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< a href = "/graphrag/" > < span class = "logotitle" > GraphRAG< / span > < / a >
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< / header >
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<!-- Sidebar -->
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< a href = "/graphrag/" > Welcome< / a >
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< / li >
<!-- Get Started Links -->
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< a href = "/graphrag/posts/get_started/" > Get Started< / a >
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< a href = "/graphrag/posts/developing/" > Developing< / a >
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<!-- Indexing Links -->
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< a href = "/graphrag/posts/index/overview/" class = "is-active" aria-current = "page" > Indexing< / a >
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< a href = "/graphrag/posts/index/0-architecture/" > Architecture< / a >
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< / li > < li >
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< a href = "/graphrag/posts/index/1-default_dataflow/" > Dataflow< / a >
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< / li > < li >
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< a href = "/graphrag/posts/index/2-cli/" > CLI< / a >
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< / li > < li >
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< a href = "/graphrag/posts/index/3-prompt_tuning/" > Prompt Tuning< / a >
< / li > < li >
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< a href = "/graphrag/posts/config/overview/" > Configuration< / a >
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< a href = "/graphrag/posts/config/env_vars" > Using Env Vars< / a >
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< / li >
< li >
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< a href = "/graphrag/posts/config/json_yaml" > Using JSON or YAML< / a >
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< a href = "/graphrag/posts/config/custom" > Fully Custom< / a >
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< a href = "/graphrag/posts/config/template" > Template< / a >
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< a href = "/graphrag/posts/query/overview/" > Query< / a >
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< a href = "/graphrag/posts/query/0-global_search/" > Global Search< / a >
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< a href = "/graphrag/posts/query/1-local_search/" > Local Search< / a >
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< a href = "/graphrag/posts/query/2-question_generation/" > Question Generation< / a >
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< / li > < li >
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< a href = "/graphrag/posts/query/3-cli/" > CLI< / a >
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< / li > < li >
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< a href = "/graphrag/posts/query/notebooks/overview/" > Notebooks< / a >
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< a href = "/graphrag/posts/query/notebooks/global_search_nb" > Global Search< / a >
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< a href = "/graphrag/posts/query/notebooks/local_search_nb" > Local Search< / a >
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< / li >
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<!-- Main Content -->
< main >
< h1 > GraphRAG Indexing 🤖< / h1 >
< p > The GraphRAG indexing package is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using LLMs.< / p >
< p > Indexing Pipelines are configurable. They are composed of workflows, standard and custom steps, prompt templates, and input/output adapters. Our standard pipeline is designed to:< / p >
< ul >
< li > extract entities, relationships and claims from raw text< / li >
< li > perform community detection in entities< / li >
< li > generate community summaries and reports at multiple levels of granularity< / li >
< li > embed entities into a graph vector space< / li >
< li > embed text chunks into a textual vector space< / li >
< / ul >
< p > The outputs of the pipeline can be stored in a variety of formats, including JSON and Parquet - or they can be handled manually via the Python API.< / p >
< h2 > Getting Started< / h2 >
< h3 > Requirements< / h3 >
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< p > See the < a href = "/graphrag/posts/developing#requirements" > requirements< / a > section in < a href = "/graphrag/posts/get_started" > Get Started< / a > for details on setting up a development environment.< / p >
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< p > The Indexing Engine can be used in either a default configuration mode or with a custom pipeline.
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To configure GraphRAG, see the < a href = "/graphrag/posts/config/overview" > configuration< / a > documentation.
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After you have a config file you can run the pipeline using the CLI or the Python API.< / p >
< h2 > Usage< / h2 >
< h3 > CLI< / h3 >
< div style = "position: relative" >
< pre class = "language-bash" > < code id = "code-54" class = "language-bash" > < span class = "token comment" > # Via Poetry< / span >
poetry run poe cli < span class = "token parameter variable" > --root< / span > < span class = "token operator" > < < / span > data_root< span class = "token operator" > >< / span > < span class = "token comment" > # default config mode< / span >
poetry run poe cli < span class = "token parameter variable" > --config< / span > your_pipeline.yml < span class = "token comment" > # custom config mode< / span >
< span class = "token comment" > # Via Node< / span >
< span class = "token function" > yarn< / span > run:index < span class = "token parameter variable" > --root< / span > < span class = "token operator" > < < / span > data_root< span class = "token operator" > >< / span > < span class = "token comment" > # default config mode< / span >
< span class = "token function" > yarn< / span > run:index < span class = "token parameter variable" > --config< / span > your_pipeline.yml < span class = "token comment" > # custom config mode< / span >
< / code > < / pre >
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< h3 > Python API< / h3 >
< div style = "position: relative" >
< pre class = "language-python" > < code id = "code-58" class = "language-python" > < span class = "token keyword" > from< / span > graphrag< span class = "token punctuation" > .< / span > index < span class = "token keyword" > import< / span > run_pipeline
< span class = "token keyword" > from< / span > graphrag< span class = "token punctuation" > .< / span > index< span class = "token punctuation" > .< / span > config < span class = "token keyword" > import< / span > PipelineWorkflowReference
workflows< span class = "token punctuation" > :< / span > < span class = "token builtin" > list< / span > < span class = "token punctuation" > [< / span > PipelineWorkflowReference< span class = "token punctuation" > ]< / span > < span class = "token operator" > =< / span > < span class = "token punctuation" > [< / span >
PipelineWorkflowReference< span class = "token punctuation" > (< / span >
steps< span class = "token operator" > =< / span > < span class = "token punctuation" > [< / span >
< span class = "token punctuation" > {< / span >
< span class = "token comment" > # built-in verb< / span >
< span class = "token string" > "verb"< / span > < span class = "token punctuation" > :< / span > < span class = "token string" > "derive"< / span > < span class = "token punctuation" > ,< / span > < span class = "token comment" > # https://github.com/microsoft/datashaper/blob/main/python/datashaper/datashaper/engine/verbs/derive.py< / span >
< span class = "token string" > "args"< / span > < span class = "token punctuation" > :< / span > < span class = "token punctuation" > {< / span >
< span class = "token string" > "column1"< / span > < span class = "token punctuation" > :< / span > < span class = "token string" > "col1"< / span > < span class = "token punctuation" > ,< / span > < span class = "token comment" > # from above< / span >
< span class = "token string" > "column2"< / span > < span class = "token punctuation" > :< / span > < span class = "token string" > "col2"< / span > < span class = "token punctuation" > ,< / span > < span class = "token comment" > # from above< / span >
< span class = "token string" > "to"< / span > < span class = "token punctuation" > :< / span > < span class = "token string" > "col_multiplied"< / span > < span class = "token punctuation" > ,< / span > < span class = "token comment" > # new column name< / span >
< span class = "token string" > "operator"< / span > < span class = "token punctuation" > :< / span > < span class = "token string" > "*"< / span > < span class = "token punctuation" > ,< / span > < span class = "token comment" > # multiply the two columns< / span >
< span class = "token punctuation" > }< / span > < span class = "token punctuation" > ,< / span >
< span class = "token comment" > # Since we're trying to act on the default input, we don't need explicitly to specify an input< / span >
< span class = "token punctuation" > }< / span >
< span class = "token punctuation" > ]< / span >
< span class = "token punctuation" > )< / span > < span class = "token punctuation" > ,< / span >
< span class = "token punctuation" > ]< / span >
dataset < span class = "token operator" > =< / span > pd< span class = "token punctuation" > .< / span > DataFrame< span class = "token punctuation" > (< / span > < span class = "token punctuation" > [< / span > < span class = "token punctuation" > {< / span > < span class = "token string" > "col1"< / span > < span class = "token punctuation" > :< / span > < span class = "token number" > 2< / span > < span class = "token punctuation" > ,< / span > < span class = "token string" > "col2"< / span > < span class = "token punctuation" > :< / span > < span class = "token number" > 4< / span > < span class = "token punctuation" > }< / span > < span class = "token punctuation" > ,< / span > < span class = "token punctuation" > {< / span > < span class = "token string" > "col1"< / span > < span class = "token punctuation" > :< / span > < span class = "token number" > 5< / span > < span class = "token punctuation" > ,< / span > < span class = "token string" > "col2"< / span > < span class = "token punctuation" > :< / span > < span class = "token number" > 10< / span > < span class = "token punctuation" > }< / span > < span class = "token punctuation" > ]< / span > < span class = "token punctuation" > )< / span >
outputs < span class = "token operator" > =< / span > < span class = "token punctuation" > [< / span > < span class = "token punctuation" > ]< / span >
< span class = "token keyword" > async< / span > < span class = "token keyword" > for< / span > output < span class = "token keyword" > in< / span > < span class = "token keyword" > await< / span > run_pipeline< span class = "token punctuation" > (< / span > dataset< span class = "token operator" > =< / span > dataset< span class = "token punctuation" > ,< / span > workflows< span class = "token operator" > =< / span > workflows< span class = "token punctuation" > )< / span > < span class = "token punctuation" > :< / span >
outputs< span class = "token punctuation" > .< / span > append< span class = "token punctuation" > (< / span > output
pipeline_result < span class = "token operator" > =< / span > outputs< span class = "token punctuation" > [< / span > < span class = "token operator" > -< / span > < span class = "token number" > 1< / span > < span class = "token punctuation" > ]< / span >
< span class = "token keyword" > print< / span > < span class = "token punctuation" > (< / span > pipeline_result< span class = "token punctuation" > )< / span > < / code > < / pre >
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< h2 > Further Reading< / h2 >
< ul >
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< li > To start developing within the < em > GraphRAG< / em > project, see < a href = "/graphrag/posts/developing/" > getting started< / a > < / li >
< li > To understand the underlying concepts and execution model of the indexing library, see < a href = "/graphrag/posts/index/0-architecture/" > the architecture documentation< / a > < / li >
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< li > To get running with a series of examples, see < a href = "https://github.com/microsoft/graphrag/blob/main/examples/README.md" > the examples documentation< / a > < / li >
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< li > To read more about configuring the indexing engine, see < a href = "/graphrag/posts/config/overview" > the configuration documentation< / a > < / li >
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