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LlamaIndex is a data framework specifically designed for connecting custom data sources to large language models (LLMs). It offers a simple and flexible approach to integrate various types of data with LLM applications.

With LlamaIndex, users can connect their existing data sources and formats, including APIs, PDFs, documents, and SQL, to be utilized within LLM applications.

The tool provides data ingestion capabilities, allowing the storage and indexing of data for different use cases. Integration with downstream vector store and database providers is also supported.LlamaIndex stands out with its query interface, which allows users to input prompts and receive knowledge-augmented responses based on their data.

This feature enables the creation of powerful end-user applications such as document Q&A and data augmented chatbots. Additionally, LlamaIndex can be used to index knowledge bases and task lists, supporting the development of automated decision machines.The tool supports various types of data sources, including unstructured sources like documents, raw text files, PDFs, videos, and images.

It also seamlessly integrates structured data sources from Excel and SQL, as well as semi-structured data from APIs like Slack, Salesforce, and Notion.LlamaIndex provides several resources for users, including documentation, a Discord community, an official Twitter account, and a blog.

It is available on GitHub under the LlamaIndex repository, and related products such as LlamaIndex.TS, LlamaHub, and LlamaLab are also accessible. Users can unleash the power of LLMs over their data by leveraging the capabilities of LlamaIndex.

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Initial release

June 6, 2023

Initial release of Llamaindex.

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Pricing

Pricing model

Freemium

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$50/month

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Monthly

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Llamaindex was manually vetted by our editorial team and was first featured on August 8th 2023.

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Pros and Cons

Pros

Connects custom data sources

Supports large language models

Flexible data integration

Supports APIs, PDFs, documents, SQL

Data ingestion capabilities

Storage and indexing of data

Integrated with vector store

Integrated with database providers

Input prompts in query interface

Knowledge-augmented responses

Creates document Q&A applications

Enables data augmented chatbots

Can index knowledge bases

Supports automated decision machines

Integrates unstructured data sources

Connects raw text files, videos, images

Seamlessly integrates Excel, SQL

Integrates semi-structured data APIs

Community support via Discord

Active Twitter account

Blog updates

Available on GitHub

View 19 more pros

Cons

No dedicated customer support

Restricted data ingestion capabilities

Limited types of structured data

No explicit security measures

No data cleansing feature

Limited vector store providers

Unclear update frequency

Exclusive reliance on GitHub

No multi-language support

No information on scalability

View 5 more cons

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Q&A

What is LlamaIndex?

LlamaIndex is a data framework specifically designed for connecting custom data sources to large language models (LLMs). It offers a flexible approach to integrate various types of data with LLM applications. The tool supports different use cases by providing data ingestion capabilities, data indexing, and a query interface for receiving knowledge-augmented responses based on user data.

How does LlamaIndex connect with large language models?

LlamaIndex connects with large language models through its data framework. This framework allows users to connect their existing data sources and formats, like APIs, PDFs, documents, and SQL, to be utilized within LLM applications.

What types of data sources can LlamaIndex support?

LlamaIndex can support various types of data sources. These include unstructured sources like documents, raw text files, PDFs, videos, and images. It also supports structured data sources from Excel and SQL, and semi-structured data from APIs like Slack, Salesforce, and Notion.

How does LlamaIndex handle data ingestion?

LlamaIndex handles data ingestion by allowing the storage and indexing of data for different use cases. Users can connect their existing data sources and data formats to use with a large language model application.

What types of integrations does LlamaIndex offer with downstream vector store and database providers?

LlamaIndex offers integration with downstream vector store and database providers. This ensures seamless storage and retrieval of data for user applications.

What is the query interface in LlamaIndex?

The query interface in LlamaIndex is a feature that accepts any input prompt over user data and returns a knowledge-augmented response. This interface allows users to gain insights and information directly from their data.

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How can I use the LlamaIndex query interface to receive knowledge-augmented responses?

You can use the LlamaIndex query interface to receive knowledge-augmented responses simply by inputting prompts. The interface processes these prompts and returns responses based on the data attached to your LLM applications.

How does LlamaIndex support the creation of document Q&A applications?

LlamaIndex supports the creation of document Q&A applications by offering a flexible data framework that can connect with unstructured data sources like PDFs, PPTs, web pages, and images and generate answers over this data.

Can I use LlamaIndex to build data augmented chatbots?

Yes, LlamaIndex can be used to build data augmented chatbots. By indexing your knowledge base and task list, you can converse with an agent over your knowledge corpus.

How does LlamaIndex handle knowledge bases and task lists?

LlamaIndex handles knowledge bases and task lists by allowing users to index them. This enables the tool to support the development of automated decision machines.

What are some examples of unstructured data sources that LlamaIndex can connect with?

Some examples of unstructured data sources that LlamaIndex can connect with are documents, raw text files, PDFs, videos, and images.

Can LlamaIndex integrate with structured data sources like Excel and SQL?

Yes, LlamaIndex can indeed integrate with structured data sources like Excel and SQL. It provides a simple and flexible approach for connecting these structured data sources to LLM applications.

What semi-structured data sources, like APIs, can LlamaIndex work with?

LlamaIndex can work with various semi-structured data sources. These include APIs like Slack, Salesforce, and Notion.

What resources does LlamaIndex provide for its users?

LlamaIndex provides several resources for its users. These include documentation, a Discord community, an official Twitter account, and a blog. LlamaIndex and its related products are also available on GitHub.

Where can I find LlamaIndex's repository on GitHub?

LlamaIndex's repository is located on GitHub under the handle jerryjliu/llama_index.

What related products are available alongside LlamaIndex?

Related products available alongside LlamaIndex include LlamaIndex.TS, LlamaHub, and LlamaLab.

What is the LlamaIndex.TS product?

LlamaIndex.TS is a related product but exact information regarding its specifics isn't available.

What features does LlamaHub offer?

Information about the specific features provided by LlamaHub isn't available.

Can I access LlamaIndex's community on Discord?

Yes, you can access LlamaIndex's community on Discord at https://discord.com/invite/eN6D2HQ4aX.

How can I follow LlamaIndex on Twitter?

You can follow LlamaIndex on Twitter at https://twitter.com/llama_index.