Skip to content

Commit

Permalink
Add slides template to website (#7661)
Browse files Browse the repository at this point in the history
Co-authored-by: berkecanrizai <63911408+berkecanrizai@users.noreply.github.com>
GitOrigin-RevId: 30f4b17febd445384b93549c3dc4f353ced8911a
  • Loading branch information
2 people authored and Manul from Pathway committed Nov 15, 2024
1 parent 3629b52 commit b1f4c4e
Showing 1 changed file with 1 addition and 1 deletion.
2 changes: 1 addition & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -27,9 +27,9 @@ The application templates provided in this repo scale up to **millions of pages
| [`Live Document Indexing (Vector Store / Retriever)`](examples/pipelines/demo-document-indexing/) | A real-time document indexing pipeline for RAG that acts as a vector store service. It performs live indexing on your documents (PDF, DOCX,...) from a connected data source (files, Google Drive, Sharepoint,...). It can be used with any frontend, or integrated as a retriever backend for a [Langchain](https://pathway.com/developers/templates/langchain-integration) or [Llamaindex](https://pathway.com/developers/templates/llamaindex-pathway) application. You can also try out a [demo REST endpoint](https://pathway.com/solutions/ai-contract-management#try-it-out). |
| [`Multimodal RAG pipeline with GPT4o`](examples/pipelines/gpt_4o_multimodal_rag/) | Multimodal RAG using GPT-4o in the parsing stage to index PDFs and other documents from a connected data source files, Google Drive, Sharepoint,...). It is perfect for extracting information from unstructured financial documents in your folders (including charts and tables), updating results as documents change or new ones arrive.|
| [`Unstructured-to-SQL pipeline + SQL question-answering`](examples/pipelines/unstructured_to_sql_on_the_fly/) | A RAG example which connects to unstructured financial data sources (financial report PDFs), structures the data into SQL, and loads it into a PostgreSQL table. It also answers natural language user queries to these financial documents by translating them into SQL using an LLM and executing the query on the PostgreSQL table. |
| [`Alerting when answers change on Google Drive`](examples/pipelines/drive_alert/) | Ask questions about your private data (docs), and tell the app to alert you whenever responses change. The app is always connected to your Google Docs folder and listening for changes. Whenever new relevant information is added to the data sources, the LLM decides if there is a substantial difference in response and notifies the user with a Slack message.|
| [`Adaptive RAG App`](examples/pipelines/adaptive-rag/) | A RAG application using Adaptive RAG, a technique developed by Pathway to reduce token cost in RAG up to 4x while maintaining accuracy. |
| [`Private RAG App with Mistral and Ollama`](examples/pipelines/private-rag/) | A fully private (local) version of the `demo-question-answering` RAG pipeline using Pathway, Mistral, and Ollama. |
| [`Slides AI Search App`](examples/pipelines/slides_ai_search/) | An indexing pipeline for retrieving slides. It performs multi-modal of PowerPoint and PDF and maintains live index of your slides."|


## How do these LLM Apps work?
Expand Down

0 comments on commit b1f4c4e

Please sign in to comment.