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https://github.com/topoteretes/cognee

Deterministic LLMs Outputs for AI Applications and AI Agents
https://github.com/topoteretes/cognee

agents ai ai-agents ai-memory chatgpt cognitive cognitive-architecture cognitive-memory cognitive-modeling graph-database graphql knowledge knowledge-graph neo4j openai rag vector-database

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Deterministic LLMs Outputs for AI Applications and AI Agents

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README 

        
# cognee

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We build for developers who need a reliable, production-ready data layer for AI applications



## What is cognee? 



Cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow you to interconnect and retrieve past conversations, documents, and audio transcriptions while reducing hallucinations, developer effort, and cost.

Try it in a Google Colab  notebook  or have a look at our documentation



If you have questions, join our  Discord community



## 📦 Installation



### With pip



```bash

pip install cognee

```



### With poetry



```bash

poetry add cognee

```



## 💻 Basic Usage



### Setup



```

import os



os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"



```

or 

```

import cognee

cognee.config.llm_api_key = "YOUR_OPENAI_API_KEY"

```

You can also set the variables by creating .env file, here is our template.

To use different LLM providers, for more info check out our documentation



If you are using Networkx, create an account on Graphistry to visualize results:

```

    cognee.config.set_graphistry_config({

        "username": "YOUR_USERNAME",

        "password": "YOUR_PASSWORD"

    })

```



(Optional) To run the UI, go to cognee-frontend directory and run:

```

npm run dev

```

or run everything in a docker container:

```

docker-compose up

```

Then navigate to localhost:3000



### Simple example



Run the default cognee pipeline:



```

import cognee



text = """Natural language processing (NLP) is an interdisciplinary

       subfield of computer science and information retrieval"""



await cognee.add(text) # Add a new piece of information



await cognee.cognify() # Use LLMs and cognee to create a knowledge graph



search_results = await cognee.search("INSIGHTS", {'query': 'NLP'}) # Query cognee for the insights



for result in search_results:

    do_something_with_result(result)

```



### Create your own memory store



cognee framework consists of tasks that can be grouped into pipelines.

Each task can be an independent part of business logic, that can be tied to other tasks to form a pipeline.

These tasks persist data into your memory store enabling you to search for relevant context of past conversations, documents, or any other data you have stored.



### Example: Classify your documents



Here is an example of how it looks for a default cognify pipeline:



1. To prepare the data for the pipeline run, first we need to add it to our metastore and normalize it:



Start with:

```

text = """Natural language processing (NLP) is an interdisciplinary

       subfield of computer science and information retrieval"""



await cognee.add(text) # Add a new piece of information

```



2. In the next step we make a task. The task can be any business logic we need, but the important part is that it should be encapsulated in one function.



Here we show an example of creating a naive LLM classifier that takes a Pydantic model and then stores the data in both the graph and vector stores after analyzing each chunk.

We provided just a snippet for reference, but feel free to check out the implementation in our repo. 



```

async def chunk_naive_llm_classifier(

    data_chunks: list[DocumentChunk],

    classification_model: Type[BaseModel]

):

    # Extract classifications asynchronously

    chunk_classifications = await asyncio.gather(

        *(extract_categories(chunk.text, classification_model) for chunk in data_chunks)

    )



    # Collect classification data points using a set to avoid duplicates

    classification_data_points = {

        uuid5(NAMESPACE_OID, cls.label.type)

        for cls in chunk_classifications

    } | {

        uuid5(NAMESPACE_OID, subclass.value)

        for cls in chunk_classifications

        for subclass in cls.label.subclass

    }



    vector_engine = get_vector_engine()

    collection_name = "classification"



    # Define the payload schema

    class Keyword(BaseModel):

        uuid: str

        text: str

        chunk_id: str

        document_id: str



    # Ensure the collection exists and retrieve existing data points

    if not await vector_engine.has_collection(collection_name):

        await vector_engine.create_collection(collection_name, payload_schema=Keyword)

        existing_points_map = {}

    else:

        existing_points_map = {}

    return data_chunks



...



```



We have a large number of tasks that can be used in your pipelines, and you can also create your own tasks to fit your business logic.



3. Once we have our tasks, it is time to group them into a pipeline.

This simplified snippet demonstrates how tasks can be added to a pipeline, and how they can pass the information forward from one to another. 



```

            



Task(

    chunk_naive_llm_classifier,

    classification_model = cognee_config.classification_model,

)



pipeline = run_tasks(tasks, documents)



```



To see the working code, check cognee.api.v1.cognify default pipeline in our repo.



## Vector retrieval, Graphs and LLMs



Cognee supports a variety of tools and services for different operations:

- **Modular**: Cognee is modular by nature, using tasks grouped into pipelines



- **Local Setup**: By default, LanceDB runs locally with NetworkX and OpenAI.



- **Vector Stores**: Cognee supports Qdrant and Weaviate for vector storage.



- **Language Models (LLMs)**: You can use either Anyscale or Ollama as your LLM provider.



- **Graph Stores**: In addition to LanceDB, Neo4j is also supported for graph storage.

  

- **User management**: Create individual user graphs and manage permissions



## Demo



Check out our demo notebook [here](https://github.com/topoteretes/cognee/blob/main/notebooks/cognee%20-%20Get%20Started.ipynb)



[](https://www.youtube.com/watch?v=BDFt4xVPmro "Learn about cognee: 55")



## Get Started



### Install Server



Please see the [cognee Quick Start Guide](https://topoteretes.github.io/cognee/quickstart/) for important configuration information.



```bash

docker compose up

```



### Install SDK



Please see the cognee [Development Guide](https://topoteretes.github.io/cognee/quickstart/) for important beta information and usage instructions.



```bash

pip install cognee

```



## Star History



[![Star History Chart](https://api.star-history.com/svg?repos=topoteretes/cognee&type=Date)](https://star-history.com/#topoteretes/cognee&Date)



## 💫 Contributors





  contributors