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haystack
:mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
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Now we just need to return these inferred solutions together with additional information (context, confidence level,...) to help our users understand why we think this is the answer they were looking for. All thanks to a completely free and open source NLP framework!
A Retriever selects a set of candidate documents from all the available information. Among other options, we can rely on ElasticSearch to index the documents and return those that most likely contain the answer to the question
The easiest part is to create the chatbot. We'll obviously use Xatkit for this. The bot can have as many intents as you wish. The only part that we care about here is the default fallback state. Here, instead of saying something useless, e.g. "sorry I didn't get your question, can you rephrase it and try again?", we will ask Haystack to find us a solution.
Let's see how we can benefit from Haystack by creating a chatbot able to answer software design questions for the WordPress website modeling-languages.com. Haystack website is full of useful examples, so we'll adapt them in our scenario.