Dharm1287 patch 1 #83
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WalkthroughThis update introduces two new files in the project. The README file provides a project overview that explains the unified query interface combining Retrieval-Augmented Generation (RAG) and Text-to-SQL functionalities, along with detailed installation and API key configuration instructions. The new Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant S as Streamlit App
participant QE as Query Engine
participant DB as SQLite Database
U->>S: Enter query in input field
S->>QE: Process natural language query
QE->>DB: Execute SQL query against city_stats table
DB-->>QE: Return results
QE-->>S: Format and return answer
S->>U: Display result on UI
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Actionable comments posted: 6
🧹 Nitpick comments (5)
Hybrid-RAG-dharm/app.py (3)
15-16: Use more specific LLM model configurationThe OpenAI model is configured with minimal parameters, which might not provide optimal performance for this specific task.
Consider adding more configuration options to improve output quality:
- Settings.llm = OpenAI("gpt-3.5-turbo") + Settings.llm = OpenAI( + model="gpt-3.5-turbo", + temperature=0.1, # Lower temperature for more deterministic outputs + max_tokens=512, # Set appropriate token limit + system_prompt="You are a helpful assistant specialized in querying and analyzing city statistics." + )
18-31: Improve database structure and schema managementThe database table schema is defined inline with no error handling or verification, which could lead to issues if the schema needs to change.
Consider implementing a more robust database setup:
# Create SQLite in-memory database engine = create_engine("sqlite:///:memory:", future=True) metadata_obj = MetaData() # Define city statistics table table_name = "city_stats" city_stats_table = Table( table_name, metadata_obj, Column("city_name", String(16), primary_key=True), + # Increase string length for city names to accommodate longer entries + # Column("city_name", String(64), primary_key=True), Column("population", Integer), Column("state", String(16), nullable=False), + # Add additional useful columns + # Column("country", String(32), nullable=False, default="USA"), + # Column("last_updated", Date, nullable=True), ) -metadata_obj.create_all(engine) +try: + metadata_obj.create_all(engine) + st.sidebar.success("Database initialized successfully") +except Exception as e: + st.sidebar.error(f"Error initializing database: {e}") + st.stop()
33-48: Enhance sample data managementThe current implementation inserts sample data directly in the application code, which mixes data with application logic and lacks error handling.
Consider separating data insertion into a dedicated function with error handling:
-# Insert sample data -from sqlalchemy.dialects.sqlite import insert as sqlite_insert -rows = [ - {"city_name": "New York City", "population": 8336000, "state": "New York"}, - {"city_name": "Los Angeles", "population": 3822000, "state": "California"}, - {"city_name": "Chicago", "population": 2665000, "state": "Illinois"}, - {"city_name": "Houston", "population": 2303000, "state": "Texas"}, - {"city_name": "Miami", "population": 449514, "state": "Florida"}, - {"city_name": "Seattle", "population": 749256, "state": "Washington"}, -] -for row in rows: - stmt = sqlite_insert(city_stats_table).values(**row) - stmt = stmt.on_conflict_do_update(index_elements=['city_name'], set_=row) - with engine.begin() as connection: - connection.execute(stmt) +# Import at the top of the file +from sqlalchemy.dialects.sqlite import insert as sqlite_insert + +def load_sample_data(engine, table): + """Load sample data into the database with error handling.""" + rows = [ + {"city_name": "New York City", "population": 8336000, "state": "New York"}, + {"city_name": "Los Angeles", "population": 3822000, "state": "California"}, + {"city_name": "Chicago", "population": 2665000, "state": "Illinois"}, + {"city_name": "Houston", "population": 2303000, "state": "Texas"}, + {"city_name": "Miami", "population": 449514, "state": "Florida"}, + {"city_name": "Seattle", "population": 749256, "state": "Washington"}, + ] + + try: + for row in rows: + stmt = sqlite_insert(table).values(**row) + stmt = stmt.on_conflict_do_update(index_elements=['city_name'], set_=row) + with engine.begin() as connection: + connection.execute(stmt) + return True + except Exception as e: + st.sidebar.error(f"Error loading sample data: {e}") + return False + +# Call the function +load_sample_data(engine, city_stats_table)Hybrid-RAG-dharm/README (2)
38-40: Update app execution instructions for clarityThe app execution instructions don't specify the full path to the app.py file, which might confuse users if they're not in the correct directory.
- streamlit run app.py + # Navigate to the project directory first + cd Hybrid-RAG-dharm + streamlit run app.py
1-9: Improve project description with usage examplesThe README provides a good overview but lacks specific examples of the types of queries users can perform with the system.
Consider adding a "Usage Examples" section:
## Usage Examples Once the application is running, you can ask natural language questions about city statistics, such as: - "Which city has the highest population?" - "List all cities in California" - "What is the average population of cities in the database?" - "Compare the population of New York and Los Angeles" The application will convert your natural language query into SQL, execute it against the database, and return the results in a human-readable format.
| import streamlit as st | ||
| import os | ||
| import nest_asyncio | ||
| from llama_index.core import SQLDatabase, Settings | ||
| from llama_index.llms.openai import OpenAI | ||
| from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer | ||
| from llama_index.core.query_engine import NLSQLTableQueryEngine | ||
| from llama_index.core.tools import QueryEngineTool | ||
|
|
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🛠️ Refactor suggestion
Missing main function and modular structure
The code currently runs sequentially without a proper main function or modular structure, which makes it harder to maintain and test.
Refactor the code to use a main function and improve modularity:
+def initialize_database():
+ """Initialize database with schema and sample data."""
+ engine = create_engine("sqlite:///:memory:", future=True)
+ metadata_obj = MetaData()
+
+ # Define city statistics table
+ table_name = "city_stats"
+ city_stats_table = Table(
+ table_name,
+ metadata_obj,
+ Column("city_name", String(64), primary_key=True),
+ Column("population", Integer),
+ Column("state", String(16), nullable=False),
+ )
+ metadata_obj.create_all(engine)
+
+ # Load sample data
+ load_sample_data(engine, city_stats_table)
+
+ return engine, table_name
+
+def setup_query_engine(engine, table_name):
+ """Set up the SQL database query engine."""
+ sql_database = SQLDatabase(engine, include_tables=[table_name])
+ sql_query_engine = NLSQLTableQueryEngine(
+ sql_database=sql_database,
+ tables=[table_name]
+ )
+ return sql_query_engine
+
+def main():
+ """Main function to run the Streamlit application."""
+ st.title("RAG and Text-to-SQL")
+ st.write("Ask questions about city populations and states!")
+
+ # Initialize database
+ engine, table_name = initialize_database()
+
+ # Set up query engine
+ sql_query_engine = setup_query_engine(engine, table_name)
+
+ # UI code here...
+
+if __name__ == "__main__":
+ main()Committable suggestion skipped: line range outside the PR's diff.
| # Set up API key (Ensure to store securely in production) | ||
| os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>" |
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Security risk: Hardcoded API key placeholder
The current implementation has a placeholder for the OpenAI API key directly in the code. This is a security risk as API keys should never be hardcoded in source code, especially if they might be committed to version control.
Consider using environment variables or a secure configuration file approach:
- # Set up API key (Ensure to store securely in production)
- os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
+ # Load API key from environment or .env file
+ from dotenv import load_dotenv
+ load_dotenv()
+
+ # Ensure the API key is available
+ if "OPENAI_API_KEY" not in os.environ:
+ st.error("OpenAI API key is missing. Please set the OPENAI_API_KEY environment variable.")
+ st.stop()📝 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.
| # Set up API key (Ensure to store securely in production) | |
| os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>" | |
| # Load API key from environment or .env file | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # Ensure the API key is available | |
| if "OPENAI_API_KEY" not in os.environ: | |
| st.error("OpenAI API key is missing. Please set the OPENAI_API_KEY environment variable.") | |
| st.stop() |
| user_query = st.text_input("Enter your query:", "Which city has the highest population?") | ||
|
|
||
| if st.button("Get Answer"): | ||
| with st.spinner("Processing..."): | ||
| response = sql_query_engine.query(user_query) | ||
| st.write("### Answer:") | ||
| st.write(response.response) |
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🛠️ Refactor suggestion
Enhance user interface with better error handling and examples
The current UI implementation lacks comprehensive error handling and doesn't provide examples to help users understand what kinds of queries are supported.
Improve the UI with error handling and query examples:
-user_query = st.text_input("Enter your query:", "Which city has the highest population?")
+# Add example queries to help users
+example_queries = [
+ "Which city has the highest population?",
+ "List all cities in California",
+ "What is the average population of cities in the database?",
+ "Which state has the most cities in our database?"
+]
+
+st.write("### Example queries:")
+for example in example_queries:
+ if st.button(example):
+ user_query = example
+ st.session_state.user_query = example
+
+if 'user_query' not in st.session_state:
+ st.session_state.user_query = "Which city has the highest population?"
+
+user_query = st.text_input("Enter your query:", value=st.session_state.user_query)
if st.button("Get Answer"):
with st.spinner("Processing..."):
- response = sql_query_engine.query(user_query)
- st.write("### Answer:")
- st.write(response.response)
+ if not user_query.strip():
+ st.error("Please enter a query")
+ else:
+ try:
+ response = sql_query_engine.query(user_query)
+ st.write("### Answer:")
+ st.write(response.response)
+
+ # Show the SQL query generated (optional)
+ with st.expander("View generated SQL"):
+ if hasattr(response, 'metadata') and 'sql_query' in response.metadata:
+ st.code(response.metadata['sql_query'], language='sql')
+ else:
+ st.write("SQL query not available")
+ except Exception as e:
+ st.error(f"Error processing query: {str(e)}")📝 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.
| user_query = st.text_input("Enter your query:", "Which city has the highest population?") | |
| if st.button("Get Answer"): | |
| with st.spinner("Processing..."): | |
| response = sql_query_engine.query(user_query) | |
| st.write("### Answer:") | |
| st.write(response.response) | |
| # Add example queries to help users | |
| example_queries = [ | |
| "Which city has the highest population?", | |
| "List all cities in California", | |
| "What is the average population of cities in the database?", | |
| "Which state has the most cities in our database?" | |
| ] | |
| st.write("### Example queries:") | |
| for example in example_queries: | |
| if st.button(example): | |
| user_query = example | |
| st.session_state.user_query = example | |
| if 'user_query' not in st.session_state: | |
| st.session_state.user_query = "Which city has the highest population?" | |
| user_query = st.text_input("Enter your query:", value=st.session_state.user_query) | |
| if st.button("Get Answer"): | |
| with st.spinner("Processing..."): | |
| if not user_query.strip(): | |
| st.error("Please enter a query") | |
| else: | |
| try: | |
| response = sql_query_engine.query(user_query) | |
| st.write("### Answer:") | |
| st.write(response.response) | |
| # Show the SQL query generated (optional) | |
| with st.expander("View generated SQL"): | |
| if hasattr(response, 'metadata') and 'sql_query' in response.metadata: | |
| st.code(response.metadata['sql_query'], language='sql') | |
| else: | |
| st.write("SQL query not available") | |
| except Exception as e: | |
| st.error(f"Error processing query: {str(e)}") |
|
|
||
| **Setup OpenAI**: | ||
|
|
||
| Get an API key from [OpenAihttps://platform.openai.com/) and set it in the `.env` file as follows: |
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Fix incorrect URL format in OpenAI link
There's a syntax error in the OpenAI URL - missing opening bracket and the URL is concatenated with the text.
- Get an API key from [OpenAihttps://platform.openai.com/) and set it in the `.env` file as follows:
+ Get an API key from [OpenAI](https://platform.openai.com/) and set it in the `.env` file as follows:📝 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.
| Get an API key from [OpenAihttps://platform.openai.com/) and set it in the `.env` file as follows: | |
| Get an API key from [OpenAI](https://platform.openai.com/) and set it in the `.env` file as follows: |
| Ensure you have Python 3.1 or later installed. | ||
| ```bash | ||
| pip install -U llama-index-callbacks-arize-phoenix streamlit | ||
| ``` |
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Python version requirement is incorrect
The README specifies Python 3.1, which is a very old version released in 2009. Modern projects typically use Python 3.8+ or newer.
- Ensure you have Python 3.1 or later installed.
+ Ensure you have Python 3.8 or later installed.Additionally, the dependency installation command seems incomplete as it doesn't include all required packages mentioned in the app.py file:
- pip install -U llama-index-callbacks-arize-phoenix streamlit
+ pip install -U llama-index-core llama-index-llms-openai streamlit sqlalchemy python-dotenv nest-asyncio📝 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.
| Ensure you have Python 3.1 or later installed. | |
| ```bash | |
| pip install -U llama-index-callbacks-arize-phoenix streamlit | |
| ``` | |
| Ensure you have Python 3.8 or later installed. |
| - Qdrant VectorDB for storing the embeddings. | ||
| - NLSQLTableQueryEngine to enable natural language queries on a SQL database. |
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Inconsistency between README and implementation
The README mentions Qdrant VectorDB for storing embeddings, but the implementation in app.py doesn't use Qdrant or any vector database.
Either update the code to include the vector database functionality or correct the README:
- - Qdrant VectorDB for storing the embeddings.
+ (Remove this line or implement vector database functionality in the code)Alternatively, if the intention is to include this functionality in a future update, clearly mark it as a planned feature:
- - Qdrant VectorDB for storing the embeddings.
+ - Qdrant VectorDB for storing the embeddings (planned for future implementation).📝 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.
| - Qdrant VectorDB for storing the embeddings. | |
| - NLSQLTableQueryEngine to enable natural language queries on a SQL database. | |
| - Qdrant VectorDB for storing the embeddings (planned for future implementation). | |
| - NLSQLTableQueryEngine to enable natural language queries on a SQL database. |
Added ReadMe and Streamlit app
Summary by CodeRabbit
Documentation
New Features