Question 1

How to deploy a Pathway RAG app on AWS?

Accepted Answer

Pathway templates are Docker-friendly and can be deployed on AWS using services like ECS or EKS. Each template's README includes cloud deployment instructions, and the project emphasizes compatibility with major cloud providers.

Question 2

Pathway AI Pipelines vs LangChain for building RAG apps?

Accepted Answer

Pathway provides a unified, real-time sync solution with built-in indexing, reducing infrastructure dependencies, while LangChain offers more modularity and tool integrations but requires separate setup for live data updates. Pathway is better for all-in-one deployment, LangChain for flexibility.

Question 3

Can I use my own LLM model with Pathway templates?

Accepted Answer

Yes, Pathway templates are configurable and allow integration of custom LLM models by modifying pipeline steps, such as swapping the default GPT model for another API or local model like Ollama, as shown in the private RAG template.

Question 4

Does Pathway support real-time updates from PostgreSQL?

Accepted Answer

Yes, Pathway AI Pipelines can sync with PostgreSQL in real-time, handling data changes to keep the RAG index current. This is listed as a supported data source in the key features.

Question 5

What's the performance of Pathway's vector search vs Pinecone?

Accepted Answer

Pathway uses usearch for fast in-memory vector search, but for very large-scale deployments, it may lack the distributed scalability of cloud-managed services like Pinecone. Performance is optimized for real-time sync within memory constraints.

Question 6

How to add a custom data source to a Pathway template?

Accepted Answer

You can extend data sources by modifying the Pathway pipeline code, often with a one-line change to include new connectors. The README notes flexibility for adding sources, but it requires Python coding and understanding of Pathway's dataflow API.

LLM App

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