Build a search engine, not a vector DB

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  • txtai

    💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

    I agree that RAG doesn't have to be paired with vector search. Other types of search can work in some cases.

    Where vector search excels is that it can encode a complex question as a vector and does a good job bringing back the top n results. Its not impossible to do some of this with keyword search (term expansion, stopwords and so forth). Vector search just makes it easy.

    In the end, yes this is a better search system. And thinking about this step is a good point. I would go a step further and say it's also worth thinking about the RAG framework. Lots of examples use a OpenAI/Langchain/Chroma stack. But it's also worth evaluating RAG framework options. There might be frameworks that are easier to integrate and perform better for your use case.

    Disclaimer: I am the author of txtai (https://github.com/neuml/txtai).

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  • langroid

    Harness LLMs with Multi-Agent Programming

    This resonates with the approach we’ve taken in Langroid (the Multi-Agent framework from ex-CMU/UW-Madison researchers): our DocChatAgent uses a combination of lexical and semantic retrieval, reranking and relevance extraction to improve precision and recall:

    https://github.com/langroid/langroid/blob/main/langroid/agen...

  • bootcamp

    Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc. (by milvus-io)

    Partially agree.

    Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important.

    As someone working on vector DB, I find many users struggling in building their own retrieval systems with building blocks such as embedding service (openai,cohere), logic orchestration framework (langchain/llamaindex) and vector databases, some even with reranker models. Putting them together is not as easy as it looks. A fairly changeling system work. Letting alone quality tuning and devops.

    The struggle is no surprise to me, as tech companies who are experts on this (google,meta) all have dedicated teams working on retrieval system alone, making tons of optimizations and develop a whole feedback loop of evaluating and improving the quality. Most developers don't get access to such resource.

    No one size fits all. I think there shall exist a service that democratize AI-powered retrieval, in simple words the know-how of using embedding+vectordb and a bunch of tricks to achieve SOTA retrieval quality.

    With this idea I built a Retrieval-as-a-service solution, and here is its demo:

    https://github.com/milvus-io/bootcamp/blob/master/bootcamp/R...

    Curious to learn your thoughts.

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