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# rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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Classic Topic Modeling with BM25

Classic Topic Modeling with BM25

4
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5 min read
Introducing the Semantic Graph

Introducing the Semantic Graph

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Comments 1
9 min read
Embeddings index components

Embeddings index components

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7 min read
Run txtai in native code

Run txtai in native code

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Comments 1
7 min read
Pictures are a worth a thousand words

Pictures are a worth a thousand words

8
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3 min read
Build a QA database

Build a QA database

12
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3 min read
Query translation

Query translation

8
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4 min read
Model explainability

Model explainability

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5 min read
Near duplicate image detection

Near duplicate image detection

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3 min read
Embeddings SQL custom functions

Embeddings SQL custom functions

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4 min read
Anatomy of a txtai index

Anatomy of a txtai index

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7 min read
Push notifications with workflows

Push notifications with workflows

12
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7 min read
Workflow Scheduling

Workflow Scheduling

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6 min read
Entity extraction workflows

Entity extraction workflows

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3 min read
Generate image captions and detect objects

Generate image captions and detect objects

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2 min read
💡 What's new in txtai 4.0

💡 What's new in txtai 4.0

9
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7 min read
Tensor workflows

Tensor workflows

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5 min read
Transform tabular data with composable workflows

Transform tabular data with composable workflows

8
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14 min read
Export and run other machine learning models

Export and run other machine learning models

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6 min read
Extractive QA to build structured data

Extractive QA to build structured data

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3 min read
Train a QA model

Train a QA model

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Comments 1
3 min read
Export and run models with ONNX

Export and run models with ONNX

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12 min read
Train a text labeler

Train a text labeler

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2 min read
Train without labels

Train without labels

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3 min read
Distributed embeddings cluster

Distributed embeddings cluster

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5 min read
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