feat: add inference backends

.env.example

@@ -1,7 +1,29 @@
+# Tokenizer
 TOKENIZER_MODEL=
-SYNTHESIZER_MODEL=
+
+# LLM
+# Support different backends: http_api, openai_api, ollama_api, ollama, huggingface, tgi, sglang, tensorrt
+
+# http_api / openai_api
+SYNTHESIZER_BACKEND=openai_api
+SYNTHESIZER_MODEL=gpt-4o-mini
 SYNTHESIZER_BASE_URL=
 SYNTHESIZER_API_KEY=
-TRAINEE_MODEL=
+TRAINEE_BACKEND=openai_api
+TRAINEE_MODEL=gpt-4o-mini
 TRAINEE_BASE_URL=
 TRAINEE_API_KEY=
+
+# # ollama_api
+# SYNTHESIZER_BACKEND=ollama_api
+# SYNTHESIZER_MODEL=gemma3
+# SYNTHESIZER_BASE_URL=http://localhost:11434
+#
+# Note: TRAINEE with ollama_api backend is not supported yet as ollama_api does not support logprobs.
+
+# # huggingface
+# SYNTHESIZER_BACKEND=huggingface
+# SYNTHESIZER_MODEL=Qwen/Qwen2.5-0.5B-Instruct
+#
+# TRAINEE_BACKEND=huggingface
+# TRAINEE_MODEL=Qwen/Qwen2.5-0.5B-Instruct

graphgen/bases/__init__.py

@@ -1,6 +1,6 @@
 from .base_generator import BaseGenerator
 from .base_kg_builder import BaseKGBuilder
-from .base_llm_client import BaseLLMClient
+from .base_llm_wrapper import BaseLLMWrapper
 from .base_partitioner import BasePartitioner
 from .base_reader import BaseReader
 from .base_splitter import BaseSplitter

graphgen/bases/base_generator.py

@@ -1,15 +1,15 @@
 from abc import ABC, abstractmethod
 from typing import Any
 
-from graphgen.bases.base_llm_client import BaseLLMClient
+from graphgen.bases.base_llm_wrapper import BaseLLMWrapper
 
 
 class BaseGenerator(ABC):
     """
     Generate QAs based on given prompts.
     """
 
-    def __init__(self, llm_client: BaseLLMClient):
+    def __init__(self, llm_client: BaseLLMWrapper):
         self.llm_client = llm_client
 
     @staticmethod

graphgen/bases/base_kg_builder.py

@@ -2,13 +2,13 @@
 from collections import defaultdict
 from typing import Dict, List, Tuple
 
-from graphgen.bases.base_llm_client import BaseLLMClient
+from graphgen.bases.base_llm_wrapper import BaseLLMWrapper
 from graphgen.bases.base_storage import BaseGraphStorage
 from graphgen.bases.datatypes import Chunk
 
 
 class BaseKGBuilder(ABC):
-    def __init__(self, llm_client: BaseLLMClient):
+    def __init__(self, llm_client: BaseLLMWrapper):
         self.llm_client = llm_client
         self._nodes: Dict[str, List[dict]] = defaultdict(list)
         self._edges: Dict[Tuple[str, str], List[dict]] = defaultdict(list)

graphgen/bases/base_llm_client.py → graphgen/bases/base_llm_wrapper.py

@@ -8,7 +8,7 @@
 from graphgen.bases.datatypes import Token
 
 
-class BaseLLMClient(abc.ABC):
+class BaseLLMWrapper(abc.ABC):
     """
     LLM client base class, agnostic to specific backends (OpenAI / Ollama / ...).
     """
@@ -66,3 +66,9 @@ def filter_think_tags(text: str, think_tag: str = "think") -> str:
         think_pattern = re.compile(rf"<{think_tag}>.*?</{think_tag}>", re.DOTALL)
         filtered_text = think_pattern.sub("", text).strip()
         return filtered_text if filtered_text else text.strip()
+
+    def shutdown(self) -> None:
+        """Shutdown the LLM engine if applicable."""
+
+    def restart(self) -> None:
+        """Reinitialize the LLM engine if applicable."""

graphgen/graphgen.py

@@ -1,11 +1,11 @@
 import asyncio
 import os
 import time
-from dataclasses import dataclass
 from typing import Dict, cast
 
 import gradio as gr
 
+from graphgen.bases import BaseLLMWrapper
 from graphgen.bases.base_storage import StorageNameSpace
 from graphgen.bases.datatypes import Chunk
 from graphgen.models import (
@@ -20,6 +20,7 @@
     build_text_kg,
     chunk_documents,
     generate_qas,
+    init_llm,
     judge_statement,
     partition_kg,
     quiz,
@@ -31,40 +32,28 @@
 sys_path = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 
 
-@dataclass
 class GraphGen:
-    unique_id: int = int(time.time())
-    working_dir: str = os.path.join(sys_path, "cache")
-
-    # llm
-    tokenizer_instance: Tokenizer = None
-    synthesizer_llm_client: OpenAIClient = None
-    trainee_llm_client: OpenAIClient = None
-
-    # webui
-    progress_bar: gr.Progress = None
-
-    def __post_init__(self):
-        self.tokenizer_instance: Tokenizer = self.tokenizer_instance or Tokenizer(
+    def __init__(
+        self,
+        unique_id: int = int(time.time()),
+        working_dir: str = os.path.join(sys_path, "cache"),
+        tokenizer_instance: Tokenizer = None,
+        synthesizer_llm_client: OpenAIClient = None,
+        trainee_llm_client: OpenAIClient = None,
+        progress_bar: gr.Progress = None,
+    ):
+        self.unique_id: int = unique_id
+        self.working_dir: str = working_dir
+
+        # llm
+        self.tokenizer_instance: Tokenizer = tokenizer_instance or Tokenizer(
             model_name=os.getenv("TOKENIZER_MODEL")
         )
 
-        self.synthesizer_llm_client: OpenAIClient = (
-            self.synthesizer_llm_client
-            or OpenAIClient(
-                model_name=os.getenv("SYNTHESIZER_MODEL"),
-                api_key=os.getenv("SYNTHESIZER_API_KEY"),
-                base_url=os.getenv("SYNTHESIZER_BASE_URL"),
-                tokenizer=self.tokenizer_instance,
-            )
-        )
-
-        self.trainee_llm_client: OpenAIClient = self.trainee_llm_client or OpenAIClient(
-            model_name=os.getenv("TRAINEE_MODEL"),
-            api_key=os.getenv("TRAINEE_API_KEY"),
-            base_url=os.getenv("TRAINEE_BASE_URL"),
-            tokenizer=self.tokenizer_instance,
+        self.synthesizer_llm_client: BaseLLMWrapper = (
+            synthesizer_llm_client or init_llm("synthesizer")
         )
+        self.trainee_llm_client: BaseLLMWrapper = trainee_llm_client
 
         self.full_docs_storage: JsonKVStorage = JsonKVStorage(
             self.working_dir, namespace="full_docs"
@@ -86,6 +75,9 @@ def __post_init__(self):
             namespace="qa",
         )
 
+        # webui
+        self.progress_bar: gr.Progress = progress_bar
+
     @async_to_sync_method
     async def insert(self, read_config: Dict, split_config: Dict):
         """
@@ -272,16 +264,29 @@ async def quiz_and_judge(self, quiz_and_judge_config: Dict):
         )
 
         # TODO： assert trainee_llm_client is valid before judge
+        if not self.trainee_llm_client:
+            # TODO: shutdown existing synthesizer_llm_client properly
+            logger.info("No trainee LLM client provided, initializing a new one.")
+            self.synthesizer_llm_client.shutdown()
+            self.trainee_llm_client = init_llm("trainee")
+
         re_judge = quiz_and_judge_config["re_judge"]
         _update_relations = await judge_statement(
             self.trainee_llm_client,
             self.graph_storage,
             self.rephrase_storage,
             re_judge,
         )
+
         await self.rephrase_storage.index_done_callback()
         await _update_relations.index_done_callback()
 
+        logger.info("Shutting down trainee LLM client.")
+        self.trainee_llm_client.shutdown()
+        self.trainee_llm_client = None
+        logger.info("Restarting synthesizer LLM client.")
+        self.synthesizer_llm_client.restart()
+
     @async_to_sync_method
     async def generate(self, partition_config: Dict, generate_config: Dict):
         # Step 1: partition the graph

graphgen/models/__init__.py

@@ -7,8 +7,7 @@
     VQAGenerator,
 )
 from .kg_builder import LightRAGKGBuilder, MMKGBuilder
-from .llm.openai_client import OpenAIClient
-from .llm.topk_token_model import TopkTokenModel
+from .llm import HTTPClient, OllamaClient, OpenAIClient
 from .partitioner import (
     AnchorBFSPartitioner,
     BFSPartitioner,

graphgen/models/kg_builder/light_rag_kg_builder.py

@@ -2,7 +2,7 @@
 from collections import Counter, defaultdict
 from typing import Dict, List, Tuple
 
-from graphgen.bases import BaseGraphStorage, BaseKGBuilder, BaseLLMClient, Chunk
+from graphgen.bases import BaseGraphStorage, BaseKGBuilder, BaseLLMWrapper, Chunk
 from graphgen.templates import KG_EXTRACTION_PROMPT, KG_SUMMARIZATION_PROMPT
 from graphgen.utils import (
     detect_main_language,
@@ -15,7 +15,7 @@
 
 
 class LightRAGKGBuilder(BaseKGBuilder):
-    def __init__(self, llm_client: BaseLLMClient, max_loop: int = 3):
+    def __init__(self, llm_client: BaseLLMWrapper, max_loop: int = 3):
         super().__init__(llm_client)
         self.max_loop = max_loop
 

graphgen/models/llm/__init__.py

@@ -0,0 +1,4 @@
+from .api.http_client import HTTPClient
+from .api.ollama_client import OllamaClient
+from .api.openai_client import OpenAIClient
+from .local.hf_wrapper import HuggingFaceWrapper