Update architecture diagram and refactor the main workflow into multiple ones.

Author: tuandung222

.env.example

@@ -1,18 +1,32 @@
-OPENAI_API_KEY=...
-# API Keys
-WEAVIATE_URL=http://weaviate:8080
-WEAVIATE_API_KEY=
+# OpenAI configuration
+OPENAI_API_KEY=your_openai_api_key_here
 
-# Weaviate Configuration
-WEAVIATE_HOST=localhost
-WEAVIATE_PORT=8082  # External port for local access
-WEAVIATE_SCHEMA_CLASS=Articles
+# Weaviate configuration
+WEAVIATE_URL=http://weaviate:8080
+# WEAVIATE_API_KEY=your_weaviate_api_key_here
 
-# Application Settings
+# API configuration
 API_HOST=0.0.0.0
 API_PORT=8000
+
+# Demo app configuration
 DEMO_PORT=8501
+API_URL=http://search_server:8000
+
+# Application settings
 LOG_LEVEL=INFO
+LOAD_SAMPLE_DATA=true
+
+# Embedding configuration
+OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+OPENAI_COMPLETION_MODEL=gpt-3.5-turbo
+OPENAI_TEMPERATURE=0.7
+OPENAI_MAX_TOKENS=1000
+
+# Weaviate Configuration
+WEAVIATE_HOST=localhost
+WEAVIATE_PORT=8082  # External port for local access
+WEAVIATE_SCHEMA_CLASS=Articles
 
 # Kubernetes Settings (for production)
 K8S_NAMESPACE=semantic-search

.github/workflows/docker-build-push.yml

@@ -27,9 +27,17 @@ jobs:
 
       - name: Set up .env file
         run: |
-          # Copy the example file first
-          cp .env.example .env
-          # No need to add secrets here, they'll be passed at runtime via Docker secrets
+          # Create .env file directly since .env.example might not exist
+          cat << EOF > .env
+          OPENAI_API_KEY=dummy-key-for-ci
+          WEAVIATE_URL=http://weaviate:8080
+          API_HOST=0.0.0.0
+          API_PORT=8000
+          DEMO_PORT=8501
+          LOG_LEVEL=INFO
+          LOAD_SAMPLE_DATA=true
+          EOF
+          echo ".env file created successfully"
 
       - name: Create secret files for build context
         run: |

.github/workflows/main.yml

@@ -24,14 +24,27 @@ jobs:
           echo "${{ secrets.OPENAI_API_KEY }}" > openai_api_key.txt
           echo "${{ secrets.WEAVIATE_API_KEY }}" > weaviate_api_key.txt
           
+      - name: Create .env file
+        run: |
+          cat << EOF > .env
+          OPENAI_API_KEY=dummy-key-for-ci
+          WEAVIATE_URL=http://weaviate:8080
+          API_HOST=0.0.0.0
+          API_PORT=8000
+          DEMO_PORT=8501
+          LOG_LEVEL=INFO
+          LOAD_SAMPLE_DATA=true
+          EOF
+          echo ".env file created successfully"
+          
       - name: Pull latest images
         run: |
           docker pull tuandung12092002/semantic-search-server:latest
           docker pull tuandung12092002/semantic-search-demo:latest
           
       - name: Deploy with Docker Compose
         run: |
-          docker compose -f docker/docker-compose.full.yml up -d
+          docker compose --env-file .env -f docker/docker-compose.full.yml up -d
           
       - name: Wait for services to start
         run: |
@@ -53,5 +66,5 @@ jobs:
           
       - name: Clean up
         run: |
-          docker compose -f docker/docker-compose.full.yml down
+          docker compose --env-file .env -f docker/docker-compose.full.yml down
           rm openai_api_key.txt weaviate_api_key.txt 

.github/workflows/test-deployment.yml

@@ -22,13 +22,13 @@ A production-ready semantic search application that combines vector embeddings a
 
 ```mermaid
 flowchart TB
-    subgraph "Demo Frontend (Streamlit)"
+    subgraph Frontend["Demo Frontend (Streamlit)"]
         DocUI["Document Upload UI"]
         QuesUI["Question Input UI"]
         ResUI["Results Display UI"]
     end
 
-    subgraph "Search Server (FastAPI)"
+    subgraph Server["Search Server (FastAPI)"]
         TextProc["TextProcessor<br>Clean & Chunk Text"]
         EmbMgr["EmbeddingManager<br>Create & Cache Embeddings"]
         GenSearch["GenerativeSearch<br>Answer Gen. with Context"]
@@ -37,7 +37,7 @@ flowchart TB
         EmbMgr --> GenSearch
     end
     
-    subgraph "External Services"
+    subgraph External["External Services"]
         VecDB["Vector Database<br>(Weaviate)"]
         OpenAI["OpenAI API"]
         SampleData["Sample Data<br>Text Samples & Metadata"]
@@ -51,9 +51,13 @@ flowchart TB
     EmbMgr <-- Vectors --> VecDB
     GenSearch <-- Prompts --> OpenAI
     
-    style Demo%20Frontend%20\(Streamlit\) fill:#e6f7ff,stroke:#0099cc
-    style Search%20Server%20\(FastAPI\) fill:#e6ffe6,stroke:#00cc66
-    style External%20Services fill:#fff0e6,stroke:#ff9933
+    classDef frontend fill:#e6f7ff,stroke:#0099cc
+    classDef server fill:#e6ffe6,stroke:#00cc66
+    classDef external fill:#fff0e6,stroke:#ff9933
+    
+    class Frontend frontend
+    class Server server
+    class External external
 ```
 
 ### Core Components and Data Flow