[ADD]Add functions related to knowledge base graph:
Add functions related to knowledge base graph: 1. Entity type generation, 2. Knowledge base graph acquisition, 3. Hard deletion of knowledge base graph, 4. Knowledge base graph reconstruction (asynchronous)
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lixiangcheng1
290
api/app/core/rag/llm/embedding_model.py
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290
api/app/core/rag/llm/embedding_model.py
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import json
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from abc import ABC
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from urllib.parse import urljoin
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import dashscope
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import numpy as np
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import requests
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from openai import OpenAI
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from app.core.rag.common.log_utils import log_exception
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from app.core.rag.common.token_utils import num_tokens_from_string, truncate
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class Base(ABC):
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def __init__(self, key, model_name, **kwargs):
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"""
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Constructor for abstract base class.
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Parameters are accepted for interface consistency but are not stored.
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Subclasses should implement their own initialization as needed.
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"""
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pass
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def encode(self, texts: list):
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raise NotImplementedError("Please implement encode method!")
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def encode_queries(self, text: str):
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raise NotImplementedError("Please implement encode method!")
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def total_token_count(self, resp):
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try:
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return resp.usage.total_tokens
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except Exception:
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pass
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try:
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return resp["usage"]["total_tokens"]
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except Exception:
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pass
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return 0
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class OpenAIEmbed(Base):
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_FACTORY_NAME = "OpenAI"
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def __init__(self, key, model_name="text-embedding-ada-002", base_url="https://api.openai.com/v1"):
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if not base_url:
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base_url = "https://api.openai.com/v1"
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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def encode(self, texts: list):
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# OpenAI requires batch size <=16
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batch_size = 16
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texts = [truncate(t, 8191) for t in texts]
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ress = []
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total_tokens = 0
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for i in range(0, len(texts), batch_size):
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res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name, encoding_format="float", extra_body={"drop_params": True})
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try:
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ress.extend([d.embedding for d in res.data])
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total_tokens += self.total_token_count(res)
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except Exception as _e:
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log_exception(_e, res)
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return np.array(ress), total_tokens
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def encode_queries(self, text):
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res = self.client.embeddings.create(input=[truncate(text, 8191)], model=self.model_name, encoding_format="float",extra_body={"drop_params": True})
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return np.array(res.data[0].embedding), self.total_token_count(res)
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class LocalAIEmbed(Base):
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_FACTORY_NAME = "LocalAI"
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def __init__(self, key, model_name, base_url):
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if not base_url:
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raise ValueError("Local embedding model url cannot be None")
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key="empty", base_url=base_url)
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self.model_name = model_name.split("___")[0]
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def encode(self, texts: list):
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batch_size = 16
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ress = []
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for i in range(0, len(texts), batch_size):
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res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name)
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try:
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ress.extend([d.embedding for d in res.data])
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except Exception as _e:
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log_exception(_e, res)
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# local embedding for LmStudio donot count tokens
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return np.array(ress), 1024
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def encode_queries(self, text):
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embds, cnt = self.encode([text])
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return np.array(embds[0]), cnt
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class AzureEmbed(OpenAIEmbed):
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_FACTORY_NAME = "Azure-OpenAI"
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def __init__(self, key, model_name, **kwargs):
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from openai.lib.azure import AzureOpenAI
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api_key = json.loads(key).get("api_key", "")
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api_version = json.loads(key).get("api_version", "2024-02-01")
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self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
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self.model_name = model_name
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class BaiChuanEmbed(OpenAIEmbed):
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_FACTORY_NAME = "BaiChuan"
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def __init__(self, key, model_name="Baichuan-Text-Embedding", base_url="https://api.baichuan-ai.com/v1"):
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if not base_url:
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base_url = "https://api.baichuan-ai.com/v1"
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super().__init__(key, model_name, base_url)
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class QWenEmbed(Base):
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_FACTORY_NAME = "Tongyi-Qianwen"
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def __init__(self, key, model_name="text_embedding_v2", **kwargs):
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self.key = key
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self.model_name = model_name
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def encode(self, texts: list):
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import time
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import dashscope
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batch_size = 4
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res = []
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token_count = 0
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texts = [truncate(t, 2048) for t in texts]
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for i in range(0, len(texts), batch_size):
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retry_max = 5
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resp = dashscope.TextEmbedding.call(model=self.model_name, input=texts[i : i + batch_size], api_key=self.key, text_type="document")
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while (resp["output"] is None or resp["output"].get("embeddings") is None) and retry_max > 0:
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time.sleep(10)
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resp = dashscope.TextEmbedding.call(model=self.model_name, input=texts[i : i + batch_size], api_key=self.key, text_type="document")
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retry_max -= 1
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if retry_max == 0 and (resp["output"] is None or resp["output"].get("embeddings") is None):
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if resp.get("message"):
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log_exception(ValueError(f"Retry_max reached, calling embedding model failed: {resp['message']}"))
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else:
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log_exception(ValueError("Retry_max reached, calling embedding model failed"))
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raise
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try:
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embds = [[] for _ in range(len(resp["output"]["embeddings"]))]
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for e in resp["output"]["embeddings"]:
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embds[e["text_index"]] = e["embedding"]
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res.extend(embds)
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token_count += self.total_token_count(resp)
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except Exception as _e:
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log_exception(_e, resp)
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raise
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return np.array(res), token_count
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def encode_queries(self, text):
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resp = dashscope.TextEmbedding.call(model=self.model_name, input=text[:2048], api_key=self.key, text_type="query")
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try:
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return np.array(resp["output"]["embeddings"][0]["embedding"]), self.total_token_count(resp)
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except Exception as _e:
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log_exception(_e, resp)
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class XinferenceEmbed(Base):
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_FACTORY_NAME = "Xinference"
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def __init__(self, key, model_name="", base_url=""):
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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def encode(self, texts: list):
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batch_size = 16
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ress = []
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total_tokens = 0
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for i in range(0, len(texts), batch_size):
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res = None
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try:
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res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name)
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ress.extend([d.embedding for d in res.data])
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total_tokens += self.total_token_count(res)
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except Exception as _e:
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log_exception(_e, res)
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return np.array(ress), total_tokens
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def encode_queries(self, text):
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res = None
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try:
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res = self.client.embeddings.create(input=[text], model=self.model_name)
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return np.array(res.data[0].embedding), self.total_token_count(res)
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except Exception as _e:
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log_exception(_e, res)
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class NvidiaEmbed(Base):
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_FACTORY_NAME = "NVIDIA"
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def __init__(self, key, model_name, base_url="https://integrate.api.nvidia.com/v1/embeddings"):
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if not base_url:
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base_url = "https://integrate.api.nvidia.com/v1/embeddings"
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self.api_key = key
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self.base_url = base_url
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self.headers = {
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"accept": "application/json",
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"Content-Type": "application/json",
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"authorization": f"Bearer {self.api_key}",
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}
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self.model_name = model_name
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if model_name == "nvidia/embed-qa-4":
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self.base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings"
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self.model_name = "NV-Embed-QA"
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if model_name == "snowflake/arctic-embed-l":
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self.base_url = "https://ai.api.nvidia.com/v1/retrieval/snowflake/arctic-embed-l/embeddings"
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def encode(self, texts: list):
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batch_size = 16
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ress = []
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token_count = 0
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for i in range(0, len(texts), batch_size):
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payload = {
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"input": texts[i : i + batch_size],
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"input_type": "query",
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"model": self.model_name,
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"encoding_format": "float",
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"truncate": "END",
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}
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response = requests.post(self.base_url, headers=self.headers, json=payload)
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try:
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res = response.json()
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except Exception as _e:
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log_exception(_e, response)
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ress.extend([d["embedding"] for d in res["data"]])
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token_count += self.total_token_count(res)
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return np.array(ress), token_count
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def encode_queries(self, text):
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embds, cnt = self.encode([text])
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return np.array(embds[0]), cnt
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class HuggingFaceEmbed(Base):
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_FACTORY_NAME = "HuggingFace"
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def __init__(self, key, model_name, base_url=None, **kwargs):
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if not model_name:
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raise ValueError("Model name cannot be None")
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self.key = key
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self.model_name = model_name.split("___")[0]
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self.base_url = base_url or "http://127.0.0.1:8080"
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def encode(self, texts: list):
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response = requests.post(f"{self.base_url}/embed", json={"inputs": texts}, headers={"Content-Type": "application/json"})
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if response.status_code == 200:
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embeddings = response.json()
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else:
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raise Exception(f"Error: {response.status_code} - {response.text}")
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return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts])
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def encode_queries(self, text: str):
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response = requests.post(f"{self.base_url}/embed", json={"inputs": text}, headers={"Content-Type": "application/json"})
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if response.status_code == 200:
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embedding = response.json()[0]
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return np.array(embedding), num_tokens_from_string(text)
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else:
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raise Exception(f"Error: {response.status_code} - {response.text}")
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class VolcEngineEmbed(OpenAIEmbed):
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_FACTORY_NAME = "VolcEngine"
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def __init__(self, key, model_name, base_url="https://ark.cn-beijing.volces.com/api/v3"):
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if not base_url:
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base_url = "https://ark.cn-beijing.volces.com/api/v3"
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ark_api_key = json.loads(key).get("ark_api_key", "")
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model_name = json.loads(key).get("ep_id", "") + json.loads(key).get("endpoint_id", "")
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super().__init__(ark_api_key, model_name, base_url)
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class GPUStackEmbed(OpenAIEmbed):
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_FACTORY_NAME = "GPUStack"
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def __init__(self, key, model_name, base_url):
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if not base_url:
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raise ValueError("url cannot be None")
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base_url = urljoin(base_url, "v1")
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self.client = OpenAI(api_key=key, base_url=base_url)
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self.model_name = model_name
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