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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 PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema.runnable import RunnablePassthrough
from langchain.prompts import PromptTemplate
from langchain_community.vectorstores.utils import filter_complex_metadata
class ChatPDF:
vector_store = None
retriever = None
chain = None
def __init__(self):
self.model = ChatOllama(model="qwen:0.5b")
self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=96)
self.prompt = PromptTemplate.from_template(
"""
You are an assistant for question-answering tasks. Use the following pieces of context
to answer the question. If you don't know the answer, just say that you don't know.
Question: {question}
Context: {context}
Answer:
"""
)
def ingest(self, pdf_file_path: str):
docs = PyMuPDFLoader(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": 7,
"score_threshold": 0.1,
},
)
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[:512])
def clear(self):
self.vector_store = None
self.retriever = None
self.chain = None |