Revert "first commit"

lightrag/llm.py

@@ -1,88 +0,0 @@
-import os
-import numpy as np
-from openai import AsyncOpenAI, APIConnectionError, RateLimitError, Timeout
-from tenacity import (
-    retry,
-    stop_after_attempt,
-    wait_exponential,
-    retry_if_exception_type,
-)
-
-from .base import BaseKVStorage
-from .utils import compute_args_hash, wrap_embedding_func_with_attrs
-
-@retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
-    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
-)
-async def openai_complete_if_cache(
-    model, prompt, system_prompt=None, history_messages=[], **kwargs
-) -> str:
-    openai_async_client = AsyncOpenAI()
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
-    messages = []
-    if system_prompt:
-        messages.append({"role": "system", "content": system_prompt})
-    messages.extend(history_messages)
-    messages.append({"role": "user", "content": prompt})
-    if hashing_kv is not None:
-        args_hash = compute_args_hash(model, messages)
-        if_cache_return = await hashing_kv.get_by_id(args_hash)
-        if if_cache_return is not None:
-            return if_cache_return["return"]
-
-    response = await openai_async_client.chat.completions.create(
-        model=model, messages=messages, **kwargs
-    )
-
-    if hashing_kv is not None:
-        await hashing_kv.upsert(
-            {args_hash: {"return": response.choices[0].message.content, "model": model}}
-        )
-    return response.choices[0].message.content
-
-async def gpt_4o_complete(
-    prompt, system_prompt=None, history_messages=[], **kwargs
-) -> str:
-    return await openai_complete_if_cache(
-        "gpt-4o",
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        **kwargs,
-    )
-
-
-async def gpt_4o_mini_complete(
-    prompt, system_prompt=None, history_messages=[], **kwargs
-) -> str:
-    return await openai_complete_if_cache(
-        "gpt-4o-mini",
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        **kwargs,
-    )
-
-@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
-@retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
-    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
-)
-async def openai_embedding(texts: list[str]) -> np.ndarray:
-    openai_async_client = AsyncOpenAI()
-    response = await openai_async_client.embeddings.create(
-        model="text-embedding-3-small", input=texts, encoding_format="float"
-    )
-    return np.array([dp.embedding for dp in response.data])
-
-if __name__ == "__main__":
-    import asyncio
-
-    async def main():
-        result = await gpt_4o_mini_complete('How are you?')
-        print(result)
-
-    asyncio.run(main())