Metadata-Version: 2.1
Name: llama_index
Version: 0.4.21
Summary: Interface between LLMs and your data.
Home-page: https://github.com/jerryjliu/gpt_index
License: MIT
Description: # 🗂️ LlamaIndex 🦙 (GPT Index)
        
        > ⚠️ **NOTE**: We are rebranding GPT Index as LlamaIndex! We will carry out this transition gradually.
        
        > **2/25/2023**: By default, our docs/notebooks/instructions now reference "LlamaIndex"
        instead of "GPT Index".
        
        > **2/19/2023**: By default, our docs/notebooks/instructions now use the `llama-index` package. However the `gpt-index` package still exists as a duplicate!
        
        > **2/16/2023**: We have a duplicate `llama-index` pip package. Simply replace all imports of `gpt_index` with `llama_index` if you choose to `pip install llama-index`.
        
        LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.
        
        PyPi: 
        - LlamaIndex: https://pypi.org/project/llama-index/.
        - GPT Index (duplicate): https://pypi.org/project/gpt-index/.
        
        Documentation: https://gpt-index.readthedocs.io/en/latest/.
        
        Twitter: https://twitter.com/gpt_index.
        
        Discord: https://discord.gg/dGcwcsnxhU.
        
        LlamaHub (community library of data loaders): https://llamahub.ai
        
        ## 🚀 Overview
        
        **NOTE**: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!
        
        #### Context
        - LLMs are a phenomenonal piece of technology for knowledge generation and reasoning.
        - A big limitation of LLMs is context size (e.g. Davinci's limit is 4096 tokens. Large, but not infinite).
        - The ability to feed "knowledge" to LLMs is restricted to this limited prompt size and model weights.
        
        #### Proposed Solution
        
        At its core, LlamaIndex contains a toolkit designed to easily connect LLM's with your external data.
        LlamaIndex helps to provide the following:
        - A set of **data structures** that allow you to index your data for various LLM tasks, and remove concerns over prompt size limitations.
        - Data connectors to your common data sources (Google Docs, Slack, etc.).
        - Cost transparency + tools that reduce cost while increasing performance.
        
        
        Each data structure offers distinct use cases and a variety of customizable parameters. These indices can then be 
        *queried* in a general purpose manner, in order to achieve any task that you would typically achieve with an LLM:
        - Question-Answering
        - Summarization
        - Text Generation (Stories, TODO's, emails, etc.)
        - and more!
        
        
        ## 💡 Contributing
        
        Interesting in contributing? See our [Contribution Guide](CONTRIBUTING.md) for more details.
        
        ## 📄 Documentation
        
        Full documentation can be found here: https://gpt-index.readthedocs.io/en/latest/. 
        
        Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources! 
        
        
        ## 💻 Example Usage
        
        ```
        pip install llama-index
        ```
        
        Examples are in the `examples` folder. Indices are in the `indices` folder (see list of indices below).
        
        To build a simple vector store index:
        ```python
        import os
        os.environ["OPENAI_API_KEY"] = 'YOUR_OPENAI_API_KEY'
        
        from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader
        documents = SimpleDirectoryReader('data').load_data()
        index = GPTSimpleVectorIndex(documents)
        ```
        
        To save to and load from disk:
        ```python
        # save to disk
        index.save_to_disk('index.json')
        # load from disk
        index = GPTSimpleVectorIndex.load_from_disk('index.json')
        ```
        
        To query:
        ```python
        index.query("<question_text>?")
        ```
        
        ## 🔧 Dependencies
        
        The main third-party package requirements are `tiktoken`, `openai`, and `langchain`.
        
        All requirements should be contained within the `setup.py` file. To run the package locally without building the wheel, simply run `pip install -r requirements.txt`. 
        
        
        ## 📖 Citation
        
        Reference to cite if you use LlamaIndex in a paper:
        
        ```
        @software{Liu_LlamaIndex_2022,
        author = {Liu, Jerry},
        doi = {10.5281/zenodo.1234},
        month = {11},
        title = {{LlamaIndex}},
        url = {https://github.com/jerryjliu/gpt_index},
        year = {2022}
        }
        ```
        
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