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94 lines
2.6 KiB
Python
94 lines
2.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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import json
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from typing import Any, List, Mapping, Optional
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from urllib.parse import urljoin
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import requests
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from langchain.embeddings.base import Embeddings
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from langchain.llms.base import LLM
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from dbgpt._private.config import Config
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from dbgpt._private.pydantic import BaseModel
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CFG = Config()
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class VicunaLLM(LLM):
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vicuna_generate_path = "generate_stream"
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def _call(
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self,
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prompt: str,
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temperature: float,
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max_new_tokens: int,
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stop: Optional[List[str]] = None,
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) -> str:
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params = {
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"prompt": prompt,
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"temperature": temperature,
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"max_new_tokens": max_new_tokens,
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"stop": stop,
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}
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response = requests.post(
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url=urljoin(CFG.MODEL_SERVER, self.vicuna_generate_path),
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data=json.dumps(params),
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)
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skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
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for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
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if chunk:
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data = json.loads(chunk.decode())
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if data["error_code"] == 0:
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output = data["text"][skip_echo_len:].strip()
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yield output
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@property
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def _llm_type(self) -> str:
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return "custome"
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def _identifying_params(self) -> Mapping[str, Any]:
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return {}
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class VicunaEmbeddingLLM(BaseModel, Embeddings):
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vicuna_embedding_path = "embedding"
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def _call(self, prompt: str) -> str:
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p = prompt.strip()
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print("Sending prompt ", p)
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response = requests.post(
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url=urljoin(CFG.MODEL_SERVER, self.vicuna_embedding_path),
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json={"prompt": p},
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)
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response.raise_for_status()
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return response.json()["response"]
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Call out to Vicuna's server embedding endpoint for embedding search docs.
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Args:
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texts: The list of text to embed
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Returns:
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List of embeddings. one for each text.
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"""
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results = []
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for text in texts:
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response = self.embed_query(text)
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results.append(response)
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return results
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def embed_query(self, text: str) -> List[float]:
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"""Call out to Vicuna's server embedding endpoint for embedding query text.
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Args:
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text: The text to embed.
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Returns:
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Embedding for the text
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"""
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embedding = self._call(text)
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return embedding
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