Adarsh-RAG-&-TextToSQL-Task-Submission (Technical Writer) #98
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WalkthroughThis update introduces the Agentic RAG + Text-to-SQL system. The changes include a README with system details and setup instructions, a Streamlit web interface for interactive chat sessions, and a RAG workflow to process queries using SQL and vector searches with asynchronous error handling. The code initializes session state, maintains conversational memory, and routes queries based on context. Additionally, a dependencies file and a file containing a related URL have been added. Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant UI as Streamlit Interface (app.py)
participant WK as Workflow (rag.py)
participant DB as Database / Tools
U->>UI: Enter query
UI->>UI: Append user message to chat history
UI->>WK: Call async process_query(user_input)
WK->>WK: prepare_chat (update chat history)
WK->>WK: chat (determine tool routing)
WK->>DB: call_tools (execute SQL/vector queries)
DB-->>WK: Return tool outputs
WK->>WK: gather (aggregate responses)
WK-->>UI: Return aggregated response
UI->>UI: Update chat display with result
UI->>U: Display assistant response
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Actionable comments posted: 1
🧹 Nitpick comments (13)
agentic_rag_texttosql/app.py (4)
15-18: Consider the necessity of manual garbage collection.Calling
gc.collect()manually is rarely necessary in Python and could impact performance. The Python garbage collector is generally effective at managing memory automatically.def reset_chat(): st.session_state.messages = [] - gc.collect()
43-54: Improve error handling with more descriptive messages.The current error handling simply displays "Error: {e}", which isn't very informative for users. Consider providing more context-specific error messages and logging the full error details for debugging.
async def handle_query(): try: # Fetch the response asynchronously response = await process_query(prompt) # Update the message placeholder with the response message_placeholder.markdown(response) # Return the response to be appended to the session history return response except Exception as e: - message_placeholder.markdown(f"Error: {e}") - return f"Error: {e}" + import traceback + error_details = traceback.format_exc() + print(f"Error processing query: {error_details}") + + user_message = "I encountered an error processing your query. This might be due to API connection issues or invalid input format. Please try again or rephrase your question." + message_placeholder.markdown(user_message) + return user_message
56-57: Consider Streamlit's execution model with asyncio.Using
asyncio.run()within a Streamlit app can lead to issues since Streamlit has its own execution model. Consider usingst.experimental_singletonfor long-running operations or implementing a different approach for asynchronous tasks.- # Run the async query processing task - full_response = asyncio.run(handle_query()) + # Use a more Streamlit-friendly approach for async operations + import nest_asyncio + nest_asyncio.apply() + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + full_response = loop.run_until_complete(handle_query()) + loop.close()You'll need to add
nest_asyncioto your requirements.txt:nest_asyncio==1.5.8
7-8: Enhance the UI with more guidance for users.The current UI provides minimal guidance for users. Consider adding a brief introduction or examples of questions the system can answer to improve the user experience.
st.title("🧠 RAG + Text-to-SQL Interface") + +st.markdown(""" +This assistant combines: +- **Text-to-SQL**: Ask questions about structured database information +- **RAG (Retrieval Augmented Generation)**: Query unstructured document data + +**Example questions you can ask:** +- "What are the top 5 products by sales?" +- "Summarize the key points from the latest quarterly report." +- "Compare customer satisfaction between our premium and basic service tiers." +""")agentic_rag_texttosql/rag.py (4)
80-80: Remove the unnecessaryfprefix in this string.
This string has no placeholders, making thefprefix extraneous.- f"Useful for answering semantic questions about certain cities in the US." + "Useful for answering semantic questions about certain cities in the US."🧰 Tools
🪛 Ruff (0.8.2)
80-80: f-string without any placeholders
Remove extraneous
fprefix(F541)
87-87: Remove the unusedDictimport.
The static analysis tool indicates thatDictis never referenced.-from typing import Dict, List, Any, Optional +from typing import List, Any, Optional🧰 Tools
🪛 Ruff (0.8.2)
87-87:
typing.Dictimported but unusedRemove unused import:
typing.Dict(F401)
92-92: Format the class definition for clarity.
Declaring classes in a single line is flagged by style guidelines. Movingpassto a new line clarifies the definition.-class InputEvent(Event): pass +class InputEvent(Event): + pass🧰 Tools
🪛 Ruff (0.8.2)
92-92: Multiple statements on one line (colon)
(E701)
133-146: Consider logging or error handling for tool call failures.
While a "tool not found" response is appended as aChatMessage, it may be beneficial to add logging or a specialized fallback strategy for scenarios where a tool returns an error or raises an exception, especially when scaled to production loads.agentic_rag_texttosql/requirements.txt (1)
1-7: Dependency List Inclusion.
The requirements file lists all the necessary packages for the project. For enhanced reproducibility, consider pinning version numbers or specifying version ranges to prevent unexpected breaking changes in the future.agentic_rag_texttosql/README.md (4)
37-47: Fenced Code Block Language Specification.
The code blocks that show the configuration for API keys (lines 38–41 and 44–47) currently lack a language specifier. For better readability and to comply with markdown lint rules (MD040), consider adding a language identifier (e.g.,bash ortext).🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
38-38: Fenced code blocks should have a language specified
null(MD040, fenced-code-language)
44-44: Fenced code blocks should have a language specified
null(MD040, fenced-code-language)
49-51: Heading Formatting for Installation Section.
The "## 🔧 Install Dependencies:" heading includes a trailing colon, which is flagged by markdown lint (MD026). Consider removing the colon to adhere to best practices.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
49-49: Trailing punctuation in heading
Punctuation: ':'(MD026, no-trailing-punctuation)
52-54: Code Block Language Identifier.
The fenced code block containing the pip install command does not specify a language. Adding a language (such as ```bash) will improve syntax highlighting and readability.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
52-52: Fenced code blocks should have a language specified
null(MD040, fenced-code-language)
56-60: Run the App Section Formatting.
The "## Run the app:" heading also has a trailing colon. Removing the colon would improve consistency with markdown style guidelines. Additionally, specifying a language (e.g., ```bash) in the fenced code block for thestreamlit run app.pycommand will enhance clarity.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
56-56: Trailing punctuation in heading
Punctuation: ':'(MD026, no-trailing-punctuation)
58-58: Fenced code blocks should have a language specified
null(MD040, fenced-code-language)
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⛔ Files ignored due to path filters (1)
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📒 Files selected for processing (5)
agentic_rag_texttosql/README.md(1 hunks)agentic_rag_texttosql/app.py(1 hunks)agentic_rag_texttosql/rag.py(1 hunks)agentic_rag_texttosql/requirements.txt(1 hunks)agentic_rag_texttosql/typefully_thread_link.txt(1 hunks)
🧰 Additional context used
🪛 markdownlint-cli2 (0.17.2)
agentic_rag_texttosql/README.md
38-38: Fenced code blocks should have a language specified
null
(MD040, fenced-code-language)
44-44: Fenced code blocks should have a language specified
null
(MD040, fenced-code-language)
49-49: Trailing punctuation in heading
Punctuation: ':'
(MD026, no-trailing-punctuation)
52-52: Fenced code blocks should have a language specified
null
(MD040, fenced-code-language)
56-56: Trailing punctuation in heading
Punctuation: ':'
(MD026, no-trailing-punctuation)
58-58: Fenced code blocks should have a language specified
null
(MD040, fenced-code-language)
🪛 Ruff (0.8.2)
agentic_rag_texttosql/rag.py
80-80: f-string without any placeholders
Remove extraneous f prefix
(F541)
87-87: typing.Dict imported but unused
Remove unused import: typing.Dict
(F401)
92-92: Multiple statements on one line (colon)
(E701)
🔇 Additional comments (5)
agentic_rag_texttosql/rag.py (1)
60-60: Ensure proper handling ofLLAMA_CLOUD_API_KEY.
The code directly accesses an environment variable without verifying if it's set. Consider adding fallback logic or error handling to avoid unexpected failures if the key is missing.Would you like to add a brief check to ensure that
LLAMA_CLOUD_API_KEYis set?agentic_rag_texttosql/typefully_thread_link.txt (1)
1-1: Valid URL Reference Added.
The file now includes the Typefully thread URL, and it appears to be correctly formatted. Ensure that this link is kept up-to-date or consider adding a brief comment for context if future maintainers need clarity about its purpose.agentic_rag_texttosql/README.md (3)
1-10: Introduction and Features Section Reviewed.
The introduction and features clearly explain the system overview. The use of emojis and headings helps engage readers. No functional issues found here.
11-17: How It Works Section Clarity.
The step-by-step breakdown of how the system processes a query is concise and clear. This helps users understand the workflow adequately.
19-32: API Keys Setup Instructions.
The instructions for acquiring API keys for OpenAI and LlamaIndex Cloud are clear and easy to follow. Consider confirming that the links remain valid over time.
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Task #1 RAG-&-TextToSQL.
Summary by CodeRabbit
New Features
Documentation
README.mddetailing the operational flow and setup instructions for the project.Chores