You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


GA Release of NEST 2.0, our .NET client for Elasticsearch

This marks the first GA release of our 2.0 client with well over a 1000 commits since 1.7.1 (the currently last released NEST version in the 1.x range).

Back to the drawing board

We took some time to go back to the drawing board. NEST was originally started in 2010 and there are many choices that have accumulated in the code base that don't make sense anymore. So we stepped back to properly formalize how we see the lifetime of a call and worked off of that. Armed with the following diagram, we completely rewrote NEST's internals; The old Task Parallel Library (TPL) based code is now replaced with async/await with a much saner approach to exceptions and errors in addition to exposing enough information as an audit trail so you don't ever have to guess what went down during a call.

the default ITransport uses the passed in IRequestPipelineFactory to create an RequestPipeline which implements IPipeline.

This pipeline now handles all of the failover/sniffing/pinging logic and directly reflects the flow diagram.

We also simplified IConnection down just 2 methods. This means the outer edges are clean (ITransport and IConnection) and implementing your own should be really really simple. All of these (and also IMemoryStreamProvider and IDateTimeProvider) can be injected on the constructor of the client.

Test Framework

Another huge endeavour is the rework of our test framework. NEST 1.x was always well tested but used 5 different test projects and 5 years worth of changing our minds as how best to write tests and assertions, thus becoming a big hodgepodge of NUnit assertions, Fluent assertions, FakeItEasy, Moq combined with several different ways to compare json with object graphs and vice-versa. Trying to write a new test quickly became an exercise in yak shaving because there was no clear cut way for how best to write said test.

So the first thing we did as part of our 2.0 branch was to completely delete all of our tests. This act of insanity gave us carte blanche during our rewrite.

As of 2.0, we have one test project, Tests, with all tests written in such a way that they can be run in unit test mode and integration test mode. Write once, run differently.
All of the API endpoint tests test all four request variations - two DSL's (using fluent and object initializer syntax) with both synchronous and asynchronous variations of each. We also test all of the moving parts of the Elasticsearch DSL (Aggregations, Sorting, IndexSettings, etc) in the same way.

In addition to the more formal unit and integration tests, we also implemented a thing we dubbed Literate Testing to allow us to write tests in a more story telling form, with multi-line comments serving as the narrative for our asciidoc documentation while using the Roslyn compiler to pick out the interesting bits of code. This gives us the benefit of always compiling our documentation in addition to having only one place where we document, test and assert how a piece of code is supposed to work.

Another huge component of our testing framework is the Virtual Cluster that allows us to write tests for any situation and how we expect the client to behave.

This showcases the Virtual Cluster tests combined with Literate Tests and the extensive audit trail information available on each response (or exception).

I'm pleased to say we are back at a decent coverage rate (60%) and will continue to iterate and improve this.

Exception handling

Another big change in NEST 2.0 is how we deal with exceptions.

In NEST 1.x, the client threw a multitude of exceptions: MaxRetryException, ElasticsearchAuthException, ElasticsearchServerException, DslException, etc.. This made it challenging for users to handle exceptions and invalid responses, and understand the root cause of errors. On top of that, the types of exceptions thrown depended on what kind of IConnectionPool was injected, in order to maintain maximum backwards compatibility with NEST 0.x.

In NEST 2.x, exceptions are much more deterministic. The former ThrowOnElasticsearchServerExceptions() setting has been replaced with the more succinct ThrowExceptions(), which determines whether the client should ever throw an exception or not (client side and server exceptions). Furthermore, the types of exceptions have been reduced and simplified down to three types of exceptions:

ElasticsearchClientException: These are known exceptions, either an exception that occurred in the request pipeline(such as max retries or timeout reached, bad authentication, etc...) or Elasticsearch itself returned an error (could not parse the request, bad query, missing field, etc...). If it is an Elasticsearch error, the ServerError property on the response will contain the the actual error that was returned. The inner exception will always contain the root causing exception.

UnexpectedElasticsearchClientException: These are unknown exceptions, for instance a response from Elasticsearch not properly deserialized. These are usually bugs in the client and we encourage you to report them. This exception also inherits from ElasticsearchClientException so an additional catch block isn't necessary to handle but can be helpful in distinguishing between the two.

Runtime exceptions: These are CLR exceptions like ArgumentException, NullArgumentException etc. that are thrown when an API in the client is misused.

Breaking Changes

Even though a lot of work went into the interior, the exterior did not escape unscathed! On top of the many breaking changes that Elasticsearch 2.0 introduces, there are more then a few NEST 2.0 introduces.
We revalidated all the request and response domain objects against Elasticsearch 2.0.

This encapsulates all of the sort options properly and adheres stricter to the 1-to-1 mapping. NEST 1.x did also contain this full descriptor however the mix and matching
of the convenience methods of the parent meant that some fluent methods were additive whilst others overwrote what was previously set. With NEST 2.0, this discrepancy is gone!

This happens in more places e.g index settings and mappings.

Filtered query deprecation

With the removal of filters, NEST has added a special construct in its Query DSL to easily create a bool query with a filter clause

.Query(q=> +q.Term(p=>p.Name, "NEST"))

the + will cause the term query to be wrapped inside a bool query's filter clause

You can even combine + with !

.Query(q=> !+q.Term(p=>p.Name, "NEST"))

This will wrap the term query inside a bool filter and subsequently inside a bool must_not clause. This approach also works with the object initializer syntax (OIS)

!+new TermQuery {}

Attribute based mapping

The single ElasticPropertyAttribute has been broken up into individual attributes per property type.

Aside from a simpler and cleaner API, this allows each attribute to only reflect the options that are available for the particular type instead of exposing options that may not be relevant (as ElasticPropertyAttribute did).

Inferred types

Many places that only took a string or primitive type now take a more strongly typed object such as Id, Field, Fields, Index, Indices, Type, Types, DocumentPath<T>, etc. It's good to know that in most cases you can still pass a string or primitive type and it will be implicitly converted to the type where it makes sense.

If you are using C# 6 you can also statically import the Infer static class using using static Nest.Infer, allowing to write

so on and so forth. If you are using the fluent API using these infer methods is not required since the fluent API is strongly typed through lambda expressions, but they are another tool at your disposal nonetheless.

C# 6 support

NEST's codebase has been largely rewritten to take advantage of all the cool new c# features making almost all the fluent code one liners

DNX Support

The 2.0 release has been released with a dotnet5.1 version on nuget that can be used on both the Desktop and Core CLR runtimes of DNX rc1. We are actively tracking rc2, the .NET Commandline interface (dotnet-cli) and the new netstandard NuGet target framework, with a plan to release compatible packages once these hit the scene.

Feedback

We'd like to thank everyone who took the alpha and rc releases out for a spin and provided invaluable input while we incubated the 2.0 release. A special shoutout to Blake Niemyjski for sharing his screen with us so we could see firsthand where the pain points were during an upgrade from 1.x to 2.0.