=0.95):
   if (model_id.startswith("http")):
     print(f"Creating TextGen LLM base_url:{model_id}")
     return TextGen(model_url=model_id, callbacks=[StreamingStdOutCallbackHandler()])
@@ -228,34 +254,36 @@ class RBotAgent:
     formatted_history = get_buffer_string(chat_history, human_prefix="USER")
     
     prompt = f"""
-EXAMPLE 1:
+### EXAMPLE 1:
 USER: Find me a recipe for chocolate chip cookies. 
 AI: SearchAndReply("chocolate chip cookies recipe", 5)
 
-EXAMPLE 2:
+### EXAMPLE 2:
 USER: Show me pictures of cute puppies.
 AI: ImageGenerator("cute puppies", 512, 512)
-EXAMPLE 3:
+
+### EXAMPLE 3:
 USER: Explain the concept of blockchain.
 AI: KnowledgeBaseQuery("Explain blockchain")
 
-EXAMPLE 4:  
+### EXAMPLE 4:  
 USER: Find me recent news about cryptocurrency.
 AI: SearchAndReply("recent cryptocurrency news")
 
-EXAMPLE 5:
+### EXAMPLE 5:
 USER: Can you calculate the factorial of 5?
 AI: Calculator("factorial(5)")
 
-###REAL CONVERSATION:\n
-SYS:Today is {str(datetime.now().date())},  
-You are {AI_NAME} a smart and helpful AI assistant with access to external tools and knowledge.
-Please reply to the user with a truth and useful response, if you do not know the answer or you are not sure or you need more recent informations, delegate the task replying with ActionName(action_input) with the most appropriate of the available actions (you call them like functions).\nCurrent Conversation History:
+### CURRENT CONTEXT:\n
+SYS: Today is {str(datetime.now().date())},  
+SYS: You are {AI_NAME} a smart and helpful AI assistant with access to external tools and knowledge.
+SYS: Please reply to the user with a truth and useful response, if you do not know the answer or you are not sure or you need more recent informations, delegate the task replying with ActionName(action_input) with the most appropriate of the available actions (you call them like functions).
 
-###AVAILABLE TOOL ACTIONS  
+### CURRENT CONVERSATION:
+{formatted_history}
+SYS: Follow the most current user message, you can reply directly or invoke a valid action from the following available: 
 {self.tools_prompt()}  
 
-{formatted_history}
 USER: {input}
 AI:
 """
@@ -283,34 +311,32 @@ AI:
           if match[0].strip().lower() in tool_names:
             action_name = match[0].strip().lower()
             action_input = match[1].strip().replace("query_params", "").strip().replace("()","")
-            break
+            if (action_name and action_input): break
 
       # Try unformatted            
       if not action_name or not action_input:   
         lines = output.split("\n")
-
-        
         for line in lines:
           for tool in tool_names:
             if f"{tool}:" in line.lower() or f"{tool}(" in line.lower():
               action_name = tool
               action_input = line[line.lower().find(tool)+len(tool):].strip().replace("query_params", "").strip().replace("()","")
               print(f"Matched unformatted action request. {action_name}:{action_input} from line: {line}")
-              break
+              if (action_name and action_input): break
       
       # Call tool if found
       if action_name and action_input:                
         for tool in self.tools:
           if tool.name.lower() in action_name:
-            print(f"Calling action:{tool.name} with input:{action_input}")
-            observations.append(f"Calling action:{tool.name} with input:{action_input}")
 
             params_list  = action_input.split(",")
             try:
-              try:
-                res = tool.func(*params_list)
-              except:
-                res = tool.func(action_input)
+              print(f"Fixing input for tool {tool.name}, Original params list: {str(params_list)}")
+              params_list = validate_and_fix_params(tool.name, params_list)
+              print(f"Fixed params list: {str(params_list)}")
+              print(f"Calling action:{tool.name} with input:{str(params_list)}")
+              observations.append(f"Calling action:{tool.name} with input:{str(params_list)}")
+              res = tool.func(*params_list)
             except Exception as ex:
               res = f"{action_name} execution error: {str(ex)}"
             
@@ -361,7 +387,7 @@ class SmartAgent:
       self.chat_history.clear()
       
     # Create image 
-  def createImage(self, prompt, width=512, height=512, denoise_strength=0.75, guidance_scale=7.5, model_id = 'dreamshaper_8.safetensors'):
+  def createImage(self, prompt : str, width : int=512, height : int=512, denoise_strength : float=0.75, guidance_scale: float=7.5, model_id : str= 'dreamshaper_8.safetensors'):
     try:
         init_image = None
         if (os.path.exists("./image_gen_guide.jpg")):
@@ -372,11 +398,11 @@ class SmartAgent:
             if self.text2image_gen_pipe is None:
                 if torch.cuda.is_available():
                     print(f"Loading Stable model {model_id} into GPU")
-                    self.text2image_gen_pipe = StableDiffusionPipeline.from_single_file("./models/" + model_id, torch_dtype=torch.float16, verbose=True, use_safetensors=True)
+                    self.text2image_gen_pipe = StableDiffusionPipeline.from_single_file(f"{MODELS_DIR}/" + model_id, torch_dtype=torch.float16, verbose=True, use_safetensors=True)
                     self.text2image_gen_pipe = self.text2image_gen_pipe.to("cuda")   
                 else:
                     print(f"Loading Stable model {model_id} into CPU")
-                    self.text2image_gen_pipe = StableDiffusionPipeline.from_single_file("./models/" + model_id, torch_dtype=torch.float32, verbose=True, use_safetensors=True)
+                    self.text2image_gen_pipe = StableDiffusionPipeline.from_single_file(f"{MODELS_DIR}/" + model_id, torch_dtype=torch.float32, verbose=True, use_safetensors=True)
                     self.text2image_gen_pipe = self.text2image_gen_pipe.to("cpu")                   
             print("generating image from promt...")
             images = self.text2image_gen_pipe(prompt, width=width, height=height).images
@@ -384,11 +410,11 @@ class SmartAgent:
             if self.image2image_gen_pipe is None:
                 if torch.cuda.is_available():
                     print(f"Loading Stable model {model_id} into GPU")
-                    self.image2image_gen_pipe = StableDiffusionImg2ImgPipeline.from_single_file("./models/" + model_id, torch_dtype=torch.float16, verbose=True, use_safetensors=True)
+                    self.image2image_gen_pipe = StableDiffusionImg2ImgPipeline.from_single_file(f"{MODELS_DIR}/" + model_id, torch_dtype=torch.float16, verbose=True, use_safetensors=True)
                     self.image2image_gen_pipe = self.image2image_gen_pipe.to("cuda")   
                 else:
                     print(f"Loading Stable model {model_id} into CPU")
-                    self.image2image_gen_pipe = StableDiffusionImg2ImgPipeline.from_single_file("./models/" + model_id, torch_dtype=torch.float32, verbose=True, use_safetensors=True)
+                    self.image2image_gen_pipe = StableDiffusionImg2ImgPipeline.from_single_file(f"{MODELS_DIR}/" + model_id, torch_dtype=torch.float32, verbose=True, use_safetensors=True)
                     self.image2image_gen_pipe = self.image2image_gen_pipe.to("cpu")                   
             print("generating image from promt+image...")
             init_image = init_image.convert("RGB")
@@ -404,7 +430,7 @@ class SmartAgent:
             paths.append(file_path)
         return f"Generated images from prompt \"{prompt}\" saved to files: {', '.join(paths)}"        
     except Exception as e:
-        print(f"error in createImageLocal: {e}")            
+        print(f"error in createImage: {e}")            
         return "Unable to generate file"
     
   def load_and_split_documents(self, url, max_depth=2):

agent_llama_ui.py

@@ -7,7 +7,7 @@ import requests
 import glob
 import json
 import shutil
-from RBotReloaded import SmartAgent
+from RBotReloaded import SmartAgent, MODELS_DIR
 import time
 from PIL import Image
 from langchain.schema import AIMessage, HumanMessage
@@ -15,15 +15,13 @@ from langchain.schema import AIMessage, HumanMessage
 load_dotenv()
 
 
-default_model = ""
+default_model = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
 default_context = 8192
 default_load_type = "Auto"
 default_iterations = 2
 default_temperature = 0.5
 default_topp = 0.95
 
-MODELS_DIR = "models"
-
 @st.cache_resource
 def agent(model, temperature, top_p, context_length, load_8bit, load_4bit, max_iterations):    
     ag = SmartAgent(f"{MODELS_DIR}/{model}" if os.path.exists(f"{MODELS_DIR}/{model}") else model, temp=temperature, top_p=top_p, load_in_4bit=load_4bit, load_in_8bit=load_8bit, ctx_len=context_length, max_iterations=max_iterations) if model else None

requirements.txt

@@ -6,6 +6,8 @@ bs4
 diffusers
 transformers
 faiss-cpu
+lxml
+ctransformers
 fastapi
 git-python
 google-search-results
@@ -41,6 +43,4 @@ torchaudio
 #torchaudio==2.0.1+cu117
 torchvision
 python-dotenv
-lxml
-ctransformers
 omegaconf

start_agent.bat

@@ -13,7 +13,7 @@ rem Activate the virtual environment
 call %ENV_NAME%\Scripts\activate
 
 rem Install the required packages from requirements.txt
-python -m pip install -r requirements.txt
+python -m pip install --user -r requirements.txt
 
 rem Run your Streamlit application
 python -m streamlit run agent_llama_ui.py