repo_id stringlengths 15 132 | file_path stringlengths 34 176 | content stringlengths 2 3.52M | __index_level_0__ int64 0 0 |
|---|---|---|---|
promptflow_repo/promptflow/examples/flows/chat | promptflow_repo/promptflow/examples/flows/chat/chat-with-wikipedia/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
chat_history:
type: list
default: []
question:
type: string
default: What is ChatGPT?
is_chat_input: true
outputs:
answer:
type: string
reference: ${augmented_chat.output}
is_chat_output: true
... | 0 |
promptflow_repo/promptflow/examples/flows/chat | promptflow_repo/promptflow/examples/flows/chat/chat-with-wikipedia/search_result_from_url.py | import random
import time
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import bs4
import requests
from promptflow import tool
session = requests.Session()
def decode_str(string):
return string.encode().decode("unicode-escape").encode("latin1").decode("utf-8")
def get_page_s... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-perceived-intelligence/parse_score.py | from promptflow import tool
import re
@tool
def parse_score(gpt_score: str):
return float(extract_float(gpt_score))
def extract_float(s):
match = re.search(r"[-+]?\d*\.\d+|\d+", s)
if match:
return float(match.group())
else:
return None
| 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-perceived-intelligence/data.jsonl | {"question": "What is the name of the new language representation model introduced in the document?", "variant_id": "v1", "line_number":1, "answer":"The document mentions multiple language representation models, so it is unclear which one is being referred to as \"new\". Can you provide more specific information or con... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-perceived-intelligence/gpt_perceived_intelligence.md | user:
# Instructions
* There are many chatbots that can answer users questions based on the context given from different sources like search results, or snippets from books/papers. They try to understand users's question and then get context by either performing search from search engines, databases or books/papers fo... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-perceived-intelligence/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-perceived-intelligence/aggregate.py | from typing import List
from promptflow import tool
@tool
def aggregate(perceived_intelligence_score: List[float]):
aggregated_results = {"perceived_intelligence_score": 0.0, "count": 0}
# Calculate average perceived_intelligence_score
for i in range(len(perceived_intelligence_score)):
aggregated... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-perceived-intelligence/README.md | # Perceived Intelligence Evaluation
This is a flow leverage llm to eval perceived intelligence.
Perceived intelligence is the degree to which a bot can impress the user with its responses, by showing originality, insight, creativity, knowledge, and adaptability.
Tools used in this flow：
- `python` tool
- built-in `ll... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-perceived-intelligence/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
environment:
python_requirements_txt: requirements.txt
inputs:
question:
type: string
default: What is the name of the new language representation model introduced in
the document?
answer:
type: string
default: ... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/gpt_fluency_prompt.jinja2 | system:
You are an AI assistant. You will be given the definition of an evaluation metric for assessing the quality of an answer in a question-answering task. Your job is to compute an accurate evaluation score using the provided evaluation metric.
user:
Fluency measures the quality of individual sentences in the answe... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/data.jsonl | {"question":"Which tent is the most waterproof?","ground_truth":"The Alpine Explorer Tent has the highest rainfly waterproof rating at 3000m","answer":"The Alpine Explorer Tent is the most waterproof.","context":"From the our product list, the alpine explorer tent is the most waterproof. The Adventure Dining Table has ... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/gpt_similarity_prompt.jinja2 | system:
You are an AI assistant. You will be given the definition of an evaluation metric for assessing the quality of an answer in a question-answering task. Your job is to compute an accurate evaluation score using the provided evaluation metric.
user:
Equivalence, as a metric, measures the similarity between the pre... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/ada_cosine_similarity_score.py | from promptflow import tool
import numpy as np
from numpy.linalg import norm
@tool
def compute_ada_cosine_similarity(a, b) -> float:
return np.dot(a, b)/(norm(a)*norm(b))
| 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/validate_input.py | from promptflow import tool
@tool
def validate_input(question: str, answer: str, context: str, ground_truth: str, selected_metrics: dict) -> dict:
input_data = {"question": question, "answer": answer, "context": context, "ground_truth": ground_truth}
expected_input_cols = set(input_data.keys())
dict_metri... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/aggregate_variants_results.py | from typing import List
from promptflow import tool, log_metric
import numpy as np
@tool
def aggregate_variants_results(results: List[dict], metrics: List[str]):
aggregate_results = {}
for result in results:
for name, value in result.items():
if name in metrics[0]:
if name ... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/gpt_relevance_prompt.jinja2 | system:
You are an AI assistant. You will be given the definition of an evaluation metric for assessing the quality of an answer in a question-answering task. Your job is to compute an accurate evaluation score using the provided evaluation metric.
user:
Relevance measures how well the answer addresses the main aspects... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/README.md | # Q&A Evaluation:
This is a flow evaluating the Q&A systems by leveraging Large Language Models (LLM) to measure the quality and safety of responses. Utilizing GPT and GPT embedding model to assist with measurements aims to achieve a high agreement with human evaluations compared to traditional mathematical measuremen... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
question:
type: string
default: Which tent is the most waterproof?
is_chat_input: false
answer:
type: string
default: The Alpine Explorer Tent is the most waterproof.
is_chat_input: false
context:
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/concat_scores.py | from promptflow import tool
import numpy as np
import re
@tool
def concat_results(gpt_coherence_score: str = None,
gpt_similarity_score: str = None,
gpt_fluency_score: str = None,
gpt_relevance_score: str = None,
gpt_groundedness_score: str =... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/select_metrics.py | from promptflow import tool
@tool
def select_metrics(metrics: str) -> str:
supported_metrics = ('gpt_coherence', 'gpt_similarity', 'gpt_fluency', 'gpt_relevance', 'gpt_groundedness',
'f1_score', 'ada_similarity')
user_selected_metrics = [metric.strip() for metric in metrics.split(',')... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/f1_score.py | from promptflow import tool
from collections import Counter
@tool
def compute_f1_score(ground_truth: str, answer: str) -> str:
import string
import re
class QASplitTokenizer:
def __call__(self, line):
"""Tokenizes an input line using split() on whitespace
:param line: a s... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/gpt_groundedness_prompt.jinja2 | system:
You are an AI assistant. You will be given the definition of an evaluation metric for assessing the quality of an answer in a question-answering task. Your job is to compute an accurate evaluation score using the provided evaluation metric.
user:
You will be presented with a CONTEXT and an ANSWER about that CON... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-non-rag/gpt_coherence_prompt.jinja2 | system:
You are an AI assistant. You will be given the definition of an evaluation metric for assessing the quality of an answer in a question-answering task. Your job is to compute an accurate evaluation score using the provided evaluation metric.
user:
Coherence of an answer is measured by how well all the sentences... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-basic/data.jsonl | {"groundtruth": "Tomorrow's weather will be sunny.","prediction": "The weather will be sunny tomorrow."}
| 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-basic/line_process.py | from promptflow import tool
@tool
def line_process(groundtruth: str, prediction: str):
"""
This tool processes the prediction of a single line and returns the processed result.
:param groundtruth: the groundtruth of a single line.
:param prediction: the prediction of a single line.
"""
# Add... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-basic/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-basic/aggregate.py | from typing import List
from promptflow import tool
@tool
def aggregate(processed_results: List[str]):
"""
This tool aggregates the processed result of all lines to the variant level and log metric for each variant.
:param processed_results: List of the output of line_process node.
"""
# Add yo... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-basic/README.md | # Basic Eval
This example shows how to create a basic evaluation flow.
Tools used in this flow：
- `python` tool
## Prerequisites
Install promptflow sdk and other dependencies in this folder:
```bash
pip install -r requirements.txt
```
## What you will learn
In this flow, you will learn
- how to compose a point ba... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-basic/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
groundtruth:
type: string
default: groundtruth
prediction:
type: string
default: prediction
outputs:
results:
type: string
reference: ${line_process.output}
nodes:
- name: line_process
type: python
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/parse_retrival_score.py | from promptflow import tool
import re
@tool
def parse_retrieval_output(retrieval_output: str) -> str:
score_response = [sent.strip() for sent in
retrieval_output.strip("\"").split("# Result")[-1].strip().split('.') if sent.strip()]
parsed_score_response = re.findall(r"\d+", score_respons... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/data.jsonl | {"question": "What is the purpose of the LLM Grounding Score, and what does a higher score mean in this context?", "answer": "The LLM Grounding Score is a metric used in the context of in-context learning with large-scale pretrained language models (LLMs) [doc1]. It measures the ability of the LLM to understand and con... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/rag_retrieval_prompt.jinja2 | system:
You are a helpful assistant.
user:
A chat history between user and bot is shown below
A list of documents is shown below in json format, and each document has one unique id.
These listed documents are used as contex to answer the given question.
The task is to score the relevance between the documents and the ... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/validate_input.py | from promptflow import tool
def is_valid(input_item):
return True if input_item and input_item.strip() else False
@tool
def validate_input(question: str, answer: str, documents: str, selected_metrics: dict) -> dict:
input_data = {"question": is_valid(question), "answer": is_valid(answer), "documents": is_va... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/aggregate_variants_results.py | from typing import List
from promptflow import tool, log_metric
import numpy as np
@tool
def aggregate_variants_results(results: List[dict], metrics: List[str]):
aggregate_results = {}
for result in results:
for name, value in result.items():
if name not in aggregate_results.keys():
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/rag_groundedness_prompt.jinja2 | system:
You are a helpful assistant.
user:
Your task is to check and rate if factual information in chatbot's reply is all grounded to retrieved documents.
You will be given a question, chatbot's response to the question, a chat history between this chatbot and human, and a list of retrieved documents in json format.
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/README.md | # Q&A Evaluation:
This is a flow evaluating the Q&A RAG (Retrieval Augmented Generation) systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of responses. Utilizing GPT model to assist with measurements aims to achieve a high agreement with human evaluations compare... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
metrics:
type: string
default: gpt_groundedness,gpt_relevance,gpt_retrieval_score
is_chat_input: false
answer:
type: string
default: Of the tents mentioned in the retrieved documents, the Alpine Explorer
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/parse_generation_score.py | from promptflow import tool
import re
@tool
def parse_generation_output(rag_generation_score: str) -> str:
quality_score = float('nan')
quality_reasoning = ''
for sent in rag_generation_score.split('\n'):
sent = sent.strip()
if re.match(r"\s*(<)?Quality score:", sent):
numbers_... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/parse_groundedness_score.py | from promptflow import tool
import re
@tool
def parse_grounding_output(rag_grounding_score: str) -> str:
try:
numbers_found = re.findall(r"Quality score:\s*(\d+)\/\d", rag_grounding_score)
score = float(numbers_found[0]) if len(numbers_found) > 0 else 0
except Exception:
score = float(... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/concat_scores.py | from promptflow import tool
import numpy as np
@tool
def concat_results(rag_retrieval_score: dict = None,
rag_grounding_score: dict = None, rag_generation_score: dict = None):
load_list = [{'name': 'gpt_groundedness', 'result': rag_grounding_score},
{'name': 'gpt_retrieval_sco... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/select_metrics.py | from promptflow import tool
@tool
def select_metrics(metrics: str) -> str:
supported_metrics = ('gpt_relevance', 'gpt_groundedness', 'gpt_retrieval_score')
user_selected_metrics = [metric.strip() for metric in metrics.split(',') if metric]
metric_selection_dict = {}
for metric in supported_metrics:
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-qna-rag-metrics/rag_generation_prompt.jinja2 | system:
You will be provided a question, a conversation history, fetched documents related to the question and a response to the question in the domain. You task is to evaluate the quality of the provided response by following the steps below:
- Understand the context of the question based on the conversation history.
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/data.jsonl | {"entities": ["software engineer","CEO"],"ground_truth": "\"CEO, Software Engineer, Finance Manager\""}
{"entities": ["Software Engineer","CEO", "Finance Manager"],"ground_truth": "\"CEO, Software Engineer, Finance Manager\""}
| 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/match.py | from promptflow import tool
from typing import List
@tool
def match(answer: List[str], ground_truth: List[str]):
exact_match = 0
partial_match = 0
if is_match(answer, ground_truth, ignore_case=True, ignore_order=True, allow_partial=False):
exact_match = 1
if is_match(answer, ground_truth, ig... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/is_match_test.py | import unittest
from match import is_match
class IsMatchTest(unittest.TestCase):
def test_normal(self):
self.assertEqual(is_match(["a", "b"], ["B", "a"], True, True, False), True)
self.assertEqual(is_match(["a", "b"], ["B", "a"], True, False, False), False)
self.assertEqual(is_match(["a",... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/README.md | # Entity match rate evaluation
This is a flow evaluates: entity match rate.
Tools used in this flow：
- `python` tool
## Prerequisites
Install promptflow sdk and other dependencies:
```bash
pip install -r requirements.txt
```
### 1. Test flow/node
```bash
# test with default input value in flow.dag.yaml
pf flow te... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
entities:
type: list
default:
- software engineer
- CEO
ground_truth:
type: string
default: '"CEO, Software Engineer, Finance Manager"'
outputs:
match_cnt:
type: object
reference: ${match.outpu... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/log_metrics.py | from promptflow import tool
from typing import List
from promptflow import log_metric
# The inputs section will change based on the arguments of the tool function, after you save the code
# Adding type to arguments and return value will help the system show the types properly
# Please update the function name/signatur... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-entity-match-rate/cleansing.py | from typing import List
from promptflow import tool
@tool
def cleansing(entities_str: str) -> List[str]:
# Split, remove leading and trailing spaces/tabs/dots
parts = entities_str.split(",")
cleaned_parts = [part.strip(" \t.\"") for part in parts]
entities = [part for part in cleaned_parts if len(part... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-chat-math/data.jsonl | {"groundtruth": "10","prediction": "10"}
{"groundtruth": "253","prediction": "506"}
{"groundtruth": "1/3","prediction": "2/6"} | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-chat-math/line_process.py | from promptflow import tool
def string_to_number(raw_string: str) -> float:
''' Try to parse the prediction string and groundtruth string to float number.
Support parse int, float, fraction and recognize non-numeric string with wrong format.
Wrong format cases: 'the answer is \box{2/3}', '0, 5, or any num... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-chat-math/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-chat-math/aggregate.py | from typing import List
from promptflow import tool
from promptflow import log_metric
@tool
def accuracy_aggregate(processed_results: List[int]):
num_exception = 0
num_correct = 0
for i in range(len(processed_results)):
if processed_results[i] == -1:
num_exception += 1
elif p... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-chat-math/README.md | # Eval chat math
This example shows how to evaluate the answer of math questions, which can compare the output results with the standard answers numerically.
Learn more on corresponding [tutorials](../../../tutorials/flow-fine-tuning-evaluation/promptflow-quality-improvement.md)
Tools used in this flow：
- `python` t... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-chat-math/flow.dag.yaml | inputs:
groundtruth:
type: string
default: "10"
is_chat_input: false
prediction:
type: string
default: "10"
is_chat_input: false
outputs:
score:
type: string
reference: ${line_process.output}
nodes:
- name: line_process
type: python
source:
type: code
path: line_process... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-classification-accuracy/data.jsonl | {"groundtruth": "App","prediction": "App"}
{"groundtruth": "Channel","prediction": "Channel"}
{"groundtruth": "Academic","prediction": "Academic"}
| 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-classification-accuracy/calculate_accuracy.py | from typing import List
from promptflow import log_metric, tool
@tool
def calculate_accuracy(grades: List[str]):
result = []
for index in range(len(grades)):
grade = grades[index]
result.append(grade)
# calculate accuracy for each variant
accuracy = round((result.count("Correct") / l... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-classification-accuracy/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-classification-accuracy/grade.py | from promptflow import tool
@tool
def grade(groundtruth: str, prediction: str):
return "Correct" if groundtruth.lower() == prediction.lower() else "Incorrect"
| 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-classification-accuracy/README.md | # Classification Accuracy Evaluation
This is a flow illustrating how to evaluate the performance of a classification system. It involves comparing each prediction to the groundtruth and assigns a "Correct" or "Incorrect" grade, and aggregating the results to produce metrics such as accuracy, which reflects how good th... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-classification-accuracy/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
groundtruth:
type: string
description: Please specify the groundtruth column, which contains the true label
to the outputs that your flow produces.
default: APP
prediction:
type: string
description: Pl... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-groundedness/data.jsonl | {"question": "What is the name of the new language representation model introduced in the document?", "variant_id": "v1", "line_number":1, "answer":"The document mentions multiple language representation models, so it is unclear which one is being referred to as \"new\". Can you provide more specific information or con... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-groundedness/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-groundedness/aggregate.py | from typing import List
from promptflow import tool
@tool
def aggregate(groundedness_scores: List[float]):
"""
This tool aggregates the processed result of all lines to the variant level and log metric for each variant.
:param processed_results: List of the output of line_process node.
:param variant... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-groundedness/calc_groundedness.py | from promptflow import tool
import re
@tool
def parse_score(gpt_score: str):
return float(extract_float(gpt_score))
def extract_float(s):
match = re.search(r"[-+]?\d*\.\d+|\d+", s)
if match:
return float(match.group())
else:
return None
| 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-groundedness/README.md | # Groundedness Evaluation
This is a flow leverage llm to eval groundedness: whether answer is stating facts that are all present in the given context.
Tools used in this flow：
- `python` tool
- built-in `llm` tool
### 0. Setup connection
Prepare your Azure Open AI resource follow this [instruction](https://learn.mi... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-groundedness/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
environment:
python_requirements_txt: requirements.txt
inputs:
question:
type: string
default: What is the name of the new language representation model introduced in
the document?
answer:
type: string
default: ... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-groundedness/gpt_groundedness.md | user:
# Instructions
* There are many chatbots that can answer users questions based on the context given from different sources like search results, or snippets from books/papers. They try to understand users's question and then get context by either performing search from search engines, databases or books/papers fo... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/test_data.jsonl | {"question": "What is the sum of 5 and 3?", "groundtruth": "8", "answer": "8"}
{"question": "Subtract 7 from 10.", "groundtruth": "3", "answer": "3"}
{"question": "Multiply 6 by 4.", "groundtruth": "24", "answer": "24"}
{"question": "Divide 20 by 5.", "groundtruth": "4", "answer": "4"}
{"question": "What is the square ... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/line_process.py | from promptflow import tool
@tool
def line_process(groundtruth: str, prediction: str) -> int:
processed_result = 0
if prediction == "JSONDecodeError" or prediction.startswith("Unknown Error:"):
processed_result = -1
return processed_result
try:
groundtruth = float(groundtruth)
... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/aggregate.py | from typing import List
from promptflow import tool
from promptflow import log_metric
@tool
def accuracy_aggregate(processed_results: List[int]):
num_exception = 0
num_correct = 0
for i in range(len(processed_results)):
if processed_results[i] == -1:
num_exception += 1
elif p... | 0 |
promptflow_repo/promptflow/examples/flows/evaluation | promptflow_repo/promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
groundtruth:
type: string
default: "1"
prediction:
type: string
default: "2"
outputs:
score:
type: string
reference: ${line_process.output}
nodes:
- name: line_process
type: python
source:
type... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/describe-image/flip_image.py | import io
from promptflow import tool
from promptflow.contracts.multimedia import Image
from PIL import Image as PIL_Image
@tool
def passthrough(input_image: Image) -> Image:
image_stream = io.BytesIO(input_image)
pil_image = PIL_Image.open(image_stream)
flipped_image = pil_image.transpose(PIL_Image.FLIP_... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/describe-image/data.jsonl | {"question": "How many colors are there in the image?", "input_image": {"data:image/png;url": "https://developer.microsoft.com/_devcom/images/logo-ms-social.png"}}
{"question": "What's this image about?", "input_image": {"data:image/png;url": "https://developer.microsoft.com/_devcom/images/404.png"}} | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/describe-image/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/describe-image/README.md | # Describe image flow
A flow that take image input, flip it horizontally and uses OpenAI GPT-4V tool to describe it.
Tools used in this flow：
- `OpenAI GPT-4V` tool
- custom `python` Tool
Connections used in this flow:
- OpenAI Connection
## Prerequisites
Install promptflow sdk and other dependencies, create connec... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/describe-image/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
question:
type: string
default: Please describe this image.
input_image:
type: image
default: https://developer.microsoft.com/_devcom/images/logo-ms-social.png
outputs:
answer:
type: string
reference: ... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/describe-image/question_on_image.jinja2 | # system:
As an AI assistant, your task involves interpreting images and responding to questions about the image.
Remember to provide accurate answers based on the information present in the image.
# user:
{{question}}
| 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/generate_result.py | from promptflow import tool
@tool
def generate_result(llm_result="", default_result="") -> str:
if llm_result:
return llm_result
else:
return default_result
| 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/data.jsonl | {"question": "What is Prompt flow?"}
{"question": "What is ChatGPT?"} | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/requirements.txt | promptflow
promptflow-tools | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/default_result.py | from promptflow import tool
@tool
def default_result(question: str) -> str:
return f"I'm not familiar with your query: {question}."
| 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/README.md | # Conditional flow for if-else scenario
This example is a conditional flow for if-else scenario.
By following this example, you will learn how to create a conditional flow using the `activate config`.
## Flow description
In this flow, it checks if an input query passes content safety check. If it's denied, we'll re... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
question:
type: string
default: What is Prompt flow?
outputs:
answer:
type: string
reference: ${generate_result.output}
nodes:
- name: content_safety_check
type: python
source:
type: code
path: conte... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/llm_result.py | from promptflow import tool
@tool
def llm_result(question: str) -> str:
# You can use an LLM node to replace this tool.
return (
"Prompt flow is a suite of development tools designed to streamline "
"the end-to-end development cycle of LLM-based AI applications."
)
| 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/conditional-flow-for-if-else/content_safety_check.py | from promptflow import tool
import random
@tool
def content_safety_check(text: str) -> str:
# You can use a content safety node to replace this tool.
return random.choice([True, False])
| 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/hello.py | import os
from openai.version import VERSION as OPENAI_VERSION
from dotenv import load_dotenv
from promptflow import tool
# The inputs section will change based on the arguments of the tool function, after you save the code
# Adding type to arguments and return value will help the system show the types properly
# Ple... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/data.jsonl | {"text": "Python Hello World!"}
{"text": "C Hello World!"}
{"text": "C# Hello World!"}
| 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/hello.jinja2 | {# Please replace the template with your own prompt. #}
Write a simple {{text}} program that displays the greeting message when executed. | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/requirements.txt | promptflow[azure]
promptflow-tools
python-dotenv | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/.env.example | AZURE_OPENAI_API_KEY=<your_AOAI_key>
AZURE_OPENAI_API_BASE=<your_AOAI_endpoint>
AZURE_OPENAI_API_TYPE=azure
| 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/run.yml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json
flow: .
data: data.jsonl
environment_variables:
# environment variables from connection
AZURE_OPENAI_API_KEY: ${open_ai_connection.api_key}
AZURE_OPENAI_API_BASE: ${open_ai_connection.api_base}
AZURE_OPENAI_API_TYPE: azure
column_ma... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/README.md | # Basic standard flow
A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables.
Tools used in this flow：
- `prompt` tool
- custom `python` Tool
Connections used in this flow:
- None
## Prerequisites
Install promptflow sdk and other dependencies:
``... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/basic/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
environment:
python_requirements_txt: requirements.txt
inputs:
text:
type: string
default: Hello World!
outputs:
output:
type: string
reference: ${llm.output}
nodes:
- name: hello_prompt
type: prompt
source:
t... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/web-classification/classify_with_llm.jinja2 | system:
Your task is to classify a given url into one of the following categories:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
user:
The selection range of the value of "category" must... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/web-classification/fetch_text_content_from_url.py | import bs4
import requests
from promptflow import tool
@tool
def fetch_text_content_from_url(url: str):
# Send a request to the URL
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/113.0.0.0 Safari/537.3... | 0 |
promptflow_repo/promptflow/examples/flows/standard | promptflow_repo/promptflow/examples/flows/standard/web-classification/convert_to_dict.py | import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
try:
return json.loads(input_str)
except Exception as e:
print("The input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
| 0 |
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