Pranay Patel RAG & Text-To-SQL Task1 #94
Conversation
WalkthroughThe changes introduce a new application project that integrates Retrieval-Augmented Generation with Text-to-SQL functionalities. The update includes comprehensive documentation, an architecture diagram detailing component interactions, and code snippets for implementation. Two Streamlit applications are provided—one full-featured and one simplified—with new workflow classes managing query processing, tool dispatch, and asynchronous response generation. Additionally, dependency files have been added to manage project requirements. Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant UI as Streamlit UI
participant WF as RouterOutputAgentWorkflow
participant SQL as SQL Tool/DB
participant RAG as RAG Tool
U->>UI: Submit query
UI->>WF: Forward query input
WF->>WF: Analyze and prepare chat
alt Query requires SQL
WF->>SQL: Execute SQL query
SQL-->>WF: Return SQL results
else Query requires RAG
WF->>RAG: Retrieve unstructured data
RAG-->>WF: Return RAG results
end
WF->>UI: Display formatted response
UI->>U: Show response
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Actionable comments posted: 2
🧹 Nitpick comments (6)
rag-text2sql/README.md (1)
22-46: Detailed setup instructions.The setup and installation instructions are comprehensive, listing all prerequisites and step-by-step instructions for getting the application running. Including the API key requirements upfront is particularly helpful.
Consider adding a note about which version of Python was tested with the application, as you specify Python 3.9+ as a requirement.
🧰 Tools
🪛 LanguageTool
[uncategorized] ~33-~33: Possible missing preposition found.
Context: ...stallation 1. Clone this repository 2. Install the required packages: ```bash pip ins...(AI_HYDRA_LEO_MISSING_TO)
rag-text2sql/simple_app.py (2)
19-38: Well-structured sidebar for configuration.The sidebar is well-organized with clear sections for API keys and configuration settings. The "About" section provides a concise summary of the application's capabilities.
Consider adding validation for the API keys format or a "Test Connection" button to verify the keys work before proceeding.
95-122: Informative data explorer tab.The data explorer tab provides a clear view of both the structured and unstructured data available to the application. The sample data is representative of what would be used in a real implementation.
Consider adding a note that this is sample data and not connected to a real database yet, to set proper expectations for users exploring the demo.
rag-text2sql/code_snippets.md (1)
75-111: Workflow class clarity.The
RouterOutputAgentWorkflowadequately demonstrates how to structure tools and manage chat history. Including in-depth docstrings or additional usage examples could help onboard new contributors more quickly.rag-text2sql/app.py (2)
113-114: Consolidate nested context managers.Consider combining the two
withstatements into one. This improves readability and aligns with static analysis suggestions.-def init_db(engine): - with db_lock: - with engine.begin() as conn: +def init_db(engine): + with db_lock, engine.begin() as conn: ...🧰 Tools
🪛 Ruff (0.8.2)
113-114: Use a single
withstatement with multiple contexts instead of nestedwithstatements(SIM117)
340-340: Remove or utilize the unused variable.The
num_tool_callsassignment is never used, which can cause confusion.- num_tool_calls = await ctx.get("num_tool_calls") + await ctx.get("num_tool_calls")🧰 Tools
🪛 Ruff (0.8.2)
340-340: Local variable
num_tool_callsis assigned to but never usedRemove assignment to unused variable
num_tool_calls(F841)
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⛔ Files ignored due to path filters (1)
rag-text2sql/llamacloud_sql_router_img.pngis excluded by!**/*.png
📒 Files selected for processing (7)
rag-text2sql/README.md(1 hunks)rag-text2sql/app.py(1 hunks)rag-text2sql/architecture_diagram.txt(1 hunks)rag-text2sql/code_snippets.md(1 hunks)rag-text2sql/requirements.txt(1 hunks)rag-text2sql/simple_app.py(1 hunks)rag-text2sql/simple_requirements.txt(1 hunks)
🧰 Additional context used
🪛 LanguageTool
rag-text2sql/simple_requirements.txt
[uncategorized] ~1-~1: Hier scheint es einen Fehler zu geben.
Context: streamlit>=1.24.0 pandas>=1.5.3
(AI_DE_MERGED_MATCH)
rag-text2sql/README.md
[uncategorized] ~33-~33: Possible missing preposition found.
Context: ...stallation 1. Clone this repository 2. Install the required packages: ```bash pip ins...
(AI_HYDRA_LEO_MISSING_TO)
[style] ~51-~51: Consider removing “of” to be more concise
Context: ...e is the Space Needle located?" - "List all of the places to visit in Miami." - "How do pe...
(ALL_OF_THE)
rag-text2sql/architecture_diagram.txt
[duplication] ~18-~18: Possible typo: you repeated a word.
Context: ... | | v v +----------------+ +----------------...
(ENGLISH_WORD_REPEAT_RULE)
[duplication] ~25-~25: Possible typo: you repeated a word.
Context: ... | | v v +----------------+ +----------------...
(ENGLISH_WORD_REPEAT_RULE)
[duplication] ~95-~95: Possible typo: you repeated a word.
Context: ...urces 5. Results are passed back to the LLM 6. LLM generates a coherent natural language r...
(ENGLISH_WORD_REPEAT_RULE)
[duplication] ~96-~96: Possible typo: you repeated a word.
Context: ...M generates a coherent natural language response 7. Response is displayed to the user
(ENGLISH_WORD_REPEAT_RULE)
🪛 Ruff (0.8.2)
rag-text2sql/app.py
61-61: Do not use bare except
(E722)
113-114: Use a single with statement with multiple contexts instead of nested with statements
(SIM117)
340-340: Local variable num_tool_calls is assigned to but never used
Remove assignment to unused variable num_tool_calls
(F841)
🔇 Additional comments (18)
rag-text2sql/simple_requirements.txt (1)
1-2: LGTM! Dependencies are appropriate for a simplified version.The requirements specify appropriate minimum versions for both Streamlit and pandas, which are essential for running the simplified application. These align perfectly with what's needed for the UI components and data handling in
simple_app.py.🧰 Tools
🪛 LanguageTool
[uncategorized] ~1-~1: Hier scheint es einen Fehler zu geben.
Context: streamlit>=1.24.0 pandas>=1.5.3(AI_DE_MERGED_MATCH)
rag-text2sql/requirements.txt (1)
1-7: Complete dependency list for the full application.This comprehensive requirements file includes all necessary dependencies for both the RAG and Text-to-SQL functionality:
- UI framework (Streamlit)
- Data manipulation (pandas)
- RAG implementation (LlamaIndex and related packages)
- LLM integration (OpenAI)
- Database connectivity (SQLAlchemy)
The minimum versions specified are appropriate for the described functionality.
rag-text2sql/README.md (7)
1-4: Well-structured introduction to the application.The title and introduction clearly explain the purpose of the application - combining RAG and Text-to-SQL in a unified query interface. This sets appropriate expectations for users and aligns with the implementation in the provided code files.
5-12: Comprehensive feature list.The feature list effectively communicates the key capabilities of the application, from the unified interface to the intelligent query routing. These features align with the implementation in
simple_app.pyand provide users with a clear understanding of what the application can do.
13-21: Clear architecture overview.The architecture section concisely describes the workflow of the application, from query analysis to response formatting. This high-level overview helps users understand the system's operation without getting into implementation details.
47-54: Helpful example queries.The example queries demonstrate the range of questions the application can handle, from structured data queries about population to unstructured data queries about attractions and transportation. These examples align with the query handling in
simple_app.py.🧰 Tools
🪛 LanguageTool
[style] ~51-~51: Consider removing “of” to be more concise
Context: ...e is the Space Needle located?" - "List all of the places to visit in Miami." - "How do pe...(ALL_OF_THE)
55-63: Clear implementation details.The implementation details section succinctly lists the key technologies used in the application, providing users with an understanding of the tech stack without overwhelming them with technical details.
64-70: Transparent about limitations.The limitations section honestly acknowledges the constraints of the demo, which is good practice. The mention of
simple_app.pymocking capabilities aligns with what we see in the code review.
71-74: Proper attribution.The credits section appropriately acknowledges the source of inspiration for the application, demonstrating good open-source citizenship.
rag-text2sql/simple_app.py (7)
1-9: LGTM - Appropriate imports and page configuration.The imports and page configuration are correctly set up for a Streamlit application. The page layout is set to "wide" which is appropriate for a dashboard-style application with multiple components.
11-18: Clear application title and description.The title and description effectively communicate the purpose of the application to users. The markdown formatting makes the description readable and professional.
40-43: Proper session state initialization.Session state is correctly initialized for the chat history, which is essential for maintaining conversation context across Streamlit reruns.
44-46: Clear tab structure.The tab structure provides a clean separation of concerns between the chat interface, data explorer, and architectural explanation.
124-189: Excellent architectural explanation.The "How It Works" tab provides a detailed explanation of the application's architecture and query routing process. The ASCII diagram effectively visualizes the flow of information through the system.
190-222: Helpful code implementation example.The code example in the expandable section provides users with a clear understanding of how the core functionality would be implemented. This is valuable for users who want to understand the technical details or extend the application.
223-225: Appropriate footer.The footer provides proper attribution for the task context.
rag-text2sql/architecture_diagram.txt (1)
1-97: Well-structured diagram.Your ASCII-based architecture diagram is clear and provides a solid overview of the RAG + Text-to-SQL workflow. The static analysis warnings regarding repeated words (“v”, “LLM”) appear to be false positives in the context of ASCII art and the repeated LLM references. No further changes needed.
🧰 Tools
🪛 LanguageTool
[duplication] ~18-~18: Possible typo: you repeated a word.
Context: ... | | v v +----------------+ +----------------...(ENGLISH_WORD_REPEAT_RULE)
[duplication] ~25-~25: Possible typo: you repeated a word.
Context: ... | | v v +----------------+ +----------------...(ENGLISH_WORD_REPEAT_RULE)
[duplication] ~95-~95: Possible typo: you repeated a word.
Context: ...urces 5. Results are passed back to the LLM 6. LLM generates a coherent natural language r...(ENGLISH_WORD_REPEAT_RULE)
[duplication] ~96-~96: Possible typo: you repeated a word.
Context: ...M generates a coherent natural language response 7. Response is displayed to the user(ENGLISH_WORD_REPEAT_RULE)
rag-text2sql/code_snippets.md (1)
5-24: Tool initialization looks good.The initial snippet for setting up
sql_toolandllama_cloud_toolis straightforward and self-explanatory. No issues found.
| # Tab 1: Chat Interface | ||
| with tab1: | ||
| # Display chat history | ||
| for message in st.session_state.chat_history: | ||
| with st.chat_message(message["role"]): | ||
| st.markdown(message["content"]) | ||
|
|
||
| # Chat input | ||
| if prompt := st.chat_input("Ask a question about US cities..."): | ||
| # Add user message to chat history | ||
| st.session_state.chat_history.append({"role": "user", "content": prompt}) | ||
|
|
||
| # Display user message | ||
| with st.chat_message("user"): | ||
| st.markdown(prompt) | ||
|
|
||
| # Display assistant response | ||
| with st.chat_message("assistant"): | ||
| if not openai_api_key or not llama_cloud_api_key: | ||
| st.markdown("⚠️ Please enter your API keys in the sidebar to continue.") | ||
| else: | ||
| with st.spinner("Thinking..."): | ||
| # Simulate response - in a real app, this would call the actual workflow | ||
| if "population" in prompt.lower() or "largest" in prompt.lower() or "smallest" in prompt.lower(): | ||
| st.markdown("I'll use the SQL database to answer this question.") | ||
| st.markdown("New York City has the highest population among US cities in our database.") | ||
| elif "space needle" in prompt.lower(): | ||
| st.markdown("I'll use the RAG system to answer this question.") | ||
| st.markdown("The Space Needle is located in Seattle, Washington.") | ||
| elif "visit" in prompt.lower() and any(city in prompt.lower() for city in ["miami", "new york", "los angeles", "chicago", "houston", "seattle"]): | ||
| st.markdown("I'll use the RAG system to answer this question about attractions.") | ||
| st.markdown("""Here are some places to visit in Miami: | ||
| - Beaches and parks | ||
| - Zoo Miami | ||
| - Jungle Island | ||
| - Miami Seaquarium | ||
| - Botanic Garden | ||
| - Key Biscayne | ||
| - South Beach | ||
| - Lincoln Road | ||
| - Bayside Marketplace""") | ||
| else: | ||
| st.markdown("I'll analyze your question and determine the best way to answer it.") | ||
| st.markdown("Based on my analysis, I would need to use both structured and unstructured data sources to fully answer this question. In a complete implementation, I would route this to the appropriate tool or combine results from multiple tools.") | ||
|
|
||
| # Add assistant response to chat history | ||
| st.session_state.chat_history.append({"role": "assistant", "content": "Response simulation for: " + prompt}) | ||
|
|
There was a problem hiding this comment.
Functional chat interface implementation.
The chat interface correctly displays the conversation history and handles user input. The conditional logic for simulating responses based on query content provides a realistic demonstration of the application's capabilities.
One issue: The chat history is updated with a generic "Response simulation for: " message (line 93) instead of the actual detailed response content generated in lines 71-90.
Apply this fix to correctly store the actual response in the chat history:
- st.session_state.chat_history.append({"role": "assistant", "content": "Response simulation for: " + prompt})
+ # Determine the response based on the prompt
+ if "population" in prompt.lower() or "largest" in prompt.lower() or "smallest" in prompt.lower():
+ response = "I'll use the SQL database to answer this question.\n\nNew York City has the highest population among US cities in our database."
+ elif "space needle" in prompt.lower():
+ response = "I'll use the RAG system to answer this question.\n\nThe Space Needle is located in Seattle, Washington."
+ elif "visit" in prompt.lower() and any(city in prompt.lower() for city in ["miami", "new york", "los angeles", "chicago", "houston", "seattle"]):
+ response = """I'll use the RAG system to answer this question about attractions.
+
+Here are some places to visit in Miami:
+- Beaches and parks
+- Zoo Miami
+- Jungle Island
+- Miami Seaquarium
+- Botanic Garden
+- Key Biscayne
+- South Beach
+- Lincoln Road
+- Bayside Marketplace"""
+ else:
+ response = """I'll analyze your question and determine the best way to answer it.
+
+Based on my analysis, I would need to use both structured and unstructured data sources to fully answer this question. In a complete implementation, I would route this to the appropriate tool or combine results from multiple tools."""
+
+ st.session_state.chat_history.append({"role": "assistant", "content": response})📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Tab 1: Chat Interface | |
| with tab1: | |
| # Display chat history | |
| for message in st.session_state.chat_history: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Chat input | |
| if prompt := st.chat_input("Ask a question about US cities..."): | |
| # Add user message to chat history | |
| st.session_state.chat_history.append({"role": "user", "content": prompt}) | |
| # Display user message | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Display assistant response | |
| with st.chat_message("assistant"): | |
| if not openai_api_key or not llama_cloud_api_key: | |
| st.markdown("⚠️ Please enter your API keys in the sidebar to continue.") | |
| else: | |
| with st.spinner("Thinking..."): | |
| # Simulate response - in a real app, this would call the actual workflow | |
| if "population" in prompt.lower() or "largest" in prompt.lower() or "smallest" in prompt.lower(): | |
| st.markdown("I'll use the SQL database to answer this question.") | |
| st.markdown("New York City has the highest population among US cities in our database.") | |
| elif "space needle" in prompt.lower(): | |
| st.markdown("I'll use the RAG system to answer this question.") | |
| st.markdown("The Space Needle is located in Seattle, Washington.") | |
| elif "visit" in prompt.lower() and any(city in prompt.lower() for city in ["miami", "new york", "los angeles", "chicago", "houston", "seattle"]): | |
| st.markdown("I'll use the RAG system to answer this question about attractions.") | |
| st.markdown("""Here are some places to visit in Miami: | |
| - Beaches and parks | |
| - Zoo Miami | |
| - Jungle Island | |
| - Miami Seaquarium | |
| - Botanic Garden | |
| - Key Biscayne | |
| - South Beach | |
| - Lincoln Road | |
| - Bayside Marketplace""") | |
| else: | |
| st.markdown("I'll analyze your question and determine the best way to answer it.") | |
| st.markdown("Based on my analysis, I would need to use both structured and unstructured data sources to fully answer this question. In a complete implementation, I would route this to the appropriate tool or combine results from multiple tools.") | |
| # Add assistant response to chat history | |
| st.session_state.chat_history.append({"role": "assistant", "content": "Response simulation for: " + prompt}) | |
| # Tab 1: Chat Interface | |
| with tab1: | |
| # Display chat history | |
| for message in st.session_state.chat_history: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Chat input | |
| if prompt := st.chat_input("Ask a question about US cities..."): | |
| # Add user message to chat history | |
| st.session_state.chat_history.append({"role": "user", "content": prompt}) | |
| # Display user message | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Display assistant response | |
| with st.chat_message("assistant"): | |
| if not openai_api_key or not llama_cloud_api_key: | |
| st.markdown("⚠️ Please enter your API keys in the sidebar to continue.") | |
| else: | |
| with st.spinner("Thinking..."): | |
| # Simulate response - in a real app, this would call the actual workflow | |
| if "population" in prompt.lower() or "largest" in prompt.lower() or "smallest" in prompt.lower(): | |
| st.markdown("I'll use the SQL database to answer this question.") | |
| st.markdown("New York City has the highest population among US cities in our database.") | |
| elif "space needle" in prompt.lower(): | |
| st.markdown("I'll use the RAG system to answer this question.") | |
| st.markdown("The Space Needle is located in Seattle, Washington.") | |
| elif "visit" in prompt.lower() and any(city in prompt.lower() for city in ["miami", "new york", "los angeles", "chicago", "houston", "seattle"]): | |
| st.markdown("I'll use the RAG system to answer this question about attractions.") | |
| st.markdown("""Here are some places to visit in Miami: | |
| - Beaches and parks | |
| - Zoo Miami | |
| - Jungle Island | |
| - Miami Seaquarium | |
| - Botanic Garden | |
| - Key Biscayne | |
| - South Beach | |
| - Lincoln Road | |
| - Bayside Marketplace""") | |
| else: | |
| st.markdown("I'll analyze your question and determine the best way to answer it.") | |
| st.markdown("Based on my analysis, I would need to use both structured and unstructured data sources to fully answer this question. In a complete implementation, I would route this to the appropriate tool or combine results from multiple tools.") | |
| # Determine the response based on the prompt | |
| if "population" in prompt.lower() or "largest" in prompt.lower() or "smallest" in prompt.lower(): | |
| response = "I'll use the SQL database to answer this question.\n\nNew York City has the highest population among US cities in our database." | |
| elif "space needle" in prompt.lower(): | |
| response = "I'll use the RAG system to answer this question.\n\nThe Space Needle is located in Seattle, Washington." | |
| elif "visit" in prompt.lower() and any(city in prompt.lower() for city in ["miami", "new york", "los angeles", "chicago", "houston", "seattle"]): | |
| response = """I'll use the RAG system to answer this question about attractions. | |
| Here are some places to visit in Miami: | |
| - Beaches and parks | |
| - Zoo Miami | |
| - Jungle Island | |
| - Miami Seaquarium | |
| - Botanic Garden | |
| - Key Biscayne | |
| - South Beach | |
| - Lincoln Road | |
| - Bayside Marketplace""" | |
| else: | |
| response = """I'll analyze your question and determine the best way to answer it. | |
| Based on my analysis, I would need to use both structured and unstructured data sources to fully answer this question. In a complete implementation, I would route this to the appropriate tool or combine results from multiple tools.""" | |
| st.session_state.chat_history.append({"role": "assistant", "content": response}) |
| try: | ||
| st.image("llamacloud_sql_router_img.png", use_container_width=True) | ||
| except: | ||
| st.warning("Image file not found. The diagram is not displayed.") | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Avoid bare except statements.
Use a more specific exception type (e.g., FileNotFoundError) to prevent hiding critical errors.
-try:
- st.image("llamacloud_sql_router_img.png", use_container_width=True)
-except:
- st.warning("Image file not found. The diagram is not displayed.")
+try:
+ st.image("llamacloud_sql_router_img.png", use_container_width=True)
+except FileNotFoundError:
+ st.warning("Image file not found. The diagram is not displayed.")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| st.image("llamacloud_sql_router_img.png", use_container_width=True) | |
| except: | |
| st.warning("Image file not found. The diagram is not displayed.") | |
| try: | |
| st.image("llamacloud_sql_router_img.png", use_container_width=True) | |
| except FileNotFoundError: | |
| st.warning("Image file not found. The diagram is not displayed.") |
🧰 Tools
🪛 Ruff (0.8.2)
61-61: Do not use bare except
(E722)
I have developed RAG and Text-to-SQL based unified query interface that can intelligently route natural language queries to either a SQL database (for structured data) or a RAG system (for unstructured data).
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