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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*: https://pypi.org/project/llama-index/[`llama-index`]. A starter Python package that includes core LlamaIndex as well as a selection of integrations. . *Customized*: https://pypi.org/project/llama-index-core/[`llama-index-core`]. Install core LlamaIndex and add your chosen LlamaIndex integration packages on https://llamahub.ai/[LlamaHub] 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. [,python] ----

from llama_index.core.xxx import ClassABC # core submodule xxx from llama_index.xxx.yyy import ( SubclassABC, ) # integration yyy for submodule xxx

from llama_index.core.llms import LLM from llama_index.llms.openai import OpenAI ----

*NOTE*: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!

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 xref:CONTRIBUTING.adoc[Contribution Guide] for more details. New integrations should meaningfully integrate with existing LlamaIndex framework components. At the discretion of LlamaIndex maintainers, some integrations may be declined.

Full documentation can be found https://docs.llamaindex.ai/en/latest/[here] Please check it out for the most up-to-date tutorials, how-to guides, references, and other resources!

[,sh] ----

pip install llama-index-core pip install llama-index-llms-openai pip install llama-index-llms-replicate pip install llama-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: [,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 LLMs, e.g. Llama 2 hosted on https://replicate.com/[Replicate], where you can easily create a free trial API token: [,python] ---- 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

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}, )

Settings.tokenizer = AutoTokenizer.from_pretrained( "NousResearch/Llama-2-7b-chat-hf" )

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: [,python] ---- query_engine = index.as_query_engine() query_engine.query("YOUR_QUESTION") ---- 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

storage_context = StorageContext.from_defaults(persist_dir="./storage")

index = load_index_from_storage(storage_context) ----

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. [,bash] ---- cd pip install poetry poetry install --with dev ----

By default, `llama-index-core` includes a `_static` folder that contains the nltk and tiktoken cache that is included with the package installation. This ensures that you can easily run `llama-index` in environments with restrictive disk access permissions at runtime. To verify that these files are safe and valid, we use the github `attest-build-provenance` action. This action will verify that the files in the `_static` folder are the same as the files in the `llama-index-core/llama_index/core/_static` folder. To verify this, you can run the following script (pointing to your installed package): [,bash] ---- #!/bin/bash STATIC_DIR="venv/lib/python3.13/site-packages/llama_index/core/_static" REPO="run-llama/llama_index" find "$STATIC_DIR" -type f | while read -r file; do echo "Verifying: $file" gh attestation verify "$file" -R "$REPO" || echo "Failed to verify: $file" done ----

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} } ----