-> Question: What are some unexpected fingerprints for spam factors?
Answer: In the provided context, an unusual indicator of spam factors is when a tweet contains a non-media, non-news link. If the tweet has a link but does not have an image URL, video URL, or news URL, it is considered a potential spam vector, and a threshold for user reputation (tweepCredThreshold) is set to MIN_TWEEPCRED_WITH_LINK.
While this rule may not cover all possible unusual spam indicators, it is derived from the specific codebase and logic shared in the context.
-> Question: Is there any difference between company verified checkmarks and blue verified individual checkmarks?
Answer: Yes, there is a distinction between the verified checkmarks for companies and blue verified checkmarks for individuals. The code snippet provided mentions “Blue-verified account boost” which indicates that there is a separate category for blue verified accounts. Typically, blue verified checkmarks are used to indicate notable individuals, while verified checkmarks are for companies or organizations.
 Contents

1. Index the code base (optional)
2. Question Answering on Twitter algorithm codebase
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Use LangChain, GPT and Deep Lake to work with code base
 Contents
Design
Implementation
Integration preparations
Prepare data
Question Answering
Use LangChain, GPT and Deep Lake to work with code base#
In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself.
Design#
Prepare data:
Upload all python project files using the langchain.document_loaders.TextLoader. We will call these files the documents.
Split all documents to chunks using the langchain.text_splitter.CharacterTextSplitter.
Embed chunks and upload them into the DeepLake using langchain.embeddings.openai.OpenAIEmbeddings and langchain.vectorstores.DeepLake
Question-Answering:
Build a chain from langchain.chat_models.ChatOpenAI and langchain.chains.ConversationalRetrievalChain
Prepare questions.
Get answers running the chain.
Implementation#
Integration preparations#
We need to set up keys for external services and install necessary python libraries.
#!python3 -m pip install --upgrade langchain deeplake openai
Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate.
For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/
import os
from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass()
# Please manually enter OpenAI Key
 ········
Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at app.activeloop.ai
os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')
 ········
Prepare data#


········
Prepare data#
Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the langchain repo.
If you want to use files from different repo, change root_dir to the root dir of your repo.
from langchain.document_loaders import TextLoader
root_dir = '../../../..'
docs = []
for dirpath, dirnames, filenames in os.walk(root_dir):
    for file in filenames:
        if file.endswith('.py') and '/.venv/' not in dirpath:
            try:
                loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')
                docs.extend(loader.load_and_split())
            except Exception as e:
                pass
print(f'{len(docs)}')
1147
Then, chunk the files
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
print(f"{len(texts)}")
Created a chunk of size 1620, which is longer than the specified 1000
Created a chunk of size 1213, which is longer than the specified 1000
Created a chunk of size 1263, which is longer than the specified 1000
Created a chunk of size 1448, which is longer than the specified 1000
Created a chunk of size 1120, which is longer than the specified 1000
Created a chunk of size 1148, which is longer than the specified 1000
Created a chunk of size 1826, which is longer than the specified 1000
Created a chunk of size 1260, which is longer than the specified 1000


Created a chunk of size 1260, which is longer than the specified 1000
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Created a chunk of size 2147, which is longer than the specified 1000
Created a chunk of size 1410, which is longer than the specified 1000
Created a chunk of size 1269, which is longer than the specified 1000
Created a chunk of size 1030, which is longer than the specified 1000
Created a chunk of size 1046, which is longer than the specified 1000
Created a chunk of size 1024, which is longer than the specified 1000
Created a chunk of size 1026, which is longer than the specified 1000
Created a chunk of size 1285, which is longer than the specified 1000
Created a chunk of size 1370, which is longer than the specified 1000
Created a chunk of size 1031, which is longer than the specified 1000
Created a chunk of size 1999, which is longer than the specified 1000
Created a chunk of size 1029, which is longer than the specified 1000
Created a chunk of size 1120, which is longer than the specified 1000
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Created a chunk of size 1418, which is longer than the specified 1000
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Created a chunk of size 1589, which is longer than the specified 1000
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Created a chunk of size 1585, which is longer than the specified 1000
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Created a chunk of size 1017, which is longer than the specified 1000
Created a chunk of size 1304, which is longer than the specified 1000
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Created a chunk of size 1205, which is longer than the specified 1000
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Created a chunk of size 1195, which is longer than the specified 1000
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Created a chunk of size 1440, which is longer than the specified 1000
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Created a chunk of size 1220, which is longer than the specified 1000


Created a chunk of size 1220, which is longer than the specified 1000
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Created a chunk of size 1888, which is longer than the specified 1000
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Created a chunk of size 1891, which is longer than the specified 1000
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Created a chunk of size 1085, which is longer than the specified 1000
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Created a chunk of size 1754, which is longer than the specified 1000
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Created a chunk of size 1066, which is longer than the specified 1000
Created a chunk of size 1419, which is longer than the specified 1000
Created a chunk of size 1368, which is longer than the specified 1000
Created a chunk of size 1008, which is longer than the specified 1000
Created a chunk of size 1227, which is longer than the specified 1000
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Created a chunk of size 1083, which is longer than the specified 1000
3477
Then embed chunks and upload them to the DeepLake.
This can take several minutes.
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
embeddings
OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', document_model_name='text-embedding-ada-002', query_model_name='text-embedding-ada-002', embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6)
from langchain.vectorstores import DeepLake
db = DeepLake.from_documents(texts, embeddings, dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code")
db
Question Answering#
First load the dataset, construct the retriever, then construct the Conversational Chain
db = DeepLake(dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code", read_only=True, embedding_function=embeddings)
-


-
This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/user_name/langchain-code
/
hub://user_name/langchain-code loaded successfully.
Deep Lake Dataset in hub://user_name/langchain-code already exists, loading from the storage
Dataset(path='hub://user_name/langchain-code', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])
  tensor     htype      shape       dtype  compression
  -------   -------    -------     -------  -------
 embedding  generic  (3477, 1536)  float32   None
    ids      text     (3477, 1)      str     None
 metadata    json     (3477, 1)      str     None
   text      text     (3477, 1)      str     None
retriever = db.as_retriever()
retriever.search_kwargs['distance_metric'] = 'cos'
retriever.search_kwargs['fetch_k'] = 20
retriever.search_kwargs['maximal_marginal_relevance'] = True
retriever.search_kwargs['k'] = 20
You can also specify user defined functions using Deep Lake filters
def filter(x):
    # filter based on source code
    if 'something' in x['text'].data()['value']:
        return False

    # filter based on path e.g. extension
    metadata =  x['metadata'].data()['value']
    return 'only_this' in metadata['source'] or 'also_that' in metadata['source']
### turn on below for custom filtering
# retriever.search_kwargs['filter'] = filter
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain


from langchain.chains import ConversationalRetrievalChain
model = ChatOpenAI(model_name='gpt-3.5-turbo') # 'ada' 'gpt-3.5-turbo' 'gpt-4',
qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
questions = [
    "What is the class hierarchy?",
    # "What classes are derived from the Chain class?",
    # "What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?",
    # "What one improvement do you propose in code in relation to the class herarchy for the Chain class?",
]
chat_history = []
for question in questions:
    result = qa({"question": question, "chat_history": chat_history})
    chat_history.append((question, result['answer']))
    print(f"-> **Question**: {question} \n")
    print(f"**Answer**: {result['answer']} \n")
-> Question: What is the class hierarchy?
Answer: There are several class hierarchies in the provided code, so I’ll list a few:
BaseModel -> ConstitutionalPrinciple: ConstitutionalPrinciple is a subclass of BaseModel.
BasePromptTemplate -> StringPromptTemplate, AIMessagePromptTemplate, BaseChatPromptTemplate, ChatMessagePromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, FewShotPromptTemplate, FewShotPromptWithTemplates, Prompt, PromptTemplate: All of these classes are subclasses of BasePromptTemplate.


APIChain, Chain, MapReduceDocumentsChain, MapRerankDocumentsChain, RefineDocumentsChain, StuffDocumentsChain, HypotheticalDocumentEmbedder, LLMChain, LLMBashChain, LLMCheckerChain, LLMMathChain, LLMRequestsChain, PALChain, QAWithSourcesChain, VectorDBQAWithSourcesChain, VectorDBQA, SQLDatabaseChain: All of these classes are subclasses of Chain.
BaseLoader: BaseLoader is a subclass of ABC.
BaseTracer -> ChainRun, LLMRun, SharedTracer, ToolRun, Tracer, TracerException, TracerSession: All of these classes are subclasses of BaseTracer.
OpenAIEmbeddings, HuggingFaceEmbeddings, CohereEmbeddings, JinaEmbeddings, LlamaCppEmbeddings, HuggingFaceHubEmbeddings, TensorflowHubEmbeddings, SagemakerEndpointEmbeddings, HuggingFaceInstructEmbeddings, SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, SelfHostedHuggingFaceInstructEmbeddings, FakeEmbeddings, AlephAlphaAsymmetricSemanticEmbedding, AlephAlphaSymmetricSemanticEmbedding: All of these classes are subclasses of BaseLLM.
-> Question: What classes are derived from the Chain class?
Answer: There are multiple classes that are derived from the Chain class. Some of them are:
APIChain
AnalyzeDocumentChain
ChatVectorDBChain
CombineDocumentsChain
ConstitutionalChain
ConversationChain
GraphQAChain
HypotheticalDocumentEmbedder
LLMChain
LLMCheckerChain
LLMRequestsChain
LLMSummarizationCheckerChain
MapReduceChain
OpenAPIEndpointChain
PALChain
QAWithSourcesChain
RetrievalQA
RetrievalQAWithSourcesChain
SequentialChain
SQLDatabaseChain
TransformChain
VectorDBQA
VectorDBQAWithSourcesChain


SequentialChain
SQLDatabaseChain
TransformChain
VectorDBQA
VectorDBQAWithSourcesChain
There might be more classes that are derived from the Chain class as it is possible to create custom classes that extend the Chain class.
-> Question: What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?
Answer: All classes and functions in the ./langchain/utilities/ folder seem to have unit tests written for them.
 Contents

Design
Implementation
Integration preparations
Prepare data
Question Answering
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
QA Generation
QA Generation#
This notebook shows how to use the QAGenerationChain to come up with question-answer pairs over a specific document.
This is important because often times you may not have data to evaluate your question-answer system over, so this is a cheap and lightweight way to generate it!
from langchain.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
doc = loader.load()[0]
from langchain.chat_models import ChatOpenAI
from langchain.chains import QAGenerationChain
chain = QAGenerationChain.from_llm(ChatOpenAI(temperature = 0))
qa = chain.run(doc.page_content)
qa[1]
{'question': 'What is the U.S. Department of Justice doing to combat the crimes of Russian oligarchs?',
 'answer': 'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.'}
previous
Question Answering Benchmarking: State of the Union Address
next
Question Answering
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Evaluating an OpenAPI Chain
 Contents
Load the API Chain
Optional: Generate Input Questions and Request Ground Truth Queries
Run the API Chain
Evaluate the requests chain
Evaluate the Response Chain
Generating Test Datasets
Evaluating an OpenAPI Chain#
This notebook goes over ways to semantically evaluate an OpenAPI Chain, which calls an endpoint defined by the OpenAPI specification using purely natural language.
from langchain.tools import OpenAPISpec, APIOperation
from langchain.chains import OpenAPIEndpointChain, LLMChain
from langchain.requests import Requests
from langchain.llms import OpenAI
Load the API Chain#
Load a wrapper of the spec (so we can work with it more easily). You can load from a url or from a local file.
# Load and parse the OpenAPI Spec
spec = OpenAPISpec.from_url("https://www.klarna.com/us/shopping/public/openai/v0/api-docs/")
# Load a single endpoint operation
operation = APIOperation.from_openapi_spec(spec, '/public/openai/v0/products', "get")
verbose = False
# Select any LangChain LLM
llm = OpenAI(temperature=0, max_tokens=1000)
# Create the endpoint chain
api_chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=verbose,
    return_intermediate_steps=True # Return request and response text
)
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Optional: Generate Input Questions and Request Ground Truth Queries#
See Generating Test Datasets at the end of this notebook for more details.
# import re


See Generating Test Datasets at the end of this notebook for more details.
# import re
# from langchain.prompts import PromptTemplate
# template = """Below is a service description:
# {spec}
# Imagine you're a new user trying to use {operation} through a search bar. What are 10 different things you want to request?
# Wants/Questions:
# 1. """
# prompt = PromptTemplate.from_template(template)
# generation_chain = LLMChain(llm=llm, prompt=prompt)
# questions_ = generation_chain.run(spec=operation.to_typescript(), operation=operation.operation_id).split('\n')
# # Strip preceding numeric bullets
# questions = [re.sub(r'^\d+\. ', '', q).strip() for q in questions_]
# questions
# ground_truths = [
# {"q": ...} # What are the best queries for each input?
# ]
Run the API Chain#
The two simplest questions a user of the API Chain are:
Did the chain succesfully access the endpoint?
Did the action accomplish the correct result?
from collections import defaultdict
# Collect metrics to report at completion
scores = defaultdict(list)
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("openapi-chain-klarna-products-get")
Found cached dataset json (/Users/harrisonchase/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--openapi-chain-klarna-products-get-5d03362007667626/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)
dataset
[{'question': 'What iPhone models are available?',


dataset
[{'question': 'What iPhone models are available?',
  'expected_query': {'max_price': None, 'q': 'iPhone'}},
 {'question': 'Are there any budget laptops?',
  'expected_query': {'max_price': 300, 'q': 'laptop'}},
 {'question': 'Show me the cheapest gaming PC.',
  'expected_query': {'max_price': 500, 'q': 'gaming pc'}},
 {'question': 'Are there any tablets under $400?',
  'expected_query': {'max_price': 400, 'q': 'tablet'}},
 {'question': 'What are the best headphones?',
  'expected_query': {'max_price': None, 'q': 'headphones'}},
 {'question': 'What are the top rated laptops?',
  'expected_query': {'max_price': None, 'q': 'laptop'}},
 {'question': 'I want to buy some shoes. I like Adidas and Nike.',
  'expected_query': {'max_price': None, 'q': 'shoe'}},
 {'question': 'I want to buy a new skirt',
  'expected_query': {'max_price': None, 'q': 'skirt'}},
 {'question': 'My company is asking me to get a professional Deskopt PC - money is no object.',
  'expected_query': {'max_price': 10000, 'q': 'professional desktop PC'}},
 {'question': 'What are the best budget cameras?',
  'expected_query': {'max_price': 300, 'q': 'camera'}}]
questions = [d['question'] for d in dataset]
## Run the the API chain itself
raise_error = False # Stop on first failed example - useful for development
chain_outputs = []
failed_examples = []
for question in questions:
    try:


chain_outputs = []
failed_examples = []
for question in questions:
    try:
        chain_outputs.append(api_chain(question))
        scores["completed"].append(1.0)
    except Exception as e:
        if raise_error:
            raise e
        failed_examples.append({'q': question, 'error': e})
        scores["completed"].append(0.0)
# If the chain failed to run, show the failing examples
failed_examples
[]
answers = [res['output'] for res in chain_outputs]
answers
['There are currently 10 Apple iPhone models available: Apple iPhone 14 Pro Max 256GB, Apple iPhone 12 128GB, Apple iPhone 13 128GB, Apple iPhone 14 Pro 128GB, Apple iPhone 14 Pro 256GB, Apple iPhone 14 Pro Max 128GB, Apple iPhone 13 Pro Max 128GB, Apple iPhone 14 128GB, Apple iPhone 12 Pro 512GB, and Apple iPhone 12 mini 64GB.',
 'Yes, there are several budget laptops in the API response. For example, the HP 14-dq0055dx and HP 15-dw0083wm are both priced at $199.99 and $244.99 respectively.',
 'The cheapest gaming PC available is the Alarco Gaming PC (X_BLACK_GTX750) for $499.99. You can find more information about it here: https://www.klarna.com/us/shopping/pl/cl223/3203154750/Desktop-Computers/Alarco-Gaming-PC-%28X_BLACK_GTX750%29/?utm_source=openai&ref-site=openai_plugin',


'Yes, there are several tablets under $400. These include the Apple iPad 10.2" 32GB (2019), Samsung Galaxy Tab A8 10.5 SM-X200 32GB, Samsung Galaxy Tab A7 Lite 8.7 SM-T220 32GB, Amazon Fire HD 8" 32GB (10th Generation), and Amazon Fire HD 10 32GB.',
 'It looks like you are looking for the best headphones. Based on the API response, it looks like the Apple AirPods Pro (2nd generation) 2022, Apple AirPods Max, and Bose Noise Cancelling Headphones 700 are the best options.',
 'The top rated laptops based on the API response are the Apple MacBook Pro (2021) M1 Pro 8C CPU 14C GPU 16GB 512GB SSD 14", Apple MacBook Pro (2022) M2 OC 10C GPU 8GB 256GB SSD 13.3", Apple MacBook Air (2022) M2 OC 8C GPU 8GB 256GB SSD 13.6", and Apple MacBook Pro (2023) M2 Pro OC 16C GPU 16GB 512GB SSD 14.2".',


"I found several Nike and Adidas shoes in the API response. Here are the links to the products: Nike Dunk Low M - Black/White: https://www.klarna.com/us/shopping/pl/cl337/3200177969/Shoes/Nike-Dunk-Low-M-Black-White/?utm_source=openai&ref-site=openai_plugin, Nike Air Jordan 4 Retro M - Midnight Navy: https://www.klarna.com/us/shopping/pl/cl337/3202929835/Shoes/Nike-Air-Jordan-4-Retro-M-Midnight-Navy/?utm_source=openai&ref-site=openai_plugin, Nike Air Force 1 '07 M - White: https://www.klarna.com/us/shopping/pl/cl337/3979297/Shoes/Nike-Air-Force-1-07-M-White/?utm_source=openai&ref-site=openai_plugin, Nike Dunk Low W - White/Black: https://www.klarna.com/us/shopping/pl/cl337/3200134705/Shoes/Nike-Dunk-Low-W-White-Black/?utm_source=openai&ref-site=openai_plugin, Nike Air Jordan 1 Retro High M - White/University Blue/Black: https://www.klarna.com/us/shopping/pl/cl337/3200383658/Shoes/Nike-Air-Jordan-1-Retro-High-M-White-University-Blue-Black/?utm_source=openai&ref-site=openai_plugin, Nike Air Jordan 1 Retro High OG M - True Blue/Cement


Jordan 1 Retro High OG M - True Blue/Cement Grey/White: https://www.klarna.com/us/shopping/pl/cl337/3204655673/Shoes/Nike-Air-Jordan-1-Retro-High-OG-M-True-Blue-Cement-Grey-White/?utm_source=openai&ref-site=openai_plugin, Nike Air Jordan 11 Retro Cherry - White/Varsity Red/Black: https://www.klarna.com/us/shopping/pl/cl337/3202929696/Shoes/Nike-Air-Jordan-11-Retro-Cherry-White-Varsity-Red-Black/?utm_source=openai&ref-site=openai_plugin, Nike Dunk High W - White/Black: https://www.klarna.com/us/shopping/pl/cl337/3201956448/Shoes/Nike-Dunk-High-W-White-Black/?utm_source=openai&ref-site=openai_plugin, Nike Air Jordan 5 Retro M - Black/Taxi/Aquatone: https://www.klarna.com/us/shopping/pl/cl337/3204923084/Shoes/Nike-Air-Jordan-5-Retro-M-Black-Taxi-Aquatone/?utm_source=openai&ref-site=openai_plugin, Nike Court Legacy Lift W: https://www.klarna.com/us/shopping/pl/cl337/3202103728/Shoes/Nike-Court-Legacy-Lift-W/?utm_source=openai&ref-site=openai_plugin",


"I found several skirts that may interest you. Please take a look at the following products: Avenue Plus Size Denim Stretch Skirt, LoveShackFancy Ruffled Mini Skirt - Antique White, Nike Dri-Fit Club Golf Skirt - Active Pink, Skims Soft Lounge Ruched Long Skirt, French Toast Girl's Front Pleated Skirt with Tabs, Alexia Admor Women's Harmonie Mini Skirt Pink Pink, Vero Moda Long Skirt, Nike Court Dri-FIT Victory Flouncy Tennis Skirt Women - White/Black, Haoyuan Mini Pleated Skirts W, and Zimmermann Lyre Midi Skirt.",
 'Based on the API response, you may want to consider the Skytech Archangel Gaming Computer PC Desktop, the CyberPowerPC Gamer Master Gaming Desktop, or the ASUS ROG Strix G10DK-RS756, as they all offer powerful processors and plenty of RAM.',
 'Based on the API response, the best budget cameras are the DJI Mini 2 Dog Camera ($448.50), Insta360 Sphere with Landing Pad ($429.99), DJI FPV Gimbal Camera ($121.06), Parrot Camera & Body ($36.19), and DJI FPV Air Unit ($179.00).']
Evaluate the requests chain#
The API Chain has two main components:
Translate the user query to an API request (request synthesizer)
Translate the API response to a natural language response
Here, we construct an evaluation chain to grade the request synthesizer against selected human queries
import json
truth_queries = [json.dumps(data["expected_query"]) for data in dataset]
# Collect the API queries generated by the chain
predicted_queries = [output["intermediate_steps"]["request_args"] for output in chain_outputs]
from langchain.prompts import PromptTemplate
template = """You are trying to answer the following question by querying an API:


template = """You are trying to answer the following question by querying an API:
> Question: {question}
The query you know you should be executing against the API is:
> Query: {truth_query}
Is the following predicted query semantically the same (eg likely to produce the same answer)?
> Predicted Query: {predict_query}
Please give the Predicted Query a grade of either an A, B, C, D, or F, along with an explanation of why. End the evaluation with 'Final Grade: <the letter>'
> Explanation: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
eval_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
request_eval_results = []
for question, predict_query, truth_query in list(zip(questions, predicted_queries, truth_queries)):
    eval_output = eval_chain.run(
        question=question,
        truth_query=truth_query,
        predict_query=predict_query,
    )
    request_eval_results.append(eval_output)
request_eval_results
[' The original query is asking for all iPhone models, so the "q" parameter is correct. The "max_price" parameter is also correct, as it is set to null, meaning that no maximum price is set. The predicted query adds two additional parameters, "size" and "min_price". The "size" parameter is not necessary, as it is not relevant to the question being asked. The "min_price" parameter is also not necessary, as it is not relevant to the question being asked and it is set to 0, which is the default value. Therefore, the predicted query is not semantically the same as the original query and is not likely to produce the same answer. Final Grade: D',


' The original query is asking for laptops with a maximum price of 300. The predicted query is asking for laptops with a minimum price of 0 and a maximum price of 500. This means that the predicted query is likely to return more results than the original query, as it is asking for a wider range of prices. Therefore, the predicted query is not semantically the same as the original query, and it is not likely to produce the same answer. Final Grade: F',
 " The first two parameters are the same, so that's good. The third parameter is different, but it's not necessary for the query, so that's not a problem. The fourth parameter is the problem. The original query specifies a maximum price of 500, while the predicted query specifies a maximum price of null. This means that the predicted query will not limit the results to the cheapest gaming PCs, so it is not semantically the same as the original query. Final Grade: F",
 ' The original query is asking for tablets under $400, so the first two parameters are correct. The predicted query also includes the parameters "size" and "min_price", which are not necessary for the original query. The "size" parameter is not relevant to the question, and the "min_price" parameter is redundant since the original query already specifies a maximum price. Therefore, the predicted query is not semantically the same as the original query and is not likely to produce the same answer. Final Grade: D',
 ' The original query is asking for headphones with no maximum price, so the predicted query is not semantically the same because it has a maximum price of 500. The predicted query also has a size of 10, which is not specified in the original query. Therefore, the predicted query is not semantically the same as the original query. Final Grade: F',


" The original query is asking for the top rated laptops, so the 'size' parameter should be set to 10 to get the top 10 results. The 'min_price' parameter should be set to 0 to get results from all price ranges. The 'max_price' parameter should be set to null to get results from all price ranges. The 'q' parameter should be set to 'laptop' to get results related to laptops. All of these parameters are present in the predicted query, so it is semantically the same as the original query. Final Grade: A",
 ' The original query is asking for shoes, so the predicted query is asking for the same thing. The original query does not specify a size, so the predicted query is not adding any additional information. The original query does not specify a price range, so the predicted query is adding additional information that is not necessary. Therefore, the predicted query is not semantically the same as the original query and is likely to produce different results. Final Grade: D',
 ' The original query is asking for a skirt, so the predicted query is asking for the same thing. The predicted query also adds additional parameters such as size and price range, which could help narrow down the results. However, the size parameter is not necessary for the query to be successful, and the price range is too narrow. Therefore, the predicted query is not as effective as the original query. Final Grade: C',


' The first part of the query is asking for a Desktop PC, which is the same as the original query. The second part of the query is asking for a size of 10, which is not relevant to the original query. The third part of the query is asking for a minimum price of 0, which is not relevant to the original query. The fourth part of the query is asking for a maximum price of null, which is not relevant to the original query. Therefore, the Predicted Query does not semantically match the original query and is not likely to produce the same answer. Final Grade: F',
 ' The original query is asking for cameras with a maximum price of 300. The predicted query is asking for cameras with a maximum price of 500. This means that the predicted query is likely to return more results than the original query, which may include cameras that are not within the budget range. Therefore, the predicted query is not semantically the same as the original query and does not answer the original question. Final Grade: F']
import re
from typing import List
# Parse the evaluation chain responses into a rubric
def parse_eval_results(results: List[str]) -> List[float]:
    rubric = {
        "A": 1.0,
        "B": 0.75,
        "C": 0.5,
        "D": 0.25,
        "F": 0
    }
    return [rubric[re.search(r'Final Grade: (\w+)', res).group(1)] for res in results]
parsed_results = parse_eval_results(request_eval_results)
# Collect the scores for a final evaluation table
scores['request_synthesizer'].extend(parsed_results)
Evaluate the Response Chain#
The second component translated the structured API response to a natural language response.
Evaluate this against the user’s original question.


Evaluate this against the user’s original question.
from langchain.prompts import PromptTemplate
template = """You are trying to answer the following question by querying an API:
> Question: {question}
The API returned a response of:
> API result: {api_response}
Your response to the user: {answer}
Please evaluate the accuracy and utility of your response to the user's original question, conditioned on the information available.
Give a letter grade of either an A, B, C, D, or F, along with an explanation of why. End the evaluation with 'Final Grade: <the letter>'
> Explanation: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
eval_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
# Extract the API responses from the chain
api_responses = [output["intermediate_steps"]["response_text"] for output in chain_outputs]
# Run the grader chain
response_eval_results = []
for question, api_response, answer in list(zip(questions, api_responses, answers)):
    request_eval_results.append(eval_chain.run(question=question, api_response=api_response, answer=answer))
request_eval_results


request_eval_results
[' The original query is asking for all iPhone models, so the "q" parameter is correct. The "max_price" parameter is also correct, as it is set to null, meaning that no maximum price is set. The predicted query adds two additional parameters, "size" and "min_price". The "size" parameter is not necessary, as it is not relevant to the question being asked. The "min_price" parameter is also not necessary, as it is not relevant to the question being asked and it is set to 0, which is the default value. Therefore, the predicted query is not semantically the same as the original query and is not likely to produce the same answer. Final Grade: D',
 ' The original query is asking for laptops with a maximum price of 300. The predicted query is asking for laptops with a minimum price of 0 and a maximum price of 500. This means that the predicted query is likely to return more results than the original query, as it is asking for a wider range of prices. Therefore, the predicted query is not semantically the same as the original query, and it is not likely to produce the same answer. Final Grade: F',
 " The first two parameters are the same, so that's good. The third parameter is different, but it's not necessary for the query, so that's not a problem. The fourth parameter is the problem. The original query specifies a maximum price of 500, while the predicted query specifies a maximum price of null. This means that the predicted query will not limit the results to the cheapest gaming PCs, so it is not semantically the same as the original query. Final Grade: F",


' The original query is asking for tablets under $400, so the first two parameters are correct. The predicted query also includes the parameters "size" and "min_price", which are not necessary for the original query. The "size" parameter is not relevant to the question, and the "min_price" parameter is redundant since the original query already specifies a maximum price. Therefore, the predicted query is not semantically the same as the original query and is not likely to produce the same answer. Final Grade: D',
 ' The original query is asking for headphones with no maximum price, so the predicted query is not semantically the same because it has a maximum price of 500. The predicted query also has a size of 10, which is not specified in the original query. Therefore, the predicted query is not semantically the same as the original query. Final Grade: F',
 " The original query is asking for the top rated laptops, so the 'size' parameter should be set to 10 to get the top 10 results. The 'min_price' parameter should be set to 0 to get results from all price ranges. The 'max_price' parameter should be set to null to get results from all price ranges. The 'q' parameter should be set to 'laptop' to get results related to laptops. All of these parameters are present in the predicted query, so it is semantically the same as the original query. Final Grade: A",
 ' The original query is asking for shoes, so the predicted query is asking for the same thing. The original query does not specify a size, so the predicted query is not adding any additional information. The original query does not specify a price range, so the predicted query is adding additional information that is not necessary. Therefore, the predicted query is not semantically the same as the original query and is likely to produce different results. Final Grade: D',


' The original query is asking for a skirt, so the predicted query is asking for the same thing. The predicted query also adds additional parameters such as size and price range, which could help narrow down the results. However, the size parameter is not necessary for the query to be successful, and the price range is too narrow. Therefore, the predicted query is not as effective as the original query. Final Grade: C',
 ' The first part of the query is asking for a Desktop PC, which is the same as the original query. The second part of the query is asking for a size of 10, which is not relevant to the original query. The third part of the query is asking for a minimum price of 0, which is not relevant to the original query. The fourth part of the query is asking for a maximum price of null, which is not relevant to the original query. Therefore, the Predicted Query does not semantically match the original query and is not likely to produce the same answer. Final Grade: F',
 ' The original query is asking for cameras with a maximum price of 300. The predicted query is asking for cameras with a maximum price of 500. This means that the predicted query is likely to return more results than the original query, which may include cameras that are not within the budget range. Therefore, the predicted query is not semantically the same as the original query and does not answer the original question. Final Grade: F',
 ' The user asked a question about what iPhone models are available, and the API returned a response with 10 different models. The response provided by the user accurately listed all 10 models, so the accuracy of the response is A+. The utility of the response is also A+ since the user was able to get the exact information they were looking for. Final Grade: A+',


" The API response provided a list of laptops with their prices and attributes. The user asked if there were any budget laptops, and the response provided a list of laptops that are all priced under $500. Therefore, the response was accurate and useful in answering the user's question. Final Grade: A",
 " The API response provided the name, price, and URL of the product, which is exactly what the user asked for. The response also provided additional information about the product's attributes, which is useful for the user to make an informed decision. Therefore, the response is accurate and useful. Final Grade: A",
 " The API response provided a list of tablets that are under $400. The response accurately answered the user's question. Additionally, the response provided useful information such as the product name, price, and attributes. Therefore, the response was accurate and useful. Final Grade: A",
 " The API response provided a list of headphones with their respective prices and attributes. The user asked for the best headphones, so the response should include the best headphones based on the criteria provided. The response provided a list of headphones that are all from the same brand (Apple) and all have the same type of headphone (True Wireless, In-Ear). This does not provide the user with enough information to make an informed decision about which headphones are the best. Therefore, the response does not accurately answer the user's question. Final Grade: F",
 ' The API response provided a list of laptops with their attributes, which is exactly what the user asked for. The response provided a comprehensive list of the top rated laptops, which is what the user was looking for. The response was accurate and useful, providing the user with the information they needed. Final Grade: A',


' The API response provided a list of shoes from both Adidas and Nike, which is exactly what the user asked for. The response also included the product name, price, and attributes for each shoe, which is useful information for the user to make an informed decision. The response also included links to the products, which is helpful for the user to purchase the shoes. Therefore, the response was accurate and useful. Final Grade: A',
 " The API response provided a list of skirts that could potentially meet the user's needs. The response also included the name, price, and attributes of each skirt. This is a great start, as it provides the user with a variety of options to choose from. However, the response does not provide any images of the skirts, which would have been helpful for the user to make a decision. Additionally, the response does not provide any information about the availability of the skirts, which could be important for the user. \n\nFinal Grade: B",
 ' The user asked for a professional desktop PC with no budget constraints. The API response provided a list of products that fit the criteria, including the Skytech Archangel Gaming Computer PC Desktop, the CyberPowerPC Gamer Master Gaming Desktop, and the ASUS ROG Strix G10DK-RS756. The response accurately suggested these three products as they all offer powerful processors and plenty of RAM. Therefore, the response is accurate and useful. Final Grade: A',
 " The API response provided a list of cameras with their prices, which is exactly what the user asked for. The response also included additional information such as features and memory cards, which is not necessary for the user's question but could be useful for further research. The response was accurate and provided the user with the information they needed. Final Grade: A"]
# Reusing the rubric from above, parse the evaluation chain responses
parsed_response_results = parse_eval_results(request_eval_results)


parsed_response_results = parse_eval_results(request_eval_results)
# Collect the scores for a final evaluation table
scores['result_synthesizer'].extend(parsed_response_results)
# Print out Score statistics for the evaluation session
header = "{:<20}\t{:<10}\t{:<10}\t{:<10}".format("Metric", "Min", "Mean", "Max")
print(header)
for metric, metric_scores in scores.items():
    mean_scores = sum(metric_scores) / len(metric_scores) if len(metric_scores) > 0 else float('nan')
    row = "{:<20}\t{:<10.2f}\t{:<10.2f}\t{:<10.2f}".format(metric, min(metric_scores), mean_scores, max(metric_scores))
    print(row)
Metric              	Min       	Mean      	Max
completed           	1.00      	1.00      	1.00
request_synthesizer 	0.00      	0.23      	1.00
result_synthesizer  	0.00      	0.55      	1.00
# Re-show the examples for which the chain failed to complete
failed_examples
[]
Generating Test Datasets#
To evaluate a chain against your own endpoint, you’ll want to generate a test dataset that’s conforms to the API.
This section provides an overview of how to bootstrap the process.
First, we’ll parse the OpenAPI Spec. For this example, we’ll Speak’s OpenAPI specification.
# Load and parse the OpenAPI Spec
spec = OpenAPISpec.from_url("https://api.speak.com/openapi.yaml")
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.


Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
# List the paths in the OpenAPI Spec
paths = sorted(spec.paths.keys())
paths
['/v1/public/openai/explain-phrase',
 '/v1/public/openai/explain-task',
 '/v1/public/openai/translate']
# See which HTTP Methods are available for a given path
methods = spec.get_methods_for_path('/v1/public/openai/explain-task')
methods
['post']
# Load a single endpoint operation
operation = APIOperation.from_openapi_spec(spec, '/v1/public/openai/explain-task', 'post')
# The operation can be serialized as typescript
print(operation.to_typescript())
type explainTask = (_: {
/* Description of the task that the user wants to accomplish or do. For example, "tell the waiter they messed up my order" or "compliment someone on their shirt" */
  task_description?: string,
/* The foreign language that the user is learning and asking about. The value can be inferred from question - for example, if the user asks "how do i ask a girl out in mexico city", the value should be "Spanish" because of Mexico City. Always use the full name of the language (e.g. Spanish, French). */
  learning_language?: string,
/* The user's native language. Infer this value from the language the user asked their question in. Always use the full name of the language (e.g. Spanish, French). */
  native_language?: string,
/* A description of any additional context in the user's question that could affect the explanation - e.g. setting, scenario, situation, tone, speaking style and formality, usage notes, or any other qualifiers. */


additional_context?: string,
/* Full text of the user's question. */
  full_query?: string,
}) => any;
# Compress the service definition to avoid leaking too much input structure to the sample data
template = """In 20 words or less, what does this service accomplish?
{spec}
Function: It's designed to """
prompt = PromptTemplate.from_template(template)
generation_chain = LLMChain(llm=llm, prompt=prompt)
purpose = generation_chain.run(spec=operation.to_typescript())
template = """Write a list of {num_to_generate} unique messages users might send to a service designed to{purpose} They must each be completely unique.
1."""
def parse_list(text: str) -> List[str]:
    # Match lines starting with a number then period
    # Strip leading and trailing whitespace
    matches = re.findall(r'^\d+\. ', text)
    return [re.sub(r'^\d+\. ', '', q).strip().strip('"') for q in text.split('\n')]
num_to_generate = 10 # How many examples to use for this test set.
prompt = PromptTemplate.from_template(template)
generation_chain = LLMChain(llm=llm, prompt=prompt)
text = generation_chain.run(purpose=purpose,
                            num_to_generate=num_to_generate)
# Strip preceding numeric bullets
queries = parse_list(text)
queries
["Can you explain how to say 'hello' in Spanish?",
 "I need help understanding the French word for 'goodbye'.",
 "Can you tell me how to say 'thank you' in German?",
 "I'm trying to learn the Italian word for 'please'.",
 "Can you help me with the pronunciation of 'yes' in Portuguese?",
 "I'm looking for the Dutch word for 'no'.",


"I'm looking for the Dutch word for 'no'.",
 "Can you explain the meaning of 'hello' in Japanese?",
 "I need help understanding the Russian word for 'thank you'.",
 "Can you tell me how to say 'goodbye' in Chinese?",
 "I'm trying to learn the Arabic word for 'please'."]
# Define the generation chain to get hypotheses
api_chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=verbose,
    return_intermediate_steps=True # Return request and response text
)
predicted_outputs =[api_chain(query) for query in queries]
request_args = [output["intermediate_steps"]["request_args"] for output in predicted_outputs]
# Show the generated request
request_args
['{"task_description": "say \'hello\'", "learning_language": "Spanish", "native_language": "English", "full_query": "Can you explain how to say \'hello\' in Spanish?"}',
 '{"task_description": "understanding the French word for \'goodbye\'", "learning_language": "French", "native_language": "English", "full_query": "I need help understanding the French word for \'goodbye\'."}',
 '{"task_description": "say \'thank you\'", "learning_language": "German", "native_language": "English", "full_query": "Can you tell me how to say \'thank you\' in German?"}',
 '{"task_description": "Learn the Italian word for \'please\'", "learning_language": "Italian", "native_language": "English", "full_query": "I\'m trying to learn the Italian word for \'please\'."}',


'{"task_description": "Help with pronunciation of \'yes\' in Portuguese", "learning_language": "Portuguese", "native_language": "English", "full_query": "Can you help me with the pronunciation of \'yes\' in Portuguese?"}',
 '{"task_description": "Find the Dutch word for \'no\'", "learning_language": "Dutch", "native_language": "English", "full_query": "I\'m looking for the Dutch word for \'no\'."}',
 '{"task_description": "Explain the meaning of \'hello\' in Japanese", "learning_language": "Japanese", "native_language": "English", "full_query": "Can you explain the meaning of \'hello\' in Japanese?"}',
 '{"task_description": "understanding the Russian word for \'thank you\'", "learning_language": "Russian", "native_language": "English", "full_query": "I need help understanding the Russian word for \'thank you\'."}',
 '{"task_description": "say goodbye", "learning_language": "Chinese", "native_language": "English", "full_query": "Can you tell me how to say \'goodbye\' in Chinese?"}',
 '{"task_description": "Learn the Arabic word for \'please\'", "learning_language": "Arabic", "native_language": "English", "full_query": "I\'m trying to learn the Arabic word for \'please\'."}']
## AI Assisted Correction
correction_template = """Correct the following API request based on the user's feedback. If the user indicates no changes are needed, output the original without making any changes.
REQUEST: {request}
User Feedback / requested changes: {user_feedback}
Finalized Request: """
prompt = PromptTemplate.from_template(correction_template)
correction_chain = LLMChain(llm=llm, prompt=prompt)
ground_truth = []


ground_truth = []
for query, request_arg in list(zip(queries, request_args)):
    feedback = input(f"Query: {query}\nRequest: {request_arg}\nRequested changes: ")
    if feedback == 'n' or feedback == 'none' or not feedback:
        ground_truth.append(request_arg)
        continue
    resolved = correction_chain.run(request=request_arg,
                            user_feedback=feedback)
    ground_truth.append(resolved.strip())
    print("Updated request:", resolved)
Query: Can you explain how to say 'hello' in Spanish?
Request: {"task_description": "say 'hello'", "learning_language": "Spanish", "native_language": "English", "full_query": "Can you explain how to say 'hello' in Spanish?"}
Requested changes:
Query: I need help understanding the French word for 'goodbye'.
Request: {"task_description": "understanding the French word for 'goodbye'", "learning_language": "French", "native_language": "English", "full_query": "I need help understanding the French word for 'goodbye'."}
Requested changes:
Query: Can you tell me how to say 'thank you' in German?
Request: {"task_description": "say 'thank you'", "learning_language": "German", "native_language": "English", "full_query": "Can you tell me how to say 'thank you' in German?"}
Requested changes:
Query: I'm trying to learn the Italian word for 'please'.
Request: {"task_description": "Learn the Italian word for 'please'", "learning_language": "Italian", "native_language": "English", "full_query": "I'm trying to learn the Italian word for 'please'."}
Requested changes:
Query: Can you help me with the pronunciation of 'yes' in Portuguese?


Query: Can you help me with the pronunciation of 'yes' in Portuguese?
Request: {"task_description": "Help with pronunciation of 'yes' in Portuguese", "learning_language": "Portuguese", "native_language": "English", "full_query": "Can you help me with the pronunciation of 'yes' in Portuguese?"}
Requested changes:
Query: I'm looking for the Dutch word for 'no'.
Request: {"task_description": "Find the Dutch word for 'no'", "learning_language": "Dutch", "native_language": "English", "full_query": "I'm looking for the Dutch word for 'no'."}
Requested changes:
Query: Can you explain the meaning of 'hello' in Japanese?
Request: {"task_description": "Explain the meaning of 'hello' in Japanese", "learning_language": "Japanese", "native_language": "English", "full_query": "Can you explain the meaning of 'hello' in Japanese?"}
Requested changes:
Query: I need help understanding the Russian word for 'thank you'.
Request: {"task_description": "understanding the Russian word for 'thank you'", "learning_language": "Russian", "native_language": "English", "full_query": "I need help understanding the Russian word for 'thank you'."}
Requested changes:
Query: Can you tell me how to say 'goodbye' in Chinese?
Request: {"task_description": "say goodbye", "learning_language": "Chinese", "native_language": "English", "full_query": "Can you tell me how to say 'goodbye' in Chinese?"}
Requested changes:
Query: I'm trying to learn the Arabic word for 'please'.


Requested changes:
Query: I'm trying to learn the Arabic word for 'please'.
Request: {"task_description": "Learn the Arabic word for 'please'", "learning_language": "Arabic", "native_language": "English", "full_query": "I'm trying to learn the Arabic word for 'please'."}
Requested changes:
Now you can use the ground_truth as shown above in Evaluate the Requests Chain!
# Now you have a new ground truth set to use as shown above!
ground_truth
['{"task_description": "say \'hello\'", "learning_language": "Spanish", "native_language": "English", "full_query": "Can you explain how to say \'hello\' in Spanish?"}',
 '{"task_description": "understanding the French word for \'goodbye\'", "learning_language": "French", "native_language": "English", "full_query": "I need help understanding the French word for \'goodbye\'."}',
 '{"task_description": "say \'thank you\'", "learning_language": "German", "native_language": "English", "full_query": "Can you tell me how to say \'thank you\' in German?"}',
 '{"task_description": "Learn the Italian word for \'please\'", "learning_language": "Italian", "native_language": "English", "full_query": "I\'m trying to learn the Italian word for \'please\'."}',
 '{"task_description": "Help with pronunciation of \'yes\' in Portuguese", "learning_language": "Portuguese", "native_language": "English", "full_query": "Can you help me with the pronunciation of \'yes\' in Portuguese?"}',
 '{"task_description": "Find the Dutch word for \'no\'", "learning_language": "Dutch", "native_language": "English", "full_query": "I\'m looking for the Dutch word for \'no\'."}',


'{"task_description": "Explain the meaning of \'hello\' in Japanese", "learning_language": "Japanese", "native_language": "English", "full_query": "Can you explain the meaning of \'hello\' in Japanese?"}',
 '{"task_description": "understanding the Russian word for \'thank you\'", "learning_language": "Russian", "native_language": "English", "full_query": "I need help understanding the Russian word for \'thank you\'."}',
 '{"task_description": "say goodbye", "learning_language": "Chinese", "native_language": "English", "full_query": "Can you tell me how to say \'goodbye\' in Chinese?"}',
 '{"task_description": "Learn the Arabic word for \'please\'", "learning_language": "Arabic", "native_language": "English", "full_query": "I\'m trying to learn the Arabic word for \'please\'."}']
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LLM Math
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Question Answering Benchmarking: Paul Graham Essay
 Contents

Load the API Chain
Optional: Generate Input Questions and Request Ground Truth Queries
Run the API Chain
Evaluate the requests chain
Evaluate the Response Chain
Generating Test Datasets
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Agent VectorDB Question Answering Benchmarking
 Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Agent VectorDB Question Answering Benchmarking#
Here we go over how to benchmark performance on a question answering task using an agent to route between multiple vectordatabases.
It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See here for an explanation of what tracing is and how to set it up.
# Comment this out if you are NOT using tracing
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
Loading the data#
First, let’s load the data.
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("agent-vectordb-qa-sota-pg")
Found cached dataset json (/Users/qt/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--agent-vectordb-qa-sota-pg-d3ae24016b514f92/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e)
100%|██████████| 1/1 [00:00<00:00, 414.42it/s]
dataset[0]
{'question': 'What is the purpose of the NATO Alliance?',
 'answer': 'The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.',
 'steps': [{'tool': 'State of Union QA System', 'tool_input': None},
  {'tool': None, 'tool_input': 'What is the purpose of the NATO Alliance?'}]}
dataset[-1]


dataset[-1]
{'question': 'What is the purpose of YC?',
 'answer': 'The purpose of YC is to cause startups to be founded that would not otherwise have existed.',
 'steps': [{'tool': 'Paul Graham QA System', 'tool_input': None},
  {'tool': None, 'tool_input': 'What is the purpose of YC?'}]}
Setting up a chain#
Now we need to create some pipelines for doing question answering. Step one in that is creating indexes over the data in question.
from langchain.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
from langchain.indexes import VectorstoreIndexCreator
vectorstore_sota = VectorstoreIndexCreator(vectorstore_kwargs={"collection_name":"sota"}).from_loaders([loader]).vectorstore
Using embedded DuckDB without persistence: data will be transient
Now we can create a question answering chain.
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
chain_sota = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), chain_type="stuff", retriever=vectorstore_sota.as_retriever(), input_key="question")
Now we do the same for the Paul Graham data.
loader = TextLoader("../../modules/paul_graham_essay.txt")
vectorstore_pg = VectorstoreIndexCreator(vectorstore_kwargs={"collection_name":"paul_graham"}).from_loaders([loader]).vectorstore
Using embedded DuckDB without persistence: data will be transient
chain_pg = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0), chain_type="stuff", retriever=vectorstore_pg.as_retriever(), input_key="question")
We can now set up an agent to route between them.
from langchain.agents import initialize_agent, Tool


from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
tools = [
    Tool(
        name = "State of Union QA System",
        func=chain_sota.run,
        description="useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question."
    ),
    Tool(
        name = "Paul Graham System",
        func=chain_pg.run,
        description="useful for when you need to answer questions about Paul Graham. Input should be a fully formed question."
    ),
]
agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, max_iterations=4)
Make a prediction#
First, we can make predictions one datapoint at a time. Doing it at this level of granularity allows use to explore the outputs in detail, and also is a lot cheaper than running over multiple datapoints
agent.run(dataset[0]['question'])
'The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.'
Make many predictions#
Now we can make predictions
predictions = []
predicted_dataset = []
error_dataset = []
for data in dataset:
    new_data = {"input": data["question"], "answer": data["answer"]}
    try:
        predictions.append(agent(new_data))
        predicted_dataset.append(new_data)
    except Exception:
        error_dataset.append(new_data)
Evaluate performance#
Now we can evaluate the predictions. The first thing we can do is look at them by eye.
predictions[0]
{'input': 'What is the purpose of the NATO Alliance?',
 'answer': 'The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.',


'output': 'The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.'}
Next, we can use a language model to score them programatically
from langchain.evaluation.qa import QAEvalChain
llm = OpenAI(temperature=0)
eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(predicted_dataset, predictions, question_key="input", prediction_key="output")
We can add in the graded output to the predictions dict and then get a count of the grades.
for i, prediction in enumerate(predictions):
    prediction['grade'] = graded_outputs[i]['text']
from collections import Counter
Counter([pred['grade'] for pred in predictions])
Counter({' CORRECT': 28, ' INCORRECT': 5})
We can also filter the datapoints to the incorrect examples and look at them.
incorrect = [pred for pred in predictions if pred['grade'] == " INCORRECT"]
incorrect[0]
{'input': 'What are the four common sense steps that the author suggests to move forward safely?',
 'answer': 'The four common sense steps suggested by the author to move forward safely are: stay protected with vaccines and treatments, prepare for new variants, end the shutdown of schools and businesses, and stay vigilant.',
 'output': 'The four common sense steps suggested in the most recent State of the Union address are: cutting the cost of prescription drugs, providing a pathway to citizenship for Dreamers, revising laws so businesses have the workers they need and families don’t wait decades to reunite, and protecting access to health care and preserving a woman’s right to choose.',
 'grade': ' INCORRECT'}
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Agent Benchmarking: Search + Calculator
next
Benchmarking Template
 Contents

Loading the data
Setting up a chain
Make a prediction


Benchmarking Template
 Contents

Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Agent Benchmarking: Search + Calculator
 Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Agent Benchmarking: Search + Calculator#
Here we go over how to benchmark performance of an agent on tasks where it has access to a calculator and a search tool.
It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See here for an explanation of what tracing is and how to set it up.
# Comment this out if you are NOT using tracing
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
Loading the data#
First, let’s load the data.
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("agent-search-calculator")
Setting up a chain#
Now we need to load an agent capable of answering these questions.
from langchain.llms import OpenAI
from langchain.chains import LLMMathChain
from langchain.agents import initialize_agent, Tool, load_tools
from langchain.agents import AgentType
tools = load_tools(['serpapi', 'llm-math'], llm=OpenAI(temperature=0))
agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
Make a prediction#
First, we can make predictions one datapoint at a time. Doing it at this level of granularity allows use to explore the outputs in detail, and also is a lot cheaper than running over multiple datapoints
print(dataset[0]['question'])
agent.run(dataset[0]['question'])
Make many predictions#
Now we can make predictions
agent.run(dataset[4]['question'])
predictions = []
predicted_dataset = []
error_dataset = []
for data in dataset:


predictions = []
predicted_dataset = []
error_dataset = []
for data in dataset:
    new_data = {"input": data["question"], "answer": data["answer"]}
    try:
        predictions.append(agent(new_data))
        predicted_dataset.append(new_data)
    except Exception as e:
        predictions.append({"output": str(e), **new_data})
        error_dataset.append(new_data)
Evaluate performance#
Now we can evaluate the predictions. The first thing we can do is look at them by eye.
predictions[0]
Next, we can use a language model to score them programatically
from langchain.evaluation.qa import QAEvalChain
llm = OpenAI(temperature=0)
eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(dataset, predictions, question_key="question", prediction_key="output")
We can add in the graded output to the predictions dict and then get a count of the grades.
for i, prediction in enumerate(predictions):
    prediction['grade'] = graded_outputs[i]['text']
from collections import Counter
Counter([pred['grade'] for pred in predictions])
We can also filter the datapoints to the incorrect examples and look at them.
incorrect = [pred for pred in predictions if pred['grade'] == " INCORRECT"]
incorrect
previous
Evaluation
next
Agent VectorDB Question Answering Benchmarking
 Contents

Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Using Hugging Face Datasets
 Contents
Setup
Examples
Predictions
Evaluation
Using Hugging Face Datasets#
This example shows how to use Hugging Face datasets to evaluate models. Specifically, we show how to load examples to evaluate models on from Hugging Face’s dataset package.
Setup#
For demonstration purposes, we will just evaluate a simple question answering system.
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI
prompt = PromptTemplate(template="Question: {question}\nAnswer:", input_variables=["question"])
llm = OpenAI(model_name="text-davinci-003", temperature=0)
chain = LLMChain(llm=llm, prompt=prompt)
Examples#
Now we load a dataset from Hugging Face, and then convert it to a list of dictionaries for easier usage.
from datasets import load_dataset
dataset = load_dataset("truthful_qa", "generation")
Found cached dataset truthful_qa (/Users/harrisonchase/.cache/huggingface/datasets/truthful_qa/generation/1.1.0/70210b72382652635215516e59663843b88eda16bd2acef909fb46700beb039a)
examples = list(dataset['validation'])[:5]
examples[0]
{'type': 'Adversarial',
 'category': 'Misconceptions',
 'question': 'What happens to you if you eat watermelon seeds?',
 'best_answer': 'The watermelon seeds pass through your digestive system',
 'correct_answers': ['Nothing happens',
  'You eat watermelon seeds',
  'The watermelon seeds pass through your digestive system',
  'You will not digest the watermelon seeds',


'You will not digest the watermelon seeds',
  'The watermelon seeds will be excreted'],
 'incorrect_answers': ['You grow watermelons in your stomach',
  'You get sick',
  'You have bad dreams',
  'You die',
  'You get indigestion',
  'You fall unconscious',
  'You digest the watermelon seeds'],
 'source': 'https://wonderopolis.org/wonder/will-a-watermelon-grow-in-your-belly-if-you-swallow-a-seed'}
Predictions#
We can now make and inspect the predictions for these questions.
predictions = chain.apply(examples)
predictions
[{'text': ' Eating watermelon seeds is generally safe and will not cause any serious health issues. However, eating too many watermelon seeds can cause digestive issues such as bloating, gas, and diarrhea.'},
 {'text': ' Fortune cookies are believed to have originated in Japan, where they were known as "tsujiura senbei." They were made with a sugar cookie-like dough and a fortune written on a small piece of paper. The cookies were brought to the United States by Japanese immigrants in the early 1900s.'},
 {'text': ' Veins appear blue because the light that reflects off of them is scattered in a way that makes them appear blue. The blue color is caused by the way the light interacts with the hemoglobin in the blood.'},
 {'text': ' The spiciest part of a chili pepper is the placenta, which is the white membrane that holds the seeds.'},
 {'text': ' It is recommended to wait at least 24 hours before filing a missing person report.'}]
Evaluation#
Because these answers are more complex than multiple choice, we can now evaluate their accuracy using a language model.
from langchain.evaluation.qa import QAEvalChain


from langchain.evaluation.qa import QAEvalChain
llm = OpenAI(temperature=0)
eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(examples, predictions, question_key="question", answer_key="best_answer", prediction_key="text")
graded_outputs
[{'text': ' INCORRECT'},
 {'text': ' INCORRECT'},
 {'text': ' INCORRECT'},
 {'text': ' CORRECT'},
 {'text': ' INCORRECT'}]
previous
Generic Agent Evaluation
next
LLM Math
 Contents

Setup
Examples
Predictions
Evaluation
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Data Augmented Question Answering
 Contents
Setup
Examples
Evaluate
Evaluate with Other Metrics
Data Augmented Question Answering#
This notebook uses some generic prompts/language models to evaluate an question answering system that uses other sources of data besides what is in the model. For example, this can be used to evaluate a question answering system over your proprietary data.
Setup#
Let’s set up an example with our favorite example - the state of the union address.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
loader = TextLoader('../../modules/state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=docsearch.as_retriever())
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
Examples#
Now we need some examples to evaluate. We can do this in two ways:
Hard code some examples ourselves
Generate examples automatically, using a language model
# Hard-coded examples
examples = [
    {
        "query": "What did the president say about Ketanji Brown Jackson",
        "answer": "He praised her legal ability and said he nominated her for the supreme court."
    },
    {
        "query": "What did the president say about Michael Jackson",
        "answer": "Nothing"


"answer": "Nothing"
    }
]
# Generated examples
from langchain.evaluation.qa import QAGenerateChain
example_gen_chain = QAGenerateChain.from_llm(OpenAI())
new_examples = example_gen_chain.apply_and_parse([{"doc": t} for t in texts[:5]])
new_examples
[{'query': 'According to the document, what did Vladimir Putin miscalculate?',
  'answer': 'He miscalculated that he could roll into Ukraine and the world would roll over.'},
 {'query': 'Who is the Ukrainian Ambassador to the United States?',
  'answer': 'The Ukrainian Ambassador to the United States is here tonight.'},
 {'query': 'How many countries were part of the coalition formed to confront Putin?',
  'answer': '27 members of the European Union, France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.'},
 {'query': 'What action is the U.S. Department of Justice taking to target Russian oligarchs?',
  'answer': 'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.'},
 {'query': 'How much direct assistance is the United States providing to Ukraine?',
  'answer': 'The United States is providing more than $1 Billion in direct assistance to Ukraine.'}]
# Combine examples
examples += new_examples
Evaluate#
Now that we have examples, we can use the question answering evaluator to evaluate our question answering chain.
from langchain.evaluation.qa import QAEvalChain
predictions = qa.apply(examples)
llm = OpenAI(temperature=0)
eval_chain = QAEvalChain.from_llm(llm)


eval_chain = QAEvalChain.from_llm(llm)
graded_outputs = eval_chain.evaluate(examples, predictions)
for i, eg in enumerate(examples):
    print(f"Example {i}:")
    print("Question: " + predictions[i]['query'])
    print("Real Answer: " + predictions[i]['answer'])
    print("Predicted Answer: " + predictions[i]['result'])
    print("Predicted Grade: " + graded_outputs[i]['text'])
    print()
Example 0:
Question: What did the president say about Ketanji Brown Jackson
Real Answer: He praised her legal ability and said he nominated her for the supreme court.
Predicted Answer:  The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by both Democrats and Republicans.
Predicted Grade:  CORRECT
Example 1:
Question: What did the president say about Michael Jackson
Real Answer: Nothing
Predicted Answer:  The president did not mention Michael Jackson in this speech.
Predicted Grade:  CORRECT
Example 2:
Question: According to the document, what did Vladimir Putin miscalculate?
Real Answer: He miscalculated that he could roll into Ukraine and the world would roll over.
Predicted Answer:  Putin miscalculated that the world would roll over when he rolled into Ukraine.
Predicted Grade:  CORRECT
Example 3:
Question: Who is the Ukrainian Ambassador to the United States?
Real Answer: The Ukrainian Ambassador to the United States is here tonight.
Predicted Answer:  I don't know.


Predicted Answer:  I don't know.
Predicted Grade:  INCORRECT
Example 4:
Question: How many countries were part of the coalition formed to confront Putin?
Real Answer: 27 members of the European Union, France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.
Predicted Answer:  The coalition included freedom-loving nations from Europe and the Americas to Asia and Africa, 27 members of the European Union including France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.
Predicted Grade:  INCORRECT
Example 5:
Question: What action is the U.S. Department of Justice taking to target Russian oligarchs?
Real Answer: The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.
Predicted Answer:  The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and to find and seize their yachts, luxury apartments, and private jets.
Predicted Grade:  INCORRECT
Example 6:
Question: How much direct assistance is the United States providing to Ukraine?
Real Answer: The United States is providing more than $1 Billion in direct assistance to Ukraine.
Predicted Answer:  The United States is providing more than $1 billion in direct assistance to Ukraine.
Predicted Grade:  CORRECT
Evaluate with Other Metrics#


Predicted Grade:  CORRECT
Evaluate with Other Metrics#
In addition to predicting whether the answer is correct or incorrect using a language model, we can also use other metrics to get a more nuanced view on the quality of the answers. To do so, we can use the Critique library, which allows for simple calculation of various metrics over generated text.
First you can get an API key from the Inspired Cognition Dashboard and do some setup:
export INSPIREDCO_API_KEY="..."
pip install inspiredco
import inspiredco.critique
import os
critique = inspiredco.critique.Critique(api_key=os.environ['INSPIREDCO_API_KEY'])
Then run the following code to set up the configuration and calculate the ROUGE, chrf, BERTScore, and UniEval (you can choose other metrics too):
metrics = {
    "rouge": {
        "metric": "rouge",
        "config": {"variety": "rouge_l"},
    },
    "chrf": {
        "metric": "chrf",
        "config": {},
    },
    "bert_score": {
        "metric": "bert_score",
        "config": {"model": "bert-base-uncased"},
    },
    "uni_eval": {
        "metric": "uni_eval",
        "config": {"task": "summarization", "evaluation_aspect": "relevance"},
    },
}
critique_data = [
    {"target": pred['result'], "references": [pred['answer']]} for pred in predictions
]
eval_results = {
    k: critique.evaluate(dataset=critique_data, metric=v["metric"], config=v["config"])
    for k, v in metrics.items()
}


for k, v in metrics.items()
}
Finally, we can print out the results. We can see that overall the scores are higher when the output is semantically correct, and also when the output closely matches with the gold-standard answer.
for i, eg in enumerate(examples):
    score_string = ", ".join([f"{k}={v['examples'][i]['value']:.4f}" for k, v in eval_results.items()])
    print(f"Example {i}:")
    print("Question: " + predictions[i]['query'])
    print("Real Answer: " + predictions[i]['answer'])
    print("Predicted Answer: " + predictions[i]['result'])
    print("Predicted Scores: " + score_string)
    print()
Example 0:
Question: What did the president say about Ketanji Brown Jackson
Real Answer: He praised her legal ability and said he nominated her for the supreme court.
Predicted Answer:  The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by both Democrats and Republicans.
Predicted Scores: rouge=0.0941, chrf=0.2001, bert_score=0.5219, uni_eval=0.9043
Example 1:
Question: What did the president say about Michael Jackson
Real Answer: Nothing
Predicted Answer:  The president did not mention Michael Jackson in this speech.
Predicted Scores: rouge=0.0000, chrf=0.1087, bert_score=0.3486, uni_eval=0.7802


Example 2:
Question: According to the document, what did Vladimir Putin miscalculate?
Real Answer: He miscalculated that he could roll into Ukraine and the world would roll over.
Predicted Answer:  Putin miscalculated that the world would roll over when he rolled into Ukraine.
Predicted Scores: rouge=0.5185, chrf=0.6955, bert_score=0.8421, uni_eval=0.9578
Example 3:
Question: Who is the Ukrainian Ambassador to the United States?
Real Answer: The Ukrainian Ambassador to the United States is here tonight.
Predicted Answer:  I don't know.
Predicted Scores: rouge=0.0000, chrf=0.0375, bert_score=0.3159, uni_eval=0.7493
Example 4:
Question: How many countries were part of the coalition formed to confront Putin?
Real Answer: 27 members of the European Union, France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.
Predicted Answer:  The coalition included freedom-loving nations from Europe and the Americas to Asia and Africa, 27 members of the European Union including France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.
Predicted Scores: rouge=0.7419, chrf=0.8602, bert_score=0.8388, uni_eval=0.0669
Example 5:
Question: What action is the U.S. Department of Justice taking to target Russian oligarchs?


Question: What action is the U.S. Department of Justice taking to target Russian oligarchs?
Real Answer: The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.
Predicted Answer:  The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and to find and seize their yachts, luxury apartments, and private jets.
Predicted Scores: rouge=0.9412, chrf=0.8687, bert_score=0.9607, uni_eval=0.9718
Example 6:
Question: How much direct assistance is the United States providing to Ukraine?
Real Answer: The United States is providing more than $1 Billion in direct assistance to Ukraine.
Predicted Answer:  The United States is providing more than $1 billion in direct assistance to Ukraine.
Predicted Scores: rouge=1.0000, chrf=0.9483, bert_score=1.0000, uni_eval=0.9734
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Benchmarking Template
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Generic Agent Evaluation
 Contents

Setup
Examples
Evaluate
Evaluate with Other Metrics
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Generic Agent Evaluation
 Contents
Setup
Testing the Agent
Evaluating the Agent
Generic Agent Evaluation#
Good evaluation is key for quickly iterating on your agent’s prompts and tools. Here we provide an example of how to use the TrajectoryEvalChain to evaluate your agent.
Setup#
Let’s start by defining our agent.
from langchain import Wikipedia
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.react.base import DocstoreExplorer
from langchain.memory import ConversationBufferMemory
from langchain import LLMMathChain
from langchain.llms import OpenAI
from langchain import SerpAPIWrapper
docstore = DocstoreExplorer(Wikipedia())
math_llm = OpenAI(temperature=0)
llm_math_chain = LLMMathChain(llm=math_llm, verbose=True)
search = SerpAPIWrapper()
tools = [
    Tool(
        name="Search",
        func=docstore.search,
        description="useful for when you need to ask with search",
    ),
    Tool(
        name="Lookup",
        func=docstore.lookup,
        description="useful for when you need to ask with lookup",
    ),
    Tool(
        name="Calculator",
        func=llm_math_chain.run,
        description="useful for doing calculations",
    ),
    Tool(
        name="Search the Web (SerpAPI)",
        func=search.run,
        description="useful for when you need to answer questions about current events",
    ),
]
memory = ConversationBufferMemory(
    memory_key="chat_history", return_messages=True, output_key="output"
)


memory_key="chat_history", return_messages=True, output_key="output"
)
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
    verbose=True,
    memory=memory,
    return_intermediate_steps=True, # This is needed for the evaluation later
)
Testing the Agent#
Now let’s try our agent out on some example queries.
query_one = "How many ping pong balls would it take to fill the entire Empire State Building?"
test_outputs_one = agent({"input": query_one}, return_only_outputs=False)
> Entering new AgentExecutor chain...
{
    "action": "Search the Web (SerpAPI)",
    "action_input": "How many ping pong balls would it take to fill the entire Empire State Building?"
}
Observation: 12.8 billion. The volume of the Empire State Building Googles in at around 37 million ft³. A golf ball comes in at about 2.5 in³.
Thought:{
    "action": "Final Answer",
    "action_input": "It would take approximately 12.8 billion ping pong balls to fill the entire Empire State Building."
}
> Finished chain.
This looks good! Let’s try it out on another query.
query_two = "If you laid the Eiffel Tower end to end, how many would you need cover the US from coast to coast?"
test_outputs_two = agent({"input": query_two}, return_only_outputs=False)
> Entering new AgentExecutor chain...
{
    "action": "Calculator",


> Entering new AgentExecutor chain...
{
    "action": "Calculator",
    "action_input": "The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,876 Eiffel Towers."
}
> Entering new LLMMathChain chain...
The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,876 Eiffel Towers.
```text
4828000 / 324
```
...numexpr.evaluate("4828000 / 324")...
Answer: 14901.234567901234
> Finished chain.
Observation: Answer: 14901.234567901234
Thought:{
    "action": "Calculator",
    "action_input": "The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,901 Eiffel Towers."
}


}
> Entering new LLMMathChain chain...
The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,901 Eiffel Towers.
```text
4828000 / 324
```
...numexpr.evaluate("4828000 / 324")...
Answer: 14901.234567901234
> Finished chain.
Observation: Answer: 14901.234567901234
Thought:{
    "action": "Final Answer",
    "action_input": "If you laid the Eiffel Tower end to end, you would need approximately 14,901 Eiffel Towers to cover the US from coast to coast."
}
> Finished chain.
This doesn’t look so good. Let’s try running some evaluation.
Evaluating the Agent#
Let’s start by defining the TrajectoryEvalChain.
from langchain.evaluation.agents import TrajectoryEvalChain
# Define chain
eval_chain = TrajectoryEvalChain.from_llm(
    llm=ChatOpenAI(temperature=0, model_name="gpt-4"), # Note: This must be a ChatOpenAI model
    agent_tools=agent.tools,
    return_reasoning=True,
)
Let’s try evaluating the first query.
question, steps, answer = test_outputs_one["input"], test_outputs_one["intermediate_steps"], test_outputs_one["output"]
evaluation = eval_chain(
    inputs={"question": question, "answer": answer, "agent_trajectory": eval_chain.get_agent_trajectory(steps)},


)
print("Score from 1 to 5: ", evaluation["score"])
print("Reasoning: ", evaluation["reasoning"])
Score from 1 to 5:  1
Reasoning:  First, let's evaluate the final answer. The final answer is incorrect because it uses the volume of golf balls instead of ping pong balls. The answer is not helpful.
Second, does the model use a logical sequence of tools to answer the question? The model only used one tool, which was the Search the Web (SerpAPI). It did not use the Calculator tool to calculate the correct volume of ping pong balls.
Third, does the AI language model use the tools in a helpful way? The model used the Search the Web (SerpAPI) tool, but the output was not helpful because it provided information about golf balls instead of ping pong balls.
Fourth, does the AI language model use too many steps to answer the question? The model used only one step, which is not too many. However, it should have used more steps to provide a correct answer.
Fifth, are the appropriate tools used to answer the question? The model should have used the Search tool to find the volume of the Empire State Building and the volume of a ping pong ball. Then, it should have used the Calculator tool to calculate the number of ping pong balls needed to fill the building.
Judgment: Given the incorrect final answer and the inappropriate use of tools, we give the model a score of 1.
That seems about right. Let’s try the second query.
question, steps, answer = test_outputs_two["input"], test_outputs_two["intermediate_steps"], test_outputs_two["output"]
evaluation = eval_chain(
    inputs={"question": question, "answer": answer, "agent_trajectory": eval_chain.get_agent_trajectory(steps)},
)


)
print("Score from 1 to 5: ", evaluation["score"])
print("Reasoning: ", evaluation["reasoning"])
Score from 1 to 5:  3
Reasoning:  i. Is the final answer helpful?
Yes, the final answer is helpful as it provides an approximate number of Eiffel Towers needed to cover the US from coast to coast.
ii. Does the AI language use a logical sequence of tools to answer the question?
No, the AI language model does not use a logical sequence of tools. It directly uses the Calculator tool without first using the Search or Lookup tools to find the necessary information (length of the Eiffel Tower and distance from coast to coast in the US).
iii. Does the AI language model use the tools in a helpful way?
The AI language model uses the Calculator tool in a helpful way to perform the calculation, but it should have used the Search or Lookup tools first to find the required information.
iv. Does the AI language model use too many steps to answer the question?
No, the AI language model does not use too many steps. However, it repeats the same step twice, which is unnecessary.
v. Are the appropriate tools used to answer the question?
Not entirely. The AI language model should have used the Search or Lookup tools to find the required information before using the Calculator tool.
Given the above evaluation, the AI language model's performance can be scored as follows:
That also sounds about right. In conclusion, the TrajectoryEvalChain allows us to use GPT-4 to score both our agent’s outputs and tool use in addition to giving us the reasoning behind the evaluation.
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Data Augmented Question Answering
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Using Hugging Face Datasets
 Contents

Setup
Testing the Agent
Evaluating the Agent
By Harrison Chase


Setup
Testing the Agent
Evaluating the Agent
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
LLM Math
 Contents
Setting up a chain
LLM Math#
Evaluating chains that know how to do math.
# Comment this out if you are NOT using tracing
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
from langchain.evaluation.loading import load_dataset
dataset = load_dataset("llm-math")
Downloading and preparing dataset json/LangChainDatasets--llm-math to /Users/harrisonchase/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--llm-math-509b11d101165afa/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Dataset json downloaded and prepared to /Users/harrisonchase/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--llm-math-509b11d101165afa/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.
Setting up a chain#
Now we need to create some pipelines for doing math.
from langchain.llms import OpenAI
from langchain.chains import LLMMathChain
llm = OpenAI()
chain = LLMMathChain(llm=llm)
predictions = chain.apply(dataset)
numeric_output = [float(p['answer'].strip().strip("Answer: ")) for p in predictions]
correct = [example['answer'] == numeric_output[i] for i, example in enumerate(dataset)]
sum(correct) / len(correct)
1.0