-Figure 1. An entire DRIFT search hierarchy highlighting the three core phases of the DRIFT search process. A (Primer): DRIFT compares the user’s query with the top K most semantically relevant community reports, generating a broad initial answer and follow-up questions to steer further exploration. B (Follow-Up): DRIFT uses local search to refine queries, producing additional intermediate answers and follow-up questions that enhance specificity, guiding the engine towards context-rich information. A glyph on each node in the diagram shows the confidence the algorithm has to continue the query expansion step. C (Output Hierarchy): The final output is a hierarchical structure of questions and answers ranked by relevance, reflecting a balanced mix of global insights and local refinements, making the results adaptable and comprehensive.
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+Figure 1. An entire DRIFT search hierarchy highlighting the three core phases of the DRIFT search process. A (Primer): DRIFT compares the user’s query with the top K most semantically relevant community reports, generating a broad initial answer and follow-up questions to steer further exploration. B (Follow-Up): DRIFT uses local search to refine queries, producing additional intermediate answers and follow-up questions that enhance specificity, guiding the engine towards context-rich information. A glyph on each node in the diagram shows the confidence the algorithm has to continue the query expansion step. C (Output Hierarchy): The final output is a hierarchical structure of questions and answers ranked by relevance, reflecting a balanced mix of global insights and local refinements, making the results adaptable and comprehensive.
+ DRIFT Search introduces a new approach to local search queries by including community information in the search process. This greatly expands the breadth of the query’s starting point and leads to retrieval and usage of a far higher variety of facts in the final answer. This addition expands the GraphRAG query engine by providing a more comprehensive option for local search, which uses community insights to refine a query into detailed follow-up questions. diff --git a/docs/visualization_guide.md b/docs/visualization_guide.md new file mode 100644 index 0000000000..539dd509c2 --- /dev/null +++ b/docs/visualization_guide.md @@ -0,0 +1,100 @@ +# Visualizing and Debugging Your Knowledge Graph + +The following step-by-step guide walks through the process to visualize a knowledge graph after it's been constructed by graphrag. Note that some of the settings recommended below are based on our own experience of what works well. Feel free to change and explore other settings for a better visualization experience! + +## 1. Run the Pipeline +Before building an index, please review your `settings.yaml` configuration file and ensure that graphml snapshots is enabled. +```yaml +snapshots: + graphml: true +``` +(Optional) To support other visualization tools and exploration, additional parameters can be enabled that provide access to vector embeddings. +```yaml +embed_graph: + enabled: true # will generate node2vec embeddings for nodes +umap: + enabled: true # will generate UMAP embeddings for nodes +``` +After running the indexing pipeline over your data, there will be an output folder (defined by the `storage.base_dir` setting). + +- **Output Folder**: Contains artifacts from the LLM’s indexing pass. + +## 2. Locate the Knowledge Graph +In the output folder, look for a file named `merged_graph.graphml`. graphml is a standard [file format](http://graphml.graphdrawing.org) supported by many visualization tools. We recommend trying [Gephi](https://gephi.org). + +## 3. Open the Graph in Gephi +1. Install and open Gephi +2. Navigate to the `output` folder containing the various parquet files. +3. Import the `merged_graph.graphml` file into Gephi. This will result in a fairly plain view of the undirected graph nodes and edges. + +
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