Automated File Generation from Docs Notebook Changes (#682)

Co-authored-by: joshreini1 <[email protected]> Co-authored-by: Josh Reini <[email protected]>
truera · Dec 15, 2023 · 6630bd0 · 6630bd0
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diff --git a/trulens_eval/examples/quickstart/py_script_quickstarts/all_tools.py b/trulens_eval/examples/quickstart/py_script_quickstarts/all_tools.py
diff --git a/trulens_eval/examples/quickstart/py_script_quickstarts/langchain_quickstart.py b/trulens_eval/examples/quickstart/py_script_quickstarts/langchain_quickstart.py
@@ -2,9 +2,9 @@
 # coding: utf-8
 
 # # Langchain Quickstart
-# 
+#
 # In this quickstart you will create a simple LLM Chain and learn how to log it and get feedback on an LLM response.
-# 
+#
 # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/truera/trulens/blob/main/trulens_eval/examples/quickstart/langchain_quickstart.ipynb)
 
 # ## Setup
@@ -13,46 +13,45 @@
 
 # In[ ]:
 
-
 # ! pip install trulens_eval==0.19.0 openai==1.3.7
 
-
 # In[ ]:
 
-
 import os
+
 os.environ["OPENAI_API_KEY"] = "..."
 os.environ["HUGGINGFACE_API_KEY"] = "..."
 
-
 # ### Import from LangChain and TruLens
 
 # In[ ]:
 
-
 from IPython.display import JSON
 
 # Imports main tools:
-from trulens_eval import TruChain, Feedback, Huggingface, Tru
+from trulens_eval import Feedback
+from trulens_eval import Huggingface
+from trulens_eval import Tru
+from trulens_eval import TruChain
 from trulens_eval.schema import FeedbackResult
+
 tru = Tru()
 
 # Imports from langchain to build app. You may need to install langchain first
 # with the following:
 # ! pip install langchain>=0.0.170
 from langchain.chains import LLMChain
 from langchain.llms import OpenAI
-from langchain.prompts import ChatPromptTemplate, PromptTemplate
+from langchain.prompts import ChatPromptTemplate
 from langchain.prompts import HumanMessagePromptTemplate
-
+from langchain.prompts import PromptTemplate
 
 # ### Create Simple LLM Application
-# 
+#
 # This example uses a LangChain framework and OpenAI LLM
 
 # In[ ]:
 
-
 full_prompt = HumanMessagePromptTemplate(
     prompt=PromptTemplate(
         template=
@@ -67,28 +66,22 @@
 
 chain = LLMChain(llm=llm, prompt=chat_prompt_template, verbose=True)
 
-
 # ### Send your first request
 
 # In[ ]:
 
-
 prompt_input = '¿que hora es?'
 
-
 # In[ ]:
 
-
 llm_response = chain(prompt_input)
 
 display(llm_response)
 
-
 # ## Initialize Feedback Function(s)
 
 # In[ ]:
 
-
 # Initialize Huggingface-based feedback function collection class:
 hugs = Huggingface()
 
@@ -97,67 +90,56 @@
 # By default this will check language match on the main app input and main app
 # output.
 
-
 # ## Instrument chain for logging with TruLens
 
 # In[ ]:
 
-
-tru_recorder = TruChain(chain,
-    app_id='Chain1_ChatApplication',
-    feedbacks=[f_lang_match])
-
+tru_recorder = TruChain(
+    chain, app_id='Chain1_ChatApplication', feedbacks=[f_lang_match]
+)
 
 # In[ ]:
 
-
 with tru_recorder as recording:
     llm_response = chain(prompt_input)
 
 display(llm_response)
 
-
 # ## Retrieve records and feedback
 
 # In[ ]:
 
-
 # The record of the ap invocation can be retrieved from the `recording`:
 
-rec = recording.get() # use .get if only one record
+rec = recording.get()  # use .get if only one record
 # recs = recording.records # use .records if multiple
 
 display(rec)
 
-
 # In[ ]:
 
-
 # The results of the feedback functions can be rertireved from the record. These
 # are `Future` instances (see `concurrent.futures`). You can use `as_completed`
 # to wait until they have finished evaluating.
 
 from concurrent.futures import as_completed
 
-for feedback_future in  as_completed(rec.feedback_results):
+for feedback_future in as_completed(rec.feedback_results):
     feedback, feedback_result = feedback_future.result()
-    
+
     feedback: Feedback
     feedbac_result: FeedbackResult
 
     display(feedback.name, feedback_result.result)
 
-
 # ## Explore in a Dashboard
 
 # In[ ]:
 
-
-tru.run_dashboard() # open a local streamlit app to explore
+tru.run_dashboard()  # open a local streamlit app to explore
 
 # tru.stop_dashboard() # stop if needed
 
-
 # Alternatively, you can run `trulens-eval` from a command line in the same folder to start the dashboard.
 
 # Note: Feedback functions evaluated in the deferred manner can be seen in the "Progress" page of the TruLens dashboard.
@@ -166,6 +148,5 @@
 
 # In[ ]:
 
-
-tru.get_records_and_feedback(app_ids=[])[0] # pass an empty list of app_ids to get all
-
+tru.get_records_and_feedback(app_ids=[]
+                            )[0]  # pass an empty list of app_ids to get all
diff --git a/trulens_eval/examples/quickstart/py_script_quickstarts/llama_index_quickstart.py b/trulens_eval/examples/quickstart/py_script_quickstarts/llama_index_quickstart.py
@@ -2,78 +2,70 @@
 # coding: utf-8
 
 # # Llama-Index Quickstart
-# 
+#
 # In this quickstart you will create a simple Llama Index App and learn how to log it and get feedback on an LLM response.
-# 
+#
 # For evaluation, we will leverage the "hallucination triad" of groundedness, context relevance and answer relevance.
-# 
+#
 # [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/truera/trulens/blob/main/trulens_eval/examples/quickstart/llama_index_quickstart.ipynb)
 
 # ## Setup
-# 
+#
 # ### Install dependencies
 # Let's install some of the dependencies for this notebook if we don't have them already
 
 # In[ ]:
 
-
-# pip install trulens_eval==0.19.0 llama_index>=0.9.15post2 html2text>=2020.1.16 
-
+# pip install trulens_eval==0.19.0 llama_index>=0.9.15post2 html2text>=2020.1.16
 
 # ### Add API keys
 # For this quickstart, you will need Open AI and Huggingface keys. The OpenAI key is used for embeddings and GPT, and the Huggingface key is used for evaluation.
 
 # In[ ]:
 
-
 import os
-os.environ["OPENAI_API_KEY"] = "..."
 
+os.environ["OPENAI_API_KEY"] = "..."
 
 # ### Import from LlamaIndex and TruLens
 
 # In[ ]:
 
-
-from trulens_eval import Feedback, Tru, TruLlama
+from trulens_eval import Feedback
+from trulens_eval import Tru
+from trulens_eval import TruLlama
 from trulens_eval.feedback import Groundedness
 from trulens_eval.feedback.provider.openai import OpenAI
 
 tru = Tru()
 
-
 # ### Create Simple LLM Application
-# 
+#
 # This example uses LlamaIndex which internally uses an OpenAI LLM.
 
 # In[ ]:
 
-
 from llama_index import VectorStoreIndex
 from llama_index.readers.web import SimpleWebPageReader
 
-documents = SimpleWebPageReader(
-    html_to_text=True
-).load_data(["http://paulgraham.com/worked.html"])
+documents = SimpleWebPageReader(html_to_text=True).load_data(
+    ["http://paulgraham.com/worked.html"]
+)
 index = VectorStoreIndex.from_documents(documents)
 
 query_engine = index.as_query_engine()
 
-
 # ### Send your first request
 
 # In[ ]:
 
-
 response = query_engine.query("What did the author do growing up?")
 print(response)
 
-
 # ## Initialize Feedback Function(s)
 
 # In[ ]:
 
-
 import numpy as np
 
 # Initialize provider class
@@ -84,46 +76,40 @@
 # Define a groundedness feedback function
 f_groundedness = Feedback(grounded.groundedness_measure_with_cot_reasons).on(
     TruLlama.select_source_nodes().node.text.collect()
-    ).on_output(
-    ).aggregate(grounded.grounded_statements_aggregator)
+).on_output().aggregate(grounded.grounded_statements_aggregator)
 
 # Question/answer relevance between overall question and answer.
 f_qa_relevance = Feedback(openai.relevance).on_input_output()
 
 # Question/statement relevance between question and each context chunk.
 f_qs_relevance = Feedback(openai.qs_relevance).on_input().on(
     TruLlama.select_source_nodes().node.text
-    ).aggregate(np.mean)
-
+).aggregate(np.mean)
 
 # ## Instrument app for logging with TruLens
 
 # In[ ]:
 
-
-tru_query_engine_recorder = TruLlama(query_engine,
+tru_query_engine_recorder = TruLlama(
+    query_engine,
     app_id='LlamaIndex_App1',
-    feedbacks=[f_groundedness, f_qa_relevance, f_qs_relevance])
-
+    feedbacks=[f_groundedness, f_qa_relevance, f_qs_relevance]
+)
 
 # In[ ]:
 
-
 # or as context manager
 with tru_query_engine_recorder as recording:
     query_engine.query("What did the author do growing up?")
 
-
 # ## Explore in a Dashboard
 
 # In[ ]:
 
-
-tru.run_dashboard() # open a local streamlit app to explore
+tru.run_dashboard()  # open a local streamlit app to explore
 
 # tru.stop_dashboard() # stop if needed
 
-
 # Alternatively, you can run `trulens-eval` from a command line in the same folder to start the dashboard.
 
 # Note: Feedback functions evaluated in the deferred manner can be seen in the "Progress" page of the TruLens dashboard.
@@ -132,6 +118,5 @@
 
 # In[ ]:
 
-
-tru.get_records_and_feedback(app_ids=[])[0] # pass an empty list of app_ids to get all
-
+tru.get_records_and_feedback(app_ids=[]
+                            )[0]  # pass an empty list of app_ids to get all