langchain-foxnose 0.2.0
LangChain integration for FoxNose - the knowledge layer for RAG and AI agents
License: Apache-2.0
Requires Python: >=3.9
Keywords: ai, embeddings, foxnose, knowledge-base, langchain, rag, retriever, vector-search

# langchain-foxnose

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LangChain integration for [FoxNose](https://foxnose.net?utm_source=github&utm_medium=repository&utm_campaign=langchain-foxnose) — the serverless knowledge platform purpose-built as the knowledge layer for RAG and AI agents.

- **`FoxNoseRetriever`** — query-based retrieval for RAG pipelines
- **`FoxNoseLoader`** — bulk document loading with cursor-based pagination
- **`create_foxnose_tool`** — search tool for LLM agents

## Installation

```bash
pip install langchain-foxnose
```

## Quick Start

```python
from foxnose_sdk.flux import FluxClient
from foxnose_sdk.auth import SimpleKeyAuth
from langchain_foxnose import FoxNoseRetriever

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

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

# Use it
docs = retriever.invoke("How do I reset my password?")
for doc in docs:
    print(doc.page_content)
    print(doc.metadata)
```

## Features

- **All search modes**: text, vector, hybrid, and vector-boosted search
- **Bulk document loading**: cursor-based pagination with lazy loading for large folders
- **Agent-ready search tool**: wrap any retriever as a tool for LLM agents
- **Flexible content mapping**: single field, multiple fields, or custom mapper function
- **Metadata control**: whitelist, blacklist, or include system metadata
- **Native async**: uses `AsyncFluxClient` for true async when available
- **Structured filtering**: pass FoxNose `where` filters for precise retrieval
- **Full configuration**: search fields, thresholds, hybrid weights, sort, and more

## Search Modes

```python
# Pure vector (semantic) search
retriever = FoxNoseRetriever(
    client=client,
    folder_path="articles",
    page_content_field="body",
    search_mode="vector",
)

# Hybrid search (text + vector)
retriever = FoxNoseRetriever(
    client=client,
    folder_path="articles",
    page_content_field="body",
    search_mode="hybrid",
    hybrid_config={"vector_weight": 0.6, "text_weight": 0.4},
)

# Text search with vector boost
retriever = FoxNoseRetriever(
    client=client,
    folder_path="articles",
    page_content_field="body",
    search_mode="vector_boosted",
    vector_boost_config={"boost_factor": 1.3},
)
```

## Filtered Retrieval

```python
retriever = FoxNoseRetriever(
    client=client,
    folder_path="articles",
    page_content_field="body",
    where={
        "$": {
            "all_of": [
                {"status__eq": "published"},
                {"category__in": ["tech", "science"]},
            ]
        }
    },
)
```

## Document Loader

`FoxNoseLoader` iterates over all resources in a folder using cursor-based pagination. Use it to bulk-load documents for indexing, batch processing, or seeding a local vector store.

```python
from langchain_foxnose import FoxNoseLoader

loader = FoxNoseLoader(
    client=client,
    folder_path="knowledge-base",
    page_content_field="body",
    batch_size=50,
)

# Load all documents at once
docs = loader.load()

# Or iterate lazily for large folders
for doc in loader.lazy_load():
    print(doc.metadata.get("key"), doc.page_content[:100])
```

## Agent Tool

`create_foxnose_tool` wraps a retriever as a LangChain tool that LLM agents can call.

```python
from langchain_foxnose import create_foxnose_tool

tool = create_foxnose_tool(
    client=client,
    folder_path="knowledge-base",
    page_content_field="body",
    name="kb_search",
    description="Search the knowledge base for relevant information.",
    search_mode="hybrid",
    top_k=5,
)

# Use directly
result = tool.invoke("How do I reset my password?")

# Or plug into any LangChain agent
# from langgraph.prebuilt import create_react_agent
# agent = create_react_agent(llm, tools=[tool])
```

## Async Usage

```python
from foxnose_sdk.flux import AsyncFluxClient

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

retriever = FoxNoseRetriever(
    async_client=async_client,
    folder_path="knowledge-base",
    page_content_field="body",
)

docs = await retriever.ainvoke("search query")
```

## Documentation

- [Getting Started](https://langchain-foxnose.readthedocs.io/getting-started/)
- [Retriever](https://langchain-foxnose.readthedocs.io/retriever/)
- [Document Loader](https://langchain-foxnose.readthedocs.io/loader/)
- [Search Tool](https://langchain-foxnose.readthedocs.io/tool/)
- [Configuration](https://langchain-foxnose.readthedocs.io/configuration/)
- [Examples](https://langchain-foxnose.readthedocs.io/examples/)
- [API Reference](https://langchain-foxnose.readthedocs.io/api-reference/)
- [FoxNose Documentation](https://foxnose.net/docs?utm_source=github&utm_medium=repository&utm_campaign=langchain-foxnose)

## License

Apache-2.0 — see [LICENSE](LICENSE) for details.