Delete rag.py

rag.py

@@ -1,72 +0,0 @@
-from langchain.vectorstores import Chroma
-from langchain.chat_models import ChatOllama
-from langchain.embeddings import FastEmbedEmbeddings
-from langchain.schema.output_parser import StrOutputParser
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.prompts import PromptTemplate
-from langchain.vectorstores.utils import filter_complex_metadata
-#add new import
-from langchain_community.document_loaders.csv_loader import CSVLoader
-
-from sentence_transformers import SentenceTransformer
-
-from langchain_community.embeddings import HuggingFaceEmbeddings
-model_name = "sentence-transformers/all-mpnet-base-v2"
-embedding = HuggingFaceEmbeddings(
-    model_name=model_name,
-   )
-
-
-
-class ChatPDF:
-    vector_store = None
-    retriever = None
-    chain = None
-
-    def __init__(self):
-        self.model = ChatOllama(model="mistral")
-        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=100)
-        self.prompt = PromptTemplate.from_template(
-            """
-            <s> [INST] You are an assistant for question-answering tasks. Use only the following pieces of retrieved context 
-            to build an answer for the user. If you don't know the answer, just say that you don't know. Use three sentences
-             maximum and keep the answer concise. [/INST] </s> 
-            [INST] Question: {question} 
-            Context: {context} 
-            Answer: [/INST]
-            """
-        )
-
-    def ingest(self, pdf_file_path: str):
-        docs = PyPDFLoader(file_path=pdf_file_path).load()
-       
-        
-        chunks = self.text_splitter.split_documents(docs)
-        chunks = filter_complex_metadata(chunks)
-
-        vector_store = Chroma.from_documents(documents=chunks, embedding=embedding)
-        self.retriever = vector_store.as_retriever(
-            search_type="similarity_score_threshold",
-            search_kwargs={
-                "k": 3,
-                "score_threshold": 0.5,
-            },
-        )
-
-        self.chain = ({"context": self.retriever, "question": RunnablePassthrough()}
-                      | self.prompt
-                      | self.model
-                      | StrOutputParser())
-
-    def ask(self, query: str):
-        if not self.chain:
-            return "Please, add a PDF document first."
-
-        return self.chain.invoke(query)
-
-    def clear(self):
-        self.vector_store = None
-        self.retriever = None
-        self.chain = None