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rag.py
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from langchain_community.vectorstores import Chroma
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import FastEmbedEmbeddings
from langchain.schema.output_parser import StrOutputParser
from langchain_community.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
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 the following pieces of retrieved context
to answer the question. 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)
self.vector_store = Chroma.from_documents(documents=chunks, embedding=FastEmbedEmbeddings())
self.retriever = self.vector_store.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
"k": 3,
"score_threshold": 0.1,
},
)
self.chain = ({"context": self.retriever, "question": RunnablePassthrough()}
| self.prompt
| self.model
| StrOutputParser())
#except Exception as e:
#print(f"Error during ingestion: {e}")
#self.clear()
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
# from langchain_community.vectorstores import Chroma
# from langchain_community.chat_models import ChatOllama
# from langchain_community.embeddings import FastEmbedEmbeddings
# from langchain.schema.output_parser import StrOutputParser
# from langchain_community.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
# 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 the following pieces of retrieved context
# to answer the question. 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=FastEmbedEmbeddings())
# self.retriever = vector_store.as_retriever(
# search_type="similarity_score_threshold",
# search_kwargs={
# "k": 3,
# "score_threshold": 1.0,
# },
# )
# 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