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Slightly related: what's the difference in use cases of llama-index vs LangChain? I know that LangChain can make retrieval using embeddings and I suspect that t

• LawTalkingGuy 4y ago
  I think Langchain is currently broader, containing those text-splitting and indexing tools as well as summarization and memory, 'Tool' integration, etc.
  • deleted 4y ago
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• freezed88 4y ago
  author of llamaindex here: there's some overlap but our primary use case is going deep into indexing+retrieval for LLM's (and going deep in that area). Not really doing agents/chatbots/prompt management/etc. E.g. this is a good example of our use cases: https://gpt-index.readthedocs.io/en/latest/use_cases/queries.html https://gpt-index.readthedocs.io/en/latest/use_cases/queries... You can actually use us as a retrieval/tool in langchain too: https://gpt-index.readthedocs.io/en/latest/how_to/integrations/using_with_langchain.html https://gpt-index.readthedocs.io/en/latest/how_to/integratio...
• jn2clark 4y ago
  Not sure on your interest/use case but something that is designed for "documents in" -> "documents out" is here https://github.com/marqo-ai/marqo https://github.com/marqo-ai/marqo. It does retrieval using embeddings and combines all the text splitting and inference operations and can be easily deployed to production (its designed for that, not pip install). Works across images and allows for multi-vector representations.