canopy VS httpx

Pinecone: A scalable vector database service that facilitates efficient similarity search in high-dimensional spaces. Ideal for building real-time applications in AI, such as personalized recommendation engines and content-based retrieval systems.

In this post we’ll build a Python script that uses StripeDocs Reader, a loader on LlamaIndex, that creates vector embeddings of Stripe's documentation in Pinecone. This allows a user to ask questions about Stripe Docs to an LLM, in this case OpenAI, and receive a generated response.

Pinecone is a managed vector database service that simplifies the process of building and scaling vector search applications. It offers a simple API for embedding vector search into applications, providing accurate, scalable similarity search with minimal setup and maintenance.

In case of AIMD, I am doing this all in-memory, but you could also do this in a database (e.g. Pinecone). It all depends on how much data you have and how much compute you have available.

A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving to meet the new challenge.

In the dynamic world of web development, Python has emerged as a dominant force, especially in backend development – the primary focus of this blog post. Although it's worth mentioning that there are ongoing efforts to use Python for the frontend as well, like Reflex (previously known as Pynecone, they presumably had to change their name because of Pinecone vector database), which even garnered support from Y Combinator. Samuel Colvin (creator of Pydantic) is also working on FastUI (he literally just released the first version in December 2023).

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 ~

There are already some implementations for Python HTTP clients. One of them is aiometer. But it's not suitable for my use case. Since httpx already has the internal pool, it would be better to reuse the design.

Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.

We can use the async HTTP client provided by httpx, a fully featured HTTP client for Python with an API broadly compatible with requests, so it can be used in pretty much the same way in most cases.

httpx in place of requests library

The main value of Requests is that it provided an abstract interface on top of HTTP, which was designed well-enough to become a standard. But today it has fallen way behind in its field, and there are much better alternatives such as HTTPX [0].

[0] https://www.python-httpx.org/

HTTPX is a popular Python library that provides an asynchronous HTTP client, and it can be beneficial for enabling async support in Django. While Django itself does not require HTTPX for async support, using HTTPX in combination with Django's async views can bring several advantages:

The underlying library for both sync and async is httpx (https://www.python-httpx.org/) which may be limited from the HTTP Client perspective but it may be possible to add rate limiting at a Session level.