Package: minio-weaviate-langchain
This will guide users on how to deploy the application using Docker, which simplifies the setup process by handling dependencies and configurations through Docker containers. Here's an updated version of theREADME.mdwith Docker Compose deployment instructions:README.mdMy FastAPI Application with Langchain, Weaviate, and MinIO IntegrationsThis FastAPI application demonstrates an integrated text processing pipeline using Weaviate and MinIO. It's designed to efficiently process, store, and retrieve documents through a Dockerized environment.FeaturesProcess documents from a MinIO bucket.Use OpenAI's LangChain for advanced text processing.Index and manage documents in Weaviate.Easy deployment with Docker Compose.RequirementsDockerDocker ComposeInstallation and DeploymentClone the Repository:gitclonehttps://github.com/Cdaprod/minio-weaviate-langchaincdminio-weaviate-langchainStart the Application with Docker Compose:Run the following command to start all services defined in yourdocker-compose.yaml:docker-composeupThis command will build and start containers for your FastAPI app, Weaviate, and MinIO.Proof of ConceptThe application showcases the following workflow:Load Documents from MinIO: The application retrieves documents stored in a MinIO bucket.Document Processing: Documents are processed via LangChain's OpenAI integration.Indexing in Weaviate: Processed documents are indexed in Weaviate for full-text search and data retrieval.API Endpoints:POST /process_documents: Processes and indexes documents from MinIO into Weaviate.GET /: Health check endpoint.POST /index_from_minio: Indexes documents from MinIO into Weaviate.POST /query: Queries documents in Weaviate.POST /update/{uuid}: Updates a document in Weaviate.DELETE /delete/{uuid}: Deletes a document from Weaviate.Example UsageIndex Documents from MinIO:importrequestsresponse=requests.post('http://localhost:8000/index_from_minio')print(response.json())Query Documents in Weaviate:query='{"query": "your_query_here"}'response=requests.post('http://localhost:8000/query',json=query)print(response.json())ContributingContributions are welcome! Fork the repository, make your changes, and submit a pull request.Further Development for Notebook EnvironmentTo adapt your FastAPI application with Weaviate and MinIO integration for use in a Jupyter notebook, you'll need to structure it as a Python package that can be installed and used interactively. The structure of the package should facilitate easy installation and usage within a notebook environment.Here's a basic outline of steps to restructure your application:1. Organize Your Application as a PackageYour project directory might look something like this:minio-weaviate-langchain/
    my_fastapi_lib/
        __init__.py
        main.py
        weaviate_operations.py
        minio_operations.py
        # ... other modules ...
    setup.py
    README.md
    LICENSEInminio-weaviate-langchain/my_fastapi_lib/__init__.py, you can import the main components:from.mainimportappfrom.weaviate_operationsimportWeaviateOperationsfrom.minio_operationsimportload_documents_from_minio2. Setup.py for InstallationYoursetup.pyallows the package to be installed via pip. Here's an example:fromsetuptoolsimportsetup,find_packagessetup(name='my_fastapi_lib',version='0.1.0',packages=find_packages(),include_package_data=True,install_requires=['fastapi','uvicorn','weaviate-client','minio','langchain',# ... other dependencies ...],)3. Using the Library in a Jupyter NotebookOnce your library is structured and installable, you can create a Jupyter notebook to demonstrate its usage. For example:Cell 1: Install the library!pipinstallgit+https://github.com/yourusername/my_fastapi_lib.gitCell 2: Import and Initialize Componentsfrommy_fastapi_libimportWeaviateOperations,load_documents_from_minio# Setup Weaviate and MinIO clientsweaviate_ops=WeaviateOperations(WEAVIATE_ENDPOINT)minio_documents=load_documents_from_minio(MINIO_BUCKET,MINIO_ENDPOINT,MINIO_ACCESS_KEY,MINIO_SECRET_KEY)Cell 3: Use the Library# Example operationfordocinminio_documents:processed_doc=process_document(doc)# Define this function as per your processing logicweaviate_ops.index_document(MINIO_BUCKET,"DocumentName",processed_doc)4. Documentation and ExamplesProvide clear instructions and examples in yourREADME.mdand include Jupyter notebooks that demonstrate the use of your library.5. Running the FastAPI AppSince the FastAPI app is designed to be run as a server, it might not be directly applicable in a Jupyter notebook for interactive use. Instead, focus on demonstrating how to interact with the server (which could be running locally or remotely) using requests or similar in the notebook.NoteAdapting a FastAPI application for use as a library in a Jupyter notebook involves a shift in focus. The interactive notebook environment is more suited for demonstrating API calls to the running FastAPI server, rather than running the server itself within the notebook.