httpx VS pydantic

Using the createSession, deleteSession endpoints and httpx, we can create a session for API interaction.

I've mostly ditched requests in favour of httpx these days. https://www.python-httpx.org

Now that your environment is set up, you’re going to need to install the HTTPX library for making requests, both asynchronous and synchronous which we will compare. Install this with the following command after activating your virtual environment:

Now time to code the implementation, as I wanted to both read from and write to ffmpeg concurrently, so this is going to be an asyncio application. The http client library we are using this time is httpx, which has a method to fetch download in smaller batches:

The concept of a Client in httpx is analogous to a Session in requests. However, httpx.Client is more powerful and efficient. You can read the article from the httpx documentation, to learn more about httpx.Client.

Let's look at a simple code example. This will work for requests, httpx, and aiohttp with a clean installation and no extensions.

For reference, it's a butterfly, not a moth.

Source: https://github.com/encode/httpx/issues/834

Very cool, first I'm hearing of httpx https://www.python-httpx.org/

I think most people would start with trying out requests or something for this kind of work, I'm guessing that didn't work out? You've got a star from me.

To access 10 different commands at the same time, that is tricky but definitely doable.

First thing that comes to mind, you can use aliases.

To keep it simple, lets use 3 examples instead of 10: harlequin (this project), pgcli (https://www.pgcli.com/) and httpx (https://www.python-httpx.org/)

Setup a main home for all your venvs:

    cd ~

Pydantic is bundled with FastAPI and is excellent for modelling, validating, and serialising API responses.

Pydantic Settings is a python library that extends 🚀 Pydantic for dealing with settings management.

And here is where FastAPI ✨ shines: It leverages Pydantic powerful data validation to offer out of the box JSON Schema and OpenAPI specs via Swagger and Redoc.

If you carefully think about it, the system that extracts raw text from the PDF needs to both detect and render PDF form elements like checkboxes and radiobuttons in a way that LLMs can understand. In this example, we’ll use LLMWhisperer to extract PDF raw text representing checkboxes and radiobuttons. You can use LLMWhisperer completely free for processing up to 100 pages per day. As for structuring the output from LLMWhisperer, we’ll use GPT3.5-Turbo and we’ll use Langchain and Pydantic to help make our job easy.

Pydantic: Use Pydantic to declare your data model. This output parser allows users to specify an arbitrary Pydantic Model and query LLMs for outputs that conform to that schema.

1. Pydantic: Ultimate Data Validation

I'm really surprised ijl got angry that his mail was quoted, it looks innocent enough to me.

For reference it's been edited out here: https://github.com/pydantic/pydantic/issues/589

But github shows edits, so the edit is meaningless for privacy. Here's the original mail (yes, I'm blatantly ignoring his request to not publish this, I'm just this evil.)

    I've looked into replacing ujson in pydantic with orjson

First, note the method prefix_allowed_tokens_fn. This method applies a Pydantic model to constrain/guide how the LLM generates tokens. Next, see how that constrain can be applied to txtai's LLM pipeline.

utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses