Been a while since the last big release, here's v1.3.9 Flowise (BREAKING) API response Output Parser View Messages Unstructured Embedding Cache SearchAPI We have lots of first time contributors in this release, thanks to all of you! Now, let's dive deep
@flowiseai
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OSS Startup Featured in ROSS Q3 2023 Index
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Extremely grateful to be featured as top trending OSS startup in ROSS Q3 2023 index by @RunaCapital
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Collaboration avec Vectara pour améliorer les solutions IA
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Would love to work with Vectara team and see how we can improve that!
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Flowise 1.3.8 Release Update and Patch Notes
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As always, find more details from the patch note https://
github.com/FlowiseAI/Flow
ise/releases/tag/flowise1.3.8
… Happy hacking! -
Upstash Redis: HTTP REST Based Database for Conversations
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💬 Upstash Memory
— FlowiseAI (@FlowiseAI) 13 octobre 2023
Having trouble setting up your own Redis? @upstash, a HTTP/REST based Redis client can help with that.
Store the conversations in Upstash Redis server, separating between different users using session id. pic.twitter.com/MjdgXL2jEjUpstash Memory Having trouble setting up your own Redis? @upstash
, a HTTP/REST based Redis client can help with that. Store the conversations in Upstash Redis server, separating between different users using session id. -
Similarity Threshold Retriever for Vector Store Queries
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Similarity Threshold Retriever The "Top K" parameter allows you to specify the number of results to retrieve from the vector store. With the similarity threshold retriever, you can return all possible results to your question based on a minimum similarity percentage.
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S3 File Loader for RAG Pipeline Integration
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S3 File Loader Load your file from AWS S3 as part of your RAG pipeline
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Elasticsearch Relevance Engine Now Supports Vector Embeddings
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🔎 Elasticsearch Vector Store@elastic is a distributed, RESTful search and analytics engine. With their new Elasticsearch Relevance Engine, you can now use to store vector embeddings pic.twitter.com/WDfnUrzeVS
— FlowiseAI (@FlowiseAI) 13 octobre 2023Elasticsearch Vector Store @elastic is a distributed, RESTful search and analytics engine. With their new Elasticsearch Relevance Engine, you can now use to store vector embeddings
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LLM Cache: Reduce API Costs with Response Caching
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LLM Cache Save on API costs by caching the LLM response. Instead of making new calls to LLM, retrieve the answer from cache when the same question is asked.
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Ollama Now Available in Flowise for Local LLM Deployment
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🦙 Ollama
— FlowiseAI (@FlowiseAI) 13 octobre 2023
Highly requested @Ollama_ai is now available to be used in Flowise!
Ollama let your run open source LLM like Llama2, Mistral locally, including embeddings too pic.twitter.com/imVx5dCoEjOllama Highly requested @Ollama_ai is now available to be used in Flowise! Ollama let your run open source LLM like Llama2, Mistral locally, including embeddings too