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weaviate
recipes
Jupyter Notebook2023–2024
weaviate / recipes

recipes

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!

AI & Machine LearningData & DatabasesSearch & Information Retrieval
Explore on GitHubRead Hacker News discussions
5Distinct people
6Mentions
0Explicit recommenders
953GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month6 dated mentions

Observed mentions: 2023-07-25 15:22:00 UTC to 2024-04-04 21:27:48 UTC.

0122023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJul 20232023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsNov 20232023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsApr 2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC110
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC220
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC000
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC000
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC000
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC000
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC110
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC211

What Hacker News says

6 sources
stanbiryukov·Apr 4, 2024
I think of dspy as a programmatic way to guide LLMs with information, whether from context based on retrieval or from input and output pairs, rather than traditional low-rank fine-tuning. Their readme has a high-level introduction to using RAG with a user defined way to pass relevant context. I also found their link to Weaviate's notebooks, where dspy is used with a vector DB, helpful in understanding an end-to-end workflow: [1] https://github.com/weaviate/recipes/tree/main/integrations/d...
In Ask HN: Most efficient way to fine-tune an LLM in 2024?
CShorten·Apr 1, 2024Submission

Structured Outputs with DSPy

Unfortunately, Large Language Models will not consistently follow the instructions that you give them. This is a massive problem when you are building AI systems that require a particular type of output from the previous step to feed into the next one! For example, imagine you are building a blog post writing system that first takes a question and retrieved context to output a list of topics. These topics have to be formatted in a particular way, such as a comma-separated list or a JSON of Topic objects, such that the system can continue writing the blog post! I am SUPER excited to share the 4th video in my DSPy series, diving into 3 solutions to structuring outputs in DSPy programs: (1) TypedPredictors, (2) DSPy Assertions, and (3) Custom Guardrails with the DSPy programming model! TypedPredictors follow the line of thinking around JSON
Read the rest
mode and using Pydantic BaseModels to interface types and custom objects into a JSON template for LLMs. The output can then be validated to provide a more structured retry prompt to correct the output structure! DSPy Assertions are one of the core building blocks of DSPy, offering an interface to input a boolean-valued function and a retry prompt which is templated alongside the past output to retry the call to the LLM! Custom Guardrails with the DSPy Programming Model are one of the things I love the most about DSPy — we have unlimited flexibility to control these systems however we want. The video will also show you how to write custom guardrails and retry Signatures and discussion around using TypedPredictors for your Custom Guardrails and potentially feeding your Custom Guardrails into a DSPy Assertion. I had so much fun exploring this topic! Further seeing how well OpenAI’s GPT-4 and GPT-3.5-Turbo, Cohere’s Command R, and Mistral 7B hosted with Ollama perform with each Structured Output strategy! I also found monitoring structured output retries to be another fantastic application of Arize Phoenix! I hope you find the video useful! Video: https://www.youtube.com/watch?v=tVw3CwrN5-8 If interested, all the code examples for this DSPy series can be found here! - https://github.com/weaviate/recipes
1 points · Open discussion
eriicar·Aug 14, 2023Submission

Show HN: Efficient and secure management of data across multiple users

Weaviate released a native multi-tenancy feature as part of the v1.20 release. It is now easy to store data from millions of tenants on a single setup. This notebook shows you how to get started with this feature! To learn more about multi-tenancy, check out: Multi-Tenancy blog post: https://weaviate.io/blog/multi-tenancy-vector-search Documentation: https://weaviate.io/developers/weaviate/concepts/data#multi-...
2 points · Open discussion

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