Recomendation to run llama-index examples for a newbie

[MarcSunyer]

I started to use llama-index to build a RAG app but I found many problems with import and from sentences through diferent examples. I'd like a recomendation on which version of python libraries to install, in order to avoid the "not found" module according to the most recent documentation and examples given in the llama-index web (https://docs.llamaindex.ai/) . Thank you.

[dosubot[bot]]

Hey @MarcSunyer! I'm here to help you with any bugs, questions, or contributions you might have regarding llama-index. Let's get that issue sorted out for you!

To avoid "not found" module errors when using LlamaIndex for a RAG app, you should install the following Python libraries according to the most recent documentation:

• llama-index-core>=0.12.0
• llama-index-indices-managed-vectara>=0.4.0
• llama-index-embeddings-huggingface
• llama-index-llms-openai
• llama-index-readers-file
• llama-index-vector-stores-chroma
• llama-index-vector-stores-qdrant
• google-cloud-aiplatform
• llama-index-vector-stores-vertexaivectorsearch
• llama-index-llms-vertex
• llama-index-llms-gemini
• llama-index-indices-managed-vertexai

These dependencies are required for MetaGPT's RAG functionality with the latest LlamaIndex version and for using Vertex AI managed index. It's also recommended to create a fresh virtual environment before installing these packages to avoid conflicts ^[1][2][3].

Additionally, ensure you are using Python 3.9 or higher, but less than 4.0, as this is the recommended version for running LlamaIndex ^[4].

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[logan-markewich]

@MarcSunyer all the docs are up to date. Can you give some specific examples? Or make a google colab to reproduce?

pip isntall llama-index installs a bundle of starter packages. Beyond that, any integration like Ollama or Qdrant, etc is a seperate install, like pip install llama-index-llms-ollama or pip install llama-index-vector-stores-qdrant