Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update readme file #65

Merged
merged 1 commit into from
Oct 2, 2023
Merged

Update readme file #65

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 11 additions & 4 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,11 +16,18 @@

Pathway's **LLM (Large Language Model) App** is a Python library that helps you build innovative AI applications by providing real-time human-like responses to user queries based on the most up-to-date knowledge available in your data sources.

LLM App **does not require** a separate vector database and **avoids the need** for complex and fragmented typical LLM stacks (such as ~Pinecone/Weaviate + Langchain + Redis + FastAPI +...~). Your data remains secure and undisturbed in its original storage location. LLM App's design ensures high performance and offers flexibility for easy customization and expansion. It is particularly recommended for privacy-preserving LLM applications.

**Quick links** - 💡[Use cases](#use-cases) 📚 [How it works](#how-it-works) 🎬 [Showcases](#showcases) 🌟 [Key Features](#key-features) 🏁 [Get Started](#get-started) 🛠️ [Troubleshooting](#troubleshooting)
**Quick links** - 👀[Why LLM App?](#why-llm-app) 💡[Use cases](#use-cases) 📚 [How it works](#how-it-works) 🎬 [Showcases](#showcases) 🌟 [Key Features](#key-features) 🏁 [Get Started](#get-started) 🛠️ [Troubleshooting](#troubleshooting)
👥 [Contributing](#troubleshooting)

## Why LLM App?

1. **Simplicity** - Simplifies your AI pipeline by consolidating capabilities into one platform. No need to integrate and maintain separate modules for your Gen AI app: ~Vector Databases (e.g. Pinecone/Weaviate/Qdrant) + LangChain + Cache (e.g. Redis) + API Framework (e.g. Fast API)~.
2. **Real-time data syncing** - Syncs both structured and unstructured data from diverse sources, enabling real-time Retrieval Augmented Generation (RAG).
3. **Easy alert setup** - Configure alerts for key business events with simple configurations.
4. **Scalability** - Handles heavy data loads and usage without degradation in performance. Metrics help track usage and scalability.
5. **Monitoring** - Provide visibility into model behavior via monitoring, tracing errors, anomaly detection, and replay for debugging. Helps with response quality.
6. **Security** - Designed for the enterprise with capabilities like Personally Identifiable Information (PII) detection, content moderation, permissions, and version control. Run this in your private cloud with local LLMs.

## Use cases

LLM App examples can be used as templates for developing multiple applications running on top of Pathway. Here are examples of possible uses:
Expand All @@ -35,7 +42,7 @@ LLM App examples can be used as templates for developing multiple applications r

## How it works

The default [`contextful`](examples/pipelines/contextful/app.py) LLM App takes a bunch of documents that might be stored in [AWS S3](https://aws.amazon.com/s3/) or locally on your computer. Then it processes and organizes these documents by building a 'vector index' using the Pathway package. It waits for user queries that come as HTTP REST requests, then uses the index to find relevant documents and responds using [OpenAI API](https://openai.com/blog/openai-api) or [Hugging Face](https://huggingface.co/) in natural language. The cool part is that the app is always aware of changes in the documents. If new pieces of information are added, it updates its index in real-time and uses this new knowledge to answer the next questions. In this way, it provides the most accurate **real-time data** answers.
The default [`LLM-Augmented Pipeline`](examples/pipelines/contextful/app.py) takes a bunch of documents that might be stored in [AWS S3](https://aws.amazon.com/s3/) or locally on your computer. Then it processes and organizes these documents by building a 'vector index' using the Pathway package. It waits for user queries that come as HTTP REST requests, then uses the index to find relevant documents and responds using [OpenAI API](https://openai.com/blog/openai-api) or [Hugging Face](https://huggingface.co/) in natural language. The cool part is that the app is always aware of changes in the documents. If new pieces of information are added, it updates its index in real-time and uses this new knowledge to answer the next questions. In this way, it provides the most accurate **real-time data** answers.

The app can also be combined with streams of fresh data, such as news feeds or status reports, either through REST or a technology like Kafka. It can also be combined with extra static data sources and user-specific contexts, for example, to eliminate **the ambiguity problems** of natural language with clearer prompts and better contexts.

Expand Down