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#Json query github python code

For CloudQuery this is important as it makes it much easier to develop source or destination plugins in different languages.

Cross-language with extensive libraries for different languages - The format is defined via flatbuffers in such way that you can parse it in any language and already has extensive support in C/C++, C#, Go, Java, JavaScript, Julia, Matlab, Python, R, Ruby and Rust (at the time of writing).

Apache Arrow defines a language-independent columnar format for flat and hierarchical data, and brings the following advantages: Also, performance-wise, lots of the time spent in an ELT process is around converting data from one format to another, so we wanted to take a step back and see if we can avoid this famous XKCD (by building yet another format): For example, in database to database replication, we needed to support many more types, including nested types. This served us well, but we started to hit limitations in various use-cases. Why Arrow?īefore Arrow, we used our own type system that supported more than 14 types. In Arrow terminology, these are a schema and a record batch. So to recap, the source plugin sends mainly two things to a destination: 1) the schema 2) the records that fit the defined schema. The destination plugin can then easily create the schema for its database and transform the incoming data to the destination types. Source plugins extract information from APIs in the most performant way possible, defining a schema and then transforming the result from the API (JSON or any other format) to a well-defined type system.

#Json query github python code

This is crucial to allowing the addition of new destinations and updating old destinations without requiring updates to source plugin code (which otherwise would introduce an unmaintainable architecture). Sources and destinations are decoupled and communicate via gRPC. gpt-json will attempt to fix these values. The most common is generating True instead of true. Boolean variables: GPT will sometimes confuse valid JSON boolean values with the boolean tokens that are used in other languages.In this case, gpt-json will attempt to fix the truncated payload by recreating the JSON object and closing it. There are many cases where this behavior is actually okay for production applications - for instance, if you list 100 generated strings, it's sometimes okay for you to take the 70 that actually rendered. This results in a broken JSON payload where much of the data is valid but the JSON object is not closed, which is not valid syntax. Response truncation: Since GPT is not internally aware of its response length limit, JSON payloads will sometimes exhaust the available token space.GPT (especially GPT-4) is relatively good at formatting responses at JSON, but it's not perfect. (default: 0) - If you're using auto_trim, configures the max amount of tokens to allow in the model's response.Īny other parameters you want to pass to the underlying GPT class, will just be a passthrough.

(default: False) - If your input prompt is too long, perhaps because of dynamic injected content, will automatically truncate the text to create enough room for the model's response. You can also pass a string value if you want to use another more specific architecture. (default: GPTModelVersion.GPT_4) - For convenience we provide the currently supported GPT model versions in the GPTModelVersion enum. The GPTJSON class supports other configuration parameters at initialization. Quotes: list = Field( description = "Max quantity ,