Merge remote-tracking branch 'origin/main'

haikalbot/Optimizing Qwen3-8B for Arabic Language Support in Django AI Analyst.md

@@ -18,7 +18,7 @@ Update your Django settings to use Qwen3-8B:
 
 ```python
 # In settings.py
-OLLAMA_BASE_URL = "http://localhost:11434"
+OLLAMA_BASE_URL = "http://10.10.1.132:11434"
 OLLAMA_MODEL = "qwen3:8b"
 OLLAMA_TIMEOUT = 120  # Seconds
 ```
@@ -34,10 +34,10 @@ def get_ollama_llm():
     """
     try:
         # Get settings from Django settings or use defaults
-        base_url = getattr(settings, 'OLLAMA_BASE_URL', 'http://localhost:11434')
+        base_url = getattr(settings, 'OLLAMA_BASE_URL', 'http://10.10.1.132:11434')
         model = getattr(settings, 'OLLAMA_MODEL', 'qwen3:8b')
         timeout = getattr(settings, 'OLLAMA_TIMEOUT', 120)
-        
+
         # Configure Ollama with parameters optimized for Qwen3-8B with Arabic
         return Ollama(
             base_url=base_url,
@@ -71,7 +71,7 @@ def create_prompt_analyzer_chain(language='ar'):
     llm = get_ollama_llm()
     if not llm:
         return None
-    
+
     # Define the prompt template optimized for Qwen3-8B
     if language == 'ar':
         template = """
@@ -79,9 +79,9 @@ def create_prompt_analyzer_chain(language='ar'):
         1. نوع التحليل المطلوب
         2. نماذج البيانات المستهدفة
         3. أي معلمات استعلام
-        
+
         الاستعلام: {prompt}
-        
+
         قم بتقديم إجابتك بتنسيق JSON فقط، بدون أي نص إضافي، كما يلي:
         ```json
         {{
@@ -97,9 +97,9 @@ def create_prompt_analyzer_chain(language='ar'):
         1. The type of analysis required
         2. Target data models
         3. Any query parameters
-        
+
         Prompt: {prompt}
-        
+
         Provide your answer in JSON format only, without any additional text, as follows:
         ```json
         {
@@ -109,13 +109,13 @@ def create_prompt_analyzer_chain(language='ar'):
         }
         ```
         """
-    
+
     # Create the prompt template
     prompt_template = PromptTemplate(
         input_variables=["prompt"],
         template=template
     )
-    
+
     # Create and return the LLM chain
     return LLMChain(llm=llm, prompt=prompt_template)
 ```
@@ -135,13 +135,13 @@ def _parse_llm_json_response(result):
         if json_match:
             json_str = json_match.group(1).strip()
             return json.loads(json_str)
-        
+
         # If no markdown blocks, try to find JSON object directly
         json_match = re.search(r'({[\s\S]*})', result)
         if json_match:
             json_str = json_match.group(1).strip()
             return json.loads(json_str)
-        
+
         # If still no match, try to parse the entire response as JSON
         return json.loads(result.strip())
     except Exception as e:
@@ -159,7 +159,7 @@ def _parse_llm_json_response(result):
 ## Handling Arabic-Specific Challenges with Qwen3-8B
 
 1. **Diacritics**: Qwen3-8B handles Arabic diacritics well, but for consistency, consider normalizing input by removing diacritics
-   
+
 2. **Text Direction**: When displaying results in frontend, ensure proper RTL (right-to-left) support
 
 3. **Dialectal Variations**: Qwen3-8B performs best with Modern Standard Arabic (MSA), but has reasonable support for major dialects

requirements_dev.txt

@@ -92,3 +92,5 @@ typing_extensions==4.13.0
 tzdata==2025.2
 urllib3==2.3.0
 wcwidth==0.2.13
+langchain
+langchain_ollama