From d404966152e3473427a85f015abf1b6e46cbef16 Mon Sep 17 00:00:00 2001
From: Andri Joos <andri@joos.io>
Date: Thu, 13 Jun 2024 21:25:36 +0200
Subject: [PATCH] fix langchain haystack
---
src/04_Appendix/tooling-evaluations/langchain-haystack.tex | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/src/04_Appendix/tooling-evaluations/langchain-haystack.tex b/src/04_Appendix/tooling-evaluations/langchain-haystack.tex
index dd34c72..9a01c6c 100644
--- a/src/04_Appendix/tooling-evaluations/langchain-haystack.tex
+++ b/src/04_Appendix/tooling-evaluations/langchain-haystack.tex
@@ -21,7 +21,7 @@
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.
@@ -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.
--
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