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| title: README | |
| emoji: 🦙 | |
| colorFrom: yellow | |
| colorTo: purple | |
| sdk: static | |
| pinned: false | |
| # 🗂️ LlamaIndex 🦙 | |
| [](https://pypi.org/project/llama-index/) | |
| [](https://github.com/jerryjliu/llama_index/graphs/contributors) | |
| [](https://discord.gg/dGcwcsnxhU) | |
| [](https://www.phorm.ai/query?projectId=c5863b56-6703-4a5d-87b6-7e6031bf16b6) | |
| LlamaIndex (GPT Index) is a data framework for your LLM application. Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins). There are two ways to start building with LlamaIndex in | |
| Python: | |
| 1. **Starter**: [`pip install llama-index`](https://pypi.org/project/llama-index/). A starter Python package that includes core LlamaIndex as well as a selection of integrations. | |
| 2. **Customized**: [`pip install llama-index-core`](https://pypi.org/project/llama-index-core/). Install core LlamaIndex and add your chosen LlamaIndex integration packages on [LlamaHub](https://llamahub.ai/) | |
| that are required for your application. There are over 300 LlamaIndex integration | |
| packages that work seamlessly with core, allowing you to build with your preferred | |
| LLM, embedding, and vector store providers. | |
| ### Important Links | |
| - LlamaIndex.TS [(Typescript/Javascript)](https://github.com/run-llama/LlamaIndexTS) | |
| - [Documentation](https://docs.llamaindex.ai/en/stable/) | |
| - [Twitter](https://twitter.com/llama_index) | |
| - [Discord](https://discord.gg/dGcwcsnxhU) | |
| ### Ecosystem | |
| - LlamaHub [(community library of data loaders)](https://llamahub.ai) | |
| - LlamaLab [(cutting-edge AGI projects using LlamaIndex)](https://github.com/run-llama/llama-lab) | |
| ## 🚀 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 phenomenal piece of technology for knowledge generation and reasoning. They are pre-trained on large amounts of publicly available data. | |
| - How do we best augment LLMs with our own private data? | |
| We need a comprehensive toolkit to help perform this data augmentation for LLMs. | |
| ### Proposed Solution | |
| That's where **LlamaIndex** comes in. LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools: | |
| - Offers **data connectors** to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.). | |
| - Provides ways to **structure your data** (indices, graphs) so that this data can be easily used with LLMs. | |
| - Provides an **advanced retrieval/query interface over your data**: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output. | |
| - Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, or anything else). | |
| LlamaIndex provides tools for both beginner users and advanced users. Our high-level API allows beginner users to use LlamaIndex to ingest and query their data in | |
| 5 lines of code. 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. | |
| ## 📄 Documentation | |
| Full documentation can be found [here](https://docs.llamaindex.ai/en/latest/) | |
| Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources! | |
| ## 💻 Example Usage | |
| The LlamaIndex Python library is namespaced such that import statements which | |
| include `core` imply that the core package is being used. In contrast, those | |
| statements without `core` imply that an integration package is being used. | |
| ```python | |
| # typical pattern | |
| from llama_index.core.xxx import ClassABC # core submodule xxx | |
| from llama_index.xxx.yyy import ( | |
| SubclassABC, | |
| ) # integration yyy for submodule xxx | |
| # concrete example | |
| from llama_index.core.llms import LLM | |
| from llama_index.llms.openai import OpenAI | |
| ``` | |
| To get started, we can install llama-index directly using the starter dependencies (mainly OpenAI): | |
| ```sh | |
| pip install llama-index | |
| ``` | |
| Or we can do a more custom isntallation: | |
| ```sh | |
| # custom selection of integrations to work with core | |
| pip install llama-index-core | |
| pip install llama-index-llms-openai | |
| pip install llama-index-llms-ollama | |
| pip install llama-index-embeddings-huggingface | |
| pip install llama-index-readers-file | |
| ``` | |
| To build a simple vector store index using OpenAI: | |
| ```python | |
| import os | |
| os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" | |
| from llama_index.core import VectorStoreIndex, SimpleDirectoryReader | |
| documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data() | |
| index = VectorStoreIndex.from_documents(documents) | |
| ``` | |
| To build a simple vector store index using non-OpenAI models, we can leverage Ollama and HuggingFace. This assumes you've already installed Ollama and have pulled the model you want to use. | |
| ```python | |
| from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader | |
| from llama_index.embeddings.huggingface import HuggingFaceEmbedding | |
| from llama_index.llms.ollama import Ollama | |
| # set the LLM | |
| llama2_7b_chat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e" | |
| Settings.llm = Ollama( | |
| model="llama3.1:latest", | |
| temperature=0.1, | |
| request_timeout=360.0, | |
| ) | |
| # set the embed model | |
| Settings.embed_model = HuggingFaceEmbedding( | |
| model_name="BAAI/bge-small-en-v1.5", | |
| embed_batch_size=2, | |
| ) | |
| documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data() | |
| index = VectorStoreIndex.from_documents( | |
| documents, | |
| ) | |
| ``` | |
| To query: | |
| ```python | |
| query_engine = index.as_query_engine() | |
| query_engine.query("YOUR_QUESTION") | |
| ``` | |
| Or chat: | |
| ```python | |
| chat_engine = index.as_chat_engine(chat_mode="condense_plus_context") | |
| chat_engine.chat("YOUR MESSAGE") | |
| ``` | |
| By default, data is stored in-memory. | |
| To persist to disk (under `./storage`): | |
| ```python | |
| index.storage_context.persist() | |
| ``` | |
| To reload from disk: | |
| ```python | |
| from llama_index.core import StorageContext, load_index_from_storage | |
| # rebuild storage context | |
| storage_context = StorageContext.from_defaults(persist_dir="./storage") | |
| # load index | |
| index = load_index_from_storage(storage_context) | |
| ``` | |