Django Langchain
Project description
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
- Store a file in
SourceDocument
model - Perform the admin action
Parse and index file
- Check for
DocumentChunk
instances
- 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)
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