fix sample commands, rename, text fixes for langserve

13_sandboxes/codelangchain/agent.py

@@ -18,8 +18,9 @@
 # ## Setup
 
 import modal
-from src import edges, nodes, retrieval
-from src.common import COLOR, PYTHON_VERSION, image
+
+from .src import edges, nodes, retrieval
+from .src.common import COLOR, PYTHON_VERSION, image
 
 # You will need two [Modal Secrets](https://modal.com/docs/guide/secrets) to run this example:
 # one to access the OpenAI API and another to access the LangSmith API for logging the agent's behavior.
@@ -43,7 +44,7 @@
 # library to generate text with a pre-trained model. Let's create a Sandbox with the necessary dependencies.
 
 
-def create_sandbox() -> modal.Sandbox:
+def create_sandbox(app) -> modal.Sandbox:
     # Change this image (and the retrieval logic in the retrieval module)
     # if you want the agent to give coding advice on other libraries!
     agent_image = modal.Image.debian_slim(
@@ -101,7 +102,8 @@ def run(code: str, sb: modal.Sandbox) -> tuple[str, str]:
 
 def construct_graph(sandbox: modal.Sandbox, debug: bool = False):
     from langgraph.graph import StateGraph
-    from src.common import GraphState
+
+    from .src.common import GraphState
 
     # Crawl the transformers documentation to inform our code generation
     context = retrieval.retrieve_docs(debug=debug)
@@ -137,7 +139,7 @@ def go(
     debug: bool = False,
 ):
     """Compiles the Python code generation agent graph and runs it, returning the result."""
-    sb = create_sandbox()
+    sb = create_sandbox(app)
 
     graph = construct_graph(sb, debug=debug)
     runnable = graph.compile()

13_sandboxes/codelangchain/langserve.py

@@ -0,0 +1,86 @@
+# ---
+# pytest: false
+# cmd: ["modal", "serve", "13_sandboxes.codelangchain.langserve"]
+# ---
+
+# # Deploy LangChain and LangGraph applications with LangServe
+
+# This code demonstrates how to deploy a
+# [LangServe](https://python.langchain.com/docs/langserve/) application on Modal.
+# LangServe makes it easy to wrap LangChain and LangGraph applications in a FastAPI server,
+# and Modal makes it easy to deploy FastAPI servers.
+
+# The LangGraph application that it serves is from our [sandboxed LLM coding agent example](https://modal.com/docs/examples/agent).
+
+# You can find the code for the agent and several other code files associated with this example in the
+# [`codelangchain` directory of our examples repo](https://github.com/modal-labs/modal-examples/tree/main/13_sandboxes/codelangchain).
+
+import modal
+
+from .agent import construct_graph, create_sandbox
+from .src.common import image
+
+app = modal.App("example-langserve")
+
+image = image.pip_install("langserve[all]==0.3.0")
+
+
+@app.function(
+    image=image,
+    secrets=[  # see the agent.py file for more information on Secrets
+        modal.Secret.from_name("openai-secret"),
+        modal.Secret.from_name("my-langsmith-secret"),
+    ],
+)
+@modal.asgi_app()
+def serve():
+    from fastapi import FastAPI, responses
+    from fastapi.middleware.cors import CORSMiddleware
+    from langchain_core.runnables import RunnableLambda
+    from langserve import add_routes
+
+    # create a FastAPI app
+    web_app = FastAPI(
+        title="CodeLangChain Server",
+        version="1.0",
+        description="Writes code and checks if it runs.",
+    )
+
+    # set all CORS enabled origins
+    web_app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+        expose_headers=["*"],
+    )
+
+    def inp(question: str) -> dict:
+        return {"keys": {"question": question, "iterations": 0}}
+
+    def out(state: dict) -> str:
+        if "finish" in state:
+            return state["finish"]["keys"]["response"]
+        elif len(state) > 0 and "finish" in state[-1]:
+            return state[-1]["finish"]["keys"]["response"]
+        else:
+            return str(state)
+
+    graph = construct_graph(create_sandbox(app), debug=False).compile()
+
+    chain = RunnableLambda(inp) | graph | RunnableLambda(out)
+
+    add_routes(
+        web_app,
+        chain,
+        path="/codelangchain",
+    )
+
+    # redirect the root to the interactive playground
+    @web_app.get("/")
+    def redirect():
+        return responses.RedirectResponse(url="/codelangchain/playground")
+
+    # return the FastAPI app and Modal will deploy it for us
+    return web_app

13_sandboxes/codelangchain/src/common.py

@@ -14,7 +14,6 @@
     "langgraph==0.2.39",
     "langchain-community==0.3.3",
     "langchain-openai==0.2.3",
-    "langserve[all]==0.3.0",
 )