Serp API Loader Previously you can use Serp as tool for agent, now you can use Serp as a document loader! Thanks @fantastigenius for the contribution!
AI
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Postgres Vector Store Using TypeORM and pgvector Extension
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Postgres Vector Store Postgres Vector Store is using TypeORM with the pgvector extension – https://
github.com/pgvector/pgvec
tor
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Milvus Vector Store Integration Brings Trillion-Scale Indexing
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🍃 Milvus Vector Store@milvusio is specifically designed to handle queries over input vectors, and capable of indexing vectors on a trillion scale.
— FlowiseAI (@FlowiseAI) 23 août 2023
Excited to have them on Flowise! pic.twitter.com/6chKdBEbLdMilvus Vector Store @milvusio is specifically designed to handle queries over input vectors, and capable of indexing vectors on a trillion scale. Excited to have them on Flowise!
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Zep Vector Store Now Available for Flowise Integration
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Zep Vector Store Many of you guys have been using @zep_ai as memory node, finally you can now use Zep for vector store as well! Here's a Render yaml file that spins up both Zep + Flowise by @derekcheungsa https://
github.com/derekcheungsa/
flowise-zep-deploy/blob/master/render.yaml
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Vector Store Document Loader Integration for Prompt Enhancement
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VectorDB Document Loader Many of you have asked: how to use retrieved documents from vector store into the prompt? With this new integration, you can now do so! Even with the flexibility of specifying a minimum score for documents to be included.
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ConversationalQAChain Templates LangChain Marketplace
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Chat history variable Use previous chat history inside the prompt. You can now create your own ConversationalQAChain from scratch using LLMChain + PromptTemplate, and more customization! A template is readily available in the marketplace.
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Flowise v1.3.4 Release: VectorDB and SQLChain Enhancements
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It's release time again Flowise v1.3.4 is now freshly baked with: VectorDB Document Loader Chat history variable 3 new vector stores: Milvus, PgVector, Zep SQLChain now supports MySQL, MsSQL, PostgreSQL Serp API Loader
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LoRA as Cost-Effective Solution for Multi-Customer LLM Deployment
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I believe this would be easiest and most cost-effective because they won't have to store a new 175B parameter LLM for each customer then. With LoRA, you can use the same base LLM but only need to store the LoRA weights.
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LLMate Launches Solution for Structured Data QA
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QA over structured data is hard
— Harrison Chase (@hwchase17) 23 août 2023
Excited to so LLMate working with @manuelsoria_ and @fpingham to bring a solution to market
We've been chatting with them for a while – they've got some good ideas https://t.co/4ZLpFG4StDQA over structured data is hard Excited to so LLMate working with @manuelsoria_ and @fpingham to bring a solution to market We've been chatting with them for a while – they've got some good ideas
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New Short Course on Fine-tuning Large Language Models
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New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs – that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, @realSharonZhou, CEO… pic.twitter.com/kAocaAgX0k
— Andrew Ng (@AndrewYNg) 23 août 2023New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs – that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, @realSharonZhou
, CEO