Hotfixes done

Author: DSoftwareArtist

.env.example

@@ -1,5 +1,5 @@
 DEBUG=True
-OPENAI_API_KEY=your-openai-api-key-here
+GROQ_API_KEY=your-groq-api-key-here
 SENTENCE_TRANSFORMER_MODEL=all-MiniLM-L6-v2
 
 PGVECTOR_DB_NAME=llm_analytics

README.md

@@ -29,21 +29,25 @@
 ### Layout Structure
 
 **Header**
+
 - Application title: "LLM SQL Analytics"
 - Navigation: Dashboard, Query Interface, Schema Docs, Settings
 
 **Main Content Area**
+
 - Dashboard: Overview of connected databases and recent queries
 - Query Interface: Natural language input + SQL preview + results table
 - Schema Docs: List of uploaded documents with semantic search
 - Settings: Database connection configuration
 
 **Footer**
+
 - Version info and copyright
 
 ### Visual Design
 
 **Color Palette**
+
 - Primary: `#1E3A5F` (Deep Navy)
 - Secondary: `#3D5A80` (Slate Blue)
 - Accent: `#48CAE4` (Cyan)
@@ -56,11 +60,13 @@
 - Warning: `#FFC107` (Yellow)
 
 **Typography**
+
 - Headings: "Inter", sans-serif, 600 weight
 - Body: "Inter", sans-serif, 400 weight
 - Monospace (SQL): "JetBrains Mono", monospace
 
 **Spacing**
+
 - Base unit: 8px
 - Container max-width: 1200px
 - Card padding: 24px
@@ -69,23 +75,27 @@
 ### Components
 
 **Query Input Card**
+
 - Textarea for natural language input
 - "Generate SQL" button (primary style)
 - "Execute" button (accent style)
 - SQL preview panel with syntax highlighting
 
 **Results Table**
+
 - Sortable columns
 - Pagination for large results
 - Export to CSV option
 
 **Schema Doc Card**
+
 - Document name
 - Upload date
 - Preview snippet
 - Semantic search input
 
 **Database Connection Card**
+
 - Connection name
 - Database type indicator
 - Status indicator (connected/disconnected)
@@ -98,33 +108,39 @@
 ### Core Features
 
 #### 4.1 Database Connection Management
+
 - Add PostgreSQL database connections with connection details
 - Test database connectivity
 - Store connection configurations securely
 - Support multiple database connections
 
 #### 4.2 Schema Introspection
+
 - Automatically fetch table names and column information
 - Store schema in PostgreSQL with pgvector for semantic search
 - Support uploading schema documentation (PDF)
 
 #### 4.3 Natural Language to SQL
+
 - Accept natural language queries
 - Use LLM to convert natural language to SQL
 - Support SELECT, INSERT, UPDATE, DELETE operations
 - Include validation and error handling
 
 #### 4.4 Semantic Schema Search
+
 - Embed schema documentation using sentence-transformers
 - Store embeddings in pgvector
 - Search schema semantically to find relevant tables/columns
 
 #### 4.5 Query Execution
+
 - Execute generated SQL against target database
 - Return formatted results
 - Handle errors gracefully with user-friendly messages
 
 #### 4.6 Query History
+
 - Store all queries with timestamps
 - View past queries and their results
 - Re-run previous queries
@@ -192,20 +208,20 @@ QueryHistory
 
 ## 6. API Endpoints
 
-| Method | Endpoint | Description |
-|--------|----------|-------------|
-| GET | /api/connections/ | List all database connections |
-| POST | /api/connections/ | Add new database connection |
-| GET | /api/connections/{id}/ | Get connection details |
-| PUT | /api/connections/{id}/ | Update connection |
-| DELETE | /api/connections/{id}/ | Delete connection |
-| POST | /api/connections/{id}/test/ | Test connection |
-| GET | /api/connections/{id}/schema/ | Get database schema |
-| POST | /api/query/ | Execute natural language query |
-| GET | /api/history/ | Get query history |
-| POST | /api/docs/ | Upload schema document |
-| GET | /api/docs/ | List schema documents |
-| POST | /api/docs/search/ | Semantic search in documents |
+| Method | Endpoint                      | Description                    |
+| ------ | ----------------------------- | ------------------------------ |
+| GET    | /api/connections/             | List all database connections  |
+| POST   | /api/connections/             | Add new database connection    |
+| GET    | /api/connections/{id}/        | Get connection details         |
+| PUT    | /api/connections/{id}/        | Update connection              |
+| DELETE | /api/connections/{id}/        | Delete connection              |
+| POST   | /api/connections/{id}/test/   | Test connection                |
+| GET    | /api/connections/{id}/schema/ | Get database schema            |
+| POST   | /api/query/                   | Execute natural language query |
+| GET    | /api/history/                 | Get query history              |
+| POST   | /api/docs/                    | Upload schema document         |
+| GET    | /api/docs/                    | List schema documents          |
+| POST   | /api/docs/search/             | Semantic search in documents   |
 
 ---
 
@@ -220,4 +236,4 @@ QueryHistory
 7. ✓ System performs semantic search over schema docs
 8. ✓ Query history is maintained
 9. ✓ Web interface is responsive and functional
-10. ✓ Error handling provides helpful feedback
+10. ✓ Error handling provides helpful feedback

core/services.py

@@ -4,7 +4,7 @@
 import psycopg2
 from psycopg2.extras import RealDictCursor
 from django.conf import settings
-from langchain_openai import ChatOpenAI
+from langchain_groq import ChatGroq
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 from sentence_transformers import SentenceTransformer
@@ -122,13 +122,13 @@ class SQLGenerationService:
     @classmethod
     def get_llm(cls):
         if cls._llm is None:
-            api_key = os.environ.get('OPENAI_API_KEY', '')
+            api_key = os.environ.get('GROQ_API_KEY', '')
             if not api_key:
-                raise ValueError("OPENAI_API_KEY not set")
-            cls._llm = ChatOpenAI(
-                model='gpt-4',
+                raise ValueError("GROQ_API_KEY not set")
+            cls._llm = ChatGroq(
+                model='llama-3.3-70b-versatile',
                 temperature=0,
-                openai_api_key=api_key
+                api_key=api_key
             )
         return cls._llm
 
@@ -139,21 +139,28 @@ def generate_sql(cls, natural_language: str, schema_info: str) -> str:
         prompt = PromptTemplate(
             input_variables=["question", "schema"],
             template="""
-You are a SQL expert. Given a natural language question and a database schema, generate a valid PostgreSQL query.
+                You are a SQL expert. Given a natural language question and a database schema, generate a valid PostgreSQL query.
 
-Schema:
-{schema}
+                Schema:
+                {schema}
 
-Question: {question}
+                Question: {question}
 
-Generate only the SQL query, no explanation. If the question cannot be answered with the given schema, generate a query that returns an appropriate empty result.
-"""
-        )
+                Generate only the SQL query and format it as string, no explanation. If the question cannot be answered with the given schema, generate a query that returns an appropriate empty result.
+                """
+            )
 
         chain = LLMChain(llm=llm, prompt=prompt)
         result = chain.run(question=natural_language, schema=schema_info)
-        
-        return result.strip()
+        print('LLM Output:', result)
+        sql = result.strip()
+        if sql.lower().startswith('sql'):
+            sql = sql[3:].strip()
+        if sql.lower().startswith('```sql'):
+            sql = sql[6:].strip()
+        if sql.endswith('```'):
+            sql = sql[:-3].strip()
+        return sql
 
     @classmethod
     def format_schema_for_prompt(cls, schema: Dict[str, Any]) -> str:

llm_sql_analytics/settings.py

@@ -1,5 +1,8 @@
 import os
 from pathlib import Path
+from dotenv import load_dotenv
+
+load_dotenv()
 
 BASE_DIR = Path(__file__).resolve().parent.parent
 
@@ -84,7 +87,6 @@
     ],
 }
 
-OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', '')
 SENTENCE_TRANSFORMER_MODEL = os.environ.get('SENTENCE_TRANSFORMER_MODEL', 'all-MiniLM-L6-v2')
 
 PGVECTOR_CONNECTION = {

requirements.txt

@@ -5,6 +5,7 @@ langchain>=0.1.0
 langchain-core>=0.1.0
 langchain-text-splitters>=0.3.0
 langchain-openai>=0.1.0
+langchain-groq>=0.1.0
 sentence-transformers>=2.2.2
 python-dotenv>=1.0.0
 PyPDF2>=3.0.0

templates/core/query.html

@@ -50,7 +50,7 @@ <h3 class="card-title">Query Result</h3>
 
     {% if query_result.success %}
         <div class="alert alert-success">
-            Query executed successfully. {{ query_result.row_count|default:query_result.rows_affected|default:0 }} row(s) returned.
+            Query executed successfully. {% if query_result.row_count %}{{ query_result.row_count }}{% elif query_result.rows_affected %}{{ query_result.rows_affected }}{% else %}0{% endif %} row(s) returned.
         </div>
 
         {% if query_result.rows %}