by AkiRusProd · AI 工具 · ★ 52
long-term-memory-llm RAG-based LLM using long-term memory through vector database Description This repository enables the large language model to use long-term memory through a vector database (This method is called RAG (Retrieval Augmented Generation) — this is a technique that allows LLM to retrieve facts from an external database). The application is built with mistral-7b-instruct-v0.2.Q4KM.gguf (using LLAMAcpppython binding) and chromadb. User can ask in natural language to add information to db, find information from db or the Internet using guidance.
| Stars | 52 |
| Forks | 9 |
| Language | Python |
| Category | AI 工具 |
| Quality Score | 35.2/100 |
| Last Updated | 2024-04-09 |
| Created | 2023-09-19 |
| Platforms | python |
| Est. Tokens | ~19k |
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llm-agent is LLM using long-term memory through vector database. It is categorized as a AI 工具 with 52 GitHub stars.
llm-agent is primarily written in Python. It covers topics such as chromadb, gpt, gpt4all.
You can find installation instructions and usage details in the llm-agent GitHub repository at github.com/AkiRusProd/llm-agent. The project has 52 stars and 9 forks, indicating an active community.