rag-golang-postgresql-langchain
langchaingo
| rag-golang-postgresql-langchain | langchaingo | |
|---|---|---|
| 1 | 9 | |
| 6 | 3,396 | |
| - | - | |
| 4.7 | 9.7 | |
| about 1 month ago | 4 days ago | |
| Go | Go | |
| MIT No Attribution | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rag-golang-postgresql-langchain
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How to use Retrieval Augmented Generation (RAG) for Go applications
git clone https://github.com/build-on-aws/rag-golang-postgresql-langchain cd rag-golang-postgresql-langchain
langchaingo
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How to use Retrieval Augmented Generation (RAG) for Go applications
Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!
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Build a Serverless GenAI solution with Lambda, DynamoDB, LangChain and Amazon Bedrock
This use-case here is a similar one - a chat application. I will switch back to implementing things in Go using langchaingo (I used Python for the previous one) and continue to use Amazon Bedrock. But there are few unique things you can explore in this blog post:
- LangChain for Go, the easiest way to write LLM-based programs in Go
- Langchaingo – LangChain in Idiomatic Go
- Agency: Pure Go LangChain Alternative
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Building LangChain applications with Amazon Bedrock and Go - An introduction
langchaingo is the LangChain implementation for the Go programming language. This blog post covers how to extend langchaingo to use foundation model from Amazon Bedrock.
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Zep: A long-term memory store for LLM apps, written in Go
Langchain Go is being actively developed https://github.com/tmc/langchaingo
What are some alternatives?
amazon-bedrock-go-sdk-examples - AWS Go SDK examples for Amazon Bedrock
yao - :rocket: A performance app engine to create web services and applications in minutes.Suitable for AI, IoT, Industrial Internet, Connected Vehicles, DevOps, Energy, Finance and many other use-cases.
amazon-bedrock-langchain-go - langchaingo wrapper implementations for Amazon Bedrock
langchain - 🦜🔗 Build context-aware reasoning applications
langchaingo-dynamodb-chat-history - Chat history (memory) implementation for LangChain based on DynamoDB
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
zep - Zep: Long-Term Memory for AI Assistants.
TaskEaseGPT - (WIP) A user-friendly, AI-powered task manager emphasizing efficient work over planning. Streamlines workflow with intelligent task generation & execution. Boost your productivity today!
langchaingo-amazon-bedrock-llm - Amazon Bedrock extension for langchaingo
casdoor - An open-source UI-first Identity and Access Management (IAM) / Single-Sign-On (SSO) platform with web UI supporting OAuth 2.0, OIDC, SAML, CAS, LDAP, SCIM, WebAuthn, TOTP, MFA and RADIUS [Moved to: https://github.com/casdoor/casdoor]
chatbot-bedrock-dynamodb-lambda-langchain
Gin - Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.