Added Test Code - Submission #82
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*Total -- 3,970.60kb -> 2,746.60kb (30.83%) /LaTeX-OCR-with-Llama/kl_div.png -- 101.87kb -> 24.38kb (76.07%) /Youtube-trend-analysis/assets/brightdata.png -- 12.14kb -> 8.10kb (33.24%) /agentic_rag/thumbnail/thumbnail.png -- 754.95kb -> 517.49kb (31.45%) /agentic_rag/thumbnail/youtube.png -- 754.95kb -> 517.49kb (31.45%) /Youtube-trend-analysis/assets/crewai.png -- 217.12kb -> 149.43kb (31.18%) /agentic_rag_deepseek/assets/thumbnail.png -- 770.29kb -> 530.36kb (31.15%) /content_planner_flow/resources/thumbnail.png -- 737.68kb -> 529.40kb (28.23%) /deepseek-thinking-ui/assets/deep-seek.png -- 28.03kb -> 21.02kb (25.01%) /fastest-rag-stack/resources/thumbnail.png -- 284.26kb -> 213.87kb (24.76%) /document-chat-rag/resources/thumbnail.png -- 284.26kb -> 213.87kb (24.76%) /Website-to-API-with-FireCrawl/assets/firecrawl.png -- 15.21kb -> 11.44kb (24.74%) /chat-with-audios/assets/AssemblyAI.png -- 9.84kb -> 9.76kb (0.9%) Signed-off-by: ImgBotApp <ImgBotHelp@gmail.com>
Converted .ipynb file to .py file with all the needed implementations.
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Important Review skippedReview was skipped due to path filters ⛔ Files ignored due to path filters (12)
CodeRabbit blocks several paths by default. You can override this behavior by explicitly including those paths in the path filters. For example, including You can disable this status message by setting the WalkthroughA new Python file has been added to implement a Streamlit application for a Retrieval-Augmented Generation demo. The application integrates a SQLite in-memory database with city statistics and defines a Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant S as Streamlit UI
participant Q as CityQueryEngine
participant D as SQLite DB
participant L as LlamaCloud
U->>S: Enter query & API keys
S->>Q: Process user query
alt SQL Query
Q->>D: Execute SQL query
D-->>Q: Return query results
else Augmented Query
Q->>L: Send query for augmentation
L-->>Q: Return augmented response
end
Q->>S: Provide final answer or error message
S->>U: Display results
Poem
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Actionable comments posted: 1
🧹 Nitpick comments (4)
Hybrid_RAG_Test_Vedant/requirements.txt (1)
1-7: Consider pinning versions for reproducibility.
Specifying exact versions or version ranges helps ensure consistent environments and prevents unexpected breakages if the packages get updated with breaking changes.Hybrid_RAG_Test_Vedant/gemini_sql_router.py (3)
9-9: Remove unused imports.
Per the static analysis hints,desc,asc,funcin line 9 andList,Dict,Any,Tuplein line 11 are unused and can be safely removed.-from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, insert, text, desc, asc, func +from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, insert, text -from typing import List, Dict, Any, TupleAlso applies to: 11-11
🧰 Tools
🪛 Ruff (0.8.2)
9-9:
sqlalchemy.descimported but unusedRemove unused import
(F401)
9-9:
sqlalchemy.ascimported but unusedRemove unused import
(F401)
9-9:
sqlalchemy.funcimported but unusedRemove unused import
(F401)
55-123: Add unit tests for the new query engine methods.
The methodsquery_highest_population,query_lowest_population,query_by_state, and others provide vital functionality for this application. Consider adding unit tests to ensure correctness, prevent regressions, and fully cover edge cases.
250-251: Combine nested context managers.
Refactor the nestedwithstatements into a single line for cleaner code, as suggested by static analysis.-with st.chat_message("assistant"): - with st.spinner("Thinking..."): +with st.chat_message("assistant"), st.spinner("Thinking..."): message_placeholder = st.empty() ...🧰 Tools
🪛 Ruff (0.8.2)
250-251: Use a single
withstatement with multiple contexts instead of nestedwithstatements(SIM117)
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Hybrid_RAG_Test_Vedant/gemini_sql_router.py(1 hunks)Hybrid_RAG_Test_Vedant/requirements.txt(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py
9-9: sqlalchemy.desc imported but unused
Remove unused import
(F401)
9-9: sqlalchemy.asc imported but unused
Remove unused import
(F401)
9-9: sqlalchemy.func imported but unused
Remove unused import
(F401)
11-11: typing.List imported but unused
Remove unused import
(F401)
11-11: typing.Dict imported but unused
Remove unused import
(F401)
11-11: typing.Any imported but unused
Remove unused import
(F401)
11-11: typing.Tuple imported but unused
Remove unused import
(F401)
250-251: Use a single with statement with multiple contexts instead of nested with statements
(SIM117)
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
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Actionable comments posted: 2
🧹 Nitpick comments (8)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py (8)
9-11: Remove unused imports.Several imports are declared but never used in the code, which affects readability and maintainability.
-from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, insert, text, desc, asc, func +from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, insert, text import re -from typing import List, Dict, Any, Tuple🧰 Tools
🪛 Ruff (0.8.2)
9-9:
sqlalchemy.descimported but unusedRemove unused import
(F401)
9-9:
sqlalchemy.ascimported but unusedRemove unused import
(F401)
9-9:
sqlalchemy.funcimported but unusedRemove unused import
(F401)
11-11:
typing.Listimported but unusedRemove unused import
(F401)
11-11:
typing.Dictimported but unusedRemove unused import
(F401)
11-11:
typing.Anyimported but unusedRemove unused import
(F401)
11-11:
typing.Tupleimported but unusedRemove unused import
(F401)
110-113: Enhance state name extraction logic.The current regex pattern for extracting state names has limitations. It only matches state names at the end of the query and requires the preposition "in".
- state_match = re.search(r"in\s+([a-zA-Z\s]+)(?:\?)?$", query_lower) + state_match = re.search(r"(?:in|from|of)\s+([a-zA-Z\s]+)(?:\?|\.)?", query_lower) if state_match: state_name = state_match.group(1).strip() return self.query_by_state(state_name)This improvement will recognize phrases like "cities of California" and "population from Texas" as well.
251-252: Simplify nested with statements.Multiple nested
withstatements can be combined for better readability.- with st.chat_message("assistant"): - with st.spinner("Thinking..."): + with st.chat_message("assistant"), st.spinner("Thinking..."):🧰 Tools
🪛 Ruff (0.8.2)
251-252: Use a single
withstatement with multiple contexts instead of nestedwithstatements(SIM117)
62-75: Add error handling for SQL queries.The current implementation doesn't handle potential exceptions that could occur during query execution.
def execute_query(self, query_text): """Execute a raw SQL query and return formatted results""" - with self.engine.connect() as conn: - result = conn.execute(text(query_text)) - rows = result.fetchall() - if not rows: - return "No matching cities found in the database." - - # Format the results - if len(rows) == 1: - row = rows[0] - return f"{row[0]} has a population of {row[1]:,} people and is located in {row[2]}." - else: - formatted_rows = "\n".join([f"- {row[0]}: {row[1]:,} people in {row[2]}" for row in rows]) - return f"City information:\n\n{formatted_rows}" + try: + with self.engine.connect() as conn: + result = conn.execute(text(query_text)) + rows = result.fetchall() + if not rows: + return "No matching cities found in the database." + + # Format the results + if len(rows) == 1: + row = rows[0] + return f"{row[0]} has a population of {row[1]:,} people and is located in {row[2]}." + else: + formatted_rows = "\n".join([f"- {row[0]}: {row[1]:,} people in {row[2]}" for row in rows]) + return f"City information:\n\n{formatted_rows}" + except Exception as e: + return f"Error executing query: {str(e)}"
260-266: Improve query detection logic.The current approach to detecting population queries relies on a simple keyword check. This can be improved using a more systematic approach.
- if any(word in prompt.lower() for word in ['population', 'populous', 'big city', 'large city', 'small city']): + # Define query types with associated keywords + query_types = { + "population": ['population', 'populous', 'big city', 'large city', 'small city', 'people', 'residents'], + "location": ['where is', 'located', 'location', 'state', 'country', 'find'], + "general": ['tell me about', 'what is', 'describe', 'information'] + } + + # Determine query type + prompt_lower = prompt.lower() + query_type = next((qtype for qtype, keywords in query_types.items() + if any(word in prompt_lower for word in keywords)), "general") + + if query_type == "population":This modular approach makes it easier to extend the system with additional query types in the future.
97-123: Add docstring examples to the process_population_query method.This method would benefit from examples in the docstring to clarify what kinds of queries it can handle.
def process_population_query(self, query_text): - """Process a natural language query about population""" + """Process a natural language query about population. + + Examples: + - "What is the city with the highest population?" + - "Which city has the smallest population?" + - "List all cities by population." + - "What cities are in California?" + """
38-46: Consider making the city data more comprehensive.The current dataset is very limited with only 6 cities. For a real-world application, consider expanding this dataset or loading it from an external source.
You could enhance this by:
- Loading data from a CSV file or external API
- Adding more cities and data points
- Implementing a data refresh mechanism
This would make the application more robust and realistic for demonstration purposes.
286-287: Consider adding modular testing capabilities.The current implementation only has an entry point for the main application. Consider adding a way to test components individually.
if __name__ == "__main__": main() +else: + # When imported as a module, expose key components for testing + __all__ = ['setup_database', 'CityQueryEngine']
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Hybrid_RAG_Test_Vedant/gemini_sql_router.py(1 hunks)
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🪛 Gitleaks (8.21.2)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py
18-18: Uncovered a GCP API key, which could lead to unauthorized access to Google Cloud services and data breaches.
(gcp-api-key)
🪛 Ruff (0.8.2)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py
9-9: sqlalchemy.desc imported but unused
Remove unused import
(F401)
9-9: sqlalchemy.asc imported but unused
Remove unused import
(F401)
9-9: sqlalchemy.func imported but unused
Remove unused import
(F401)
11-11: typing.List imported but unused
Remove unused import
(F401)
11-11: typing.Dict imported but unused
Remove unused import
(F401)
11-11: typing.Any imported but unused
Remove unused import
(F401)
11-11: typing.Tuple imported but unused
Remove unused import
(F401)
251-252: Use a single with statement with multiple contexts instead of nested with statements
(SIM117)
🔇 Additional comments (1)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py (1)
93-96: Good use of parameterized queries.The implementation correctly uses parameterized queries to prevent SQL injection attacks.
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♻️ Duplicate comments (1)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py (1)
145-152:⚠️ Potential issueRemove hardcoded sensitive credentials.
This code contains hardcoded API keys and IDs, which is a security risk.
- llamacloud_api_key = st.text_input("LlamaCloud API Key:", type="password", - value="llx-CssfMkf0ENH0TTeU6xCxZC9hmOYm656gHu7fkexPHsu2hACz") - llamacloud_org_id = st.text_input("Organization ID:", - value="ea3321f4-0226-41b8-9929-5f5f8c396086") - llamacloud_project = st.text_input("Project Name:", - value="Default") - llamacloud_index = st.text_input("Index Name:", - value="overwhelming-felidae-2025-03-13") + llamacloud_api_key = st.text_input("LlamaCloud API Key:", type="password") + llamacloud_org_id = st.text_input("Organization ID:") + llamacloud_project = st.text_input("Project Name:") + llamacloud_index = st.text_input("Index Name:")Consider using environment variables or a secure configuration mechanism.
🧹 Nitpick comments (3)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py (3)
9-11: Remove unused imports.Several imports in your code are declared but never used:
- From SQLAlchemy:
desc,asc,func- From typing:
List,Dict,Any,Tuple-from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, insert, text, desc, asc, func -import re -from typing import List, Dict, Any, Tuple +from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, insert, text +import re🧰 Tools
🪛 Ruff (0.8.2)
9-9:
sqlalchemy.descimported but unusedRemove unused import
(F401)
9-9:
sqlalchemy.ascimported but unusedRemove unused import
(F401)
9-9:
sqlalchemy.funcimported but unusedRemove unused import
(F401)
11-11:
typing.Listimported but unusedRemove unused import
(F401)
11-11:
typing.Dictimported but unusedRemove unused import
(F401)
11-11:
typing.Anyimported but unusedRemove unused import
(F401)
11-11:
typing.Tupleimported but unusedRemove unused import
(F401)
60-75: Improve error handling in execute_query method.The method appropriately formats results, but could benefit from more robust error handling.
def execute_query(self, query_text): """Execute a raw SQL query and return formatted results""" try: with self.engine.connect() as conn: result = conn.execute(text(query_text)) rows = result.fetchall() if not rows: return "No matching cities found in the database." # Format the results if len(rows) == 1: row = rows[0] return f"{row[0]} has a population of {row[1]:,} people and is located in {row[2]}." else: formatted_rows = "\n".join([f"- {row[0]}: {row[1]:,} people in {row[2]}" for row in rows]) return f"City information:\n\n{formatted_rows}" + except Exception as e: + return f"Error executing database query: {str(e)}"
251-253: Simplify nested with statements.Multiple nested
withstatements can be combined into a single statement with multiple contexts.- with st.chat_message("assistant"): - with st.spinner("Thinking..."): - message_placeholder = st.empty() + with st.chat_message("assistant"), st.spinner("Thinking..."): + message_placeholder = st.empty()🧰 Tools
🪛 Ruff (0.8.2)
251-252: Use a single
withstatement with multiple contexts instead of nestedwithstatements(SIM117)
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py
9-9: sqlalchemy.desc imported but unused
Remove unused import
(F401)
9-9: sqlalchemy.asc imported but unused
Remove unused import
(F401)
9-9: sqlalchemy.func imported but unused
Remove unused import
(F401)
11-11: typing.List imported but unused
Remove unused import
(F401)
11-11: typing.Dict imported but unused
Remove unused import
(F401)
11-11: typing.Any imported but unused
Remove unused import
(F401)
11-11: typing.Tuple imported but unused
Remove unused import
(F401)
251-252: Use a single with statement with multiple contexts instead of nested with statements
(SIM117)
🔇 Additional comments (5)
Hybrid_RAG_Test_Vedant/gemini_sql_router.py (5)
16-19: Good security practice with API key handling.The code appropriately initializes the API key to an empty string and stores it in the session state, avoiding hardcoded credentials in this section.
20-54: Well-structured database setup function.The
setup_database()function is well-organized and follows good practices:
- Uses in-memory SQLite appropriate for a demo
- Creates a proper schema with appropriate column types
- Populates with sample data using SQLAlchemy's proper insertion methods
- Returns both the engine and table for further use
91-97: SQL injection protection is properly implemented.Good job implementing parameterized queries using the
textfunction with parameter binding instead of f-strings. This helps protect against SQL injection attacks.
97-124: Good implementation of natural language query processing.The
process_population_querymethod effectively handles various natural language queries by:
- Using pattern matching for different query intents
- Regular expressions to extract state names
- Handling different variations of highest/lowest queries
- Providing a default case for general population queries
255-284: Good error handling and routing logic for queries.The application effectively:
- Routes population queries to the specialized SQL engine
- Uses LlamaCloud for general information when available
- Provides appropriate fallbacks when services aren't available
- Handles exceptions and provides user-friendly error messages
[ImgBot] Optimize images
Converted .ipynb file to .py file with all the needed implementations.
PR: Implemented hybrid RAG system with LlamaCloud for document retrieval and SQL database for structured city data, powered by Gemini 2.0 and Streamlit UI.
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