first commit

AIEC-RAG/atlas_rag/kg_construction/concept_generation.py

@@ -0,0 +1,282 @@
+from tqdm import tqdm
+import random
+import logging
+import csv
+import os
+import hashlib
+import re
+from atlas_rag.llm_generator import LLMGenerator
+from atlas_rag.kg_construction.triple_config import ProcessingConfig
+from atlas_rag.kg_construction.utils.csv_processing.csv_to_graphml import get_node_id
+from atlas_rag.llm_generator.prompt.triple_extraction_prompt import CONCEPT_INSTRUCTIONS
+import pickle
+# Increase the field size limit
+csv.field_size_limit(10 * 1024 * 1024)  # 10 MB limit
+
+
+
+def build_batch_data(sessions, batch_size):
+    batched_sessions = []
+    for i in range(0, len(sessions), batch_size):
+        batched_sessions.append(sessions[i:i+batch_size])
+    return batched_sessions
+
+# Function to compute a hash ID from text
+def compute_hash_id(text):
+    # Use SHA-256 to generate a hash
+    hash_object = hashlib.sha256(text.encode('utf-8'))
+    return hash_object.hexdigest()  # Return hash as a hex string
+
+def convert_attribute(value):
+    """ Convert attributes to GDS-compatible types. """
+    if isinstance(value, list):
+        return [str(v) for v in value]
+    elif isinstance(value, (int, float)):
+        return value
+    else:
+        return str(value)
+
+def clean_text(text):
+    # remove NUL as well
+    
+    new_text = text.replace("\n", " ").replace("\r", " ").replace("\t", " ").replace("\v", " ").replace("\f", " ").replace("\b", " ").replace("\a", " ").replace("\e", " ").replace(";", ",")
+    new_text = new_text.replace("\x00", "")
+    new_text = re.sub(r'\s+', ' ', new_text).strip()
+
+    return new_text
+
+def remove_NUL(text):
+    return text.replace("\x00", "")
+
+def build_batched_events(all_node_list, batch_size):
+    "The types are in Entity Event Relation"
+    event_nodes = [node[0] for node in all_node_list if node[1].lower() == "event"]
+    batched_events = []
+
+    for i in range(0, len(event_nodes), batch_size):
+        batched_events.append(event_nodes[i:i+batch_size])
+    
+    return batched_events
+
+def build_batched_entities(all_node_list, batch_size):
+    
+    entity_nodes = [node[0] for node in all_node_list if node[1].lower() == "entity"]
+    batched_entities = []
+
+    for i in range(0, len(entity_nodes), batch_size):
+        batched_entities.append(entity_nodes[i:i+batch_size])
+    
+    return batched_entities
+
+def build_batched_relations(all_node_list, batch_size):
+
+    relations = [node[0] for node in all_node_list if node[1].lower() == "relation"]
+    # relations = list(set(relations))
+    batched_relations = []
+
+    for i in range(0, len(relations), batch_size):
+        batched_relations.append(relations[i:i+batch_size])
+    
+    return batched_relations
+
+def batched_inference(model:LLMGenerator, inputs, record=False, **kwargs):
+    responses = model.generate_response(inputs, return_text_only = not record,  **kwargs)
+    answers = []
+    if record:
+        text_responses = [response[0] for response in responses]
+        usages = [response[1] for response in responses]
+    else:
+        text_responses = responses
+    for i in range(len(text_responses)):
+        answer = text_responses[i]
+        answers.append([x.strip().lower() for x in answer.split(",")])
+    
+    if record:
+        return answers, usages
+    else:
+        return answers
+
+def load_data_with_shard(input_file, shard_idx, num_shards):
+
+    with open(input_file, "r") as f:
+        csv_reader = list(csv.reader(f))
+    
+    # data = csv_reader  
+    data = csv_reader[1:]
+    # Random shuffle the data before splitting into shards
+    random.shuffle(data)
+    
+    total_lines = len(data)
+    lines_per_shard = (total_lines + num_shards - 1) // num_shards
+    start_idx = shard_idx * lines_per_shard
+    end_idx = min((shard_idx + 1) * lines_per_shard, total_lines)
+    
+    return data[start_idx:end_idx]
+
+def generate_concept(model: LLMGenerator,
+            input_file = 'processed_data/triples_csv', 
+            output_folder = 'processed_data/triples_conceptualized', 
+            output_file = 'output.json', 
+            logging_file = 'processed_data/logging.txt', 
+            config:ProcessingConfig=None, 
+            batch_size=32, 
+            shard=0, 
+            num_shards=1,
+            **kwargs):
+    log_dir = os.path.dirname(logging_file)
+    if log_dir and not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+
+    # Create the log file if it doesn't exist
+    if not os.path.exists(logging_file):
+        open(logging_file, 'w').close()
+
+    language = kwargs.get('language', 'en')
+    record = kwargs.get('record', False)
+
+    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+    file_handler = logging.FileHandler(logging_file)
+    file_handler.setLevel(logging.INFO)
+    file_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
+    logging.getLogger().addHandler(file_handler)
+    
+    with open(f"{config.output_directory}/kg_graphml/{config.filename_pattern}_without_concept.pkl", "rb") as f: 
+        temp_kg = pickle.load(f)
+
+    # read data
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+    all_missing_nodes = load_data_with_shard(
+        input_file,
+        shard_idx=shard,
+        num_shards=num_shards
+    )
+        
+    batched_events = build_batched_events(all_missing_nodes, batch_size)
+    batched_entities = build_batched_entities(all_missing_nodes, batch_size)
+    batched_relations = build_batched_relations(all_missing_nodes, batch_size)
+    
+    all_batches = []
+    all_batches.extend(('event', batch) for batch in batched_events)
+    all_batches.extend(('entity', batch) for batch in batched_entities)
+    all_batches.extend(('relation', batch) for batch in batched_relations)
+    
+    print("all_batches", len(all_batches))
+
+
+
+    
+    output_file = output_folder + f"/{output_file.rsplit('.', 1)[0]}_shard_{shard}.csv"
+    with open(output_file, "w", newline='') as file:
+        csv_writer = csv.writer(file)
+        csv_writer.writerow(["node", "conceptualized_node", "node_type"])
+
+        # for batch_type, batch in tqdm(all_batches, total=total_batches, desc="Generating concepts"):
+        # don't use tqdm for now
+        for batch_type, batch in tqdm(all_batches, desc="Shard_{}".format(shard)):
+            # print("batch_type", batch_type)
+            # print("batch", batch)
+            replace_context_token = None
+            if batch_type == 'event':
+                template = CONCEPT_INSTRUCTIONS[language]['event']
+                node_type = 'event'
+                replace_token = '[EVENT]'
+            elif batch_type == 'entity':
+                template = CONCEPT_INSTRUCTIONS[language]['entity']
+                node_type = 'entity'
+                replace_token = '[ENTITY]'
+                replace_context_token = '[CONTEXT]'
+            elif batch_type == 'relation':
+                template = CONCEPT_INSTRUCTIONS[language]['relation']
+                node_type = 'relation'
+                replace_token = '[RELATION]'
+
+            inputs = []
+            for node in batch:
+                # sample node from given node and replace context token.
+                if replace_context_token:
+                    node_id = get_node_id(node)
+                    entity_predecessors = list(temp_kg.predecessors(node_id))
+                    entity_successors = list(temp_kg.successors(node_id))
+
+                    context = ""
+
+                    if len(entity_predecessors) > 0:
+                        random_two_neighbors = random.sample(entity_predecessors, min(1, len(entity_predecessors)))
+                        context += ", ".join([f"{temp_kg.nodes[neighbor]['id']} {temp_kg[neighbor][node_id]['relation']}" for neighbor in random_two_neighbors])
+                    
+                    if len(entity_successors) > 0:
+                        random_two_neighbors = random.sample(entity_successors, min(1, len(entity_successors)))
+                        context += ", ".join([f"{temp_kg[node_id][neighbor]['relation']} {temp_kg.nodes[neighbor]['id']}" for neighbor in random_two_neighbors])
+                    
+                    prompt = template.replace(replace_token, node).replace(replace_context_token, context)
+                else:
+                    prompt = template.replace(replace_token, node)
+                constructed_input = [
+                    {"role": "system", "content": "You are a helpful AI assistant."},
+                    {"role": "user", "content": f"{prompt}"},
+                ]
+                inputs.append(constructed_input)
+
+            try:
+                # print("inputs", inputs)
+                if record:
+                    # If recording, we will get both answers and responses
+                    answers, usages = batched_inference(model, inputs, record=record, max_workers = config.max_workers)
+                else:
+                    answers = batched_inference(model, inputs, record=record, max_workers = config.max_workers)
+                    usages = None
+                # print("answers", answers)
+            except Exception as e:
+                logging.error(f"Error processing {batch_type} batch: {e}")
+                raise e
+            # try:
+            #     answers = batched_inference(llm, sampling_params, inputs)
+            # except Exception as e:
+            #     logging.error(f"Error processing {batch_type} batch: {e}")
+            #     continue
+
+            for i,(node, answer) in enumerate(zip(batch, answers)):
+                # print(node, answer, node_type)
+                if usages is not None:
+                    logging.info(f"Usage log: Node {node}, completion_usage: {usages[i]}")
+                csv_writer.writerow([node, ", ".join(answer), node_type])
+                file.flush()
+    # count unique conceptualized nodes
+    conceptualized_nodes = []
+
+    conceptualized_events = []
+    conceptualized_entities = []
+    conceptualized_relations = []
+
+    with open(output_file, "r") as file:
+        reader = csv.reader(file)
+        next(reader)
+        for row in reader:
+            conceptualized_nodes.extend(row[1].split(","))
+            if row[2] == "event":
+                conceptualized_events.extend(row[1].split(","))
+            elif row[2] == "entity":
+                conceptualized_entities.extend(row[1].split(","))
+            elif row[2] == "relation":
+                conceptualized_relations.extend(row[1].split(","))
+    
+    conceptualized_nodes = [x.strip() for x in conceptualized_nodes]
+    conceptualized_events = [x.strip() for x in conceptualized_events]
+    conceptualized_entities = [x.strip() for x in conceptualized_entities]
+    conceptualized_relations = [x.strip() for x in conceptualized_relations]
+
+    unique_conceptualized_nodes = list(set(conceptualized_nodes))
+    unique_conceptualized_events = list(set(conceptualized_events))
+    unique_conceptualized_entities = list(set(conceptualized_entities))
+    unique_conceptualized_relations = list(set(conceptualized_relations))
+
+    print(f"Number of unique conceptualized nodes: {len(unique_conceptualized_nodes)}")
+    print(f"Number of unique conceptualized events: {len(unique_conceptualized_events)}")
+    print(f"Number of unique conceptualized entities: {len(unique_conceptualized_entities)}")
+    print(f"Number of unique conceptualized relations: {len(unique_conceptualized_relations)}")
+
+    return 
+    
+
+

AIEC-RAG/atlas_rag/kg_construction/concept_to_csv.py

@@ -0,0 +1,153 @@
+import ast
+import uuid
+import csv
+from tqdm import tqdm
+import hashlib
+import os
+
+def generate_uuid():
+    """Generate a random UUID"""
+    return str(uuid.uuid4())
+
+def parse_concepts(s):
+    """Parse concepts field and filter empty values"""
+    try:
+        parsed = ast.literal_eval(s) if s and s != '[]' else []
+        return [c.strip() for c in parsed if c.strip()]
+    except:
+        return []
+    
+
+# Function to compute a hash ID from text
+def compute_hash_id(text):
+    # Use SHA-256 to generate a hash
+    text = text + '_concept'
+    hash_object = hashlib.sha256(text.encode('utf-8'))
+    return hash_object.hexdigest()  # Return hash as a hex string
+
+
+def all_concept_triples_csv_to_csv(node_file, edge_file, concepts_file, output_node_file, output_edge_file, output_full_concept_triple_edges):
+
+    # to deal add output the concepts nodes, edges, and new full_triple_edges, 
+    # we need to read the concepts maps to the memory, as it is usually not too large.
+    # Then we need to iterate over the triple nodes to create concept edges 
+    # Finally we iterate over the triple edges to create the full_triple_edges
+     
+    # Read missing concept
+    # relation_concepts_mapping = {}
+    # all_missing_concepts = []
+    
+    # check if all output directories exist
+    output_dir = os.path.dirname(output_node_file)
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    output_dir = os.path.dirname(output_edge_file)
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    output_dir = os.path.dirname(output_full_concept_triple_edges)
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    
+    node_to_concepts = {}
+    relation_to_concepts = {}
+
+    all_concepts = set()
+    with open(concepts_file, 'r', encoding='utf-8') as f:
+        reader = csv.DictReader(f)
+
+        # Load missing concepts list
+        print("Loading concepts...")
+        for row in tqdm(reader):
+
+
+            if row['node_type'] == 'relation':
+                relation = row['node']
+                concepts = [c.strip() for c in row['conceptualized_node'].split(',') if c.strip()]
+
+                if relation not in relation_to_concepts:
+                    relation_to_concepts[relation] = concepts
+                else:
+                    relation_to_concepts[relation].extend(concepts)
+                    relation_to_concepts[relation] = list(set(relation_to_concepts[relation]))
+                
+            else:
+                node = row['node']
+                concepts = [c.strip() for c in row['conceptualized_node'].split(',') if c.strip()]
+
+                if node not in node_to_concepts:
+                    node_to_concepts[node] = concepts
+                else:
+                    node_to_concepts[node].extend(concepts)
+                    node_to_concepts[node] = list(set(node_to_concepts[node]))
+
+    print("Loading concepts done.")
+    print(f"Relation to concepts: {len(relation_to_concepts)}")
+    print(f"Node to concepts: {len(node_to_concepts)}")
+
+    # Read triple nodes and write to output concept edges files
+    print("Processing triple nodes...")
+    with open(node_file, 'r', encoding='utf-8') as f:
+        reader = csv.reader(f)
+        # name:ID,type,concepts,synsets,:LABEL
+        header = next(reader)
+        
+        with open (output_edge_file, 'w', newline='', encoding='utf-8') as f_out:
+            
+            writer = csv.writer(f_out, quoting=csv.QUOTE_ALL)
+            writer.writerow([':START_ID', ':END_ID', 'relation', ':TYPE'])
+            
+            for row in tqdm(reader):
+                node_name = row[0]
+                if node_name in node_to_concepts:
+                   
+                    for concept in node_to_concepts[node_name]:
+                        concept_id = compute_hash_id(concept)
+                        writer.writerow([row[0], concept_id, 'has_concept', 'Concept'])
+                        all_concepts.add(concept)
+                    
+
+                for concept in parse_concepts(row[2]):
+                    concept_id = compute_hash_id(concept)
+                    writer.writerow([row[0], concept_id, 'has_concept', 'Concept'])
+                    all_concepts.add(concept)
+
+    # Read the concept nodes and write to output concept nodes file
+    print("Processing concept nodes...")
+    with open (output_node_file, 'w', newline='', encoding='utf-8') as f_out:
+        writer = csv.writer(f_out, quoting=csv.QUOTE_ALL)
+        writer.writerow(['concept_id:ID', 'name', ':LABEL'])
+
+        for concept in tqdm(all_concepts):
+            concept_id = compute_hash_id(concept)
+            writer.writerow([concept_id, concept, 'Concept'])
+           
+    
+    # Read triple edges and write to output full concept triple edges file
+    print("Processing triple edges...")
+    with open(edge_file, 'r', encoding='utf-8') as f:
+        with open(output_full_concept_triple_edges, 'w', newline='', encoding='utf-8') as f_out:
+            reader = csv.reader(f)
+            writer = csv.writer(f_out, quoting=csv.QUOTE_ALL)
+
+
+            header = next(reader)
+            writer.writerow([':START_ID', ':END_ID', 'relation', 'concepts', 'synsets', ':TYPE'])
+
+            for row in tqdm(reader):
+                src_id = row[0]
+                end_id = row[1]
+                relation = row[2]
+                concepts = row[3]
+                synsets = row[4]
+
+                original_concepts = parse_concepts(concepts)
+             
+
+                if relation in relation_to_concepts:
+                    for concept in relation_to_concepts[relation]:
+                        if concept not in original_concepts:
+                            original_concepts.append(concept)
+                            original_concepts = list(set(original_concepts))
+                
+                writer.writerow([src_id, end_id, relation, original_concepts, synsets, 'Relation'])
+    return

AIEC-RAG/atlas_rag/kg_construction/neo4j/neo4j_api.py

@@ -0,0 +1,267 @@
+import time
+import uvicorn
+from fastapi import FastAPI, HTTPException, Response
+from typing import List, Literal, Optional, Union
+from pydantic import BaseModel, Field
+from logging import Logger
+from atlas_rag.retriever.lkg_retriever.base import BaseLargeKGRetriever, BaseLargeKGEdgeRetriever
+from atlas_rag.kg_construction.neo4j.utils import start_up_large_kg_index_graph
+from atlas_rag.llm_generator import LLMGenerator
+from neo4j import Driver
+from dataclasses import dataclass
+import traceback
+
+@dataclass
+class LargeKGConfig:
+    largekg_retriever: BaseLargeKGRetriever | BaseLargeKGEdgeRetriever = None
+    reader_llm_generator : LLMGenerator = None
+    driver: Driver = None
+    logger: Logger = None
+    is_felm: bool = False
+    is_mmlu: bool = False
+    
+    rag_exemption_list = [
+    """I will show you a question and a list of text segments. All the segments can be concatenated to form a complete answer to the question. Your task is to assess whether each text segment contains errors or not. \nPlease generate using the following format:\nAnswer: List the ids of the segments with errors (separated by commas). Please only output the ids, no more details. If all the segments are correct, output \"ALL_CORRECT\".\n\nHere is one example:\nQuestion: 8923164*7236571?\nSegments: \n1. The product of 8923164 and 7236571 is: 6,461,216,222,844\n2. So, 8923164 multiplied by 7236571 is equal to 6,461,216,222,844.\n\nBelow are your outputs:\nAnswer: 1,2\nIt means segment 1,2 contain errors.""",
+    """I will show you a question and a list of text segments. All the segments can be concatenated to form a complete answer to the question. Your task is to determine whether each text segment contains factual errors or not. \nPlease generate using the following format:\nAnswer: List the ids of the segments with errors (separated by commas). Please only output the ids, no more details. If all the segments are correct, output \"ALL_CORRECT\".\n\nHere is one example:\nQuestion: A company offers a 10% discount on all purchases over $100. A customer purchases three items, each costing $80. Does the customer qualify for the discount?\nSegments: \n1. To solve this problem, we need to use deductive reasoning. We know that the company offers a 10% discount on purchases over $100, so we need to calculate the total cost of the customer's purchase.\n2. The customer purchased three items, each costing $80, so the total cost of the purchase is: 3 x $80 = $200.\n3. Since the total cost of the purchase is greater than $100, the customer qualifies for the discount. \n4. To calculate the discounted price, we can multiply the total cost by 0.1 (which represents the 10% discount): $200 x 0.1 = $20.\n5. So the customer is eligible for a discount of $20, and the final cost of the purchase would be: $200 - $20 = $180.\n6. Therefore, the customer would pay a total of $216 for the three items with the discount applied.\n\nBelow are your outputs:\nAnswer: 2,3,4,5,6\nIt means segment 2,3,4,5,6 contains errors.""",
+    ]
+
+    mmlu_check_list = [
+        """Given the following question and four candidate answers (A, B, C and D), choose the answer."""
+    ]
+
+app = FastAPI()
+
+@app.on_event("startup")
+async def startup():
+    global large_kg_config
+    start_up_large_kg_index_graph(large_kg_config.driver)
+
+@app.on_event("shutdown")
+async def shutdown():
+    global large_kg_config
+    print("Shutting down the model...")
+    del large_kg_config
+
+class ModelCard(BaseModel):
+    id: str
+    object: str = "model"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    owned_by: str = "test"
+    root: Optional[str] = None
+    parent: Optional[str] = None
+    permission: Optional[list] = None
+
+class ModelList(BaseModel):
+    object: str = "list"
+    data: List[ModelCard] = []
+
+class ChatMessage(BaseModel):
+    role: Literal["user", "system", "assistant"]
+    content: str = None
+    name: Optional[str] = None
+
+class DeltaMessage(BaseModel):
+    role: Optional[Literal["user", "assistant", "system"]] = None
+    content: Optional[str] = None
+
+class UsageInfo(BaseModel):
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: List[ChatMessage]
+    temperature: Optional[float] = 0.8
+    top_p: Optional[float] = 0.8
+    max_tokens: Optional[int] = None
+    stream: Optional[bool] = False
+    tools: Optional[Union[dict, List[dict]]] = None
+    repetition_penalty: Optional[float] = 1.1
+    retriever_config: Optional[dict] = {
+        "topN": 5,
+        "number_of_source_nodes_per_ner": 10,
+        "sampling_area": 250,
+        "Dmax": 2,
+        "Wmax": 3
+    }
+    class Config:
+        extra = "allow"
+    
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: Literal["stop", "length", "function_call"]
+
+class ChatCompletionResponseStreamChoice(BaseModel):
+    delta: DeltaMessage
+    finish_reason: Optional[Literal["stop", "length"]]
+    index: int
+
+class ChatCompletionResponse(BaseModel):
+    model: str
+    id: str
+    object: Literal["chat.completion", "chat.completion.chunk"]
+    choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
+    created: Optional[int] = Field(default_factory=lambda: int(time.time()))
+    usage: Optional[UsageInfo] = None
+
+@app.get("/health")
+async def health_check():
+    return Response(status_code=200)
+
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def create_chat_completion(request: ChatCompletionRequest):
+    global large_kg_config
+    try:
+        if len(request.messages) < 1 or request.messages[-1].role == "assistant":
+            print(request)
+            # raise HTTPException(status_code=400, detail="Invalid request")
+        if large_kg_config.logger is not None:
+            large_kg_config.logger.info(f"Request: {request}")
+        gen_params = dict(
+            messages=request.messages,
+            temperature=0.8,
+            top_p=request.top_p,
+            max_tokens=request.max_tokens or 1024,
+            echo=False,
+            stream=False,
+            repetition_penalty=request.repetition_penalty,
+            tools=request.tools,
+        )
+
+        last_message = request.messages[-1]
+        system_prompt = 'You are a helpful assistant.'
+        question = last_message.content if last_message.role == 'user' else request.messages[-2].content
+
+        is_exemption = any(exemption in question for exemption in LargeKGConfig.rag_exemption_list)
+        is_mmlu = any(exemption in question for exemption in LargeKGConfig.mmlu_check_list)
+        print(f"Is exemption: {is_exemption}, Is MMLU: {is_mmlu}")
+        if is_mmlu:
+            rag_text = question 
+        else:
+            parts = question.rsplit("Question:", 1)
+            rag_text = parts[-1] if len(parts) > 1 else None
+        print(f"RAG text: {rag_text}")
+        if not is_exemption:
+            passages, passages_score = large_kg_config.largekg_retriever.retrieve_passages(rag_text)
+            context = "No retrieved context, Please answer the question with your own knowledge." if not passages else "\n".join([f"Passage {i+1}: {text}" for i, text in enumerate(reversed(passages))])
+            
+        if is_mmlu:
+            rag_chat_content = [
+                {
+                    "role": "system",
+                    "content": f"{system_prompt}"
+                },
+                {
+                    "role": "user",
+                    "content": f"""Here is the context: {context} \n\n
+                    If the context is not useful, you can answer the question with your own knowledge. \n {question}\nThink step by step. Your response should end with 'The answer is ([the_answer_letter])' where the [the_answer_letter] is one of A, B, C and D."""
+                }
+            ]
+        elif not is_exemption:
+            rag_chat_content = [
+                {
+                    "role": "system",
+                    "content": f"{system_prompt}"
+                },
+                {
+                    "role": "user",
+                    "content": f"""{question} Reference doc: {context}"""
+                }
+            ]
+        else:
+            rag_chat_content = [
+                {
+                    "role": "system",
+                    "content": f"{system_prompt}"
+                },
+                {
+                    "role": "user",
+                    "content": f"""{question} """
+                }
+            ]
+        if large_kg_config.logger is not None:
+            large_kg_config.logger.info(rag_chat_content)
+
+        response = large_kg_config.reader_llm_generator.generate_response(
+            batch_messages=rag_chat_content,
+            max_new_tokens=gen_params["max_tokens"],
+            temperature=gen_params["temperature"],
+            frequency_penalty = 1.1
+        )
+        message = ChatMessage(
+            role="assistant",
+            content=response.strip()
+        )
+        choice_data = ChatCompletionResponseChoice(
+            index=0,
+            message=message,
+            finish_reason="stop"
+        )
+        return ChatCompletionResponse(
+            model=request.model,
+            id="",
+            object="chat.completion",
+            choices=[choice_data]
+        )
+    except Exception as e:
+        print("ERROR: ", e)
+        print("Catched error")
+        traceback.print_exc()
+        system_prompt = 'You are a helpful assistant.'
+        gen_params = dict(
+            messages=request.messages,
+            temperature=0.8,
+            top_p=request.top_p,
+            max_tokens=request.max_tokens or 1024,
+            echo=False,
+            stream=False,
+            repetition_penalty=request.repetition_penalty,
+            tools=request.tools,
+        )
+        last_message = request.messages[-1]
+        system_prompt = 'You are a helpful assistant.'
+        question = last_message.content if last_message.role == 'user' else request.messages[-2].content
+        rag_chat_content = [
+                {
+                    "role": "system",
+                    "content": f"{system_prompt}"
+                },
+                {
+                    "role": "user",
+                    "content": f"""{question} """
+                }
+            ]
+        response = large_kg_config.reader_llm_generator.generate_response(
+            batch_messages=rag_chat_content,
+            max_new_tokens=gen_params["max_tokens"],
+            temperature=gen_params["temperature"],
+            frequency_penalty = 1.1
+        )
+        message = ChatMessage(
+            role="assistant",
+            content=response.strip()
+        )
+        choice_data = ChatCompletionResponseChoice(
+            index=0,
+            message=message,
+            finish_reason="stop"
+        )
+        return ChatCompletionResponse(
+            model=request.model,
+            id="",
+            object="chat.completion",
+            choices=[choice_data]
+        )
+
+
+def start_app(user_config:LargeKGConfig, host="0.0.0.0", port=10090, reload=False):
+    """Function to start the FastAPI application."""
+    global large_kg_config
+    large_kg_config = user_config  # Use the passed context if provided
+
+    uvicorn.run(f"atlas_rag.kg_construction.neo4j.neo4j_api:app", host=host, port=port, reload=reload)
+    

AIEC-RAG/atlas_rag/kg_construction/neo4j/utils.py

@@ -0,0 +1,73 @@
+import faiss
+from neo4j import Driver
+import time
+from graphdatascience import GraphDataScience
+from atlas_rag.retriever.lkg_retriever.base import BaseLargeKGRetriever
+
+def build_projection_graph(driver: GraphDataScience):
+    project_graph_1 = "largekgrag_graph"
+    is_project_graph_1_exist = False
+    # is_project_graph_2_exist = False
+    result = driver.graph.list()
+    for index, row in result.iterrows():
+        if row['graphName'] == project_graph_1:
+            is_project_graph_1_exist = True
+        # if row['graphName'] == project_graph_2:
+        #     is_project_graph_2_exist = True
+    
+    if not is_project_graph_1_exist:
+        start_time = time.time()
+        node_properties = ["Node"]
+        relation_projection = [ "Relation"]
+        result = driver.graph.project(
+            project_graph_1,
+            node_properties,
+            relation_projection
+        )
+        graph = driver.graph.get(project_graph_1)
+        print(f"Projection graph {project_graph_1} created in {time.time() - start_time:.2f} seconds")
+
+def build_neo4j_label_index(driver: GraphDataScience):
+    with driver.session() as session:
+        index_name = f"NodeNumericIDIndex"
+        # Check if the index already exists
+        existing_indexes = session.run("SHOW INDEXES").data()
+        index_exists = any(index['name'] == index_name for index in existing_indexes)
+        # Drop the index if it exists
+        if not index_exists:
+            start_time = time.time()
+            session.run(f"CREATE INDEX {index_name} FOR (n:Node) ON (n.numeric_id)")
+            print(f"Index {index_name} created in {time.time() - start_time:.2f} seconds")
+            
+        index_name = f"TextNumericIDIndex"
+        index_exists = any(index['name'] == index_name for index in existing_indexes)
+        if not index_exists:
+            start_time = time.time()
+            session.run(f"CREATE INDEX {index_name} FOR (t:Text) ON (t.numeric_id)")
+            print(f"Index {index_name} created in {time.time() - start_time:.2f} seconds")
+        
+        index_name = f"EntityEventEdgeNumericIDIndex"
+        index_exists = any(index['name'] == index_name for index in existing_indexes)
+        if not index_exists:
+            start_time = time.time()
+            session.run(f"CREATE INDEX {index_name} FOR ()-[r:Relation]-() on (r.numeric_id)")
+            print(f"Index {index_name} created in {time.time() - start_time:.2f} seconds")
+
+def load_indexes(path_dict):
+    for key, value in path_dict.items():
+        if key == 'node':
+            node_index = faiss.read_index(value, faiss.IO_FLAG_MMAP)
+            print(f"Node index loaded from {value}")
+        elif key == 'edge':
+            edge_index = faiss.read_index(value, faiss.IO_FLAG_MMAP)
+            print(f"Edge index loaded from {value}")
+        elif key == 'text':
+            passage_index = faiss.read_index(value, faiss.IO_FLAG_MMAP)
+            print(f"Passage index loaded from {value}")
+    return node_index, edge_index, passage_index
+
+def start_up_large_kg_index_graph(neo4j_driver: Driver)->BaseLargeKGRetriever:    
+    gds_driver = GraphDataScience(neo4j_driver)
+    # build label index and projection graph
+    build_neo4j_label_index(neo4j_driver)
+    build_projection_graph(gds_driver)

AIEC-RAG/atlas_rag/kg_construction/triple_config.py

@@ -0,0 +1,22 @@
+from dataclasses import dataclass
+
+@dataclass
+class ProcessingConfig:
+    """Configuration for text processing pipeline."""
+    model_path: str
+    data_directory: str
+    filename_pattern: str
+    batch_size_triple: int = 16
+    batch_size_concept: int = 64
+    output_directory: str = "./generation_result_debug"
+    total_shards_triple: int = 1
+    current_shard_triple: int = 0
+    total_shards_concept: int = 1
+    current_shard_concept: int = 0
+    use_8bit: bool = False
+    debug_mode: bool = False
+    resume_from: int = 0
+    record : bool = False
+    max_new_tokens: int = 8192
+    max_workers: int = 8
+    remove_doc_spaces: bool = False

AIEC-RAG/atlas_rag/kg_construction/triple_extraction.py

@@ -0,0 +1,497 @@
+#!/usr/bin/env python3
+"""
+Knowledge Graph Extraction Pipeline
+Extracts entities, relations, and events from text data using transformer models.
+"""
+import re
+import json
+import os
+import argparse
+from datetime import datetime
+from typing import List, Dict, Any, Tuple
+from pathlib import Path
+import torch
+from datasets import load_dataset
+from tqdm import tqdm
+import json_repair
+from atlas_rag.llm_generator import LLMGenerator
+from atlas_rag.kg_construction.utils.json_processing.json_to_csv import json2csv
+from atlas_rag.kg_construction.concept_generation import generate_concept
+from atlas_rag.kg_construction.utils.csv_processing.merge_csv import merge_csv_files
+from atlas_rag.kg_construction.utils.csv_processing.csv_to_graphml import csvs_to_graphml, csvs_to_temp_graphml
+from atlas_rag.kg_construction.concept_to_csv import all_concept_triples_csv_to_csv
+from atlas_rag.kg_construction.utils.csv_processing.csv_add_numeric_id import add_csv_columns
+from atlas_rag.vectorstore.embedding_model import BaseEmbeddingModel
+from atlas_rag.vectorstore.create_neo4j_index import create_faiss_index
+from atlas_rag.llm_generator.prompt.triple_extraction_prompt import TRIPLE_INSTRUCTIONS
+from atlas_rag.kg_construction.triple_config import ProcessingConfig
+# Constants
+TOKEN_LIMIT = 1024
+INSTRUCTION_TOKEN_ESTIMATE = 200
+CHAR_TO_TOKEN_RATIO = 3.5
+
+
+
+class TextChunker:
+    """Handles text chunking based on token limits."""
+    
+    def __init__(self, max_tokens: int = TOKEN_LIMIT, instruction_tokens: int = INSTRUCTION_TOKEN_ESTIMATE):
+        self.max_tokens = max_tokens
+        self.instruction_tokens = instruction_tokens
+        self.char_ratio = CHAR_TO_TOKEN_RATIO
+        
+    def calculate_max_chars(self) -> int:
+        """Calculate maximum characters per chunk."""
+        available_tokens = self.max_tokens - self.instruction_tokens
+        return int(available_tokens * self.char_ratio)
+    
+    def split_text(self, text: str) -> List[str]:
+        """Split text into chunks that fit within token limits."""
+        max_chars = self.calculate_max_chars()
+        chunks = []
+        
+        while len(text) > max_chars:
+            chunks.append(text[:max_chars])
+            text = text[max_chars:]
+        
+        if text:  # Add remaining text
+            chunks.append(text)
+            
+        return chunks
+
+
+class DatasetProcessor:
+    """Processes and prepares dataset for knowledge graph extraction."""
+    
+    def __init__(self, config: ProcessingConfig):
+        self.config = config
+        self.chunker = TextChunker()
+        
+    def filter_language_content(self, sample: Dict[str, Any]) -> bool:
+        """Check if content is in English."""
+        metadata = sample.get("metadata", {})
+        language = metadata.get("lang", "en")  # Default to English if not specified
+        supported_languages = list(TRIPLE_INSTRUCTIONS.keys())
+        return language in supported_languages
+    
+    
+    def create_sample_chunks(self, sample: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Create chunks from a single sample."""
+        original_text = sample.get("text", "")
+        if self.config.remove_doc_spaces:
+            original_text = re.sub(r'\s+', ' ',original_text).strip()
+        text_chunks = self.chunker.split_text(original_text)
+        chunks = []
+        
+        for chunk_idx, chunk_text in enumerate(text_chunks):
+            chunk_data = {
+                "id": sample["id"],
+                "text": chunk_text,
+                "chunk_id": chunk_idx,
+                "metadata": sample["metadata"]
+            }
+            chunks.append(chunk_data)
+            
+        return chunks
+    
+    def prepare_dataset(self, raw_dataset) -> List[Dict[str, Any]]:
+        """Process raw dataset into chunks suitable for processing with generalized slicing."""
+        
+        processed_samples = []
+        total_texts = len(raw_dataset)
+        
+        # Handle edge cases
+        if total_texts == 0:
+            print(f"No texts found for shard {self.config.current_shard_triple+1}/{self.config.total_shards_triple}")
+            return processed_samples
+        
+        # Calculate base and remainder for fair distribution
+        base_texts_per_shard = total_texts // self.config.total_shards_triple
+        remainder = total_texts % self.config.total_shards_triple
+        
+        # Calculate start index
+        if self.config.current_shard_triple < remainder:
+            start_idx = self.config.current_shard_triple * (base_texts_per_shard + 1)
+        else:
+            start_idx = remainder * (base_texts_per_shard + 1) + (self.config.current_shard_triple - remainder) * base_texts_per_shard
+        
+        # Calculate end index
+        if self.config.current_shard_triple < remainder:
+            end_idx = start_idx + (base_texts_per_shard + 1)
+        else:
+            end_idx = start_idx + base_texts_per_shard
+        
+        # Ensure indices are within bounds
+        start_idx = min(start_idx, total_texts)
+        end_idx = min(end_idx, total_texts)
+        
+        print(f"Processing shard {self.config.current_shard_triple+1}/{self.config.total_shards_triple} "
+            f"(texts {start_idx}-{end_idx-1} of {total_texts}, {end_idx - start_idx} documents)")
+        
+        # Process documents in assigned shard
+        for idx in range(start_idx, end_idx):
+            sample = raw_dataset[idx]
+            
+            # Filter by language
+            if not self.filter_language_content(sample):
+                print(f"Unsupported language in sample {idx}, skipping.")
+                continue
+                
+            # Create chunks
+            chunks = self.create_sample_chunks(sample)
+            processed_samples.extend(chunks)
+            
+            # Debug mode early termination
+            if self.config.debug_mode and len(processed_samples) >= 20:
+                print("Debug mode: Stopping at 20 chunks")
+                break
+        
+        print(f"Generated {len(processed_samples)} chunks for shard {self.config.current_shard_triple+1}/{self.config.total_shards_triple}")
+        return processed_samples
+
+
+class CustomDataLoader:
+    """Custom data loader for knowledge graph extraction."""
+    
+    def __init__(self, dataset, processor: DatasetProcessor):
+        self.raw_dataset = dataset
+        self.processor = processor
+        self.processed_data = processor.prepare_dataset(dataset)
+        self.stage_to_prompt_dict = {
+            "stage_1": "entity_relation",
+            "stage_2": "event_entity",
+            "stage_3": "event_relation"
+        }
+        
+    def __len__(self) -> int:
+        return len(self.processed_data)
+    
+    def create_batch_instructions(self, batch_data: List[Dict[str, Any]]) -> List[str]:
+        messages_dict = {
+            'stage_1': [],
+            'stage_2': [],
+            'stage_3': []
+        }
+        for item in batch_data:
+            # get language
+            language = item.get("metadata",{}).get("lang", "en")
+            system_msg = TRIPLE_INSTRUCTIONS.get(language, TRIPLE_INSTRUCTIONS["en"])['system'] 
+            stage_1_msg = TRIPLE_INSTRUCTIONS.get(language, TRIPLE_INSTRUCTIONS["en"])['entity_relation'] + TRIPLE_INSTRUCTIONS.get(language, TRIPLE_INSTRUCTIONS["en"])['passage_start'] + '\n' + item["text"]
+            stage_2_msg = TRIPLE_INSTRUCTIONS.get(language, TRIPLE_INSTRUCTIONS["en"])['event_entity'] + TRIPLE_INSTRUCTIONS.get(language, TRIPLE_INSTRUCTIONS["en"])['passage_start'] + '\n'+ item["text"]
+            stage_3_msg = TRIPLE_INSTRUCTIONS.get(language, TRIPLE_INSTRUCTIONS["en"])['event_relation'] + TRIPLE_INSTRUCTIONS.get(language, TRIPLE_INSTRUCTIONS["en"])['passage_start'] + '\n'+ item["text"]
+            stage_one_message = [
+                {"role": "system", "content": system_msg},
+                {"role": "user", "content": stage_1_msg}
+            ]
+            stage_two_message = [
+                {"role": "system", "content": system_msg},
+                {"role": "user", "content": stage_2_msg}
+            ]
+            stage_three_message = [
+                {"role": "system", "content": system_msg},
+                {"role": "user", "content": stage_3_msg}
+            ]
+            messages_dict['stage_1'].append(stage_one_message)
+            messages_dict['stage_2'].append(stage_two_message)
+            messages_dict['stage_3'].append(stage_three_message)
+        
+        return messages_dict
+    
+    def __iter__(self):
+        """Iterate through batches."""
+        batch_size = self.processor.config.batch_size_triple
+        start_idx = self.processor.config.resume_from * batch_size
+        
+        for i in tqdm(range(start_idx, len(self.processed_data), batch_size)):
+            batch_data = self.processed_data[i:i + batch_size]
+            
+            # Prepare instructions
+            instructions = self.create_batch_instructions(batch_data)
+            
+            # Extract batch information
+            batch_ids = [item["id"] for item in batch_data]
+            batch_metadata = [item["metadata"] for item in batch_data]
+            batch_texts = [item["text"] for item in batch_data]
+            
+            yield instructions, batch_ids, batch_texts, batch_metadata
+
+
+class OutputParser:
+    """Parses model outputs and extracts structured data."""
+    def __init__(self):
+        pass
+
+    def extract_structured_data(self, outputs: List[str]) -> List[List[Dict[str, Any]]]:
+        """Extract structured data from model outputs."""
+        results = []
+        for output in outputs:
+            parsed_data = json_repair.loads(output)
+            results.append(parsed_data)
+            
+        return results
+
+
+class KnowledgeGraphExtractor:
+    """Main class for knowledge graph extraction pipeline."""
+    
+    def __init__(self, model:LLMGenerator, config: ProcessingConfig):
+        self.config = config
+        self.model = None
+        self.parser = None
+        self.model = model
+        self.model_name = model.model_name
+        self.parser = OutputParser()
+    
+    def load_dataset(self) -> Any:
+        """Load and prepare dataset."""
+        data_path = Path(self.config.data_directory)
+        all_files = os.listdir(data_path)
+        
+        valid_files = [
+            filename for filename in all_files
+            if filename.startswith(self.config.filename_pattern) and
+            (filename.endswith(".json.gz") or filename.endswith(".json") or filename.endswith(".jsonl") or filename.endswith(".jsonl.gz"))
+        ]
+        
+        print(f"Found data files: {valid_files}")
+        data_files = valid_files
+        dataset_config = {"train": data_files}
+        return load_dataset(self.config.data_directory, data_files=dataset_config["train"])
+    
+    def process_stage(self, instructions: Dict[str, str], stage = 1) -> Tuple[List[str], List[List[Dict[str, Any]]]]:
+        """Process first stage: entity-relation extraction."""
+        outputs = self.model.triple_extraction(messages=instructions, max_tokens=self.config.max_new_tokens, stage=stage, record=self.config.record)
+        if self.config.record:
+            text_outputs = [output[0] for output in outputs]
+        else:
+            text_outputs = outputs
+        structured_data = self.parser.extract_structured_data(text_outputs)
+        return outputs, structured_data
+    
+    def create_output_filename(self) -> str:
+        """Create output filename with timestamp and shard info."""
+        timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
+        model_name_safe = self.config.model_path.replace("/", "_")
+        
+        filename = (f"{model_name_safe}_{self.config.filename_pattern}_output_"
+                   f"{timestamp}_{self.config.current_shard_triple + 1}_in_{self.config.total_shards_triple}.json")
+        
+        extraction_dir = os.path.join(self.config.output_directory, "kg_extraction")
+        os.makedirs(extraction_dir, exist_ok=True)
+        
+        return os.path.join(extraction_dir, filename)
+    
+    def prepare_result_dict(self, batch_data: Tuple, stage_outputs: Tuple, index: int) -> Dict[str, Any]:
+        """Prepare result dictionary for a single sample."""
+        ids, original_texts, metadata = batch_data
+        (stage1_results, entity_relations), (stage2_results, event_entities), (stage3_results, event_relations) = stage_outputs
+        if self.config.record:
+            stage1_outputs = [output[0] for output in stage1_results]
+            stage1_usage = [output[1] for output in stage1_results]
+            stage2_outputs = [output[0] for output in stage2_results]
+            stage2_usage = [output[1] for output in stage2_results]
+            stage3_outputs = [output[0] for output in stage3_results]
+            stage3_usage = [output[1] for output in stage3_results]
+        else:
+            stage1_outputs = stage1_results
+            stage2_outputs = stage2_results
+            stage3_outputs = stage3_results
+        result = {
+            "id": ids[index],
+            "metadata": metadata[index],
+            "original_text": original_texts[index],
+            "entity_relation_dict": entity_relations[index],
+            "event_entity_relation_dict": event_entities[index],
+            "event_relation_dict": event_relations[index],
+            "output_stage_one": stage1_outputs[index],
+            "output_stage_two": stage2_outputs[index],
+            "output_stage_three": stage3_outputs[index],
+        }
+        if self.config.record:
+            result['usage_stage_one'] = stage1_usage[index]
+            result['usage_stage_two'] = stage2_usage[index]
+            result['usage_stage_three'] = stage3_usage[index]
+        
+        # Handle date serialization
+        if 'date_download' in result['metadata']:
+            result['metadata']['date_download'] = str(result['metadata']['date_download'])
+            
+        return result
+    
+    def debug_print_result(self, result: Dict[str, Any]):
+        """Print result for debugging."""
+        for key, value in result.items():
+            print(f"{key}: {value}")
+            print("-" * 100)
+    
+    def run_extraction(self):
+        """Run the complete knowledge graph extraction pipeline."""
+        # Setup
+        os.makedirs(self.config.output_directory+'/kg_extraction', exist_ok=True)
+        dataset = self.load_dataset()
+        
+        if self.config.debug_mode:
+            print("Debug mode: Processing only 20 samples")
+        
+        # Create data processor and loader
+        processor = DatasetProcessor(self.config)
+        data_loader = CustomDataLoader(dataset["train"], processor)
+        
+        output_file = self.create_output_filename()
+        print(f"Model: {self.config.model_path}")
+        
+        batch_counter = 0
+        
+        with torch.no_grad():
+            with open(output_file, "w") as output_stream:
+                for batch in data_loader:
+                    batch_counter += 1
+                    messages_dict, batch_ids, batch_texts, batch_metadata = batch
+                    
+                    # Process all three stages
+                    stage1_results = self.process_stage(messages_dict['stage_1'],1)
+                    stage2_results = self.process_stage(messages_dict['stage_2'],2)
+                    stage3_results = self.process_stage(messages_dict['stage_3'],3)
+                    
+                    # Combine results
+                    batch_data = (batch_ids, batch_texts, batch_metadata)
+                    stage_outputs = (stage1_results, stage2_results, stage3_results)
+
+                    # Write results
+                    print(f"Processed {batch_counter} batches ({batch_counter * self.config.batch_size_triple} chunks)")
+                    for i in range(len(batch_ids)):
+                        result = self.prepare_result_dict(batch_data, stage_outputs, i)
+                        
+                        if self.config.debug_mode:
+                            self.debug_print_result(result)
+   
+                        output_stream.write(json.dumps(result, ensure_ascii=False) + "\n")
+                        output_stream.flush()
+
+    def convert_json_to_csv(self):
+        json2csv(
+            dataset = self.config.filename_pattern, 
+            output_dir=f"{self.config.output_directory}/triples_csv",
+            data_dir=f"{self.config.output_directory}/kg_extraction"
+        )
+        csvs_to_temp_graphml(
+            triple_node_file=f"{self.config.output_directory}/triples_csv/triple_nodes_{self.config.filename_pattern}_from_json_without_emb.csv",
+            triple_edge_file=f"{self.config.output_directory}/triples_csv/triple_edges_{self.config.filename_pattern}_from_json_without_emb.csv",
+            config = self.config
+        )
+    
+    def generate_concept_csv_temp(self, batch_size: int = None, **kwargs):
+        generate_concept(
+            model=self.model,
+            input_file=f"{self.config.output_directory}/triples_csv/missing_concepts_{self.config.filename_pattern}_from_json.csv",
+            output_folder=f"{self.config.output_directory}/concepts",
+            output_file="concept.json",
+            logging_file=f"{self.config.output_directory}/concepts/logging.txt",
+            config=self.config,
+            batch_size=batch_size if batch_size else self.config.batch_size_concept,
+            shard=self.config.current_shard_concept,
+            num_shards=self.config.total_shards_concept,
+            record = self.config.record,
+            **kwargs
+        )
+    
+    def create_concept_csv(self):
+        merge_csv_files(
+            output_file=f"{self.config.output_directory}/triples_csv/{self.config.filename_pattern}_from_json_with_concept.csv",
+            input_dir=f"{self.config.output_directory}/concepts",
+        )
+        all_concept_triples_csv_to_csv(
+            node_file=f'{self.config.output_directory}/triples_csv/triple_nodes_{self.config.filename_pattern}_from_json_without_emb.csv',
+            edge_file=f'{self.config.output_directory}/triples_csv/triple_edges_{self.config.filename_pattern}_from_json_without_emb.csv',
+            concepts_file=f'{self.config.output_directory}/triples_csv/{self.config.filename_pattern}_from_json_with_concept.csv',
+            output_node_file=f'{self.config.output_directory}/concept_csv/concept_nodes_{self.config.filename_pattern}_from_json_with_concept.csv',
+            output_edge_file=f'{self.config.output_directory}/concept_csv/concept_edges_{self.config.filename_pattern}_from_json_with_concept.csv',
+            output_full_concept_triple_edges=f'{self.config.output_directory}/concept_csv/triple_edges_{self.config.filename_pattern}_from_json_with_concept.csv',
+        )
+        
+    def convert_to_graphml(self):
+        csvs_to_graphml(
+            triple_node_file=f"{self.config.output_directory}/triples_csv/triple_nodes_{self.config.filename_pattern}_from_json_without_emb.csv",
+            text_node_file=f"{self.config.output_directory}/triples_csv/text_nodes_{self.config.filename_pattern}_from_json.csv",
+            concept_node_file=f"{self.config.output_directory}/concept_csv/concept_nodes_{self.config.filename_pattern}_from_json_with_concept.csv",
+            triple_edge_file=f"{self.config.output_directory}/concept_csv/triple_edges_{self.config.filename_pattern}_from_json_with_concept.csv",
+            text_edge_file=f"{self.config.output_directory}/triples_csv/text_edges_{self.config.filename_pattern}_from_json.csv",
+            concept_edge_file=f"{self.config.output_directory}/concept_csv/concept_edges_{self.config.filename_pattern}_from_json_with_concept.csv",
+            output_file=f"{self.config.output_directory}/kg_graphml/{self.config.filename_pattern}_graph.graphml",
+        )
+    
+    def add_numeric_id(self):
+        add_csv_columns(
+            node_csv=f"{self.config.output_directory}/triples_csv/triple_nodes_{self.config.filename_pattern}_from_json_without_emb.csv",
+            edge_csv=f"{self.config.output_directory}/concept_csv/triple_edges_{self.config.filename_pattern}_from_json_with_concept.csv",
+            text_csv=f"{self.config.output_directory}/triples_csv/text_nodes_{self.config.filename_pattern}_from_json.csv",
+            node_with_numeric_id=f"{self.config.output_directory}/triples_csv/triple_nodes_{self.config.filename_pattern}_from_json_without_emb_with_numeric_id.csv",
+            edge_with_numeric_id=f"{self.config.output_directory}/triples_csv/triple_edges_{self.config.filename_pattern}_from_json_without_emb_with_numeric_id.csv",
+            text_with_numeric_id=f"{self.config.output_directory}/triples_csv/text_nodes_{self.config.filename_pattern}_from_json_with_numeric_id.csv",
+        )
+
+    def compute_kg_embedding(self, encoder_model:BaseEmbeddingModel, batch_size: int = 2048):
+        encoder_model.compute_kg_embedding(
+            node_csv_without_emb=f"{self.config.output_directory}/triples_csv/triple_nodes_{self.config.filename_pattern}_from_json_without_emb.csv",
+            node_csv_file=f"{self.config.output_directory}/triples_csv/triple_nodes_{self.config.filename_pattern}_from_json_with_emb.csv",
+            edge_csv_without_emb=f"{self.config.output_directory}/concept_csv/triple_edges_{self.config.filename_pattern}_from_json_with_concept.csv",
+            edge_csv_file=f"{self.config.output_directory}/triples_csv/triple_edges_{self.config.filename_pattern}_from_json_with_concept_with_emb.csv",
+            text_node_csv_without_emb=f"{self.config.output_directory}/triples_csv/text_nodes_{self.config.filename_pattern}_from_json.csv",
+            text_node_csv=f"{self.config.output_directory}/triples_csv/text_nodes_{self.config.filename_pattern}_from_json_with_emb.csv",
+            batch_size = 2048
+        )
+
+    def create_faiss_index(self, index_type="HNSW,Flat"):
+        create_faiss_index(self.config.output_directory, self.config.filename_pattern, index_type)
+
+def parse_command_line_arguments() -> ProcessingConfig:
+    """Parse command line arguments and return configuration."""
+    parser = argparse.ArgumentParser(description="Knowledge Graph Extraction Pipeline")
+    
+    parser.add_argument("-m", "--model", type=str, required=True,
+                       default="meta-llama/Meta-Llama-3-8B-Instruct",
+                       help="Model path for knowledge extraction")
+    parser.add_argument("--data_dir", type=str, default="your_data_dir",
+                       help="Directory containing input data")
+    parser.add_argument("--file_name", type=str, default="en_simple_wiki_v0",
+                       help="Filename pattern to match")
+    parser.add_argument("-b", "--batch_size", type=int, default=16,
+                       help="Batch size for processing")
+    parser.add_argument("--output_dir", type=str, default="./generation_result_debug",
+                       help="Output directory for results")
+    parser.add_argument("--total_shards_triple", type=int, default=1,
+                       help="Total number of data shards")
+    parser.add_argument("--shard", type=int, default=0,
+                       help="Current shard index")
+    parser.add_argument("--bit8", action="store_true",
+                       help="Use 8-bit quantization")
+    parser.add_argument("--debug", action="store_true",
+                       help="Enable debug mode")
+    parser.add_argument("--resume", type=int, default=0,
+                       help="Resume from specific batch")
+    
+    args = parser.parse_args()
+    
+    return ProcessingConfig(
+        model_path=args.model,
+        data_directory=args.data_dir,
+        filename_pattern=args.file_name,
+        batch_size=args.batch_size,
+        output_directory=args.output_dir,
+        total_shards_triple=args.total_shards_triple,
+        current_shard_triple=args.shard,
+        use_8bit=args.bit8,
+        debug_mode=args.debug,
+        resume_from=args.resume
+    )
+
+
+def main():
+    """Main entry point for the knowledge graph extraction pipeline."""
+    config = parse_command_line_arguments()
+    extractor = KnowledgeGraphExtractor(config)
+    extractor.run_extraction()
+
+
+if __name__ == "__main__":
+    main() 

AIEC-RAG/atlas_rag/kg_construction/utils/csv_processing/csv_add_numeric_id.py

@@ -0,0 +1,160 @@
+import csv
+from tqdm import tqdm
+
+def check_created_csv_header(keyword, csv_dir):
+    keyword_to_paths ={
+        'cc_en':{
+            'node_with_numeric_id': f"{csv_dir}/triple_nodes_cc_en_from_json_without_emb_with_numeric_id.csv",
+            'edge_with_numeric_id': f"{csv_dir}/triple_edges_cc_en_from_json_without_emb_with_numeric_id.csv",
+            'text_with_numeric_id': f"{csv_dir}/text_nodes_cc_en_from_json_with_numeric_id.csv",
+            'concept_with_numeric_id': f"{csv_dir}/concept_nodes_pes2o_abstract_from_json_without_emb_with_numeric_id.csv",
+        },
+        'pes2o_abstract':{
+            'node_with_numeric_id': f"{csv_dir}/triple_nodes_pes2o_abstract_from_json_without_emb_with_numeric_id.csv",
+            'edge_with_numeric_id': f"{csv_dir}/triple_edges_pes2o_abstract_from_json_without_emb_full_concept_with_numeric_id.csv",
+            'text_with_numeric_id': f"{csv_dir}/text_nodes_pes2o_abstract_from_json_with_numeric_id.csv",
+        },
+        'en_simple_wiki_v0':{
+            'node_with_numeric_id': f"{csv_dir}/triple_nodes_en_simple_wiki_v0_from_json_without_emb_with_numeric_id.csv",
+            'edge_with_numeric_id': f"{csv_dir}/triple_edges_en_simple_wiki_v0_from_json_without_emb_full_concept_with_numeric_id.csv",
+            'text_with_numeric_id': f"{csv_dir}/text_nodes_en_simple_wiki_v0_from_json_with_numeric_id.csv",
+        },
+    }
+    for key, path in keyword_to_paths[keyword].items():
+        with open(path) as infile:
+            reader = csv.reader(infile)
+            header = next(reader)
+            print(f"Header of {key}: {header}")
+            
+            # print first 5 rows
+            for i, row in enumerate(reader):
+                if i < 1:
+                    print(row)
+                else:
+                    break
+
+def add_csv_columns(node_csv, edge_csv, text_csv, node_with_numeric_id, edge_with_numeric_id, text_with_numeric_id):
+    with open(node_csv) as infile, open(node_with_numeric_id, 'w', newline='') as outfile:
+        reader = csv.reader(infile)
+        writer = csv.writer(outfile)
+        header = next(reader)
+        print(header)
+        label_index = header.index(':LABEL')
+        header.insert(label_index, 'numeric_id')  # Add new column name
+        writer.writerow(header)
+        for row_number, row in tqdm(enumerate(reader), desc="Adding numeric ID"):
+            row.insert(label_index, row_number)  # Add numeric ID before ':LABEL'
+            writer.writerow(row)
+    with open(edge_csv) as infile, open(edge_with_numeric_id, 'w', newline='') as outfile:
+        reader = csv.reader(infile)
+        writer = csv.writer(outfile)
+        header = next(reader)
+        print(header)
+        label_index = header.index(':TYPE')
+        header.insert(label_index, 'numeric_id')  # Add new column name
+        writer.writerow(header)
+        for row_number, row in tqdm(enumerate(reader), desc="Adding numeric ID"):
+            row.insert(label_index, row_number)  # Add numeric ID before ':LABEL'
+            writer.writerow(row)
+    with open(text_csv) as infile, open(text_with_numeric_id, 'w', newline='') as outfile:
+        reader = csv.reader(infile)
+        writer = csv.writer(outfile)
+        header = next(reader)
+        print(header)
+        label_index = header.index(':LABEL')
+        header.insert(label_index, 'numeric_id')  # Add new column name
+        writer.writerow(header)
+        for row_number, row in tqdm(enumerate(reader), desc="Adding numeric ID"):
+            row.insert(label_index, row_number)  # Add numeric ID before ':LABEL'
+            writer.writerow(row)
+            
+
+# def add_csv_columns(keyword, csv_dir):
+#     keyword_to_paths ={
+#         'cc_en':{
+#             'node_csv': f"{csv_dir}/triple_nodes_cc_en_from_json_without_emb.csv",
+#             'edge_csv': f"{csv_dir}/triple_edges_cc_en_from_json_without_emb.csv",
+#             'text_csv': f"{csv_dir}/text_nodes_cc_en_from_json.csv",
+            
+#             'node_with_numeric_id': f"{csv_dir}/triple_nodes_cc_en_from_json_without_emb_with_numeric_id.csv",
+#             'edge_with_numeric_id': f"{csv_dir}/triple_edges_cc_en_from_json_without_emb_with_numeric_id.csv",
+#             'text_with_numeric_id': f"{csv_dir}/text_nodes_cc_en_from_json_with_numeric_id.csv"
+#         },
+#         'pes2o_abstract':{
+#             'node_csv': f"{csv_dir}/triple_nodes_pes2o_abstract_from_json_without_emb.csv",
+#             'edge_csv': f"{csv_dir}/triple_edges_pes2o_abstract_from_json_without_emb_full_concept.csv",
+#             'text_csv': f"{csv_dir}/text_nodes_pes2o_abstract_from_json.csv",
+            
+#             'node_with_numeric_id': f"{csv_dir}/triple_nodes_pes2o_abstract_from_json_without_emb_with_numeric_id.csv",
+#             'edge_with_numeric_id': f"{csv_dir}/triple_edges_pes2o_abstract_from_json_without_emb_full_concept_with_numeric_id.csv",
+#             'text_with_numeric_id': f"{csv_dir}/text_nodes_pes2o_abstract_from_json_with_numeric_id.csv"
+#         },
+#         'en_simple_wiki_v0':{
+#             'node_csv': f"{csv_dir}/triple_nodes_en_simple_wiki_v0_from_json_without_emb.csv",
+#             'edge_csv': f"{csv_dir}/triple_edges_en_simple_wiki_v0_from_json_without_emb_full_concept.csv",
+#             'text_csv': f"{csv_dir}/text_nodes_en_simple_wiki_v0_from_json.csv",
+            
+#             'node_with_numeric_id': f"{csv_dir}/triple_nodes_en_simple_wiki_v0_from_json_without_emb_with_numeric_id.csv",
+#             'edge_with_numeric_id': f"{csv_dir}/triple_edges_en_simple_wiki_v0_from_json_without_emb_full_concept_with_numeric_id.csv",
+#             'text_with_numeric_id': f"{csv_dir}/text_nodes_en_simple_wiki_v0_from_json_with_numeric_id.csv"
+#         },
+#     }
+#     # ouput node
+#     with open(keyword_to_paths[keyword]['node_csv']) as infile, open(keyword_to_paths[keyword]['node_with_numeric_id'], 'w') as outfile:
+#         reader = csv.reader(infile)
+#         writer = csv.writer(outfile)
+
+#         # Read the header
+#         header = next(reader)
+#         print(header)
+#         # Insert 'numeric_id' before ':LABEL'
+#         label_index = header.index(':LABEL')
+#         header.insert(label_index, 'numeric_id')  # Add new column name
+#         writer.writerow(header)
+
+#         # Process each row and add a numeric ID
+#         for row_number, row in tqdm(enumerate(reader), desc="Adding numeric ID"):
+#             row.insert(label_index, row_number)  # Add numeric ID before ':LABEL'
+#             writer.writerow(row)
+            
+#     # output edge (TYPE instead of LABEL for edge)
+#     with open(keyword_to_paths[keyword]['edge_csv']) as infile, open(keyword_to_paths[keyword]['edge_with_numeric_id'], 'w') as outfile:
+#         reader = csv.reader(infile)
+#         writer = csv.writer(outfile)
+
+#         # Read the header
+#         header = next(reader)
+#         print(header)
+#         # Insert 'numeric_id' before ':TYPE'
+#         label_index = header.index(':TYPE')
+#         header.insert(label_index, 'numeric_id')  # Add new column name
+#         writer.writerow(header)
+
+#         # Process each row and add a numeric ID
+#         for row_number, row in tqdm(enumerate(reader), desc="Adding numeric ID"):
+#             row.insert(label_index, row_number)  # Add numeric ID before ':LABEL'
+#             writer.writerow(row)
+            
+#     # output text
+#     with open(keyword_to_paths[keyword]['text_csv']) as infile, open(keyword_to_paths[keyword]['text_with_numeric_id'], 'w') as outfile:
+#         reader = csv.reader(infile)
+#         writer = csv.writer(outfile)
+
+#         # Read the header
+#         header = next(reader)
+#         print(header)
+#         # Insert 'numeric_id' before ':LABEL'
+#         label_index = header.index(':LABEL')
+#         header.insert(label_index, 'numeric_id')  # Add new column name
+#         writer.writerow(header)
+
+#         # Process each row and add a numeric ID
+#         for row_number, row in tqdm(enumerate(reader), desc="Adding numeric ID"):
+#             row.insert(label_index, row_number)  # Add numeric ID before ':LABEL'
+#             writer.writerow(row)
+
+if __name__ == "__main__":
+    keyword = "en_simple_wiki_v0"
+    csv_dir = "./import"  # Change this to your CSV directory
+    add_csv_columns(keyword, csv_dir)
+    # check_created_csv_header(keyword)

AIEC-RAG/atlas_rag/kg_construction/utils/csv_processing/csv_to_graphml.py

@@ -0,0 +1,189 @@
+import networkx as nx
+import csv
+import ast
+import hashlib
+import os
+from atlas_rag.kg_construction.triple_config import ProcessingConfig
+import pickle
+
+def get_node_id(entity_name, entity_to_id={}):
+    """Returns existing or creates new nX ID for an entity using a hash-based approach."""
+    if entity_name not in entity_to_id:
+        # Use a hash function to generate a unique ID
+        hash_object = hashlib.sha256(entity_name.encode('utf-8'))
+        hash_hex = hash_object.hexdigest()  # Get the hexadecimal representation of the hash
+        # Use the first 8 characters of the hash as the ID (you can adjust the length as needed)
+        entity_to_id[entity_name] = hash_hex
+    return entity_to_id[entity_name]
+
+def csvs_to_temp_graphml(triple_node_file, triple_edge_file, config:ProcessingConfig=None):
+    g = nx.DiGraph()
+    entity_to_id = {}
+
+    # Add triple nodes
+    with open(triple_node_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            node_id = row["name:ID"]
+            mapped_id = get_node_id(node_id, entity_to_id)
+            if mapped_id not in g.nodes:
+                g.add_node(mapped_id, id=node_id, type=row["type"]) 
+            
+
+    # Add triple edges
+    with open(triple_edge_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            start_id = get_node_id(row[":START_ID"], entity_to_id)
+            end_id = get_node_id(row[":END_ID"], entity_to_id)
+            # Check if edge already exists to prevent duplicates
+            if not g.has_edge(start_id, end_id):
+                g.add_edge(start_id, end_id, relation=row["relation"], type=row[":TYPE"])
+
+    # save graph to 
+    output_name = f"{config.output_directory}/kg_graphml/{config.filename_pattern}_without_concept.pkl"
+    # check if output file directory exists
+    output_dir = os.path.dirname(output_name)
+    if output_dir and not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    # store the graph to a pickle file
+    with open(output_name, 'wb') as output_file:
+        pickle.dump(g, output_file)
+    
+    
+
+def csvs_to_graphml(triple_node_file, text_node_file, concept_node_file,
+                    triple_edge_file, text_edge_file, concept_edge_file,
+                    output_file):
+    '''
+    Convert multiple CSV files into a single GraphML file.
+    
+    Types of nodes to be added to the graph:
+    - Triple nodes: Nodes representing triples, with properties like subject, predicate, object.
+    - Text nodes: Nodes representing text, with properties like text content.
+    - Concept nodes: Nodes representing concepts, with properties like concept name and type.
+
+    Types of edges to be added to the graph:
+    - Triple edges: Edges representing relationships between triples, with properties like relation type.
+    - Text edges: Edges representing relationships between text and nodes, with properties like text type.
+    - Concept edges: Edges representing relationships between concepts and nodes, with properties like concept type.
+    
+    DiGraph networkx attributes:
+    Node:
+    - type: Type of the node (e.g., entity, event, text, concept).
+    - file_id: List of text IDs the node is associated with.
+    - id: Node Name 
+    Edge:
+    - relation: relation name
+    - file_id: List of text IDs the edge is associated with.
+    - type: Type of the edge (e.g., Source, Relation, Concept).
+    - synsets: List of synsets associated with the edge.
+    
+    '''
+    g = nx.DiGraph()
+    entity_to_id = {}
+
+    # Add triple nodes
+    with open(triple_node_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            node_id = row["name:ID"]
+            mapped_id = get_node_id(node_id, entity_to_id)
+            # Check if node already exists to prevent duplicates
+            if mapped_id not in g.nodes:
+                g.add_node(mapped_id, id=node_id, type=row["type"])
+            
+    # Add text nodes
+    with open(text_node_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            node_id = row["text_id:ID"]
+            # Check if node already exists to prevent duplicates
+            if node_id not in g.nodes:
+                g.add_node(node_id, file_id=node_id, id=row["original_text"], type="passage")
+
+    # Add concept nodes
+    with open(concept_node_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            node_id = row["concept_id:ID"]
+            # Check if node already exists to prevent duplicates
+            if node_id not in g.nodes:
+                g.add_node(node_id, file_id="concept_file", id=row["name"], type="concept")
+
+    # Add file id for triple nodes and concept nodes when add the edges
+    
+    # Add triple edges
+    with open(triple_edge_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            start_id = get_node_id(row[":START_ID"], entity_to_id)
+            end_id = get_node_id(row[":END_ID"], entity_to_id)
+            # Check if edge already exists to prevent duplicates
+            if not g.has_edge(start_id, end_id):
+                g.add_edge(start_id, end_id, relation=row["relation"], type=row[":TYPE"])
+                # Add file_id to start and end nodes if they are triple or concept nodes
+                for node_id in [start_id, end_id]:
+                    if g.nodes[node_id]['type'] in ['triple', 'concept'] and 'file_id' not in g.nodes[node_id]:
+                        g.nodes[node_id]['file_id'] = row.get("file_id", "triple_file")
+            
+            # Add concepts to the edge
+            concepts = ast.literal_eval(row["concepts"])
+            for concept in concepts:
+                if "concepts" not in g.edges[start_id, end_id]:
+                    g.edges[start_id, end_id]['concepts'] = str(concept)
+                else:
+                    # Avoid duplicate concepts by checking if concept is already in the list
+                    current_concepts = g.edges[start_id, end_id]['concepts'].split(",")
+                    if str(concept) not in current_concepts:
+                        g.edges[start_id, end_id]['concepts'] += "," + str(concept)
+            
+
+    # Add text edges
+    with open(text_edge_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            start_id = get_node_id(row[":START_ID"], entity_to_id)
+            end_id = row[":END_ID"]
+            # Check if edge already exists to prevent duplicates
+            if not g.has_edge(start_id, end_id):
+                g.add_edge(start_id, end_id, relation="mention in", type=row[":TYPE"])
+                # Add file_id to start node if it is a triple or concept node
+                if 'file_id' in g.nodes[start_id]:
+                    g.nodes[start_id]['file_id'] += "," + str(end_id)
+                else:
+                    g.nodes[start_id]['file_id'] = str(end_id)
+
+    # Add concept edges between triple nodes and concept nodes
+    with open(concept_edge_file, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            start_id = get_node_id(row[":START_ID"], entity_to_id)
+            end_id = row[":END_ID"] # end id is concept node id
+            if not g.has_edge(start_id, end_id):
+                g.add_edge(start_id, end_id, relation=row["relation"], type=row[":TYPE"])
+
+    # Write to GraphML
+    # check if output file directory exists
+    output_dir = os.path.dirname(output_file)
+    if output_dir and not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    nx.write_graphml(g, output_file, infer_numeric_types=True)
+    
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description='Convert CSV files to GraphML format.')
+    parser.add_argument('--triple_node_file', type=str, required=True, help='Path to the triple node CSV file.')
+    parser.add_argument('--text_node_file', type=str, required=True, help='Path to the text node CSV file.')
+    parser.add_argument('--concept_node_file', type=str, required=True, help='Path to the concept node CSV file.')
+    parser.add_argument('--triple_edge_file', type=str, required=True, help='Path to the triple edge CSV file.')
+    parser.add_argument('--text_edge_file', type=str, required=True, help='Path to the text edge CSV file.')
+    parser.add_argument('--concept_edge_file', type=str, required=True, help='Path to the concept edge CSV file.')
+    parser.add_argument('--output_file', type=str, required=True, help='Path to the output GraphML file.')
+
+    args = parser.parse_args()
+    
+    csvs_to_graphml(args.triple_node_file, args.text_node_file, args.concept_node_file,
+                    args.triple_edge_file, args.text_edge_file, args.concept_edge_file,
+                    args.output_file)

AIEC-RAG/atlas_rag/kg_construction/utils/csv_processing/csv_to_npy.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import numpy as np
+from ast import literal_eval  # Safer string-to-list conversion
+import os
+
+CHUNKSIZE = 100_000  # Adjust based on your RAM (100K rows per chunk)
+EMBEDDING_COL = "embedding:STRING"  # Column name with embeddings
+# DIMENSION = 32  # Update with your embedding dimension
+ENTITY_ONLY = True
+def parse_embedding(embed_str):
+    """Convert embedding string to numpy array"""
+    # Remove brackets and convert to list
+    return np.array(literal_eval(embed_str), dtype=np.float32)
+
+# Create memory-mapped numpy file
+def convert_csv_to_npy(csv_path, npy_path):
+    total_embeddings = 0
+    # check dir exist, if not then create it
+    os.makedirs(os.path.dirname(npy_path), exist_ok=True)
+    
+    with open(npy_path, "wb") as f:
+        pass  # Initialize empty file
+
+    # Process CSV in chunks
+    for chunk_idx, df_chunk in enumerate(
+        pd.read_csv(csv_path, chunksize=CHUNKSIZE, usecols=[EMBEDDING_COL])
+    ):  
+        
+        
+        # Parse embeddings
+        embeddings = np.stack(
+            df_chunk[EMBEDDING_COL].apply(parse_embedding).values
+        )
+        
+        # Verify dimensions
+        # assert embeddings.shape[1] == DIMENSION, \
+        #     f"Dimension mismatch at chunk {chunk_idx}"
+        total_embeddings += embeddings.shape[0]
+        # Append to .npy file
+        with open(npy_path, "ab") as f:
+            np.save(f, embeddings.astype(np.float32))
+        
+        print(f"Processed chunk {chunk_idx} ({CHUNKSIZE*(chunk_idx+1)} rows)")
+    print(f"Total number of embeddings: {total_embeddings}")
+    print("Conversion complete!")
+    
+if __name__ == "__main__":
+    keyword = 'cc_en'  # Change this to your desired keyword
+    csv_dir="./import" # Change this to your CSV directory
+    keyword_to_paths ={
+        'cc_en':{
+            'node_csv': f"{csv_dir}/triple_nodes_cc_en_from_json_2.csv",
+            # 'edge_csv': f"{csv_dir}/triple_edges_cc_en_from_json_2.csv",
+            'text_csv': f"{csv_dir}/text_nodes_cc_en_from_json_with_emb.csv",
+        },
+        'pes2o_abstract':{
+            'node_csv': f"{csv_dir}/triple_nodes_pes2o_abstract_from_json.csv",
+            # 'edge_csv': f"{csv_dir}/triple_edges_pes2o_abstract_from_json.csv",
+            'text_csv': f"{csv_dir}/text_nodes_pes2o_abstract_from_json_with_emb.csv",
+        },
+        'en_simple_wiki_v0':{
+            'node_csv': f"{csv_dir}/triple_nodes_en_simple_wiki_v0_from_json.csv",
+            # 'edge_csv': f"{csv_dir}/triple_edges_en_simple_wiki_v0_from_json.csv",
+            'text_csv': f"{csv_dir}/text_nodes_en_simple_wiki_v0_from_json_with_emb.csv",
+        },
+    }
+    for key, path in keyword_to_paths[keyword].items():
+        npy_path = path.replace(".csv", ".npy")
+        convert_csv_to_npy(path, npy_path)
+        print(f"Converted {path} to {npy_path}")

AIEC-RAG/atlas_rag/kg_construction/utils/csv_processing/merge_csv.py

@@ -0,0 +1,27 @@
+import os
+import glob
+
+def merge_csv_files(output_file, input_dir):
+    """
+    Merge all CSV files in the input directory into a single output file.
+
+    Args:
+        output_file (str): Path to the output CSV file.
+        input_dir (str): Directory containing the input CSV files.
+    """
+    # Delete the output file if it exists
+    if os.path.exists(output_file):
+        os.remove(output_file)
+
+    # Write the header to the output file
+    with open(output_file, 'w') as outfile:
+        outfile.write("node,conceptualized_node,node_type\n")
+
+    # Append the contents of all CSV files in the input directory
+    for csv_file in glob.glob(os.path.join(input_dir, '*.csv')):
+        with open(csv_file, 'r') as infile:
+            # Skip the header line
+            next(infile)
+            # Append the remaining lines to the output file
+            with open(output_file, 'a') as outfile:
+                outfile.writelines(infile)

AIEC-RAG/atlas_rag/kg_construction/utils/json_processing/json_to_csv.py

@@ -0,0 +1,277 @@
+from tqdm import tqdm
+import argparse
+import os
+import csv 
+import json
+import re
+import hashlib
+
+# Increase the field size limit
+csv.field_size_limit(10 * 1024 * 1024)  # 10 MB limit
+
+
+# Function to compute a hash ID from text
+def compute_hash_id(text):
+    # Use SHA-256 to generate a hash
+    hash_object = hashlib.sha256(text.encode('utf-8'))
+    return hash_object.hexdigest()  # Return hash as a hex string
+
+def clean_text(text):
+    # remove NUL as well
+    
+    new_text = text.replace("\n", " ").replace("\r", " ").replace("\t", " ").replace("\v", " ").replace("\f", " ").replace("\b", " ").replace("\a", " ").replace("\e", " ").replace(";", ",")
+    new_text = new_text.replace("\x00", "")
+    new_text = re.sub(r'\s+', ' ', new_text).strip()
+
+    return new_text
+
+def remove_NUL(text):
+    return text.replace("\x00", "")
+
+def json2csv(dataset, data_dir, output_dir, test=False):
+    """
+    Convert JSON files to CSV files for nodes, edges, and missing concepts.
+
+    Args:
+        dataset (str): Name of the dataset.
+        data_dir (str): Directory containing the JSON files.
+        output_dir (str): Directory to save the output CSV files.
+        test (bool): If True, run in test mode (process only 3 files).
+    """
+    visited_nodes = set()
+    visited_hashes = set()
+
+    all_entities = set()
+    all_events = set()
+    all_relations = set()
+
+    file_dir_list = [f for f in os.listdir(data_dir) if dataset in f]
+    file_dir_list = sorted(file_dir_list)
+    if test:
+        file_dir_list = file_dir_list[:3]
+    print("Loading data from the json files")
+    print("Number of files: ", len(file_dir_list))
+
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    # Define output file paths
+    node_csv_without_emb = os.path.join(output_dir, f"triple_nodes_{dataset}_from_json_without_emb.csv")
+    edge_csv_without_emb = os.path.join(output_dir, f"triple_edges_{dataset}_from_json_without_emb.csv")
+    node_text_file = os.path.join(output_dir, f"text_nodes_{dataset}_from_json.csv")
+    edge_text_file = os.path.join(output_dir, f"text_edges_{dataset}_from_json.csv")
+    missing_concepts_file = os.path.join(output_dir, f"missing_concepts_{dataset}_from_json.csv")
+
+    if test:
+        node_text_file = os.path.join(output_dir, f"text_nodes_{dataset}_from_json_test.csv")
+        edge_text_file = os.path.join(output_dir, f"text_edges_{dataset}_from_json_test.csv")
+        node_csv_without_emb = os.path.join(output_dir, f"triple_nodes_{dataset}_from_json_without_emb_test.csv")
+        edge_csv_without_emb = os.path.join(output_dir, f"triple_edges_{dataset}_from_json_without_emb_test.csv")
+        missing_concepts_file = os.path.join(output_dir, f"missing_concepts_{dataset}_from_json_test.csv")
+
+    # Open CSV files for writing
+    with open(node_text_file, "w", newline='', encoding='utf-8', errors='ignore') as csvfile_node_text, \
+         open(edge_text_file, "w", newline='', encoding='utf-8', errors='ignore') as csvfile_edge_text, \
+         open(node_csv_without_emb, "w", newline='', encoding='utf-8', errors='ignore') as csvfile_node, \
+         open(edge_csv_without_emb, "w", newline='', encoding='utf-8', errors='ignore') as csvfile_edge:
+
+        csv_writer_node_text = csv.writer(csvfile_node_text)
+        csv_writer_edge_text = csv.writer(csvfile_edge_text)
+        writer_node = csv.writer(csvfile_node)
+        writer_edge = csv.writer(csvfile_edge)
+
+        # Write headers
+        csv_writer_node_text.writerow(["text_id:ID", "original_text", ":LABEL"])
+        csv_writer_edge_text.writerow([":START_ID", ":END_ID", ":TYPE"])
+        writer_node.writerow(["name:ID", "type", "concepts", "synsets", ":LABEL"])
+        writer_edge.writerow([":START_ID", ":END_ID", "relation", "concepts", "synsets", ":TYPE"])
+
+        # Process each file
+        for file_dir in tqdm(file_dir_list):
+            print("Processing file for file ids: ", file_dir)
+            with open(os.path.join(data_dir, file_dir), "r") as jsonfile:
+                for line in jsonfile:
+                    data = json.loads(line.strip())
+                    original_text = data["original_text"]
+                    original_text = remove_NUL(original_text)
+                    if "Here is the passage." in original_text:
+                        original_text = original_text.split("Here is the passage.")[-1]
+                    eot_token = "<|eot_id|>"
+                    original_text = original_text.split(eot_token)[0]
+
+                    text_hash_id = compute_hash_id(original_text)
+
+                    # Write the original text as nodes
+                    if text_hash_id not in visited_hashes:
+                        visited_hashes.add(text_hash_id)
+                        csv_writer_node_text.writerow([text_hash_id, original_text, "Text"])
+
+                    file_id = str(data["id"])
+                    entity_relation_dict = data["entity_relation_dict"]
+                    event_entity_relation_dict = data["event_entity_relation_dict"]
+                    event_relation_dict = data["event_relation_dict"]
+
+                    # Process entity triples
+                    entity_triples = []
+                    for entity_triple in entity_relation_dict:
+                        try:
+                            assert isinstance(entity_triple["Head"], str)
+                            assert isinstance(entity_triple["Relation"], str)
+                            assert isinstance(entity_triple["Tail"], str)
+
+                            head_entity = entity_triple["Head"]
+                            relation = entity_triple["Relation"]
+                            tail_entity = entity_triple["Tail"]
+
+                            # Clean the text
+                            head_entity = clean_text(head_entity)
+                            relation = clean_text(relation)
+                            tail_entity = clean_text(tail_entity)
+
+                            if head_entity.isspace() or len(head_entity) == 0 or tail_entity.isspace() or len(tail_entity) == 0:
+                                continue
+
+                            entity_triples.append((head_entity, relation, tail_entity))
+                        except:
+                            print(f"Error processing entity triple: {entity_triple}")
+                            continue
+
+                    # Process event triples
+                    event_triples = []
+                    for event_triple in event_relation_dict:
+                        try:
+                            assert isinstance(event_triple["Head"], str)
+                            assert isinstance(event_triple["Relation"], str)
+                            assert isinstance(event_triple["Tail"], str)
+                            head_event = event_triple["Head"]
+                            relation = event_triple["Relation"]
+                            tail_event = event_triple["Tail"]
+                            
+                            # Clean the text
+                            head_event = clean_text(head_event)
+                            relation = clean_text(relation)
+                            tail_event = clean_text(tail_event)
+
+                            if head_event.isspace() or len(head_event) == 0 or tail_event.isspace() or len(tail_event) == 0:
+                                continue
+
+                            event_triples.append((head_event, relation, tail_event))
+                        except:
+                            print(f"Error processing event triple: {event_triple}")
+
+                    # Process event-entity triples
+                    event_entity_triples = []
+                    for event_entity_participations in event_entity_relation_dict:
+                        if "Event" not in event_entity_participations or "Entity" not in event_entity_participations:
+                            continue
+                        if not isinstance(event_entity_participations["Event"], str) or not isinstance(event_entity_participations["Entity"], list):
+                            continue
+
+                        for entity in event_entity_participations["Entity"]:
+                            if not isinstance(entity, str):
+                                continue
+
+                            entity = clean_text(entity)
+                            event = clean_text(event_entity_participations["Event"])
+
+                            if event.isspace() or len(event) == 0 or entity.isspace() or len(entity) == 0:
+                                continue
+
+                            event_entity_triples.append((event, "is participated by", entity))
+
+                    # Write nodes and edges to CSV files
+                    for entity_triple in entity_triples:
+                        head_entity, relation, tail_entity = entity_triple
+                        if head_entity is None or tail_entity is None or relation is None:
+                            continue
+                        if head_entity.isspace() or tail_entity.isspace() or relation.isspace():
+                            continue
+                        if len(head_entity) == 0 or len(tail_entity) == 0 or len(relation) == 0:
+                            continue
+
+                        # Add nodes to files
+                        if head_entity not in visited_nodes:
+                            visited_nodes.add(head_entity)
+                            all_entities.add(head_entity)
+                            writer_node.writerow([head_entity, "entity", [], [], "Node"])
+                            csv_writer_edge_text.writerow([head_entity, text_hash_id, "Source"])
+
+                        if tail_entity not in visited_nodes:
+                            visited_nodes.add(tail_entity)
+                            all_entities.add(tail_entity)
+                            writer_node.writerow([tail_entity, "entity", [], [], "Node"])
+                            csv_writer_edge_text.writerow([tail_entity, text_hash_id, "Source"])
+
+                        all_relations.add(relation)
+                        writer_edge.writerow([head_entity, tail_entity, relation, [], [], "Relation"])
+
+                    for event_triple in event_triples:
+                        head_event, relation, tail_event = event_triple
+                        if head_event is None or tail_event is None or relation is None:
+                            continue
+                        if head_event.isspace() or tail_event.isspace() or relation.isspace():
+                            continue
+                        if len(head_event) == 0 or len(tail_event) == 0 or len(relation) == 0:
+                            continue
+
+                        # Add nodes to files
+                        if head_event not in visited_nodes:
+                            visited_nodes.add(head_event)
+                            all_events.add(head_event)
+                            writer_node.writerow([head_event, "event", [], [], "Node"])
+                            csv_writer_edge_text.writerow([head_event, text_hash_id, "Source"])
+
+                        if tail_event not in visited_nodes:
+                            visited_nodes.add(tail_event)
+                            all_events.add(tail_event)
+                            writer_node.writerow([tail_event, "event", [], [], "Node"])
+                            csv_writer_edge_text.writerow([tail_event, text_hash_id, "Source"])
+
+                        all_relations.add(relation)
+                        writer_edge.writerow([head_event, tail_event, relation, [], [], "Relation"])
+
+                    for event_entity_triple in event_entity_triples:
+                        head_event, relation, tail_entity = event_entity_triple
+                        if head_event is None or tail_entity is None or relation is None:
+                            continue
+                        if head_event.isspace() or tail_entity.isspace() or relation.isspace():
+                            continue
+                        if len(head_event) == 0 or len(tail_entity) == 0 or len(relation) == 0:
+                            continue
+
+                        # Add nodes to files
+                        if head_event not in visited_nodes:
+                            visited_nodes.add(head_event)
+                            all_events.add(head_event)
+                            writer_node.writerow([head_event, "event", [], [], "Node"])
+                            csv_writer_edge_text.writerow([head_event, text_hash_id, "Source"])
+
+                        if tail_entity not in visited_nodes:
+                            visited_nodes.add(tail_entity)
+                            all_entities.add(tail_entity)
+                            writer_node.writerow([tail_entity, "entity", [], [], "Node"])
+                            csv_writer_edge_text.writerow([tail_entity, text_hash_id, "Source"])
+
+                        all_relations.add(relation)
+                        writer_edge.writerow([head_event, tail_entity, relation, [], [], "Relation"])
+
+    # Write missing concepts to CSV
+    with open(missing_concepts_file, "w", newline='', encoding='utf-8', errors='ignore') as csvfile:
+        writer = csv.writer(csvfile)
+        writer.writerow(["Name", "Type"])
+        for entity in all_entities:
+            writer.writerow([entity, "Entity"])
+        for event in all_events:
+            writer.writerow([event, "Event"])
+        for relation in all_relations:
+            writer.writerow([relation, "Relation"])
+
+    print("Data to CSV completed successfully.")
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", type=str, required=True, help="[pes2o_abstract, en_simple_wiki_v0, cc_en]")
+    parser.add_argument("--data_dir", type=str, required=True, help="Directory containing the graph raw JSON files")
+    parser.add_argument("--output_dir", type=str, required=True, help="Directory to save the output CSV files")
+    parser.add_argument("--test", action="store_true", help="Test the script")
+    args = parser.parse_args()
+    json2csv(dataset=args.dataset, data_dir=args.data_dir, output_dir=args.output_dir, test=args.test)

AIEC-RAG/atlas_rag/kg_construction/utils/json_processing/json_to_graphml.py

@@ -0,0 +1,169 @@
+import networkx as nx
+import json
+from tqdm import tqdm
+import os
+import hashlib
+
+def get_node_id(entity_name, entity_to_id):
+    """Returns existing or creates new nX ID for an entity using a hash-based approach."""
+    if entity_name not in entity_to_id:
+        # Use a hash function to generate a unique ID
+        hash_object = hashlib.md5(entity_name.encode())  # Use MD5 or another hashing algorithm
+        hash_hex = hash_object.hexdigest()  # Get the hexadecimal representation of the hash
+        # Use the first 8 characters of the hash as the ID (you can adjust the length as needed)
+        entity_to_id[entity_name] = f'n{hash_hex[:16]}'
+    return entity_to_id[entity_name]
+
+def clean_text(text):
+    # remove NUL as well
+    new_text = text.replace("\n", " ").replace("\r", " ").replace("\t", " ").replace("\v", " ").replace("\f", " ").replace("\b", " ").replace("\a", " ").replace("\e", " ").replace(";", ",")
+    new_text = new_text.replace("\x00", "")
+    return new_text
+
+
+def process_kg_data(input_passage_dir, input_triple_dir, output_dir, keyword):
+    # Get file names containing the keyword
+    file_names = [file for file in list(os.listdir(input_triple_dir)) if keyword in file]
+    print(f"Keyword: {keyword}")
+    print(f"Number of files: {len(file_names)}")
+    print(file_names)
+
+    passage_file_names = [file for file in list(os.listdir(input_passage_dir)) if keyword in file]
+    print(f'Passage file names: {passage_file_names}')
+
+    g = nx.DiGraph()
+    print("Graph created.")
+    entity_to_id = {}
+    # check if output directory exists, if not create it
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        print(f"Output directory {output_dir} created.")
+        
+    output_path = f"{output_dir}/{keyword}_kg_from_corpus.graphml"
+
+    # Create the original_text to node_id dictionary and add passage node to the graph
+    with open(f"{input_passage_dir}/{passage_file_names[0]}") as f:
+        data = json.load(f)
+        for item in tqdm(data, desc="Processing passages"):
+            passage_id = item["id"]
+            passage_text = item["text"]
+            node_id = get_node_id(passage_text, entity_to_id)
+            if passage_text.isspace() or len(passage_text) == 0:
+                continue
+            # Add the passage node to the graph
+            g.add_node(node_id, type="passage", id=passage_text, file_id=passage_id)
+
+    for file_name in tqdm(file_names):
+        print(f"Processing {file_name}")
+        input_file_path = f"{input_triple_dir}/{file_name}"
+        with open(input_file_path) as f:
+            for line in tqdm(f):
+                data = json.loads(line)
+                metadata = data["metadata"]
+                file_id = data["id"]
+                original_text = data["original_text"]
+                entity_relation_dict = data["entity_relation_dict"]
+                event_entity_relation_dict = data["event_entity_relation_dict"]
+                event_relation_dict = data["event_relation_dict"]
+
+                # Process entity triples
+                entity_triples = []
+                for entity_triple in entity_relation_dict:
+                    if not all(key in entity_triple for key in ["Head", "Relation", "Tail"]):
+                        continue
+                    head_entity = clean_text(entity_triple["Head"])
+                    relation = clean_text(entity_triple["Relation"])
+                    tail_entity = clean_text(entity_triple["Tail"])
+                    if head_entity.isspace() or len(head_entity) == 0 or tail_entity.isspace() or len(tail_entity) == 0:
+                        continue
+                    entity_triples.append((head_entity, relation, tail_entity))
+
+                # Add entity triples to the graph
+                for triple in entity_triples:
+                    head_id = get_node_id(triple[0], entity_to_id)
+                    tail_id = get_node_id(triple[2], entity_to_id)
+                    g.add_node(head_id, type="entity", id=triple[0])
+                    g.add_node(tail_id, type="entity", id=triple[2])
+                    g.add_edge(head_id, get_node_id(original_text, entity_to_id), relation='mention in')
+                    g.add_edge(tail_id, get_node_id(original_text, entity_to_id), relation='mention in')
+                    g.add_edge(head_id, tail_id, relation=triple[1])
+                    for node_id in [head_id, tail_id]:
+                        if "file_id" not in g.nodes[node_id]:
+                            g.nodes[node_id]["file_id"] = str(file_id)
+                        else:
+                            g.nodes[node_id]["file_id"] += "," + str(file_id)
+                    edge = g.edges[head_id, tail_id]
+                    if "file_id" not in edge:
+                        edge["file_id"] = str(file_id)
+                    else:
+                        edge["file_id"] += "," + str(file_id)
+
+                # Process event triples
+                event_triples = []
+                for event_triple in event_relation_dict:
+                    if not all(key in event_triple for key in ["Head", "Relation", "Tail"]):
+                        continue
+                    head_event = clean_text(event_triple["Head"])
+                    relation = clean_text(event_triple["Relation"])
+                    tail_event = clean_text(event_triple["Tail"])
+                    if head_event.isspace() or len(head_event) == 0 or tail_event.isspace() or len(tail_event) == 0:
+                        continue
+                    event_triples.append((head_event, relation, tail_event))
+
+                # Add event triples to the graph
+                for triple in event_triples:
+                    head_id = get_node_id(triple[0], entity_to_id)
+                    tail_id = get_node_id(triple[2], entity_to_id)
+                    g.add_node(head_id, type="event", id=triple[0])
+                    g.add_node(tail_id, type="event", id=triple[2])
+                    g.add_edge(head_id, get_node_id(original_text, entity_to_id), relation='mention in')
+                    g.add_edge(tail_id, get_node_id(original_text, entity_to_id), relation='mention in')
+                    g.add_edge(head_id, tail_id, relation=triple[1])
+                    for node_id in [head_id, tail_id]:
+                        if "file_id" not in g.nodes[node_id]:
+                            g.nodes[node_id]["file_id"] = str(file_id)
+                        else:
+                            g.nodes[node_id]["file_id"] += "," + str(file_id)
+                    edge = g.edges[head_id, tail_id]
+                    if "file_id" not in edge:
+                        edge["file_id"] = str(file_id)
+                    else:
+                        edge["file_id"] += "," + str(file_id)
+
+                # Process event-entity triples
+                event_entity_triples = []
+                for event_entity_participations in event_entity_relation_dict:
+                    if not all(key in event_entity_participations for key in ["Event", "Entity"]):
+                        continue
+                    event = clean_text(event_entity_participations["Event"])
+                    if event.isspace() or len(event) == 0:
+                        continue
+                    for entity in event_entity_participations["Entity"]:
+                        if not isinstance(entity, str) or entity.isspace() or len(entity) == 0:
+                            continue
+                        entity = clean_text(entity)
+                        event_entity_triples.append((event, "is participated by", entity))
+
+                # Add event-entity triples to the graph
+                for triple in event_entity_triples:
+                    head_id = get_node_id(triple[0], entity_to_id)
+                    tail_id = get_node_id(triple[2], entity_to_id)
+                    g.add_node(head_id, type="event", id=triple[0])
+                    g.add_node(tail_id, type="entity", id=triple[2])
+                    g.add_edge(head_id, tail_id, relation=triple[1])
+                    for node_id in [head_id, tail_id]:
+                        if "file_id" not in g.nodes[node_id]:
+                            g.nodes[node_id]["file_id"] = str(file_id)
+                    edge = g.edges[head_id, tail_id]
+                    if "file_id" not in edge:
+                        edge["file_id"] = str(file_id)
+                    else:
+                        edge["file_id"] += "," + str(file_id)
+
+    print(f"Number of nodes: {g.number_of_nodes()}")
+    print(f"Number of edges: {g.number_of_edges()}")
+    print(f"Graph density: {nx.density(g)}")
+    with open(output_path, 'wb') as f:
+        nx.write_graphml(g, f, infer_numeric_types=True)
+
+

AIEC-RAG/atlas_rag/kg_construction/utils/md_processing/markdown_to_json.py

@@ -0,0 +1,63 @@
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+
+# Set up argument parser
+parser = argparse.ArgumentParser(description="Convert all Markdown files in a folder to separate JSON files.")
+parser.add_argument(
+    "--input", required=True, help="Path to the folder containing Markdown files"
+)
+parser.add_argument(
+    "--output", default=None, help="Output folder for JSON files (defaults to input folder if not specified)"
+)
+
+# Parse arguments
+args = parser.parse_args()
+
+# Resolve input folder path
+input_folder = Path(args.input)
+if not input_folder.is_dir():
+    print(f"Error: '{args.input}' is not a directory.", file=sys.stderr)
+    sys.exit(1)
+
+# Set output folder (use input folder if not specified)
+output_folder = Path(args.output) if args.output else input_folder
+output_folder.mkdir(parents=True, exist_ok=True)
+
+# Find all .md files in the input folder
+markdown_files = [f for f in input_folder.iterdir() if f.suffix.lower() == ".md"]
+
+if not markdown_files:
+    print(f"Error: No Markdown files found in '{args.input}'.", file=sys.stderr)
+    sys.exit(1)
+
+# Process each Markdown file
+for file in markdown_files:
+    try:
+        # Read the content of the file
+        with open(file, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        # Create the JSON object
+        obj = {
+            "id": "1",
+            "text": content,
+            "metadata": {
+                "lang": "en"
+            }
+        }
+
+        # Create output JSON filename (e.g., file1.md -> file1.json)
+        output_file = output_folder / f"{file.stem}.json"
+
+        # Write JSON to file
+        with open(output_file, "w", encoding="utf-8") as f:
+            json.dump([obj], f, indent=4)
+
+        print(f"Successfully converted '{file}' to '{output_file}'")
+    except FileNotFoundError:
+        print(f"Error: File '{file}' not found.", file=sys.stderr)
+    except Exception as e:
+        print(f"Error processing file '{file}': {e}", file=sys.stderr)