removed never used method

lightrag/llm/ollama.py

@@ -13,7 +13,9 @@ if not pm.is_installed("ollama"):
 if not pm.is_installed("tenacity"):
     pm.install("tenacity")
 
+
 import ollama
+
 from tenacity import (
     retry,
     stop_after_attempt,
@@ -26,7 +28,7 @@ from lightrag.exceptions import (
     APITimeoutError,
 )
 from lightrag.api import __api_version__
-from lightrag.utils import extract_reasoning
+
 import numpy as np
 from typing import Union
 
@@ -38,7 +40,7 @@ from typing import Union
         (RateLimitError, APIConnectionError, APITimeoutError)
     ),
 )
-async def ollama_model_if_cache(
+async def _ollama_model_if_cache(
     model,
     prompt,
     system_prompt=None,
@@ -46,7 +48,7 @@ async def ollama_model_if_cache(
     **kwargs,
 ) -> Union[str, AsyncIterator[str]]:
     stream = True if kwargs.get("stream") else False
-    reasoning_tag = kwargs.pop("reasoning_tag", None)
+    
     kwargs.pop("max_tokens", None)
     # kwargs.pop("response_format", None) # allow json
     host = kwargs.pop("host", None)
@@ -84,11 +86,7 @@ async def ollama_model_if_cache(
         response and can simply be trimmed.
         """
 
-        return (
-            model_response
-            if reasoning_tag is None
-            else extract_reasoning(model_response, reasoning_tag).response_content
-        )
+        return model_response
 
 
 async def ollama_model_complete(
@@ -98,7 +96,7 @@ async def ollama_model_complete(
     if keyword_extraction:
         kwargs["format"] = "json"
     model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
-    return await ollama_model_if_cache(
+    return await _ollama_model_if_cache(
         model_name,
         prompt,
         system_prompt=system_prompt,
@@ -131,4 +129,4 @@ async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray:
     kwargs["headers"] = headers
     ollama_client = ollama.Client(**kwargs)
     data = ollama_client.embed(model=embed_model, input=texts)
-    return data["embeddings"]
+    return data["embeddings"]

lightrag/utils.py

@@ -18,13 +18,7 @@ import tiktoken
 
 from lightrag.prompt import PROMPTS
 
-import pipmaster as pm  # Pipmaster for dynamic library install
 
-# install specific modules
-if not pm.is_installed("bs4"):
-    pm.install("bs4")
-    
-import bs4
 
 VERBOSE_DEBUG = os.getenv("VERBOSE", "false").lower() == "true"
 
@@ -90,12 +84,6 @@ class EmbeddingFunc:
         return await self.func(*args, **kwargs)
 
 
-@dataclass
-class ReasoningResponse:
-    reasoning_content: str | None
-    response_content: str
-    tag: str
-
 
 def locate_json_string_body_from_string(content: str) -> str | None:
     """Locate the JSON string body from a string"""
@@ -728,27 +716,3 @@ def get_conversation_turns(
 
     return "\n".join(formatted_turns)
 
-
-def extract_reasoning(response: str, tag: str) -> ReasoningResponse:
-    """Extract the reasoning section and the following section from the LLM response.
-
-    Args:
-        response: LLM response
-        tag: Tag to extract
-    Returns:
-        ReasoningResponse: Reasoning section and following section
-
-    """
-    soup = bs4.BeautifulSoup(response, "html.parser")
-
-    reasoning_section = soup.find(tag)
-    if reasoning_section is None:
-        return ReasoningResponse(None, response, tag)
-    reasoning_content = reasoning_section.get_text().strip()
-
-    after_reasoning_section = reasoning_section.next_sibling
-    if after_reasoning_section is None:
-        return ReasoningResponse(reasoning_content, "", tag)
-    after_reasoning_content = after_reasoning_section.get_text().strip()
-
-    return ReasoningResponse(reasoning_content, after_reasoning_content, tag)