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| import pinecone | |
| from langchain_community.llms import Ollama | |
| from langchain_community.embeddings import OllamaEmbeddings | |
| from langchain.prompts import ChatPromptTemplate | |
| from langchain_pinecone import PineconeVectorStore | |
| from tqdm import tqdm | |
| from pinecone import Pinecone | |
| from langchain_core.output_parsers import StrOutputParser | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_core.runnables import RunnableParallel, RunnablePassthrough | |
| from langchain_pinecone import PineconeVectorStore | |
| from concurrent.futures import ThreadPoolExecutor | |
| import gradio as gr | |
| from langchain_nomic import NomicEmbeddings | |
| from transformers import AutoModel | |
| from gradio_app.backend.embedders import EmbedderFactory | |
| # import requests | |
| class Retrieving: | |
| def __init__(self, apikey, index_name): | |
| self.prompts = self.setup_prompt() | |
| self.pinecone_api_key = apikey | |
| self.index_name = index_name | |
| # self.llm = Ollama(model="llama3") | |
| # self.embeddings = OllamaEmbeddings(model="nomic-embed-text") | |
| # self.embeddings = AutoModel.from_pretrained("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) | |
| # self.embeddings = OllamaEmbeddings(model="nomic-embed-text") | |
| self.llm = gr.load("models/mistralai/Mistral-7B-Instruct-v0.3") | |
| # self.embeddings = AutoModel.from_pretrained("models/nomic-ai/nomic-bert-2048", trust_remote_code=True) | |
| # self.embeddings = EmbedderFactory.get_embedder(model="nomic-embed-text") | |
| self.embeddings = NomicEmbeddings() | |
| self.vectorstore = self.load_vectorstore() | |
| self.retriever = self.create_retriever() | |
| # def setup_llm(self, payload): | |
| # API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large" | |
| # response = requests.post(API_URL, headers=headers, json=payload) | |
| # return response.json() | |
| def load_vectorstore(self): | |
| tqdm.write("Established Pinecone Connection") | |
| pc = PineconeVectorStore(index_name=self.index_name, embedding=self.embeddings, pinecone_api_key= self.pinecone_api_key) | |
| return pc | |
| def create_retriever(self): | |
| tqdm.write("Creating retriever") | |
| return self.vectorstore.as_retriever() | |
| def setup_prompt(self): | |
| template = """ | |
| You are an assistant specialized in providing restaurant information from the retrieved documents. You only allow to give answer for restaruant in the state of florida. | |
| Use the retrieved information to answer the question accurately. If you don't know the answer based on the provided context, | |
| simply respond that you don't know. Do not make up any information. You should act on behalf of my company and say the word "I" instead of based on the provided documentation. | |
| Context: {context} | |
| Question: {question} | |
| """ | |
| tqdm.write("Prompt setup completed") | |
| return ChatPromptTemplate.from_template(template) | |
| def get_response(self, user_input): | |
| chain = ( | |
| RunnableParallel({"context": self.retriever, "question": RunnablePassthrough()}) | |
| | self.prompts | |
| | self.llm | |
| | StrOutputParser() | |
| ) | |
| # return chain.invoke(user_input) | |
| result = chain.invoke(user_input) | |
| # print("Result type:", type(result)) | |
| # print("Result content:", result) | |
| return result |