mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
92 lines
2.6 KiB
Python
92 lines
2.6 KiB
Python
import getpass
|
|
import os
|
|
|
|
from langchain_community.document_loaders import PyPDFLoader
|
|
from langchain_community.vectorstores import Milvus
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
from langchain_core.prompts import ChatPromptTemplate
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.runnables import (
|
|
RunnableLambda,
|
|
RunnableParallel,
|
|
RunnablePassthrough,
|
|
)
|
|
from langchain_nvidia_aiplay import ChatNVIDIA, NVIDIAEmbeddings
|
|
from langchain_text_splitters.character import CharacterTextSplitter
|
|
|
|
EMBEDDING_MODEL = "nvolveqa_40k"
|
|
CHAT_MODEL = "llama2_13b"
|
|
HOST = "127.0.0.1"
|
|
PORT = "19530"
|
|
COLLECTION_NAME = "test"
|
|
INGESTION_CHUNK_SIZE = 500
|
|
INGESTION_CHUNK_OVERLAP = 0
|
|
|
|
if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
|
|
print("Valid NVIDIA_API_KEY already in environment. Delete to reset")
|
|
else:
|
|
nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ")
|
|
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key"
|
|
os.environ["NVIDIA_API_KEY"] = nvapi_key
|
|
|
|
# Read from Milvus Vector Store
|
|
embeddings = NVIDIAEmbeddings(model=EMBEDDING_MODEL)
|
|
vectorstore = Milvus(
|
|
connection_args={"host": HOST, "port": PORT},
|
|
collection_name=COLLECTION_NAME,
|
|
embedding_function=embeddings,
|
|
)
|
|
retriever = vectorstore.as_retriever()
|
|
|
|
# RAG prompt
|
|
template = """<s>[INST] <<SYS>>
|
|
Use the following context to answer the user's question. If you don't know the answer,
|
|
just say that you don't know, don't try to make up an answer.
|
|
<</SYS>>
|
|
<s>[INST] Context: {context} Question: {question} Only return the helpful
|
|
answer below and nothing else. Helpful answer:[/INST]"
|
|
"""
|
|
prompt = ChatPromptTemplate.from_template(template)
|
|
|
|
# RAG
|
|
model = ChatNVIDIA(model=CHAT_MODEL)
|
|
chain = (
|
|
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
|
|
| prompt
|
|
| model
|
|
| StrOutputParser()
|
|
)
|
|
|
|
|
|
# Add typing for input
|
|
class Question(BaseModel):
|
|
__root__: str
|
|
|
|
|
|
chain = chain.with_types(input_type=Question)
|
|
|
|
|
|
def _ingest(url: str) -> dict:
|
|
"""Load and ingest the PDF file from the URL"""
|
|
|
|
loader = PyPDFLoader(url)
|
|
data = loader.load()
|
|
|
|
# Split docs
|
|
text_splitter = CharacterTextSplitter(
|
|
chunk_size=INGESTION_CHUNK_SIZE, chunk_overlap=INGESTION_CHUNK_OVERLAP
|
|
)
|
|
docs = text_splitter.split_documents(data)
|
|
|
|
# Insert the documents in Milvus Vector Store
|
|
_ = Milvus.from_documents(
|
|
documents=docs,
|
|
embedding=embeddings,
|
|
collection_name=COLLECTION_NAME,
|
|
connection_args={"host": HOST, "port": PORT},
|
|
)
|
|
return {}
|
|
|
|
|
|
ingest = RunnableLambda(_ingest)
|