File size: 5,458 Bytes
a8bf50c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
import logging
import os
import requests
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from openai import OpenAI
from huggingface_hub import snapshot_download

from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings


class RAG:
    NO_ANSWER_MESSAGE: str = "Ho sento, no he pogut respondre la teva pregunta."

    

    # Download the vectorstore from Hugging Face Hub
    
    def __init__(self, hf_token, embeddings_model, repo_name):

        vectorstore = snapshot_download(repo_name)


        # self.model_name = model_name
        self.hf_token = hf_token
        # self.rerank_model = rerank_model
        # self.rerank_number_contexts = rerank_number_contexts
        
        # load vectore store
        embeddings = HuggingFaceEmbeddings(model_name=embeddings_model, model_kwargs={'device': 'cpu'})
        self.vectore_store = FAISS.load_local(vectorstore, embeddings, allow_dangerous_deserialization=True)#, allow_dangerous_deserialization=True)

        logging.info("RAG loaded!")
    
    # def rerank_contexts(self, instruction, contexts, number_of_contexts=1):
    #     """
    #     Rerank the contexts based on their relevance to the given instruction.
    #     """

    #     rerank_model = self.rerank_model
        

    #     tokenizer = AutoTokenizer.from_pretrained(rerank_model)
    #     model = AutoModelForSequenceClassification.from_pretrained(rerank_model)

    #     def get_score(query, passage):
    #         """Calculate the relevance score of a passage with respect to a query."""


    #         inputs = tokenizer(query, passage, return_tensors='pt', truncation=True, padding=True, max_length=512)
            

    #         with torch.no_grad():
    #             outputs = model(**inputs)
            

    #         logits = outputs.logits
            

    #         score = logits.view(-1, ).float()  
            

    #         return score

    #     scores = [get_score(instruction, c[0].page_content) for c in contexts]
    #     combined = list(zip(contexts, scores))
    #     sorted_combined = sorted(combined, key=lambda x: x[1], reverse=True)
    #     sorted_texts, _ = zip(*sorted_combined)

    #     return sorted_texts[:number_of_contexts]

    def get_context(self, instruction, number_of_contexts=2):
        """Retrieve the most relevant contexts for a given instruction."""
        documentos = self.vectore_store.similarity_search_with_score(instruction, k=4)

        # documentos = self.rerank_contexts(instruction, documentos, number_of_contexts=number_of_contexts)

        print("Reranked documents")
        return documentos
        
    def predict_dolly(self, instruction, context, model_parameters):

        api_key = os.getenv("HF_TOKEN")


        headers = {
        "Accept" : "application/json",
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json" 
        }

        query = f"### Instruction\n{instruction}\n\n### Context\n{context}\n\n### Answer\n "
        #prompt = "You are a helpful assistant. Answer the question using only the context you are provided with. If it is not possible to do it with the context, just say 'I can't answer'. <|endoftext|>"


        payload = {
        "inputs": query,
        "parameters": model_parameters
        }
        
        response = requests.post(self.model_name, headers=headers, json=payload)

        return response.json()[0]["generated_text"].split("###")[-1][8:]

    def predict_completion(self, instruction, context, model_parameters):

        client = OpenAI(
                base_url=os.getenv("MODEL"),
                api_key=os.getenv("HF_TOKEN")
            )

        query = f"Context:\n{context}\n\nQuestion:\n{instruction}"

        chat_completion = client.chat.completions.create(
            model="tgi",
            messages=[
                {"role": "user", "content": instruction}
            ],
            temperature=model_parameters["temperature"],
            max_tokens=model_parameters["max_new_tokens"],
            stream=False,
            stop=["<|im_end|>"],
            extra_body = {
                "presence_penalty": model_parameters["repetition_penalty"] - 2,
                "do_sample": False
            }
        )

        response = chat_completion.choices[0].message.content

        return response
        
    
    def beautiful_context(self, docs):

        text_context = ""

        full_context = ""
        source_context = []
        for doc in docs:
            text_context += doc[0].page_content
            full_context += doc[0].page_content + "\n"
            full_context += doc[0].metadata["url"] + "\n\n"
            source_context.append(doc[0].metadata["url"])

        return text_context, full_context, source_context

    def get_response(self, prompt: str, model_parameters: dict) -> str:
        try:
            docs = self.get_context(prompt, model_parameters["NUM_CHUNKS"])
            text_context, full_context, source = self.beautiful_context(docs)

            del model_parameters["NUM_CHUNKS"]

            # response = self.predict_completion(prompt, text_context, model_parameters)
            response = "Output"

            if not response:
                return self.NO_ANSWER_MESSAGE

            return response, full_context, source
        except Exception as err:
            print(err)