基于文件的节点解析器¶
SimpleFileNodeParser 和 FlatReader 旨在支持打开多种文件类型，并自动选择最佳的 NodeParser 来处理文件。FlatReader 以原始文本格式加载文件，并将文件信息附加到元数据中，随后 SimpleFileNodeParser 将文件类型映射到 node_parser/file 中的节点解析器，为当前任务选择最合适的节点解析器。
SimpleFileNodeParser 不会执行基于令牌的文本分块，设计初衷是与令牌节点解析器配合使用。
让我们通过示例了解如何使用 FlatReader 和 SimpleFileNodeParser 加载内容。本例中将使用 LlamaIndex 的 README 文件作为演示文档，HTML 文件则采用 Stack Overflow 的着陆页，当然任何 README 和 HTML 文件均可适用。
如果您在 Colab 上打开此 Notebook，可能需要安装 LlamaIndex 🦙。
In [ ]:
Copied!
%pip install llama-index-readers-file
%pip install llama-index-readers-file
In [ ]:
Copied!
!pip install llama-index
!pip install llama-index
In [ ]:
Copied!
from llama_index.core.node_parser import SimpleFileNodeParser
from llama_index.readers.file import FlatReader
from pathlib import Path
from llama_index.core.node_parser import SimpleFileNodeParser
from llama_index.readers.file import FlatReader
from pathlib import Path
/Users/adamhofmann/opt/anaconda3/lib/python3.9/site-packages/langchain/__init__.py:24: UserWarning: Importing BasePromptTemplate from langchain root module is no longer supported. warnings.warn( /Users/adamhofmann/opt/anaconda3/lib/python3.9/site-packages/langchain/__init__.py:24: UserWarning: Importing PromptTemplate from langchain root module is no longer supported. warnings.warn(
In [ ]:
Copied!
reader = FlatReader()
html_file = reader.load_data(Path("./stack-overflow.html"))
md_file = reader.load_data(Path("./README.md"))
print(html_file[0].metadata)
print(html_file[0])
print("----")
print(md_file[0].metadata)
print(md_file[0])
reader = FlatReader()
html_file = reader.load_data(Path("./stack-overflow.html"))
md_file = reader.load_data(Path("./README.md"))
print(html_file[0].metadata)
print(html_file[0])
print("----")
print(md_file[0].metadata)
print(md_file[0])
{'filename': 'stack-overflow.html', 'extension': '.html'}
Doc ID: a6750408-b0fa-466d-be28-ff2fcbcbaa97
Text: <!DOCTYPE html> <html class="html__responsive
html__unpinned-leftnav" lang="en"> <head> <title>Stack
Overflow - Where Developers Learn, Share, & Build Careers</title>
<link rel="shortcut icon" href="https://cdn.sstatic.net/Sites/stackove
rflow/Img/favicon.ico?v=ec617d715196"> <link rel="apple-touch-
icon" hr...
----
{'filename': 'README.md', 'extension': '.md'}
Doc ID: 1d872f44-2bb3-4693-a1b8-a59392c23be2
Text: # 🗂️ LlamaIndex 🦙 [](https://pypi.org/project/llama-index/) [![GitHub contributors]
(https://img.shields.io/github/contributors/jerryjliu/llama_index)](ht
tps://github.com/jerryjliu/llama_index/graphs/contributors) [](https:...
解析文件¶
平面阅读器已简单地将文件内容加载到 Document 对象中以供进一步处理。我们可以看到文件信息被保留在元数据中。接下来我们将这些文档传递给节点解析器进行解析。
In [ ]:
Copied!
parser = SimpleFileNodeParser()
md_nodes = parser.get_nodes_from_documents(md_file)
html_nodes = parser.get_nodes_from_documents(html_file)
print(md_nodes[0].metadata)
print(md_nodes[0].text)
print(md_nodes[1].metadata)
print(md_nodes[1].text)
print("----")
print(html_nodes[0].metadata)
print(html_nodes[0].text)
parser = SimpleFileNodeParser()
md_nodes = parser.get_nodes_from_documents(md_file)
html_nodes = parser.get_nodes_from_documents(html_file)
print(md_nodes[0].metadata)
print(md_nodes[0].text)
print(md_nodes[1].metadata)
print(md_nodes[1].text)
print("----")
print(html_nodes[0].metadata)
print(html_nodes[0].text)
{'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙'}
🗂️ LlamaIndex 🦙
[](https://pypi.org/project/llama-index/)
[](https://github.com/jerryjliu/llama_index/graphs/contributors)
[](https://discord.gg/dGcwcsnxhU)
LlamaIndex (GPT Index) is a data framework for your LLM application.
PyPI:
- LlamaIndex: https://pypi.org/project/llama-index/.
- GPT Index (duplicate): https://pypi.org/project/gpt-index/.
LlamaIndex.TS (Typescript/Javascript): https://github.com/run-llama/LlamaIndexTS.
Documentation: https://gpt-index.readthedocs.io/.
Twitter: https://twitter.com/llama_index.
Discord: https://discord.gg/dGcwcsnxhU.
{'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 3': 'Ecosystem'}
Ecosystem
- LlamaHub (community library of data loaders): https://llamahub.ai
- LlamaLab (cutting-edge AGI projects using LlamaIndex): https://github.com/run-llama/llama-lab
----
{'filename': 'stack-overflow.html', 'extension': '.html', 'tag': 'li'}
About
Products
For Teams
Stack Overflow
Public questions & answers
Stack Overflow for Teams
Where developers & technologists share private knowledge with coworkers
Talent
Build your employer brand
Advertising
Reach developers & technologists worldwide
Labs
The future of collective knowledge sharing
About the company
current community
Stack Overflow
help
chat
Meta Stack Overflow
your communities
Sign up or log in to customize your list.
more stack exchange communities
company blog
文件的进一步处理¶
我们可以看到 Markdown 和 HTML 文件已根据文档结构被分割成多个块。Markdown 节点解析器会依据所有标题进行分割，并将标题层级结构附加到元数据中。HTML 节点解析器则从常见文本元素中提取文本以简化 HTML 文件，并合并相邻的同类型节点。与处理原始 HTML 相比，这在获取有意义文本内容方面已有显著改进。
由于这些文件仅根据文件结构进行分割，我们可以通过文本分割器进行进一步处理，将内容准备成有限令牌长度的节点。
In [ ]:
Copied!
from llama_index.core.node_parser import SentenceSplitter
# For clarity in the demo, make small splits without overlap
splitting_parser = SentenceSplitter(chunk_size=200, chunk_overlap=0)
html_chunked_nodes = splitting_parser(html_nodes)
md_chunked_nodes = splitting_parser(md_nodes)
print(f"\n\nHTML parsed nodes: {len(html_nodes)}")
print(html_nodes[0].text)
print(f"\n\nHTML chunked nodes: {len(html_chunked_nodes)}")
print(html_chunked_nodes[0].text)
print(f"\n\nMD parsed nodes: {len(md_nodes)}")
print(md_nodes[0].text)
print(f"\n\nMD chunked nodes: {len(md_chunked_nodes)}")
print(md_chunked_nodes[0].text)
from llama_index.core.node_parser import SentenceSplitter
# For clarity in the demo, make small splits without overlap
splitting_parser = SentenceSplitter(chunk_size=200, chunk_overlap=0)
html_chunked_nodes = splitting_parser(html_nodes)
md_chunked_nodes = splitting_parser(md_nodes)
print(f"\n\nHTML parsed nodes: {len(html_nodes)}")
print(html_nodes[0].text)
print(f"\n\nHTML chunked nodes: {len(html_chunked_nodes)}")
print(html_chunked_nodes[0].text)
print(f"\n\nMD parsed nodes: {len(md_nodes)}")
print(md_nodes[0].text)
print(f"\n\nMD chunked nodes: {len(md_chunked_nodes)}")
print(md_chunked_nodes[0].text)
HTML parsed nodes: 67
About
Products
For Teams
Stack Overflow
Public questions & answers
Stack Overflow for Teams
Where developers & technologists share private knowledge with coworkers
Talent
Build your employer brand
Advertising
Reach developers & technologists worldwide
Labs
The future of collective knowledge sharing
About the company
current community
Stack Overflow
help
chat
Meta Stack Overflow
your communities
Sign up or log in to customize your list.
more stack exchange communities
company blog
HTML chunked nodes: 87
About
Products
For Teams
Stack Overflow
Public questions & answers
Stack Overflow for Teams
Where developers & technologists share private knowledge with coworkers
Talent
Build your employer brand
Advertising
Reach developers & technologists worldwide
Labs
The future of collective knowledge sharing
About the company
current community
Stack Overflow
help
chat
Meta Stack Overflow
your communities
MD parsed nodes: 10
🗂️ LlamaIndex 🦙
[](https://pypi.org/project/llama-index/)
[](https://github.com/jerryjliu/llama_index/graphs/contributors)
[](https://discord.gg/dGcwcsnxhU)
LlamaIndex (GPT Index) is a data framework for your LLM application.
PyPI:
- LlamaIndex: https://pypi.org/project/llama-index/.
- GPT Index (duplicate): https://pypi.org/project/gpt-index/.
LlamaIndex.TS (Typescript/Javascript): https://github.com/run-llama/LlamaIndexTS.
Documentation: https://gpt-index.readthedocs.io/.
Twitter: https://twitter.com/llama_index.
Discord: https://discord.gg/dGcwcsnxhU.
MD chunked nodes: 13
🗂️ LlamaIndex 🦙
[](https://pypi.org/project/llama-index/)
[](https://github.com/jerryjliu/llama_index/graphs/contributors)
[](https://discord.gg/dGcwcsnxhU)
摘要¶
可以看到文件在 SimpleFileNodeParser 创建的切分基础上进行了进一步处理，现在已准备好被索引或向量存储库摄取。以下代码单元格展示了从原始文件到分块节点的完整解析器链式调用过程：
In [ ]:
Copied!
from llama_index.core.ingestion import IngestionPipeline
pipeline = IngestionPipeline(
documents=reader.load_data(Path("./README.md")),
transformations=[
SimpleFileNodeParser(),
SentenceSplitter(chunk_size=200, chunk_overlap=0),
],
)
md_chunked_nodes = pipeline.run()
print(md_chunked_nodes)
from llama_index.core.ingestion import IngestionPipeline
pipeline = IngestionPipeline(
documents=reader.load_data(Path("./README.md")),
transformations=[
SimpleFileNodeParser(),
SentenceSplitter(chunk_size=200, chunk_overlap=0),
],
)
md_chunked_nodes = pipeline.run()
print(md_chunked_nodes)
[TextNode(id_='e6236169-45a1-4699-9762-c8d3d89f8fa0', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='e7bc328f-85c1-430a-9772-425e59909a58', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙'}, hash='e538ad7c04f635f1c707eba290b55618a9f0942211c4b5ca2a4e54e1fdf04973'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='51b40b54-dfd3-48ed-b377-5ca58a0f48a3', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙'}, hash='ca9e3590b951f1fca38687fd12bb43fbccd0133a38020c94800586b3579c3218')}, hash='ec733c85ad1dca248ae583ece341428ee20e4d796bc11adea1618c8e4ed9246a', text='🗂️ LlamaIndex 🦙\n[](https://pypi.org/project/llama-index/)\n[](https://github.com/jerryjliu/llama_index/graphs/contributors)\n[](https://discord.gg/dGcwcsnxhU)', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='51b40b54-dfd3-48ed-b377-5ca58a0f48a3', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='e7bc328f-85c1-430a-9772-425e59909a58', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙'}, hash='e538ad7c04f635f1c707eba290b55618a9f0942211c4b5ca2a4e54e1fdf04973'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='e6236169-45a1-4699-9762-c8d3d89f8fa0', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙'}, hash='ec733c85ad1dca248ae583ece341428ee20e4d796bc11adea1618c8e4ed9246a')}, hash='ca9e3590b951f1fca38687fd12bb43fbccd0133a38020c94800586b3579c3218', text='LlamaIndex (GPT Index) is a data framework for your LLM application.\n\nPyPI: \n- LlamaIndex: https://pypi.org/project/llama-index/.\n- GPT Index (duplicate): https://pypi.org/project/gpt-index/.\n\nLlamaIndex.TS (Typescript/Javascript): https://github.com/run-llama/LlamaIndexTS.\n\nDocumentation: https://gpt-index.readthedocs.io/.\n\nTwitter: https://twitter.com/llama_index.\n\nDiscord: https://discord.gg/dGcwcsnxhU.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='ce269047-4718-4a08-b170-34fef19cdafe', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 3': 'Ecosystem'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='953934dc-dd4f-4069-9e2a-326ee8a593bf', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 3': 'Ecosystem'}, hash='ede2843c0f18e0f409ae9e2bb4090bca4409eaa992fe8ca149295406d3d7adac')}, hash='52b03025c73d7218bd4d66b9812f6e1f6fab6ccf64e5660dc31d123bf1caf5be', text='Ecosystem\n\n- LlamaHub (community library of data loaders): https://llamahub.ai\n- LlamaLab (cutting-edge AGI projects using LlamaIndex): https://github.com/run-llama/llama-lab', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='5ef55167-1fa1-4cae-b2b5-4a86beffbef6', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='2223925f-93a8-45db-9044-41838633e8cc', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview'}, hash='adc49240ff2bdd007e3462b2c3d3f6b6f3b394abbf043d4c291b1a029302c909')}, hash='dc3f175a9119976866e3e6fb2233a12590e8861dc91c621db131521d84e490c4', text='🚀 Overview\n\n**NOTE**: This README is not updated as frequently as the documentation. Please check out the documentation above for the latest updates!', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='8b8e4778-7943-424c-a160-b7da845dd7da', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Context'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='c1ea3027-aad7-4a6f-b8dc-460a8ffbc258', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Context'}, hash='632c76181233b32c03377ccc3d41e458aaec7de845d123a20ace6e3036bbdcd7')}, hash='b867ce7afa1cee176db4e5d0b147276c2e4c724223d590dd5017e68fab3aa29a', text='Context\n- LLMs are a phenomenonal piece of technology for knowledge generation and reasoning. They are pre-trained on large amounts of publicly available data.\n- How do we best augment LLMs with our own private data?\n\nWe need a comprehensive toolkit to help perform this data augmentation for LLMs.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='be9d228a-91f6-4c39-845d-b79d3b8fa874', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Proposed Solution'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f57a202a-cb3d-4a74-ab09-70bf93a0bf51', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Proposed Solution'}, hash='4d338f21570da1564e407877e2fceac4dc9e9f8c90cb3b34876507f85d29f41e'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='a18e1c90-0455-47be-9411-8e098df9c951', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Proposed Solution'}, hash='7b9bbe433d53e727b353864a38ad8a9e78b74c84dbef4ca931422f0f45a4906d')}, hash='b02a43b52686c62c8c4a2f32aa7b8a5bcf2a9e9ea7a033430645ec492f04a4fd', text='Proposed Solution\n\nThat\'s where **LlamaIndex** comes in. LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools:\n\n- Offers **data connectors** to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.)\n- Provides ways to **structure your data** (indices, graphs) so that this data can be easily used with LLMs.\n- Provides an **advanced retrieval/query interface over your data**: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.\n- Allows easy integrations with your outer application framework (e.g.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='a18e1c90-0455-47be-9411-8e098df9c951', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Proposed Solution'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f57a202a-cb3d-4a74-ab09-70bf93a0bf51', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Proposed Solution'}, hash='4d338f21570da1564e407877e2fceac4dc9e9f8c90cb3b34876507f85d29f41e'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='be9d228a-91f6-4c39-845d-b79d3b8fa874', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🚀 Overview', 'Header 3': 'Proposed Solution'}, hash='b02a43b52686c62c8c4a2f32aa7b8a5bcf2a9e9ea7a033430645ec492f04a4fd')}, hash='7b9bbe433d53e727b353864a38ad8a9e78b74c84dbef4ca931422f0f45a4906d', text='with LangChain, Flask, Docker, ChatGPT, anything else).\n\nLlamaIndex 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\n5 lines of code. Our lower-level APIs allow advanced users to customize and extend any module (data connectors, indices, retrievers, query engines, reranking modules),\nto fit their needs.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='b3c6544a-6f68-4060-b3ec-27e5d4b9a599', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💡 Contributing'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='6abcec78-98c1-4f74-b57b-d8cae4aa7112', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💡 Contributing'}, hash='cdb950bc1703132df9c05c607702201177c1ad5f8f0de9dcfa3f6154a12a3acd')}, hash='4892fb635ac6b11743ca428676ed492ef7d264e440a205a68a0d752d43e3a19c', text='💡 Contributing\n\nInterested in contributing? See our [Contribution Guide](CONTRIBUTING.md) for more details.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='e0fc56d6-ec94-476d-a3e4-c007daa2e405', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '📄 Documentation'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f44afbd2-0bf3-46f5-8662-309e0cf7fa9c', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '📄 Documentation'}, hash='b01a7435fcbe2962f9b6a2cb397a07c1fed6632941e06a1814f4c4ea2300dc67')}, hash='f0215c48bf198d05ee1d6dcc74e12f70d9310c43f4b4dcea71452c9aec051612', text='📄 Documentation\n\nFull documentation can be found here: https://gpt-index.readthedocs.io/en/latest/. \n\nPlease check it out for the most up-to-date tutorials, how-to guides, references, and other resources!', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='b583e1f6-e696-42e3-9c87-fa1a12af5cc9', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💻 Example Usage'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f25c47c0-b8bd-451b-81bf-3879c48c55f4', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💻 Example Usage'}, hash='dfe232d846ceae9f0ccbf96e053b01a00cf24382ff4f49f1380830522d8ae86c'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='82fcab04-4346-4fba-86ae-612e95285c8a', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💻 Example Usage'}, hash='fe6196075f613ebae9f64bf5b1e04d8324c239e8f256d4455653ccade1da5541')}, hash='9073dfc928908788a3e174fe06f4689c081a6eeafe002180134a57c28c640c83', text='💻 Example Usage\n\n```\npip install llama-index\n```\n\nExamples are in the `examples` folder. Indices are in the `indices` folder (see list of indices below).\n\nTo build a simple vector store index:\n```python\nimport os\nos.environ["OPENAI_API_KEY"] = \'YOUR_OPENAI_API_KEY\'\n\nfrom llama_index import VectorStoreIndex, SimpleDirectoryReader\ndocuments = SimpleDirectoryReader(\'data\').load_data()\nindex = VectorStoreIndex.from_documents(documents)\n```', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='82fcab04-4346-4fba-86ae-612e95285c8a', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💻 Example Usage'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='f25c47c0-b8bd-451b-81bf-3879c48c55f4', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💻 Example Usage'}, hash='dfe232d846ceae9f0ccbf96e053b01a00cf24382ff4f49f1380830522d8ae86c'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='b583e1f6-e696-42e3-9c87-fa1a12af5cc9', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '💻 Example Usage'}, hash='9073dfc928908788a3e174fe06f4689c081a6eeafe002180134a57c28c640c83')}, hash='fe6196075f613ebae9f64bf5b1e04d8324c239e8f256d4455653ccade1da5541', text='To query:\n```python\nquery_engine = index.as_query_engine()\nquery_engine.query("<question_text>?")\n```\n\n\nBy default, data is stored in-memory.\nTo persist to disk (under `./storage`):\n\n```python\nindex.storage_context.persist()\n```\n\nTo reload from disk:\n```python\nfrom llama_index import StorageContext, load_index_from_storage\n\n# rebuild storage context\nstorage_context = StorageContext.from_defaults(persist_dir=\'./storage\')\n# load index\nindex = load_index_from_storage(storage_context)\n```', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='b2c3437a-7cef-4990-ab3e-6b3f293f3d9f', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🔧 Dependencies'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='0f9e96b7-9a47-4053-8a43-b27a444910ee', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '🔧 Dependencies'}, hash='3302ab107310e381d572f2410e8994d0b3737b78acc7729c18f8b7f100fd0078')}, hash='28d0ed4496c3bd0a8f0ace18c11be509eadfae4693a3a239c80a5ec1a6eaedd6', text='🔧 Dependencies\n\nThe main third-party package requirements are `tiktoken`, `openai`, and `langchain`.\n\nAll 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`.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='a5af8ac3-57dd-4ed7-ab7f-fab6fb435a42', embedding=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '📖 Citation'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='12629a60-c584-4ec9-888d-ea120813f4df', node_type=None, metadata={'filename': 'README.md', 'extension': '.md', 'Header 1': '🗂️ LlamaIndex 🦙', 'Header 2': '📖 Citation'}, hash='ad2d72754f9faa42727bd38ba84f71ad43c9d65bc1b12a8c46d5dc951212f863')}, hash='f7df46992fbea69c394e73961c4d17ea0b49a587420b0c9f47986af12f787950', text='📖 Citation\n\nReference to cite if you use LlamaIndex in a paper:\n\n```\n@software{Liu_LlamaIndex_2022,\nauthor = {Liu, Jerry},\ndoi = {10.5281/zenodo.1234},\nmonth = {11},\ntitle = {{LlamaIndex}},\nurl = {https://github.com/jerryjliu/llama_index},\nyear = {2022}\n}\n```', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n')]