id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
|---|---|---|
9d4fc9925642-9 | "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... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-10 | # 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:
> Question: {question}
The query you know you sho... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-11 | 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 nece... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-12 | ' 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 ... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-13 | ' 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 n... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-14 | ' 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 pred... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-15 | > 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:... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-16 | 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 nece... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-17 | ' 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 ... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-18 | ' 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 n... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-19 | ' 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 pred... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-20 | " 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 sa... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-21 | " 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, w... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-22 | # 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{:<... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-23 | 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/transla... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-24 | 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 explanat... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-25 | 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 = par... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-26 | # 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", "nati... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-27 | '{"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_q... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-28 | ## 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 = PromptTemp... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-29 | 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 h... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-30 | 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: {"ta... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-31 | 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... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-32 | '{"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_lang... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
9d4fc9925642-33 | '{"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 ... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/openapi_eval.html |
8836ee92a0ec-0 | .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... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/agent_benchmarking.html |
8836ee92a0ec-1 | 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:
new_data = {"input": data["question"], "answer": data["answer"]}
try:
predi... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/agent_benchmarking.html |
8836ee92a0ec-2 | 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 predict... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/agent_benchmarking.html |
d2d8dee29e2d-0 | .ipynb
.pdf
Question Answering Benchmarking: Paul Graham Essay
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Question Answering Benchmarking: Paul Graham Essay#
Here we go over how to benchmark performance on a question answering task over a Paul Graham essa... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_benchmarking_pg.html |
d2d8dee29e2d-1 | from langchain.indexes import VectorstoreIndexCreator
vectorstore = VectorstoreIndexCreator().from_loaders([loader]).vectorstore
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
Now we can create a question answering chain.
from langchain.chains import RetrievalQA
from... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_benchmarking_pg.html |
d2d8dee29e2d-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(dataset, predictions, question_key="question", prediction_key="result")
We can add in the graded output ... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_benchmarking_pg.html |
e1f8a56f6cf3-0 | .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 prepar... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/llm_math.html |
e1f8a56f6cf3-1 | 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
for i, example in enumerate(dataset):
print("i... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/llm_math.html |
e1f8a56f6cf3-2 | input: 209758.857 divided by 2714.31
expected output : 77.27888745205964
prediction: 77.27888745205964
previous
Using Hugging Face Datasets
next
Evaluating an OpenAPI Chain
Contents
Setting up a chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/use_cases/evaluation/llm_math.html |
1635a89ef050-0 | .ipynb
.pdf
Question Answering Benchmarking: State of the Union Address
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
Question Answering Benchmarking: State of the Union Address#
Here we go over how to benchmark performance on a question answering task over ... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
1635a89ef050-1 | from langchain.indexes import VectorstoreIndexCreator
vectorstore = VectorstoreIndexCreator().from_loaders([loader]).vectorstore
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
Now we can create a question answering chain.
from langchain.chains import RetrievalQA
from... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
1635a89ef050-2 | 'result': ' 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.eval... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
1635a89ef050-3 | 'grade': ' INCORRECT'}
previous
Question Answering Benchmarking: Paul Graham Essay
next
QA Generation
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 Apr 26, 2023. | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_benchmarking_sota.html |
7d714c652d0b-0 | .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 o... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7d714c652d0b-1 | ),
]
memory = ConversationBufferMemory(
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,
... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7d714c652d0b-2 | test_outputs_two = agent({"input": query_two}, return_only_outputs=False)
> 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 k... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7d714c652d0b-3 | 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... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7d714c652d0b-4 | 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_st... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7d714c652d0b-5 | 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 vol... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
7d714c652d0b-6 | 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 wa... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/generic_agent_evaluation.html |
b8837be3c706-0 | .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!
f... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/qa_generation.html |
a0e149834054-0 | .ipynb
.pdf
SQL Question Answering Benchmarking: Chinook
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
SQL Question Answering Benchmarking: Chinook#
Here we go over how to benchmark performance on a question answering task over a SQL database.
It is highly r... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
a0e149834054-1 | Dataset json downloaded and prepared to /Users/harrisonchase/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--sql-qa-chinook-7528565d2d992b47/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.
dataset[0]
{'question': 'How many employees ... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
a0e149834054-2 | chain(dataset[0])
{'question': 'How many employees are there?',
'answer': '8',
'result': ' There are 8 employees.'}
Make many predictions#
Now we can make predictions. Note that we add a try-except because this chain can sometimes error (if SQL is written incorrectly, etc)
predictions = []
predicted_dataset = []
erro... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
a0e149834054-3 | incorrect[0]
{'question': 'How many employees are also customers?',
'answer': 'None',
'result': ' 59 employees are also customers.',
'grade': ' INCORRECT'}
previous
Question Answering
next
Installation
Contents
Loading the data
Setting up a chain
Make a prediction
Make many predictions
Evaluate performance
By Ha... | /content/https://python.langchain.com/en/latest/use_cases/evaluation/sql_qa_benchmarking_chinook.html |
5a826525fb45-0 | .ipynb
.pdf
Voice Assistant
Voice Assistant#
This chain creates a clone of ChatGPT with a few modifications to make it a voice assistant.
It uses the pyttsx3 and speech_recognition libraries to convert text to speech and speech to text respectively. The prompt template is also changed to make it more suitable for voice... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-1 | Assistant is aware that human input is being transcribed from audio and as such there may be some errors in the transcription. It will attempt to account for some words being swapped with similar-sounding words or phrases. Assistant will also keep responses concise, because human attention spans are more limited over t... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-2 | # other speech recognition models are also available.
text = r.recognize_whisper(audio, model='medium.en', show_dict=True, )['text']
except Exception as e:
unrecognized_speech_text = f'Sorry, I didn\'t catch that. Exception was: {e}s'
text = unrecognized_speec... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-3 | Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based ... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-4 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-5 | Assistant:
> Finished chain.
That's great to hear! What can I do for you today?
listening now...
Recognizing...
Thank you.
> Entering new LLMChain chain...
Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answe... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-6 | Human: Hello, Assistant. What's going on?
AI: Hi there! It's great to hear from you. I'm doing well. How can I help you today?
Human: That's cool. Isn't that neat? Yeah, I'm doing great.
AI: That's great to hear! What can I do for you today?
Human: Thank you.
Assistant:
> Finished chain.
You're welcome! Is there... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-7 | Assistant is aware that human input is being transcribed from audio and as such there may be some errors in the transcription. It will attempt to account for some words being swapped with similar-sounding words or phrases. Assistant will also keep responses concise, because human attention spans are more limited over t... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-8 | Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based ... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-9 | Assistant:
> Finished chain.
Neural networks are inspired by the way the human brain works. They are composed of interconnected nodes that process data and make decisions, just like neurons in the brain. Neural networks can learn from their mistakes and improve their performance over time, just like humans do.
listeni... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-10 | Assistant is aware that human input is being transcribed from audio and as such there may be some errors in the transcription. It will attempt to account for some words being swapped with similar-sounding words or phrases. Assistant will also keep responses concise, because human attention spans are more limited over t... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-11 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-12 | Human: Tell me a fun fact about neural networks.
AI: Neural networks are inspired by the way the human brain works. They are composed of interconnected nodes that process data and make decisions, just like neurons in the brain. Neural networks can learn from their mistakes and improve their performance over time, jus... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-13 | > Entering new LLMChain chain...
Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assis... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-14 | Human: Tell me about a brand new discovered bird species.
AI: A new species of bird was recently discovered in the Amazon rainforest. The species, called the Spix's Macaw, is a small, blue parrot that is believed to be extinct in the wild. It is the first new species of bird to be discovered in the Amazon in over 100... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-15 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-16 | Human: Tell me a children's story about the importance of honesty and trust.
AI: Once upon a time, there was a young boy named Jack who lived in a small village. Jack was always honest and trustworthy, and his friends and family knew they could always count on him. One day, Jack was walking through the forest when he... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-17 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-18 | Human: Thank you.
AI: You're welcome!
Human: Do you know of online brands like Photoshop and Freq that you don't have to download in some sort of way? Do you know of online brands like Photoshop and Freq that you don't have to download in some sort of way?
Assistant:
> Finished chain.
Yes, there are several online ... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-19 | Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
Assistant is aware that human ... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-20 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-21 | AI: Yes, there are several online brands that offer photo editing and other creative tools without the need to download any software. Adobe Photoshop Express, Pixlr, and Fotor are some of the most popular online photo editing tools. Freq is an online music production platform that allows users to create and share musi... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
5a826525fb45-22 | 521 break
--> 523 buffer = source.stream.read(source.CHUNK)
524 if len(buffer) == 0: break # reached end of the stream
525 frames.append(buffer)
File c:\ProgramData\miniconda3\envs\lang\lib\site-packages\speech_recognition\__init__.py:199, in Microphone.MicrophoneStream.read(self, size)
198 def read(se... | /content/https://python.langchain.com/en/latest/use_cases/chatbots/voice_assistant.html |
41fc07588e8a-0 | .ipynb
.pdf
Question answering over a group chat messages
Contents
1. Install required packages
2. Add API keys
2. Create sample data
3. Ingest chat embeddings
4. Ask questions
Question answering over a group chat messages#
In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to semantically search a... | /content/https://python.langchain.com/en/latest/use_cases/question_answering/semantic-search-over-chat.html |
41fc07588e8a-1 | You can generate a sample group chat conversation using ChatGPT with this prompt:
Generate a group chat conversation with three friends talking about their day, referencing real places and fictional names. Make it funny and as detailed as possible.
I’ve already generated such a chat in messages.txt. We can keep it simp... | /content/https://python.langchain.com/en/latest/use_cases/question_answering/semantic-search-over-chat.html |
41fc07588e8a-2 | retriever.search_kwargs['k'] = 4
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=False)
# What was the restaurant the group was talking about called?
query = input("Enter query:")
# The Hungry Lobster
ans = qa({"query": query})
print(ans)
Contents
1. I... | /content/https://python.langchain.com/en/latest/use_cases/question_answering/semantic-search-over-chat.html |
76223b70a2a5-0 | .md
.pdf
Quickstart Guide
Contents
Installation
Environment Setup
Building a Language Model Application: LLMs
LLMs: Get predictions from a language model
Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add S... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-1 | LangChain provides many modules that can be used to build language model applications. Modules can be combined to create more complex applications, or be used individually for simple applications.
LLMs: Get predictions from a language model#
The most basic building block of LangChain is calling an LLM on some input.
Le... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-2 | This is easy to do with LangChain!
First lets define the prompt template:
from langchain.prompts import PromptTemplate
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
Let’s now see how this works! We can call the .format method to forma... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-3 | from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
Now we can run that chain only specifying the product!
chain.run("colorful socks")
# -> '\n\nSocktastic!'
There we go! There’s the first chain - an LLM Chain.
This is one of the simpler types of chains, but understanding how it works will se... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-4 | Agents: For a list of supported agents and their specifications, see here.
Tools: For a list of predefined tools and their specifications, see here.
For this example, you will also need to install the SerpAPI Python package.
pip install google-search-results
And set the appropriate environment variables.
import os
os.e... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-5 | Action: Search
Action Input: "High temperature in SF yesterday"
Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ...
Thought: I now have the temperature, so I can use the calculator to raise it to th... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-6 | By default, the ConversationChain has a simple type of memory that remembers all previous inputs/outputs and adds them to the context that is passed. Let’s take a look at using this chain (setting verbose=True so we can see the prompt).
from langchain import OpenAI, ConversationChain
llm = OpenAI(temperature=0)
convers... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-7 | Building a Language Model Application: Chat Models#
Similarly, you can use chat models instead of LLMs. Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a “text in, text out” API, they expose an interfa... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-8 | ]
chat(messages)
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
You can go one step further and generate completions for multiple sets of messages using generate. This returns an LLMResult with an additional message parameter:
batch_messages = [
[
SystemMessage(content="You are a helpful... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-9 | result.llm_output['token_usage']
# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
Chat Prompt Templates#
Similar to LLMs, you can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s f... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-10 | # get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
Chains with Chat Models#
The LLMChain discussed in the above section can be... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-11 | # -> "J'aime programmer."
Agents with Chat Models#
Agents can also be used with chat models, you can initialize one using AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION as the agent type.
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langch... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-12 | "action_input": "Olivia Wilde boyfriend"
}
Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting d... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-13 | prompt = ChatPromptTemplate.from_messages([
SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
76223b70a2a5-14 | Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add State to Chains and Agents
Building a Language Model Application: Chat Models
Get Message Completions from a Chat Model
Chat Prompt Templates
Chains with Cha... | /content/https://python.langchain.com/en/latest/getting_started/getting_started.html |
64e1c0801cb5-0 | .rst
.pdf
Memory
Memory#
Note
Conceptual Guide
By default, Chains and Agents are stateless,
meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
In some applications (chatbots being a GREAT example) it is highly important
to remember previous interactions, both at a sh... | /content/https://python.langchain.com/en/latest/modules/memory.html |
ccee5bb2a730-0 | .rst
.pdf
Chains
Chains#
Note
Conceptual Guide
Using an LLM in isolation is fine for some simple applications,
but many more complex ones require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of... | /content/https://python.langchain.com/en/latest/modules/chains.html |
35de4076c5e9-0 | .rst
.pdf
Indexes
Contents
Go Deeper
Indexes#
Note
Conceptual Guide
Indexes refer to ways to structure documents so that LLMs can best interact with them.
This module contains utility functions for working with documents, different types of indexes, and then examples for using those indexes in chains.
The most common... | /content/https://python.langchain.com/en/latest/modules/indexes.html |
35de4076c5e9-1 | Go Deeper#
Document Loaders
Text Splitters
Vectorstores
Retrievers
previous
Structured Output Parser
next
Getting Started
Contents
Go Deeper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/modules/indexes.html |
a95e8f37182a-0 | .rst
.pdf
Agents
Contents
Go Deeper
Agents#
Note
Conceptual Guide
Some applications will require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the user’s input.
In these types of chains, there is a “agent” which has access to a suite of tools.
Depending ... | /content/https://python.langchain.com/en/latest/modules/agents.html |
1e2a7caf9bf6-0 | .rst
.pdf
Models
Contents
Go Deeper
Models#
Note
Conceptual Guide
This section of the documentation deals with different types of models that are used in LangChain.
On this page we will go over the model types at a high level,
but we have individual pages for each model type.
The pages contain more detailed “how-to” ... | /content/https://python.langchain.com/en/latest/modules/models.html |
a9cacdad1570-0 | .rst
.pdf
Prompts
Contents
Go Deeper
Prompts#
Note
Conceptual Guide
The new way of programming models is through prompts.
A “prompt” refers to the input to the model.
This input is rarely hard coded, but rather is often constructed from multiple components.
A PromptTemplate is responsible for the construction of this... | /content/https://python.langchain.com/en/latest/modules/prompts.html |
ab48e5322750-0 | .rst
.pdf
LLMs
LLMs#
Note
Conceptual Guide
Large Language Models (LLMs) are a core component of LangChain.
LangChain is not a provider of LLMs, but rather provides a standard interface through which
you can interact with a variety of LLMs.
The following sections of documentation are provided:
Getting Started: An overvi... | /content/https://python.langchain.com/en/latest/modules/models/llms.html |
3aa44f1b5395-0 | .rst
.pdf
Text Embedding Models
Text Embedding Models#
Note
Conceptual Guide
This documentation goes over how to use the Embedding class in LangChain.
The Embedding class is a class designed for interfacing with embeddings. There are lots of Embedding providers (OpenAI, Cohere, Hugging Face, etc) - this class is design... | /content/https://python.langchain.com/en/latest/modules/models/text_embedding.html |
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