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Apr 15, 2024
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docs(rag): fix broken link #825

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2 changes: 1 addition & 1 deletion docs/docs/tutorials/rag.md
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Expand Up @@ -10,7 +10,7 @@ RAG ensures LLMs can dynamically utilize real-time knowledge even if not origina

## Configuring LM and RM

We'll start by setting up the language model (LM) and retrieval model (RM), which **DSPy** supports through multiple [LM](https://dspy-docs.vercel.app/docs/category/language-model-clients) and [RM](https://dspy-docs.vercel.app/docs/category/retrieval-model-clients) APIs and [local models hosting](https://dspy-docs.vercel.app/docs/category/local-language-model-clients).
We'll start by setting up the language model (LM) and retrieval model (RM), which **DSPy** supports through multiple [LM](https://dspy-docs.vercel.app/docs/category/language-model-clients) and [RM](https://dspy-docs.vercel.app/docs/category/retrieval-model-clients) APIs and [local models hosting](https://dspy-docs.vercel.app/docs/deep-dive/language_model_clients/local_models/HFClientTGI).

In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the `ColBERTv2` retriever (a free server hosting a Wikipedia 2017 "abstracts" search index containing the first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)). We configure the LM and RM within DSPy, allowing DSPy to internally call the respective module when needed for generation or retrieval.

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