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Andrew AgostiniandChris Chinchilla
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* docs: remove link to token-transformer since that repo has since been refactored and no particular file contains the code originally linked to * docs: update Redis link in rate limiting examples to point to the correct URL * docs: update Supabase URLs to current magic link docs * docs: update README for postgres-on-the-edge function to remove polyscale line (service no longer exists) and add resource links to relevant deno blog post * docs: update Open Graph image generation examples links to the new documentation URL * docs: update README with new Quickstart: Swift guide URL * docs: update Supabase Vector links to reflect new URL * docs: add two new link suggestions the replace the current broken one. Choose one. * docs: update documentation links for Postgres Language Server to reflect new URL * docs: update event trigger matrix documentation link to compensate for fact that 'current' verb does not work for some of their links * Update apps/docs/content/troubleshooting/supabase-grafana-memory-charts.mdx Co-authored-by: Chris Chinchilla <chris@chrischinchilla.com> --------- Co-authored-by: Chris Chinchilla <chris@chrischinchilla.com>
79 lines
4.9 KiB
Plaintext
79 lines
4.9 KiB
Plaintext
---
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name: Firecrawl
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title: Firecrawl switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.
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# Use meta_title to add a custom meta title. Otherwise it defaults to '{name} | Supabase Customer Stories':
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meta_title: Firecrawl switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.
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description: How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.
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# Use meta_description to add a custom meta description. Otherwise it defaults to {description}:
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meta_description: How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.
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author: paul_copplestone
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author_title: Supabase
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author_url: https://github.com/kiwicopple
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author_image_url: https://avatars2.githubusercontent.com/u/10214025?s=400&u=c6775be2ae667e2acae3ccd347fed62bb3f5b3e7&v=4
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logo: /images/customers/logos/firecrawl.png
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logo_inverse: /images/customers/logos/light/firecrawl.png
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og_image: /images/customers/og/firecrawl.jpg
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tags:
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- supabase
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date: '2023-05-05'
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company_url: 'https://firecrawl.dev/'
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stats:
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[
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{ stat: '00,000', label: Example stat },
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{ stat: '00,000', label: Example stat },
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{ stat: '00,000', label: Example stat },
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]
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misc: [{ label: 'Backed by', text: 'Y Combinator' }]
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about: Firecrawl is Chat Powered Search for Documentation.
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# "healthcare" | "fintech" | "ecommerce" | "education" | "gaming" | "media" | "real-estate" | "saas" | "social" | "analytics" | "ai" | "developer-tools"
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industry: ['ai', 'saas', 'developer-tools']
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# "startup" | "enterprise" | "indie_dev"
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company_size: 'startup'
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# "Asia" | "Europe" | "North America" | "South America" | "Africa" | "Oceania"
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region: 'North America'
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# "database" | "auth" | "storage" | "realtime" | "functions" | "vector"
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supabase_products: ['database', 'vector']
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---
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[Firecrawl](http://firecrawl.dev/) provides a chat-powered search engine for technical documentation. Their AI-powered search tool makes it easier for developers and other technical users to find relevant information in complex documentation. Users can simply ask questions in natural language, and the tool returns the most relevant answers. Firecrawl's search engine also provides detailed analytics, which helps teams identify knowledge gaps and areas for improvement in their documentation. Firecrawl has integrated with some of the largest open source projects in the space such as LangChain and LlamaIndex.
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## The Challenge
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Firecrawl was experiencing tremendous success, growing Weekly Active Users by nearly 300% since March. They needed a tool to store and search through large amounts of vector data to improve the efficiency and accuracy of their similarity search operations. They tried Faiss, Weaviate, and Pinecone, but found them to be expensive and not very intuitive, especially when it came to storing metadata along with the vectors.
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## Why they chose Supabase
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Firecrawl lear that Supabase supports [pgvector](https://supabase.com/docs/guides/database/extensions/pgvector) and found it to be a simple and cost-effective solution. They were impressed with the open source nature of Supabase, as well as its ability to store metadata alongside the vectors. They also appreciated the intuitive interface and ease of use.
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<Quote img="caleb-peffer.jpg" caption="Caleb Peffer - CEO, Firecrawl">
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We tried other vector databases - we tried Faiss, we tried Weaviate, we tried Pinecone. We found
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them to be incredibly expensive and not very intuitive. If you're just doing vector search they're
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great, but if you need to store a bunch of metadata that becomes a huge pain.
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</Quote>
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## What They Built
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Using [Supabase Vector](https://supabase.com/modules/vector), Firecrawl was able to build a more efficient and accurate search function for their AI chatbot. By storing vector data alongside metadata in Supabase, Firecrawl was able to quickly and easily search through their customers documentation to find the most relevant responses to queries. They found that Supabase's solution was just as performant as dedicated vector databases, but without the high cost.
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## The Results
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Thanks to Supabase Vector, Firecrawl was able to significantly improve the efficiency and accuracy of their Chat Powered Search for Documentation. They were able to build faster and more cost-effectively using Supabase's open source stack.
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<Quote img="caleb-peffer.jpg" caption="Caleb Peffer - CEO, Firecrawl">
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We looked at the alternatives and chose Supabase because it's open source, it's simpler, and, for
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all the ways we need to use it, Supabase has been just as performant - if not more performant -
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than the other vector databases.
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</Quote>
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To learn more about how Supabase Vector can help you store vector embeddings at scale and build AI apps with ease, [reach out to us](https://forms.supabase.com/enterprise).
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## Tech stack
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- React
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- Vercel
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- Next.js
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- Express
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- Supabase
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