mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
|
import os
|
||
|
|
|
||
|
|
from langchain_community.vectorstores.azuresearch import AzureSearch
|
||
|
|
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 RunnableParallel, RunnablePassthrough
|
||
|
|
from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings
|
||
|
|
|
||
|
|
if not os.getenv("AZURE_OPENAI_ENDPOINT"):
|
||
|
|
raise ValueError("Please set the environment variable AZURE_OPENAI_ENDPOINT")
|
||
|
|
|
||
|
|
if not os.getenv("AZURE_OPENAI_API_KEY"):
|
||
|
|
raise ValueError("Please set the environment variable AZURE_OPENAI_API_KEY")
|
||
|
|
|
||
|
|
if not os.getenv("AZURE_EMBEDDINGS_DEPLOYMENT"):
|
||
|
|
raise ValueError("Please set the environment variable AZURE_EMBEDDINGS_DEPLOYMENT")
|
||
|
|
|
||
|
|
if not os.getenv("AZURE_CHAT_DEPLOYMENT"):
|
||
|
|
raise ValueError("Please set the environment variable AZURE_CHAT_DEPLOYMENT")
|
||
|
|
|
||
|
|
if not os.getenv("AZURE_SEARCH_ENDPOINT"):
|
||
|
|
raise ValueError("Please set the environment variable AZURE_SEARCH_ENDPOINT")
|
||
|
|
|
||
|
|
if not os.getenv("AZURE_SEARCH_KEY"):
|
||
|
|
raise ValueError("Please set the environment variable AZURE_SEARCH_KEY")
|
||
|
|
|
||
|
|
|
||
|
|
api_version = os.getenv("OPENAI_API_VERSION", "2023-05-15")
|
||
|
|
index_name = os.getenv("AZURE_SEARCH_INDEX_NAME", "rag-azure-search")
|
||
|
|
|
||
|
|
embeddings = AzureOpenAIEmbeddings(
|
||
|
|
deployment=os.environ["AZURE_EMBEDDINGS_DEPLOYMENT"],
|
||
|
|
api_version=api_version,
|
||
|
|
chunk_size=1,
|
||
|
|
)
|
||
|
|
|
||
|
|
vector_store: AzureSearch = AzureSearch(
|
||
|
|
azure_search_endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
|
||
|
|
azure_search_key=os.environ["AZURE_SEARCH_KEY"],
|
||
|
|
index_name=index_name,
|
||
|
|
embedding_function=embeddings.embed_query,
|
||
|
|
)
|
||
|
|
|
||
|
|
"""
|
||
|
|
(Optional) Example document -
|
||
|
|
Uncomment the following code to load the document into the vector store
|
||
|
|
or substitute with your own.
|
||
|
|
"""
|
||
|
|
# import pathlib
|
||
|
|
# from langchain.text_splitter import CharacterTextSplitter
|
||
|
|
# from langchain_community.document_loaders import TextLoader
|
||
|
|
|
||
|
|
# current_file_path = pathlib.Path(__file__).resolve()
|
||
|
|
# root_directory = current_file_path.parents[3]
|
||
|
|
# target_file_path = \
|
||
|
|
# root_directory / "docs" / "docs" / "modules" / "state_of_the_union.txt"
|
||
|
|
|
||
|
|
# loader = TextLoader(str(target_file_path), encoding="utf-8")
|
||
|
|
|
||
|
|
# documents = loader.load()
|
||
|
|
# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
|
||
|
|
# docs = text_splitter.split_documents(documents)
|
||
|
|
|
||
|
|
# vector_store.add_documents(documents=docs)
|
||
|
|
|
||
|
|
# RAG prompt
|
||
|
|
template = """Answer the question based only on the following context:
|
||
|
|
{context}
|
||
|
|
Question: {question}
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Perform a similarity search
|
||
|
|
retriever = vector_store.as_retriever()
|
||
|
|
|
||
|
|
_prompt = ChatPromptTemplate.from_template(template)
|
||
|
|
_model = AzureChatOpenAI(
|
||
|
|
deployment_name=os.environ["AZURE_CHAT_DEPLOYMENT"],
|
||
|
|
api_version=api_version,
|
||
|
|
)
|
||
|
|
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)
|