feat: add pdf_reader & tests for MinerUParser

graphgen/configs/aggregated_config.yaml

@@ -1,5 +1,5 @@
 read:
-  input_file: resources/input_examples/jsonl_demo.jsonl # input file path, support json, jsonl, txt. See resources/input_examples for examples
+  input_file: resources/input_examples/jsonl_demo.jsonl # input file path, support json, jsonl, txt, pdf. See resources/input_examples for examples
 split:
   chunk_size: 1024 # chunk size for text splitting
   chunk_overlap: 100 # chunk overlap for text splitting
@@ -18,5 +18,5 @@ partition: # graph partition configuration
     max_tokens_per_community: 10240 # max tokens per community
     unit_sampling: max_loss # unit sampling strategy, support: random, max_loss, min_loss
 generate:
-  mode: aggregated # atomic, aggregated, multi_hop, cot
+  mode: aggregated # atomic, aggregated, multi_hop, cot, vqa
   data_format: ChatML # Alpaca, Sharegpt, ChatML

graphgen/configs/atomic_config.yaml

@@ -1,5 +1,5 @@
 read:
-  input_file: resources/input_examples/json_demo.json # input file path, support json, jsonl, txt, csv. See resources/input_examples for examples
+  input_file: resources/input_examples/json_demo.json # input file path, support json, jsonl, txt, csv, pdf. See resources/input_examples for examples
 split:
   chunk_size: 1024 # chunk size for text splitting
   chunk_overlap: 100 # chunk overlap for text splitting
@@ -15,5 +15,5 @@ partition: # graph partition configuration
   method_params:
     max_units_per_community: 1 # atomic partition, one node or edge per community
 generate:
-  mode: atomic # atomic, aggregated, multi_hop, cot
+  mode: atomic # atomic, aggregated, multi_hop, cot, vqa
   data_format: Alpaca # Alpaca, Sharegpt, ChatML

graphgen/configs/cot_config.yaml

@@ -1,5 +1,5 @@
 read:
-  input_file: resources/input_examples/txt_demo.txt  # input file path, support json, jsonl, txt. See resources/input_examples for examples
+  input_file: resources/input_examples/txt_demo.txt  # input file path, support json, jsonl, txt, pdf. See resources/input_examples for examples
 split:
   chunk_size: 1024 # chunk size for text splitting
   chunk_overlap: 100 # chunk overlap for text splitting
@@ -15,5 +15,5 @@ partition: # graph partition configuration
     use_lcc: false # whether to use the largest connected component
     random_seed: 42 # random seed for partitioning
 generate:
-  mode: cot # atomic, aggregated, multi_hop, cot
+  mode: cot # atomic, aggregated, multi_hop, cot, vqa
   data_format: Sharegpt # Alpaca, Sharegpt, ChatML

graphgen/configs/multi_hop_config.yaml

@@ -1,5 +1,5 @@
 read:
-  input_file: resources/input_examples/csv_demo.csv # input file path, support json, jsonl, txt. See resources/input_examples for examples
+  input_file: resources/input_examples/csv_demo.csv # input file path, support json, jsonl, txt, pdf. See resources/input_examples for examples
 split:
   chunk_size: 1024 # chunk size for text splitting
   chunk_overlap: 100 # chunk overlap for text splitting
@@ -18,5 +18,5 @@ partition: # graph partition configuration
     max_tokens_per_community: 10240 # max tokens per community
     unit_sampling: random # unit sampling strategy, support: random, max_loss, min_loss
 generate:
-  mode: multi_hop # strategy for generating multi-hop QA pairs
+  mode: multi_hop # atomic, aggregated, multi_hop, cot, vqa
   data_format: ChatML # Alpaca, Sharegpt, ChatML

graphgen/configs/vqa_config.yaml

@@ -0,0 +1,22 @@
+read:
+  input_file: resources/input_examples/pdf_demo.pdf # input file path, support json, jsonl, txt, pdf. See resources/input_examples for examples
+split:
+  chunk_size: 1024 # chunk size for text splitting
+  chunk_overlap: 100 # chunk overlap for text splitting
+search: # web search configuration
+  enabled: false # whether to enable web search
+  search_types: ["google"] # search engine types, support: google, bing, uniprot, wikipedia
+quiz_and_judge: # quiz and test whether the LLM masters the knowledge points
+  enabled: true
+  quiz_samples: 2 # number of quiz samples to generate
+  re_judge: false # whether to re-judge the existing quiz samples
+partition: # graph partition configuration
+  method: ece # ece is a custom partition method based on comprehension loss
+  method_params:
+    max_units_per_community: 20 # max nodes and edges per community
+    min_units_per_community: 5 # min nodes and edges per community
+    max_tokens_per_community: 10240 # max tokens per community
+    unit_sampling: max_loss # unit sampling strategy, support: random, max_loss, min_loss
+generate:
+  mode: vqa # atomic, aggregated, multi_hop, cot, vqa
+  data_format: ChatML # Alpaca, Sharegpt, ChatML

graphgen/generate.py

@@ -72,24 +72,11 @@ def main():
 
     graph_gen.search(search_config=config["search"])
 
-    # Use pipeline according to the output data type
-    if mode in ["atomic", "aggregated", "multi_hop"]:
-        logger.info("Generation mode set to '%s'. Start generation.", mode)
-        if "quiz_and_judge" in config and config["quiz_and_judge"]["enabled"]:
-            graph_gen.quiz_and_judge(quiz_and_judge_config=config["quiz_and_judge"])
-        else:
-            logger.warning(
-                "Quiz and Judge strategy is disabled. Edge sampling falls back to random."
-            )
-            assert (
-                config["partition"]["method"] == "ece"
-                and "method_params" in config["partition"]
-            ), "Only ECE partition with edge sampling is supported."
-            config["partition"]["method_params"]["edge_sampling"] = "random"
-    elif mode == "cot":
-        logger.info("Generation mode set to 'cot'. Start generation.")
-    else:
-        raise ValueError(f"Unsupported output data type: {mode}")
+    if config.get("quiz_and_judge", {}).get("enabled"):
+        graph_gen.quiz_and_judge(quiz_and_judge_config=config["quiz_and_judge"])
+
+    # TODO: add data filtering step here in the future
+    # graph_gen.filter(filter_config=config["filter"])
 
     graph_gen.generate(
         partition_config=config["partition"],

graphgen/graphgen.py

@@ -91,7 +91,7 @@ async def insert(self, read_config: Dict, split_config: Dict):
         insert chunks into the graph
         """
         # Step 1: Read files
-        data = read_files(read_config["input_file"])
+        data = read_files(read_config["input_file"], self.working_dir)
         if len(data) == 0:
             logger.warning("No data to process")
             return
@@ -105,6 +105,7 @@ async def insert(self, read_config: Dict, split_config: Dict):
                 "content": doc["content"]
             }
             for doc in data
+            if doc.get("type", "text") == "text"
         }
         _add_doc_keys = await self.full_docs_storage.filter_keys(list(new_docs.keys()))
         new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}

graphgen/models/__init__.py

@@ -4,6 +4,7 @@
     AtomicGenerator,
     CoTGenerator,
     MultiHopGenerator,
+    VQAGenerator,
 )
 from .kg_builder import LightRAGKGBuilder
 from .llm.openai_client import OpenAIClient
@@ -14,7 +15,7 @@
     ECEPartitioner,
     LeidenPartitioner,
 )
-from .reader import CsvReader, JsonlReader, JsonReader, TxtReader
+from .reader import CSVReader, JSONLReader, JSONReader, PDFReader, TXTReader
 from .search.db.uniprot_search import UniProtSearch
 from .search.kg.wiki_search import WikiSearch
 from .search.web.bing_search import BingSearch

graphgen/models/generator/__init__.py

@@ -2,3 +2,4 @@
 from .atomic_generator import AtomicGenerator
 from .cot_generator import CoTGenerator
 from .multi_hop_generator import MultiHopGenerator
+from .vqa_generator import VQAGenerator

graphgen/models/generator/vqa_generator.py

@@ -0,0 +1,23 @@
+from dataclasses import dataclass
+from typing import Any
+
+from graphgen.bases import BaseGenerator
+
+
+@dataclass
+class VQAGenerator(BaseGenerator):
+    @staticmethod
+    def build_prompt(
+        batch: tuple[list[tuple[str, dict]], list[tuple[Any, Any, dict]]]
+    ) -> str:
+        raise NotImplementedError(
+            "VQAGenerator.build_prompt is not implemented. "
+            "Please provide an implementation for VQA prompt construction."
+        )
+
+    @staticmethod
+    def parse_response(response: str) -> Any:
+        raise NotImplementedError(
+            "VQAGenerator.parse_response is not implemented. "
+            "Please provide an implementation for VQA response parsing."
+        )

graphgen/models/reader/__init__.py

@@ -1,4 +1,5 @@
-from .csv_reader import CsvReader
-from .json_reader import JsonReader
-from .jsonl_reader import JsonlReader
-from .txt_reader import TxtReader
+from .csv_reader import CSVReader
+from .json_reader import JSONReader
+from .jsonl_reader import JSONLReader
+from .pdf_reader import PDFReader
+from .txt_reader import TXTReader

graphgen/models/reader/csv_reader.py

@@ -5,7 +5,7 @@
 from graphgen.bases.base_reader import BaseReader
 
 
-class CsvReader(BaseReader):
+class CSVReader(BaseReader):
     def read(self, file_path: str) -> List[Dict[str, Any]]:
 
         df = pd.read_csv(file_path)

graphgen/models/reader/json_reader.py

@@ -4,7 +4,7 @@
 from graphgen.bases.base_reader import BaseReader
 
 
-class JsonReader(BaseReader):
+class JSONReader(BaseReader):
     def read(self, file_path: str) -> List[Dict[str, Any]]:
         with open(file_path, "r", encoding="utf-8") as f:
             data = json.load(f)

graphgen/models/reader/jsonl_reader.py

@@ -5,7 +5,7 @@
 from graphgen.utils import logger
 
 
-class JsonlReader(BaseReader):
+class JSONLReader(BaseReader):
     def read(self, file_path: str) -> List[Dict[str, Any]]:
         docs = []
         with open(file_path, "r", encoding="utf-8") as f: