local-rag-with-lightweight-elasticsearch

Author: Delacrobix

supporting-blog-content/local-rag-with-lightweight-elasticsearch/app-logs/llama-smoltalk-3.2-1b-instruct_results.md

@@ -0,0 +1,19 @@
+📥 Indexing documents...
+
+🔍 Search: 'Can you summarize the performance issues in the API?'
+
+🤖 Asking to model: llama-smoltalk-3.2-1b-instruct
+
+## 💡 Question: 
+Can you summarize the performance issues in the API?
+
+## 📝 Answer: 
+The primary performance issue in the API is the slow response times of 3 seconds or more from the 1,000+ queries per minute. The search API, in particular, is experiencing performance degradations, with complex Elasticsearch queries causing the issues. A proposed solution is to implement a 15-minute TTL cache with event-based invalidation to improve response times. Additionally, a three-tiered approach involving optimization of bool queries and added calculated index fields is being implemented to improve query performance. Finally, auto-scaling for the infrastructure is set up to scale to 6 instances at 70% CPU.
+
+
+## Stats
+✅ Indexed 5 documents in 250ms
+
+🔍 Search Latency: 57ms
+
+🤖 AI Latency: 21019ms | 5.8 tokens/s

supporting-blog-content/local-rag-with-lightweight-elasticsearch/app-logs/qwen3:4b-results.md

@@ -2,22 +2,24 @@
 
 🔍 Search: 'Can you summarize the performance issues in the API?'
 
-## 🤖 Asking to model: llama3.2
+🤖 Asking to model: dolphin3.0-qwen2.5-0.5b
 
-### 💡 Question: 
+## 💡 Question: 
 Can you summarize the performance issues in the API?
-### 📝 Answer: 
-According to the transcript, the performance issues in the API are:
+## 📝 Answer: 
 
-1. Response times increase from 200ms to 3 seconds when handling 1,000+ queries per minute.
-2. Complex Elasticsearch queries are slow, with an average execution time of 1.2 seconds.
-3. Performance degrades during spikes.
+The performance issues in the Search API deployed on September 16, 2025, include:
 
-These issues are attributed to the lack of caching and a complex Elasticsearch query setup.
+- Degradation in performance at 1,000+ queries per minute, resulting in a 200ms to 3-second response time for complex queries.
+- High response times for queries that do not utilize caching, causing them to take significantly longer than 2 seconds.
+- Inability to scale to handle spikes in query traffic, leading to increased CPU limits.
 
-## App performance metrics:
-✅ Indexed 5 documents in 96ms
+These issues are primarily attributed to the complexity and inefficiency of the Elasticsearch queries, as well as the lack of caching layer. This indicates a need for optimization and addressing these specific performance bottlenecks to ensure the API's scalability and effectiveness for the development team.
 
-🔍 Search Latency: 20ms
+## Stats
 
-🤖 Ollama Latency: 36772ms | 24.7 tokens/s
+✅ Indexed 5 documents in 627ms
+
+🔍 Search Latency: 81ms
+
+🤖 AI Latency: 16044ms | 9.5 tokens/s

supporting-blog-content/local-rag-with-lightweight-elasticsearch/app-logs/results.md

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+📥 Indexing documents...
+
+🔍 Search: 'Can you summarize the performance issues in the API?'
+
+🤖 Asking to model: smollm2-1.7b-instruct
+
+## 💡 Question: 
+
+Can you summarize the performance issues in the API?
+## 📝 Answer: 
+
+The summary of the performance issues in the API can be summarized as follows:
+
+- The API works but performance degrades at 1,000+ queries per minute, resulting in response times jumping from 200ms to 3 seconds.
+- The root cause of these issues is the lack of a caching layer in the Elasticsearch queries.
+- The team proposed a few solutions, including a 15-minute TTL cache with event-based invalidation, which would be implemented by Sarah.
+- They also proposed optimizing boolean queries and adding calculated index fields, which would be taken care of by John.
+- To handle the performance spikes, they suggested auto-scaling the infrastructure, with Mike working on this and aiming to scale to 6 instances at 70% CPU by Wednesday.
+- They also proposed implementing Redis cache, which would be done by Sarah.
+- The team discussed the timeline and timeline of the changes and proposed a phased migration approach: complete migration on October 30th, followed by a partial migration on October 15th.
+
+## Stats
+
+✅ Indexed 5 documents in 141ms
+
+🔍 Search Latency: 26ms
+
+🤖 AI Latency: 47561ms | 4.8 tokens/s

supporting-blog-content/local-rag-with-lightweight-elasticsearch/app-logs/smollm2-1.7b-instruct_results.md

@@ -1,62 +1,11 @@
 >>> Why Elastic is so cool?
 
-That's a **great question**, but there's a **critical clarification first**: **"Elastic" alone isn't a product** — it's almost always a shorthand for **Elasticsearch** (the core search
-engine) and the **Elastic Stack** (Elasticsearch + Kibana + Logstash + Beats).
+## Raw Response
 
-**Why people say "Elastic is so cool" (and why it's actually *Elasticsearch* that's the star):**
+```json
+{"created":1762881411,"object":"chat.completion","id":"0178b570-4e13-4c1b-9ff4-e2ca5bff1c67","model":"dolphin3.0-qwen2.5-0.5b","choices":[{"index":0,"finish_reason":"stop","message":{"role":"assistant","content":"Elastic is a versatile technology that supports a wide range of applications. Its coolness stems from its ability to manage complex environments and provide a seamless integration with other technologies."}}],"usage":{"prompt_tokens":14,"completion_tokens":35,"total_tokens":49}}
+```
 
-1. **Real-Time Search & Analytics (The "Cool" Part!)**
-   Elasticsearch doesn't just *search* — it **indexes, searches, and analyzes data in real time** (millions of events/sec). Unlike traditional databases (which need slow queries or
-batch processing), it gives you instant answers.
-   *Example:* Netflix uses it to show personalized movie recommendations *as you browse* — not after you click "Next" or "Save."
+## Answer
 
-2. **Handles "Wild" Data (Unstructured + Structured)**
-   Most data today is messy (text, logs, images, JSON, CSV). Elasticsearch **natively understands** this.
-   *Example:* A company can search *both* "user feedback in Slack messages" *and* "product prices from a spreadsheet" in one query.
-
-3. **Scalability That Doesn’t Break**
-   It’s built to scale **horizontally** (add more servers) without downtime. Handles **petabytes** of data.
-   *Example:* Airbnb uses it to power their 10M+ listing search across 200+ countries — *without* slowing down.
-
-4. **The Elastic Stack = Full Power**
-   Elasticsearch isn’t alone — it’s part of a **complete suite**:
-   - **Logstash**: Ingests data from anywhere (websites, apps, logs).
-   - **Kibana**: Visualize data (dashboards, maps, charts).
-   - **Beats**: Lightweight data shippers (for apps).
-   *This lets you build end-to-end data pipelines:* **Collect → Process → Search → Visualize** in one flow.
-
-5. **No More "Slow Queries" (The Real Pain Point)**
-   Traditional SQL databases struggle with:
-   - Full-text search (e.g., "show me products with 'sneakers' AND 'black'")
-   - Real-time analytics (e.g., "how many users clicked 'checkout' in the last 5 mins?")
-   Elasticsearch solves both **with one query**.
-
-6. **Open Source (with Enterprise Support)**
-   Free to use — but Elastic also offers enterprise features (security, ML, etc.) for large teams. *This is why it’s so widely adopted.*
-
-### Why It’s "So Cool" in Practice:
-| **Problem**                | **Traditional Tool**       | **Elasticsearch**                     |
-|----------------------------|----------------------------|---------------------------------------|
-| Real-time product search    | Slow (seconds)             | Instant (milliseconds)               |
-| Analyze user behavior       | Requires complex SQL       | Simple queries + real-time dashboards|
-| Handle messy logs           | Needs ETL pipelines        | Ingests logs *directly*              |
-| Scale to 10M+ users        | Databases become slow      | Scales horizontally effortlessly    |
-
-### Real-World Examples:
-- **Netflix**: Uses Elasticsearch for 1B+ users to personalize content.
-- **GitHub**: Uses it to search code repositories (text + code structure).
-- **Healthcare**: Analyzes patient data for real-time alerts (e.g., "risk of sepsis").
-- **Security**: Real-time threat detection (e.g., "suspicious login from Brazil").
-
-### Why People Get Confused:
-- **"Elastic" = Elasticsearch** (the product) → Not a standalone tool.
-- **"The Elastic Stack"** = The full suite (Elasticsearch + Kibana + Logstash + Beats).
-- **Not "Elastic" as in rubber bands** (that’s physics, not tech!).
-
-### The Bottom Line:
-**Elasticsearch is "so cool" because it turns messy, real-time data into instant insights — without slowing down.** It’s the reason companies can build **search, analytics, and
-monitoring** at scale *without* writing complex code or waiting for results.
-
-If you meant **"Elastic"** as in the rubber band (physics), that’s **not cool** 😄 — but in tech? **100% cool**. 😎
-
-*So next time someone says "Elastic is so cool," you know exactly what they mean!* 🔥
+Elastic is a versatile technology that supports a wide range of applications. Its coolness stems from its ability to manage complex environments and provide a seamless integration with other technologies.