Harrison/banana fix (#1311)

Co-authored-by: Erik Dunteman <44653944+erik-dunteman@users.noreply.github.com>

docs/ecosystem/bananadev.md

@@ -4,24 +4,28 @@ This page covers how to use the Banana ecosystem within LangChain.
 It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
 
 ## Installation and Setup
+
 - Install with `pip3 install banana-dev`
-- Get an CerebriumAI api key and set it as an environment variable (`BANANA_API_KEY`)
+- Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
 
 ## Define your Banana Template
 
-If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base). 
-This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/). 
+If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base).
+This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/).
 You can check out an example Banana repository [here](https://github.com/conceptofmind/serverless-template-palmyra-base).
 
 ## Build the Banana app
 
-You must include a output in the result. There is a rigid response structure.
+Banana Apps must include the "output" key in the return json. 
+There is a rigid response structure.
+
 ```python
 # Return the results as a dictionary
 result = {'output': result}
 ```
 
 An example inference function would be:
+
 ```python
 def inference(model_inputs:dict) -> dict:
     global model
@@ -31,22 +35,22 @@ def inference(model_inputs:dict) -> dict:
     prompt = model_inputs.get('prompt', None)
     if prompt == None:
         return {'message': "No prompt provided"}
-    
+
     # Run the model
     input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()
     output = model.generate(
-        input_ids, 
-        max_length=100, 
-        do_sample=True, 
-        top_k=50, 
-        top_p=0.95, 
-        num_return_sequences=1, 
-        temperature=0.9, 
-        early_stopping=True, 
-        no_repeat_ngram_size=3, 
-        num_beams=5, 
-        length_penalty=1.5, 
-        repetition_penalty=1.5, 
+        input_ids,
+        max_length=100,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+        temperature=0.9,
+        early_stopping=True,
+        no_repeat_ngram_size=3,
+        num_beams=5,
+        length_penalty=1.5,
+        repetition_penalty=1.5,
         bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
         )
 
@@ -58,17 +62,18 @@ def inference(model_inputs:dict) -> dict:
 
 You can find a full example of a Banana app [here](https://github.com/conceptofmind/serverless-template-palmyra-base/blob/main/app.py).
 
-
 ## Wrappers
 
 ### LLM
 
-There exists an Banana LLM wrapper, which you can access with 
+There exists an Banana LLM wrapper, which you can access with
+
 ```python
 from langchain.llms import Banana
 ```
 
 You need to provide a model key located in the dashboard:
+
 ```python
 llm = Banana(model_key="YOUR_MODEL_KEY")
-```
+```

langchain/llms/bananadev.py

@@ -100,10 +100,15 @@ class Banana(LLM, BaseModel):
         response = banana.run(api_key, model_key, model_inputs)
         try:
             text = response["modelOutputs"][0]["output"]
-        except KeyError:
+        except (KeyError, TypeError):
+            returned = response["modelOutputs"][0]
             raise ValueError(
-                f"Response should be {'modelOutputs': [{'output': 'text'}]}."
-                f"Response was: {response}"
+                "Response should be of schema: {'output': 'text'}."
+                f"\nResponse was: {returned}"
+                "\nTo fix this:"
+                "\n- fork the source repo of the Banana model"
+                "\n- modify app.py to return the above schema"
+                "\n- deploy that as a custom repo"
             )
         if stop is not None:
             # I believe this is required since the stop tokens