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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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Understanding vector search and HNSW index with pgvector

Understanding vector search and HNSW index with pgvector

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10 min read
Introducing GPT4All
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Introducing GPT4All

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4 min read
External database integration

External database integration

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9 min read
Benefits of hybrid search

Benefits of hybrid search

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5 min read
Building an efficient sparse keyword index in Python

Building an efficient sparse keyword index in Python

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9 min read
đź’ˇ What's new in txtai 6.0

đź’ˇ What's new in txtai 6.0

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9 min read
Customize your own embeddings database

Customize your own embeddings database

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9 min read
Prompt templates and task chains

Prompt templates and task chains

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4 min read
Postgres as Rest
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Postgres as Rest

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5 min read
Commitar ou nĂŁo commitar o package-lock.json?

Commitar ou nĂŁo commitar o package-lock.json?

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2 min read
Embeddings in the Cloud

Embeddings in the Cloud

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6 min read
Initialization and creating a database for testing the installation of Postgres and Age part 4.

Initialization and creating a database for testing the installation of Postgres and Age part 4.

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2 min read
Functions in Solidity
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Functions in Solidity

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2 min read
How to build a Barcode Widget in React Native (part I: Android)

How to build a Barcode Widget in React Native (part I: Android)

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9 min read
How to extract an audio file from a video with Python
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How to extract an audio file from a video with Python

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2 min read
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