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Added Amazon Bedrock for Agents, LLMs and Vector Databases from AWS #45

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13 changes: 11 additions & 2 deletions README.md
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Expand Up @@ -52,6 +52,8 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
| [OpenAI Ada Embedding 2](https://platform.openai.com/docs/guides/embeddings) | OpenAI's most popular embedding model for capturing semantic relationships in text | n/a | <a href=https://pypi.org/project/openai><img src="https://img.shields.io/pypi/dw/openai" width=150/></a> |
| [Cohere AI](https://docs.cohere.com/docs/embeddings) | An independent commerical provider of LLMs, with particular focus on embeddings for semantic search, topic clustering, and vertical applications | <a href=https://github.com/cohere-ai/notebooks><img src="https://img.shields.io/github/stars/cohere-ai/notebooks?style=social" width=90/></a> | <a href=https://pypi.org/project/cohere><img src="https://img.shields.io/pypi/dw/cohere" width=150/></a> |
| [Sentence Transformers](https://www.sbert.net/) | An open-source Python framework for sentence, text, and image embeddings | <a href=https://github.com/UKPLab/sentence-transformers><img src="https://img.shields.io/github/stars/UKPLab/sentence-transformers?style=social" width=90/></a> | <a href=https://pypi.org/project/sentence-transformers><img src="https://img.shields.io/pypi/dw/sentence-transformers" width=150/></a> |
| [Amazon Titan Text Embeddings models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | Amazon Titan Text Embeddings models generate meaningful semantic representations of documents, paragraphs, and sentences, providing vectors for text retrieval, semantic similarity, and clustering in over 100 languages | <a href=https://github.com/aws-samples/titan-multimodal-embeddings-workshop> <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/aws-samples/titan-multimodal-embeddings-workshop?style=social" width=90></a> | <a href=https://pypi.org/project/boto3/><img src="https://img.shields.io/pypi/dw/boto3" width=150/></a> |
| [Amazon Titan Multimodal Embeddings G1 model](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | Amazon's Multimodal Embeddings G1 model generates semantic representations for text and images, enabling applications like personalized search and image-text similarity comparisons | <a href=https://github.com/aws-samples/titan-multimodal-embeddings-workshop> <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/aws-samples/titan-multimodal-embeddings-workshop?style=social" width=90></a> | <a href=https://pypi.org/project/boto3/><img src="https://img.shields.io/pypi/dw/boto3" width=150/></a> |

<p style="text-align: right;"><a href="#table-of-contents">^ Back to Contents ^</a></p>

Expand All @@ -66,16 +68,20 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
| [Qdrant](https://qdrant.tech/) | A vector database and vector similarity search engine | <a href=https://github.com/qdrant/qdrant><img src="https://img.shields.io/github/stars/qdrant/qdrant?style=social" width=90/></a> | <a href=https://pypi.org/project/qdrant-client><img src="https://img.shields.io/pypi/dw/qdrant-client" width=150/></a> |
| [Metal io](https://getmetal.io/) | A managed service for developers to build applications with ML embeddings | N/A | <a href=https://pypi.org/project/metal-python><img src="https://img.shields.io/pypi/dw/metal-python" width=150/></a> |
| [LanceDB](https://lancedb.com/) | A serverless vector database for AI applications | <a href=https://github.com/lancedb/lancedb><img src="https://img.shields.io/github/stars/lancedb/lancedb?style=social" width=90/></a> | <a href=https://pypi.org/project/lancedb><img src="https://img.shields.io/pypi/dw/lancedb" width=150/></a> |
| [Amazon OpenSearch Service](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless-vector-search.html) | Amazon OpenSearch Service enables interactive log analytics, real-time application monitoring, and website search with k-Nearest Neighbor (k-NN) search capabilities for vector databases | <a href=https://github.com/aws-samples/AI-search-with-amazon-opensearch-service><img src="https://img.shields.io/github/stars/aws-samples/AI-search-with-amazon-opensearch-service?style=social" width=90/></a> | <a href=https://pypi.org/project/pinecone-client><img src="https://img.shields.io/pypi/dw/pinecone-client" width=150/></a> |
| [Amazon Aurora PostgreSQL-Compatible Edition](https://community.aws/concepts/vector-embeddings-and-rag-demystified-1) | Amazon Aurora PostgreSQL-Compatible Edition supports the pgvector extension for storing embeddings and performing efficient similarity searches in your database | <a href=https://github.com/aws-samples/aurora-postgresql-pgvector><img src="https://img.shields.io/github/stars/pgvector/pgvector?style=social" width=90/></a> | <a href=https://pypi.org/project/pgvector><img src="https://img.shields.io/pypi/dw/pgvector" width=150/></a> |
| [Vector search for Amazon DocumentDB (with MongoDB compatibility)](https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html) | Amazon DocumentDB supports vector search, allowing you to store, index, and search millions of vectors with millisecond response times within your document database, ideal for ML applications | <a href=https://github.com/aws-samples/amazon-documentdb-samples/tree/master/blogs><img src="https://img.shields.io/github/stars/aws-samples/amazon-documentdb-samples?style=social" width=90/></a> | <a href=https://pypi.org/project/boto3/><img src="https://img.shields.io/pypi/dw/boto3" width=150/></a> |

<p style="text-align: right;"><a href="#table-of-contents">^ Back to Contents ^</a></p>

### Playgrounds
| Name (site) | Description | Github | Pip Installs |
|---------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|
| [OpenAI Playground](https://platform.openai.com/) | A web-based platform for experimenting with various machine-learning models developed by OpenAI | N/A | N/A |
| [OpenAI Playground](https://platform.openai.com/) | A web-based platform for experimenting with various machine-learning models developed by OpenAI | N/A | N/A |
| [nat.dev](https://nat.dev) | A platform that allows users to test prompts with multiple language models and compare their performance | <a href=https://github.com/nat/openplayground><img src="https://img.shields.io/github/stars/nat/openplayground?style=social" width=90/></a> | <a href=https://pypi.org/project/openplayground><img src="https://img.shields.io/pypi/dw/openplayground" width=150/></a> |
| [Humanloop](https://humanloop.com/) | A platform that helps developers build applications on top of LLMs | <a href=https://github.com/humanloop/humanloop-tutorial-python><img src="https://img.shields.io/github/stars/humanloop/humanloop-tutorial-python?style=social" width=90/></a> | <a href=https://pypi.org/project/humanloop><img src="https://img.shields.io/pypi/dw/humanloop" width=150/></a> |
| [Parea AI](https://www.parea.ai/) | Platform and SDK for AI Engineers providing tools for LLM evaluation, observability, and a version-controlled enhanced prompt playground. | <a href=https://github.com/parea-ai><img src="https://img.shields.io/github/stars/parea-ai/parea-sdk-py?style=social" width=90/></a> | <a href=https://pypi.org/project/parea-ai/><img src="https://img.shields.io/pypi/dw/parea-ai" width=150/></a> |
| [Playgrounds - Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/playgrounds.html) | The Amazon Bedrock playgrounds provide a console environment to experiment with running inference on different models and configurations before using them in applications | N/A | N/A |

<p style="text-align: right;"><a href="#table-of-contents">^ Back to Contents ^</a></p>

Expand All @@ -90,6 +96,7 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
| [Vercel AI SDK](https://sdk.vercel.ai/docs) | An open-source library for developers to build streaming UIs in JavaScript and TypeScript | <a href=https://github.com/vercel/ai><img src="https://img.shields.io/github/stars/vercel-labs/ai?style=social" width=90/></a> | <a href=https://pypi.org/project/vercel-ai-sdk><img src="https://img.shields.io/npm/dw/ai" width=150/></a>(node/npm)|
| [Vectara AI](https://vectara.com/) | A search and discovery platform for AI conversations utilizing your own data | <a href=https://github.com/vectara/vectara-ingest><img src="https://img.shields.io/github/stars/vectara/vectara-ingest?style=social" width=90/></a> | N/A |
| [ChatGPT](https://chat.openai.com) | An AI chatbot that uses natural language processing to create humanlike conversational dialogue | N/A| N/A |
| [Agents for Amazon Bedrock](https://aws.amazon.com/bedrock/agents/) | Agents for Amazon Bedrock enhance operational efficiency, customer service, and decision-making while reducing costs and facilitating innovation | <a href=https://github.com/build-on-aws/amazon-bedrock-agents-quickstart><img src="https://img.shields.io/github/stars/build-on-aws/amazon-bedrock-agents-quickstart?style=social" width=90/></a> | <a href=https://pypi.org/project/boto3/><img src="https://img.shields.io/pypi/dw/boto3" width=150/></a> |

<p style="text-align: right;"><a href="#table-of-contents">^ Back to Contents ^</a></p>

Expand All @@ -109,6 +116,7 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
| [Redis](https://redis.io/) | An in-memory data structure store used as a database, cache, message broker, and streaming engine | <a href=https://github.com/redis/redis><img src="https://img.shields.io/github/stars/redis/redis?style=social" width=90/></a> | <a href=https://pypi.org/project/redis/><img src="https://img.shields.io/pypi/dw/redis" width=150/></a> |
| [SQLite](https://sqlite.org/) | A self-contained, serverless, zero-configuration, transactional SQL database engine | <a href=https://github.com/sqlite/sqlite><img src="https://img.shields.io/github/stars/sqlite/sqlite?style=social" width=90/></a> | <a href=https://pypi.org/project/pysqlite3/><img src="https://img.shields.io/pypi/dw/pysqlite3" width=150/></a> |
| [GPTCache](https://github.com/zilliztech/GPTCache) | An open-source tool for improving the efficiency and speed of GPT-based applications by implementing a cache to store the responses | <a href=https://github.com/zilliztech/GPTCache><img src="https://img.shields.io/github/stars/zilliztech/GPTCache?style=social" width=90/></a> | N/A |
| [MemoryDB for Redis](https://aws.amazon.com/memorydb) | Amazon MemoryDB for Redis is a durable, in-memory database service delivering ultra-fast performance for modern applications requiring low latency and high scalability | <a href=https://github.com/aws-samples/amazon-memorydb-for-redis-samples><img src="https://img.shields.io/github/stars/aws-samples/amazon-memorydb-for-redis-samples?style=social" width=90/></a> | <a href=https://pypi.org/project/boto3/><img src="https://img.shields.io/pypi/dw/boto3" width=150/></a> |

<p style="text-align: right;"><a href="#table-of-contents">^ Back to Contents ^</a></p>

Expand Down Expand Up @@ -137,7 +145,7 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
| [promptfoo](https://www.promptfoo.dev/) | Open-source LLM eval framework with support for model/prompt/RAG eval, dataset generation, local models, and self-hosting. | <a href=https://github.com/promptfoo/promptfoo><img src="https://img.shields.io/github/stars/promptfoo/promptfoo?style=social" width=90/></a> | <a href=https://www.npmjs.com/package/promptfoo><img src="https://img.shields.io/npm/dw/promptfoo" width=150/></a> (node/npm) |
| [Parea AI](https://www.parea.ai/) | Platform and SDK for AI Engineers providing tools for LLM evaluation, observability, and a version-controlled enhanced prompt playground. | <a href=https://github.com/parea-ai><img src="https://img.shields.io/github/stars/parea-ai/parea-sdk-py?style=social" width=90/></a> | <a href=https://pypi.org/project/parea-ai/><img src="https://img.shields.io/pypi/dw/parea-ai" width=150/></a> |
| [Galileo](https://www.rungalileo.io/) | Galileo is a platform for evaluation, fine-tuning and real-time observability, powered by high-accuracy hallucination guardrails. | N/A | N/A |

| [Amazon Bedrock model evaluation](https://aws.amazon.com/blogs/aws/amazon-bedrock-model-evaluation-is-now-generally-available/) | Amazon Bedrock's model evaluation helps you select the best foundation model for your use case by comparing model outputs, incorporating both automatic and human evaluations | <a href=https://github.com/aws-samples/genai-model-evaluator><img src="https://img.shields.io/github/stars/aws-samples/genai-model-evaluator?style=social" width=90/></a> | <a href=https://pypi.org/project/boto3/><img src="https://img.shields.io/pypi/dw/boto3" width=150/></a> |

<p style="text-align: right;"><a href="#table-of-contents">^ Back to Contents ^</a></p>

Expand All @@ -161,6 +169,7 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
| [Anthropic](https://anthropic.com) | The developer of Claude, an AI assistant based on Anthropic’s research | N/A | <a href=https://pypi.org/project/anthropic><img src="https://img.shields.io/pypi/dw/anthropic" width=150/></a> |
| [Cohere AI](https://docs.cohere.com/docs/embeddings) | An LLM vendor with particular focus on embeddings for semantic search, topic clustering, and vertical applications | <a href=https://github.com/cohere-ai/notebooks><img src="https://img.shields.io/github/stars/cohere-ai/notebooks?style=social" width=90/></a> | <a href=https://pypi.org/project/cohere><img src="https://img.shields.io/pypi/dw/cohere" width=150/></a> |
| [LLM](https://llm.datasette.io/en/stable/) | A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. | <a href=https://github.com/simonw/llm><img src="https://img.shields.io/github/stars/simonw/llm?style=social" width=90/></a> | <a href=https://pypi.org/project/llm/><img src="https://img.shields.io/pypi/dw/llm" width=150/></a> |
| [Amazon Bedrock](https://aws.amazon.com/bedrock/) | Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI | <a href=https://github.com/boto/boto3><img src="https://img.shields.io/github/stars/boto/boto3?style=social" width=90/></a> | <a href=https://pypi.org/project/boto3/><img src="https://img.shields.io/pypi/dw/boto3" width=150/></a> |

<p style="text-align: right;"><a href="#table-of-contents">^ Back to Contents ^</a></p>

Expand Down