View source for LlamaIndex Python-based Framework

A [[LlamaIndex Python-based Framework]] is a [[LLM framework]] that enables [[data ingestion]], [[data structuring]], and [[data access]] for [[LLM augmentation]] with [[domain-specific data]].
* <B>Context:</B>
** It can (typically) provide [[data connector]]s through [[API integration]], [[PDF processing]], [[SQL connection]], and [[document handling]].
** It can (typically) enable [[data indexing]] through [[vector store]], [[summary store]], [[tree store]], and [[keyword table]] mechanisms.
** It can (typically) support [[natural language interface]]s through [[query engine]]s and [[chat engine]]s.
** It can (typically) implement [[data agent]]s for [[LLM-powered task execution]] and [[tool integration]].
** It can (typically) facilitate [[application integration]] with [[LangChain]], [[Flask]], [[Docker]], and [[ChatGPT]].
** ...
** It can (often) manage [[data processing]] through [[document parsing]], [[node creation]], and [[metadata management]].
** It can (often) maintain [[node relationship]]s between [[text chunk]]s and [[image content]].
** It can (often) support [[storage backend]]s including [[local filesystem]], [[AWS S3]], and [[Cloudflare R2]].
** It can (often) enable [[workflow customization]] through [[callback system]]s and [[custom function]]s.
** It can (often) provide [[monitoring capability]]s for [[performance tracking]] and [[system debugging]].
** ...
** It can range from being a [[Basic Data Connector]] to being an [[Advanced RAG Pipeline]], depending on its [[implementation complexity]].
** It can range from being a [[Simple Query Interface]] to being a [[Complex Knowledge Assistant]], depending on its [[feature activation]].
** It can range from being a [[Beginner Framework]] to being an [[Advanced Development Platform]], depending on its [[API level]].
** ...
* <B>Examples:</B>
** [[LlamaIndex Release]]s, such as:
*** [[Major Version]]s, such as:
**** [[LlamaIndex v0.11]] ([[2023]]) introducing [[workflow feature]]s and [[async streaming]].
**** [[LlamaIndex v0.8.24]] ([[2023]]) enhancing [[core functionality]].
**** [[LlamaIndex v0.7.16]] ([[2023]]) improving [[framework stability]].
*** [[Framework Implementation]]s, such as:
**** [[LlamaIndex Python]] for [[Python ecosystem integration]].
**** [[LlamaIndex.TS]] for [[TypeScript environment]].
** ...
* <B>Counter-Examples:</B>
** [[LangChain Framework]], which focuses on [[LLM application composition]] rather than [[data framework functionality]].
** [[LLMFlows Framework]], which specializes in [[simple LLM application]] development without [[comprehensive data management]].
** [[LLMApp Framework]], which emphasizes [[real-time pipeline]]s over [[structured data access]].
* <B>See:</B> [[LlamaHub Data Loader]], [[LLM Data Framework]], [[RAG System]], [[Knowledge Assistant Platform]].

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== References ==

=== 2024-01-02 ===
* Perplexity.ai
** [[Question]]: What is [[LlamaIndex Framework|LlamaIndex]] and what are its core components? Please explain its [[LlamaIndex Framework Feature|key features]], [[LlamaIndex Framework Architecture|architecture]], and recent [[LlamaIndex Framework Development|developments]].
** [[Answer]]: [[LlamaIndex Framework|LlamaIndex]] is a powerful [[Python Framework|Python framework]] specifically designed for building [[LLM Data Application|LLM-powered data applications]]. It provides comprehensive [[LlamaIndex Framework Component|components]] and [[LlamaIndex Framework Feature|features]] for [[LLM Data Processing|data processing]] and [[LLM Application Development|application development]].
*** [[LlamaIndex Core Component|Core Components]]:
**** [[LlamaIndex Module System|Module System]]:
***** Every [[LlamaIndex Core Module|core module]] can be [[LlamaIndex Module Customization|subclassed]] and [[LlamaIndex Module Extension|customized]].
***** Provides [[LlamaIndex Custom Module|custom modules]] for [[LlamaIndex LLM Module|LLMs]], [[LlamaIndex Embedding Module|embeddings]], [[LlamaIndex Parser Module|output parsers]], [[LlamaIndex Retriever Module|retrievers]], [[LlamaIndex Query Engine|query engines]], and [[LlamaIndex Agent Module|agents]].
**** [[LlamaIndex Data Processing|Data Processing]]:
***** Enables parsing of [[LlamaIndex Document|Documents]] into [[LlamaIndex Node|Nodes]].
***** Supports various [[LlamaIndex Node Type|node types]] including [[LlamaIndex Text Node|text chunks]] and [[LlamaIndex Image Node|images]].
***** Maintains [[LlamaIndex Node Metadata|metadata]] and [[LlamaIndex Node Relationship|relationship information]].
*** [[LlamaIndex Framework Feature|Key Features]]:
**** [[LlamaIndex Indexing Capability|Indexing Capabilities]]:
***** [[LlamaIndex Vector Store|Vector Store Index]] for [[LlamaIndex Node Embedding|node embeddings]] storage.
***** [[LlamaIndex Summary Store|Summary Index]] for [[LlamaIndex Sequential Chain|sequential chains]].
***** [[LlamaIndex Tree Store|Tree Index]] for [[LlamaIndex Hierarchical Structure|hierarchical structures]].
***** [[LlamaIndex Keyword Table|Keyword Table Index]] for [[LlamaIndex Keyword Mapping|keyword-based mappings]].
**** [[LlamaIndex Storage Integration|Storage and Integration]]:
***** [[LlamaIndex Document Store|Document Stores]] for [[LlamaIndex Node Storage|node storage]].
***** [[LlamaIndex Metadata Store|Index Stores]] for [[LlamaIndex Metadata Storage|metadata]].
***** [[LlamaIndex Vector Store|Vector Stores]] for [[LlamaIndex Embedding Storage|embeddings]].
***** Supports [[LlamaIndex Storage Backend|multiple backends]] including [[LlamaIndex Local Storage|local filesystem]], [[LlamaIndex AWS Integration|AWS S3]], and [[LlamaIndex Cloudflare Integration|Cloudflare R2]].
*** [[LlamaIndex Recent Development|Recent Developments]]:
**** [[LlamaIndex Version 0.11|Version 0.11]] introduced:
***** New [[LlamaIndex Workflow Feature|Workflows feature]] for [[LlamaIndex Complex Application|complex gen AI applications]].
***** Enhanced [[LlamaIndex Instrumentation|instrumentation]] for better [[LlamaIndex Monitoring|monitoring]] and [[LlamaIndex Debugging|debugging]].
***** Improved [[LlamaIndex Property Graph|property graph support]].
***** Reduced [[LlamaIndex Package Size|package size]] by 42%.
***** Added [[LlamaIndex Async Streaming|async streaming support]].
***** Introduced [[LlamaIndex Structured Planning Agent|Structured Planning Agent]].
*** [[LlamaIndex Framework Architecture|Framework Architecture]]:
**** [[LlamaIndex RAG Pipeline|RAG Pipelines]]:
***** Provides [[LlamaIndex Task Completion|tools for completing tasks]].
***** Enables building [[LlamaIndex Knowledge Assistant|knowledge assistants]] using [[LLM Enterprise Data Connection|LLMs connected to enterprise data]].
***** Supports [[LlamaIndex Framework Customization|customization]] through [[LlamaIndex Callback System|callbacks]] and [[LlamaIndex Custom Function|custom functions]].
***** Maintains [[LlamaIndex Low Level Abstraction|lower-level abstractions]].
** Citations:
 [1] https://docs.llamaindex.ai
 [2] https://docs.llamaindex.ai/en/stable/optimizing/custom_modules/
 [3] https://www.llamaindex.ai/blog/introducing-llamaindex-0-11
 [4] https://www.npmjs.com/package/llamaindex/v/0.5.4
 [5] https://www.datastax.com/guides/what-is-langchain
 [6] https://adasci.org/llmflows-for-building-flow-based-chat-application-a-hands-on-guide/
 [7] https://nanonets.com/blog/llamaindex/
 [8] https://www.ibm.com/think/topics/langchain
 [9] https://llmflows.readthedocs.io/en/latest/
 [10] https://datasciencedojo.com/blog/llamaindex-vs-langchain/

=== 2023 ===
* https://gpt-index.readthedocs.io/en/latest/
** QUOTE: [[LlamaIndex Framework|LlamaIndex]] (formerly [[GPT Index]]) is a [[data framework for LLM application]]s to ingest, structure, and access private or domain-specific data.
** At their core, [[LLM]]s offer a natural language interface between humans and inferred data. Widely available models come pre-trained on huge amounts of publicly available data, from Wikipedia and mailing lists to textbooks and source code.
** Applications built on top of [[LLM]]s often require augmenting these models with private or domain-specific data. Unfortunately, that data can be distributed across siloed applications and data stores. It’s behind APIs, in SQL databases, or trapped in PDFs and slide decks.
** [[LlamaIndex Framework|LlamaIndex]] provides the following tools:
*** Data connectors ingest your existing data from their native source and format. These could be APIs, PDFs, SQL, and (much) more.
*** Data indexes structure your data in intermediate representations that are easy and performant for [[LLM]]s to consume.
*** Engines provide natural language access to your data. For example:
**** Query engines are powerful retrieval interfaces for knowledge-augmented output.
**** Chat engines are conversational interfaces for multi-message, “back and forth” interactions with your data.
*** Data agents are LLM-powered knowledge workers augmented by tools, from simple helper functions to API integrations and more.
*** Application integrations tie [[LlamaIndex Framework|LlamaIndex]] back into the rest of your ecosystem. This could be [[LangChain]], Flask, Docker, ChatGPT, or… anything else!
** [[LlamaIndex Framework|LlamaIndex]] provides tools for beginners, advanced users, and everyone in between.
** Our high-level API allows beginner users to use [[LlamaIndex Framework|LlamaIndex]] to ingest and query their data in 5 lines of code.
** For more complex applications, our lower-level APIs allow advanced users to customize and extend any module—data connectors, indices, retrievers, query engines, reranking modules—to fit their needs.

=== 2023 ===
* Google Bard (~2023-09-11)
** [[LlamaIndex Framework|LlamaIndex]] is a [[data framework]] for indexing and querying data that can be used by LLMs. [[LlamaIndex Framework|It]] provides a way to store data in a way that is efficient for [[LLM]]s to access, as well as a way to query the data in a way that is natural for humans to express.
** It can provide the following tools:
*** [[Data connector]]s to ingest your existing data sources and data formats (APIs, PDFs, documents, SQL, etc.)
*** [[Data indexing]] to store and index your data for different use cases
*** [[Query interface]] that accepts any input prompt over your data and returns a knowledge-augmented response
** [[LlamaIndex Framework|LlamaIndex]] can be used for a variety of purposes, including:
*** Adding [[personal data to LLMs]]. [[LlamaIndex Framework|LlamaIndex]] can be used to ingest and index personal data, such as medical records, financial records, or customer data. This data can then be used to train [[LLM]]s to generate more personalized responses.
*** Building [[conversational AI application]]s. [[LlamaIndex Framework|LlamaIndex]] can be used to build conversational AI applications that can access and process information from a variety of sources. For example, a chatbot that can answer customer questions about products or services could use [[LlamaIndex Framework|LlamaIndex]] to access product data, customer data, and other relevant information.
*** Improving the [[performance of LLMs]]. [[LlamaIndex Framework|LlamaIndex]] can be used to improve the performance of [[LLM]]s by providing them with access to more data and by making it easier for them to process that data. For example, an LLM that is trained on a large corpus of text data will be able to generate more accurate and relevant responses than an LLM that is trained on a smaller corpus of data.
** [[LlamaIndex Framework|LlamaIndex]] is a powerful tool that can be used to augment LLM applications with data. It is easy to use and can be customized to meet the specific needs of your application.
** Here are some of the benefits of using [[LlamaIndex Framework|LlamaIndex]]:
*** It is easy to use. [[LlamaIndex Framework|LlamaIndex]] provides a high-level API that makes it easy to ingest and query data.
*** It is flexible. [[LlamaIndex Framework|LlamaIndex]] can be used to ingest and index a variety of data sources and formats.
*** It is scalable. [[LlamaIndex Framework|LlamaIndex]] can be scaled to handle large amounts of data.
*** It is open source. [[LlamaIndex Framework|LlamaIndex]] is open source, so you can customize it to meet your specific needs.

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[[Category:Concept]]
[[Category:AI]]
[[Category:Data Framework]]
[[Category:LLM Platform]]
[[Category:Quality Silver]]