updated guides

fern/docs/pages/guides/ada.mdx

@@ -39,15 +39,17 @@ from langchain import PromptTemplate
 from sentence_transformers import SentenceTransformer
 import lancedb
 from lancedb.embeddings import with_embeddings
-import predictionguard as pg
+from predictionguard import PredictionGuard
 import pandas as pd
 from getpass import getpass
 ```
 
 ## Authenticate to Prediction Guard API
 ```python
-pg_access_token = getpass('Enter your Prediction Guard access token: ')
-os.environ['PREDICTIONGUARD_TOKEN'] = pg_access_token
+# Set your Prediction Guard token as an environmental variable.
+os.environ["PREDICTIONGUARD_API_KEY"] = "<your access token>"
+
+client = PredictionGuard()
 ```
 
 ## Create a SQLite Database
@@ -209,7 +211,7 @@ def generate_sql_query(question, injected_schema):
   prompt_filled = sql_prompt.format(question=question, schema_description=injected_schema)
 
   try:
-      result = pg.Completion.create(
+      result = client.completions.create(
           model="deepseek-coder-6.7b-instruct",
           prompt=prompt_filled,
           max_tokens=300,
@@ -270,7 +272,7 @@ def get_answer(question, data, sql_query):
   prompt_filled = qa_prompt.format(question=question, data=data, sql_query=sql_query)
 
   # Respond to the user
-  output = pg.Completion.create(
+  output = client.completions.create(
       model="Neural-Chat-7B",
       prompt=prompt_filled,
       max_tokens=200,

fern/docs/pages/guides/data-extraction.mdx

@@ -15,10 +15,12 @@ import itertools
 
 import pandas as pd
 from langchain import PromptTemplate
-import predictionguard as pg
+from predictionguard import PredictionGuard
 
+# Set your Prediction Guard token as an environmental variable.
+os.environ["PREDICTIONGUARD_API_KEY"] = "<your access token>"
 
-os.environ["PREDICTIONGUARD_TOKEN"] = "<your access token>"
+client = PredictionGuard()
 
 # Load the JSON data into a dataframe
 data = []
@@ -65,7 +67,7 @@ summary_prompt = PromptTemplate(template=summarize_template,
 # Loop over the rows summarizing the data
 summaries = []
 for i,row in df.iterrows():
-    result=pg.Completion.create(
+    result=client.completions.create(
         model="Nous-Hermes-Llama2-13B",
         prompt=summary_prompt.format(
             transcript=row['transcript']
@@ -121,7 +123,7 @@ for i, row in df.iterrows():
     for q in questions:
 
         # Extract the information
-        result = pg.Completion.create(
+        result = client.completions.create(
             model="Nous-Hermes-Llama2-13B",
             prompt=q_and_a_prompt.format(
                 question=q, transcript_summary=row["summary"]
@@ -132,7 +134,7 @@ for i, row in df.iterrows():
         )
 
         # Generate a factual consistency score
-        fact_score = pg.Factuality.check(
+        fact_score =client.factuality.check(
             reference=row['summary'],
             text=result['choices'][0]['text']
         )

fern/docs/pages/guides/langchainllm.mdx

@@ -7,7 +7,7 @@ helps you "Build applications with LLMs through composability." LangChain doesn'
 
 ## Installation and Setup
 - Install the Python SDK with `pip install predictionguard`
-- Get a Prediction Guard access token (as described [here](https://docs.predictionguard.com/)) and set it as the environment variable `PREDICTIONGUARD_TOKEN`.
+- Get a Prediction Guard access token (as described [here](https://docs.predictionguard.com/)) and set it as the environment variable `PREDICTIONGUARD_API_KEY`.
 ## LLM Wrapper
 
 There exists a Prediction Guard LLM wrapper, which you can access with 
@@ -36,12 +36,14 @@ Basic usage of the controlled or guarded LLM wrapper:
 ```python
 import os
 
-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 access token>"
+
+# Set your Prediction Guard token as an environmental variable.
+os.environ["PREDICTIONGUARD_API_KEY"] = "<your access token>"
+
+client = PredictionGuard()
 
 # Define a prompt template
 template = """Respond to the following query based on the context.
@@ -73,7 +75,7 @@ from langchain import PromptTemplate, LLMChain
 from langchain.llms import PredictionGuard
 
 # Your Prediction Guard API key. Get one at predictionguard.com
-os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
+os.environ["PREDICTIONGUARD_API_KEY"] = "<your Prediction Guard access token>"
 
 pgllm = PredictionGuard(model="Nous-Hermes-Llama2-13B")
 

fern/docs/pages/guides/output.mdx

@@ -15,14 +15,17 @@ This example demonstrates how to integrate PredictionGuard with custom data stru
 1. **Set Environment Variable for PredictionGuard**: Ensure that your PredictionGuard API token is correctly set up in your environment variables.
 
 ```python
-import os
-os.environ["PREDICTIONGUARD_TOKEN"] = "<your access token>"
+from predictionguard import PredictionGuard
+
+# Set your Prediction Guard token as an environmental variable.
+os.environ["PREDICTIONGUARD_API_KEY"] = "<your access token>"
+
+client = PredictionGuard()
 ```
 
 2. **Import Necessary Libraries**: Import PredictionGuard, Pydantic for data validation, and LangChain for output parsing and prompt templating.
 
 ```python
-import predictionguard as pg
 from langchain.output_parsers import PydanticOutputParser
 from langchain.prompts import PromptTemplate
 from pydantic import BaseModel, Field, validator
@@ -61,7 +64,7 @@ prompt = PromptTemplate(
 6. **Generate and Parse Output**: Call PredictionGuard's text completion model "Neural-Chat-7B" to generate an output based on the formatted prompt, then parse the output into the Pydantic model. Handle exceptions for parsing errors.
 
 ```python
-result = pg.Completion.create(
+result = client.completions.create(
     model="Neural-Chat-7B",
     prompt=prompt.format(query="Tell me a joke."),
     max_tokens=200,