How to Build a RAG Solution with Llama Index, ChromaDB, and Ollama

1. chroma

   1 55 25,095 9.9 Rust

   Open-source search and retrieval database for AI applications.

   

   Llama index is an open-source RAG orchestrator; this is the brain behind RAG. It loads your documents (PDF, TXT, CSV files, or webpages), splits them into chunks (bits or pieces), and saves them in a vector database. A vector database is not like a regular (SQL or NoSQL) database, which stores image files as image files or text as text files; instead, it saves every piece of data as vector embeddings, a list of numbers that encodes a text. If a text like "cat sitting on a mat" is saved, it saves like this [0.12, -0.87, 0.44, …]. ChromaDB is a type of vector database, and the vectors are what the LLM uses for retrieval in RAG. The whole beauty of RAG lies in the implementation of this search.

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. streamlit

   2 323 42,767 9.9 Python

   Streamlit — A faster way to build and share data apps.

   

   With a few lines of Python, you can build a basic retrieval-augmented generation (RAG) solution, but it doesn’t stop here. You can extend this project to search for multiple web pages, load large documents, add a simple web UI using either Streamlit or Anvil, or even experiment with different models in Ollama.

4. ollama

   3 604 157,773 9.9 Go

   Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.

   

   In case you are unfamiliar with Ollama. Ollama is an open source that lets you download local models and use them in your projects. You can download the software here and follow the on-screen instructions to install it.

5. llama_index

   4 82 45,854 9.9 Python

   LlamaIndex is the leading framework for building LLM-powered agents over your data.

   

   Step 2: Set up LlamaIndex and Chroma DB

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