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Update tagline to the all-in-one AI framework, closes #901
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davidmezzetti
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cff-version: 1.2.0
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date-released: 2020-08-11
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message: "If you use this software, please cite it as below."
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title: "txtai: the all-in-one embeddings database"
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abstract: "txtai is an all-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows"
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title: "txtai: the all-in-one AI framework"
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abstract: "txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows"
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url: "https://github.com/neuml/txtai"
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authors:
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- family-names: "Mezzetti"
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@@ -3,7 +3,7 @@
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</p>
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<p align="center">
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<b>All-in-one embeddings database</b>
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<b>All-in-one AI framework</b>
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</p>
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<p align="center">
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@@ -27,12 +27,12 @@
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</a>
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</p>
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txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.
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Embeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases.
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The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases.
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This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.
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@@ -130,7 +130,7 @@ Agents connect embeddings, pipelines, workflows and other agents together to aut
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txtai agents are built on top of the Transformers Agent framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI / Claude / AWS Bedrock via LiteLLM).
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txtai agents are built on top of the [smolagents](https://github.com/huggingface/smolagents) framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI / Claude / AWS Bedrock via LiteLLM).
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See the link below to learn more.
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@@ -235,7 +235,7 @@ In addition to this list, there are also many other [open-source projects](https
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- [Introducing txtai, the all-in-one embeddings database](https://medium.com/neuml/introducing-txtai-the-all-in-one-embeddings-database-c721f4ff91ad)
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- [Introducing txtai, the all-in-one AI framework](https://medium.com/neuml/introducing-txtai-the-all-in-one-ai-framework-0660ecfc39d7)
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- [Tutorial series on Hashnode](https://neuml.hashnode.dev/series/txtai-tutorial) | [dev.to](https://dev.to/neuml/tutorial-series-on-txtai-ibg)
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- [What's new in txtai 8.0](https://medium.com/neuml/whats-new-in-txtai-8-0-2d7d0ab4506b) | [7.0](https://medium.com/neuml/whats-new-in-txtai-7-0-855ad6a55440) | [6.0](https://medium.com/neuml/whats-new-in-txtai-6-0-7d93eeedf804) | [5.0](https://medium.com/neuml/whats-new-in-txtai-5-0-e5c75a13b101) | [4.0](https://medium.com/neuml/whats-new-in-txtai-4-0-bbc3a65c3d1c)
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- [Getting started with semantic search](https://medium.com/neuml/getting-started-with-semantic-search-a9fd9d8a48cf) | [workflows](https://medium.com/neuml/getting-started-with-semantic-workflows-2fefda6165d9) | [rag](https://medium.com/neuml/getting-started-with-rag-9a0cca75f748)
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@@ -3,7 +3,7 @@
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- [Introducing txtai, the all-in-one embeddings database](https://medium.com/neuml/introducing-txtai-the-all-in-one-embeddings-database-c721f4ff91ad)
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- [Introducing txtai, the all-in-one AI framework](https://medium.com/neuml/introducing-txtai-the-all-in-one-ai-framework-0660ecfc39d7)
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- [Tutorial series on Hashnode](https://neuml.hashnode.dev/series/txtai-tutorial) | [dev.to](https://dev.to/neuml/tutorial-series-on-txtai-ibg)
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- [What's new in txtai 8.0](https://medium.com/neuml/whats-new-in-txtai-8-0-2d7d0ab4506b) | [7.0](https://medium.com/neuml/whats-new-in-txtai-7-0-855ad6a55440) | [6.0](https://medium.com/neuml/whats-new-in-txtai-6-0-7d93eeedf804) | [5.0](https://medium.com/neuml/whats-new-in-txtai-5-0-e5c75a13b101) | [4.0](https://medium.com/neuml/whats-new-in-txtai-4-0-bbc3a65c3d1c)
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- [Getting started with semantic search](https://medium.com/neuml/getting-started-with-semantic-search-a9fd9d8a48cf) | [workflows](https://medium.com/neuml/getting-started-with-semantic-workflows-2fefda6165d9) | [rag](https://medium.com/neuml/getting-started-with-rag-9a0cca75f748)
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@@ -5,7 +5,7 @@
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</p>
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<p align="center">
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<b>All-in-one embeddings database</b>
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<b>All-in-one AI framework</b>
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</p>
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<p align="center">
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@@ -29,17 +29,17 @@
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</a>
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</p>
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txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.
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Embeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases.
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Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.
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The key component of txtai is it's embeddings database which is a union of vector indexes (sparse and dense), graph networks and relational databases.
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This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.
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Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.
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Summary of txtai features:
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- 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing
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@@ -42,7 +42,7 @@ Agents connect embeddings, pipelines, workflows and other agents together to aut
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txtai agents are built on top of the Transformers Agent framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI / Claude / AWS Bedrock via LiteLLM).
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txtai agents are built on top of the [smolagents](https://github.com/huggingface/smolagents) framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI / Claude / AWS Bedrock via LiteLLM).
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See the link below to learn more.
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@@ -33,9 +33,9 @@
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"source": [
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"# Introducing txtai\n",
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"\n",
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"[txtai](https://github.com/neuml/txtai) is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.\n",
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"[txtai](https://github.com/neuml/txtai) is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.\n",
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"\n",
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"Embeddings databases are a union of vector indexes (sparse and dense), graph networks and relational databases.\n",
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"The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases.\n",
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"\n",
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"This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.\n",
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"\n",
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@@ -735,7 +735,7 @@
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"source": [
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"# LLM orchestration\n",
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"\n",
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"txtai is an all-in-one embeddings database. It is the only vector database that also supports sparse indexes, graph networks and relational databases with inline SQL support. In addition to this, txtai has support for LLM orchestration.\n",
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"txtai is an all-in-one AI framework. txtai supports building autonomous agents, retrieval augmented generation (RAG), chat with your data, pipelines and workflows that interface with large language models (LLMs).\n",
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"\n",
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"The [RAG pipeline](https://neuml.github.io/txtai/pipeline/text/rag/) is txtai's spin on retrieval augmented generation (RAG). This pipeline extracts knowledge from content by joining a prompt, context data store and generative model together.\n",
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"\n",
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@@ -1095,7 +1095,7 @@
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"source": [
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"# Wrapping up\n",
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"\n",
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"NLP is advancing at a rapid pace. Things not possible even a year ago are now possible. This notebook introduced txtai, an all-in-one embeddings database. The possibilities are limitless and we're excited to see what can be built on top of txtai!\n",
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"AI is advancing at a rapid pace. Things not possible even a year ago are now possible. This notebook introduced txtai, an all-in-one AI framework. The possibilities are limitless and we’re excited to see what can be built on top of txtai!\n",
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"\n",
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"Visit the links below for more.\n",
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"\n",
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#
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site_name: txtai
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site_description: "txtai is an all-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows"
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site_description: "txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows"
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repo_name: neuml/txtai
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repo_url: https://github.com/neuml/txtai
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copyright: © NeuML LLC, Apache-2.0 License
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@@ -137,7 +137,7 @@ setup(
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name="txtai",
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version="8.6.0",
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author="NeuML",
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description="All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows",
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description="All-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows",
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long_description=DESCRIPTION,
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long_description_content_type="text/markdown",
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url="https://github.com/neuml/txtai",
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