Harrison/prediction guard update (#5404)

Co-authored-by: Daniel Whitenack <whitenack.daniel@gmail.com>

docs/integrations/predictionguard.md

@@ -14,41 +14,85 @@ There exists a Prediction Guard LLM wrapper, which you can access with
 from langchain.llms import PredictionGuard
 ```
 
-You can provide the name of your Prediction Guard "proxy" as an argument when initializing the LLM:
+You can provide the name of the Prediction Guard model as an argument when initializing the LLM:
 ```python
-pgllm = PredictionGuard(name="your-text-gen-proxy")
-```
-
-Alternatively, you can use Prediction Guard's default proxy for SOTA LLMs:
-```python
-pgllm = PredictionGuard(name="default-text-gen")
+pgllm = PredictionGuard(model="MPT-7B-Instruct")
 ```
 
 You can also provide your access token directly as an argument:
 ```python
-pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")
+pgllm = PredictionGuard(model="MPT-7B-Instruct", token="<your access token>")
+```
+
+Finally, you can provide an "output" argument that is used to structure/ control the output of the LLM:
+```python
+pgllm = PredictionGuard(model="MPT-7B-Instruct", output={"type": "boolean"})
 ```
 
 ## Example usage
 
-Basic usage of the LLM wrapper:
+Basic usage of the controlled or guarded LLM wrapper:
 ```python
-from langchain.llms import PredictionGuard
+import os
 
-pgllm = PredictionGuard(name="default-text-gen")
-pgllm("Tell me a joke")
+import predictionguard as pg
+from langchain.llms import PredictionGuard
+from langchain import PromptTemplate, LLMChain
+
+# Your Prediction Guard API key. Get one at predictionguard.com
+os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
+
+# Define a prompt template
+template = """Respond to the following query based on the context.
+
+Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦
+Exclusive Candle Box - $80 
+Monthly Candle Box - $45 (NEW!)
+Scent of The Month Box - $28 (NEW!)
+Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉
+
+Query: {query}
+
+Result: """
+prompt = PromptTemplate(template=template, input_variables=["query"])
+
+# With "guarding" or controlling the output of the LLM. See the 
+# Prediction Guard docs (https://docs.predictionguard.com) to learn how to 
+# control the output with integer, float, boolean, JSON, and other types and
+# structures.
+pgllm = PredictionGuard(model="MPT-7B-Instruct", 
+                        output={
+                                "type": "categorical",
+                                "categories": [
+                                    "product announcement", 
+                                    "apology", 
+                                    "relational"
+                                    ]
+                                })
+pgllm(prompt.format(query="What kind of post is this?"))
 ```
 
 Basic LLM Chaining with the Prediction Guard wrapper:
 ```python
+import os
+
 from langchain import PromptTemplate, LLMChain
 from langchain.llms import PredictionGuard
 
+# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows
+# you to access all the latest open access models (see https://docs.predictionguard.com)
+os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"
+
+# Your Prediction Guard API key. Get one at predictionguard.com
+os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
+
+pgllm = PredictionGuard(model="OpenAI-text-davinci-003")
+
 template = """Question: {question}
 
 Answer: Let's think step by step."""
 prompt = PromptTemplate(template=template, input_variables=["question"])
-llm_chain = LLMChain(prompt=prompt, llm=PredictionGuard(name="default-text-gen"), verbose=True)
+llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)
 
 question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"