fix takeaways

qdrant-landing/content/articles/why-rust.md

@@ -13,18 +13,6 @@ keywords: rust, programming, development
 aliases: [ /articles/why_rust/ ]
 ---
 
-### Key Takeaways:
-
-- **Rust's Advantages for Qdrant:** Rust provides memory safety and control without a garbage collector, which is crucial for Qdrant's high-performance cloud services.
-
-- **Low Overhead:** Qdrant's Rust-based system offers efficiency, with small Docker container sizes and robust performance benchmarks.
-
-- **Complexity vs. Simplicity:** Rust's strict type system reduces bugs early in development, making it faster in the long run despite initial learning curves.
-
-- **Adoption by Major Players:** Large tech companies like Amazon, Google, and Microsoft are embracing Rust, further validating Qdrant's choice.
-
-- **Community and Talent:** The supportive Rust community and increasing availability of Rust developers make it easier for Qdrant to grow and innovate.
-
 # Building Qdrant in Rust
 
 Looking at the [github repository](https://github.com/qdrant/qdrant), you can see that Qdrant is built in [Rust](https://rust-lang.org). Other offerings may be written in C++, Go, Java or even Python. So why does Qdrant chose Rust? Our founder Andrey had built the first prototype in C++, but didn’t trust his command of the language to scale to a production system (to be frank, he likened it to cutting his leg off). He was well versed in Java and Scala and also knew some Python. However, he considered neither a good fit:
@@ -58,3 +46,15 @@ The job market for Rust programmers is certainly not as big as that for Java or
 Finally, the Rust community is a very friendly bunch, and we are delighted to be part of that. And we don’t seem to be alone. Most large IT companies (notably Amazon, Google, Huawei, Meta and Microsoft) have already started investing in Rust. It’s in the Windows font system already and in the process of coming to the Linux kernel (build support has already been included). In machine learning applications, Rust has been tried and proven by the likes of Aleph Alpha and Huggingface, among many others.
 
 To sum up, choosing Rust was a lucky guess that has brought huge benefits to Qdrant. Rust continues to be our not-so-secret weapon.
+
+### Key Takeaways:
+
+- **Rust's Advantages for Qdrant:** Rust provides memory safety and control without a garbage collector, which is crucial for Qdrant's high-performance cloud services.
+
+- **Low Overhead:** Qdrant's Rust-based system offers efficiency, with small Docker container sizes and robust performance benchmarks.
+
+- **Complexity vs. Simplicity:** Rust's strict type system reduces bugs early in development, making it faster in the long run despite initial learning curves.
+
+- **Adoption by Major Players:** Large tech companies like Amazon, Google, and Microsoft are embracing Rust, further validating Qdrant's choice.
+
+- **Community and Talent:** The supportive Rust community and increasing availability of Rust developers make it easier for Qdrant to grow and innovate.

qdrant-landing/content/blog/dspy-vs-langchain.md

@@ -18,20 +18,6 @@ keywords: # Keywords for SEO
     - chatbots
 ---
 
-### Key Takeaways:
-
-- **LangChain's Flexibility:** LangChain integrates seamlessly with Qdrant, enabling streamlined vector embedding and retrieval for AI workflows.
-
-- **Optimized Retrieval:** Automate and enhance retrieval processes in multi-stage AI reasoning applications.
-
-- **Enhanced RAG Applications:** Fast and accurate retrieval of relevant document sections through vector similarity search.
-
-- **Support for Complex AI:** LangChain integration facilitates the creation of advanced AI architectures requiring precise information retrieval.
-
-- **Streamlined AI Development:** Simplify managing and retrieving large datasets, leading to more efficient AI development cycles in LangChain and DSPy.
-
-- **Future AI Workflows:** Qdrant's role in optimizing retrieval will be crucial as AI frameworks like DSPy continue to evolve and scale.
-
 # The Evolving Landscape of AI Frameworks
 As Large Language Models (LLMs) and vector stores have become steadily more powerful, a new generation of frameworks has appeared which can streamline the development of AI applications by leveraging LLMs and vector search technology. These frameworks simplify the process of building everything from Retrieval Augmented Generation (RAG) applications to complex chatbots with advanced conversational abilities, and even sophisticated reasoning-driven AI applications.
 
@@ -392,6 +378,20 @@ Here are some guidelines:
 
 You can also choose to combine and use the best features of both. In fact, LangChain has released an [integration with DSPy](https://python.langchain.com/v0.1/docs/integrations/providers/dspy/) to simplify this process. This allows you to use some of the utility functions that LangChain provides, such as text splitter, directory loaders, or integrations with other data sources while using DSPy for the LM interactions.
 
+### Key Takeaways:
+
+- **LangChain's Flexibility:** LangChain integrates seamlessly with Qdrant, enabling streamlined vector embedding and retrieval for AI workflows.
+
+- **Optimized Retrieval:** Automate and enhance retrieval processes in multi-stage AI reasoning applications.
+
+- **Enhanced RAG Applications:** Fast and accurate retrieval of relevant document sections through vector similarity search.
+
+- **Support for Complex AI:** LangChain integration facilitates the creation of advanced AI architectures requiring precise information retrieval.
+
+- **Streamlined AI Development:** Simplify managing and retrieving large datasets, leading to more efficient AI development cycles in LangChain and DSPy.
+
+- **Future AI Workflows:** Qdrant's role in optimizing retrieval will be crucial as AI frameworks like DSPy continue to evolve and scale.
+
 ## **Level Up Your AI Projects with Advanced Frameworks**
 
 LangChain and DSPy both offer unique capabilities and can help you build powerful AI applications. Qdrant integrates with both LangChain and DSPy, allowing you to leverage its performance, efficiency and security features in either scenario. LangChain is ideal for projects that require extensive integration with various data sources and APIs. On the other hand, DSPy offers a powerful paradigm for building complex multi-stage applications. For pulling together an AI application that doesn’t require much prompt engineering, use LangChain. However, pick DSPy when you need a systematic approach to prompt optimization and modular design, and need robustness and scalability for complex, multi-stage reasoning applications.

qdrant-landing/content/blog/what-is-vector-similarity.md

@@ -14,17 +14,6 @@ tags:
   - similarity search
   - embeddings
 ---
-### Key Takeaways:
-
-- **Vector Similarity in AI:** Vector similarity is a crucial technique in AI, allowing for the accurate matching of queries with relevant data, driving advanced applications like semantic search and recommendation systems.
-
-- **Versatile Applications of Vector Similarity:** This technology powers a wide range of AI-driven applications, from reverse image search in e-commerce to sentiment analysis in text processing.
-
-- **Overcoming Vector Search Challenges:** Implementing vector similarity at scale poses challenges like the curse of dimensionality, but specialized systems like Qdrant provide efficient and scalable solutions.
-
-- **Qdrant's Advanced Vector Search:** Qdrant leverages Rust's performance and safety features, along with advanced algorithms, to deliver high-speed and secure vector similarity search, even for large-scale datasets.
-
-- **Future Innovations in Vector Similarity:** The field of vector similarity is rapidly evolving, with advancements in indexing, real-time search, and privacy-preserving techniques set to expand its capabilities in AI applications.
 
 # Understanding Vector Similarity: Powering Next-Gen AI Applications
 
@@ -206,6 +195,18 @@ Qdrant is one of the most secure vector stores out there. However, we are workin
 
 We have just about witnessed the tip of the iceberg in terms of what vector similarity can achieve. If you are working on an interesting use-case that uses vector similarity, we would like to hear from you.
 
+### Key Takeaways:
+
+- **Vector Similarity in AI:** Vector similarity is a crucial technique in AI, allowing for the accurate matching of queries with relevant data, driving advanced applications like semantic search and recommendation systems.
+
+- **Versatile Applications of Vector Similarity:** This technology powers a wide range of AI-driven applications, from reverse image search in e-commerce to sentiment analysis in text processing.
+
+- **Overcoming Vector Search Challenges:** Implementing vector similarity at scale poses challenges like the curse of dimensionality, but specialized systems like Qdrant provide efficient and scalable solutions.
+
+- **Qdrant's Advanced Vector Search:** Qdrant leverages Rust's performance and safety features, along with advanced algorithms, to deliver high-speed and secure vector similarity search, even for large-scale datasets.
+
+- **Future Innovations in Vector Similarity:** The field of vector similarity is rapidly evolving, with advancements in indexing, real-time search, and privacy-preserving techniques set to expand its capabilities in AI applications.
+
 ## Getting Started with Qdrant
 
 Ready to implement vector similarity in your AI applications? Explore Qdrant's vector database to enhance your data retrieval and AI capabilities. For additional resources and documentation, visit: