GraphRAG Implementation with LlamaIndex - V2¶

GraphRAG (Graphs + Retrieval Augmented Generation) combines the strengths of Retrieval Augmented Generation (RAG) and Query-Focused Summarization (QFS) to effectively handle complex queries over large text datasets. While RAG excels in fetching precise information, it struggles with broader queries that require thematic understanding, a challenge that QFS addresses but cannot scale well. GraphRAG integrates these approaches to offer responsive and thorough querying capabilities across extensive, diverse text corpora.

This notebook provides guidance on constructing the GraphRAG pipeline using the LlamaIndex PropertyGraph abstractions using Neo4J.

This notebook updates the GraphRAG pipeline to v2. If you haven’t checked v1 yet, you can find it here. Following are the updates to the existing implementation:

1. Integrate with Neo4J Graph database.
2. Embedding based retrieval.

Installation¶

graspologic is used to use hierarchical_leiden for building communities.

In [ ]:

!pip install llama-index llama-index-graph-stores-neo4j graspologic numpy==1.24.4 scipy==1.12.0

!pip install llama-index llama-index-graph-stores-neo4j graspologic numpy==1.24.4 scipy==1.12.0

Load Data¶

We will use a sample news article dataset retrieved from Diffbot, which Tomaz has conveniently made available on GitHub for easy access.

The dataset contains 2,500 samples; for ease of experimentation, we will use 50 of these samples, which include the title and text of news articles.

In [ ]:

import pandas as pd
from llama_index.core import Document

news = pd.read_csv(
    "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/news_articles.csv"
)[:50]

news.head()

import pandas as pd from llama_index.core import Document news = pd.read_csv( "https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/news_articles.csv" )[:50] news.head()

Out[ ]:

	title	date	text
0	Chevron: Best Of Breed	2031-04-06T01:36:32.000000000+00:00	JHVEPhoto Like many companies in the O&G secto...
1	FirstEnergy (NYSE:FE) Posts Earnings Results	2030-04-29T06:55:28.000000000+00:00	FirstEnergy (NYSE:FE – Get Rating) posted its ...
2	Dáil almost suspended after Sinn Féin TD put p...	2023-06-15T14:32:11.000000000+00:00	The Dáil was almost suspended on Thursday afte...
3	Epic’s latest tool can animate hyperrealistic ...	2023-06-15T14:00:00.000000000+00:00	Today, Epic is releasing a new tool designed t...
4	EU to Ban Huawei, ZTE from Internal Commission...	2023-06-15T13:50:00.000000000+00:00	The European Commission is planning to ban equ...

Prepare documents as required by LlamaIndex

In [ ]:

documents = [
    Document(text=f"{row['title']}: {row['text']}")
    for i, row in news.iterrows()
]

documents = [ Document(text=f"{row['title']}: {row['text']}") for i, row in news.iterrows() ]

Setup API Key and LLM¶

In [ ]:

import os

os.environ["OPENAI_API_KEY"] = "sk-.."

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4")

import os os.environ["OPENAI_API_KEY"] = "sk-.." from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-4")

GraphRAGExtractor¶

The GraphRAGExtractor class is designed to extract triples (subject-relation-object) from text and enrich them by adding descriptions for entities and relationships to their properties using an LLM.

This functionality is similar to that of the SimpleLLMPathExtractor, but includes additional enhancements to handle entity, relationship descriptions. For guidance on implementation, you may look at similar existing extractors.

Here's a breakdown of its functionality:

Key Components:

1. llm: The language model used for extraction.
2. extract_prompt: A prompt template used to guide the LLM in extracting information.
3. parse_fn: A function to parse the LLM's output into structured data.
4. max_paths_per_chunk: Limits the number of triples extracted per text chunk.
5. num_workers: For parallel processing of multiple text nodes.

Main Methods:

1. __call__: The entry point for processing a list of text nodes.
2. acall: An asynchronous version of call for improved performance.
3. _aextract: The core method that processes each individual node.

Extraction Process:

For each input node (chunk of text):

1. It sends the text to the LLM along with the extraction prompt.
2. The LLM's response is parsed to extract entities, relationships, descriptions for entities and relations.
3. Entities are converted into EntityNode objects. Entity description is stored in metadata
4. Relationships are converted into Relation objects. Relationship description is stored in metadata.
5. These are added to the node's metadata under KG_NODES_KEY and KG_RELATIONS_KEY.

NOTE: In the current implementation, we are using only relationship descriptions. In the next implementation, we will utilize entity descriptions during the retrieval stage.

In [ ]:

import asyncio
import nest_asyncio

nest_asyncio.apply()

from typing import Any, List, Callable, Optional, Union, Dict
from IPython.display import Markdown, display

from llama_index.core.async_utils import run_jobs
from llama_index.core.indices.property_graph.utils import (
    default_parse_triplets_fn,
)
from llama_index.core.graph_stores.types import (
    EntityNode,
    KG_NODES_KEY,
    KG_RELATIONS_KEY,
    Relation,
)
from llama_index.core.llms.llm import LLM
from llama_index.core.prompts import PromptTemplate
from llama_index.core.prompts.default_prompts import (
    DEFAULT_KG_TRIPLET_EXTRACT_PROMPT,
)
from llama_index.core.schema import TransformComponent, BaseNode
from llama_index.core.bridge.pydantic import BaseModel, Field


class GraphRAGExtractor(TransformComponent):
    """Extract triples from a graph.

    Uses an LLM and a simple prompt + output parsing to extract paths (i.e. triples) and entity, relation descriptions from text.

    Args:
        llm (LLM):
            The language model to use.
        extract_prompt (Union[str, PromptTemplate]):
            The prompt to use for extracting triples.
        parse_fn (callable):
            A function to parse the output of the language model.
        num_workers (int):
            The number of workers to use for parallel processing.
        max_paths_per_chunk (int):
            The maximum number of paths to extract per chunk.
    """

    llm: LLM
    extract_prompt: PromptTemplate
    parse_fn: Callable
    num_workers: int
    max_paths_per_chunk: int

    def __init__(
        self,
        llm: Optional[LLM] = None,
        extract_prompt: Optional[Union[str, PromptTemplate]] = None,
        parse_fn: Callable = default_parse_triplets_fn,
        max_paths_per_chunk: int = 10,
        num_workers: int = 4,
    ) -> None:
        """Init params."""
        from llama_index.core import Settings

        if isinstance(extract_prompt, str):
            extract_prompt = PromptTemplate(extract_prompt)

        super().__init__(
            llm=llm or Settings.llm,
            extract_prompt=extract_prompt or DEFAULT_KG_TRIPLET_EXTRACT_PROMPT,
            parse_fn=parse_fn,
            num_workers=num_workers,
            max_paths_per_chunk=max_paths_per_chunk,
        )

    @classmethod
    def class_name(cls) -> str:
        return "GraphExtractor"

    def __call__(
        self, nodes: List[BaseNode], show_progress: bool = False, **kwargs: Any
    ) -> List[BaseNode]:
        """Extract triples from nodes."""
        return asyncio.run(
            self.acall(nodes, show_progress=show_progress, **kwargs)
        )

    async def _aextract(self, node: BaseNode) -> BaseNode:
        """Extract triples from a node."""
        assert hasattr(node, "text")

        text = node.get_content(metadata_mode="llm")
        try:
            llm_response = await self.llm.apredict(
                self.extract_prompt,
                text=text,
                max_knowledge_triplets=self.max_paths_per_chunk,
            )
            entities, entities_relationship = self.parse_fn(llm_response)
        except ValueError:
            entities = []
            entities_relationship = []

        existing_nodes = node.metadata.pop(KG_NODES_KEY, [])
        existing_relations = node.metadata.pop(KG_RELATIONS_KEY, [])
        entity_metadata = node.metadata.copy()
        for entity, entity_type, description in entities:
            entity_metadata["entity_description"] = description
            entity_node = EntityNode(
                name=entity, label=entity_type, properties=entity_metadata
            )
            existing_nodes.append(entity_node)

        relation_metadata = node.metadata.copy()
        for triple in entities_relationship:
            subj, obj, rel, description = triple
            relation_metadata["relationship_description"] = description
            rel_node = Relation(
                label=rel,
                source_id=subj,
                target_id=obj,
                properties=relation_metadata,
            )

            existing_relations.append(rel_node)

        node.metadata[KG_NODES_KEY] = existing_nodes
        node.metadata[KG_RELATIONS_KEY] = existing_relations
        return node

    async def acall(
        self, nodes: List[BaseNode], show_progress: bool = False, **kwargs: Any
    ) -> List[BaseNode]:
        """Extract triples from nodes async."""
        jobs = []
        for node in nodes:
            jobs.append(self._aextract(node))

        return await run_jobs(
            jobs,
            workers=self.num_workers,
            show_progress=show_progress,
            desc="Extracting paths from text",
        )

import asyncio import nest_asyncio nest_asyncio.apply() from typing import Any, List, Callable, Optional, Union, Dict from IPython.display import Markdown, display from llama_index.core.async_utils import run_jobs from llama_index.core.indices.property_graph.utils import ( default_parse_triplets_fn, ) from llama_index.core.graph_stores.types import ( EntityNode, KG_NODES_KEY, KG_RELATIONS_KEY, Relation, ) from llama_index.core.llms.llm import LLM from llama_index.core.prompts import PromptTemplate from llama_index.core.prompts.default_prompts import ( DEFAULT_KG_TRIPLET_EXTRACT_PROMPT, ) from llama_index.core.schema import TransformComponent, BaseNode from llama_index.core.bridge.pydantic import BaseModel, Field class GraphRAGExtractor(TransformComponent): """Extract triples from a graph. Uses an LLM and a simple prompt + output parsing to extract paths (i.e. triples) and entity, relation descriptions from text. Args: llm (LLM): The language model to use. extract_prompt (Union[str, PromptTemplate]): The prompt to use for extracting triples. parse_fn (callable): A function to parse the output of the language model. num_workers (int): The number of workers to use for parallel processing. max_paths_per_chunk (int): The maximum number of paths to extract per chunk. """ llm: LLM extract_prompt: PromptTemplate parse_fn: Callable num_workers: int max_paths_per_chunk: int def __init__( self, llm: Optional[LLM] = None, extract_prompt: Optional[Union[str, PromptTemplate]] = None, parse_fn: Callable = default_parse_triplets_fn, max_paths_per_chunk: int = 10, num_workers: int = 4, ) -> None: """Init params.""" from llama_index.core import Settings if isinstance(extract_prompt, str): extract_prompt = PromptTemplate(extract_prompt) super().__init__( llm=llm or Settings.llm, extract_prompt=extract_prompt or DEFAULT_KG_TRIPLET_EXTRACT_PROMPT, parse_fn=parse_fn, num_workers=num_workers, max_paths_per_chunk=max_paths_per_chunk, ) @classmethod def class_name(cls) -> str: return "GraphExtractor" def __call__( self, nodes: List[BaseNode], show_progress: bool = False, **kwargs: Any ) -> List[BaseNode]: """Extract triples from nodes.""" return asyncio.run( self.acall(nodes, show_progress=show_progress, **kwargs) ) async def _aextract(self, node: BaseNode) -> BaseNode: """Extract triples from a node.""" assert hasattr(node, "text") text = node.get_content(metadata_mode="llm") try: llm_response = await self.llm.apredict( self.extract_prompt, text=text, max_knowledge_triplets=self.max_paths_per_chunk, ) entities, entities_relationship = self.parse_fn(llm_response) except ValueError: entities = [] entities_relationship = [] existing_nodes = node.metadata.pop(KG_NODES_KEY, []) existing_relations = node.metadata.pop(KG_RELATIONS_KEY, []) entity_metadata = node.metadata.copy() for entity, entity_type, description in entities: entity_metadata["entity_description"] = description entity_node = EntityNode( name=entity, label=entity_type, properties=entity_metadata ) existing_nodes.append(entity_node) relation_metadata = node.metadata.copy() for triple in entities_relationship: subj, obj, rel, description = triple relation_metadata["relationship_description"] = description rel_node = Relation( label=rel, source_id=subj, target_id=obj, properties=relation_metadata, ) existing_relations.append(rel_node) node.metadata[KG_NODES_KEY] = existing_nodes node.metadata[KG_RELATIONS_KEY] = existing_relations return node async def acall( self, nodes: List[BaseNode], show_progress: bool = False, **kwargs: Any ) -> List[BaseNode]: """Extract triples from nodes async.""" jobs = [] for node in nodes: jobs.append(self._aextract(node)) return await run_jobs( jobs, workers=self.num_workers, show_progress=show_progress, desc="Extracting paths from text", )

GraphRAGStore¶

The GraphRAGStore class is an extension of the Neo4jPropertyGraphStoreclass, designed to implement GraphRAG pipeline. Here's a breakdown of its key components and functions:

The class uses community detection algorithms to group related nodes in the graph and then it generates summaries for each community using an LLM.

Key Methods:

build_communities():

1. Converts the internal graph representation to a NetworkX graph.

2. Applies the hierarchical Leiden algorithm for community detection.

3. Collects detailed information about each community.

4. Generates summaries for each community.

generate_community_summary(text):

1. Uses LLM to generate a summary of the relationships in a community.
2. The summary includes entity names and a synthesis of relationship descriptions.

_create_nx_graph():

1. Converts the internal graph representation to a NetworkX graph for community detection.

_collect_community_info(nx_graph, clusters):

1. Collects detailed information about each node based on its community.
2. Creates a string representation of each relationship within a community.

_summarize_communities(community_info):

1. Generates and stores summaries for each community using LLM.

get_community_summaries():

1. Returns the community summaries by building them if not already done.

In [ ]:

import re
import networkx as nx
from graspologic.partition import hierarchical_leiden
from collections import defaultdict

from llama_index.core.llms import ChatMessage
from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore


class GraphRAGStore(Neo4jPropertyGraphStore):
    community_summary = {}
    entity_info = None
    max_cluster_size = 5

    def generate_community_summary(self, text):
        """Generate summary for a given text using an LLM."""
        messages = [
            ChatMessage(
                role="system",
                content=(
                    "You are provided with a set of relationships from a knowledge graph, each represented as "
                    "entity1->entity2->relation->relationship_description. Your task is to create a summary of these "
                    "relationships. The summary should include the names of the entities involved and a concise synthesis "
                    "of the relationship descriptions. The goal is to capture the most critical and relevant details that "
                    "highlight the nature and significance of each relationship. Ensure that the summary is coherent and "
                    "integrates the information in a way that emphasizes the key aspects of the relationships."
                ),
            ),
            ChatMessage(role="user", content=text),
        ]
        response = OpenAI().chat(messages)
        clean_response = re.sub(r"^assistant:\s*", "", str(response)).strip()
        return clean_response

    def build_communities(self):
        """Builds communities from the graph and summarizes them."""
        nx_graph = self._create_nx_graph()
        community_hierarchical_clusters = hierarchical_leiden(
            nx_graph, max_cluster_size=self.max_cluster_size
        )
        self.entity_info, community_info = self._collect_community_info(
            nx_graph, community_hierarchical_clusters
        )
        self._summarize_communities(community_info)

    def _create_nx_graph(self):
        """Converts internal graph representation to NetworkX graph."""
        nx_graph = nx.Graph()
        triplets = self.get_triplets()
        for entity1, relation, entity2 in triplets:
            nx_graph.add_node(entity1.name)
            nx_graph.add_node(entity2.name)
            nx_graph.add_edge(
                relation.source_id,
                relation.target_id,
                relationship=relation.label,
                description=relation.properties["relationship_description"],
            )
        return nx_graph

    def _collect_community_info(self, nx_graph, clusters):
        """
        Collect information for each node based on their community,
        allowing entities to belong to multiple clusters.
        """
        entity_info = defaultdict(set)
        community_info = defaultdict(list)

        for item in clusters:
            node = item.node
            cluster_id = item.cluster

            # Update entity_info
            entity_info[node].add(cluster_id)

            for neighbor in nx_graph.neighbors(node):
                edge_data = nx_graph.get_edge_data(node, neighbor)
                if edge_data:
                    detail = f"{node} -> {neighbor} -> {edge_data['relationship']} -> {edge_data['description']}"
                    community_info[cluster_id].append(detail)

        # Convert sets to lists for easier serialization if needed
        entity_info = {k: list(v) for k, v in entity_info.items()}

        return dict(entity_info), dict(community_info)

    def _summarize_communities(self, community_info):
        """Generate and store summaries for each community."""
        for community_id, details in community_info.items():
            details_text = (
                "\n".join(details) + "."
            )  # Ensure it ends with a period
            self.community_summary[
                community_id
            ] = self.generate_community_summary(details_text)

    def get_community_summaries(self):
        """Returns the community summaries, building them if not already done."""
        if not self.community_summary:
            self.build_communities()
        return self.community_summary

import re import networkx as nx from graspologic.partition import hierarchical_leiden from collections import defaultdict from llama_index.core.llms import ChatMessage from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore class GraphRAGStore(Neo4jPropertyGraphStore): community_summary = {} entity_info = None max_cluster_size = 5 def generate_community_summary(self, text): """Generate summary for a given text using an LLM.""" messages = [ ChatMessage( role="system", content=( "You are provided with a set of relationships from a knowledge graph, each represented as " "entity1->entity2->relation->relationship_description. Your task is to create a summary of these " "relationships. The summary should include the names of the entities involved and a concise synthesis " "of the relationship descriptions. The goal is to capture the most critical and relevant details that " "highlight the nature and significance of each relationship. Ensure that the summary is coherent and " "integrates the information in a way that emphasizes the key aspects of the relationships." ), ), ChatMessage(role="user", content=text), ] response = OpenAI().chat(messages) clean_response = re.sub(r"^assistant:\s*", "", str(response)).strip() return clean_response def build_communities(self): """Builds communities from the graph and summarizes them.""" nx_graph = self._create_nx_graph() community_hierarchical_clusters = hierarchical_leiden( nx_graph, max_cluster_size=self.max_cluster_size ) self.entity_info, community_info = self._collect_community_info( nx_graph, community_hierarchical_clusters ) self._summarize_communities(community_info) def _create_nx_graph(self): """Converts internal graph representation to NetworkX graph.""" nx_graph = nx.Graph() triplets = self.get_triplets() for entity1, relation, entity2 in triplets: nx_graph.add_node(entity1.name) nx_graph.add_node(entity2.name) nx_graph.add_edge( relation.source_id, relation.target_id, relationship=relation.label, description=relation.properties["relationship_description"], ) return nx_graph def _collect_community_info(self, nx_graph, clusters): """ Collect information for each node based on their community, allowing entities to belong to multiple clusters. """ entity_info = defaultdict(set) community_info = defaultdict(list) for item in clusters: node = item.node cluster_id = item.cluster # Update entity_info entity_info[node].add(cluster_id) for neighbor in nx_graph.neighbors(node): edge_data = nx_graph.get_edge_data(node, neighbor) if edge_data: detail = f"{node} -> {neighbor} -> {edge_data['relationship']} -> {edge_data['description']}" community_info[cluster_id].append(detail) # Convert sets to lists for easier serialization if needed entity_info = {k: list(v) for k, v in entity_info.items()} return dict(entity_info), dict(community_info) def _summarize_communities(self, community_info): """Generate and store summaries for each community.""" for community_id, details in community_info.items(): details_text = ( "\n".join(details) + "." ) # Ensure it ends with a period self.community_summary[ community_id ] = self.generate_community_summary(details_text) def get_community_summaries(self): """Returns the community summaries, building them if not already done.""" if not self.community_summary: self.build_communities() return self.community_summary

GraphRAGQueryEngine¶

The GraphRAGQueryEngine class is a custom query engine designed to process queries using the GraphRAG approach. It leverages the community summaries generated by the GraphRAGStore to answer user queries. Here's a breakdown of its functionality:

Main Components:

graph_store: An instance of GraphRAGStore, which contains the community summaries. llm: A Language Model (LLM) used for generating and aggregating answers.

Key Methods:

custom_query(query_str: str)

1. This is the main entry point for processing a query. It retrieves community summaries, generates answers from each summary, and then aggregates these answers into a final response.

generate_answer_from_summary(community_summary, query):

1. Generates an answer for the query based on a single community summary. Uses the LLM to interpret the community summary in the context of the query.

aggregate_answers(community_answers):

1. Combines individual answers from different communities into a coherent final response.
2. Uses the LLM to synthesize multiple perspectives into a single, concise answer.

Query Processing Flow:

1. Retrieve community summaries from the graph store.
2. For each community summary, generate a specific answer to the query.
3. Aggregate all community-specific answers into a final, coherent response.

Example usage:

query_engine = GraphRAGQueryEngine(graph_store=graph_store, llm=llm)

response = query_engine.query("query")

In [ ]:

from llama_index.core.query_engine import CustomQueryEngine
from llama_index.core.llms import LLM
from llama_index.core import PropertyGraphIndex

import re


class GraphRAGQueryEngine(CustomQueryEngine):
    graph_store: GraphRAGStore
    index: PropertyGraphIndex
    llm: LLM
    similarity_top_k: int = 20

    def custom_query(self, query_str: str) -> str:
        """Process all community summaries to generate answers to a specific query."""

        entities = self.get_entities(query_str, self.similarity_top_k)

        community_ids = self.retrieve_entity_communities(
            self.graph_store.entity_info, entities
        )
        community_summaries = self.graph_store.get_community_summaries()
        community_answers = [
            self.generate_answer_from_summary(community_summary, query_str)
            for id, community_summary in community_summaries.items()
            if id in community_ids
        ]

        final_answer = self.aggregate_answers(community_answers)
        return final_answer

    def get_entities(self, query_str, similarity_top_k):
        nodes_retrieved = self.index.as_retriever(
            similarity_top_k=similarity_top_k
        ).retrieve(query_str)

        enitites = set()
        pattern = r"(\w+(?:\s+\w+)*)\s*\({[^}]*}\)\s*->\s*([^(]+?)\s*\({[^}]*}\)\s*->\s*(\w+(?:\s+\w+)*)"

        for node in nodes_retrieved:
            matches = re.findall(pattern, node.text, re.DOTALL)

            for match in matches:
                subject = match[0]
                obj = match[2]
                enitites.add(subject)
                enitites.add(obj)

        return list(enitites)

    def retrieve_entity_communities(self, entity_info, entities):
        """
        Retrieve cluster information for given entities, allowing for multiple clusters per entity.

        Args:
        entity_info (dict): Dictionary mapping entities to their cluster IDs (list).
        entities (list): List of entity names to retrieve information for.

        Returns:
        List of community or cluster IDs to which an entity belongs.
        """
        community_ids = []

        for entity in entities:
            if entity in entity_info:
                community_ids.extend(entity_info[entity])

        return list(set(community_ids))

    def generate_answer_from_summary(self, community_summary, query):
        """Generate an answer from a community summary based on a given query using LLM."""
        prompt = (
            f"Given the community summary: {community_summary}, "
            f"how would you answer the following query? Query: {query}"
        )
        messages = [
            ChatMessage(role="system", content=prompt),
            ChatMessage(
                role="user",
                content="I need an answer based on the above information.",
            ),
        ]
        response = self.llm.chat(messages)
        cleaned_response = re.sub(r"^assistant:\s*", "", str(response)).strip()
        return cleaned_response

    def aggregate_answers(self, community_answers):
        """Aggregate individual community answers into a final, coherent response."""
        # intermediate_text = " ".join(community_answers)
        prompt = "Combine the following intermediate answers into a final, concise response."
        messages = [
            ChatMessage(role="system", content=prompt),
            ChatMessage(
                role="user",
                content=f"Intermediate answers: {community_answers}",
            ),
        ]
        final_response = self.llm.chat(messages)
        cleaned_final_response = re.sub(
            r"^assistant:\s*", "", str(final_response)
        ).strip()
        return cleaned_final_response

from llama_index.core.query_engine import CustomQueryEngine from llama_index.core.llms import LLM from llama_index.core import PropertyGraphIndex import re class GraphRAGQueryEngine(CustomQueryEngine): graph_store: GraphRAGStore index: PropertyGraphIndex llm: LLM similarity_top_k: int = 20 def custom_query(self, query_str: str) -> str: """Process all community summaries to generate answers to a specific query.""" entities = self.get_entities(query_str, self.similarity_top_k) community_ids = self.retrieve_entity_communities( self.graph_store.entity_info, entities ) community_summaries = self.graph_store.get_community_summaries() community_answers = [ self.generate_answer_from_summary(community_summary, query_str) for id, community_summary in community_summaries.items() if id in community_ids ] final_answer = self.aggregate_answers(community_answers) return final_answer def get_entities(self, query_str, similarity_top_k): nodes_retrieved = self.index.as_retriever( similarity_top_k=similarity_top_k ).retrieve(query_str) enitites = set() pattern = r"(\w+(?:\s+\w+)*)\s*\({[^}]*}\)\s*->\s*([^(]+?)\s*\({[^}]*}\)\s*->\s*(\w+(?:\s+\w+)*)" for node in nodes_retrieved: matches = re.findall(pattern, node.text, re.DOTALL) for match in matches: subject = match[0] obj = match[2] enitites.add(subject) enitites.add(obj) return list(enitites) def retrieve_entity_communities(self, entity_info, entities): """ Retrieve cluster information for given entities, allowing for multiple clusters per entity. Args: entity_info (dict): Dictionary mapping entities to their cluster IDs (list). entities (list): List of entity names to retrieve information for. Returns: List of community or cluster IDs to which an entity belongs. """ community_ids = [] for entity in entities: if entity in entity_info: community_ids.extend(entity_info[entity]) return list(set(community_ids)) def generate_answer_from_summary(self, community_summary, query): """Generate an answer from a community summary based on a given query using LLM.""" prompt = ( f"Given the community summary: {community_summary}, " f"how would you answer the following query? Query: {query}" ) messages = [ ChatMessage(role="system", content=prompt), ChatMessage( role="user", content="I need an answer based on the above information.", ), ] response = self.llm.chat(messages) cleaned_response = re.sub(r"^assistant:\s*", "", str(response)).strip() return cleaned_response def aggregate_answers(self, community_answers): """Aggregate individual community answers into a final, coherent response.""" # intermediate_text = " ".join(community_answers) prompt = "Combine the following intermediate answers into a final, concise response." messages = [ ChatMessage(role="system", content=prompt), ChatMessage( role="user", content=f"Intermediate answers: {community_answers}", ), ] final_response = self.llm.chat(messages) cleaned_final_response = re.sub( r"^assistant:\s*", "", str(final_response) ).strip() return cleaned_final_response

Build End to End GraphRAG Pipeline¶

Now that we have defined all the necessary components, let’s construct the GraphRAG pipeline:

1. Create nodes/chunks from the text.
2. Build a PropertyGraphIndex using GraphRAGExtractor and GraphRAGStore.
3. Construct communities and generate a summary for each community using the graph built above.
4. Create a GraphRAGQueryEngine and begin querying.

Create nodes/ chunks from the text.¶

In [ ]:

from llama_index.core.node_parser import SentenceSplitter

splitter = SentenceSplitter(
    chunk_size=1024,
    chunk_overlap=20,
)
nodes = splitter.get_nodes_from_documents(documents)

from llama_index.core.node_parser import SentenceSplitter splitter = SentenceSplitter( chunk_size=1024, chunk_overlap=20, ) nodes = splitter.get_nodes_from_documents(documents)

In [ ]:

len(nodes)

len(nodes)

Out[ ]:

50

Build ProperGraphIndex using GraphRAGExtractor and GraphRAGStore¶

In [ ]:

KG_TRIPLET_EXTRACT_TMPL = """
-Goal-
Given a text document, identify all entities and their entity types from the text and all relationships among the identified entities.
Given the text, extract up to {max_knowledge_triplets} entity-relation triplets.

-Steps-
1. Identify all entities. For each identified entity, extract the following information:
- entity_name: Name of the entity, capitalized
- entity_type: Type of the entity
- entity_description: Comprehensive description of the entity's attributes and activities
Format each entity as ("entity"$$$$<entity_name>$$$$<entity_type>$$$$<entity_description>)

2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
For each pair of related entities, extract the following information:
- source_entity: name of the source entity, as identified in step 1
- target_entity: name of the target entity, as identified in step 1
- relation: relationship between source_entity and target_entity
- relationship_description: explanation as to why you think the source entity and the target entity are related to each other

Format each relationship as ("relationship"$$$$<source_entity>$$$$<target_entity>$$$$<relation>$$$$<relationship_description>)

3. When finished, output.

-Real Data-
######################
text: {text}
######################
output:"""

KG_TRIPLET_EXTRACT_TMPL = """ -Goal- Given a text document, identify all entities and their entity types from the text and all relationships among the identified entities. Given the text, extract up to {max_knowledge_triplets} entity-relation triplets. -Steps- 1. Identify all entities. For each identified entity, extract the following information: - entity_name: Name of the entity, capitalized - entity_type: Type of the entity - entity_description: Comprehensive description of the entity's attributes and activities Format each entity as ("entity"$$$$$$$$$$$$) 2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other. For each pair of related entities, extract the following information: - source_entity: name of the source entity, as identified in step 1 - target_entity: name of the target entity, as identified in step 1 - relation: relationship between source_entity and target_entity - relationship_description: explanation as to why you think the source entity and the target entity are related to each other Format each relationship as ("relationship"$$$$$$$$$$$$$$$$) 3. When finished, output. -Real Data- ###################### text: {text} ###################### output:"""

In [ ]:

entity_pattern = r'\("entity"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\)'
relationship_pattern = r'\("relationship"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\)'


def parse_fn(response_str: str) -> Any:
    entities = re.findall(entity_pattern, response_str)
    relationships = re.findall(relationship_pattern, response_str)
    return entities, relationships


kg_extractor = GraphRAGExtractor(
    llm=llm,
    extract_prompt=KG_TRIPLET_EXTRACT_TMPL,
    max_paths_per_chunk=2,
    parse_fn=parse_fn,
)

entity_pattern = r'\("entity"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\)' relationship_pattern = r'\("relationship"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\$\$\$\$"(.+?)"\)' def parse_fn(response_str: str) -> Any: entities = re.findall(entity_pattern, response_str) relationships = re.findall(relationship_pattern, response_str) return entities, relationships kg_extractor = GraphRAGExtractor( llm=llm, extract_prompt=KG_TRIPLET_EXTRACT_TMPL, max_paths_per_chunk=2, parse_fn=parse_fn, )

Docker Setup And Neo4J setup¶

To launch Neo4j locally, first ensure you have docker installed. Then, you can launch the database with the following docker command.

docker run \
    -p 7474:7474 -p 7687:7687 \
    -v $PWD/data:/data -v $PWD/plugins:/plugins \
    --name neo4j-apoc \
    -e NEO4J_apoc_export_file_enabled=true \
    -e NEO4J_apoc_import_file_enabled=true \
    -e NEO4J_apoc_import_file_use__neo4j__config=true \
    -e NEO4JLABS_PLUGINS=\[\"apoc\"\] \
    neo4j:latest

From here, you can open the db at http://localhost:7474/. On this page, you will be asked to sign in. Use the default username/password of neo4j and neo4j.

Once you login for the first time, you will be asked to change the password.

In [ ]:

from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore

# Note: used to be `Neo4jPGStore`
graph_store = GraphRAGStore(
    username="neo4j", password="<PASSWORD>", url="bolt://localhost:7687"
)

from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore # Note: used to be `Neo4jPGStore` graph_store = GraphRAGStore( username="neo4j", password="", url="bolt://localhost:7687" )

Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The procedure has a deprecated field. ('config' used by 'apoc.meta.graphSample' is deprecated.)} {position: line: 1, column: 1, offset: 0} for query: "CALL apoc.meta.graphSample() YIELD nodes, relationships RETURN nodes, [rel in relationships | {name:apoc.any.property(rel, 'type'), count: apoc.any.property(rel, 'count')}] AS relationships"

In [ ]:

from llama_index.core import PropertyGraphIndex

index = PropertyGraphIndex(
    nodes=nodes,
    kg_extractors=[kg_extractor],
    property_graph_store=graph_store,
    show_progress=True,
)

from llama_index.core import PropertyGraphIndex index = PropertyGraphIndex( nodes=nodes, kg_extractors=[kg_extractor], property_graph_store=graph_store, show_progress=True, )

Extracting paths from text: 100%|██████████| 50/50 [05:45<00:00,  6.90s/it]
Generating embeddings: 100%|██████████| 1/1 [00:02<00:00,  2.59s/it]
Generating embeddings: 100%|██████████| 2/2 [00:03<00:00,  1.90s/it]
Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: The procedure has a deprecated field. ('config' used by 'apoc.meta.graphSample' is deprecated.)} {position: line: 1, column: 1, offset: 0} for query: "CALL apoc.meta.graphSample() YIELD nodes, relationships RETURN nodes, [rel in relationships | {name:apoc.any.property(rel, 'type'), count: apoc.any.property(rel, 'count')}] AS relationships"

In [ ]:

index.property_graph_store.get_triplets()[10]

index.property_graph_store.get_triplets()[10]

Out[ ]:

[EntityNode(label='Software', embedding=None, properties={'id': 'Unreal Engine', 'entity_description': "Unreal Engine is a game engine developed by Epic. It is used in conjunction with Epic's MetaHuman Animator tool to animate hyperrealistic MetaHumans.", 'triplet_source_id': 'b6fbbdc0-cc13-4342-a70e-b0d86f3fd2ad'}, name='MetaHuman Animator'),
 Relation(label='Integrated', source_id='MetaHuman Animator', target_id='Unreal Engine', properties={'relationship_description': 'The MetaHuman Animator tool developed by Epic is integrated with the Unreal Engine. It applies the captured actor’s facial performance to a hyperrealistic “MetaHuman” in the Unreal Engine.', 'triplet_source_id': 'a6f5c123-65a8-4278-8e24-e103e767b82f'}),
 EntityNode(label='Software', embedding=None, properties={'id': 'MetaHuman Animator', 'entity_description': 'MetaHuman Animator is a tool developed by Epic that captures an actor’s facial performance using a device as simple as an iPhone and applies it to a MetaHuman in the Unreal Engine. It is designed to produce results quickly and efficiently.', 'triplet_source_id': 'b6fbbdc0-cc13-4342-a70e-b0d86f3fd2ad'}, name='Unreal Engine')]

In [ ]:

index.property_graph_store.get_triplets()[10][0].properties

index.property_graph_store.get_triplets()[10][0].properties

Out[ ]:

{'id': 'Unreal Engine',
 'entity_description': "Unreal Engine is a game engine developed by Epic. It is used in conjunction with Epic's MetaHuman Animator tool to animate hyperrealistic MetaHumans.",
 'triplet_source_id': 'b6fbbdc0-cc13-4342-a70e-b0d86f3fd2ad'}

In [ ]:

index.property_graph_store.get_triplets()[10][1].properties

index.property_graph_store.get_triplets()[10][1].properties

Out[ ]:

{'relationship_description': 'The MetaHuman Animator tool developed by Epic is integrated with the Unreal Engine. It applies the captured actor’s facial performance to a hyperrealistic “MetaHuman” in the Unreal Engine.',
 'triplet_source_id': 'a6f5c123-65a8-4278-8e24-e103e767b82f'}

Build communities¶

This will create communities and summary for each community.

In [ ]:

index.property_graph_store.build_communities()

index.property_graph_store.build_communities()

Create QueryEngine¶

In [ ]:

query_engine = GraphRAGQueryEngine(
    graph_store=index.property_graph_store,
    llm=llm,
    index=index,
    similarity_top_k=10,
)

query_engine = GraphRAGQueryEngine( graph_store=index.property_graph_store, llm=llm, index=index, similarity_top_k=10, )

Querying¶

In [ ]:

response = query_engine.query(
    "What are the main news discussed in the document?"
)
display(Markdown(f"{response.response}"))

response = query_engine.query( "What are the main news discussed in the document?" ) display(Markdown(f"{response.response}"))

The document discusses several key business news: FirstEnergy's earnings results, Tram Nguyen's appointment as the Global Head of Strategic and Sustainable Investments at Bank of America, Thomas Christl's hiring by Morgan Stanley to co-head its coverage of consumer and retail clients in Europe alongside Imran Ansari, and the significant impacts of the COVID-19 pandemic on Delta Air Lines and Southwest Airlines, including the suspension and reinstatement of their dividend payouts.

In [ ]:

response = query_engine.query("What are the main news in energy sector?")
display(Markdown(f"{response.response}"))

response = query_engine.query("What are the main news in energy sector?") display(Markdown(f"{response.response}"))

The main news in the energy sector is that GE Vernova and Amplus Solar have entered into a Supplier-Client relationship. GE Vernova has been selected by Amplus Solar to provide and install 40 units of its 2.7-132 onshore wind turbines for a 108 MW wind power project. This means that GE Vernova will be supplying the necessary equipment and services for the successful execution of the project.