results files updated

Author: Delacrobix

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

@@ -1,5 +1,3 @@
-📥 Indexing documents...
-
 🔍 Search: 'Can you summarize the performance issues in the API?'
 
 🤖 Asking to model: llama-smoltalk-3.2-1b-instruct
@@ -8,12 +6,21 @@
 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.
+Based on the documents, it appears that the main performance issue with the API is related to the search query optimization. The API degrades to around 1,000+ queries per minute (QP/min) when there are 12 of 18 API endpoints integrated with authentication. This issue is caused by complex queries without a caching layer, leading to performance degrades and slow response times.
 
+However, there is also a smaller issue with the "Search" API, where it degrades to around 3+ seconds after 1.2 seconds execution time. This is likely due to multi-filter searches and the need for a caching layer to improve performance.
 
-## Stats
-✅ Indexed 5 documents in 250ms
+To address these issues, the team is working on implementing a caching layer (Sarah) and optimizing bool queries and adding calculated index fields (John) to improve query efficiency. They are also working on setting up auto-scaling for the database (Mike) to ensure that it can handle increased traffic.
+
+A meeting was held to discuss these issues and a plan for improvement was agreed upon. The team will work together to implement a caching layer and optimize the queries, and the team will work with product team to ensure that the migration is completed on time and does not impact the October migration date.
 
-🔍 Search Latency: 57ms
+📚 Citations:
+  [1] report_development-team.txt
+  [2] meeting_development-team_monday.txt
+  [3] meeting_management-sync_friday.txt
+
+
+## Stats
+🔍 Search Latency: 12ms
 
 🤖 AI Latency: 21019ms | 5.8 tokens/s

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

@@ -1,4 +1,3 @@
-📥 Indexing documents...
 
 🔍 Search: 'Can you summarize the performance issues in the API?'
 
@@ -8,18 +7,21 @@
 Can you summarize the performance issues in the API?
 ## 📝 Answer: 
 
-The performance issues in the Search API deployed on September 16, 2025, include:
+The performance issues in the API can be summarized as follows:
 
-- 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.
+1. **Search API degrades with 1,000+ queries per minute**: The search API has degraded, with performance degrades to a time of 3 seconds compared to the target of 200ms.
 
-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.
+2. **Complex queries are slow and no caching layer**: Complex queries take longer to execute as expected.
 
-## Stats
+3. **CPU limits during spikes**: The CPU usage is at 100% at the peak hour and limits to 70% at the peak hour.
+
+📚 Citations:
+  [1] report_development-team.txt
+  [2] meeting_development-team_monday.txt
+  [3] meeting_management-sync_friday.txt
 
-✅ Indexed 5 documents in 627ms
+## Stats
 
-🔍 Search Latency: 81ms
+🔍 Search Latency: 14ms
 
 🤖 AI Latency: 16044ms | 9.5 tokens/s

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

@@ -1,5 +1,3 @@
-📥 Indexing documents...
-
 🔍 Search: 'Can you summarize the performance issues in the API?'
 
 🤖 Asking to model: smollm2-1.7b-instruct
@@ -9,20 +7,18 @@
 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 development team identified two key technical challenges for the API:
 
-- 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.
+1.  The search API degrades at 1,000+ queries per minute, causing average execution times to jump from 200ms to 3 seconds.
+2.  The root cause is complex database queries without a caching layer, leading to poor query performance.
 
-## Stats
+📚 Citations:
+  [1] report_development-team.txt
+  [2] meeting_development-team_monday.txt
+  [3] meeting_management-sync_friday.txt
 
-✅ Indexed 5 documents in 141ms
+## Stats
 
-🔍 Search Latency: 26ms
+🔍 Search Latency: 16ms
 
 🤖 AI Latency: 47561ms | 4.8 tokens/s

supporting-blog-content/local-rag-with-lightweight-elasticsearch/app-logs/why-is-the-sky-blue.md

@@ -1,4 +1,4 @@
->>> Why Elastic is so cool?
+>>> Why is the sky blue?
 
 ## Raw Response