fix workflow deployment

examples/python_fullstack/docker-compose.yml

@@ -38,17 +38,18 @@ services:
       retries: 5
     volumes:
       - ./:/opt/app
+    working_dir: /opt/app
 
   deploy_workflows:
     # Init container, it deploys python_fullstack.yaml and exits
     image: llamaindex/llama-deploy:main
     volumes:
-      - ./:/opt/app
+      - ./python_fullstack.yaml:/opt/python_fullstack.yaml
+    working_dir: /opt/
     depends_on:
       apiserver:
         condition: service_healthy
-    working_dir: /opt/app
-    entrypoint: llamactl -s http://apiserver:4501 -t 20 deploy python_fullstack.yaml
+    entrypoint: llamactl -s http://apiserver:4501 -t 30 deploy python_fullstack.yaml
 
   frontend:
     # UI for this deployment, running at http://localhost:3000

examples/python_fullstack/python_fullstack.yaml

@@ -10,12 +10,18 @@ services:
     name: Agentic Workflow
     source:
       type: local
-      name: .
+      name: ./workflows
     path: workflows:agentic_w
+    python-dependencies:
+      - llama-index-postprocessor-rankgpt-rerank>=0.2.0
+      - llama-index-vector-stores-qdrant>=0.3.0
+      - llama-index-llms-openai>=0.2.2
+      - llama-index-embeddings-openai>=0.2.4
+      - llama-index-readers-file>=0.2.0
 
   rag_workflow:
     name: RAG Workflow
     source:
       type: local
-      name: .
+      name: ./workflows
     path: workflows:rag_w

examples/python_fullstack/workflows/rag_workflow.py

@@ -1,25 +1,24 @@
 import os
 from logging import getLogger
-
-from qdrant_client import QdrantClient, AsyncQdrantClient
+from pathlib import Path
 
 from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
 from llama_index.core.node_parser import SemanticSplitterNodeParser, SentenceSplitter
 from llama_index.core.response_synthesizers import CompactAndRefine
+from llama_index.core.schema import NodeWithScore
 from llama_index.core.workflow import (
     Context,
     Event,
-    Workflow,
     StartEvent,
     StopEvent,
+    Workflow,
     step,
 )
-from llama_index.core.schema import NodeWithScore
 from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.llms.openai import OpenAI
 from llama_index.postprocessor.rankgpt_rerank import RankGPTRerank
 from llama_index.vector_stores.qdrant import QdrantVectorStore
-
+from qdrant_client import AsyncQdrantClient, QdrantClient
 
 logger = getLogger(__name__)
 
@@ -105,8 +104,9 @@ def build_rag_workflow() -> RAGWorkflow:
     )
 
     # ensure the index exists
+    here = Path(__file__).parent
     if not client.collection_exists("papers"):
-        documents = SimpleDirectoryReader("data").load_data()
+        documents = SimpleDirectoryReader(here / "data").load_data()
 
         # Double pass chunking
         first_node_parser = SemanticSplitterNodeParser(