I'm using llama_index on chroma ,but there is still a question. According to the example:[Chroma - LlamaIndex 🦙 0.7.22 (gpt-index.readthedocs.io)](https://gpt-index.readthedocs.io/en/stable/examples/vector_stores/ChromaIndexDemo.html#basic-example-using-the-docker-container) Normally, w...
吴恩达《使用LlamaIndex构建主动式RAG|Building Agentic RAG with LlamaIndex》中英字幕 吴恩达《深入模型量化|Quantization in Depth》中英字幕 【英文可关,可拖拽】吴恩达《视觉模型的提示工程|Prompt Engineering for Vision Models》中英字幕 吴恩达《Mistral入门|Getting Started with Mistral》中英字幕 吴恩达《Hugging Fac...
Edit: Refer to below provided way Author Exactly as above! You can use any llm integration from llama-index. Just make sure you install itpip install llama-index-llms-openai but note that open-source LLMs are still quite behind in terms of agentic reasoning. I would recommend keeping thing...
LlamaIndexis a powerful tool to implement the“Retrieval Augmented Generation” (RAG)concept in practical Python code. If you want to become anexponential Python developerwho wants to leverage large language models (aka.Alien Technology) to 10x your coding productivity, you’ve come to the right ...
And now you have this central document format that you can then use with LlamaIndex. The next step is our data indexes and query interface. And we'll go into a bit more detail here in just a little bit. But fundamentally, our data indexes help to abstract away some common boilerplate ...
LlamaIndex uses OpenAI's text-embedding model to vectorize the input data by default. If you don't want to regenerate the embedding data every time, you need to save the data to a vector database. For example, use the open-source Chroma vector database, because it saves data on the lo...
How to Build a RAG System With LlamaIndex, OpenAI, and MongoDB Follow along with these by creating a free MongoDB Atlas cluster and reach out to us in our Generative AI community forums if you have any questions. Top Comments in Forums There are no comments on this article yet. Start ...
We will use LangChain to create a sample RAG application and the RAGAS framework for evaluation. RAGAS is open-source, has out-of-the-box support for all the above metrics, supports custom evaluation prompts, and has integrations with frameworks such as LangChain, LlamaIndex, and observability...
Compatible with Langchain and LlamaIndex, with more tool integrations coming soon. Open source: Licensed underApache 2.0. Speed and simplicity: Focuses on simplicity and speed, designed to make analysis and retrieval efficient while being intuitive to use. ...
We use LlamaIndex to deploy and build our LLM application for this tutorial. You can build a similar application with LangChain by taking the Developing LLM Applications with LangChain short course. 3. Creating the Dockerfile In your project, create a Dockerfile to package the application script...