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arize-phoenix
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arize-phoenix
@ArizePhoenix
Open-Source AI Observability and Evaluation
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github.com/Arize-ai/phoen…
Joined February 2023
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  • user avatar
    arize-phoenix
    @ArizePhoenix
    Jul 26, 2023
    If you are building an LLM application that uses RAG , poor retrieval can be detrimental to its UX. Phoenix now supports passing in your knowledge base as a corpus dataset so that you can inspect how your retrieval system is querying for relevant documents from your vector store.
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    arize-phoenix
    @ArizePhoenix
    Feb 16, 2024
    Phoenix now supports DSPy! 🎉 With DSPy, you can declare the architecture of your LLM app and automatically generate prompts and fine-tune models to optimize for your specific task. Try out the notebook: colab.research.google.com/github/Arize-a… @lateinteraction #LLMs #AI
    Colab logo
    colab.research.google.com
    dspy_tracing_tutorial.ipynb
    Run, share, and edit Python notebooks
    13K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Jul 28, 2023
    Webinar with @llama_index! @jerryjliu0 shows us the critical components across the data lifecycle to power RAG: ingest, index, query 🪄 And with the new OpenInferenceCallback, you can easily troubleshoot the search and retrieval with @ArizePhoenix! 🔮 youtube.com/watch?v=hbQYDp…
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  • user avatar
    arize-phoenix
    @ArizePhoenix
    Feb 6, 2024
    Phoenix now supports DSPy! What real word use-cases would you like to see?
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    Omar Khattab
    @lateinteraction
    Feb 6, 2024
    Lots of requests for richer observability in DSPy. In March, @mikeldking & I are holding a DSPy <> @arizeai meetup in SF to show you how to do that w @ArizePhoenix-DSPy integration. Video by @axiomofjoy. Good chance to show something cool with DSPy. What would you like to see?
    8.8K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Oct 2, 2023
    LLM frameworks are game-changing, but the resulting abstractions can be hard to debug. Phoenix now enables you to trace through the execution of your LLM application so you can understand its internals and troubleshoot problems related to things like retrieval and tool execution!
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  • user avatar
    arize-phoenix
    @ArizePhoenix
    Jun 21, 2023
    📚@langchain @pinecone @arizeai Workshop on troubleshooting search and retrieval - the cornerstone of connecting LLMs to private data are VectorDBs and agent frameworks. pinecone-io.zoom.us/webinar/regist…
    Building and Analyzing LLM search and retrieval
    14K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Aug 10, 2023
    🙏 @hwchase17 @UnstructuredIO @trychroma on the future of RAG. From time sensitive retrieval to productionizing large vector stores, nothing was off the table. 💡 Try all the new approaches! If you are using RAG, you are on the cutting edge.
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    LangChain "Advanced Retrieval" Webinar
    From youtube.com
    5K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Mar 16, 2024
    Replying to @CShorten30
    DSPy 🤝 @@weaviate_io 🤝 Us! For anyone interested Phoenix is fully open source and fully private 🔐 Your data is your own! Great work @CShorten30 ! Let us know if there is more visibility you need! github.com/Arize-ai/phoen…
    2K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Jul 28, 2023
    Replying to @ArizePhoenix @llama_index and @jerryjliu0
    Slides: …083050.fs1.hubspotusercontent-na1.net/hubfs/20083050… Colab: colab.research.google.com/github/Arize-a…
    2.2K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Nov 21, 2023
    Evaluation 👏 Driven 👏 Development 👏 A critical concept workshop if you are building or tuning a RAG pipeline. Mark your calendars! 📅
    user avatar
    LlamaIndex 🦙
    @llama_index
    Nov 21, 2023
    Before you try advanced retrieval techniques (query decomposition, reranking, hierarchical chunking, etc.) to improve your RAG pipeline, you should implement evals. What are the different types of evals? ✅ E2E evals (generated responses) ✅ Retrieval evals (retrieved chunks)
    5.5K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Mar 24, 2024
    Excited to see what everyone at the @MistralAI hackathon with @cerebral_valley 's been cooking up 👩‍🍳 If you need to debug your mistral calls, traces might come in super handy . It's as simple as a few lines of code in your app 👇 github.com/Arize-ai/openi…
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  • user avatar
    arize-phoenix
    @ArizePhoenix
    Feb 20, 2024
    Phoenix is now integrated with Ragas to help in evaluating and analyzing your RAG pipeline. 🤩
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  • user avatar
    arize-phoenix
    @ArizePhoenix
    May 1, 2023
    Phoenix is public! Unlock new embedding troubleshooting workflows right in your notebook. Maintained by the #MLOps team at @arizeai Designed for rapid iteration on your #LLMs #ComputerVision and #NLP models.
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    GitHub - Arize-ai/phoenix: AI Observability & Evaluation
    From github.com
    2.1K
  • user avatar
    arize-phoenix
    @ArizePhoenix
    Dec 12, 2023
    Huge S/O to @traviscline who helped add audio embeddings support to @ArizePhoenix and for winning the @AGIHouseSF hackathon! Being able to explore your samples using UMAP and HDBSCAN is not just fun, it’s crazy useful for sample discovery 🎧🎶🎼🥁🎸🎹🎺🎻
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