Build Q&A chat over unstructured text in minutes
Using no-code agents, you can create text-to-vector-database pipelines and prompt templating agents for a variety of LLMs.
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Use the latest Gen AI technologies
LangStream integrates with LLMs from OpenAI, Google Vertex AI, and Hugging Face, Vector databases like Pinecone and DataStax, Python libraries like LangChain and LlamaIndex.
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Manage embeddings in your vector database
Create pipelines that automatically create and update embeddings in your vector database for unstructured data (PDFs, Word documents, HTML) using proprietary and open-source embeddings models.
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Code and deploy in Visual Studio Code
To make it easier to build LangStream apps, we have created a VS Code extension that includes app deployment, app updating, log analysis, and code snippets.
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Declare an app and deploy it to dev or prod
In LangStream an application consists of pipelines defined using configuration files or Python. Deploy to a local development environment or to a production environment, powered by proven tech: Kubernetes and Kafka.
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Bring your existing data to the LLM
LangStream can tap into the data you already have when engineering a prompt to send to the LLM. Query SQL and no-SQL databases or use Kafka Connect connectors to access the data you already have.
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Fix your day 2 problems with an event-driven architecture
LangStream is a streaming framework using an event-driven architecture meaning it is asynchronous, decoupled, fault tolerant, and scalable.
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