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models prompt flow documentation
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The "Ask Wikipedia" is a Q&A model that employs GPT3.5 to answer questions using information sourced from Wikipedia, ensuring more grounded responses. This process involves identifying the relevant Wikipedia link and extracting its contents. These contents are then used as an augmented prompt, en...
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The "Bring Your Own Data Chat QnA" is a pre-trained chat model, enhanced by GPT3.5, that leverages your personally indexed data and chat history to deliver more concrete and relevant answers. It involves processing the raw query through an embedding procedure, followed by a "Vector Search" to pin...
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The "Bring your own data QnA" is a pre-trained Q&A model, enhanced by GPT3.5, that leverages your personally indexed data to deliver more concrete and relevant answers. It involves processing the raw query through an embedding procedure, followed by a "Vector Search" to pinpoint the most pertinen...
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The "Chat with Wikipedia" is a pre-trained chat model with GPT3.5: it combines conversation history and information from Wikipedia to make the answer more grounded. It involves finding a relevant Wikipedia link and getting page contents for the question. It can remember previous interactions and ...
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The "Classification Accuracy Evaluation" is a model designed to assess the effectiveness of a data classification system. It involves matching each prediction against the ground truth, subsequently assigning a "Correct" or "Incorrect" score. The cumulative results are then leveraged to generate p...
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how-to-use-functions-with-GPT-chat-API
The "Use Functions with Chat Models" is a chat model illustrates how to employ the LLM tool's Chat API with external functions, thereby expanding the capabilities of GPT models. The Chat Completion API includes an optional 'functions' parameter, which can be used to stipulate function specificati...
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The "QnA Ada Similarity Evaluation" is a model to evaluate the Q&A Retrieval Augmented Generation systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses. Utilizing GPT-3.5 as the Language Model to assist with measurements aims to...
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The "QnA Coherence Evaluation" is a model to evaluate the Q&A Retrieval Augmented Generation systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses. Utilizing GPT-3.5 as the Language Model to assist with measurements aims to achi...
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The "QnA F1 Score Evaluation" is a model to evaluate the Q&A Retrieval Augmented Generation systems using f1 score based on the word counts in predicted answer and ground truth.
| Inference type | Python sample (Notebook) | CLI with YAML |
|---|---|---|
| Real time | <a href="https://gi... |
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The "QnA Fluency Evaluation" is a model to evaluate the Q&A Retrieval Augmented Generation systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses. Utilizing GPT-3.5 as the Language Model to assist with measurements aims to achiev...
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The "QnA GPT Similarity Evaluation" is a model to evaluate the Q&A Retrieval Augmented Generation systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses. Utilizing GPT-3.5 as the Language Model to assist with measurements aims to...
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The "QnA Groundedness Evaluation" is a model to evaluate the Q&A Retrieval Augmented Generation systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses. Utilizing GPT-3.5 as the Language Model to assist with measurements aims to a...
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The Q&A evaluation flow will evaluate the Q&A systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses. Utilizing GPT and GPT embedding model to assist with measurements aims to achieve a high agreement with human evaluations compa...
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The Q&A RAG (Retrieval Augmented Generation) evaluation flow will evaluate the Q&A RAG systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses . Utilizing GPT model to assist with measurements aims to achieve a high agreement with...
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The "QnA Relevance Evaluation" is a model to evaluate the Q&A Retrieval Augmented Generation systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of your responses. Utilizing GPT-3.5 as the Language Model to assist with measurements aims to achi...
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qna-with-your-own-data-using-faiss-index
The "QnA with Your Own Data Using Faiss Index" is a Q&A model with GPT3.5 using information from vector search to make the answer more grounded. It involves embedding user's question with LLM, and then using Faiss Index Lookup to find relevant documents based on vectors. By utilizing vector searc...
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The "Template Chat Flow" is a chat model using GPT3.5 that generates the next message based on the conversation history and the latest chat content.
| Inference type | Python sample (Notebook) | CLI with YAML |
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| Real time | <a href="https://github.com/microsoft/promptflow... |
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The "Template Evaluation Flow" is a evaluate model to measure how well the output matches the expected criteria and goals.
| Inference type | Python sample (Notebook) | CLI with YAML |
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| Real time | <a href="https://github.com/microsoft/promptflow/blob/pm/3p-inside-materia... |
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The "Template Standard Flow" is a model using GPT3.5 to generate a joke based on user input.
| Inference type | Python sample (Notebook) | CLI with YAML |
|---|---|---|
| Real time | <a href="https://github.com/microsoft/promptflow/blob/pm/3p-inside-materials/docs/media/deploy-to-aml-co... |
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The "Web Classification" is a model demonstrating multi-class classification with LLM. Given an url, it will classify the url into one web category with just a few shots, simple summarization and classification prompts.
Inference type|Python sample (Notebook)|CLI with YAM...