# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

%pip install -qU langchain-cognee

import nest_asyncio

nest_asyncio.apply()

from langchain_cognee import CogneeRetriever

retriever = CogneeRetriever(
    llm_api_key="sk-",  # OpenAI API Key
    dataset_name="my_dataset",
    k=3,
)

# Example of adding and processing documents
from langchain_core.documents import Document

docs = [
    Document(page_content="Elon Musk is the CEO of SpaceX."),
    Document(page_content="SpaceX focuses on rockets and space travel."),
]

retriever.add_documents(docs)
retriever.process_data()

# Now let's query the retriever
query = "Tell me about Elon Musk"
results = retriever.invoke(query)

for idx, doc in enumerate(results, start=1):
    print(f"Doc {idx}: {doc.page_content}")

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

from langchain_cognee import CogneeRetriever
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Instantiate the retriever with your Cognee config
retriever = CogneeRetriever(llm_api_key="sk-", dataset_name="my_dataset", k=3)

# Optionally, prune/reset the dataset for a clean slate
retriever.prune()

# Add some documents
docs = [
    Document(page_content="Elon Musk is the CEO of SpaceX."),
    Document(page_content="SpaceX focuses on space travel."),
]
retriever.add_documents(docs)
retriever.process_data()


prompt = ChatPromptTemplate.from_template(
    """Answer the question based only on the context provided.

Context: {context}

Question: {question}"""
)


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


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

answer = chain.invoke("What companies do Elon Musk own?")

print("\nFinal chain answer:\n", answer)