Submission for Technical Writer #97
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WalkthroughThis pull request introduces new documentation and code components for an AI agent that combines Retrieval-Augmented Generation (RAG) with SQL querying capabilities. It adds a comprehensive README file outlining project components and usage instructions, a Streamlit application for PDF uploads and interactive querying, and a Jupyter Notebook demonstrating RAG integration with an in-memory SQLite database and vector indexing. A new requirements file is also provided to ensure all dependencies are met. Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant S as Streamlit App
participant D as Document Processor
participant L as LLM/Query Handler
participant O as OpenAI API
U->>S: Open App & Provide API Key
S->>S: Initialize session & UI
U->>S: Upload PDF / Select Documents
S->>D: Process documents & build vector index
D-->>S: Index ready/error handling
U->>S: Ask a question via chat
S->>L: Route query with context & chat history
L->>D: Retrieve relevant document chunks
L->>O: Request response from OpenAI
O-->>L: Return query result
L-->>S: Send back refined response
S->>U: Display response and update chat history
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Actionable comments posted: 3
🧹 Nitpick comments (11)
Rag-and-texttosql/requirement.txt (1)
1-11: Consider renaming this file and clarifying dependency versions.Typically, Python projects use a file named
requirements.txtinstead ofrequirement.txt. Additionally, confirm whether a minimum version (>=) is intended or if exact pinned versions are preferable for reproducibility.Rag-and-texttosql/README.md (1)
117-117: Refine the phrasing for a more professional tone.The phrase “feel free to” is overused and flagged by the style checker. Consider rephrasing for clarity and conciseness, such as “You are welcome to” or “We encourage you to.”
-Feel free to: +We encourage you to:🧰 Tools
🪛 LanguageTool
[style] ~117-~117: The phrase ‘feel free to’ is used quite frequently. Consider using a less frequent alternative to set your writing apart from others and make it sound more professional.
Context: ...features as needed ## 🤝 Contributing Feel free to: - Open issues - Submit pull requests -...(FEEL_FREE_TO_STYLE_ME)
Rag-and-texttosql/ragsql.ipynb (3)
1-14: Consider installing packages outside the notebook.Using
!pip install ...within a notebook can cause environment inconsistencies. It’s often preferable to maintain a single requirements file and install dependencies viapipin the project environment before running the notebook.
32-32: Protect sensitive credentials.Manually setting
OPENAI_API_KEYin code is discouraged. Store and load them from a.envfile or a secure environment variable for production usage.
219-232: Avoid hardcoded absolute paths.The path
C:/Users/prita/Documents/DailyDoseis user-specific. Consider using relative paths, environment variables, or an application configuration for portability.-pdf_dir = "C:/Users/prita/Documents/DailyDose" +pdf_dir = "./data/pdfs"Rag-and-texttosql/rag_sql_app_upload.py (6)
1-14: Clean up unused importsThere are several imports that are declared but never used in the code:
ServiceContext(line 4)SentenceSplitteris imported twice (lines 5 and 9)ListandDictfrom typing (line 11)json(line 12)-from llama_index.core import VectorStoreIndex, ServiceContext +from llama_index.core import VectorStoreIndex from llama_index.core.node_parser import SentenceSplitter from llama_index.core.readers import SimpleDirectoryReader from llama_index.llms.openai import OpenAI from llama_index.embeddings.huggingface import HuggingFaceEmbedding -from llama_index.core.node_parser import SentenceSplitter import pandas as pd -from typing import List, Dict -import json +from typing import Optional import openai🧰 Tools
🪛 Ruff (0.8.2)
4-4:
llama_index.core.ServiceContextimported but unusedRemove unused import:
llama_index.core.ServiceContext(F401)
9-9: Redefinition of unused
SentenceSplitterfrom line 5Remove definition:
SentenceSplitter(F811)
11-11:
typing.Listimported but unusedRemove unused import
(F401)
11-11:
typing.Dictimported but unusedRemove unused import
(F401)
12-12:
jsonimported but unusedRemove unused import:
json(F401)
112-139: Consider adding error specificity and type annotationsThe error handling in
process_documentscatches all exceptions with a generic handler. Consider adding more specific exception handling for common errors (like API failures, file access issues).Also, the return type annotation could be improved to indicate that the function might return None.
-def process_documents(directory: str) -> VectorStoreIndex: +def process_documents(directory: str) -> Optional[VectorStoreIndex]: """Process documents and create a vector index.""" try: # Set up embedding model embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2") # Set up OpenAI LLM llm = get_llm() # Create node parser node_parser = SentenceSplitter(chunk_size=1024, chunk_overlap=20) # Load documents documents = SimpleDirectoryReader(directory).load_data() # Create index with components directly index = VectorStoreIndex.from_documents( documents, llm=llm, embed_model=embed_model, transformations=[node_parser] ) return index + except FileNotFoundError as e: + st.error(f"Directory or files not found: {str(e)}") + return None + except ValueError as e: + st.error(f"Value error: {str(e)}") + return None except Exception as e: st.error(f"Error processing documents: {str(e)}") return None
165-172: Security consideration about API key handlingWhile the UI states "Your key is stored securely and never shared", the API key is stored in the session state which persists only during the active session. Consider adding a disclaimer that the key is not stored permanently and will need to be re-entered when the session expires.
<h4 style="margin-bottom: 0.5rem;">🔑 OpenAI API Key</h4> <p style="font-size: 0.8rem; color: #6c757d; margin-bottom: 0.5rem;"> - Enter your OpenAI API key to enable AI features. Your key is stored securely and never shared. + Enter your OpenAI API key to enable AI features. Your key is stored only for the current session and never shared. </p>
181-185: Potential redundancy in API key settingThe OpenAI API key is set globally twice - once here and once in the
get_llm()function. This duplication could lead to inconsistencies if one is updated and the other isn't.Consider refactoring to set the API key in a single location, possibly by moving the global setting to the
get_llm()function only.
235-254: Consider adding rate limiting for API callsWhen making calls to external APIs like OpenAI, it's good practice to implement rate limiting to avoid excessive API usage and potential costs.
Consider adding a simple rate limiter or a cooldown period between consecutive queries to prevent rapid-fire API calls.
import time from datetime import datetime # Add to session state initialization if 'last_query_time' not in st.session_state: st.session_state.last_query_time = None # Add this before making the API call if query and query != st.session_state.last_query: # Check if we need to apply rate limiting current_time = datetime.now() if st.session_state.last_query_time: time_diff = (current_time - st.session_state.last_query_time).total_seconds() if time_diff < 1.0: # 1 second cooldown st.warning("Please wait a moment before sending another query.") time.sleep(1.0 - time_diff) # Wait the remaining time st.session_state.last_query_time = current_time # Continue with the query processing...
223-255: Add a way to reset or clear chat historyThe application allows for building up a chat history but doesn't provide a way to clear it when starting a new conversation or topic.
Consider adding a "Clear Chat" button that resets the chat history:
# Add this after the display_chat_history() call col1, col2 = st.columns([5, 1]) with col2: if st.button("Clear Chat"): st.session_state.chat_history = [] st.session_state.last_query = None st.rerun()
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⛔ Files ignored due to path filters (1)
Rag-and-texttosql/Streamlit UI.jpgis excluded by!**/*.jpg
📒 Files selected for processing (4)
Rag-and-texttosql/README.md(1 hunks)Rag-and-texttosql/rag_sql_app_upload.py(1 hunks)Rag-and-texttosql/ragsql.ipynb(1 hunks)Rag-and-texttosql/requirement.txt(1 hunks)
🧰 Additional context used
🪛 LanguageTool
Rag-and-texttosql/README.md
[style] ~117-~117: The phrase ‘feel free to’ is used quite frequently. Consider using a less frequent alternative to set your writing apart from others and make it sound more professional.
Context: ...features as needed ## 🤝 Contributing Feel free to: - Open issues - Submit pull requests -...
(FEEL_FREE_TO_STYLE_ME)
🪛 Ruff (0.8.2)
Rag-and-texttosql/rag_sql_app_upload.py
4-4: llama_index.core.ServiceContext imported but unused
Remove unused import: llama_index.core.ServiceContext
(F401)
9-9: Redefinition of unused SentenceSplitter from line 5
Remove definition: SentenceSplitter
(F811)
11-11: typing.List imported but unused
Remove unused import
(F401)
11-11: typing.Dict imported but unused
Remove unused import
(F401)
12-12: json imported but unused
Remove unused import: json
(F401)
🔇 Additional comments (3)
Rag-and-texttosql/ragsql.ipynb (1)
211-214:✅ Verification successful
Request verification of embedding model.
You are using
Snowflake/snowflake-arctic-embed-mwithtrust_remote_code=True. Confirm that this Hugging Face model exists, is actively maintained, and suitable for your domain.
🏁 Script executed:
#!/bin/bash # Attempt to retrieve metadata about the 'Snowflake/snowflake-arctic-embed-m' model from Hugging Face curl -s https://huggingface.co/api/models/Snowflake/snowflake-arctic-embed-m | jqLength of output: 65620
Embedding Model Verification Confirmed
The Hugging Face API output confirms that the model
"Snowflake/snowflake-arctic-embed-m"exists, is publicly available, and demonstrates active maintenance (with a recent last modified date of December 13, 2024). Its extensive metadata—including download counts, likes, and task-specific performance metrics—indicates that it is suitable for sentence-similarity and related text-embedding tasks. In this context, usingtrust_remote_code=Trueis appropriate.
- Verified that the model is present and active.
- Confirmed that its performance metrics and tags (e.g.,
sentence-transformers,sentence-similarity) align with the intended use.- No additional modifications are necessary.
Rag-and-texttosql/rag_sql_app_upload.py (2)
98-111: LGTM: Good error handling for API key validationThe
get_llm()function properly checks for the API key before initializing the OpenAI client, which is a good security practice.
211-216: LGTM: Good practice for handling uploaded files securelyUsing a temporary directory for processing uploaded files is a good security practice as it ensures files are automatically cleaned up after processing.
| ### Prerequisites | ||
| ```bash | ||
| pip install -r requirements.txt | ||
| ``` |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify file name mismatch.
In line 27, the README instructs installing from requirements.txt, but the actual file name is requirement.txt. Please align references to avoid confusion.
Run the following script to confirm all references to requirements.txt have corresponding files:
🏁 Script executed:
#!/bin/bash
# Searching for references to 'requirements.txt' in the README or code
rg "requirements\.txt" -A 2Length of output: 150
Mismatch in requirements file reference detected.
The README at line 27 instructs to install dependencies with pip install -r requirements.txt, but the repository actually contains a file named requirement.txt. Please update the README to reference the correct file or rename the file to match the README, ensuring consistency across the project.
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| if directory and os.path.isdir(directory): | ||
| st.success(f"Directory selected: {directory}") | ||
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| if st.button("Process PDFs"): | ||
| with st.spinner("Processing PDFs..."): | ||
| st.session_state.index = process_documents(directory) | ||
| if st.session_state.index is not None: | ||
| st.session_state.processed = True | ||
| st.success("PDFs processed successfully!") |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify directory path security
The application accepts a directory path as input without validating if it's a safe location to access. This could potentially lead to unauthorized file access.
Consider adding validation to ensure the directory is within expected boundaries (e.g., within the app's working directory or user's home directory). Here's a script to check if the code has any path traversal protection:
🏁 Script executed:
#!/bin/bash
# Check if there's any path validation for the directory input
rg -A 5 -B 5 "directory.*=.*st\.text_input"
rg -A 10 "isdir\(directory\)" --context=10Length of output: 5311
Action Required: Secure Directory Path Validation
The directory path provided via the text input is only checked with os.path.isdir(directory), which does not guarantee that the path is within an allowed or safe location. This lack of boundary verification can potentially expose unauthorized files or directories. Please add additional validation logic, such as:
- Restricting paths to a predefined safe base (e.g., the application's working directory or a specific subdirectory).
- Normalizing the input path (using methods like
os.path.abspath()) and checking that it starts with the allowed base path.
File: Rag-and-texttosql/rag_sql_app_upload.py (Lines 195-204)
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| # Sidebar for file upload and processing | ||
| with st.sidebar: | ||
| st.header("⚙️ Settings") | ||
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| # OpenAI API Key input with better styling | ||
| st.markdown(""" | ||
| <div style="background-color: #f8f9fa; padding: 1rem; border-radius: 0.5rem; margin-bottom: 1rem;"> | ||
| <h4 style="margin-bottom: 0.5rem;">🔑 OpenAI API Key</h4> | ||
| <p style="font-size: 0.8rem; color: #6c757d; margin-bottom: 0.5rem;"> | ||
| Enter your OpenAI API key to enable AI features. Your key is stored securely and never shared. | ||
| </p> | ||
| </div> | ||
| """, unsafe_allow_html=True) | ||
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| api_key = st.text_input( | ||
| "API Key", | ||
| type="password", | ||
| value=st.session_state.openai_api_key if st.session_state.openai_api_key else "", | ||
| help="Enter your OpenAI API key to enable the AI features." | ||
| ) | ||
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| if api_key: | ||
| st.session_state.openai_api_key = api_key | ||
| # Set the API key globally | ||
| openai.api_key = api_key | ||
| st.success("✅ OpenAI API key set successfully!") | ||
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| st.markdown("---") | ||
| st.header("📁 Document Management") | ||
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| # Option to select an existing directory or upload files | ||
| option = st.radio("Choose an option:", ["Upload PDF files", "Select directory"]) | ||
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| if option == "Select directory": | ||
| directory = st.text_input("Enter the directory path containing PDFs:") | ||
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| if directory and os.path.isdir(directory): | ||
| st.success(f"Directory selected: {directory}") | ||
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| if st.button("Process PDFs"): | ||
| with st.spinner("Processing PDFs..."): | ||
| st.session_state.index = process_documents(directory) | ||
| if st.session_state.index is not None: | ||
| st.session_state.processed = True | ||
| st.success("PDFs processed successfully!") | ||
| else: | ||
| uploaded_files = st.file_uploader("Upload PDF files", accept_multiple_files=True, type=["pdf"]) | ||
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| if uploaded_files: | ||
| st.session_state.uploaded_files = uploaded_files | ||
| if st.button("Process Uploaded PDFs"): | ||
| with tempfile.TemporaryDirectory() as temp_dir: | ||
| for uploaded_file in uploaded_files: | ||
| file_path = os.path.join(temp_dir, uploaded_file.name) | ||
| with open(file_path, "wb") as f: | ||
| f.write(uploaded_file.getbuffer()) | ||
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| with st.spinner("Processing PDFs..."): | ||
| st.session_state.index = process_documents(temp_dir) | ||
| if st.session_state.index is not None: | ||
| st.session_state.processed = True | ||
| st.success("PDFs processed successfully!") | ||
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| # Main chat interface | ||
| if st.session_state.processed and st.session_state.index is not None: | ||
| st.header("💬 Ask Your Questions") | ||
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| # Display chat history | ||
| display_chat_history() | ||
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| # Query interface | ||
| st.markdown("---") | ||
| query = st.text_input("Your question:", key="query_input") | ||
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| # Only process if query is new and not empty | ||
| if query and query != st.session_state.last_query: | ||
| st.session_state.last_query = query | ||
| add_to_chat_history("user", query) | ||
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| with st.spinner("Thinking..."): | ||
| try: | ||
| # Get a fresh LLM instance for the query | ||
| llm = get_llm() | ||
| query_engine = st.session_state.index.as_query_engine(llm=llm) | ||
| response = query_engine.query(query) | ||
| answer = response.response | ||
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| add_to_chat_history("assistant", answer) | ||
| st.rerun() # Rerun to update the UI | ||
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| except Exception as e: | ||
| error_message = f"Error generating answer: {str(e)}" | ||
| st.error(error_message) | ||
| add_to_chat_history("assistant", error_message) | ||
| st.rerun() # Rerun to update the UI | ||
| else: | ||
| if not st.session_state.openai_api_key: | ||
| st.warning("⚠️ Please add your OpenAI API key in the sidebar to enable AI features.") | ||
| st.info("👈 Please upload and process PDFs using the sidebar to start asking questions!") |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Missing SQL capabilities mentioned in the description
The application description mentions SQL capabilities ("The agent automatically determines the best way to answer your question using RAG or SQL capabilities"), but there's no implementation of SQL functionality in the code.
Given the PR is about "RAG and Text-to-SQL", consider adding the SQL functionality mentioned in the application description or clarify the feature in the UI text.
🌐 Web query:
What components are needed to implement Text-to-SQL functionality in a Python application?
💡 Result:
Implementing a Text-to-SQL system in Python requires integrating natural language processing, database schema understanding, and SQL generation. Here's a breakdown of the key components and strategies:
Core Components
1. Natural Language Processing (NLP) Engine
- Uses LLMs (GPT-3.5/4, Claude, text-bison) to interpret user questions
- Converts phrases like "top 5 customers by sales" into SQL logic
- Example implementations: OpenAI API [5], Anthropic Claude via Amazon Bedrock [4], Google's text-bison [2]
2. Database Schema Integration
- Requires metadata about tables/columns from:
- AWS Glue Data Catalog [4]
- SQLAlchemy ORM [6]
- Direct schema parsing [3]
- Handles data type conversions and table relationships [7][8]
3. SQL Query Generator
- Constructs valid SQL using:
model.predict(f"Generate SQL for: '{query}' Schema: {schema}") # [2][4]
- Supports complex features like JOINs and nested queries [4][8]
4. Execution & Validation Layer
- Runs queries via database connectors:
from google.cloud import bigquery # [2] from sqlite3 import connect # [3]
- Implements error correction loops:
-- Original failing query SELECT region FROM sales WHERE country = 'US' -- Corrected version SELECT region FROM sales WHERE upper(country) = 'US' # [4]
Advanced Features
Retrieval-Augmented Generation (RAG)
- Combines vector databases (ChromaDB) with SQL templates [7]
- Training methods:
- DDL statements:
vn.train("CREATE TABLE...")[7] - Example queries:
vn.train("SELECT...")[7] - Business glossary: "ARPU = total revenue/users" [8][9]
- DDL statements:
Multi-Database Support
- Federated query handling via:
- Athena connectors (20+ data sources) [4]
- SQLAlchemy dialects [6]
- Custom drivers for Snowflake/BigQuery [7]
Evaluation Metrics
- Syntax checking with EXPLAIN [4][6]
- Result comparison:
if generated_result == ground_truth: score += 1 # [1][9]
- Execution success rate tracking [1][8]
Implementation Tools
| Category | Recommended Libraries/Packages |
|---|---|
| LLM Integration | Vanna.ai [7], text-to-sql PyPI [5] |
| DB Connectors | SQLAlchemy [6], BigQuery Python SDK [2] |
| Visualization | Plotly [7], Streamlit [9] |
| Error Handling | Athena query validator [4], SQL parse |
For production systems, consider using frameworks like Vanna.ai[7] or AWS Bedrock[4] that provide built-in training data management and auto-scaling. Always include schema context in prompts and implement query sanitization for security.
Citations:
- 1: https://www.braintrust.dev/docs/cookbook/recipes/Text2SQL
- 2: https://www.googlecloudcommunity.com/gc/AI-ML/Vertex-AI-Text-to-SQL/m-p/681674
- 3: https://www.sql-easy.com/learn/how-to-use-sql-in-python/
- 4: https://aws.amazon.com/blogs/machine-learning/build-a-robust-text-to-sql-solution-generating-complex-queries-self-correcting-and-querying-diverse-data-sources/
- 5: https://pypi.org/project/text-to-sql/
- 6: https://docs.llamaindex.ai/en/stable/examples/index_structs/struct_indices/SQLIndexDemo/
- 7: https://github.com/vanna-ai/vanna
- 8: https://ai.gopubby.com/text-to-sql-models-from-basics-to-advanced-6df497c26a81
- 9: https://www.mindinventory.com/text-to-sql-mindsql.php
Action Required: Add or Clarify SQL Functionality
The application description indicates that the agent leverages "RAG or SQL capabilities" to answer user queries, but the current implementation only supports document processing and RAG. There’s no integration for generating or executing SQL queries from natural language (such as mapping user queries to SQL using an NLP engine, database schema integration, or SQL query generation and validation).
Suggested next steps:
- Integrate SQL Components: If SQL capabilities are intended, consider incorporating components such as:
- An NLP engine (using an LLM like OpenAI's models) to interpret queries and generate SQL.
- Database schema integration (e.g., via SQLAlchemy) to provide table/column metadata.
- A SQL query generator and execution layer to run the generated queries.
- Update the UI Description: Alternatively, if SQL functionality is not within the scope of the current PR, revise the description and UI text to accurately reflect that only RAG-based answering is available.
🧰 Tools
🪛 Ruff (0.8.2)
4-4: llama_index.core.ServiceContext imported but unused
Remove unused import: llama_index.core.ServiceContext
(F401)
9-9: Redefinition of unused SentenceSplitter from line 5
Remove definition: SentenceSplitter
(F811)
11-11: typing.List imported but unused
Remove unused import
(F401)
11-11: typing.Dict imported but unused
Remove unused import
(F401)
12-12: json imported but unused
Remove unused import: json
(F401)
I have tried to completed first task shared in the assessment. (RAG and Text-to-SQL)
Twitter thread link : https://typefully.com/t/Hoefaev
Summary by CodeRabbit
New Features
Documentation
Chores