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Integrations page #2254

Merged
merged 45 commits into from
Sep 3, 2024
Merged

Integrations page #2254

merged 45 commits into from
Sep 3, 2024

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erika-cardenas
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@erika-cardenas erika-cardenas commented Jun 28, 2024

Feature branch to gather changes for an integrations section

Type of change:

  • Documentation updates (non-breaking change to fix/update documentation)

How Has This Been Tested?

  • Github action – automated build completed without errors
  • Local build - the site works as expected when running yarn start

@daveatweaviate
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Hi @erika-cardenas is this a feature branch to collect changes for integration docs?

@daveatweaviate daveatweaviate marked this pull request as draft July 2, 2024 20:25
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staging

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Hi @erika-cardenas a couple of thoughts

  • I added a link to staging in the comments
  • The branch needs to be refreshed. It's easier to do that locally. I'm not sure if you have other changes coming so I haven't refreshed it.
  • The Data Platforms page shows up in the ToC twice. I think this page is in the wrong directory: /developers/integrations/data-platforms/confluent-cloud
  • These pages are missing content. Consider adding an underscore to the file names to hide them if you need to publish the rest before you have content.
/developers/integrations/data-pipeline/firecrawl
/developers/integrations/data-pipeline/spark
developers/integrations/data-pipeline/unstructured

Similarly, it's probably a good idea to comment out the observability section, llamaindex semantic kernel, and langchain

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Hey @daveatweaviate,
I'm merging the changes from the smaller branches now!

@erika-cardenas erika-cardenas marked this pull request as ready for review August 29, 2024 13:30
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@erika-cardenas Here are some comments on the operations sections.

I'll send LLM frameworks next

developers/integrations/operations/index.md Outdated Show resolved Hide resolved
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Thanks @erika-cardenas

Data platforms next (maybe that's not new?)

developers/integrations/llm-frameworks/index.md Outdated Show resolved Hide resolved
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developers/integrations/llm-frameworks/composio/index.md Outdated Show resolved Hide resolved
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@erika-cardenas Here's the next one.

I think we already did the compute infrastructure and cloud hyperscalers. Let me know if there are any changes I should look at there.

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Thank you so much @daveatweaviate! Just made the final edits. 🙂

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@erika-cardenas thanks for all the updates, this is a big project!

I fixed the merge conflict and made a couple of tiny tweaks.

This LGTM - you can merge whenever you want, or else I can merge it when we get back on Tuesday. Just me know.

It allows you to easily crawl and extract clean, structured data from websites. It is an API service that converts a URL into clean markdown or structured data.

## Firecrawl and Weaviate
Firecrawl handles the complexities of web scraping like proxies, caching, rate limits, and dynamic content. It produces markdown or JSON formatted output that is ready to be ingested into vector databases like Weaviate.
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Suggested change
Firecrawl handles the complexities of web scraping like proxies, caching, rate limits, and dynamic content. It produces markdown or JSON formatted output that is ready to be ingested into vector databases like Weaviate.
Firecrawl handles the complexities of web scraping like proxies, caching, rate limits, and dynamic content. It produces markdown or JSON formatted output that is ready to be ingested into vector databases like Weaviate.

## Spark and Weaviate
The Spark connector enables you to easily ingest data from Spark data structures into Weaviate.

Learn more about the connector in the Weaviate[ Spark connector repository](https://github.com/weaviate/spark-connector).
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Suggested change
Learn more about the connector in the Weaviate[ Spark connector repository](https://github.com/weaviate/spark-connector).
Learn more about the connector in the Weaviate [Spark connector repository](https://github.com/weaviate/spark-connector).

[Modal](https://modal.com/) provides a serverless platform that has on-demand access to GPUs and a custom high-performance container runtime. Use Modal to easily deploy and automatically scale high-performance applications.

## Modal and Weaviate
Weaviate leverages Modal's serverless infrastructure for fast embedding generation and for fast generative model calls.
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The words "for fast" are repetitive.

Suggested change
Weaviate leverages Modal's serverless infrastructure for fast embedding generation and for fast generative model calls.
Weaviate leverages Modal's serverless infrastructure for fast embedding generation and quick generative model calls.

## Context Data and Weaviate
Weaviate is a [target connection](https://context-data.gitbook.io/context-data-1/adding-target-connections#add-a-weaviate-target-connection) in Context Data.

To connect to ContextData, open the console and input your Weaviate instance's URL and authentication credentials when prompted.
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To connect to ContextData, open the console and input your Weaviate instance's URL and authentication credentials when prompted.
To connect to Context Data, open the console and input your Weaviate instance's URL and authentication credentials when prompted.

@eddumelendez
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eddumelendez commented Sep 3, 2024

Hi, sorry for hijacking the PR. I'm really glad to see this coming as I raised an issue #1715 some time ago. It would be great to see the libraries listed there in this section. I can also contribute

@erika-cardenas
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Thank you @network-charles for looking this over!

@eddumelendez Your excitement for this makes me super happy! I'm merging this PR into main soon; I hope you like the format and content.

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It’s a pleasure, @erika-cardenas.

@erika-cardenas erika-cardenas merged commit 59f97a8 into main Sep 3, 2024
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@network-charles thank you for your suggestions!

We'd like to give you credit for the suggestions. In order to include them, we need to verify that you have accepted our Contributor license agreement

Would you open a new PR against our main branch for your suggestions? If you accept the Contributor license, please say so in the PR comments.

Thanks!

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5 participants