removed never used method
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@@ -13,7 +13,9 @@ if not pm.is_installed("ollama"):
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if not pm.is_installed("tenacity"):
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pm.install("tenacity")
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import ollama
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from tenacity import (
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retry,
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stop_after_attempt,
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@@ -26,7 +28,7 @@ from lightrag.exceptions import (
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APITimeoutError,
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)
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from lightrag.api import __api_version__
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from lightrag.utils import extract_reasoning
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import numpy as np
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from typing import Union
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@@ -38,7 +40,7 @@ from typing import Union
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(RateLimitError, APIConnectionError, APITimeoutError)
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),
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)
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async def ollama_model_if_cache(
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async def _ollama_model_if_cache(
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model,
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prompt,
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system_prompt=None,
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@@ -46,7 +48,7 @@ async def ollama_model_if_cache(
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**kwargs,
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) -> Union[str, AsyncIterator[str]]:
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stream = True if kwargs.get("stream") else False
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reasoning_tag = kwargs.pop("reasoning_tag", None)
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kwargs.pop("max_tokens", None)
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# kwargs.pop("response_format", None) # allow json
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host = kwargs.pop("host", None)
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@@ -84,11 +86,7 @@ async def ollama_model_if_cache(
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response and can simply be trimmed.
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"""
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return (
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model_response
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if reasoning_tag is None
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else extract_reasoning(model_response, reasoning_tag).response_content
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)
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return model_response
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async def ollama_model_complete(
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@@ -98,7 +96,7 @@ async def ollama_model_complete(
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if keyword_extraction:
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kwargs["format"] = "json"
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model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
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return await ollama_model_if_cache(
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return await _ollama_model_if_cache(
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model_name,
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prompt,
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system_prompt=system_prompt,
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@@ -131,4 +129,4 @@ async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray:
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kwargs["headers"] = headers
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ollama_client = ollama.Client(**kwargs)
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data = ollama_client.embed(model=embed_model, input=texts)
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return data["embeddings"]
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return data["embeddings"]
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