Sold by: MphasisÂ
Deployed on AWS
Solution enhances the search for specific business processes by accounting for language and intents of different user roles in the domain.
Overview
In search engines, the way search is conducted varies based on purpose, information needed, and the available resources within a business process. This solution uses state-of-the-art GenAI techniques and Anthropic Claude to customize the embedding LLM model to capture the nature of questions and domain semantics. Alignment of embeddings using finetuned LLMs improves the rankings of relevant content in search results. The inputs are raw documents (most formats of documents containing text and images) along with metadata such as: role of the user intendnig to use the search engine, user intentions, description of the data and usage of the data. For example, a maintenance engineer, searching thourhg maintenance logs and troubleshooting content. The embedding model is fine-tuned on this dataset and can be used at inference to vectorize the final desired corpus and incoming queries for search. Please note that the product requires an AWS bedrock anthropic Claude V2 model subscription.
Highlights
- The GenAI solution is intended to be used as part of a search and retrieval workflow. The embeddings generated from a fine-tuned LLM given the domain-specific raw documents can be useful to embed your documents to a vector database and vectorize incoming search queries. The solution can accept all format documents and the output is a zip file with embedding in excel format.
- The input can be of any data format including docx, pdf, ppt, images, xlsx, etc., and requires no data preparation. only raw documents along with the user intent and data description to understand the data and the user requirement. The generated output captures the type of questions and also the contextual meaning of keywords. The users require AWS credentials for an account that has a Bedrock - Anthropic-Claude-v2 model subscription.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.p2.xlarge Inference (Batch) Recommended | Model inference on the ml.p2.xlarge instance type, batch mode | $4.00/host/hour |
ml.p2.xlarge Training Recommended | Algorithm training on the ml.p2.xlarge instance type | $4.00/host/hour |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $4.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.50/request |
ml.p2.8xlarge Training | Algorithm training on the ml.p2.8xlarge instance type | $4.00/host/hour |
ml.g4dn.xlarge Training | Algorithm training on the ml.g4dn.xlarge instance type | $4.00/host/hour |
ml.g4dn.2xlarge Training | Algorithm training on the ml.g4dn.2xlarge instance type | $4.00/host/hour |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
First Version
Additional details
Inputs
- Summary
The inference pipeline requires:
- A Input.zip file (case sensitive) which include a .csv file named as 'test.csv'.
- The 'test.csv' contain only one column called 'text'.
- Input MIME type
- application/zip, application/json, application/gzip, text/csv
https://github.com/Mphasis-ML-Marketplace/Task-tuned-embeddings-for-search/blob/main/inferencing_Input.zip
https://github.com/Mphasis-ML-Marketplace/Task-tuned-embeddings-for-search/blob/main/inferencing_Input.zip
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