llama-index 0.10.23
pip install llama-index==0.10.23
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Interface between LLMs and your data
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- License: MIT License (MIT)
- Author: Jerry Liu
- Maintainer: Andrei Fajardo
- Tags LLM , NLP , RAG , data , devtools , index , retrieval
- Requires: Python <4.0, >=3.8.1
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Project description
🗂️ LlamaIndex 🦙
👁 PyPI - Downloads
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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:
-
Starter:
llama-index(https://pypi.org/project/llama-index/). A starter Python package that includes core LlamaIndex as well as a selection of integrations. -
Customized:
llama-index-core(https://pypi.org/project/llama-index-core/). Install core LlamaIndex and add your chosen LlamaIndex integration packages (temporary registry) 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.
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.
# 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
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, 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.
💡 Contributing
Interested in contributing? Contributions to LlamaIndex core as well as contributing integrations that build on the core are both accepted and highly encouraged! See our Contribution Guide for more details.
📄 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
# custom selection of integrations to work with core
pipinstallllama-index-core
pipinstallllama-index-llms-openai
pipinstallllama-index-llms-replicate
pipinstallllama-index-embeddings-huggingface
Examples are in the docs/examples folder. Indices are in the indices folder (see list of indices below).
To build a simple vector store index using OpenAI:
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 LLMs, e.g. Llama 2 hosted on Replicate, where you can easily create a free trial API token:
import os os.environ["REPLICATE_API_TOKEN"] = "YOUR_REPLICATE_API_TOKEN" from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.llms.replicate import Replicate from transformers import AutoTokenizer # set the LLM llama2_7b_chat = "meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e" Settings.llm = Replicate( model=llama2_7b_chat, temperature=0.01, additional_kwargs={"top_p": 1, "max_new_tokens": 300}, ) # set tokenizer to match LLM Settings.tokenizer = AutoTokenizer.from_pretrained( "NousResearch/Llama-2-7b-chat-hf" ) # set the embed model Settings.embed_model = HuggingFaceEmbedding( model_name="BAAI/bge-small-en-v1.5" ) documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data() index = VectorStoreIndex.from_documents( documents, )
To query:
query_engine = index.as_query_engine() query_engine.query("YOUR_QUESTION")
By default, data is stored in-memory.
To persist to disk (under ./storage):
index.storage_context.persist()
To reload from disk:
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)
🔧 Dependencies
We use poetry as the package manager for all Python packages. As a result, the
dependencies of each Python package can be found by referencing the pyproject.toml
file in each of the package's folders.
cd<desired-package-folder>
pipinstallpoetry
poetryinstall--withdev
📖 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/llama_index},
year = {2022}
}
Project details
Verified details
These details have been verified by PyPIMaintainers
👁 Avatar for jerryjliu from gravatar.comjerryjliu 👁 Avatar for simonsdsuo from gravatar.com
simonsdsuo
Unverified details
These details have not been verified by PyPIProject links
Meta
- License: MIT License (MIT)
- Author: Jerry Liu
- Maintainer: Andrei Fajardo
- Tags LLM , NLP , RAG , data , devtools , index , retrieval
- Requires: Python <4.0, >=3.8.1
Classifiers
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