LangStream
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About
LangStream is an open-source framework for building Gen AI applications. It allows developers to declare Gen AI components and deploy them as event-based streaming applications. LangStream integrates with LLMs from OpenAI, Google Vertex AI, and Hugging Face, Vector databases like Pinecone and DataStax, Python libraries like LangChain and LlamaIndex. It leverages Kubernetes and Kafka for backend infrastructure, offering scalability and fault tolerance. Features include no-code agents, a VS Code extension, and support for various data sources through Kafka Connect connectors. The event-driven architecture ensures asynchronous, decoupled, fault-tolerant, and scalable applications.
Platform
Task
Features
• vs code extension
• no-code agents
• event-driven architecture
• kafka connect connectors
• integrates with python libraries like langchain and llamaindex
• integrates with vector databases like pinecone and datastax
• integrates with llms from openai, google vertex ai, and hugging face
FAQs
What is LangStream?
LangStream is an open-source project designed to facilitate the creation of streaming Gen AI applications. It combines event-based architectures with the latest Gen AI technologies.
How does LangStream help in the development of Gen AI applications?
LangStream allows you to build applications that capitalize on Large Language Models (LLMs). It ensures that data from various sources, both static and streaming, is available for constructing high-quality prompts for the LLMs.
How is LangStream different than LangChain or LlamaIndex?
LangStream is complementary to LangChain and LlamaIndex. Includes a run-time environment for your applications leveraging proven tech like Kubernetes.Is event-driven, which provides advantages around scaling, fault tolerance, and extensibility.
What are LangStream application agents?
LangStream application agents operate on event data. They modify or transform this data before passing it on to the next agent in the pipeline.
Can I customize LangStream to my needs?
While LangStream offers pre-built agents for common tasks, you can also write custom agents using Python, providing a mix-and-match capability to meet your unique requirements.
What’s the role of Kubernetes and Kafka in LangStream?
When you deploy an application using LangStream, it leverages Kubernetes and Apache Kafka for the backend infrastructure.
What kind of observability does LangStream offer for my applications?
LangStream is designed with a high level of abstraction but ensures that logs and metrics are available for debugging purposes. It supports Prometheus metrics and offers tooling for accessing Kubernetes logs.
Is LangStream open-source?
Yes, LangStream is open source. You can find the project on GitHub and even contribute to its development.
I’ve built a prototype using a popular Gen AI library. Can I use LangStream to turn it into an event-driven application?
LangStream supports popular Gen AI libraries like LangChain and LlamaIndex. You can easily convert your prototype into an event-driven application using LangStream.
What are the advantages of an event-driven architecture for Gen AI applications?
Event-driven architectures offer scalability, real-time processing, loose coupling for easy evolution, and improved fault tolerance.
How can I get started with LangStream?
To begin with LangStream, you can check out its GitHub repository. There’s also a Visual Studio code extension available for LangStream, which offers a seamless experience directly from your IDE.
Where can I seek help or give feedback on LangStream?
The LangStream team is active on Slack. You can join the channel to ask questions, share feedback, or engage in discussions about the project.
Does LangStream support RAG (retrieval augmented generation) techniques?
Yes. LLMs work with the knowledge embedded during their training and might not be updated with the latest information. RAG combines the capabilities of powerful LLMs with external retrieval or database systems.
My prompt doesn’t yield the exact desired response from the LLM what can I try?
Fine-tuning the instructions given to the model can lead to more accurate or contextually relevant outputs. This is especially useful in task-specific applications where precision is required.
What is least-to-most prompting and can I do it with LangStream?
Absolutely, LangStream makes this easy. Typically this technique is used to gradually refine prompts from being general to specific based on model responses.
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