Bex - Task 1 (RAG and Text-to-SQL) Submission #88
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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 WalkthroughThis pull request introduces several new modules and configuration files that form the backbone of the RAG-SQL Assistant application. It adds environment and dependency configuration files along with comprehensive documentation. Additionally, new modules for configuration management, SQLite-based city statistics, asynchronous query processing, LlamaCloud-assisted retrieval, event-driven query routing, and a Streamlit-based user interface have been implemented. The changes establish a structured routing workflow that integrates both SQL and RAG query tools and provides a fully interactive chat interface for end users. Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant UI as Streamlit UI
participant APP as App (app.py)
participant QP as QueryProcessor
participant RW as RouterWorkflow
participant SQL as SQL Tool (CityStatsDB)
participant RAG as RAG Tool (LlamaCloudRAG)
U->>UI: Enter query
UI->>APP: Render UI / Collect query
APP->>QP: process_query(query)
QP->>RW: Initiate event-driven workflow
RW->>SQL: Dispatch SQL tool call
RW->>RAG: Dispatch RAG tool call
SQL-->>RW: Return SQL response
RAG-->>RW: Return RAG response
RW-->>QP: Aggregate and return response
QP-->>APP: Deliver final response
APP-->>UI: Display assistant message
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Actionable comments posted: 5
🧹 Nitpick comments (32)
rag-sql-assistant/.env.example (1)
14-17: Consider adding database connection parameters for production environmentsWhile the SQLite in-memory database is suitable for development, you might want to include commented examples for production database configurations (PostgreSQL, MySQL) to help users transition to persistent storage.
# Database configuration # Using SQLite in-memory database by default # Change this if you want to use a persistent database DATABASE_URL=sqlite:///:memory: +# PostgreSQL example: +# DATABASE_URL=postgresql://username:password@localhost:5432/database_name +# MySQL example: +# DATABASE_URL=mysql://username:password@localhost:3306/database_namerag-sql-assistant/src/ui/utils.py (2)
7-11: Remove unused importThe
Listtype from typing module is imported but never used in the code.import streamlit as st import time import random -from typing import List🧰 Tools
🪛 Ruff (0.8.2)
10-10:
typing.Listimported but unusedRemove unused import:
typing.List(F401)
12-40: Well-implemented text streaming function with minor issuesThe
stream_text_with_cursorfunction provides a nice visual effect for text streaming. The implementation is clean and follows good practices with proper documentation.There are two minor issues to fix:
- The loop control variable
iis not used- Consider adding a parameter for customizing the delay between words
-def stream_text_with_cursor(text: str): +def stream_text_with_cursor(text: str, min_delay: float = 0.02, max_delay: float = 0.1): """ Stream text with a red cursor effect. Args: text: The text to stream + min_delay: Minimum delay between words in seconds (default: 0.02) + max_delay: Maximum delay between words in seconds (default: 0.1) """ # Create a placeholder for the streaming text placeholder = st.empty() # Split the text into words words = text.split() displayed_text = "" # Stream each word with a random delay - for i, word in enumerate(words): + for _, word in enumerate(words): # Add the word to the displayed text displayed_text += word + " " # Display text with red cursor placeholder.markdown(f"{displayed_text}**<span style='color:red; font-size:1.25em; font-weight:bold;'>|</span>**", unsafe_allow_html=True) # Small random delay between words (0.01 to 0.05 seconds) - time.sleep(random.uniform(0.02, 0.1)) + time.sleep(random.uniform(min_delay, max_delay))🧰 Tools
🪛 Ruff (0.8.2)
27-27: Loop control variable
inot used within loop bodyRename unused
ito_i(B007)
rag-sql-assistant/requirements.txt (1)
96-96: Duplicate SQLAlchemy entry with asyncio extraLine 96 specifies SQLAlchemy with the asyncio extra, but line 95 already includes SQLAlchemy. Consider combining these into a single entry.
-sqlalchemy==2.0.39 ; python_version >= "3.11" and python_version < "4.0" -sqlalchemy[asyncio]==2.0.39 ; python_version >= "3.11" and python_version < "4.0" +sqlalchemy[asyncio]==2.0.39 ; python_version >= "3.11" and python_version < "4.0"rag-sql-assistant/app.py (1)
9-9: Remove unused importThe
gcmodule is imported but never used in this file.-import gc🧰 Tools
🪛 Ruff (0.8.2)
9-9:
gcimported but unusedRemove unused import:
gc(F401)
rag-sql-assistant/src/query_processor.py (2)
18-41: Consider persisting chat history between queriesThe workflow is initialized with an empty chat history array for each query (
chat_history=[]), which might not be ideal for maintaining conversation context.Consider modifying the function to accept and utilize a persistent chat history:
-async def _run_workflow_async(query: str) -> str: +async def _run_workflow_async(query: str, chat_history: list = None) -> str: """ Run the router workflow asynchronously. Args: query: The user's query string + chat_history: Previous conversation history Returns: Response string from the workflow """ # Create tools rag_tool = create_rag_tool() sql_tool = create_sql_tool() # Initialize router workflow router = RouterWorkflow( tools=[rag_tool, sql_tool], verbose=True, llm=None, # Will use default from config - chat_history=[] # Start fresh for each query + chat_history=chat_history or [] # Use provided history or start fresh )
36-36: Make the LLM configuration more explicitThe comment "Will use default from config" suggests an intentional design choice, but it could be clearer.
- llm=None, # Will use default from config + llm=None, # None triggers the use of default LLM from configurationrag-sql-assistant/src/rag/cloud_index.py (2)
31-31: Remove debug print statement.This
print(config)appears to be debugging code that should be removed before production deployment. Consider replacing it with proper logging if this information is valuable for troubleshooting.- print(config) + # Use logging instead if needed + # logging.debug(f"LlamaCloud configuration: {config}")
50-60: Add error handling to query method.The query method doesn't handle potential exceptions from the query engine, which could lead to unhandled exceptions when network issues occur or the service is unavailable.
def query(self, query_text: str) -> Any: """ Query the LlamaCloud index with a natural language query. Args: query_text: The natural language query text Returns: Any: The response from the query engine """ - return self.query_engine.query(query_text) + try: + return self.query_engine.query(query_text) + except Exception as e: + logging.error(f"Error querying LlamaCloud: {e}") + return f"I'm sorry, I encountered an error while searching for information: {str(e)}"rag-sql-assistant/README.md (2)
16-17: Replace placeholder with actual Twitter thread link.The README contains a placeholder for a Twitter thread link. Consider adding the actual link or removing this line if the thread doesn't exist yet.
For a detailed explanation of this project, check out this Twitter thread: -<insert Twitter thread link here> +https://twitter.com/yourusername/status/12345678
33-34: Add actual demo video or remove placeholder.There's a placeholder for a demo video or GIF. Either add the actual media or remove this section if not available yet.
### Demo -<insert demo video or GIF here> +rag-sql-assistant/src/ui/main.py (2)
62-92: Refactor logo path checking logic.The code checks multiple possible paths for logos, which is repetitive. Consider creating a utility function to find files from a list of possible paths.
+ def find_file_from_paths(possible_paths): + """Find the first existing file from a list of possible paths.""" + for path in possible_paths: + if os.path.exists(path): + return path + return None # Define base assets directory assets_dir = Path(os.path.dirname(os.path.abspath(__file__))).parent.parent / "assets" # Define possible paths for the logos openai_possible_paths = [ os.path.join("assets", "openai-logo.png"), "repo-to-submit-pr/rag-sql-assistant/assets/openai-logo.png", "rag-sql-assistant/assets/openai-logo.png", str(assets_dir / "openai-logo.png") ] llama_cloud_possible_paths = [ os.path.join("assets", "llama-cloud.png"), "repo-to-submit-pr/rag-sql-assistant/assets/llama-cloud.png", "rag-sql-assistant/assets/llama-cloud.png", str(assets_dir / "llama-cloud.png") ] - # Find OpenAI logo - openai_logo_path = None - for path in openai_possible_paths: - if os.path.exists(path): - openai_logo_path = path - break - - # Find LlamaCloud logo - llama_cloud_logo_path = None - for path in llama_cloud_possible_paths: - if os.path.exists(path): - llama_cloud_logo_path = path - break + # Find logos using utility function + openai_logo_path = find_file_from_paths(openai_possible_paths) + llama_cloud_logo_path = find_file_from_paths(llama_cloud_possible_paths)
113-115: Add more specific error handling.The current exception handling catches all exceptions and logs them, but it doesn't provide specific handling for different types of errors. Consider adding more specific exception handlers.
try: # Code for rendering header with logos except Exception as e: - logger.exception(f"Error loading logos: {e}") + if isinstance(e, FileNotFoundError): + logger.warning(f"Logo file not found: {e}") + elif isinstance(e, PermissionError): + logger.error(f"Permission denied when accessing logo file: {e}") + else: + logger.exception(f"Unexpected error loading logos: {e}") st.header("RAG-SQL Assistant powered by OpenAI and LlamaCloud")rag-sql-assistant/src/ui/explainer.py (4)
8-11: Remove unused import.The
base64module is imported but never used in this file. Consider removing it to keep imports clean.import streamlit as st import os from pathlib import Path - import base64🧰 Tools
🪛 Ruff (0.8.2)
11-11:
base64imported but unusedRemove unused import:
base64(F401)
19-30: Simplify the valid image check logic.The condition checking can be simplified to directly return the negated condition as suggested by static analysis.
def is_valid_image(file_path): """Check if a file is a valid image.""" try: # Try to read the file with open(file_path, 'rb') as f: # Check if it's a text placeholder (our echo command created text files) content = f.read(30) # Read the first few bytes - if content.startswith(b'Placeholder'): - return False - - # If not a placeholder, assume it's a valid image for now - # In a production app, you'd verify the image format here - return True + # Return True if it's not a placeholder text file + # In a production app, you'd verify the image format more thoroughly + return not content.startswith(b'Placeholder') except Exception: return False🧰 Tools
🪛 Ruff (0.8.2)
23-28: Return the negated condition directly
(SIM103)
14-14: Consider using a function for the assets directory path.The assets directory path is defined at the module level. Since this same path calculation logic appears in multiple files, consider moving it to a utility function in a shared module.
- # Get the assets directory path - ASSETS_DIR = Path(os.path.dirname(os.path.abspath(__file__))).parent.parent / "assets" + # Get the assets directory path + from src.ui.utils import get_assets_dir + ASSETS_DIR = get_assets_dir()Then in a utility file:
def get_assets_dir(): """Get the path to the assets directory.""" return Path(os.path.dirname(os.path.abspath(__file__))).parent.parent / "assets"
16-31: Add type hints to improve code readability.The
is_valid_imagefunction would benefit from type hints to make its input and output types clear. This would enhance code documentation and IDE support.- def is_valid_image(file_path): + def is_valid_image(file_path: str) -> bool: """Check if a file is a valid image.""" try: # Function implementation...🧰 Tools
🪛 Ruff (0.8.2)
23-28: Return the negated condition directly
(SIM103)
rag-sql-assistant/src/config.py (3)
13-26: Redundantload_dotenv()callThe function is called twice - once at the module level (line 13) and again inside the
load_config()function (line 26). This redundancy is unnecessary and could potentially cause confusion about when environment variables are actually loaded.import streamlit as st load_dotenv() def load_config() -> Dict[str, Any]: """ Load environment variables and configuration parameters for the application. Returns: Dict[str, Any]: Configuration dictionary Raises: ValueError: If required environment variables are missing """ # Load environment variables from .env file - load_dotenv() # Required environment variables required_vars = [
45-51: Consider separating UI configuration from environment variable loadingThe Streamlit page configuration is currently embedded within the
load_config()function. This mixes concerns between environment variable loading and UI configuration.Consider extracting the Streamlit page configuration into a separate function, like
configure_streamlit_page(), to maintain a cleaner separation of concerns.def load_config() -> Dict[str, Any]: # ... existing code ... # Optional environment variables database_url = os.getenv("DATABASE_URL", "sqlite:///:memory:") - # Configure Streamlit page settings - st.set_page_config( - page_title="City Assistant: RAG + SQL", - page_icon="🏙️", - layout="wide", - initial_sidebar_state="expanded" - ) # Return config dictionary return { # ... config dictionary ... } +def configure_streamlit_page(): + """Configure Streamlit page settings.""" + st.set_page_config( + page_title="City Assistant: RAG + SQL", + page_icon="🏙️", + layout="wide", + initial_sidebar_state="expanded" + )
110-113: Consider lazy loading of exported constantsThe current approach initializes constants at module import time. This could lead to unwanted side effects or delayed imports if any of the getter functions perform complex operations.
Consider using properties or lazy-loading patterns for these constants, especially if they might be used in contexts where the full environment setup isn't available yet.
-# Export constants for easy access -OPENAI_API_KEY = get_openai_api_key() -LLAMA_CLOUD_CONFIG = get_llama_cloud_config() -DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///:memory:") +# Export constants for easy access with lazy loading +def get_openai_api_key_lazy(): + if not hasattr(get_openai_api_key_lazy, '_key'): + get_openai_api_key_lazy._key = get_openai_api_key() + return get_openai_api_key_lazy._key + +def get_llama_cloud_config_lazy(): + if not hasattr(get_llama_cloud_config_lazy, '_config'): + get_llama_cloud_config_lazy._config = get_llama_cloud_config() + return get_llama_cloud_config_lazy._config + +def get_database_url_lazy(): + if not hasattr(get_database_url_lazy, '_url'): + get_database_url_lazy._url = os.getenv("DATABASE_URL", "sqlite:///:memory:") + return get_database_url_lazy._url + +# Properties to access lazy-loaded constants +OPENAI_API_KEY = property(get_openai_api_key_lazy) +LLAMA_CLOUD_CONFIG = property(get_llama_cloud_config_lazy) +DATABASE_URL = property(get_database_url_lazy)Alternatively, you could use a simpler caching mechanism if the more complex implementation above isn't needed.
rag-sql-assistant/src/ui/chat.py (1)
9-10: Remove unused importsThe static analysis tools detected unused imports in this line. The
ListandOptionaltypes from the typing module are imported but not used in the code.-from typing import List, Dict, Any, Optional, Callable +from typing import Dict, Any, Callable🧰 Tools
🪛 Ruff (0.8.2)
9-9:
typing.Listimported but unusedRemove unused import
(F401)
9-9:
typing.Optionalimported but unusedRemove unused import
(F401)
rag-sql-assistant/src/router/tools.py (3)
10-10: Remove unused importThe
SQLDatabaseimport fromllama_index.coreis not used directly in this file and can be removed.from llama_index.core.query_engine import NLSQLTableQueryEngine from llama_index.core.tools import QueryEngineTool -from llama_index.core import SQLDatabase🧰 Tools
🪛 Ruff (0.8.2)
10-10:
llama_index.core.SQLDatabaseimported but unusedRemove unused import:
llama_index.core.SQLDatabase(F401)
58-58: Unused cities listThe
citieslist is defined but never used in the function. If it's meant to be informational only (to document available cities), consider adding it to the docstring instead, or use it in the tool description to provide more context.Either remove the unused variable or integrate it into the tool description:
# The cities we have information about cities = ["New York City", "Los Angeles", "Chicago", "Houston", "Miami", "Seattle"] # Get query engine from RAG instance query_engine = llama_cloud_rag.query_engine # Create a tool for the RAG query engine return QueryEngineTool.from_defaults( query_engine=query_engine, description=( - f"Useful for answering semantic questions about certain cities in the US." + f"Useful for answering semantic questions about specific US cities: {', '.join(cities)}." ), name="llama_cloud_tool" )🧰 Tools
🪛 Ruff (0.8.2)
58-58: Local variable
citiesis assigned to but never usedRemove assignment to unused variable
cities(F841)
66-68: Remove unnecessary f-string prefixThe description string uses an f-string prefix but doesn't contain any placeholders. This is unnecessary and should be removed.
return QueryEngineTool.from_defaults( query_engine=query_engine, description=( - f"Useful for answering semantic questions about certain cities in the US." + "Useful for answering semantic questions about certain cities in the US." ), name="llama_cloud_tool" )🧰 Tools
🪛 Ruff (0.8.2)
67-67: f-string without any placeholders
Remove extraneous
fprefix(F541)
rag-sql-assistant/src/database/city_stats.py (4)
52-54: Consider increasing string column lengthsThe current string column lengths are set to 16 characters, which might be too restrictive for some city or state names. For example, "North Carolina" or "South Carolina" would exceed this limit.
return Table( "city_stats", self.metadata, - Column("city_name", String(16), primary_key=True), + Column("city_name", String(50), primary_key=True), Column("population", Integer), - Column("state", String(16), nullable=False), + Column("state", String(30), nullable=False), )
69-80: Optimize for bulk insertsThe current insert implementation creates a new transaction for each city, which can be inefficient for bulk operations. Consider using batch inserts when dealing with multiple rows.
def insert_data(self, cities_data: List[Dict[str, Any]]) -> None: """ Insert data into the city_stats table. Args: cities_data: List of dictionaries with city information. Each dictionary should contain 'city_name', 'population', and 'state'. """ - for city_data in cities_data: - stmt = insert(self.city_stats_table).values(**city_data) - with self.engine.begin() as connection: - connection.execute(stmt) + with self.engine.begin() as connection: + if cities_data: + stmt = insert(self.city_stats_table) + connection.execute(stmt, cities_data)
82-91: Consider using SQLAlchemy ORM style consistentlyThe
get_all_cities()method usesexec_driver_sqlwith raw SQL, while other methods use SQLAlchemy's expression language. It's generally better to use a consistent approach throughout the codebase.def get_all_cities(self) -> List[Tuple[str, int, str]]: """ Retrieve all cities and their information from the database. Returns: List of tuples with (city_name, population, state) """ with self.engine.connect() as connection: - cursor = connection.exec_driver_sql("SELECT * FROM city_stats") - return cursor.fetchall() + stmt = select(self.city_stats_table) + result = connection.execute(stmt) + return result.fetchall()
69-80: Add error handling for duplicate insertsThe insert operation doesn't handle potential duplicate key errors, which could occur if trying to insert a city that already exists in the database.
Consider adding try-except blocks to handle SQLAlchemy integrity errors, or implement an "upsert" pattern if you want to update existing records.
def insert_data(self, cities_data: List[Dict[str, Any]]) -> None: """ Insert data into the city_stats table. Args: cities_data: List of dictionaries with city information. Each dictionary should contain 'city_name', 'population', and 'state'. """ - for city_data in cities_data: - stmt = insert(self.city_stats_table).values(**city_data) - with self.engine.begin() as connection: - connection.execute(stmt) + from sqlalchemy.exc import IntegrityError + + for city_data in cities_data: + stmt = insert(self.city_stats_table).values(**city_data) + try: + with self.engine.begin() as connection: + connection.execute(stmt) + except IntegrityError: + # Handle duplicate key (e.g., log, update existing record, or skip) + print(f"City {city_data['city_name']} already exists. Skipping.")rag-sql-assistant/src/router/workflow.py (4)
9-25: Validate environment configuration.
Currently, the code proceeds without validating if the OpenAI API key is actually found or whether any fallback logic is needed. Consider adding graceful error handling or warnings to alert the user ifget_openai_api_key()returns None or an empty value.
49-104: Consider robust handling for tool dictionary initialization.
Storing the tools in a dictionary keyed bytool.metadata.nameis convenient, but if multiple tools share the same name or if a tool’s metadata is malformed, it may lead to unintended collisions or errors. You could add checks or warnings for duplicate keys during initialization.
127-165: Watch out for parallel tool call overhead.
You allow parallel tool calls by default (allow_parallel_tool_calls=True). While this is a powerful feature, ensure your environment can handle them concurrently, especially if some tools perform I/O operations or database queries. Consider adding a concurrency limit or queue if you anticipate a high volume of concurrent requests.
230-258: Clarify return type and flow when no tool results are collected.
The methodgatherreturnsUnion[StopEvent, InputEvent, None], but returningNonecould lead to unexpected stopping of the workflow. Consider returning a consistent event type (such asStopEvent) or clarifying the logic further to avoid confusion.
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📒 Files selected for processing (17)
rag-sql-assistant/.env.example(1 hunks)rag-sql-assistant/.gitignore(1 hunks)rag-sql-assistant/README.md(1 hunks)rag-sql-assistant/app.py(1 hunks)rag-sql-assistant/requirements.txt(1 hunks)rag-sql-assistant/src/config.py(1 hunks)rag-sql-assistant/src/database/city_stats.py(1 hunks)rag-sql-assistant/src/query_processor.py(1 hunks)rag-sql-assistant/src/rag/cloud_index.py(1 hunks)rag-sql-assistant/src/router/__init__.py(1 hunks)rag-sql-assistant/src/router/tools.py(1 hunks)rag-sql-assistant/src/router/workflow.py(1 hunks)rag-sql-assistant/src/ui/__init__.py(1 hunks)rag-sql-assistant/src/ui/chat.py(1 hunks)rag-sql-assistant/src/ui/explainer.py(1 hunks)rag-sql-assistant/src/ui/main.py(1 hunks)rag-sql-assistant/src/ui/utils.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
rag-sql-assistant/src/ui/utils.py
10-10: typing.List imported but unused
Remove unused import: typing.List
(F401)
27-27: Loop control variable i not used within loop body
Rename unused i to _i
(B007)
rag-sql-assistant/app.py
9-9: gc imported but unused
Remove unused import: gc
(F401)
rag-sql-assistant/src/ui/main.py
102-102: Use a context manager for opening files
(SIM115)
103-103: Use a context manager for opening files
(SIM115)
rag-sql-assistant/src/ui/explainer.py
11-11: base64 imported but unused
Remove unused import: base64
(F401)
23-28: Return the negated condition directly
(SIM103)
rag-sql-assistant/src/ui/chat.py
9-9: typing.List imported but unused
Remove unused import
(F401)
9-9: typing.Optional imported but unused
Remove unused import
(F401)
rag-sql-assistant/src/router/tools.py
10-10: llama_index.core.SQLDatabase imported but unused
Remove unused import: llama_index.core.SQLDatabase
(F401)
58-58: Local variable cities is assigned to but never used
Remove assignment to unused variable cities
(F841)
67-67: f-string without any placeholders
Remove extraneous f prefix
(F541)
🔇 Additional comments (24)
rag-sql-assistant/.env.example (1)
1-17: Well-structured environment configuration templateThe environment file includes all the necessary configurations for the RAG-SQL Assistant application, with clear sections and helpful comments. The file correctly handles sensitive information like API keys and includes optional configurations.
rag-sql-assistant/.gitignore (1)
1-45: Comprehensive .gitignore fileThe .gitignore file correctly excludes sensitive configuration files, Python artifacts, environment directories, and other common patterns. The special rule to allow
.env.examplewhile excluding other environment files is particularly good practice.rag-sql-assistant/requirements.txt (2)
1-113: Comprehensive and well-structured requirements fileThe requirements file is thorough and follows best practices by specifying exact versions for all dependencies. The Python version constraints (>= 3.11 and < 4.0) and platform-specific conditions are properly defined.
12-14:✅ Verification successful
Verify future-dated package versions
Several packages are pinned to versions with 2025 dates (e.g., certifi==2025.1.31, fsspec==2025.3.0). These appear to be future dates, which might cause installation issues.
🌐 Web query:
Is certifi version 2025.1.31 available on PyPI?💡 Result:
Yes, certifi version 2025.1.31 is available on PyPI[4][16]. This release was published on January 31, 2025[9][11], and provides Mozilla's CA bundle for SSL/TLS certificate validation in Python applications[4].
Key details:
- Installation command:
pip install certifi[4]- Latest stable release as of March 14, 2025[16]
- Security analysis shows no detected vulnerabilities in this specific version[10][11]
The package maintains high adoption with:
- Over 573 million monthly downloads[16]
- Usage across major Linux distributions including Debian Sid (since February 13)[2] and Ubuntu Plucky Puffin[5]
Developers can verify installation using:
import certifi print(certifi.where()) # Shows path to CA bundle fileThis update follows standard packaging practices with SHA256 checksums verified for source distributions (
20f734b...)[7].Citations:
- 1: https://packages.msys2.org/packages/python-certifi?variant=x86_64
- 2: https://answers.launchpad.net/debian/+source/python-certifi/2025.1.31+ds-1
- 3: https://packages.msys2.org/base/python-certifi
- 4: https://pypi.org/project/certifi/
- 5: https://launchpad.net/ubuntu/+source/python-certifi
- 6: https://app.soos.io/research/packages/Python/-/certifi/
- 7: https://www.piwheels.org/project/certifi/
- 8: https://security.snyk.io/package/pip/certifi
- 9: https://clearlydefined.io/definitions/pypi/pypi/-/certifi/2025.1.31
- 10: https://secure.software/pypi/packages/certifi/2025.1.31
- 11: https://security.snyk.io/package/pip/certifi/2025.1.31
- 12: https://ports.macports.org/port/py-certifi/summary/
- 13: https://dl.espressif.com/pypi/certifi
- 14: https://tracker.debian.org/python-certifi
- 15: https://prefix.dev/channels/conda-forge/packages/certifi
- 16: https://pypistats.org/packages/certifi
- 17: https://deps.dev/pypi/certifi
- 18: https://deps.dev/pypi/certifi/2019.9.11/versions
Future-dated package versions are valid and no changes are needed
- The version of certifi (2025.1.31) has been verified as available on PyPI, with its release dated January 31, 2025.
- The future-dated versioning appears intentional and follows standard release practices.
- If similar future versions (e.g., for fsspec) are used elsewhere in the project, please verify that they are also officially released.
rag-sql-assistant/src/router/__init__.py (1)
1-15: Well-organized Router Module structureThe Router Module is well-structured with a clear docstring, organized imports, and a properly defined
__all__list that explicitly specifies the public interface. This implementation follows Python best practices for module organization.rag-sql-assistant/app.py (2)
26-28: Appropriate session state initializationGood use of Streamlit's session state to persist data across app reruns with a unique chat ID and file cache initialization.
30-42: Clean main function structureThe main function follows a logical flow with proper separation of UI components and clear comments explaining each step. The modular approach will make future maintenance easier.
rag-sql-assistant/src/query_processor.py (2)
53-59: Proper result handlingGood job returning a structured dictionary response that can be easily consumed by the UI layer.
60-64: Robust error handlingExcellent implementation of exception handling with proper logging and user-friendly error messages.
rag-sql-assistant/src/ui/__init__.py (1)
1-55: Well-organized UI module structureThe UI module has a clean organization with logical grouping of imports and a comprehensive
__all__list that's clearly categorized by functionality. This approach makes it easy to understand the available UI components and their purposes.rag-sql-assistant/README.md (1)
1-238: Comprehensive and well-structured documentation!The README is excellently organized with clear sections for introduction, features, setup, usage, deployment, and contribution guidelines. This provides developers and users with a thorough understanding of the project.
rag-sql-assistant/src/ui/main.py (1)
1-163: Well-structured UI implementation with good component separation.The UI code is well-organized with clear function boundaries and good use of Streamlit's features. The separation of concerns into different functions (page config, sidebar, header, main UI) makes the code modular and maintainable.
🧰 Tools
🪛 Ruff (0.8.2)
102-102: Use a context manager for opening files
(SIM115)
103-103: Use a context manager for opening files
(SIM115)
rag-sql-assistant/src/ui/explainer.py (1)
32-174: Excellent fallback mechanisms for missing images.The code gracefully handles missing diagram files by providing ASCII art diagrams as fallbacks. This ensures that users still receive visual explanations even if image assets are missing.
rag-sql-assistant/src/config.py (1)
85-107: Well-structured error handling in tracing configurationThe
enable_tracing()function has appropriate error handling with a try-except block around the import and configuration of tracing. This is a good practice for optional functionality that depends on potentially unavailable packages.rag-sql-assistant/src/ui/chat.py (3)
12-19: Effective use of Streamlit's session stateThe chat state initialization is well-implemented using Streamlit's session state for persistent storage across reruns. Using UUID for the chat ID ensures uniqueness for each session.
25-42: Clean implementation of chat display functionalityThe chat display functions make good use of Streamlit's native chat_message component and properly iterate through the message history. The code is clean, concise, and maintainable.
63-73: Well-designed input handling with callback patternThe chat input area implementation is clean and follows a good pattern using a callback function for handling user submissions. This design allows for flexible integration with different query processing implementations.
rag-sql-assistant/src/router/tools.py (3)
73-87: Well-structured tool aggregationThe
get_all_toolsfunction effectively combines both SQL and RAG tools into a single list, making it easy to use all available tools in a query router.
90-104: Well-designed factory pattern for SQL tool creationThe factory function for creating SQL tools with default configurations is well-designed and follows good separation of concerns patterns.
106-119: Well-designed factory pattern for RAG tool creationThe factory function for creating RAG tools with default configurations is well-designed and follows good separation of concerns patterns.
rag-sql-assistant/src/database/city_stats.py (1)
93-100: Well-structured integration with LlamaIndexThe integration with LlamaIndex through the SQLDatabase wrapper is well-implemented and provides a clean interface for using the database with LlamaIndex's querying capabilities.
rag-sql-assistant/src/router/workflow.py (3)
1-7: Great use of docstrings for module-level context.
The introductory documentation sets a clear expectation for future maintainers and collaborators.
27-46: Concise event classes.
The new event class declarations (InputEvent, GatherToolsEvent, ToolCallEvent, ToolCallEventResult) are straightforward and have clear docstrings. No issues found here.
105-126: Add optional message sanitization or length checks.
Pending your requirements, it might be beneficial to ensure that user messages (messagefield) are sanitized or truncated if they exceed a certain size—and to confirm they do not contain malicious content or special characters that might cause downstream issues.
| # Set LLM in Settings | ||
| api_key = get_openai_api_key() | ||
| if api_key: | ||
| Settings.llm = OpenAI(api_key=api_key) | ||
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| # Initialize the LlamaCloud index using environment variables | ||
| config = get_llama_cloud_config() | ||
| print(config) | ||
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| self.index = LlamaCloudIndex( | ||
| name=config["index"], | ||
| project_name=config["project"], | ||
| organization_id=config["org_id"], | ||
| api_key=config["api_key"] | ||
| ) | ||
|
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There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for configuration and initialization.
The current implementation lacks error handling for missing or invalid environment variables. If the API key or config values are missing, the code will likely fail with unclear errors.
# Set LLM in Settings
api_key = get_openai_api_key()
if api_key:
Settings.llm = OpenAI(api_key=api_key)
+else:
+ logging.warning("OpenAI API key not found. Some functionality may be limited.")
# Initialize the LlamaCloud index using environment variables
config = get_llama_cloud_config()
- print(config)
+
+ # Validate required configuration
+ required_keys = ["index", "project", "org_id", "api_key"]
+ missing_keys = [key for key in required_keys if key not in config or not config[key]]
+ if missing_keys:
+ raise ValueError(f"Missing required LlamaCloud configuration: {', '.join(missing_keys)}")
self.index = LlamaCloudIndex(
name=config["index"],
project_name=config["project"],
organization_id=config["org_id"],
api_key=config["api_key"]
)📝 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 LLM in Settings | |
| api_key = get_openai_api_key() | |
| if api_key: | |
| Settings.llm = OpenAI(api_key=api_key) | |
| # Initialize the LlamaCloud index using environment variables | |
| config = get_llama_cloud_config() | |
| print(config) | |
| self.index = LlamaCloudIndex( | |
| name=config["index"], | |
| project_name=config["project"], | |
| organization_id=config["org_id"], | |
| api_key=config["api_key"] | |
| ) | |
| # Set LLM in Settings | |
| api_key = get_openai_api_key() | |
| if api_key: | |
| Settings.llm = OpenAI(api_key=api_key) | |
| else: | |
| logging.warning("OpenAI API key not found. Some functionality may be limited.") | |
| # Initialize the LlamaCloud index using environment variables | |
| config = get_llama_cloud_config() | |
| # Validate required configuration | |
| required_keys = ["index", "project", "org_id", "api_key"] | |
| missing_keys = [key for key in required_keys if key not in config or not config[key]] | |
| if missing_keys: | |
| raise ValueError(f"Missing required LlamaCloud configuration: {', '.join(missing_keys)}") | |
| self.index = LlamaCloudIndex( | |
| name=config["index"], | |
| project_name=config["project"], | |
| organization_id=config["org_id"], | |
| api_key=config["api_key"] | |
| ) |
|
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|  |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify architecture diagram exists.
The README references an architecture diagram at assets/architecture.png. Make sure this file exists in the repository to avoid broken images in the documentation.
🏁 Script executed:
#!/bin/bash
# Check if architecture diagram exists
ls -la rag-sql-assistant/assets/architecture.png 2>/dev/null || echo "Architecture diagram not found"Length of output: 121
Action Required: Add or Update Reference for Missing Architecture Diagram
The README now references rag-sql-assistant/assets/architecture.png, but the file was not found in the repository. Please either add the missing diagram file or update the README to point to the correct image path.
- File:
rag-sql-assistant/README.md(Lines 29-30) - Issue: Referenced architecture diagram (
assets/architecture.png) is missing.
| base64.b64encode(open(openai_logo_path, "rb").read()).decode(), | ||
| base64.b64encode(open(llama_cloud_logo_path, "rb").read()).decode() | ||
| ), unsafe_allow_html=True) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use context managers for file operations.
The code opens files without context managers, which can lead to resource leaks if exceptions occur. Consider using with statements as recommended by static analysis.
st.markdown("""
# RAG-SQL Assistant powered by <img src="data:image/png;base64,{}" style="height: 0.9em; vertical-align: middle; padding-left: 5px; padding-right: 5px;"> and <img src="data:image/png;base64,{}" style="height: 0.9em; vertical-align: middle; padding-left: 5px;">
""".format(
- base64.b64encode(open(openai_logo_path, "rb").read()).decode(),
- base64.b64encode(open(llama_cloud_logo_path, "rb").read()).decode()
+ base64.b64encode(open(openai_logo_path, "rb").read()).decode(),
+ base64.b64encode(open(llama_cloud_logo_path, "rb").read()).decode()
), unsafe_allow_html=True)Corrected version:
# Create base64 encoded images with context managers
openai_logo_b64 = ""
llama_cloud_logo_b64 = ""
with open(openai_logo_path, "rb") as f:
openai_logo_b64 = base64.b64encode(f.read()).decode()
with open(llama_cloud_logo_path, "rb") as f:
llama_cloud_logo_b64 = base64.b64encode(f.read()).decode()
st.markdown(f"""
# RAG-SQL Assistant powered by <img src="data:image/png;base64,{openai_logo_b64}" style="height: 0.9em; vertical-align: middle; padding-left: 5px; padding-right: 5px;"> and <img src="data:image/png;base64,{llama_cloud_logo_b64}" style="height: 0.9em; vertical-align: middle; padding-left: 5px;">
""", unsafe_allow_html=True)🧰 Tools
🪛 Ruff (0.8.2)
102-102: Use a context manager for opening files
(SIM115)
103-103: Use a context manager for opening files
(SIM115)
| @step(pass_context=True) | ||
| async def dispatch_calls(self, ctx: Context, ev: GatherToolsEvent) -> ToolCallEvent: | ||
| """ | ||
| Dispatch tool calls to be executed. | ||
|
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| Args: | ||
| ctx: Workflow context | ||
| ev: GatherToolsEvent with tool calls | ||
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| Returns: | ||
| ToolCallEvent for the first tool call | ||
| """ | ||
| tool_calls = ev.tool_calls | ||
|
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| # Store number of tool calls for later use | ||
| await ctx.set("num_tool_calls", len(tool_calls)) | ||
|
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| # Send events for each tool call after the first one | ||
| for tool_call in tool_calls[1:] if len(tool_calls) > 1 else []: | ||
| ctx.send_event(ToolCallEvent(tool_call=tool_call)) | ||
|
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| # Return the first tool call event to satisfy the workflow validation | ||
| if tool_calls: | ||
| return ToolCallEvent(tool_call=tool_calls[0]) | ||
| else: | ||
| # This should not happen since we've already checked for tool calls in chat() | ||
| return InputEvent() | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Validate tool call dispatch flow for partial successes.
When multiple tool calls are dispatched, errors may occur in subsequent calls, but the workflow always sends events for them anyway. Ensure your architecture can gracefully handle partial failures, such as one tool call failing while others succeed.
| @step() | ||
| async def call_tool(self, ev: ToolCallEvent) -> ToolCallEventResult: | ||
| """ | ||
| Execute a single tool call. | ||
|
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| Args: | ||
| ev: ToolCallEvent with tool to call | ||
|
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| Returns: | ||
| ToolCallEventResult with the result message | ||
| """ | ||
| tool_call = ev.tool_call | ||
|
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| # Get tool ID | ||
| id_ = tool_call.tool_id | ||
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| if self._verbose: | ||
| print(f"Calling function {tool_call.tool_name} with args {tool_call.tool_kwargs}") | ||
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| # Call the tool and get result | ||
| tool = self.tools_dict[tool_call.tool_name] | ||
| output = await tool.acall(**tool_call.tool_kwargs) | ||
|
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| # Format result as chat message | ||
| msg = ChatMessage( | ||
| name=tool_call.tool_name, | ||
| content=str(output), | ||
| role="tool", | ||
| additional_kwargs={ | ||
| "tool_call_id": id_, | ||
| "name": tool_call.tool_name | ||
| } | ||
| ) | ||
|
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| return ToolCallEventResult(msg=msg) | ||
|
|
There was a problem hiding this comment.
Handle potential KeyError in tool lookups.
Accessing self.tools_dict[tool_call.tool_name] can raise a KeyError if tool_call.tool_name was never registered. You could handle this error gracefully by returning a StopEvent or an error message instead of letting the workflow crash.
Here is an example diff to handle the KeyError:
- tool = self.tools_dict[tool_call.tool_name]
+ try:
+ tool = self.tools_dict[tool_call.tool_name]
+ except KeyError:
+ error_msg = f"Tool '{tool_call.tool_name}' not found."
+ return ToolCallEventResult(
+ msg=ChatMessage(role="tool", content=error_msg)
+ )📝 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.
| @step() | |
| async def call_tool(self, ev: ToolCallEvent) -> ToolCallEventResult: | |
| """ | |
| Execute a single tool call. | |
| Args: | |
| ev: ToolCallEvent with tool to call | |
| Returns: | |
| ToolCallEventResult with the result message | |
| """ | |
| tool_call = ev.tool_call | |
| # Get tool ID | |
| id_ = tool_call.tool_id | |
| if self._verbose: | |
| print(f"Calling function {tool_call.tool_name} with args {tool_call.tool_kwargs}") | |
| # Call the tool and get result | |
| tool = self.tools_dict[tool_call.tool_name] | |
| output = await tool.acall(**tool_call.tool_kwargs) | |
| # Format result as chat message | |
| msg = ChatMessage( | |
| name=tool_call.tool_name, | |
| content=str(output), | |
| role="tool", | |
| additional_kwargs={ | |
| "tool_call_id": id_, | |
| "name": tool_call.tool_name | |
| } | |
| ) | |
| return ToolCallEventResult(msg=msg) | |
| @step() | |
| async def call_tool(self, ev: ToolCallEvent) -> ToolCallEventResult: | |
| """ | |
| Execute a single tool call. | |
| Args: | |
| ev: ToolCallEvent with tool to call | |
| Returns: | |
| ToolCallEventResult with the result message | |
| """ | |
| tool_call = ev.tool_call | |
| # Get tool ID | |
| id_ = tool_call.tool_id | |
| if self._verbose: | |
| print(f"Calling function {tool_call.tool_name} with args {tool_call.tool_kwargs}") | |
| # Call the tool and get result | |
| try: | |
| tool = self.tools_dict[tool_call.tool_name] | |
| except KeyError: | |
| error_msg = f"Tool '{tool_call.tool_name}' not found." | |
| return ToolCallEventResult( | |
| msg=ChatMessage(role="tool", content=error_msg) | |
| ) | |
| output = await tool.acall(**tool_call.tool_kwargs) | |
| # Format result as chat message | |
| msg = ChatMessage( | |
| name=tool_call.tool_name, | |
| content=str(output), | |
| role="tool", | |
| additional_kwargs={ | |
| "tool_call_id": id_, | |
| "name": tool_call.tool_name | |
| } | |
| ) | |
| return ToolCallEventResult(msg=msg) |
There was a problem hiding this comment.
Actionable comments posted: 3
♻️ Duplicate comments (1)
rag-sql-assistant/README.md (1)
28-31:⚠️ Potential issueAdd missing architecture diagram file
The README references an architecture diagram that doesn't exist in the repository.
Please add the referenced
assets/architecture.pngfile to the repository or update the path to point to an existing image file to avoid broken image links in documentation.
🧹 Nitpick comments (3)
rag-sql-assistant/src/ui/utils.py (1)
7-10: Remove unused importThe
typing.Listimport is not being used in this module.import streamlit as st import time import random -from typing import List🧰 Tools
🪛 Ruff (0.8.2)
10-10:
typing.Listimported but unusedRemove unused import:
typing.List(F401)
rag-sql-assistant/tweet.ipynb (2)
166-191: Incomplete class implementationThe
RouterOutputAgentWorkflowclass implementation is incomplete in this code snippet. It shows the constructor but doesn't include other methods that would be needed for the class to function.Consider including the complete class implementation or adding a comment that indicates this is a partial implementation with more methods shown in the following tweets.
26-26: Replace GIF placeholderThere's a placeholder for a demo GIF that should be replaced with an actual GIF file.
Replace
{Demo GIF showing the assistant in action}with the actual GIF or image showing the application in use.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
⛔ Files ignored due to path filters (2)
rag-sql-assistant/assets/architecture.pngis excluded by!**/*.pngrag-sql-assistant/assets/demo.mp4is excluded by!**/*.mp4
📒 Files selected for processing (3)
rag-sql-assistant/README.md(1 hunks)rag-sql-assistant/src/ui/utils.py(1 hunks)rag-sql-assistant/tweet.ipynb(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
rag-sql-assistant/src/ui/utils.py
10-10: typing.List imported but unused
Remove unused import: typing.List
(F401)
🔇 Additional comments (19)
rag-sql-assistant/src/ui/utils.py (2)
1-5: Well-structured module docstringThe docstring clearly explains the purpose of the module and follows best practices for Python documentation.
12-44: LGTM! Text streaming function is well-implementedThe
stream_text_with_cursorfunction implements a nice visual effect with good performance characteristics:
- Creates a visual typing effect with cursor
- Uses random delays for natural appearance
- Preserves markdown formatting
- Properly cleans up by removing the cursor at the end
The chunk size of 3 characters is a good balance between smooth animation and performance.
rag-sql-assistant/README.md (9)
1-27: Well-structured introduction and overviewThe introduction provides a clear explanation of the project's purpose and capabilities, along with a comprehensive tech stack section.
32-38: Clear feature highlightsThe features section effectively summarizes the key capabilities of the application with appropriate emojis for visual distinction.
39-87: Comprehensive setup instructionsThe getting started section provides detailed step-by-step instructions for installation and configuration, which is excellent for user onboarding.
89-136: Detailed usage guidelinesThe usage section provides clear instructions for running the application and adding Wikipedia files to LlamaCloud, along with example queries for both SQL and RAG capabilities.
137-165: Thorough deployment optionsThe deployment section covers multiple deployment scenarios including local, Streamlit Cloud, Heroku, and cloud providers, providing users with flexibility.
166-185: Well-defined contribution guidelinesThe contribution process and coding standards are clearly outlined, making it easier for potential contributors to understand expectations.
186-210: Helpful project structure overviewThe project structure section provides a clear map of the codebase, which helps new developers understand how the components fit together.
211-220: Concise workflow explanationThe "How It Works" section effectively summarizes the system's operation in a step-by-step format that's easy to understand.
231-233: License information providedIncluding license information is important for open-source projects.
rag-sql-assistant/tweet.ipynb (8)
1-45: Well-structured introduction to the project architectureThe first three tweets provide a clear introduction to the RAG-SQL agent concept, its capabilities, and technical architecture.
46-72: Clear setup instructions for knowledge sourcesTweets 4 provides clear instructions for obtaining API keys and setting up knowledge sources.
73-98: Informative database setup exampleThe SQL database setup is well explained, but the code snippet ends with "Add sample data below" and "..." which indicates incomplete code.
Consider completing the code example by showing the actual sample data insertion statements to make the example fully functional.
99-140: Well-explained query engine implementationsTweets 6 and 7 clearly explain how to create the Text-to-SQL and RAG query engines.
141-165: Clear explanation of tool creationTweet 8 effectively explains how to create QueryEngineTools that will be used by the router agent.
192-226: Effective explanation of the chat methodTweet 10 explains the critical
chatmethod that handles tool selection logic.
227-251: Good example of workflow initialization and usageTweet 11 demonstrates how to initialize and use the workflow with a practical example.
267-289: Effective conclusion with contact informationThe final tweets provide a good conclusion with relevant tags and contact information.
| ## 🔗 Community | ||
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There was a problem hiding this comment.
Add missing banner image
The README references a banner image that doesn't appear to exist in the repository.
Please add the referenced assets/ddods_banner.png file to the repository or update the path to point to an existing image file.
| "- @OpenAI for response generation\n", | ||
| "- @Streamlit for the UI\n", | ||
| "\n", | ||
| "" |
There was a problem hiding this comment.
Add missing architecture diagram
The notebook references an architecture diagram that doesn't exist in the repository.
Please add the referenced assets/architecture.png file to ensure the architecture visualization is available when viewing the notebook.
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Tweet 12" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "That's a wrap! Find the full code for this project from our GitHub repository👇\n", | ||
| "\n", | ||
| "{LINK TO THE PROJECT CODE}" | ||
| ] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Replace placeholder with actual repository link
The notebook contains a placeholder for the project code link.
Replace {LINK TO THE PROJECT CODE} with the actual GitHub repository URL to ensure readers can find the code.
Hey Avi and Akshay,
I've completed the Technical Writer Task 1 - RAG and Text-to-SQL integration. Here's what I've done:
Completed Requirements ✅
Looking forward to your feedback!
Architecture:
