Bring back codelangchain (#944)

* move to sandboxes, get running again

* rework as a Python agent

* Refactor to focus on sandboxes

* text updates

* minor text fixes

* fix sample commands, rename, text fixes for langserve

---------

Co-authored-by: Peyton Walters <[email protected]>

06_gpu_and_ml/langchains/codelangchain/README.md → 13_sandboxes/codelangchain/README.md

@@ -3,10 +3,8 @@
 This example deploys a "code agent": a language model that can write and execute
 code in a flexible control flow aimed at completing a task or goal.
 
-The agent is designed to help write programs in the LangChain Expression
-Language (LCEL). And, naturally, it is implemented in LangChain, using the
-LangGraph library to structure the agent and the LangServe framework to turn it
-into a FastAPI app.
+It is implemented in LangChain, using the LangGraph library to structure the agent
+and the LangServe framework to turn it into a FastAPI app.
 
 We use Modal to turn that app into a web endpoint. We also use Modal to
 "sandbox" the agent's code execution, so that it can't accidentally (or when
@@ -21,19 +19,19 @@ the project's context and implementation. Check it out if you're curious!
 To run this app, you need to `pip install modal` and then create the following
 [secrets](https://modal.com/docs/guide/secrets):
 
-- `my-openai-secret` with an OpenAI API key, so that we can query OpenAI's
+- `openai-secret` with an OpenAI API key, so that we can query OpenAI's
   models to power the agent,
 - and `my-langsmith-secret` with a LangSmith API key, so that we can monitor the
   agent's behavior with LangSmith.
 
 Head to the
-[secret creation dashboard](https://modal.com/charles-modal-labs/secrets/create)
+[secret creation dashboard](https://modal.com/secrets/)
 and follow the instructions for each secret type.
 
 Then, you can deploy the app with:
 
 ```bash
-modal deploy app.py
+modal deploy codelangchain.py
 ```
 
 Navigate to the URL that appears in the output and you'll be dropped into an
@@ -44,23 +42,20 @@ You can also navigate to the `/docs` path to see OpenAPI/Swagger docs, for
 everything you'd need to see how to incorporate the agent into your downstream
 applications via API requests.
 
-When developing the app, use `modal serve app.py` to get a hot-reloading server.
+When developing the app, use `modal serve codelangchain.py` to get a hot-reloading server.
 
 ## Repo structure
 
-The web application is defined in `app.py`.
+The web application is defined in `codelangchain.py`.
 
 It wraps the `agent.py` module, which contains the LangChain agent's definition.
 To test the agent in isolation, run `modal run agent.py` in the terminal and
-provide a `--question` about LCEL as input.
+provide a `--question` about Python programming as input.
 
 Because the agent is a graph, it is defined by specifying nodes and edges, which
 are found in `nodes.py` and `edges.py`, respectively.
 
-The logic for spinning up a `modal.Sandbox` to contain the agent's actions is in
-`sandbox.py`.
-
-The retrieval logic is very simple: all of the data from the LCEL docs is
+The retrieval logic is very simple: all of the data from the relevant docs is
 retrieved and put at the beginning of the language model's prompt. You can find
 it in `retrieval.py`.