Getting Started¶

Prerequisites¶

Python >= 3.9
A FoxNose workspace with at least one folder containing vectorized content
A Flux API key (public + secret)

See the FoxNose documentation for workspace setup and API key creation.

Installation¶

pip install langchain-foxnose

This installs langchain-foxnose along with its dependencies: foxnose-sdk and langchain-core.

Your First Retriever¶

1. Create a Flux client¶

from foxnose_sdk.flux import FluxClient
from foxnose_sdk.auth import SimpleKeyAuth

client = FluxClient(
    base_url="https://<env_key>.fxns.io",
    api_prefix="my_api",
    auth=SimpleKeyAuth("YOUR_PUBLIC_KEY", "YOUR_SECRET_KEY"),
)

Replace <env_key> with your environment key, and provide your API credentials.

2. Create a retriever¶

from langchain_foxnose import FoxNoseRetriever

retriever = FoxNoseRetriever(
    client=client,
    folder_path="knowledge-base",
    page_content_field="body",
    search_mode="hybrid",
    top_k=5,
)

folder_path — the FoxNose folder connected to your Flux API
page_content_field — which data field becomes the document's page_content
search_mode — "text", "vector", "hybrid", or "vector_boosted"
top_k — how many results to return

3. Retrieve documents¶

docs = retriever.invoke("How do I reset my password?")
for doc in docs:
    print(doc.page_content[:100])
    print(doc.metadata)

4. Use in a LangChain chain¶

from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA

qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4"),
    retriever=retriever,
    return_source_documents=True,
)
result = qa.invoke({"query": "How do I reset my password?"})
print(result["result"])

Convenience Constructor¶

If you prefer not to construct the client yourself:

from foxnose_sdk.auth import SimpleKeyAuth
from langchain_foxnose import FoxNoseRetriever

retriever = FoxNoseRetriever.from_client_params(
    base_url="https://<env_key>.fxns.io",
    api_prefix="my_api",
    auth=SimpleKeyAuth("YOUR_PUBLIC_KEY", "YOUR_SECRET_KEY"),
    folder_path="knowledge-base",
    page_content_field="body",
)