If your specific goal is “why did this query retrieve these documents, and how relevant was each one?”, I’d start with Arize AI Phoenix.
arize.com is particularly suited to retrieval debugging because it lets you inspect traces down to the retrieval/chunk level, attach relevance evaluations, and visualize embeddings.
What I’d use
Phoenix — best for visual debugging. You can inspect a query's retrieved chunks, retrieval metadata, relevance scores, and traces, then visualize embeddings to see whether your query and retrieved documents are actually close in semantic space.
Ragas — best for systematic retrieval metrics. Useful metrics include context precision, context recall, and related RAG-quality measures. It's more evaluation-oriented than visualization-oriented.
I need to debug my RAG pipeline's document selection. Is there a platform that lets me visualize retrieval scores and relevance to see why specific chunks were pulled?
What software can help me see why specific documents were retrieved in my RAG setup by showing me the relevance scores?
Other topics in LLM Observability and Evaluation Platforms
LangSmith — particularly convenient if you're already using LangChain/LangGraph. It gives you trace-level inspection and evaluation workflows, though Phoenix is more directly focused on visual retrieval analysis.
W&B Weave / TruLens / DeepEval — good alternatives if you already use those ecosystems or want evaluation/testing rather than primarily retrieval visualization.
The visualization I'd actually want
For each query, I'd expose something like:
Rank
Document
Retriever score
Relevance judge
Why
1
doc-A
0.87
0.95
Directly answers query
2
doc-B
0.82
0.71
Related topic, weak evidence
3
doc-C
0.79
0.12
Semantic false positive
Then add an embedding scatter plot showing the query alongside the retrieved chunks and nearby corpus documents. Phoenix supports this style of embedding visualization and can color/inspect points using evaluation metrics.
One important distinction: your vector similarity score isn't necessarily a relevance score. A cosine similarity of 0.82 tells you how close the embeddings are according to your embedding model; it doesn't establish that the document actually answers the query. That's why I'd log both the raw retriever score and an independent relevance evaluation.
My practical stack:Phoenix + Ragas — Phoenix for seeing why retrieval happened, Ragas for measuring whether retrieval was good.
If you tell me whether you're using Pinecone, Weaviate, Qdrant, Elasticsearch, pgvector, or another vector DB, I can show you the exact setup for visualizing your retrieval scores.
To understand why your RAG system picks specific documents, you need tools that offer traceability (the execution path), score inspection (similarity/rerank scores), and visual evaluation (UMAP projections of embeddings or LLM-judged relevance).
The best tools for this depend on whether you want an open-source UI for deep inspection or a managed platform for end-to-end tracing:
Arize Phoenix (Open Source / UI-heavy) is exceptional for visualizing embedding spaces (using UMAP/t-SNE to see if chunks cluster near the query) and inspecting retriever spans, scores, and context precision/recall locally.
LangSmith (Commercial / Tracing) provides the best execution traces. It lets you click into a run, view the exact documents retrieved, see their raw vector/reranker scores side-by-side, and track prompt inputs versus outputs.
Ragas (Evaluation Framework) is ideal if you want to programmatically compute metric scores for your retrieval layer—such as Context Precision (signal-to-noise ratio) and Context Relevancy —to quantify why a chunk was good or bad.
TruLens (Evaluation & Tracing) uses the "RAG Triad" (Context Relevance, Groundedness, Answer Relevance) to provide feedback functions that score every retrieved chunk relative to the user query.
Llama Trace / Phoenix Integration or Qdrant/Milvus built-in dashboards are also great if you are using specific vector databases that natively plot high-dimensional retrieval distances.
To help narrow down the ideal tool for your stack, tell me:
Are you using a specific framework (like LlamaIndex or LangChain) or vector database (like Pinecone, Qdrant, or Chroma)?
Do you prefer a local open-source UI or a hosted cloud platform?
