3/3 – Upstash Vector
– TogetherAI Chat & Embeddings For more, check out our latest release note –
@flowiseai
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Upstash Vector and TogetherAI integration for AI applications
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Flowise v1.7 Introduces Document Stores for RAG Data Processing
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Flowise major release v1.7 🎉
— FlowiseAI (@FlowiseAI) 7 mai 2024
Introducing – Document Stores.
Ensuring clean data pre-processing is vital when building RAG. With Document Stores, users can:
🔄 Reuse chunks for upserting
🧩 Splitter playground
📝 Edit, update & remove redundant data
Other goodies in v1.7: pic.twitter.com/U9jPuInmIjFlowise major release v1.7 Introducing – Document Stores. Ensuring clean data pre-processing is vital when building RAG. With Document Stores, users can: Reuse chunks for upserting Splitter playground Edit, update & remove redundant data Other goodies in v1.7:
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Google Deprecates PaLM Model, Suggests Vertex and Gemini
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PaLM is deprecated by Google, try changing to Vertex or Gemini model
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Record Manager: Keeping RAG Indexes Updated and Optimized
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Keeping your indexes up to date with latest changes in your documents is crucial in building a RAG.
— FlowiseAI (@FlowiseAI) 29 avril 2024
And Record Manager is here to help with:
🗄️Prevent duplicated contents
💰Save cost on embeddings
🧹Cleanup outdated content@leonvz did a great job of explaining it, watch it👇 https://t.co/2gsSFJHTvk pic.twitter.com/zUynk70O6vKeeping your indexes up to date with latest changes in your documents is crucial in building a RAG. And Record Manager is here to help with:
Prevent duplicated contents
Save cost on embeddings
Cleanup outdated content @leonvz did a great job of explaining it, watch it -
LLM Updates: Llama 3, Cohere, VoyageAI Embeddings Released
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6/ Enhancement More improvements on other nodes from the community! – UnstructuredIO update
– Llama 3, Cohere on AWS Bedrock
– VoyageAI Embedding
– Custom Dimension OpenAI Embedding
– Confluence PAT support
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Flowise Native Buffer Memory Support with Session ID
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3/ Buffer Memory SessionId If you don't want additional dependency of using external database to store/retrieve messages.. We now have native support for buffer memory nodes! Messages will be stored/retrieved from Flowise database with session ID. https://
docs.flowiseai.com/integrations/l
angchain/memory#separate-conversations-for-multiple-users
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Flowise Migrates File Storage to Blob Storage and S3
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4/Storage Files that are uploaded on Flowise used to store as base64 string on a local file path. This caused limitation and huge burden on database. We've migrated the logic to support blob storage. You can specify a folder path or use S3:
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OpenAI Assistants V2 API: Streaming, File Search, Vector Store
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1/🤖 OpenAI Assistant V2
— FlowiseAI (@FlowiseAI) 26 avril 2024
Assistants created on Flowise is now using V2 API, meaning its now supporting:
✅streaming
✅new file_search tool
✅vector store pic.twitter.com/5tqYEQuh6E1/ OpenAI Assistant V2 Assistants created on Flowise is now using V2 API, meaning its now supporting:
streaming
new file_search tool
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Flowise Enables Dynamic Model Selection via JSON Configuration
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2/ Models {JSON} You can now specify the model you want to use in a JSON file. Previously users have to wait till next release to use latest model. Now, it's automatically fetched from a specified path: https://
raw.githubusercontent.com/FlowiseAI/Flow
ise/main/packages/components/models.json
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Flowise v1.6.6 launches Tool Agent for LLM function calling
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With more LLMs supporting function calling, Flowise v1.6.6 is now updated with a new agent: Tool Agent This is a generalized tool calling agent by @langchain that is compatible with Mistral, Gemini, Claude, OpenAI, Groq. Other updates on v1.6.6