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One way to get around context length is to perform embedding and retrieval of your entire corpus. Langchain (https://langchain.readthedocs.io/en/latest/ https:/
by fzliu 4y ago
One way to get around context length is to perform embedding and retrieval of your entire corpus. Langchain (https://langchain.readthedocs.io/en/latest/ https://langchain.readthedocs.io/en/latest/) and Milvus (https://milvus.io https://milvus.io) is one of the stacks you can use.
- deleted 4y ago[deleted]
- ComplexSystems 4y agoCan you elaborate on how this works?
- teaearlgraycold 4y agoYou run the corpus through the model piecemeal, recording the model's interpretation for each chunk as a vector of floating point numbers. Then when performing a completions request you first query the vectors and include the closest matches as context.