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Normalize and denormalize llamacpp streaming reply #121
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jhrozek
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Originally, I wanted to add the normalizers to convert the `im_start`/`im_end` tags, but we worked around that by setting llamacpp to use the OpenAI format. We'll still need a normalizer for the vllm provider though. At the moment we really need the denormalizer so that the blocking pipeline can return a stream of `ModelResponse`s and the denormalizer would convert them to the CreateChatCompletionStreamResponse structure that is then serialized to the client. This avoids any guessing or special casing that would otherwise be needed in the `llamacpp_stream_generator` which currently expected `Iterator[CreateChatCompletionStreamResponse]`. Another change that simplifies the logic is that the `llamacpp_stream_generator` now accepts an `AsyncIterator` instead of just `Iterator` that the llamacpp completion hander was returning. Again, this is to simplify the logic and pass the iterator from the blocking pipeline. On the completion side we have a simple sync-to-async wrapper. Fixes: #94
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lukehinds
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At the moment we really need the denormalizer so that the blocking
pipeline can return a stream of
ModelResponses and the denormalizerwould convert them to the CreateChatCompletionStreamResponse structure
that is then serialized to the client. This avoids any guessing or
special casing that would otherwise be needed in the
llamacpp_stream_generatorwhich currently expectedIterator[CreateChatCompletionStreamResponse].Another change that simplifies the logic is that the
llamacpp_stream_generatornow accepts anAsyncIteratorinstead ofjust
Iteratorthat the llamacpp completion hander was returning.Again, this is to simplify the logic and pass the iterator from the
blocking pipeline. On the completion side we have a simple sync-to-async
wrapper.
Fixes: #94