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Fix Docs : minor grammar fixes (#2218)

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* fix(docs): fix typos and some links in documentation

* fix(docs): correct typo in filtering.md

* Update qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/generated/typescript.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

* Update qdrant-landing/content/documentation/headless/snippets/inference/multiple/generated/typescript.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

* Update qdrant-landing/content/documentation/hybrid-cloud/configure-scale-upgrade.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

* Update qdrant-landing/content/documentation/cloud-api.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

---------

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
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Mohamed Arbi
2026-03-26 17:23:36 +01:00
committed by GitHub
co-authored by Abdon Pijpelink
parent 3f3ef1ad20
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qdrant-landing/content/blog/using-qdrant-and-langchain.md
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@@ -74,7 +74,7 @@ Here is what this basic tutorial will teach you:
**3. Implement vector similarity search algorithms:** Second, you will create and test a chatbot that only uses the LLM. Then, you will enable the memory component offered by Qdrant. This will allow your chatbot to be modified and updated, giving it long-term memory.
**4. Optimize the chatbot's performance:** In the last step, you will query the chatbot in two ways. First query will retrieve parametric data from the LLM, while the second one will get contexual data via Qdrant.
**4. Optimize the chatbot's performance:** In the last step, you will query the chatbot in two ways. First query will retrieve parametric data from the LLM, while the second one will get contextual data via Qdrant.
The goal of this exercise is to show that RAG is simple to implement via LangChain and yields much better results than using LLMs by itself.