ollama/examples/langchain-python-rag-document/main.py
Matt Williams 385eeea357 remove with
Signed-off-by: Matt Williams <m@technovangelist.com>
2023-10-11 21:26:11 -07:00

61 lines
No EOL
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="llama2:13b", 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})