Fix Docs : minor grammar fixes (#2218)

* 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>

qdrant-landing/content/documentation/frameworks/camel.md

@@ -92,7 +92,7 @@ retrieved_info = ar.run_vector_retriever(
 print(retrieved_info)
 ```
 
-You can refer to the Camel [documentation](https://docs.camel-ai.org/index.html) for more information about the retrieval mechansims.
+You can refer to the Camel [documentation](https://docs.camel-ai.org/index.html) for more information about the retrieval mechanisms.
 
 ## End-To-End Examples
 

qdrant-landing/content/documentation/frameworks/deepeval.md

@@ -82,5 +82,5 @@ You can scale this process with a dataset (e.g. from Hugging Face) and evaluate
 
 ## Further Reading
 
-- [End-to-end Evalutation Example](https://github.com/qdrant/qdrant-rag-eval/blob/master/workshop-rag-eval-qdrant-deepeval/notebook/rag_eval_qdrant_deepeval.ipynb)
+- [End-to-end Evaluation Example](https://github.com/qdrant/qdrant-rag-eval/blob/master/workshop-rag-eval-qdrant-deepeval/notebook/rag_eval_qdrant_deepeval.ipynb)
 - [DeepEval documentation](https://deepeval.com)

qdrant-landing/content/documentation/frameworks/honeyhive.md

@@ -11,7 +11,7 @@ By integrating Qdrant with HoneyHive, you can:
 - Trace vector database operations
 - Monitor latency, embedding quality, and context relevance
 - Evaluate retrieval performance in your RAG pipelines
-- Optimize paramaters such as `chunk_size` or `chunk_overlap`
+- Optimize parameters such as `chunk_size` or `chunk_overlap`
 
 ## Prerequisites
 

qdrant-landing/content/documentation/frameworks/langchain.md

@@ -18,7 +18,7 @@ It might be installed with pip:
 pip install langchain-qdrant
 ```
 
-The integration supports searching for relevant documents usin dense/sparse and hybrid retrieval.
+The integration supports searching for relevant documents using dense/sparse and hybrid retrieval.
 
 Qdrant acts as a vector index that may store the embeddings with the documents used to generate them. There are various ways to use it, but calling `QdrantVectorStore.from_texts` or `QdrantVectorStore.from_documents` is probably the most straightforward way to get started:
 

qdrant-landing/content/documentation/frameworks/vanna-ai.md

@@ -79,4 +79,4 @@ vn.ask(question="<YOUR_QUESTION>")
 
 - [Getting started with Vanna.AI](https://vanna.ai/docs/app/)
 - [Vanna.AI documentation](https://vanna.ai/docs/)
-- [Source Code](https://github.com/vanna-ai/vanna/tree/main/src/vanna/qdrant)
+- [Source Code](https://github.com/vanna-ai/vanna/tree/main/src/vanna/integrations/qdrant)