# vstash End-to-End Test Report

**Date:** 2026-03-23 19:56
**Backend:** cerebras / gpt-oss-120b
**Embedding:** BAAI/bge-small-en-v1.5

**Corpus:** 8 documents, 81 chunks, 3.09 MB

---

## Query 1: "Explain the difference between DARTS and evolutionary NAS methods, including their computational costs"

### ⏱️ Timing

| Phase | Time |
|---|---|
| Embed query | 8.1ms |
| Vector + FTS search | 3.1ms |
| **Total retrieval** | **11.2ms** |
| LLM inference | 1.13s |
| **End-to-end** | **1.14s** |

### 📚 Retrieved Sources

| # | Source | Score |
|---|---|---|
| 1 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 23) | 0.01 |
| 2 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 21) | 0.009836 |
| 3 | Vstash Constitution (chunk 1) | 0.009677 |
| 4 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 10) | 0.009524 |
| 5 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 2) | 0.009375 |

### 💬 Answer (31 words)

The provided excerpts do not contain any information about DARTS, evolutionary neural‑architecture‑search (NAS) methods, or their computational costs. Consequently, I cannot explain the differences between them based on the given context.

---

## Query 2: "What are the main arguments for renewable energy, and how do costs compare to fossil fuels?"

### ⏱️ Timing

| Phase | Time |
|---|---|
| Embed query | 14.2ms |
| Vector + FTS search | 2.8ms |
| **Total retrieval** | **17.0ms** |
| LLM inference | 1.02s |
| **End-to-end** | **1.04s** |

### 📚 Retrieved Sources

| # | Source | Score |
|---|---|---|
| 1 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 10) | 0.01 |
| 2 | https://en.wikipedia.org/wiki/Model_context_protocol (chunk 10) | 0.009836 |
| 3 | https://en.wikipedia.org/wiki/Model_context_protocol (chunk 6) | 0.009677 |
| 4 | https://en.wikipedia.org/wiki/Model_context_protocol (chunk 20) | 0.009524 |
| 5 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 20) | 0.009375 |

### 💬 Answer (43 words)

The provided contexts discuss autonomous LLM agents, multi‑plan generation, reflection, and the Model Context Protocol. They do not contain any information about arguments for renewable energy or cost comparisons with fossil fuels. Therefore, I cannot answer that question based on the given material.

---

## Query 3: "Describe Alice's emotional journey and key character interactions in Wonderland"

### ⏱️ Timing

| Phase | Time |
|---|---|
| Embed query | 14.5ms |
| Vector + FTS search | 2.2ms |
| **Total retrieval** | **16.7ms** |
| LLM inference | 1.12s |
| **End-to-end** | **1.13s** |

### 📚 Retrieved Sources

| # | Source | Score |
|---|---|---|
| 1 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 22) | 0.01 |
| 2 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 26) | 0.009836 |
| 3 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 25) | 0.009677 |
| 4 | https://en.wikipedia.org/wiki/Retrieval-augmented_generation (chunk 16) | 0.009524 |
| 5 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 0) | 0.009375 |

### 💬 Answer (38 words)

The provided contexts discuss autonomous large‑language‑model agents, their architectures, surveys, and related research. They contain no information about Alice, her emotional journey, or any character interactions in *Wonderland*. Therefore, I cannot describe those details from the given material.

---

## Query 4: "What does Sun Tzu say about the importance of knowing your enemy and terrain?"

### ⏱️ Timing

| Phase | Time |
|---|---|
| Embed query | 13.1ms |
| Vector + FTS search | 1.2ms |
| **Total retrieval** | **14.3ms** |
| LLM inference | 1.14s |
| **End-to-end** | **1.16s** |

### 📚 Retrieved Sources

| # | Source | Score |
|---|---|---|
| 1 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 20) | 0.01 |
| 2 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 6) | 0.009836 |
| 3 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 24) | 0.009677 |
| 4 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 1) | 0.009524 |
| 5 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 13) | 0.009375 |

### 💬 Answer (20 words)

The provided contexts do not contain any statement from Sun Tzu about the importance of knowing your enemy and terrain.

---

## Query 5: "How should a production API implement rate limiting and caching with Redis?"

### ⏱️ Timing

| Phase | Time |
|---|---|
| Embed query | 14.8ms |
| Vector + FTS search | 0.9ms |
| **Total retrieval** | **15.7ms** |
| LLM inference | 1.14s |
| **End-to-end** | **1.16s** |

### 📚 Retrieved Sources

| # | Source | Score |
|---|---|---|
| 1 | Vstash Constitution (chunk 2) | 0.01 |
| 2 | Vstash Constitution (chunk 3) | 0.009836 |
| 3 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 18) | 0.009677 |
| 4 | Readme (chunk 1) | 0.009524 |
| 5 | Vstash Constitution (chunk 1) | 0.009375 |

### 💬 Answer (20 words)

The provided context does not contain any information about implementing rate limiting or caching with Redis for a production API.

---

## Query 6: "What is vstash's technology stack and how does it achieve fast local search?"

### ⏱️ Timing

| Phase | Time |
|---|---|
| Embed query | 11.9ms |
| Vector + FTS search | 1.9ms |
| **Total retrieval** | **13.8ms** |
| LLM inference | 0.8s |
| **End-to-end** | **0.81s** |

### 📚 Retrieved Sources

| # | Source | Score |
|---|---|---|
| 1 | Vstash Test Frontmatter (chunk 0) | 0.01 |
| 2 | Readme (chunk 0) | 0.009836 |
| 3 | Vstash Constitution (chunk 0) | 0.009677 |

### 💬 Answer (296 words)

**vstash’s technology stack**

| Layer | Technology | Why it’s used |
|-------|------------|---------------|
| Vector store | **sqlite‑vec** | Single‑file SQLite DB, cosine similarity, no external server |
| Keyword search | **FTS5** (SQLite) | Built‑in full‑text indexing with exact matches and stemming |
| Hybrid ranking | **Reciprocal Rank Fusion (RRF)** | Combines semantic (vector) and keyword results for better relevance without training |
| Embeddings | **FastEmbed** (ONNX Runtime) | Runs locally, ~700 chunks / s, no server needed |
| Embedding model | **BAAI/bge‑small‑en‑v1.5** (384‑dim) | Fastest quality‑/‑speed ratio |
| Inference back‑ends | **Cerebras API**, **OpenAI API**, **Ollama** (local) | Configurable; Ollama keeps everything on‑device |
| Document parsing | **markitdown** | Handles PDF, DOCX, PPTX, HTML, Markdown, code, URLs |
| Configuration | **Pydantic v2** | Type‑safe, validated settings |
| CLI | **Typer + Rich** | Clean terminal UI with progress feedback |
| Language | **Python 3.10+** | Easy ecosystem, rapid development |

**How fast local search is achieved**

1. **All data lives in a single SQLite file** – vector embeddings and full‑text indexes are stored together, eliminating network latency and external services.  
2. **Local vector search** via `sqlite‑vec` provides cosine‑similarity look‑ups directly in the DB.  
3. **Keyword matching** uses SQLite’s native `FTS5` engine, which is highly optimized and runs entirely on‑device.  
4. **Hybrid ranking** with Reciprocal Rank Fusion merges the two result sets, giving better relevance without extra computation or model training.  
5. **Embedding generation** is done locally with FastEmbed (ONNX), processing ~700 chunks per second, so indexing is quick.  

Together, these components let vstash retrieve and rank relevant document chunks in **sub‑second** time, all without a server or cloud dependency.  

*Sources: Context 3 “Technology Stack” table and “Hybrid ranking” description; Context 1 “Hybrid Search” and “Storage Layer”.*

---

## Query 7: "How does Retrieval-Augmented Generation reduce hallucinations in language models?"

### ⏱️ Timing

| Phase | Time |
|---|---|
| Embed query | 11.7ms |
| Vector + FTS search | 1.3ms |
| **Total retrieval** | **13.0ms** |
| LLM inference | 1.05s |
| **End-to-end** | **1.06s** |

### 📚 Retrieved Sources

| # | Source | Score |
|---|---|---|
| 1 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 21) | 0.01 |
| 2 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 23) | 0.009836 |
| 3 | The Architecture And Evolution Of Autonomous Llm Agents   Notebooklm (chunk 26) | 0.009677 |
| 4 | https://en.wikipedia.org/wiki/Retrieval-augmented_generation (chunk 10) | 0.009524 |

### 💬 Answer (29 words)

The provided excerpts do not contain an explanation of how Retrieval‑Augmented Generation (RAG) reduces hallucinations in language models. Therefore, I cannot answer the question based on the given context.

---

## Summary

| Metric | Value |
|---|---|
| Queries tested | 7 |
| Total retrieval time | 102ms (15ms avg) |
| Total LLM time | 7.4s (1.06s avg) |
| Avg end-to-end | 1.07s |
| Corpus size | 81 chunks |