Yes, if your analysis of the specific case really leads you to believe that you need to load the entire content, then you can simply pass it in the inputs of your crew. The point is, you should examine if you truly need the whole content for context. Just because you can, doesn’t mean you should. “‘All things are lawful for me,’ but not all things are helpful.” (1 Corinthians 6:12)
RAG is a technique that allows your Agent to perform a semantic search on chunks of your document (knowledge base) and automatically add these relevant chunks to your prompt. This way, your Agent receives content that’s more relevant to the task it’s trying to perform, make sense? Almost always, this approach (RAG) yields better, more scalable, and more reliable results.
If you really need to pass the full file content, convert it to text/markdown and inject it into the prompt. In this thread, I did exactly that, loading a 52k character file directly into the prompt, but that was for a specific reason: I needed to demonstrate that the LLM could handle a large context. It was just a test; I wouldn’t recommend doing this in production.
If you want to learn more about RAG and use cases, I recommend the following videos:
- What is RAG? (Retrieval Augmented Generation), by Don Woodlock
- CrewAI RAG Deep Dive [Basic & Advanced Examples], by Brandon Hancock
- Crash Course Building Agentic Rag with CrewAI and Qdrant, by Lorenze Jay