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update llm graph transformer documentation (#27905)
This commit is contained in:
Tomaz Bratanic
GitHub
parent
31f4fb790d
commit
a3bbbe6a86
@ -44,6 +44,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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@ -105,7 +108,7 @@
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"os.environ[\"NEO4J_USERNAME\"] = \"neo4j\"\n",
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"os.environ[\"NEO4J_PASSWORD\"] = \"password\"\n",
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"\n",
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"graph = Neo4jGraph()"
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"graph = Neo4jGraph(refresh_schema=False)"
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]
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},
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{
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@ -149,8 +152,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Nodes:[Node(id='Marie Curie', type='Person'), Node(id='Pierre Curie', type='Person'), Node(id='University Of Paris', type='Organization')]\n",
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"Relationships:[Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='Pierre Curie', type='Person'), type='MARRIED'), Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='University Of Paris', type='Organization'), type='PROFESSOR')]\n"
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"Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n",
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"Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='MARRIED', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='PROFESSOR', properties={})]\n"
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]
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}
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],
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@ -191,8 +194,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Nodes:[Node(id='Marie Curie', type='Person'), Node(id='Pierre Curie', type='Person'), Node(id='University Of Paris', type='Organization')]\n",
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"Relationships:[Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='Pierre Curie', type='Person'), type='SPOUSE'), Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='University Of Paris', type='Organization'), type='WORKED_AT')]\n"
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"Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n",
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"Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]\n"
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]
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}
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],
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@ -209,6 +212,44 @@
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"print(f\"Relationships:{graph_documents_filtered[0].relationships}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To define the graph schema more precisely, consider using a three-tuple approach for relationships. In this approach, each tuple consists of three elements: the source node, the relationship type, and the target node."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={})]\n",
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"Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]\n"
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]
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}
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],
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"source": [
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"allowed_relationships = [\n",
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" (\"Person\", \"SPOUSE\", \"Person\"),\n",
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" (\"Person\", \"NATIONALITY\", \"Country\"),\n",
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" (\"Person\", \"WORKED_AT\", \"Organization\"),\n",
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"]\n",
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"\n",
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"llm_transformer_tuple = LLMGraphTransformer(\n",
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" llm=llm,\n",
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" allowed_nodes=[\"Person\", \"Country\", \"Organization\"],\n",
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" allowed_relationships=allowed_relationships,\n",
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")\n",
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"llm_transformer_tuple = llm_transformer_filtered.convert_to_graph_documents(documents)\n",
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"print(f\"Nodes:{graph_documents_filtered[0].nodes}\")\n",
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"print(f\"Relationships:{graph_documents_filtered[0].relationships}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -229,15 +270,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Nodes:[Node(id='Marie Curie', type='Person', properties={'born_year': '1867'}), Node(id='Pierre Curie', type='Person'), Node(id='University Of Paris', type='Organization')]\n",
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"Relationships:[Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='Pierre Curie', type='Person'), type='SPOUSE'), Relationship(source=Node(id='Marie Curie', type='Person'), target=Node(id='University Of Paris', type='Organization'), type='WORKED_AT')]\n"
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"Nodes:[Node(id='Marie Curie', type='Person', properties={'born_year': '1867'}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Organization', properties={}), Node(id='Poland', type='Country', properties={}), Node(id='France', type='Country', properties={})]\n",
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"Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Poland', type='Country', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='France', type='Country', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Pierre Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Organization', properties={}), type='WORKED_AT', properties={})]\n"
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]
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}
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],
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@ -264,12 +305,71 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"graph.add_graph_documents(graph_documents_props)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Most graph databases support indexes to optimize data import and retrieval. Since we might not know all the node labels in advance, we can handle this by adding a secondary base label to each node using the `baseEntityLabel` parameter."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"graph.add_graph_documents(graph_documents, baseEntityLabel=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Results will look like:\n",
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"\n",
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"\n",
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"\n",
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"The final option is to also import the source documents for the extracted nodes and relationships. This approach lets us track which documents each entity appeared in."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"graph.add_graph_documents(graph_documents, include_source=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Graph will have the following structure:\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this visualization, the source document is highlighted in blue, with all entities extracted from it connected by `MENTIONS` relationships."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -288,7 +388,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.1"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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