[Question]: how to chat with custom database data in offline mode

[kaleshasheik]

Question Validation

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Question

I have an application implemented using Django with postgres database. And from admin UI, we have option to insert data to specific table..

Now, I am trying to integrate with llama index to create a chatbox that queries the custom data that I already have in my postgres database in offline mode (without internet) and send back the response to user. But, I am not able to find any example in document.

Can you suggest, if this can be done ? if yes, any example/link to follow?

[dosubot[bot]]

Hey there, @kaleshasheik! Great to see you diving into another adventurous quest with LlamaIndex. How's the coding journey treating you lately?

Yes, you can integrate LlamaIndex with your Django application using a Postgres database to create an offline chatbox that queries custom data. The provided context shows that LlamaIndex has a PostgresDocumentStore which is a subclass of KVDocumentStore. This indicates that LlamaIndex supports Postgres as a storage backend, which is compatible with Django's default database choice.

Here's a simplified example to guide you through the integration:

1. Install LlamaIndex and Django: Ensure you have both LlamaIndex and Django installed in your environment. If LlamaIndex is not available as a package, you might need to install it manually or include it in your project.

2. Configure Django to Use Postgres: In your Django settings.py, configure the database to use Postgres.

   DATABASES = {
       'default': {
           'ENGINE': 'django.db.backends.postgresql',
           'NAME': 'your_db_name',
           'USER': 'your_db_user',
           'PASSWORD': 'your_db_password',
           'HOST': 'localhost',
           'PORT': '5432',
       }
   }

3. Integrate LlamaIndex with Django: You will need to create a Django view that interacts with the PostgresDocumentStore from LlamaIndex to handle chatbox queries.

   from django.http import JsonResponse
   from llama_index.storage.docstore.postgres import PostgresDocumentStore
   
   def chatbox_query(request):
       # Initialize your PostgresDocumentStore
       doc_store = PostgresDocumentStore(db_name='your_db_name', 
                                         user='your_db_user', 
                                         password='your_db_password', 
                                         host='localhost', 
                                         port='5432')
   
       # Assuming you have a method to handle queries in your doc_store
       query = request.GET.get('query', '')
       results = doc_store.query(query)
   
       return JsonResponse({'results': results})

4. Create URLs for Your Chatbox: In your Django app, create a URL pattern that routes to your chatbox query view.

   from django.urls import path
   from .views import chatbox_query
   
   urlpatterns = [
       path('chatbox/', chatbox_query, name='chatbox_query'),
   ]

5. Frontend Integration: On the frontend, you can create a simple chatbox interface that sends queries to your Django backend and displays the results.

This example provides a basic outline for integrating LlamaIndex with a Django application using a Postgres database. Depending on the specifics of LlamaIndex's API and your application's requirements, you may need to adjust the integration details.

Regarding your question about querying the PostgresDocumentStore directly, yes, LlamaIndex provides a method to do so. The PostgresDocumentStore class inherits from KVDocumentStore, as indicated by the test checking if KVDocumentStore.__name__ is in the list of base classes of PostgresDocumentStore. This inheritance suggests that PostgresDocumentStore utilizes the interface and methods defined in KVDocumentStore, which likely includes methods for direct querying.

You can refer to the test file here for more details.

Please note that this is a simplified example and might not cover all the complexities of your specific use case. You might need to adjust the code according to your application's requirements. If you face any issues during the integration, feel free to ask.

Sources

• llama-index-integrations/storage/docstore/llama-index-storage-docstore-postgres/tests/test_storage_docstore_postgres.py

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