RAG AGent

[andsty]

Bug Description

I created a RAG agent with qtrand and an agent. I ask questions my agent sometimes is searching qtrad sometimes is answering without searching qtrand.

Reproduction

Create a n agent with chat input, qtrand as my vector and embeddings. qtrand is a tool of the agent

Expected behavior

To go through available documents everytime i ask a question

Who can help?

No response

Operating System

kubernetes

Langflow Version

1.7.3

Python Version

None

Screenshot

No response

Flow File

No response

[AntonioABLima]

Could you please share your Agent's prompt instructions?

A good strategy is to explicitly instruct the Agent in the prompt to use the tool for every query.

Also, please check if the Agent is attempting to use the tool and failing to retrieve data from Qdrant, or if it isn't trying to call the tool at all.

[xXMrNidaXx]

Building a RAG Agent in Langflow — here's the architecture:

Components needed:

1. Document Loader → Load your docs
2. Text Splitter → Chunk them
3. Embeddings → Vector representations
4. Vector Store → Store and retrieve
5. Retriever → Search interface
6. LLM → Generate answers
7. Agent → Orchestrate

Flow layout:

Docs → Splitter → Embeddings → VectorStore
                                    ↓
Query → Retriever ← ─────────────────
            ↓
        Agent + LLM → Response

Key settings:

Text Splitter:
  chunk_size: 500
  overlap: 50

Retriever:
  top_k: 5
  score_threshold: 0.7

Agent:
  type: ReAct  # or OpenAI Functions
  memory: true

Tips:

• Start simple, add complexity
• Test retrieval before adding agent
• Monitor token usage
• Use caching for repeated queries

Common issues:

• Retriever not finding relevant docs → lower threshold
• Agent loops → add max iterations
• Slow response → enable streaming

We build RAG agents at RevolutionAI. What's your document type — PDFs, web pages, or code?