Quickstart

import os
from dotenv import load_dotenv
from datasets import load_dataset
from langchain_astradb import AstraDBVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

load_dotenv()

ASTRA_DB_APPLICATION_TOKEN = os.environ.get("ASTRA_DB_APPLICATION_TOKEN")
ASTRA_DB_API_ENDPOINT = os.environ.get("ASTRA_DB_API_ENDPOINT")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
ASTRA_DB_COLLECTION = os.environ.get("ASTRA_DB_COLLECTION")

embedding = OpenAIEmbeddings()
vstore = AstraDBVectorStore(
    embedding=embedding,
    collection_name="test",
    token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
    api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
)

philo_dataset = load_dataset("datastax/philosopher-quotes")["train"]
print("An example entry:")
print(philo_dataset[16])

docs = []
for entry in philo_dataset:
    metadata = {"author": entry["author"]}
    if entry["tags"]:
        # Add metadata tags to the metadata dictionary
        for tag in entry["tags"].split(";"):
            metadata[tag] = "y"
    # Add a LangChain document with the quote and metadata tags
    doc = Document(page_content=entry["quote"], metadata=metadata)
    docs.append(doc)

inserted_ids = vstore.add_documents(docs)
print(f"\nInserted {len(inserted_ids)} documents.")

print(vstore.astra_db.collection(ASTRA_DB_COLLECTION).find())

retriever = vstore.as_retriever(search_kwargs={'k': 3})

prompt_template = """
Answer the question based only on the supplied context. If you don't know the answer, say you don't know the answer.
Context: {context}
Question: {question}
Your answer:
"""
prompt = ChatPromptTemplate.from_template(prompt_template)
model = ChatOpenAI(openai_api_key=OPENAI_API_KEY)

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

response = chain.invoke("In the given context, what subject are philosophers most concerned with?")
print(response)

An example entry:
{'author': 'aristotle', 'quote': 'Love well, be loved and do something of value.', 'tags': 'love;ethics'}

Inserted 450 documents.
The subject that philosophers are most concerned with in the given context is truth.