Couple small code changes for LangServe walkthrough

10-deploy-ai-agent-langserve/langserve-chatbot.py

@@ -9,14 +9,12 @@ import os
 from langchain_core.messages import SystemMessage, AIMessage, HumanMessage, ToolMessage
 from langserve import RemoteRunnable
 
-from runnable import get_runnable
-
 load_dotenv()
 agent_endpoint_url = os.getenv('AGENT_ENDPOINT_URL', 'http://localhost:8000')
 
 @st.cache_resource
 def create_chatbot_instance():
-    return RemoteRunnable(f"{agent_endpoint_url}/invoke/")
+    return RemoteRunnable(agent_endpoint_url)
 
 chatbot = create_chatbot_instance()
 

10-deploy-ai-agent-langserve/langserve-endpoints.py

@@ -33,12 +33,11 @@ def main():
     # Create the Fast API route to invoke the runnable
     add_routes(
         app,
-        runnable,
-        path="/invoke",
+        runnable
     )
 
     # Start the API
-    uvicorn.run(app, host="localhost", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)
 
 if __name__ == "__main__":
     main()

10-deploy-ai-agent-langserve/requirements.txt

@@ -4,6 +4,7 @@ langchain==0.2.12
 langserve==0.2.2
 uvicorn==0.30.6
 fastapi==0.112.2
+gunicorn==23.0.0
 sse_starlette==2.1.3
 langchain-anthropic==0.1.22
 langchain-groq==0.1.5

10-deploy-ai-agent-langserve/runnable.py

@@ -14,8 +14,8 @@ from langchain_anthropic import ChatAnthropic
 from langchain_core.messages import ToolMessage, AIMessage
 
 from tools.asana_tools import available_asana_functions
-from tools.google_drive_tools import available_drive_functions
-from tools.vector_db_tools import available_vector_db_functions
+# from tools.google_drive_tools import available_drive_functions
+# from tools.vector_db_tools import available_vector_db_functions
 
 load_dotenv()
 model = os.getenv('LLM_MODEL', 'gpt-4o')
@@ -26,7 +26,11 @@ model_mapping = {
     "groq": ChatGroq
 }
 
-available_functions = available_asana_functions | available_drive_functions | available_vector_db_functions
+# Google Drive and Vector DB tools are not included by default because you'll need a bigger cloud instance (DigitalOcean droplet)
+# To load the Vector DB and add Google Drive documents to the knowledgebase. You could also use something else like
+# Pinecone instead of running Chroma locally! Uncomment lines 17 and 18 + replace line 33 with 32 if you want to use these tools.
+# available_functions = available_asana_functions | available_drive_functions | available_vector_db_functions
+available_functions = available_asana_functions
 tools = [tool for _, tool in available_functions.items()]
 
 for key, chatbot_class in model_mapping.items():