Harnessing RAG: LangChain vs. Pandas for Superior Conversational Analytics

Selecting appropriate tools in conversational analytics can greatly impact the effectiveness and performance of data processing and interaction. This blog explores the differences between two popular tools used for this task, LangChain and Pandas, emphasizing their capabilities, benefits, drawbacks, and useful applications.

While LangChain uses language models for query generation and data interaction, Pandas handles manual SQL query creation, supplemented by Plotly for visualization.

About LangChain

LangChain is an advanced framework that utilizes language models to handle complex natural language processing tasks. It enables developers to create sophisticated conversational agents that can understand and respond to user queries by dynamically generating SQL queries and other data manipulations.

LangChain:

• Pros:
  • Natural Language Understanding: LangChain excels in interpreting and responding to natural language queries.
  • Dynamic Query Generation: It can generate SQL queries on the fly based on user input.
  • Integration with Language Models: Seamlessly integrates with models like GPT-3.5/4/4o for enhanced understanding and response generation.
• Cons:
  • Complexity: Requires a deeper understanding of language models and their implementation.
  • Dependency on External APIs: Relies on external services like OpenAI, which can introduce latency and cost.

About Pandas

Pandas is a powerful Python library for data manipulation and analysis. When combined with manual SQL query creation and Plotly for visualization, it offers a robust framework for handling conversational analytics through more traditional means.

Pandas with OpenAI:

• Pros:
  • Performance: Highly efficient for data manipulation and analysis.
  • Flexibility: Allows for custom SQL query creation tailored to specific needs.
  • Visualization: Integration with Plotly enables sophisticated data visualization.
  • Natural Language Understanding: Leveraging OpenAI’s language models enhances its ability to interpret and generate responses to queries.
• Cons:
  • Manual Effort: Requires manual crafting of SQL queries, which can be time-consuming.
  • Dependency on External APIs: Like LangChain, it relies on OpenAI for natural language processing, which can introduce latency and cost.
  • Steeper Learning Curve for Visualization: Plotly can be complex to master for detailed visualizations.

Feature Comparison:

There may be a question from you that Why not Pandas AI.

Here you see the pros and cons with respect to our scenarios.

Below are the key thoughts for using Pandas custom code over PandasAI :

1. Consistency in Analytics: While PandasAI can run analytics based on user prompts, the responses are inconsistent. Custom Pandas code ensures reliable and predictable outputs.
2. Flexibility and Control: Custom Pandas code offers the flexibility to design the flow exactly as planned. This allows complete freedom to customize the agent's behavior across different scenarios, ensuring it meets specific requirements.
3. Performance with Large Datasets: PandasAI's performance with larger datasets and its time complexity remain questionable. Custom Pandas code can be optimized for better performance and efficiency.
4. Customization of Output: Customizing output with PandasAI was challenging due to limited control. Custom Pandas code allows precise customization of the output, making it easier to meet specific formatting and presentation needs.

Sample Code: Conversational Analytics using LangChain

The following sample code demonstrates how to set up a conversational analytics system using LangChain. This involves creating endpoints to handle file uploads and user queries, utilizing LangChain's capabilities to dynamically generate SQL queries and produce relevant responses.

The upload_file function handles the process of uploading a CSV file, saving it to a specified directory, reading it into memory as a pandas DataFrame, initializing a LangChain agent to work with the data, and returning status messages or error responses based on the success or failure of these operations. This endpoint is crucial for preparing the uploaded CSV data and setting up the agent to respond to subsequent queries via other API endpoints or client applications.

The ask_question function is an API endpoint that handles incoming POST requests containing a JSON object with a 'question' field. It ensures that the necessary data (CSV file and agent) is available, processes the user's question using the generate_response function, and returns the response or appropriate error messages based on the outcome. This endpoint is critical for interacting with the LangChain agent and providing intelligent responses based on the uploaded CSV data and user queries.

generate_response(question): Determines the type of response based on the user's question, either generating a chart (pie or bar), stating that trend chart functionality is not implemented, or using an agent to respond to a regular question.

generate_pie_chart(): Generates a pie chart from the CSV data if possible or returns an error message if insufficient data columns are available or an error occurs during the chart generation.

generate_bar_chart(): Generates a bar chart from the CSV data if possible or returns an error message if insufficient data columns are available or an error occurs during chart generation. 

Sample Results:

Sample Code: Conversational Analytics using Pandas with Open AI

The below sample code shows how to leverage Pandas for data manipulation and OpenAI for natural language understanding to create a conversational analytics system. The code includes handling file uploads, saving data to an SQLite database, executing SQL queries, and generating responses using OpenAI's language models.

Database Handling: SQLite database handling functions (save_to_sqlite() and execute_sql_query()) are simplified to manage file uploads and execute SQL queries respectively.

OpenAI Integration: Uses openai.ChatCompletion.create() to simulate interaction with the GPT-3.5 model, simplified for generating responses based on the question.

Pattern Extraction Logic: The extract_output() method in LLMHandler class now includes example pattern extraction logic using regular expressions (re.findall()). This logic extracts SQL queries and Python code snippets from the input text.

SQLHandler manages SQL query execution against a specified SQLite database (db_path).

RunGPT extends LLMHandler and orchestrates interactions, potentially involving SQL queries (sql_query_tool), following a predefined flow (get_next_steps), and returning the resulting interaction (run).

process_file: Handles file uploads, saves data to SQLite, and returns status messages.

submit_question: Processes user-submitted questions using a conversational AI system (GPT), and returns AI-generated responses.

Sample Results:

Key Takeaways:

Here are key takeaways on when to consider using LangChain versus Pandas with OpenAI based on their respective strengths:

1. LangChain:
   • Use Case: When you need to interpret and respond to natural language queries dynamically.
   • Strengths:
     • Excels in understanding and generating responses based on natural language input (using models like GPT-3.5).
     • Ideal for building chatbots, automated customer support systems, or interactive applications, where users ask questions in natural language.
     • Dynamic query generation for SQL queries based on user input or contextual information.
     • Seamless integration with advanced language models for nuanced responses (e.g., legal document analysis).

2. Pandas with OpenAI:
   • Use Case: When extensive data manipulation, filtering, aggregation, and analysis are required.
   • Strengths:
     • Robust tools for data cleaning, transformation, statistical analysis, and complex data operations.
     • Ideal for data science projects, financial analysis tools, and data-driven decision-making applications.
     • Custom SQL query creation, especially for users with SQL expertise.
     • Advanced data visualization using libraries like Plotly for effective insights.

Choosing Between LangChain and Pandas with OpenAI:

• Use LangChain for natural language understanding, dynamic query generation, and integration with advanced language models.
• Use Pandas with OpenAI for extensive data manipulation, custom SQL query creation, and advanced data visualization.

Conclusion:

When deciding between LangChain and Pandas with OpenAI for conversational analytics, it is important to consider the specific needs of your project. LangChain excels in natural language understanding and dynamic query generation, making it ideal for interactive applications like chatbots and automated customer support systems. On the other hand, Pandas with OpenAI is well-suited for extensive data manipulation, custom SQL query creation, and advanced data visualization, making it a robust choice for data science projects and data-driven decision-making applications. By leveraging the strengths of each tool appropriately, you can significantly enhance the effectiveness and efficiency of your conversational analytics efforts.