It also has built-in support for a variety of NLP tasks.
The key feature \href{https://docs.haystack.deepset.ai/docs/pipelines}{pipelines} is the counterpart to the LangChain chains.
The key feature \href{https://docs.haystack.deepset.ai/docs/pipelines}{Pipelines} is the counterpart to the LangChain chains.
Another key feature are the \href{https://docs.haystack.deepset.ai/v1.25/docs/agent}{Agents}.
Particularly interesting is the \href{https://docs.haystack.deepset.ai/v1.25/docs/agent#conversational-agent}{Conversational Agent}, which simplifies the usage of the LLM in a chatbot scenario, similar to the LangChain ConversationChain.
The Conversational Agent also handles the memory, so the LLM can answer context-based requests.
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@@ -30,7 +30,7 @@
As it is less flexible than LangChain, Haystack is easier to get started with.
\subsection{Conclusion \& Decision}
Of course, in this evaluation, only a small subset of the available concepts were discussed.
Of course, in this evaluation, only a small subset of the available concepts and tools were discussed.
However, most of the concepts can be found in both frameworks.
That's why I focused on evaluating the concepts, that are important to this project.
