update distributions/readmes

distributions/fireworks/compose.yaml

@@ -4,11 +4,9 @@ services:
     network_mode: "host"
     volumes:
       - ~/.llama:/root/.llama
-      # Link to ollama run.yaml file
       - ./run.yaml:/root/llamastack-run-fireworks.yaml
     ports:
       - "5000:5000"
-    # Hack: wait for ollama server to start before starting docker
     entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-fireworks.yaml"
     deploy:
       restart_policy:

distributions/meta-reference-gpu/README.md

@@ -10,6 +10,13 @@ The `llamastack/distribution-meta-reference-gpu` distribution consists of the fo
 
 ### Start the Distribution (Single Node GPU)
 
+```
+$ cd distributions/meta-reference-gpu
+$ ls
+build.yaml  compose.yaml  README.md  run.yaml
+$ docker compose up
+```
+
 > [!NOTE]
 > This assumes you have access to GPU to start a local server with access to your GPU.
 
@@ -18,7 +25,7 @@ The `llamastack/distribution-meta-reference-gpu` distribution consists of the fo
 > `~/.llama` should be the path containing downloaded weights of Llama models.
 
 
-To download and start running a pre-built docker container, you may use the following commands:
+This will download and start running a pre-built docker container. Alternatively, you may use the following commands:
 
 ```
 docker run -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./run.yaml:/root/my-run.yaml --gpus=all distribution-meta-reference-gpu --yaml_config /root/my-run.yaml
@@ -26,3 +33,54 @@ docker run -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./run.yaml:/root/my-run.
 
 ### Alternative (Build and start distribution locally via conda)
 - You may checkout the [Getting Started](../../docs/getting_started.md) for more details on building locally via conda and starting up a meta-reference distribution.
+
+### Start Distribution With pgvector/chromadb Memory Provider
+##### 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.

distributions/meta-reference-gpu/compose.yaml

@@ -0,0 +1,35 @@
+services:
+  llamastack:
+    image: llamastack/distribution-meta-reference-gpu
+    network_mode: "host"
+    volumes:
+      - ~/.llama:/root/.llama
+      - ./run.yaml:/root/my-run.yaml
+    ports:
+      - "5000:5000"
+    devices:
+      - nvidia.com/gpu=all
+    environment:
+      - CUDA_VISIBLE_DEVICES=0
+    command: []
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            # that's the closest analogue to --gpus; provide
+            # an integer amount of devices or 'all'
+            count: 1
+            # Devices are reserved using a list of capabilities, making
+            # capabilities the only required field. A device MUST
+            # satisfy all the requested capabilities for a successful
+            # reservation.
+            capabilities: [gpu]
+    runtime: nvidia
+    entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml_config /root/my-run.yaml"
+    deploy:
+      restart_policy:
+        condition: on-failure
+        delay: 3s
+        max_attempts: 5
+        window: 60s

distributions/meta-reference-gpu/run.yaml

@@ -33,9 +33,14 @@ providers:
       prompt_guard_shield:
         model: Prompt-Guard-86M
   memory:
-  - provider_id: meta0
-    provider_type: meta-reference
-    config: {}
+  - provider_id: pgvector
+    provider_type: remote::pgvector
+    config:
+      host: 127.0.0.1
+      port: 5432
+      db: postgres
+      user: postgres
+      password: mysecretpassword
   agents:
   - provider_id: meta0
     provider_type: meta-reference

distributions/together/compose.yaml

@@ -4,11 +4,9 @@ services:
     network_mode: "host"
     volumes:
       - ~/.llama:/root/.llama
-      # Link to ollama run.yaml file
       - ./run.yaml:/root/llamastack-run-together.yaml
     ports:
       - "5000:5000"
-    # Hack: wait for ollama server to start before starting docker
     entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-together.yaml"
     deploy:
       restart_policy: