Django Langchain

What's this ?

This package aims to provide integration between LangChain and Django.

This is very early and the current stage is : alpha

Compatibility

LangChain is moving at a fast pace and interfaces may change. Version pinning is not enforced at the time to be able to use latest releases.

This package is built using

• LangChain v0.0.229
• Django 4.2
• Python 3.9

Database requirements

You have to use a pgvector-enabled Postgresql database

Roadmap

Never ending todo :

• Base document models (chunks and sources)
• A LangChain-compliant document store
• Embeddings are computed via SourceDocument.parse_file
• Bare-bone admin management interface
• Serializers (output) to use with Django REST Framework
• Comprehensive set of utilities binding LLM & chains
• (TBC)Serializers (input) to use with Django REST Framework
• (TBC) Add support for celery or other async queuing system
• (TBC) Add support for data visualization in django admin

Quickstart

Using the admin site

1. Store a file in SourceDocument model
2. Perform the admin action Parse and index file

3. Check for DocumentChunk instances

4. Perform a similarity search using Django's admin built-in search (returns 5 most relevant results)

Using the console

from django_langchain.models import SourceDocument

source_doc = SourceDocument(title='< Title >', file='< /path/to/file/ >')
source_doc.save()

#: This could take some time and some non-trivial amount of RAM
source_doc.parse_file()
print(source_doc.documentchunk_set.count())

from django_langchain.models import DocumentChunk, DocumentChunkStore

# Perform a similarity search
queryset = DocumentChunk.objects.search('< query >', max_results=5)

# Or using DocumentStore Langchain interface

store = DocumentChunkStore()
source, doc = store.search("< query >")

print(doc.metadata)
print(doc.page_content)