Building-Data-Driven-Applications-with-LlamaIndex

KO | EN

gitlite — search

#javascript #hacktoberfest #react #android #deep-learning #linux #docker #windows #awesome #cli #kubernetes #mcp #ios #openai

Building-Data-Driven-Applications-with-LlamaIndex

(★ 130)

Building Data-Driven Applications with LlamaIndex, published by Packt

File Explorer

Download Latest Version (.zip)

- - sample_document1.txt
  - sample_document2.txt
- sample_get_prompts.py
- sample_metadata_custom_prompt_templates.py
- sample_metadata_summary_extractor.py
- sample_update_prompts.py
- .gitignore
- - sample_document1.txt
  - sample_document2.txt
- sample1.py
- sample_App_Chat_About_Messi.py
- sample_Create_and_Query_Index.py
- sample_Create_Doc.py
- sample_Create_Doc_from_Wiki.py
- sample_Create_Node.py
- sample_Create_Node_Relationships.py
- sample_Customize_LLM.py
- sample_LLM_parameters.py
- sample_TokenTextSplitter.py
- - sample_document1.txt
  - sample_document2.txt
- sample_Estimate_Costs.py
- sample_extractor_Custom.py
- sample_extractor_EntityExtractor.py
- sample_extractor_KeywordExtractor.py
- sample_extractor_QuestionAnsweredExtractor.py
- sample_extractor_SummaryExtractor.py
- sample_extractor_TitleExtractor.py
- sample_IngestionPipeline.py
- sample_node_creation1.py
- sample_node_creation2.py
- sample_parser_HierarchicalNodeParser.py
- sample_parser_HTMLNodeParser.py
- sample_parser_in_ServiceContext.py
- sample_parser_JSONNodeParser.py
- sample_parser_LangchainNodeParser.py
- sample_parser_MarkdownNodeParser.py
- sample_parser_SentenceWindowNodeParser.py
- sample_parser_SimpleFileNodeParser.py
- sample_PII_scrub.py
- sample_reader_DatabaseReader.py
- sample_reader_DiscordReader.py
- sample_reader_GitHubRepositoryReader.py
- sample_reader_OpenMap.py
- sample_reader_S3Reader.py
- sample_reader_SimpleDirectoryReader.py
- sample_reader_SimpleWebPageReader.py
- sample_splitter_CodeSplitter.py
- sample_splitter_TokenTextSplitter.py
- - chroma.sqlite3
- - fluffy_the_cat.txt
- - sample.md
- - sample_document1.txt
  - sample_document2.txt
- - default__vector_store.json
  - docstore.json
  - graph_store.json
  - image__vector_store.json
  - index_store.json
- sample_ChromaDB.py
- sample_ComposableGraph.py
- sample_cost_estimation1.py
- sample_cost_estimation2.py
- sample_DocumentSummaryIndex.py
- sample_KeywordTableIndex.py
- sample_KnowledgeGraphIndex.py
- sample_local_embedding.py
- sample_persist.py
- sample_persist_reload.py
- sample_SummaryIndex.py
- sample_TreeIndex.py
- sample_VectorStoreIndex.py
- - sample_document1.txt
  - sample_document2.txt
- sample_bm25_retriever.py
- sample_decompose_query_transform.py
- sample_metadata_filters.py
- sample_openai_question_generator.py
- sample_retriever_async.py
- sample_retriever_direct.py
- sample_retriever_from_index1.py
- sample_retriever_tools.py
- sample_selectors.py
- - sample_document1.txt
  - sample_document2.txt
- sample_KeywordNode_Postprocessor.py
- sample_Langchain_Output_Parser.py
- sample_low_level_query_engine.py
- sample_Metadata_Replacement_Postprocessor.py
- sample_response_synthesizer.py
- sample_Router_Query_Engine.py
- sample_Similarity_Postprocessor.py
- sample_SubQuestion_Query_Engine.py
- - sample_document1.txt
  - sample_document2.txt
- - sample_document1.txt
  - sample_document2.txt
  - sample_document3.txt
- sample_agentrunner.py
- sample_chat_memory.py
- sample_condense_question.py
- sample_context_chat_engine.py
- sample_function_tool.py
- sample_llmcompiler_agent.py
- sample_loandandsearch_toolspec.py
- sample_ondemandloader_tool.py
- sample_openai_agent.py
- sample_simple_chat_engine.py
- sample_simple_chat_engine_custom_LLM.py
- - pairwise_evaluator_dataset.json
- - sample_document1.txt
  - sample_document2.txt
- requirements.txt
- sample_eval_phoenix.py
- sample_llama_packs_zephyr.py
- sample_lm_studio_LLM.py
- sample_neutrino.py
- sample_tracing_phoenix2.py
- - .gitignore
- - .gitignore
- - .gitignore
- - .gitignore
- app.py
- conversation_engine.py
- document_uploader.py
- global_settings.py
- index_builder.py
- logging_functions.py
- quiz_builder.py
- quiz_UI.py
- requirements.txt
- session_functions.py
- slides.py
- training_material_builder.py
- training_UI.py
- user_onboarding.py
LICENSE
README.md

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Building Data-Driven Applications with LlamaIndex
<a href="https://www.packtpub.com/product/building-data-driven-applications-with-llamaindex/9781835089507"><img src="https://m.media-amazon.com/images/I/71Aly61qDsL._SL1360_.jpg" alt="Building Data-Driven Applications with LlamaIndex" height="256px" align="right"></a>

This is the code repository for [Building Data-Driven Applications with LlamaIndex](https://www.packtpub.com/product/building-data-driven-applications-with-llamaindex/9781835089507), published by Packt.

**A practical guide to retrieval augmented generation (RAG) for Enhancing LLM Applications**

## What is this book about?
Generative AI, such as Large Language Models (LLMs) possess immense potential. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional "hallucinations."

This book covers the following exciting features: 
* Understand the LlamaIndex ecosystem and common use cases
* Master techniques to ingest and parse data from various sources into LlamaIndex
* Discover how to create optimized indexes tailored to your use cases
* Understand how to query LlamaIndex effectively and interpret responses
* Build an end-to-end interactive web application with LlamaIndex, Python and, Streamlit
* Customize a LlamaIndex configuration based on your project needs
* Predict costs and deal with potential privacy issues
* Deploy LlamaIndex applications that others can use

If you feel this book is for you, get your [copy](https://www.amazon.com/Building-Data-Driven-Applications-LlamaIndex-retrieval-augmented/dp/183508950X/ref=sr_1_1?sr=8-1) today!

## Project Description

This repository contains the code and resources for the book "Build Data-Driven LLM Applications with LlamaIndex: A Practical Guide to LlamaIndex for Python Developers".

The project is built using LlamaIndex and Streamlit as the core technologies and offers the following features:

- **Learning Objective Definition**: Define your learning goals and upload your own study materials in various formats including PDF, DOC and TXT.
  
- **Knowledge Assessment Quiz**: Take a quiz to measure your current understanding of the subject, storing your responses as a baseline for your learning journey.
  
- **Custom Learning Material**: PITS creates personalized educational content, including slides, narration and tests, all tailored to your level of expertise.
  
- **Modular Learning Blocks**: The course is broken down into easily digestible modules, allowing you to progress at a pace that suits you.
  
- **Question Tracking**: Keep track of all your interactions with the assistant and receive summary recaps whenever you return after 24 hours or upon request.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Installation

1. Clone this repository to your local machine.
    ```bash
    git clone https://github.com/PacktPublishing/Building-Data-Driven-LLM-Applications-with-LlamaIndex
    ```

2. Navigate to the project directory.
    ```bash
    cd PITS_APP
    ```

3. Install the required packages.
    ```bash
    pip install -r requirements.txt
    ```

## Usage

After installation, you can run the Streamlit app using the following command:

```bash
streamlit run app.py
```
## Contributing

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Get to Know the Author
**Andrei Gheorghiu** is an experienced IT consultant and trainer with over 20 years of experience in the IT industry. Holding prestigious certifications such as ITIL Master, CISA, ISO 27001 Lead Auditor and CISSP, he has enriched over 15,000 students with knowledge in IT Service Management, Information Security, IT Governance and Audit. Driven by a passion for groundbreaking innovations with transformative potential, he taps into his vast experience, offering practical advice on harnessing technology to solve real-life challenges.

For any questions, feel free to reach out at [andrei.gheorghiu@gmail.com](mailto:andrei.gheorghiu@gmail.com)

Building-Data-Driven-Applications-with-LlamaIndex

File Explorer

Today's picks

Was this content helpful?

Recent Feedback

Building-Data-Driven-Applications-with-LlamaIndex

File Explorer

Today's picks

Was this content helpful?

Recent Feedback

Download README