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/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/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/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/requirements.txt | promptflow
promptflow-tools | 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-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/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/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/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-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/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/requirements.txt | promptflow
promptflow-tools | 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-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/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/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/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/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/requirements.txt | promptflow
promptflow-tools | 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/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/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/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/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_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-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-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/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/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/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-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-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/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/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-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/requirements.txt | promptflow
promptflow-tools | 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/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-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/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/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/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-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-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/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/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/requirements.txt | promptflow
promptflow-tools | 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-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/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-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/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/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-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/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/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/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/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-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/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/requirements.txt | promptflow
promptflow-tools | 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/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/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/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/scripts | promptflow_repo/promptflow/scripts/tool/generate_tool_package_template.py | import argparse
import os
import re
from jinja2 import Environment, FileSystemLoader
def make_pythonic_variable_name(input_string):
variable_name = input_string.strip()
variable_name = re.sub(r'\W|^(?=\d)', '_', variable_name)
if not variable_name[0].isalpha() and variable_name[0] != '_':
variable... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/tool/generate_connection_config.py | import argparse
import json
from pathlib import Path
from utils.secret_manager import get_secret, get_secret_client, list_secret_names
CONNECTION_FILE_NAME = "connections.json"
PROMPTFLOW_TOOLS_ROOT = Path(__file__) / "../../../src/promptflow-tools"
CONNECTION_TPL_FILE_PATH = PROMPTFLOW_TOOLS_ROOT / "connections.json... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/tool/validate_tool_secret.py | import argparse
from utils.secret_manager import (
get_secret_client,
init_used_secret_names,
validate_secret_name,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--tenant_id",
type=str,
required=True,
help="The tenant id of the... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/tool/deploy_endpoint.py | import argparse
from utils.repo_utils import create_remote_branch_in_ADO_with_new_tool_pkg_version, deploy_test_endpoint
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--tool_pkg_version", type=str, required=True)
parser.add_argument("--ado_pat", type=str, required=True... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/tool/generate_package_tool_meta.py | import argparse
import ast
import importlib
import json
import os
import sys
from ruamel.yaml import YAML
sys.path.append("src/promptflow-tools")
sys.path.append(os.getcwd())
from utils.generate_tool_meta_utils import generate_custom_llm_tools_in_module_as_dict, generate_python_tools_in_module_as_dict # noqa: E402,... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/tool/convert_image_to_data_url.py | import argparse
import base64
import os
import io
from PIL import Image
SUPPORT_IMAGE_TYPES = ["png", "jpg", "jpeg", "bmp"]
def get_image_size(image_path):
with Image.open(image_path) as img:
width, height = img.size
return width, height
def get_image_storage_size(image_path):
file_size_bytes ... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/tool/upload_tool_secret.py | import argparse
from utils.secret_manager import get_secret_client, upload_secret
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--tenant_id",
type=str,
required=True,
help="The tenant id of the service principal",
)
parser.add_argum... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/test_tool.py.j2 | import pytest
import unittest
from promptflow.connections import CustomConnection
from {{ package_name }}.tools.{{ tool_name }} import {{ function_name }}
@pytest.fixture
def my_custom_connection() -> CustomConnection:
my_custom_connection = CustomConnection(
{
"api-key" : "my-api-key",
... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/tool.yaml.j2 | {{ package_name }}.tools.{{ tool_name }}.{{ function_name }}:
function: {{ function_name }}
inputs:
connection:
type:
- CustomConnection
input_text:
type:
- string
module: {{ package_name }}.tools.{{ tool_name }}
name: Hello World Tool
description: This is hello world tool
ty... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/setup.py.j2 | from setuptools import find_packages, setup
PACKAGE_NAME = "{{ package_name }}"
setup(
name=PACKAGE_NAME,
version="0.0.1",
description="This is my tools package",
packages=find_packages(),
entry_points={
"package_tools": ["{{ tool_name }} = {{ package_name }}.tools.utils:list_package_tools... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/tool2.py.j2 | from promptflow import ToolProvider, tool
import urllib.request
class {{ class_name }}(ToolProvider):
def __init__(self, url: str):
super().__init__()
# Load content from url might be slow, so we do it in __init__ method to make sure it is loaded only once.
self.content = urllib.request.u... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/utils.py.j2 | from ruamel.yaml import YAML
from pathlib import Path
def collect_tools_from_directory(base_dir) -> dict:
tools = {}
yaml = YAML()
for f in Path(base_dir).glob("**/*.yaml"):
with open(f, "r") as f:
tools_in_file = yaml.load(f)
for identifier, tool in tools_in_file.items():
... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/MANIFEST.in.j2 | include {{ package_name }}/yamls/*.yaml | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/tool2.yaml.j2 | {{ package_name }}.tools.{{ tool_name }}.{{ class_name }}.{{ function_name }}:
class_name: {{ class_name }}
function: {{ function_name }}
inputs:
url:
type:
- string
query:
type:
- string
module: {{ package_name }}.tools.{{ tool_name }}
name: Hello World Tool
description: Thi... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/test_tool2.py.j2 | import pytest
import unittest
from {{ package_name }}.tools.{{ tool_name }} import {{ class_name }}
@pytest.fixture
def my_url() -> str:
my_url = "https://www.bing.com"
return my_url
@pytest.fixture
def my_tool_provider(my_url) -> {{ class_name }}:
my_tool_provider = {{ class_name }}(my_url)
return... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/templates/tool.py.j2 | from promptflow import tool
from promptflow.connections import CustomConnection
@tool
def {{ function_name }}(connection: CustomConnection, input_text: str) -> str:
# Replace with your tool code.
# Usually connection contains configs to connect to an API.
# Use CustomConnection is a dict. You can use it l... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/exceptions/secret_exceptions.py | class SecretNameAlreadyExistsException(Exception):
pass
class SecretNameInvalidException(Exception):
pass
class SecretNoSetPermissionException(Exception):
pass
| 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/exceptions/__init__.py | from .secret_exceptions import SecretNameAlreadyExistsException, SecretNameInvalidException, SecretNoSetPermissionException # noqa: F401, E501
| 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/utils/generate_tool_meta_utils.py | """
This file can generate a meta file for the given prompt template or a python file.
"""
import inspect
import types
from dataclasses import asdict
from utils.tool_utils import function_to_interface
from promptflow.contracts.tool import Tool, ToolType
# Avoid circular dependencies: Use import 'from promptflow._inte... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/utils/repo_utils.py | import json
import os
import shutil
import subprocess
from datetime import datetime
from pathlib import Path
import requests
scripts_dir = os.path.join(os.getcwd(), "scripts")
index_url = "https://azuremlsdktestpypi.azureedge.net/test-promptflow/promptflow-tools"
ado_promptflow_repo_url_format = "https://{0}@dev.azur... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/utils/secret_manager.py | import re
from azure.core.exceptions import HttpResponseError, ResourceExistsError
from azure.identity import ClientSecretCredential
from azure.keyvault.secrets import SecretClient
from exceptions import (
SecretNameAlreadyExistsException,
SecretNameInvalidException,
SecretNoSetPermissionException,
)
key_... | 0 |
promptflow_repo/promptflow/scripts/tool | promptflow_repo/promptflow/scripts/tool/utils/tool_utils.py | import inspect
from enum import Enum, EnumMeta
from typing import Callable, Union, get_args, get_origin
from promptflow.contracts.tool import ConnectionType, InputDefinition, ValueType, ToolType
from promptflow.contracts.types import PromptTemplate
def value_to_str(val):
if val is inspect.Parameter.empty:
... | 0 |
promptflow_repo/promptflow/scripts/tool/utils | promptflow_repo/promptflow/scripts/tool/utils/configs/deploy-endpoint-request-body.json | {
"stagesToSkip": [],
"resources": {
"repositories": {
"self": {
"refName": "refs/heads/dev-branch"
}
}
},
"templateParameters": {
"deployEndpoint": "True"
},
"variables": {
"model-file": {
"value": "promptflow-g... | 0 |
promptflow_repo/promptflow/scripts/tool/utils | promptflow_repo/promptflow/scripts/tool/utils/configs/promptflow-gallery-tool-test.yaml | storage:
storage_account: promptflowgall5817910653
deployment:
subscription_id: 96aede12-2f73-41cb-b983-6d11a904839b
resource_group: promptflow
workspace_name: promptflow-gallery
endpoint_name: tool-test638236049123389546
deployment_name: blue
mt_service_endpoint: https://eastus2euap.api.azureml.ms | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/docs/promptflow.connections.rst | promptflow.connections package
==============================
.. autoclass:: promptflow.connections.AzureContentSafetyConnection
:members:
:undoc-members:
:show-inheritance:
:noindex:
.. autoclass:: promptflow.connections.AzureOpenAIConnection
:members:
:undoc-members:
:show-inheritance:
:noin... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/docs/doc_generation.ps1 | <#
.DESCRIPTION
Script to build doc site
.EXAMPLE
PS> ./doc_generation.ps1 -SkipInstall # skip pip install
PS> ./doc_generation.ps1 -BuildLinkCheck -WarningAsError:$true -SkipInstall
#>
[CmdletBinding()]
param(
[switch]$SkipInstall,
[switch]$WarningAsError = $false,
[switch]$BuildLinkCheck = $false,
... | 0 |
promptflow_repo/promptflow/scripts | promptflow_repo/promptflow/scripts/docs/conf.py | # -- Path setup --------------------------------------------------------------
import sys
# -- Project information -----------------------------------------------------
project = 'Prompt flow'
copyright = '2023, Microsoft'
author = 'Microsoft'
sys.path.append(".")
from gallery_directive import GalleryDirective # no... | 0 |
promptflow_repo/promptflow/scripts/docs | promptflow_repo/promptflow/scripts/docs/_static/custom.js | // Get the head element
let head = document.getElementsByTagName("head")[0];
// Create the script element
let script = document.createElement("script");
script.async = true;
script.src = "https://www.googletagmanager.com/gtag/js?id=G-KZXK5PFBZY";
// Create another script element for the gtag code
let script2 = docume... | 0 |
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