Merge pull request #67 from TamiTakamiya/TamiTakamiya/run-ansible-chatbot-stack-from-source

Run ansible-chatbot-stack from source

Author: TamiTakamiya

.gitignore

@@ -180,4 +180,5 @@ responses_store.db
 trace_store.db
 /embeddings_model
 /vector_db
-/llama-stack
+/llama-stack
+/work

Makefile

@@ -156,6 +156,7 @@ clean:
 	@echo "Cleaning up your local folders..."
 	rm -rf llama-stack/
 	rm -rf providers.d/
+	rm -rf work/
 	@echo "Removing ansible-chatbot-stack images..."
 	docker rmi -f $$(docker images -a -q --filter reference=ansible-chatbot-stack) || true
 	@echo "Removing ansible-chatbot-stack containers..."

README.md

@@ -165,3 +165,42 @@ If you have the need for re-building images, apply the following clean-ups right
     # Obtain a container shell for the Ansible Chatbot Stack.
     make shell
 ```
+
+## Appendix - Run from source (PyCharm)
+1. Clone the [lightspeed-core/lightspeed-stack](https://github.com/lightspeed-core/lightspeed-stack) repository to your development environment.
+2. In the ansible-chatbot-stack project root, create `.env` file in the project root and define following variables:
+    ```commandline
+    PYTHONDONTWRITEBYTECODE=1
+    PYTHONUNBUFFERED=1
+    PYTHONCOERCECLOCALE=0
+    PYTHONUTF8=1
+    PYTHONIOENCODING=UTF-8
+    LANG=en_US.UTF-8
+    VLLM_URL=(VLLM URL Here)
+    VLLM_API_TOKEN=(VLLM API Token Here)
+    INFERENCE_MODEL=granite-3.3-8b-instruct
+
+    LIBRARY_CLIENT_CONFIG_PATH=./ansible-chatbot-run.yaml
+    SYSTEM_PROMPT_PATH=./ansible-chatbot-system-prompt.txt
+    EMBEDDINGS_MODEL=./embeddings_model
+    VECTOR_DB_DIR=./vector_db
+    PROVIDERS_DB_DIR=./work
+    EXTERNAL_PROVIDERS_DIR=./llama-stack/providers.d
+    ```
+3. Create a Python run configuration with following values:
+    - script/module: `script`
+    - script path: `(lightspeed-stack project root)/src/lightspeed_stack.py`
+    - arguments: `--config ./lightspeed-stack_local.yaml`
+    - working directory: `(ansible-chatbot-stack project root)`
+    - path to ".env" files: `(ansible-chatbot-stack project root)/.env`
+4. Run the created configuration from PyCharm main menu.
+
+#### Note: 
+If you want to debug codes in the `lightspeed-providers` project, you 
+can add it as a local package dependency with:
+```commandline
+uv add --editable (lightspeed-providers project root)
+```
+It will update `pyproject.toml` and `uv.lock` files.  Remember that 
+they are for debugging purpose only and avoid checking in those local 
+changes.

ansible-chatbot-run.yaml

@@ -28,7 +28,7 @@ providers:
       kvstore:
         type: sqlite
         namespace: null
-        db_path: /.llama/data/distributions/ansible-chatbot/aap_faiss_store.db
+        db_path: ${env.VECTOR_DB_DIR:/.llama/data/distributions/ansible-chatbot}/aap_faiss_store.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -41,11 +41,11 @@ providers:
       persistence_store:
         type: sqlite
         namespace: null
-        db_path: /.llama/data/distributions/ansible-chatbot/agents_store.db
+        db_path: ${env.PROVIDERS_DB_DIR:/.llama/data/distributions/ansible-chatbot}/agents_store.db
       responses_store:
         type: sqlite
         namespace: null
-        db_path: /.llama/data/distributions/ansible-chatbot/responses_store.db
+        db_path: ${env.PROVIDERS_DB_DIR:/.llama/data/distributions/ansible-chatbot}/responses_store.db
       tools_filter:
         enabled: true
         model_id: ${env.INFERENCE_MODEL_FILTER:}
@@ -56,14 +56,14 @@ providers:
       kvstore:
         type: sqlite
         namespace: null
-        db_path: /.llama/data/distributions/ansible-chatbot/localfs_datasetio.db
+        db_path: ${env.PROVIDERS_DB_DIR:/.llama/data/distributions/ansible-chatbot}/localfs_datasetio.db
   telemetry:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       service_name: ${env.OTEL_SERVICE_NAME:}
       sinks: ${env.TELEMETRY_SINKS:console,sqlite}
-      sqlite_db_path: /.llama/data/distributions/ansible-chatbot/trace_store.db
+      sqlite_db_path: ${env.PROVIDERS_DB_DIR:/.llama/data/distributions/ansible-chatbot}/trace_store.db
   tool_runtime:
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
@@ -79,14 +79,14 @@ models:
   provider_model_id: null
 - metadata:
     embedding_dimension: 768
-  model_id: /.llama/data/distributions/ansible-chatbot/embeddings_model
+  model_id: ${env.EMBEDDINGS_MODEL:/.llama/data/distributions/ansible-chatbot/embeddings_model}
   provider_id: inline_sentence-transformer
   model_type: embedding
 shields: []
 vector_dbs:
 - metadata: {}
   vector_db_id: "aap-product-docs-2_5"
-  embedding_model: /.llama/data/distributions/ansible-chatbot/embeddings_model
+  embedding_model: ${env.EMBEDDINGS_MODEL:/.llama/data/distributions/ansible-chatbot/embeddings_model}
   embedding_dimension: 768
   provider_id: "aap_faiss"
 datasets: []
@@ -103,4 +103,4 @@ server:
   tls_cafile: null
   auth: null
   disable_ipv6: false
-external_providers_dir: /.llama/providers.d
+external_providers_dir: ${env.EXTERNAL_PROVIDERS_DIR:/.llama/providers.d}

lightspeed-stack_local.yaml

@@ -0,0 +1,16 @@
+name: Ansible Lightspeed Intelligent Assistant
+service:
+  host: 0.0.0.0
+  port: 8080
+  auth_enabled: false
+  workers: 1
+  color_log: true
+  access_log: true
+llama_stack:
+  use_as_library_client: true
+  library_client_config_path: ./ansible-chatbot-run.yaml
+user_data_collection:
+  feedback_disabled: true
+  transcripts_disabled: true
+customization:
+  system_prompt_path: ./ansible-chatbot-system-prompt.txt

pyproject.toml

@@ -16,3 +16,9 @@ dependencies = [
     "sentence-transformers>=5.0.0",
     "sqlalchemy~=2.0.41",
 ]
+
+[dependency-groups]
+dev = [
+    "cachetools>=6.1.0",
+    "kubernetes>=33.1.0",
+]