62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
from langchain.document_loaders import OnlinePDFLoader
|
|
from langchain.vectorstores import Chroma
|
|
from langchain.embeddings import GPT4AllEmbeddings
|
|
from langchain import PromptTemplate
|
|
from langchain.llms import Ollama
|
|
from langchain.callbacks.manager import CallbackManager
|
|
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
|
|
from langchain.chains import RetrievalQA
|
|
import sys
|
|
import os
|
|
|
|
class SuppressStdout:
|
|
def __enter__(self):
|
|
self._original_stdout = sys.stdout
|
|
self._original_stderr = sys.stderr
|
|
sys.stdout = open(os.devnull, 'w')
|
|
sys.stderr = open(os.devnull, 'w')
|
|
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
|
sys.stdout.close()
|
|
sys.stdout = self._original_stdout
|
|
sys.stderr = self._original_stderr
|
|
|
|
# load the pdf and split it into chunks
|
|
loader = OnlinePDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf")
|
|
data = loader.load()
|
|
|
|
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
|
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
|
|
all_splits = text_splitter.split_documents(data)
|
|
|
|
with SuppressStdout():
|
|
vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
|
|
|
|
while True:
|
|
query = input("\nQuery: ")
|
|
if query == "exit":
|
|
break
|
|
if query.strip() == "":
|
|
continue
|
|
|
|
# Prompt
|
|
template = """Use the following pieces of context to answer the question at the end.
|
|
If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
|
Use three sentences maximum and keep the answer as concise as possible.
|
|
{context}
|
|
Question: {question}
|
|
Helpful Answer:"""
|
|
QA_CHAIN_PROMPT = PromptTemplate(
|
|
input_variables=["context", "question"],
|
|
template=template,
|
|
)
|
|
|
|
llm = Ollama(model="llama3.2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
|
|
qa_chain = RetrievalQA.from_chain_type(
|
|
llm,
|
|
retriever=vectorstore.as_retriever(),
|
|
chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
|
|
)
|
|
|
|
result = qa_chain({"query": query})
|