Help Needed: Best Approach for Querying File Content in OneDrive/Google Drive via LangFlow

[ealss101]

Use Case

I’m building a LangFlow agent that:

• Finds and retrieves files from OneDrive/Google Drive.
• Parses content from PDFs, Word, Excel, etc.
• Answers queries based on file content.

Challenges & Issues

1. OneDrive/Google Drive Composio Components

   • Find File Action is too exact-match dependent and unreliable.
   • No built-in parsing or summarization capabilities.

2. Astra DB Vector Store

   • Returns irrelevant chunks not always tied to the queried document.
   • Likely due to similar content across multiple documents in the DB.

Approach Considerations

• Vector DB (RAG) vs. Structured Ingestion (Unstructured.io)?
• Metadata filtering in Astra DB to improve chunk-document association?
• Better search reliability for OneDrive/Google Drive in LangFlow?

Integration with LangFlow & Docker Compose

• Best way to store, retrieve, and process documents in a scalable setup?
• Any LangFlow components better suited for this?

Would appreciate any insights on the best path forward to avoid wasted development time. Thanks!

[dosubot[bot]]

For your use case with LangFlow, here are some insights and recommendations:

1. OneDrive/Google Drive Integration:

   • Currently, there is no specific information available on LangFlow's components for integrating with OneDrive and Google Drive. You might need to implement custom components or use existing APIs to handle file retrieval and search.

2. Parsing and Summarization:

   • LangFlow provides components for parsing content from PDFs and Word documents, such as PyPDFLoader for PDFs and UnstructuredWordDocumentLoader for Word files. These can be used to extract text from documents ^[1][2].

3. Astra DB Vector Store:

   • To improve the relevance of search results, consider using metadata filtering in Astra DB. LangFlow supports metadata filtering through the advanced_search_filter input, which can help refine search results based on specific criteria ^[3][4].

4. RAG (Retrieval-Augmented Generation) Approach:

   • LangFlow integrates with vector databases like Astra DB for RAG applications. Ensure you have a proper ingestion flow to store documents and a RAG flow to handle queries. This involves using components like OpenAIEmbeddings and Astra DB Search to retrieve relevant records ^[5][6][7].

5. Scalable Setup with Docker Compose:

   • While specific recommendations for Docker Compose setups are not available, consider using LangFlow's existing components for document storage and retrieval. Ensure your setup can handle the necessary scale and performance requirements.

By focusing on these areas, you can enhance the reliability and efficiency of your LangFlow agent for querying file content in OneDrive and Google Drive.

To continue talking to Dosu, mention @dosu.

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