Refactor :minor change in server

README.md

@@ -1,7 +1,7 @@
 
-# 🚀 agenticSeek: Local AI Assistant Powered by DeepSeek Agents  
+# AgenticSeek: Fully local AI Assistant Powered by Deepseek R1 Agents.
 
-**A fully local AI assistant** using Deepseek R1 agents.
+**A fully local AI assistant** using AI agents. The goal of the project is to create a truly Jarvis like assistant using reasoning model such as deepseek R1. 
 
 > 🛠️ **Work in Progress** – Looking for contributors! 🚀  
 ---
@@ -11,13 +11,19 @@
 -  **Privacy-first**: Runs 100% locally – **no data leaves your machine**  
 - ️ **Voice-enabled**: Speak and interact naturally
 - **Coding abilities**: Code in Python, Bash, C, Golang, and soon more
--  **Self-correcting**: Automatically fixes errors by itself
+-  **Trial-and-error**: Automatically fixes code or command upon execution failure
 - **Agent routing**: Select the best agent for the task
 - **Multi-agent**: For complex tasks, divide and conquer with multiple agents
--  **Web browsing (not implemented yet)**: Browse the web and search the internet  
+- **Tools:**: All agents have their respective tools ability. Basic search, flight API, files explorer, etc...
+-  **Web browsing (not implemented yet)**: Browse the web autonomously to conduct task.
 
 ---
 
+
+
+---
+
+
 ## Installation  
 
 ### 1️⃣ **Install Dependencies**  
@@ -57,21 +63,25 @@ Run the assistant:
 python3 main.py
 ```
 
-### 4️⃣ **Alternative: Run the Assistant (Own Server)**  
+### 4️⃣ **Alternative: Run the LLM on your own server**  
 
 
-Get the ip address of the machine that will run the model
+On your "server" that will run the AI model, get the ip address
 
 ```sh
 ip a | grep "inet " | grep -v 127.0.0.1 | awk '{print $2}' | cut -d/ -f1
 ```
 
-On the other machine that will run the model execute the script in stream_llm.py
+Clone the repository and then, run the script `stream_llm.py` in `server/`
 
 ```sh
 python3 stream_llm.py
 ```
 
+Now on your personal computer:
+
+Clone the repository.
+
 Change the `config.ini` file to set the `provider_name` to `server` and `provider_model` to `deepseek-r1:7b`.
 Set the `provider_server_address` to the ip address of the machine that will run the model.
 
@@ -89,21 +99,40 @@ Run the assistant:
 python3 main.py
 ```
 
+## Provider
+
+Currently the only provider are :
+- ollama -> Use ollama running on your computer. Ollama program for running locally large language models.
+- server -> A custom script that allow you to have the LLM model run on another machine. Currently it use ollama but we'll switch to other options soon.
+- openai -> Use ChatGPT API (not private).
+- deepseek -> Deepseek API (not private).
+
+To select a provider change the config.ini:
+
+```
+is_local = False
+provider_name = openai
+provider_model = gpt-4o
+provider_server_address = 127.0.0.1:5000
+```
+is_local: should be True for any locally running LLM, otherwise False.
+
+provider_name: Select the provider to use by its name, see the provider list above.
+
+provider_model: Set the model to use by the agent.
+
+provider_server_address: can be set to anything if you are not using the server provider.
+
+
 ## Current capabilities
 
 - All running locally
 - Reasoning with deepseek R1
-- Code execution capabilities (Python, Golang, C)
+- Code execution capabilities (Python, Golang, C, etc..)
 - Shell control capabilities in bash
 - Will try to fix errors by itself
 - Routing system, select the best agent for the task
 - Fast text-to-speech using kokoro.
+- Speech to text.
 - Memory compression (reduce history as interaction progresses using summary model) 
-- Recovery: recover last session from memory
-
-## UNDER DEVELOPMENT
-
-- Web browsing
-- Knowledge base RAG
-- Graphical interface
-- Speech-to-text using distil-whisper/distil-medium.en
+- Recovery: recover and save session from filesystem.

server/config.json

@@ -0,0 +1,30 @@
+{
+    "model_name": "deepseek-r1:14b",
+    "known_models": [
+        "qwq:32b",
+        "deepseek-r1:1.5b",
+        "deepseek-r1:7b",
+        "deepseek-r1:14b",
+        "deepseek-r1:32b",
+        "deepseek-r1:70b",
+        "deepseek-r1:671b",
+        "deepseek-coder:1.3b",
+        "deepseek-coder:6.7b",
+        "deepseek-coder:33b",
+        "llama2-uncensored:7b",
+        "llama2-uncensored:70b",
+        "llama3.1:8b",
+        "llama3.1:70b",
+        "llama3.3:70b",
+        "llama3:8b",
+        "llama3:70b",
+        "i4:14b",
+        "mistral:7b",
+        "mistral:70b",
+        "mistral:33b",
+        "qwen1:7b",
+        "qwen1:14b",
+        "qwen1:32b",
+        "qwen1:70b"
+    ]
+}

server/stream_llm.py

@@ -2,25 +2,43 @@ from flask import Flask, jsonify, request
 import threading
 import ollama
 import logging
+import json
 
 log = logging.getLogger('werkzeug')
 log.setLevel(logging.ERROR)
 
 app = Flask(__name__)
 
-model = 'deepseek-r1:14b'
-
 # Shared state with thread-safe locks
+
+class Config:
+    def __init__(self):
+        self.model = None 
+        self.known_models = []
+        self.allowed_models = []
+        self.model_name = None
+
+    def load(self):
+        with open('config.json', 'r') as f:
+            data = json.load(f)
+            self.known_models = data['known_models']
+            self.model_name = data['model_name']
+    
+    def validate_model(self, model):
+        if model not in self.known_models:
+            raise ValueError(f"Model {model} is not known")
+
 class GenerationState:
     def __init__(self):
         self.lock = threading.Lock()
         self.last_complete_sentence = ""
         self.current_buffer = ""
         self.is_generating = False
+        self.model = None
 
 state = GenerationState()
             
-def generate_response(history, model):
+def generate_response(history):
     global state
     try:
         with state.lock:
@@ -29,21 +47,18 @@ def generate_response(history, model):
             state.current_buffer = ""
 
         stream = ollama.chat(
-            model=model,
+            model=state.model,
             messages=history,
             stream=True,
         )
-
         for chunk in stream:
             content = chunk['message']['content']
             print(content, end='', flush=True)
-
             with state.lock:
                 state.current_buffer += content
-
     except ollama.ResponseError as e:
         if e.status_code == 404:
-            ollama.pull(model)
+            ollama.pull(state.model)
         with state.lock:
             state.is_generating = False
         print(f"Error: {e}")
@@ -61,8 +76,7 @@ def start_generation():
             return jsonify({"error": "Generation already in progress"}), 400
         
         history = data.get('messages', [])
-        # Start generation in background thread
-        threading.Thread(target=generate_response, args=(history, model)).start()
+        threading.Thread(target=generate_response, args=(history, state.model)).start()
     return jsonify({"message": "Generation started"}), 202
 
 @app.route('/get_updated_sentence')
@@ -75,4 +89,8 @@ def get_updated_sentence():
         })
 
 if __name__ == '__main__':
+    config = Config()
+    config.load()
+    config.validate_model(config.model_name)
+    state.model = config.model_name
     app.run(host='0.0.0.0', port=5000, debug=False, threaded=True)