We build for developers who need a reliable, production-ready data layer for AI applications
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
pip install cognee
pip install cognee[postgres]
poetry add cognee
poetry add cognee -E postgres
import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
or
import cognee
cognee.config.set_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 Network, 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
If you want to use the UI with PostgreSQL through docker-compose make sure to set the following values in the .env file:
DB_PROVIDER=postgres
DB_HOST=postgres
DB_PORT=5432
DB_NAME=cognee_db
DB_USERNAME=cognee
DB_PASSWORD=cognee
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)
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.
Here is an example of how it looks for a default cognify pipeline:
- 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
- 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 many tasks that can be used in your pipelines, and you can also create your tasks to fit your business logic.
- 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.
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 LanceDB, Qdrant, PGVector and Weaviate for vector storage.
-
Language Models (LLMs): You can use either Anyscale or Ollama as your LLM provider.
-
Graph Stores: In addition to NetworkX, Neo4j is also supported for graph storage.
-
User management: Create individual user graphs and manage permissions
Check out our demo notebook here
Please see the cognee Quick Start Guide for important configuration information.
docker compose up
Please see the cognee Development Guide for important beta information and usage instructions.
pip install cognee