"# Visualizing the knowledge graph with `yfiles-jupyter-graphs`\n",
"\n",
"This notebook is a partial copy of [local_search.ipynb](../../local_search.ipynb) that shows how to use `yfiles-jupyter-graphs` to add interactive graph visualizations of the parquet files and how to visualize the result context of `graphrag` queries (see at the end of this notebook)."
"Local search method generates answers by combining relevant data from the AI-extracted knowledge-graph with text chunks of the raw documents. This method is suitable for questions that require an understanding of specific entities mentioned in the documents (e.g. What are the healing properties of chamomile?)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load text units and graph data tables as context for local search\n",
"\n",
"- In this test we first load indexing outputs from parquet files to dataframes, then convert these dataframes into collections of data objects aligning with the knowledge model."
"# Visualizing nodes and relationships with `yfiles-jupyter-graphs`\n",
"\n",
"`yfiles-jupyter-graphs` is a graph visualization extension that provides interactive and customizable visualizations for structured node and relationship data.\n",
"\n",
"In this case, we use it to provide an interactive visualization for the knowledge graph of the [local_search.ipynb](../../local_search.ipynb) sample by passing node and relationship lists converted from the given parquet files. The requirements for the input data is an `id` attribute for the nodes and `start`/`end` properties for the relationships that correspond to the node ids. Additional attributes can be added in the `properties` of each node/relationship dict:"
"The widget provides different automatic layouts that serve different purposes: `Circular`, `Hierarchic`, `Organic (interactiv or static)`, `Orthogonal`, `Radial`, `Tree`, `Geo-spatial`.\n",
"\n",
"For the knowledge graph, this sample uses the `Circular` layout, though `Hierarchic` or `Organic` are also suitable choices."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use the circular layout for this visualization. For larger graphs, the default organic layout is often preferrable.\n",
"w.circular_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Display the graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(w)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing the result context of `graphrag` queries\n",
"\n",
"The result context of `graphrag` queries allow to inspect the context graph of the request. This data can similarly be visualized as graph with `yfiles-jupyter-graphs`.\n",
"\n",
"## Making the request\n",
"\n",
"The following cell recreates the sample queries from [local_search.ipynb](../../local_search.ipynb)."
" \"embedding_vectorstore_key\": EntityVectorStoreKey.ID, # set this to EntityVectorStoreKey.TITLE if the vectorstore uses entity title as ids\n",
" \"max_tokens\": 12_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)\n",
"}\n",
"\n",
"llm_params = {\n",
" \"max_tokens\": 2_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 1000=1500)\n",
" response_type=\"multiple paragraphs\", # free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run local search on sample queries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = await search_engine.asearch(\"Tell me about Agent Mercer\")\n",
"print(result.response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"Tell me about Dr. Jordan Hayes\"\n",
