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retriever_chain.py
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retriever_chain.py
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import os
import tempfile
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
import mlflow
assert "OPENAI_API_KEY" in os.environ, "Please set the OPENAI_API_KEY environment variable."
with tempfile.TemporaryDirectory() as temp_dir:
persist_dir = os.path.join(temp_dir, "faiss_index")
# Create the vector database and persist it to a local filesystem folder
loader = TextLoader("tests/langchain/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)
db.save_local(persist_dir)
# Define a loader function to recall the retriever from the persisted vectorstore
def load_retriever(persist_directory):
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.load_local(persist_directory, embeddings)
return vectorstore.as_retriever()
# Log the retriever with the loader function
with mlflow.start_run() as run:
logged_model = mlflow.langchain.log_model(
db.as_retriever(),
artifact_path="retriever",
loader_fn=load_retriever,
persist_dir=persist_dir,
)
# Load the retriever chain
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
print(loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}]))