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GraphRAG
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Getting Started
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Initializing search
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< svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 448 512" > <!-- ! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc. --> < path d = "M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81" / > < / svg >
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graphrag
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Home
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Indexing
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Prompt Tuning
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Query
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Configuration
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< li class = "md-tabs__item" >
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CLI
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Extras
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GraphRAG
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< a href = "https://github.com/microsoft/graphrag" title = "Go to repository" class = "md-source" data-md-component = "source" >
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< svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 448 512" > <!-- ! Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc. --> < path d = "M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81" / > < / svg >
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graphrag
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Home
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Home
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Welcome
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Getting Started
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Getting Started
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Table of contents
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< a href = "#requirements" class = "md-nav__link" >
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Requirements
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Quickstart
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Development Guide
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Indexing
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Indexing
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Overview
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Architecture
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Dataflow
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Outputs
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< input class = "md-nav__toggle md-toggle " type = "checkbox" id = "__nav_3" >
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< label class = "md-nav__link" for = "__nav_3" id = "__nav_3_label" tabindex = "0" >
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< span class = "md-ellipsis" >
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Prompt Tuning
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
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< span class = "md-nav__icon md-icon" > < / span >
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Prompt Tuning
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< a href = "../prompt_tuning/overview/" class = "md-nav__link" >
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< span class = "md-ellipsis" >
Overview
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< / a >
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< li class = "md-nav__item" >
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< a href = "../prompt_tuning/auto_prompt_tuning/" class = "md-nav__link" >
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< span class = "md-ellipsis" >
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Auto Tuning
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< / a >
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< li class = "md-nav__item" >
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< a href = "../prompt_tuning/manual_prompt_tuning/" class = "md-nav__link" >
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< span class = "md-ellipsis" >
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Manual Tuning
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< li class = "md-nav__item md-nav__item--nested" >
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Query
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Overview
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Notebooks
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DRIFT Search
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Configuration
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Configuration
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Overview
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Init Command
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Using YAML
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Using Env Vars
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CLI
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Microsoft Research Blog
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Operation Dulce
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Operation Dulce
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About
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Requirements
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< h1 id = "getting-started" > Getting Started< / h1 >
< h2 id = "requirements" > Requirements< / h2 >
< p > < a href = "https://www.python.org/downloads/" > Python 3.10-3.12< / a > < / p >
< p > To get started with the GraphRAG system, you have a few options:< / p >
< p > 👉 < a href = "https://github.com/Azure-Samples/graphrag-accelerator" > Use the GraphRAG Accelerator solution< / a > < br / >
👉 < a href = "https://pypi.org/project/graphrag/" > Install from pypi< / a > . < br / >
👉 < a href = "../developing/" > Use it from source< / a > < br / > < / p >
< h2 id = "quickstart" > Quickstart< / h2 >
< p > To get started with the GraphRAG system we recommend trying the < a href = "https://github.com/Azure-Samples/graphrag-accelerator" > Solution Accelerator< / a > package. This provides a user-friendly end-to-end experience with Azure resources.< / p >
< h1 id = "top-level-modules" > Top-Level Modules< / h1 >
< ul >
< li > < a href = "../index/overview/" > Indexing Pipeline Overview< / a > < / li >
< li > < a href = "../query/overview/" > Query Engine Overview< / a > < / li >
< / ul >
< h1 id = "overview" > Overview< / h1 >
< p > The following is a simple end-to-end example for using the GraphRAG system.
It shows how to use the system to index some text, and then use the indexed data to answer questions about the documents.< / p >
< h1 id = "install-graphrag" > Install GraphRAG< / h1 >
< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-0-1" name = "__codelineno-0-1" href = "#__codelineno-0-1" > < / a > pip< span class = "w" > < / span > install< span class = "w" > < / span > graphrag
< / code > < / pre > < / div >
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< p > The graphrag library includes a CLI for a no-code approach to getting started. Please review the full < a href = "../cli/" > CLI documentation< / a > for further detail.< / p >
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< h1 id = "running-the-indexer" > Running the Indexer< / h1 >
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< p > Now we need to set up a data project and some initial configuration. Let's set that up. We're using the < a href = "../config/overview/" > default configuration mode< / a > , which you can customize as needed using a < a href = "../config/yaml/" > config file< / a > , which we recommend, or < a href = "../config/env_vars/" > environment variables< / a > .< / p >
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< p > First let's get a sample dataset ready:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-1-1" name = "__codelineno-1-1" href = "#__codelineno-1-1" > < / a > mkdir< span class = "w" > < / span > -p< span class = "w" > < / span > ./ragtest/input
< / code > < / pre > < / div >
< p > Now let's get a copy of A Christmas Carol by Charles Dickens from a trusted source< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-2-1" name = "__codelineno-2-1" href = "#__codelineno-2-1" > < / a > curl< span class = "w" > < / span > https://www.gutenberg.org/cache/epub/24022/pg24022.txt< span class = "w" > < / span > -o< span class = "w" > < / span > ./ragtest/input/book.txt
< / code > < / pre > < / div >
< p > Next we'll inject some required config variables:< / p >
< h2 id = "set-up-your-workspace-variables" > Set Up Your Workspace Variables< / h2 >
< p > First let's make sure to setup the required environment variables. For details on these environment variables, and what environment variables are available, see the < a href = "../config/overview/" > variables documentation< / a > .< / p >
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< p > To initialize your workspace, first run the < code > graphrag init< / code > command.
Since we have already configured a directory named < code > ./ragtest< / code > in the previous step, run the following command:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-3-1" name = "__codelineno-3-1" href = "#__codelineno-3-1" > < / a > graphrag< span class = "w" > < / span > init< span class = "w" > < / span > --root< span class = "w" > < / span > ./ragtest
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< / code > < / pre > < / div >
< p > This will create two files: < code > .env< / code > and < code > settings.yaml< / code > in the < code > ./ragtest< / code > directory.< / p >
< ul >
< li > < code > .env< / code > contains the environment variables required to run the GraphRAG pipeline. If you inspect the file, you'll see a single environment variable defined,
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< code > GRAPHRAG_API_KEY=< API_KEY> < / code > . This is the API key for the OpenAI API or Azure OpenAI endpoint. You can replace this with your own API key. If you are using another form of authentication (i.e. managed identity), please delete this file.< / li >
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< li > < code > settings.yaml< / code > contains the settings for the pipeline. You can modify this file to change the settings for the pipeline.
< br / > < / li >
< / ul >
< h4 id = "openai-and-azure-openai" > < ins > OpenAI and Azure OpenAI< / ins > < / h4 >
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< p > If running in OpenAI mode, update the value of < code > GRAPHRAG_API_KEY< / code > in the < code > .env< / code > file with your OpenAI API key.< / p >
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< h4 id = "azure-openai" > < ins > Azure OpenAI< / ins > < / h4 >
< p > In addition, Azure OpenAI users should set the following variables in the settings.yaml file. To find the appropriate sections, just search for the < code > llm:< / code > configuration, you should see two sections, one for the chat endpoint and one for the embeddings endpoint. Here is an example of how to configure the chat endpoint:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-4-1" name = "__codelineno-4-1" href = "#__codelineno-4-1" > < / a > < span class = "nt" > type< / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "l l-Scalar l-Scalar-Plain" > azure_openai_chat< / span > < span class = "w" > < / span > < span class = "c1" > # Or azure_openai_embedding for embeddings< / span >
< a id = "__codelineno-4-2" name = "__codelineno-4-2" href = "#__codelineno-4-2" > < / a > < span class = "nt" > api_base< / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "l l-Scalar l-Scalar-Plain" > https://< instance> .openai.azure.com< / span >
< a id = "__codelineno-4-3" name = "__codelineno-4-3" href = "#__codelineno-4-3" > < / a > < span class = "nt" > api_version< / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "l l-Scalar l-Scalar-Plain" > 2024-02-15-preview< / span > < span class = "w" > < / span > < span class = "c1" > # You can customize this for other versions< / span >
< a id = "__codelineno-4-4" name = "__codelineno-4-4" href = "#__codelineno-4-4" > < / a > < span class = "nt" > deployment_name< / span > < span class = "p" > :< / span > < span class = "w" > < / span > < span class = "l l-Scalar l-Scalar-Plain" > < azure_model_deployment_name> < / span >
< / code > < / pre > < / div >
< ul >
< li > For more details about configuring GraphRAG, see the < a href = "../config/overview/" > configuration documentation< / a > .< / li >
< li > To learn more about Initialization, refer to the < a href = "../config/init/" > Initialization documentation< / a > .< / li >
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< li > For more details about using the CLI, refer to the < a href = "../cli/" > CLI documentation< / a > .< / li >
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< / ul >
< h2 id = "running-the-indexing-pipeline" > Running the Indexing pipeline< / h2 >
< p > Finally we'll run the pipeline!< / p >
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< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-5-1" name = "__codelineno-5-1" href = "#__codelineno-5-1" > < / a > graphrag< span class = "w" > < / span > index< span class = "w" > < / span > --root< span class = "w" > < / span > ./ragtest
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< / code > < / pre > < / div >
< p > < img alt = "pipeline executing from the CLI" src = "../img/pipeline-running.png" / > < / p >
< p > This process will take some time to run. This depends on the size of your input data, what model you're using, and the text chunk size being used (these can be configured in your < code > settings.yml< / code > file).
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Once the pipeline is complete, you should see a new folder called < code > ./ragtest/output< / code > with a series of parquet files.< / p >
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< h1 id = "using-the-query-engine" > Using the Query Engine< / h1 >
< h2 id = "running-the-query-engine" > Running the Query Engine< / h2 >
< p > Now let's ask some questions using this dataset.< / p >
< p > Here is an example using Global search to ask a high-level question:< / p >
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< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-6-1" name = "__codelineno-6-1" href = "#__codelineno-6-1" > < / a > graphrag< span class = "w" > < / span > query< span class = "w" > < / span > < span class = "se" > \< / span >
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< a id = "__codelineno-6-2" name = "__codelineno-6-2" href = "#__codelineno-6-2" > < / a > --root< span class = "w" > < / span > ./ragtest< span class = "w" > < / span > < span class = "se" > \< / span >
< a id = "__codelineno-6-3" name = "__codelineno-6-3" href = "#__codelineno-6-3" > < / a > --method< span class = "w" > < / span > global< span class = "w" > < / span > < span class = "se" > \< / span >
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< a id = "__codelineno-6-4" name = "__codelineno-6-4" href = "#__codelineno-6-4" > < / a > --query< span class = "w" > < / span > < span class = "s2" > " What are the top themes in this story?" < / span >
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< / code > < / pre > < / div >
< p > Here is an example using Local search to ask a more specific question about a particular character:< / p >
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< div class = "highlight" > < pre > < span > < / span > < code > < a id = "__codelineno-7-1" name = "__codelineno-7-1" href = "#__codelineno-7-1" > < / a > graphrag< span class = "w" > < / span > query< span class = "w" > < / span > < span class = "se" > \< / span >
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< a id = "__codelineno-7-2" name = "__codelineno-7-2" href = "#__codelineno-7-2" > < / a > --root< span class = "w" > < / span > ./ragtest< span class = "w" > < / span > < span class = "se" > \< / span >
< a id = "__codelineno-7-3" name = "__codelineno-7-3" href = "#__codelineno-7-3" > < / a > --method< span class = "w" > < / span > < span class = "nb" > local< / span > < span class = "w" > < / span > < span class = "se" > \< / span >
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< a id = "__codelineno-7-4" name = "__codelineno-7-4" href = "#__codelineno-7-4" > < / a > --query< span class = "w" > < / span > < span class = "s2" > " Who is Scrooge and what are his main relationships?" < / span >
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< / code > < / pre > < / div >
< p > Please refer to < a href = "../query/overview/" > Query Engine< / a > docs for detailed information about how to leverage our Local and Global search mechanisms for extracting meaningful insights from data after the Indexer has wrapped up execution.< / p >
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< h1 id = "visualizing-the-graph" > Visualizing the Graph< / h1 >
< p > Check out our < a href = "../visualization_guide/" > visualization guide< / a > for a more interactive experience in debugging and exploring the knowledge graph.< / p >
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