GITHUB: github.com/ronidas39/LLMtutor...
TELEGRAM: t.me/ttyoutubediscussion
🔹 *What You Will Learn:*
1. *Setting Up Streamlit UI:*
- Create an interactive interface for uploading multiple invoice files.
- Configure the UI to handle various file formats (JPEG, PDF).
2. *Converting Invoices to JSON:*
- Use GPT-4 to process and convert invoice images into structured JSON documents.
- Ensure data consistency and accuracy during the conversion process.
3. *Integrating with MongoDB:*
- Connect to MongoDB using PyMongo.
- Insert JSON documents into a MongoDB collection.
- Handle potential errors and ensure efficient data storage.
4. *Querying MongoDB:*
- Write effective prompts to generate MongoDB aggregation queries.
- Use LangChain to translate user queries into MongoDB queries.
- Execute and validate these queries to retrieve meaningful insights.
5. *Validating Solutions:*
- Validate the entire workflow by running various queries.
- Perform analytics on the stored invoice data.
- Ensure the system can handle real-world scenarios and large datasets.
🔹 *Source Code:* [GitHub Repository] (link to the source code)
🔹 *Chapters:*
0:00 - Introduction
2:00 - Setting Up Streamlit UI
5:15 - Converting Invoices to JSON
8:45 - MongoDB Integration
12:30 - Querying MongoDB
15:45 - Validating Solutions
18:00 - Q&A and Troubleshooting
21:30 - Conclusion
🔹 *Detailed Breakdown:*
*1. Setting Up Streamlit UI:*
We'll start by setting up a Streamlit UI, which allows users to upload multiple invoice files. This interactive interface will be designed to handle different file formats like JPEG and PDF. You’ll learn how to configure Streamlit to accept multiple files simultaneously and display the uploaded files for user verification.
*2. Converting Invoices to JSON:*
Next, we’ll dive into using GPT-4 to process these invoice files. You’ll learn how to convert each invoice into a structured JSON document. This involves extracting key information such as invoice number, date, customer name, products, total price, and mode of payment. We’ll ensure that all data is accurately captured and formatted, making it suitable for database insertion.
*3. Integrating with MongoDB:*
With the JSON documents ready, we’ll move on to integrating with MongoDB. This section covers setting up a MongoDB Atlas account, connecting to it using PyMongo, and inserting the JSON documents into a MongoDB collection. We’ll discuss best practices for handling data insertion, managing database connections, and ensuring efficient storage of invoice data.
*4. Querying MongoDB:*
One of the core parts of this tutorial is learning how to query MongoDB effectively. Using LangChain, we’ll translate natural language queries into MongoDB aggregation pipeline queries. You’ll understand how to construct complex queries to retrieve specific data from the database. This section also covers handling various types of user queries and ensuring that the system can respond accurately.
*5. Validating Solutions:*
Finally, we’ll validate our entire workflow. You’ll run a series of queries to test the system’s robustness and accuracy. We’ll perform analytics on the invoice data, such as identifying the top-selling products, calculating total sales for a specific period, and more. This validation step ensures that our solution can handle real-world business needs.
🔹 *Prerequisites:*
- Basic understanding of Python and Streamlit
- Familiarity with MongoDB and database operations
- Knowledge of LangChain and GPT-4
🔹 *Resources:*
- MongoDB Atlas: [www.mongodb.com/cloud/atlas](www.mongodb.com/cloud/atlas)
- LangChain Documentation: [www.langchain.com/docs](www.langchain.com/docs)
- Streamlit Documentation: [docs.streamlit.io/](docs.streamlit.io/)
🔹 *Code Highlights:*
- *Streamlit UI Setup:* The UI allows users to upload multiple invoice files and view them before processing.
- *GPT-4 Integration:* We leverage GPT-4’s capabilities to extract structured data from invoices and convert them into JSON format.
- *MongoDB Integration:* The JSON documents are inserted into MongoDB, where they can be queried and analyzed.
- *Query Processing:* Using LangChain, we convert user queries into MongoDB queries and execute them to retrieve relevant data.
*#GPT4 #LangChain #Streamlit #MongoDB #InvoiceChat #AI #TotalTechnologyZone #PythonTutorial #AdvancedAI #DataAnalytics*