Embedchain streamlines deploying personalized LLM apps in production, supporting conventional and configurable approaches. It simplifies personalized LLM application development by efficiently processing unstructured data, segmenting data, creating embeddings, and storing them in a vector database. It offers customizable APIs, integrates with popular LLMs (OpenAI, Anthropic, Claude, HuggingFace, Mistral), vector databases (Pinecone, Elasticsearch, OpenSearch, ChromaDB, Qdrant), and various data sources. It also provides deployment support (AWS, Azure, GCP, Fly.io, Render.com) and built-in observability for debugging. It's free to use and is backed by leaders from prominent AI companies.
• vector stores
• popular large language models
• simple but customizable apis
• from prototype to production
• full control over your data flow
• supports different use cases
• personalizing llm responses
• load data from anywhere (pdf, csv, postgres, notion, slack, discord, github, etc.)
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