alihan/agents-course
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge pull request #399 from MustafizSaadi/patch-1

Browse Source 
Update retrieval_agents.mdx
This commit is contained in:
Sergio Paniego Blanco
2025-04-03 09:46:33 +02:00
committed by GitHub
parent 21c0824aab a695ec62cc
commit 57ab56b0f7
1 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
units/en/unit2/smolagents/retrieval_agents.mdx
View File

@@ -153,9 +153,9 @@ This enhanced agent can:
When building agentic RAG systems, the agent can employ sophisticated strategies like:
1. **Query Reformulation:** Instead of using the raw user query, the agent can craft optimized search terms that better match the target documents
2. **Multi-Step Retrieval** The agent can perform multiple searches, using initial results to inform subsequent queries
3. **Source Integration** Information can be combined from multiple sources like web search and local documentation
4. **Result Validation** Retrieved content can be analyzed for relevance and accuracy before being included in responses
2. **Multi-Step Retrieval:** The agent can perform multiple searches, using initial results to inform subsequent queries
3. **Source Integration:** Information can be combined from multiple sources like web search and local documentation
4. **Result Validation:** Retrieved content can be analyzed for relevance and accuracy before being included in responses
Effective agentic RAG systems require careful consideration of several key aspects. The agent **should select between available tools based on the query type and context**. Memory systems help maintain conversation history and avoid repetitive retrievals. Having fallback strategies ensures the system can still provide value even when primary retrieval methods fail. Additionally, implementing validation steps helps ensure the accuracy and relevance of retrieved information.