phi3_instruct_graph.py

import torch 
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from textwrap import dedent
from huggingface_hub import login
import os
from dotenv import load_dotenv

load_dotenv()
login(
  token=os.environ["HF_TOKEN"],
)

MODEL_LIST = [
  "EmergentMethods/Phi-3-mini-4k-instruct-graph", 
  "EmergentMethods/Phi-3-mini-128k-instruct-graph", 
#   "EmergentMethods/Phi-3-medium-128k-instruct-graph"
]

torch.random.manual_seed(0)

class Phi3InstructGraph:
    def __init__(self, model = "EmergentMethods/Phi-3-mini-4k-instruct-graph"):
        if model not in MODEL_LIST:
            raise ValueError(f"model must be one of {MODEL_LIST}")
        
        self.model_path = model
        self.model = AutoModelForCausalLM.from_pretrained( 
            self.model_path,
            device_map="cuda",  
            torch_dtype="auto",  
            trust_remote_code=True,  
        ) 
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) 
        self.pipe = pipeline(
            "text-generation", 
            model=self.model, 
            tokenizer=self.tokenizer, 
        )

    def _generate(self, messages):
        generation_args = { 
            "max_new_tokens": 2000, 
            "return_full_text": False, 
            "temperature": 0.1, 
            "do_sample": False, 
        } 

        return self.pipe(messages, **generation_args)

    def _get_messages(self, text):
        context = dedent("""\n
                    A chat between a curious user and an artificial intelligence Assistant. The Assistant is an expert at identifying entities and relationships in text. The Assistant responds in JSON output only.

                    The User provides text in the format:

                    -------Text begin-------
                    <User provided text>
                    -------Text end-------

                    The Assistant follows the following steps before replying to the User:

                    1. **identify the most important entities** The Assistant identifies the most important entities in the text. These entities are listed in the JSON output under the key "nodes", they follow the structure of a list of dictionaries where each dict is:

                    "nodes":[{"id": <entity N>, "type": <type>, "detailed_type": <detailed type>}, ...]

                    where "type": <type> is a broad categorization of the entity. "detailed type": <detailed_type>  is a very descriptive categorization of the entity.

                    2. **determine relationships** The Assistant uses the text between -------Text begin------- and -------Text end------- to determine the relationships between the entities identified in the "nodes" list defined above. These relationships are called "edges" and they follow the structure of:

                    "edges":[{"from": <entity 1>, "to": <entity 2>, "label": <relationship>}, ...]

                    The <entity N> must correspond to the "id" of an entity in the "nodes" list.

                    The Assistant never repeats the same node twice. The Assistant never repeats the same edge twice.
                    The Assistant responds to the User in JSON only, according to the following JSON schema:

                    {"type":"object","properties":{"nodes":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string"},"type":{"type":"string"},"detailed_type":{"type":"string"}},"required":["id","type","detailed_type"],"additionalProperties":false}},"edges":{"type":"array","items":{"type":"object","properties":{"from":{"type":"string"},"to":{"type":"string"},"label":{"type":"string"}},"required":["from","to","label"],"additionalProperties":false}}},"required":["nodes","edges"],"additionalProperties":false}
                        """)
        
        user_message = dedent(f"""\n
                    -------Text begin-------
                    {text}
                    -------Text end-------
                    """)
        
        if self.model_path == "EmergentMethods/Phi-3-medium-128k-instruct-graph":
            # model without system message
            messages = [ 
                {
                    "role": "user", 
                    "content": f"{context}\n Input: {user_message}",
                }
            ]
            return messages
        else:
            messages = [ 
                {
                    "role": "system", 
                    "content": context
                }, 
                {
                    "role": "user", 
                    "content": user_message
                }
            ]
            return messages
    
    
    def extract(self, text):
        messages = self._get_messages(text)
        pipe_output = self._generate(messages)
        # print("pipe_output json", pipe_output[0]["generated_text"])
        return pipe_output[0]["generated_text"]