[Question]: BAAI/bge-small-en-v1.5 Fails to Load for HuggingFaceEmbedding in LlamaIndex (Stuck Without Progress)

[DylanNguyen2111]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

I am trying to use the BAAI/bge-small-en-v1.5 model as the embedding model with LlamaIndex's HuggingFaceEmbedding integration. The model loads successfully via Hugging Face when used independently, but when I integrate it into LlamaIndex, the process stalls indefinitely during the initialization or embedding generation phase.
Here is the code snipet I used:
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

Load FinBERT as the embedding model

embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5",
device="cuda" if torch.cuda.is_available() else "cpu"
)
from llama_index.core import PropertyGraphIndex

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

[logan-markewich]

Are you uploading a lot of data? It can be very slow especially without gpu

[DylanNguyen2111]

I use the data from sample notebook docs/docs/examples/cookbooks/GraphRAG_v2.ipynb. I adjust the code to use HuggingFace llm and embedding models instead of Open AI

[dosubot]

Hey there, @DylanNguyen2111! I'm here to help you with any bugs, questions, or contributions 🤖

There are a few steps you can take to troubleshoot the issue of the BAAI/bge-small-en-v1.5 model stalling during initialization or embedding generation in LlamaIndex:

1. Check Device Configuration: Ensure that the device parameter is correctly set. You are already using "cuda" if available, which is correct. However, verify that your CUDA setup is functioning properly outside of LlamaIndex.

2. Logging and Debugging: Enable detailed logging to diagnose the issue. You can configure Python's logging module to output debug information, which might provide insights into where the process is stalling.

   import logging
   import sys
   
   logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
   logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

3. Timeout Settings: Although not directly related to your issue, ensure that any timeout settings are appropriate for your environment. The default timeout for requests in LlamaIndex is 60 seconds, which can be adjusted if necessary.

4. Parallel Processing: If you are processing a large amount of data, consider enabling parallel_process and specifying target_devices to utilize multiple devices, which might help in reducing stalling.

5. Model Compatibility: While there are no specific known issues with the BAAI/bge-small-en-v1.5 model in LlamaIndex, ensure that all dependencies are up to date and compatible with your environment.

If these steps do not resolve the issue, consider testing with a different model to determine if the problem is specific to the BAAI/bge-small-en-v1.5 model or a broader issue with the setup.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

[DylanNguyen2111]

I mean this data from the sample notebook in this Llamaindex github repo. I dont think it take up to 10 minutes without any progress with these amount of data and my laptop is macbook M2 Pro. I'm running this notebook by cpu
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()

[DylanNguyen2111]

Are you uploading a lot of data? It can be very slow especially without gpu
I use the data from sample notebook docs/docs/examples/cookbooks/GraphRAG_v2.ipynb. I adjust the code to use HuggingFace llm and embedding models instead of Open AI
I mean this data from the sample notebook in this Llamaindex github repo. I dont think it take up to 10 minutes without any progress with these amount of data and my laptop is macbook M2 Pro. I'm running this notebook by cpu
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()

[logan-markewich]

Can you give some minimum code to reproduce? It works fine for me tbh (also on an M2)

Also works in google colab 🤔

[DylanNguyen2111]

Here is the entire code I used to run on my macbook Vscode

-- coding: utf-8 --

"""GraphRAG_v2_with_Neo4j.ipynb

Automatically generated by Colab.

Original file is located at
https://colab.research.google.com/drive/1qfnVg6xW4cOniWjPQCn8Zkhg7sU5AoPo

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.
"""

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

"""## 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.
"""

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()

"""Prepare documents as required by LlamaIndex"""

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

"""## Setup API Key and LLM"""

import os

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

from llama_index.llms.openai import OpenAI

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

!pip install llama-index

!pip install llama-index-llms-huggingface

!pip install llama-index-embeddings-huggingface

!pip install llama-index-embeddings-huggingface-api

from llama_index.llms.huggingface import HuggingFaceLLM
from transformers import AutoTokenizer
import torch

hf_token = ""

Initialize the Hugging Face LLM

llm = HuggingFaceLLM(
model_name="instruction-pretrain/finance-Llama3-8B",
model_kwargs={
"token": hf_token,
"torch_dtype": torch.bfloat16,
},
generate_kwargs={
"do_sample": True,
"temperature": 0.6,
"top_p": 0.9,
},
tokenizer_name="instruction-pretrain/finance-Llama3-8B",
tokenizer_kwargs={"token": hf_token},
stopping_ids=[
AutoTokenizer.from_pretrained("instruction-pretrain/finance-Llama3-8B", token=hf_token).eos_token_id
],
)

"""## 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.
"""

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.
   """

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 __init__(self, username, password, url, llm):
    # Pass Neo4j connection parameters to the parent class
    super().__init__(username=username, password=password, url=url)
    # Store the LLM instance
    self.llm = llm

def generate_community_summary(self, text):
    """Generate summary for a given text using an LLM."""
    prompt = (
        "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.\n\n"
        f"{text}"
    )
    # Use the LLM's complete method to generate the summary
    response = self.llm.complete(prompt)
    return response.text.strip()

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")

"""

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 __init__(self, model, tokenizer):
    self.model = model
    self.tokenizer = tokenizer

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*([a-zA-Z\s]+?)\s*->\s*(\w+(?:\s+\w+)*)$"
    )

    for node in nodes_retrieved:
        matches = re.findall(
            pattern, node.text, re.MULTILINE | re.IGNORECASE
        )

        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)
    return response.message.strip()  # No need for regex cleaning

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)
    return final_response.message.strip()  # No need for regex cleaning

"""## 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.

"""

from llama_index.core.node_parser import SentenceSplitter

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

len(nodes)

"""### Build ProperGraphIndex using GraphRAGExtractor and GraphRAGStore"""

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"$$$$<source_entity>$$$$<target_entity>$$$$$$$$<relationship_description>)

3. When finished, output.

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

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.
"""

from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore

Note: used to be Neo4jPGStore

graph_store = GraphRAGStore(
username="neo4j", password="", url="bolt://localhost:7687", llm=llm
)

Test connection

print("Neo4j connection established:", graph_store)

"""### Custom Embed FinBERT Model"""

import torch
print("CUDA available:", torch.cuda.is_available())

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

Load FinBERT as the embedding model

embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5",
device="cuda" if torch.cuda.is_available() else "cpu"
)

from llama_index.core import PropertyGraphIndex

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

index.property_graph_store.get_triplets()[10]

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

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

"""### Build communities

This will create communities and summary for each community.
"""

index.property_graph_store.build_communities()

"""### Create QueryEngine"""

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

"""### Querying"""

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 in energy sector?")
display(Markdown(f"{response.response}"))