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www.paepper.com | ||
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www.anyscale.com
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| | | | | In part 2 of this blog series, we show you how to turbocharge embeddings in LLM workflows using LangChain and Ray Data. | |
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weaviate.io
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| | | | | LangChain is one of the most exciting new tools in AI. It helps overcome many limitations of LLMs, such as hallucination and limited input lengths. | |
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www.4async.com
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| | | | | ??????????????????????????????????????????????????????????? HyDE ? RAG ??????????????????? LangChain ?????? Demo????????? LlamaIndex ????????????????????????? API ???????????????? ???? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? RAG ?????????????????????????????????????????????????????????????????? HyDE ?????????????? ???? ?????????? Python ????(pdm ??)?Ollama?chroma??? Ollama ?????????????????????????????... | |
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isthisit.nz
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| | | August 2024 Update: Now a solved problem. Use Structured Outputs. Large language models (LLMs) return unstructured output. When we prompt them they respond with one large string. This is fine for applications such as ChatGPT, but in others where we want the LLM to return structured data such as lists or key value pairs, a parseable response is needed. In Building A ChatGPT-enhanced Python REPL I used a technique to prompt the LLM to return output in a text format I could parse. | ||
