> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Cognee integration

> Integrate with the Cognee retriever using LangChain Python.

# CogneeRetriever

This will help you get started with the Cognee [retriever](/oss/python/langchain/retrieval).

### Integration details

Bring-your-own data (i.e., index and search a custom corpus of documents):

| Retriever         | Self-host | Cloud offering |       Package      |
| :---------------- | :-------- | :------------: | :----------------: |
| `CogneeRetriever` | ✅         |        ❌       | `langchain-cognee` |

## Setup

For cognee default setup, only thing you need is your OpenAI API key.

If you want to get automated tracing from individual queries, you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```

### Installation

This retriever lives in the `langchain-cognee` package:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-cognee
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import nest_asyncio

nest_asyncio.apply()
```

## Instantiation

Now we can instantiate our retriever:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_cognee import CogneeRetriever

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

## Usage

Add some documents, process them, and then run queries. Cognee retrieves relevant knowledge to your queries and generates final answers.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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}")
```

***

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