A Web UI where you can upload CSV/JSON files, create vector embeddings, and query them. Soon, you'll be able to convert unstructured data to JSON/CSV using an integrated LLM.
Step | Instructions |
---|---|
Clone the repository |
|
Install the required packages |
|
Step | Instructions |
---|---|
Start the Flask server |
|
Automatically launch React frontend | The Python endpoint will launch the React frontend in a separate subprocess. |
Open your web browser | Navigate to http://127.0.0.1:4000 |
Step | Instructions |
---|---|
Drag and Drop a File | Drag and drop a CSV or JSON file into the upload area or click to select a file from your computer. |
View Uploaded File Information | Once uploaded, the file information such as name and size will be displayed. |
To upload a file using curl
:
curl -X POST -F 'file=@/path/to/your/file.csv' http://127.0.0.1:4000/upload_file
Step | Instructions |
---|---|
Select Embedding Keys | After uploading a file, select the keys (columns) you want to include in the embeddings. |
Preview Document | View a preview of the document created from the selected keys. |
Preview Embeddings | View the generated embeddings and token count for the selected document. |
To preview a file's keys using curl
:
curl -X POST -H "Content-Type: application/json" -d '{"file_path":"uploads/your-file.csv"}' http://127.0.0.1:4000/preview_file
To preview a document's embeddings using curl
:
curl -X POST -H "Content-Type: application/json" -d '{"file_path":"uploads/your-file.csv", "selected_keys":["key1", "key2"]}' http://127.0.0.1:4000/preview_document
Step | Instructions |
---|---|
Start Embedding Creation | Click the "Create Vector DB" button to start the embedding creation process. |
View Progress | Monitor the progress of the embedding creation with a circular progress indicator. ๐ |
To create a vector database using curl
:
curl -X POST -H "Content-Type: application/json" -d '{"file_path":"uploads/your-file.csv", "selected_keys":["key1", "key2"]}' http://127.0.0.1:4000/create_vector_database
Step | Instructions |
---|---|
Enter Query | Type your query into the input field. |
Select Similarity Metric | Choose between cosine similarity or Euclidean distance. |
Submit Query | Click the "Submit" button to query the vector database. |
View Results | Inspect the results, which display the document, score, and a button to view detailed data. |
To query the vector database using curl
:
curl -X POST -H "Content-Type: application/json" -d '{"query_text":"Your query text here", "similarity_metric":"cosine"}' http://127.0.0.1:4000/query
Step | Instructions |
---|---|
Backup Database | Click the "Backup Database" button to create a backup of the current vector database. |
Delete Database | Click the "Delete Database" button to delete the current vector database. |
View Database Statistics | View statistics such as total documents and average vector length. |
To check if the vector database exists using curl
:
curl -X GET http://127.0.0.1:4000/check_vector_db
To view database statistics using curl
:
curl -X GET http://127.0.0.1:4000/db_stats
To backup the database using curl
:
curl -X POST http://127.0.0.1:4000/backup_db
To delete the database using curl
:
curl -X POST http://127.0.0.1:4000/delete_db
Feature | Description |
---|---|
Uploading Files |
|
Previewing Data |
|
Creating Vector Embeddings |
|
Querying the Vector Database |
|
Managing the Vector Database |
|