no separate doc for evals, move to ai application section

docs/source/building_applications/evals.md

@@ -1,8 +1,8 @@
-# Benchmark Evaluations
+# Evals
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing)
 
-Llama Stack provides the building blocks needed to run benchmark and application evaluations. This guide will walk you through how to use these components to run open benchmark evaluations. Visit our [Evaluation Concepts](../concepts/evaluation_concepts.md) guide for more details on how evaluations work in Llama Stack, and our [Evaluation Reference](../references/evals_reference/index.md) guide for a comprehensive reference on the APIs. Check out our [Colab notebook](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing) on working examples on how you can use Llama Stack for running benchmark evaluations.
+Llama Stack provides the building blocks needed to run benchmark and application evaluations. This guide will walk you through how to use these components to run open benchmark evaluations. Visit our [Evaluation Concepts](../concepts/evaluation_concepts.md) guide for more details on how evaluations work in Llama Stack, and our [Evaluation Reference](../references/evals_reference/index.md) guide for a comprehensive reference on the APIs.
 
 ### 1. Open Benchmark Model Evaluation
 

docs/source/building_applications/index.md

@@ -6,12 +6,14 @@ The best way to get started is to look at this notebook which walks through the
 
 **Notebook**: [Building AI Applications](docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb)
 
-## Agentic Concepts
+Here are some key topics that will help you build effective agents:
+
 - **[Agent Execution Loop](agent_execution_loop)**
 - **[RAG](rag)**
 - **[Safety](safety)**
 - **[Tools](tools)**
 - **[Telemetry](telemetry)**
+- **[Evals](evals)**
 
 
 ```{toctree}
@@ -23,4 +25,5 @@ rag
 safety
 tools
 telemetry
+evals
 ```

docs/source/contributing/memory_api.md

@@ -1,53 +0,0 @@
-# Memory API Providers
-
-This guide gives you references to switch between different memory API providers.
-
-##### pgvector
-1. Start running the pgvector server:
-
-```
-$ docker run --network host --name mypostgres -it -p 5432:5432 -e POSTGRES_PASSWORD=mysecretpassword -e POSTGRES_USER=postgres -e POSTGRES_DB=postgres pgvector/pgvector:pg16
-```
-
-2. Edit the `run.yaml` file to point to the pgvector server.
-```
-memory:
-  - provider_id: pgvector
-    provider_type: remote::pgvector
-    config:
-      host: 127.0.0.1
-      port: 5432
-      db: postgres
-      user: postgres
-      password: mysecretpassword
-```
-
-> [!NOTE]
-> If you get a `RuntimeError: Vector extension is not installed.`. You will need to run `CREATE EXTENSION IF NOT EXISTS vector;` to include the vector extension. E.g.
-
-```
-docker exec -it mypostgres ./bin/psql -U postgres
-postgres=# CREATE EXTENSION IF NOT EXISTS vector;
-postgres=# SELECT extname from pg_extension;
- extname
-```
-
-3. Run `docker compose up` with the updated `run.yaml` file.
-
-##### chromadb
-1. Start running chromadb server
-```
-docker run -it --network host --name chromadb -p 6000:6000 -v ./chroma_vdb:/chroma/chroma -e IS_PERSISTENT=TRUE chromadb/chroma:latest
-```
-
-2. Edit the `run.yaml` file to point to the chromadb server.
-```
-memory:
-  - provider_id: remote::chromadb
-    provider_type: remote::chromadb
-    config:
-      host: localhost
-      port: 6000
-```
-
-3. Run `docker compose up` with the updated `run.yaml` file.

docs/source/index.md

@@ -59,7 +59,6 @@ concepts/index
 distributions/index
 distributions/selection
 building_applications/index
-benchmark_evaluations/index
 playground/index
 contributing/index
 references/index