Main

type

0 (not classified)

status

21 (imported old-v2, waiting for another import)

review version

0

cleanup version

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pending deletion

0 (-)

created at

2025-10-20 12:01:28

updated at

2026-01-22 12:50:33

Address

url

https://docs.centml.ai/apps/rag

url length

31

url crc

13507

url crc32

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location type

1 (url matches target location, page_location is empty)

canonical status

2 (missing canonical tag in html)

canonical page id

-

Source

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originating warc id

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originating url

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source type

11 (CommonCrawl)

Server response

server ip

76.76.21.142

Publication date

2025-07-15 05:59:47

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0

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97020

Normalized and saved size

39635

Content

title

excerpt

content

Retrieval-Augmented Generation (RAG) leverages external knowledge sources to enhance the accuracy and relevance of text generated by large language models. By incorporating real-world information, RAG empowers LLMs to produce more comprehensive and informative outputs. ​1. Configure your RAG application Name your RAG application and select the embedding and language models from the drop down list. Click next. ​2. Select the cluster and the hardware to deploy Select the cluster and hardware instance where you want to deploy the embedding model. Embedding models are typically small and can be efficiently run on lower-end GPUs like A10G and L4s, making this a more cost-effective hardware option. Click deploy. ​3. Use the RAG deployment Once the deployment is ready, go to the Playground tab under deployment details page. Upload PDF, Markdown, or Text files to the RAG and ask questions about the information provided. ​What’s Next LLM ServingExplore dedicated public and private endpoints ...

author

updated

1770431918

Text analysis

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0

extracted fields

96

extracted bits

full content
content was extracted heuristically

detected location

0

detected language

1 (English)

category id

Pozostałe (16)

index version

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paywall score

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spam phrases

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text cyrillic

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text characters

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text words

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text unique words

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text lines

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text sentences

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text paragraphs

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text words per sentence

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text matched phrases

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text matched dictionaries

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