pipelines/eval_rag.py

import os
import time
from typing import List
from qdrant_client import QdrantClient, models
from langchain_core.documents import Document
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
from blueprints.rag_utils import format_docs
from blueprints.prompts import QUERY_PROMPT, evaluator_intent, basic_template, chitchat_prompt, safe_prompt, cache_prompt
from langchain.retrievers import EnsembleRetriever
from BM25 import BM25SRetriever
from langchain_mistralai import ChatMistralAI
# from database_Routing import DB_Router
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain_core.output_parsers import StrOutputParser
from langchain_core.output_parsers import BaseOutputParser
# from langchain_cohere import CohereRerank
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
# from langchain_groq import ChatGroq
import time 
from qdrant_client import QdrantClient
from langchain_community.vectorstores import Qdrant
from langchain.retrievers.document_compressors import LLMChainFilter
from langchain.retrievers.document_compressors import EmbeddingsFilter
from langchain.retrievers.document_compressors import LLMListwiseRerank
from langchain_openai import OpenAIEmbeddings
from dotenv import load_dotenv
load_dotenv()
HF_EMBEDDING =  OpenAIEmbeddings(model='text-embedding-3-small')

class LineListOutputParser(BaseOutputParser[List[str]]):
    """Output parser for a list of lines."""

    def parse(self, text: str) -> List[str]:
        lines = text.strip().split("\n")
        return list(filter(None, lines))  # Remove empty lines

# def extract_metadata(docs, headers=('Header_1', 'Header_2', 'Header_3')):
#     meta_data_docs = []
#     for doc in docs:
#         meta_data_doc = [doc.metadata[header] for header in headers if doc.metadata.get(header)]
#         meta_data_docs.append(meta_data_doc)
#     return meta_data_docs

# def search_with_filter(query, vector_store, k, headers):
#     conditions = [
#         models.FieldCondition(
#             key="metadata.Header_1",
#             match=models.MatchValue(
#                 value=headers[0]
#             ),
#         ),
#         models.FieldCondition(
#             key="metadata.Header_2",
#             match=models.MatchValue(
#                 value=headers[1]
#             ),
#         ),
#     ]


#     if len(headers) == 3:
#         conditions.append(
#             models.FieldCondition(
#                 key="metadata.Header_3",
#                 match=models.MatchValue(
#                     value=headers[2]
#                 ),
#             )
#         )


#     single_result = vector_store.similarity_search(
#         query=query,
#         k=k,
#         filter=models.Filter(
#             must=conditions
#         ),
#     )
    
#     return single_result


if __name__ == "__main__": 

    client = QdrantClient(url="http://localhost:6333")

    stsv = Qdrant(client, collection_name="eval_collection2", embeddings= HF_EMBEDDING)
    retriever = stsv.as_retriever(search_kwargs={'k': 3})

    import pickle

    with open('/home/justtuananh/AI4TUAN/DOAN2024/offical/pipelines/documents_new.pkl', 'rb') as f:
        sotaysinhvien = pickle.load(f)

    retriever_bm25 = BM25SRetriever.from_documents(sotaysinhvien, k= 5, activate_numba = True)

    # reranker = CohereRerank(model = "rerank-multilingual-v3.0", top_n = 5)
    llm = ChatMistralAI(
    model="mistral-large-2407",
    temperature=0,
    max_retries=2,
)
    # llm = ChatGroq(model_name="llama3-70b-8192", temperature=0.1,api_key= os.getenv('llm_api_4'))
    # llm2 = ChatGroq(model_name="llama3-70b-8192", temperature=0.1,api_key= os.getenv('llm_api_5'))
    output_parser = LineListOutputParser()
    llm_chain = QUERY_PROMPT | llm | output_parser

    messages = [
        {"role": "system", "content": "Dựa vào thông tin sau, trả lời câu hỏi bằng tiếng việt"}
    ]

    def duy_phen():

        user_message = input("Nhập câu hỏi của bạn: ")
        start_time = time.time()

        if retriever is not None:
            retriever_multi = MultiQueryRetriever(
                            retriever=retriever, llm_chain=llm_chain, parser_key="lines"
                        ) 

            ensemble_retriever = EnsembleRetriever(
                    retrievers=[retriever_bm25, retriever_multi], weights=[0.5, 0.5])
            
            compressor = LLMChainExtractor.from_llm(llm)
            # _filter = LLMChainFilter.from_llm(llm)
            _filter2 = LLMListwiseRerank.from_llm(llm, top_n=5)
            embeddings_filter = EmbeddingsFilter(embeddings=HF_EMBEDDING, similarity_threshold=0.5)
            compression = ContextualCompressionRetriever(
                        base_compressor=_filter2, base_retriever=ensemble_retriever
                        )

            # rag_chain = (
            #                 {"context": compression | format_docs, "question": RunnablePassthrough()}
            #                 | basic_template | llm2 | StrOutputParser()
            #             )
            
            print(compression.invoke(f"{user_message}"))
            end_time = time.time()
            print(f'TIME USING : {end_time-start_time}')

        else:
            print('Retriever is not defined. Check output results and ensure retriever is assigned correctly.')

    duy_phen()