diff --git a/.semversioner/next-release/patch-20240717000610110208.json b/.semversioner/next-release/patch-20240717000610110208.json new file mode 100644 index 0000000000..090c925282 --- /dev/null +++ b/.semversioner/next-release/patch-20240717000610110208.json @@ -0,0 +1,4 @@ +{ + "type": "patch", + "description": "Add neo4j community notebook" +} diff --git a/examples_notebooks/community_contrib/README.md b/examples_notebooks/community_contrib/README.md new file mode 100644 index 0000000000..0915dc1a60 --- /dev/null +++ b/examples_notebooks/community_contrib/README.md @@ -0,0 +1,5 @@ +## Disclaimer + +This folder contains community contributed notebooks that are not officially supported by the GraphRAG team. The notebooks are provided as-is and are not guaranteed to work with the latest version of GraphRAG. If you have any questions or issues, please reach out to the author of the notebook directly. + +For more information on how to contribute to the GraphRAG project, please refer to the [contribution guidelines](https://github.com/microsoft/graphrag/blob/main/CONTRIBUTING.md) diff --git a/examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb b/examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb new file mode 100644 index 0000000000..1553b0f692 --- /dev/null +++ b/examples_notebooks/community_contrib/neo4j/graphrag_import_neo4j_cypher.ipynb @@ -0,0 +1,1214 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "b4fea928", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) 2024 Microsoft Corporation.\n", + "# Licensed under the MIT License." + ] + }, + { + "cell_type": "markdown", + "id": "0c4bc9ba", + "metadata": {}, + "source": [ + "# Neo4j Import of GraphRAG Result Parquet files\n", + "\n", + "This notebook imports the results of the GraphRAG indexing process into the Neo4j Graph database for further processing, analysis or visualization. \n", + "\n", + "You can also build your own GenAI applications using Neo4j and a number of RAG strategies with LangChain, LlamaIndex, Haystack, and many other frameworks.\n", + "See: https://neo4j.com/labs/genai-ecosystem\n", + "\n", + "Here is what the end result looks like:\n", + "\n", + "![](https://dev.assets.neo4j.com/wp-content/uploads/graphrag-neo4j-visualization.png)" + ] + }, + { + "cell_type": "markdown", + "id": "3924e246", + "metadata": {}, + "source": [ + "## How does it work?\n", + "\n", + "The notebook loads the parquet files from the `output` folder of your indexing process and loads them into Pandas dataframes.\n", + "It then uses a batching approach to send a slice of the data into Neo4j to create nodes and relationships and add relevant properties. The id-arrays on most entities are turned into relationships. \n", + "\n", + "All operations use MERGE, so they are idempotent, and you can run the script multiple times.\n", + "\n", + "If you need to clean out the database, you can run the following statement\n", + "\n", + "```cypher\n", + "MATCH (n)\n", + "CALL { WITH n DETACH DELETE n } IN TRANSACTIONS OF 25000 ROWS;\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "adca1803", + "metadata": {}, + "outputs": [], + "source": [ + "GRAPHRAG_FOLDER = \"PATH_TO_OUTPUT/artifacts\"" + ] + }, + { + "cell_type": "markdown", + "id": "7fb27b941602401d91542211134fc71a", + "metadata": {}, + "source": [ + "### Depedendencies\n", + "\n", + "We only need Pandas and the neo4j Python driver with the rust extension for faster network transport." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b57beec0", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --quiet pandas neo4j-rust-ext" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "3eeee95f-e4f2-4052-94fb-a5dc8ab542ae", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "import pandas as pd\n", + "from neo4j import GraphDatabase" + ] + }, + { + "cell_type": "markdown", + "id": "307dd2f4", + "metadata": {}, + "source": [ + "## Neo4j Installation\n", + "\n", + "You can create a free instance of Neo4j [online](https://console.neo4j.io). You get a credentials file that you can use for the connection credentials. You can also get an instance in any of the cloud marketplaces.\n", + "\n", + "If you want to install Neo4j locally either use [Neo4j Desktop](https://neo4j.com/download) or \n", + "the official Docker image: `docker run -e NEO4J_AUTH=neo4j/password -p 7687:7687 -p 7474:7474 neo4j` " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "b6c15443-4acb-4f91-88ea-4e08abaa4c29", + "metadata": {}, + "outputs": [], + "source": [ + "NEO4J_URI = \"neo4j://localhost\" # or neo4j+s://xxxx.databases.neo4j.io\n", + "NEO4J_USERNAME = \"neo4j\"\n", + "NEO4J_PASSWORD = \"\" # your password\n", + "NEO4J_DATABASE = \"neo4j\"\n", + "\n", + "# Create a Neo4j driver\n", + "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))" + ] + }, + { + "cell_type": "markdown", + "id": "70f37ab6", + "metadata": {}, + "source": [ + "## Batched Import\n", + "\n", + "The batched import function takes a Cypher insert statement (needs to use the variable `value` for the row) and a dataframe to import.\n", + "It will send by default 1k rows at a time as query parameter to the database to be inserted." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "d787bf7b-ac9b-4bfb-b140-a50a3fd205c5", + "metadata": {}, + "outputs": [], + "source": [ + "def batched_import(statement, df, batch_size=1000):\n", + " \"\"\"\n", + " Import a dataframe into Neo4j using a batched approach.\n", + "\n", + " Parameters: statement is the Cypher query to execute, df is the dataframe to import, and batch_size is the number of rows to import in each batch.\n", + " \"\"\"\n", + " total = len(df)\n", + " start_s = time.time()\n", + " for start in range(0, total, batch_size):\n", + " batch = df.iloc[start : min(start + batch_size, total)]\n", + " result = driver.execute_query(\n", + " \"UNWIND $rows AS value \" + statement,\n", + " rows=batch.to_dict(\"records\"),\n", + " database_=NEO4J_DATABASE,\n", + " )\n", + " print(result.summary.counters)\n", + " print(f\"{total} rows in {time.time() - start_s} s.\")\n", + " return total" + ] + }, + { + "cell_type": "markdown", + "id": "0fb45f42", + "metadata": {}, + "source": [ + "## Indexes and Constraints\n", + "\n", + "Indexes in Neo4j are only used to find the starting points for graph queries, e.g. quickly finding two nodes to connect.\n", + "Constraints exist to avoid duplicates, we create them mostly on id's of Entity types.\n", + "\n", + "We use some Types as markers with two underscores before and after to distinguish them from the actual entity types.\n", + "\n", + "The default relationship type here is `RELATED` but we could also infer a real relationship-type from the description or the types of the start and end-nodes.\n", + "\n", + "* `__Entity__`\n", + "* `__Document__`\n", + "* `__Chunk__`\n", + "* `__Community__`\n", + "* `__Covariate__`" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "ed7f212e-9148-424c-adc6-d81db9f8e5a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "create constraint chunk_id if not exists for (c:__Chunk__) require c.id is unique\n", + "\n", + "create constraint document_id if not exists for (d:__Document__) require d.id is unique\n", + "\n", + "create constraint entity_id if not exists for (c:__Community__) require c.community is unique\n", + "\n", + "create constraint entity_id if not exists for (e:__Entity__) require e.id is unique\n", + "\n", + "create constraint entity_title if not exists for (e:__Entity__) require e.name is unique\n", + "\n", + "create constraint entity_title if not exists for (e:__Covariate__) require e.title is unique\n", + "\n", + "create constraint related_id if not exists for ()-[rel:RELATED]->() require rel.id is unique\n" + ] + } + ], + "source": [ + "# create constraints, idempotent operation\n", + "\n", + "statements = \"\"\"\n", + "create constraint chunk_id if not exists for (c:__Chunk__) require c.id is unique;\n", + "create constraint document_id if not exists for (d:__Document__) require d.id is unique;\n", + "create constraint entity_id if not exists for (c:__Community__) require c.community is unique;\n", + "create constraint entity_id if not exists for (e:__Entity__) require e.id is unique;\n", + "create constraint entity_title if not exists for (e:__Entity__) require e.name is unique;\n", + "create constraint entity_title if not exists for (e:__Covariate__) require e.title is unique;\n", + "create constraint related_id if not exists for ()-[rel:RELATED]->() require rel.id is unique;\n", + "\"\"\".split(\";\")\n", + "\n", + "for statement in statements:\n", + " if len((statement or \"\").strip()) > 0:\n", + " print(statement)\n", + " driver.execute_query(statement)" + ] + }, + { + "cell_type": "markdown", + "id": "beea073b", + "metadata": {}, + "source": [ + "## Import Process\n", + "\n", + "### Importing the Documents\n", + "\n", + "We're loading the parquet file for the documents and create nodes with their ids and add the title property.\n", + "We don't need to store text_unit_ids as we can create the relationships and the text content is also contained in the chunks." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "1ba023e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0c305886e4aa2f6efcf64b57762777055book.txt
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" + ], + "text/plain": [ + " id title\n", + "0 c305886e4aa2f6efcf64b57762777055 book.txt" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "doc_df = pd.read_parquet(\n", + " f\"{GRAPHRAG_FOLDER}/create_final_documents.parquet\", columns=[\"id\", \"title\"]\n", + ")\n", + "doc_df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "96391c15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'_contains_updates': True, 'labels_added': 1, 'nodes_created': 1, 'properties_set': 2}\n", + "1 rows in 0.05211496353149414 s.\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import documents\n", + "statement = \"\"\"\n", + "MERGE (d:__Document__ {id:value.id})\n", + "SET d += value {.title}\n", + "\"\"\"\n", + "\n", + "batched_import(statement, doc_df)" + ] + }, + { + "cell_type": "markdown", + "id": "f97bbadb", + "metadata": {}, + "source": [ + "### Loading Text Units\n", + "\n", + "We load the text units, create a node per id and set the text and number of tokens.\n", + "Then we connect them to the documents that we created before." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "0d825626", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idtextn_tokensdocument_ids
0680dd6d2a970a49082fa4f34bf63a34eThe Project Gutenberg eBook of A Christmas Ca...300[c305886e4aa2f6efcf64b57762777055]
195f1f8f5bdbf0bee3a2c6f2f4a4907f6THE PROJECT GUTENBERG EBOOK A CHRISTMAS CAROL...300[c305886e4aa2f6efcf64b57762777055]
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" + ], + "text/plain": [ + " id \\\n", + "0 680dd6d2a970a49082fa4f34bf63a34e \n", + "1 95f1f8f5bdbf0bee3a2c6f2f4a4907f6 \n", + "\n", + " text n_tokens \\\n", + "0 The Project Gutenberg eBook of A Christmas Ca... 300 \n", + "1 THE PROJECT GUTENBERG EBOOK A CHRISTMAS CAROL... 300 \n", + "\n", + " document_ids \n", + "0 [c305886e4aa2f6efcf64b57762777055] \n", + "1 [c305886e4aa2f6efcf64b57762777055] " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text_df = pd.read_parquet(\n", + " f\"{GRAPHRAG_FOLDER}/create_final_text_units.parquet\",\n", + " columns=[\"id\", \"text\", \"n_tokens\", \"document_ids\"],\n", + ")\n", + "text_df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "ffd3d380-8710-46f5-b90a-04ed8482192c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'_contains_updates': True, 'relationships_created': 231, 'properties_set': 462}\n", + "231 rows in 0.05993008613586426 s.\n" + ] + }, + { + "data": { + "text/plain": [ + "231" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "statement = \"\"\"\n", + "MERGE (c:__Chunk__ {id:value.id})\n", + "SET c += value {.text, .n_tokens}\n", + "WITH c, value\n", + "UNWIND value.document_ids AS document\n", + "MATCH (d:__Document__ {id:document})\n", + "MERGE (c)-[:PART_OF]->(d)\n", + "\"\"\"\n", + "\n", + "batched_import(statement, text_df)" + ] + }, + { + "cell_type": "markdown", + "id": "f01b2094", + "metadata": {}, + "source": [ + "### Loading Nodes\n", + "\n", + "For the nodes we store id, name, description, embedding (if available), human readable id." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "2392f9e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " name type \\\n", + "0 \"PROJECT GUTENBERG\" \"ORGANIZATION\" \n", + "1 \"UNITED STATES\" \"GEO\" \n", + "\n", + " description human_readable_id \\\n", + "0 Project Gutenberg is a pioneering organization... 0 \n", + "1 The United States is prominently recognized fo... 1 \n", + "\n", + " id \\\n", + "0 b45241d70f0e43fca764df95b2b81f77 \n", + "1 4119fd06010c494caa07f439b333f4c5 \n", + "\n", + " description_embedding \\\n", + "0 [-0.020793898031115532, 0.02951139025390148, 0... \n", + "1 [-0.009704762138426304, 0.013335365802049637, ... \n", + "\n", + " text_unit_ids \n", + "0 [01e84646075b255eab0a34d872336a89, 10bab8e9773... \n", + "1 [01e84646075b255eab0a34d872336a89, 28f242c4515... " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "entity_df = pd.read_parquet(\n", + " f\"{GRAPHRAG_FOLDER}/create_final_entities.parquet\",\n", + " columns=[\n", + " \"name\",\n", + " \"type\",\n", + " \"description\",\n", + " \"human_readable_id\",\n", + " \"id\",\n", + " \"description_embedding\",\n", + " \"text_unit_ids\",\n", + " ],\n", + ")\n", + "entity_df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "1d038114-0714-48ee-a48a-c421cd539661", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'_contains_updates': True, 'properties_set': 831}\n", + "277 rows in 0.6978070735931396 s.\n" + ] + }, + { + "data": { + "text/plain": [ + "277" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "entity_statement = \"\"\"\n", + "MERGE (e:__Entity__ {id:value.id})\n", + "SET e += value {.human_readable_id, .description, name:replace(value.name,'\"','')}\n", + "WITH e, value\n", + "CALL db.create.setNodeVectorProperty(e, \"description_embedding\", value.description_embedding)\n", + "CALL apoc.create.addLabels(e, case when coalesce(value.type,\"\") = \"\" then [] else [apoc.text.upperCamelCase(replace(value.type,'\"',''))] end) yield node\n", + "UNWIND value.text_unit_ids AS text_unit\n", + "MATCH (c:__Chunk__ {id:text_unit})\n", + "MERGE (c)-[:HAS_ENTITY]->(e)\n", + "\"\"\"\n", + "\n", + "batched_import(entity_statement, entity_df)" + ] + }, + { + "cell_type": "markdown", + "id": "018d4f87", + "metadata": {}, + "source": [ + "### Import Relationships\n", + "\n", + "For the relationships we find the source and target node by name, using the base `__Entity__` type.\n", + "After creating the `RELATED` relationships, we set the description as attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "b347a047", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcetargetidrankweighthuman_readable_iddescriptiontext_unit_ids
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" + ], + "text/plain": [ + " id level title text_unit_ids \\\n", + "0 2 0 Community 2 [0546d296a4d3bb0486bd0c94c01dc9be,0d6bc6e701a0... \n", + "1 4 0 Community 4 [054bdcba0a3690b43609d9226a47f84d,3a450ed2b7fb... \n", + "\n", + " relationship_ids \n", + "0 [ba481175ee1d4329bf07757a30abd3a1, 8d8da35190b... \n", + "1 [929f30875e1744b49e7b416eaf5a790c, 4920fda0318... " + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "community_df = pd.read_parquet(\n", + " f\"{GRAPHRAG_FOLDER}/create_final_communities.parquet\",\n", + " columns=[\"id\", \"level\", \"title\", \"text_unit_ids\", \"relationship_ids\"],\n", + ")\n", + "\n", + "community_df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "1351f7e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'_contains_updates': True, 'properties_set': 94}\n", + "47 rows in 0.07877922058105469 s.\n" + ] + }, + { + "data": { + "text/plain": [ + "47" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "statement = \"\"\"\n", + "MERGE (c:__Community__ {community:value.id})\n", + "SET c += value {.level, .title}\n", + "/*\n", + "UNWIND value.text_unit_ids as text_unit_id\n", + "MATCH (t:__Chunk__ {id:text_unit_id})\n", + "MERGE (c)-[:HAS_CHUNK]->(t)\n", + "WITH distinct c, value\n", + "*/\n", + "WITH *\n", + "UNWIND value.relationship_ids as rel_id\n", + "MATCH (start:__Entity__)-[:RELATED {id:rel_id}]->(end:__Entity__)\n", + "MERGE (start)-[:IN_COMMUNITY]->(c)\n", + "MERGE (end)-[:IN_COMMUNITY]->(c)\n", + "RETURn count(distinct c) as createdCommunities\n", + "\"\"\"\n", + "\n", + "batched_import(statement, community_df)" + ] + }, + { + "cell_type": "markdown", + "id": "dd9adf50", + "metadata": {}, + "source": [ + "### Importing Community Reports\n", + "\n", + "Fo the community reports we create nodes for each communitiy set the id, community, level, title, summary, rank, and rank_explanation and connect them to the entities they are about.\n", + "For the findings we create the findings in context of the communities." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "1be9e7a9-69ee-406b-bce5-95a9c41ecffe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idcommunityleveltitlesummaryfindingsrankrank_explanationfull_content
0e7822326-4da8-4954-afa9-be7f4f5791a5422Scrooge's Supernatural Encounters: Marley's Gh...This report delves into the pivotal supernatur...[{'explanation': 'Marley's Ghost plays a cruci...8.0The impact severity rating is high due to the ...# Scrooge's Supernatural Encounters: Marley's ...
18a5afac1-99ef-4f01-a1b1-f044ce392ff9432The Ghost's Influence on Scrooge's TransformationThis report delves into the pivotal role of 'T...[{'explanation': 'The Ghost, identified at tim...8.5The impact severity rating is high due to the ...# The Ghost's Influence on Scrooge's Transform...
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" + ], + "text/plain": [ + " id community level \\\n", + "0 e7822326-4da8-4954-afa9-be7f4f5791a5 42 2 \n", + "1 8a5afac1-99ef-4f01-a1b1-f044ce392ff9 43 2 \n", + "\n", + " title \\\n", + "0 Scrooge's Supernatural Encounters: Marley's Gh... \n", + "1 The Ghost's Influence on Scrooge's Transformation \n", + "\n", + " summary \\\n", + "0 This report delves into the pivotal supernatur... \n", + "1 This report delves into the pivotal role of 'T... \n", + "\n", + " findings rank \\\n", + "0 [{'explanation': 'Marley's Ghost plays a cruci... 8.0 \n", + "1 [{'explanation': 'The Ghost, identified at tim... 8.5 \n", + "\n", + " rank_explanation \\\n", + "0 The impact severity rating is high due to the ... \n", + "1 The impact severity rating is high due to the ... \n", + "\n", + " full_content \n", + "0 # Scrooge's Supernatural Encounters: Marley's ... \n", + "1 # The Ghost's Influence on Scrooge's Transform... " + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "community_report_df = pd.read_parquet(\n", + " f\"{GRAPHRAG_FOLDER}/create_final_community_reports.parquet\",\n", + " columns=[\n", + " \"id\",\n", + " \"community\",\n", + " \"level\",\n", + " \"title\",\n", + " \"summary\",\n", + " \"findings\",\n", + " \"rank\",\n", + " \"rank_explanation\",\n", + " \"full_content\",\n", + " ],\n", + ")\n", + "community_report_df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "5c6ed591-f98c-4403-9fde-8d4cb4c01cca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'_contains_updates': True, 'properties_set': 729}\n", + "47 rows in 0.02472519874572754 s.\n" + ] + }, + { + "data": { + "text/plain": [ + "47" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import communities\n", + "community_statement = \"\"\"\n", + "MERGE (c:__Community__ {community:value.community})\n", + "SET c += value {.level, .title, .rank, .rank_explanation, .full_content, .summary}\n", + "WITH c, value\n", + "UNWIND range(0, size(value.findings)-1) AS finding_idx\n", + "WITH c, value, finding_idx, value.findings[finding_idx] as finding\n", + "MERGE (c)-[:HAS_FINDING]->(f:Finding {id:finding_idx})\n", + "SET f += finding\n", + "\"\"\"\n", + "batched_import(community_statement, community_report_df)" + ] + }, + { + "cell_type": "markdown", + "id": "50a1a24a", + "metadata": {}, + "source": [ + "### Importing Covariates\n", + "\n", + "Covariates are for instance claims on entities, we connect them to the chunks where they originate from." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "523bed92-d12c-4fc4-aa44-6c62321b36bc", + "metadata": {}, + "outputs": [], + "source": [ + "cov_df = (pd.read_parquet(f\"{GRAPHRAG_FOLDER}/create_final_covariates.parquet\"),)\n", + "# columns=[\"id\",\"text_unit_id\"])\n", + "cov_df.head(2)\n", + "# Subject id do not match entity ids" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e064234-5fce-448e-8bb4-ab2f35699049", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'_contains_updates': True, 'labels_added': 89, 'relationships_created': 89, 'nodes_created': 89, 'properties_set': 1061}\n", + "89 rows in 0.13370895385742188 s.\n" + ] + }, + { + "data": { + "text/plain": [ + "89" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import covariates\n", + "cov_statement = \"\"\"\n", + "MERGE (c:__Covariate__ {id:value.id})\n", + "SET c += apoc.map.clean(value, [\"text_unit_id\", \"document_ids\", \"n_tokens\"], [NULL, \"\"])\n", + "WITH c, value\n", + "MATCH (ch:__Chunk__ {id: value.text_unit_id})\n", + "MERGE (ch)-[:HAS_COVARIATE]->(c)\n", + "\"\"\"\n", + "batched_import(cov_statement, cov_df)" + ] + }, + { + "cell_type": "markdown", + "id": "00340bae", + "metadata": {}, + "source": [ + "### Visualize your data\n", + "\n", + "You can now [Open] Neo4j on Aura, you need to log in with either SSO or your credentials.\n", + "\n", + "Or open https://workspace-preview.neo4j.io and connect to your local instance, remember the URI is `neo4j://localhost` and `neo4j` as username and `password` as password.\n", + "\n", + "In \"Explore\" you can explore by using visual graph patterns and then explore and expand further.\n", + "\n", + "In \"Query\", you can open the left sidebar and explore by clicking on the nodes and relationships.\n", + "You can also use the co-pilot to generate Cypher queries for your, here are some examples.\n", + "\n", + "#### Show a few `__Entity__` nodes and their relationships (Entity Graph)\n", + "\n", + "```cypher\n", + "MATCH path = (:__Entity__)-[:RELATED]->(:__Entity__)\n", + "RETURN path LIMIT 200\n", + "```\n", + "\n", + "#### Show the Chunks and the Document (Lexical Graph)\n", + "\n", + "```cypher\n", + "MATCH (d:__Document__) WITH d LIMIT 1\n", + "MATCH path = (d)<-[:PART_OF]-(c:__Chunk__)\n", + "RETURN path LIMIT 100\n", + "```\n", + "\n", + "#### Show a Community and it's Entities\n", + "\n", + "```cypher\n", + "MATCH (c:__Community__) WITH c LIMIT 1\n", + "MATCH path = (c)<-[:IN_COMMUNITY]-()-[:RELATED]-(:__Entity__)\n", + "RETURN path LIMIT 100\n", + "```\n", + "\n", + "#### Show everything\n", + "\n", + "```cypher\n", + "MATCH (d:__Document__) WITH d LIMIT 1\n", + "MATCH path = (d)<-[:PART_OF]-(:__Chunk__)-[:HAS_ENTIY]->()-[:RELATED]-()-[:IN_COMMUNITY]->()\n", + "RETURN path LIMIT 250\n", + "```\n", + "\n", + "We showed the visualization of this last query at the beginning." + ] + }, + { + "cell_type": "markdown", + "id": "a0aa8529", + "metadata": {}, + "source": [ + "If you have questions, feel free to reach out in the GraphRAG discord server: \n", + "https://discord.gg/graphrag" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples_notebooks/community/yfiles-jupyter-graphs/graph-visualization.ipynb b/examples_notebooks/community_contrib/yfiles-jupyter-graphs/graph-visualization.ipynb similarity index 100% rename from examples_notebooks/community/yfiles-jupyter-graphs/graph-visualization.ipynb rename to examples_notebooks/community_contrib/yfiles-jupyter-graphs/graph-visualization.ipynb diff --git a/graphrag/index/verbs/graph/merge/merge_graphs.py b/graphrag/index/verbs/graph/merge/merge_graphs.py index 8ab3fa47f7..c93037baaf 100644 --- a/graphrag/index/verbs/graph/merge/merge_graphs.py +++ b/graphrag/index/verbs/graph/merge/merge_graphs.py @@ -129,7 +129,13 @@ def merge_edges( if not target_graph.has_edge(source, target): target_graph.add_edge(source, target, **(edge_data or {})) else: - merge_attributes(target_graph.edges[(source, target)], edge_data, edge_ops) + merge_attributes( + target_graph.edges[ + (source, target) # noqa: RUF031 Parenthesis needed, false positive + ], + edge_data, + edge_ops, + ) def merge_attributes( diff --git a/poetry.lock b/poetry.lock index c39b4a0089..e7e06a097b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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