Local Ollama Pipeline

A minimal guide to running a complete cognee pipeline on your own machine: Ollama for both generation and embeddings, and cognee’s embedded stores for the graph, vectors, and metadata. Use it when you want the whole remember → recall round trip to run without any hosted API call, in a throwaway directory you can delete afterwards.

Before You Start

Complete Quickstart to understand basic operations
Install Ollama, start it with ollama serve, and pull the two models the script uses:
ollama pull llama3.1:8b ollama pull nomic-embed-text
Read Local Setup (No API Key) for the equivalent .env configuration and the local-run troubleshooting list
Check LLM Providers and Embedding Providers if you want to point the script at a different local model
Nothing else to install: the graph, vector, and relational stores this script selects are embedded and ship with cognee

Code in Action

import os
import asyncio
import tempfile
from pathlib import Path

# Setup temp directory to keep this example self-contained
_DATA_DIR = tempfile.mkdtemp(prefix="cognee_ollama_example_")
os.environ["ENABLE_BACKEND_ACCESS_CONTROL"] = "false"
os.environ["CACHING"] = "false"

# Configure Ollama environment settings
os.environ["LLM_PROVIDER"] = "ollama"
os.environ["LLM_MODEL"] = "llama3.1:8b"
os.environ["LLM_ENDPOINT"] = "http://localhost:11434/v1"
os.environ["LLM_API_KEY"] = "ollama"
os.environ["LLM_TEMPERATURE"] = "0.0"

os.environ["EMBEDDING_PROVIDER"] = "ollama"
os.environ["EMBEDDING_MODEL"] = "nomic-embed-text"
os.environ["EMBEDDING_ENDPOINT"] = "http://localhost:11434/api/embed"
os.environ["EMBEDDING_DIMENSIONS"] = "768"
os.environ["HUGGINGFACE_TOKENIZER"] = "nomic-ai/nomic-embed-text-v1.5"

import cognee  # noqa: E402
from cognee.modules.search.types import SearchType  # noqa: E402
from cognee.infrastructure.llm.config import get_llm_config  # noqa: E402

# Force local embedded stack configuration
cognee.config.set_graph_database_provider("kuzu")
cognee.config.set_vector_db_provider("lancedb")
cognee.config.data_root_directory(str(Path(_DATA_DIR) / "data"))
cognee.config.system_root_directory(str(Path(_DATA_DIR) / "system"))


SAMPLE_TEXT = """\
Cognee is an open-source library that helps developers turn documents into AI memory.
It builds semantic graphs, indexes entities, and stores vectors to enable structured retrieval.
Cognee supports local execution via Ollama as well as hosted cloud providers.
"""


def banner(title: str) -> None:
    print("\n" + "=" * 78)
    print(title)
    print("=" * 78)


async def main() -> None:
    # Start from a clean slate in isolated directory
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

    banner("LOCAL PIPELINE: REMEMBER USING OLLAMA")
    llm_config = get_llm_config()
    print(f"Using LLM: {llm_config.llm_model}")
    print(f"Using Embeddings: {os.environ.get('EMBEDDING_MODEL')}")

    # Ingest and build the knowledge graph (this will trigger a warning if an
    # unvalidated model is used)
    await cognee.remember(SAMPLE_TEXT, dataset_name="ollama_local_demo", self_improvement=False)
    print("Local knowledge graph built successfully.")

    banner("LOCAL RECALL")
    query = "What does Cognee help developers do?"
    results = await cognee.recall(
        query_text=query,
        query_type=SearchType.GRAPH_COMPLETION,
        datasets=["ollama_local_demo"],
    )
    print(f"Query: {query}")
    print("Recall Results:")
    print(results[0].text if results else "<no results>")


if __name__ == "__main__":
    asyncio.run(main())

What Just Happened

Step 1: Isolate the Example’s Storage

# Setup temp directory to keep this example self-contained
_DATA_DIR = tempfile.mkdtemp(prefix="cognee_ollama_example_")
os.environ["ENABLE_BACKEND_ACCESS_CONTROL"] = "false"
os.environ["CACHING"] = "false"

A fresh temporary directory keeps this run’s databases away from your default cognee directories, so the example leaves nothing behind. Access control stays off because the script runs as a plain single-user script, and caching stays off so every run really exercises the local models instead of replaying earlier answers.

Step 2: Point Cognee at Ollama

# Configure Ollama environment settings
os.environ["LLM_PROVIDER"] = "ollama"
os.environ["LLM_MODEL"] = "llama3.1:8b"
os.environ["LLM_ENDPOINT"] = "http://localhost:11434/v1"
os.environ["LLM_API_KEY"] = "ollama"
os.environ["LLM_TEMPERATURE"] = "0.0"

os.environ["EMBEDDING_PROVIDER"] = "ollama"
os.environ["EMBEDDING_MODEL"] = "nomic-embed-text"
os.environ["EMBEDDING_ENDPOINT"] = "http://localhost:11434/api/embed"
os.environ["EMBEDDING_DIMENSIONS"] = "768"
os.environ["HUGGINGFACE_TOKENIZER"] = "nomic-ai/nomic-embed-text-v1.5"

Both halves have to be set together: configuring only the LLM leaves embeddings falling back to OpenAI, which would need an API key. The two endpoints differ because generation goes to Ollama’s OpenAI-compatible path and embeddings go to its native one, LLM_API_KEY is a placeholder Ollama never validates, and EMBEDDING_DIMENSIONS plus HUGGINGFACE_TOKENIZER describe nomic-embed-text so cognee sizes its vectors and counts tokens correctly. The environment is set before import cognee so the configuration is in place when cognee reads it — hence the # noqa: E402 markers on the imports that follow.

Step 3: Pin the Embedded Local Stack

# Force local embedded stack configuration
cognee.config.set_graph_database_provider("kuzu")
cognee.config.set_vector_db_provider("lancedb")
cognee.config.data_root_directory(str(Path(_DATA_DIR) / "data"))
cognee.config.system_root_directory(str(Path(_DATA_DIR) / "system"))

These four calls make the storage side local too: an embedded graph database, LanceDB for vectors, and SQLite metadata, all written under the temporary directory from Step 1. Both calls select what cognee already uses out of the box (kuzu is an accepted alias for the default embedded graph store, and LanceDB is the default vector store), so they are explicit rather than required — they keep the script behaving the same way even if GRAPH_DATABASE_PROVIDER or VECTOR_DB_PROVIDER is set in your environment. See Graph Stores for the other providers these names select between.

Step 4: Remember the Sample Text

SAMPLE_TEXT = """\
Cognee is an open-source library that helps developers turn documents into AI memory.
It builds semantic graphs, indexes entities, and stores vectors to enable structured retrieval.
Cognee supports local execution via Ollama as well as hosted cloud providers.
"""

await cognee.remember(SAMPLE_TEXT, dataset_name="ollama_local_demo", self_improvement=False)
print("Local knowledge graph built successfully.")

remember() runs the whole extraction pipeline against the local models: llama3.1:8b pulls entities and relationships out of the text and nomic-embed-text embeds the chunks. Three sentences is deliberately small — an 8B model on CPU is far slower than a hosted one, so keep test inputs short. llama3.1 is on cognee’s recommended list for structured extraction, so this script runs without warnings; swap in a model cognee hasn’t validated and the log opens with an advisory model-support warning instead — not a failure, extraction continues either way.

Step 5: Recall From the Local Graph

query = "What does Cognee help developers do?"
results = await cognee.recall(
    query_text=query,
    query_type=SearchType.GRAPH_COMPLETION,
    datasets=["ollama_local_demo"],
)
print(f"Query: {query}")
print("Recall Results:")
print(results[0].text if results else "<no results>")

SearchType.GRAPH_COMPLETION retrieves the triplets around the query and asks the local LLM to answer from them, which is the search type that proves the graph was actually populated. datasets=["ollama_local_demo"] scopes the search to the dataset built above, and printing results[0].text shows the generated answer rather than the raw result object.

Local Setup (No API Key)

The same configuration as .env variables, plus local-run troubleshooting.

LLM Providers

Swap in another local or hosted model for generation.

Embedding Providers

Pick a different embedding model and its matching dimensions.

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

Rust SDK

TypeScript SDK

OSS

Before You Start

Code in Action

What Just Happened

Step 1: Isolate the Example’s Storage

Step 2: Point Cognee at Ollama

Step 3: Pin the Embedded Local Stack

Step 4: Remember the Sample Text

Step 5: Recall From the Local Graph

Local Setup (No API Key)

LLM Providers

Embedding Providers

​Before You Start

​Code in Action

​What Just Happened

​Step 1: Isolate the Example’s Storage

​Step 2: Point Cognee at Ollama

​Step 3: Pin the Embedded Local Stack

​Step 4: Remember the Sample Text

​Step 5: Recall From the Local Graph

Local Setup (No API Key)

LLM Providers

Embedding Providers

Before You Start

Code in Action

What Just Happened

Step 1: Isolate the Example’s Storage

Step 2: Point Cognee at Ollama

Step 3: Pin the Embedded Local Stack

Step 4: Remember the Sample Text

Step 5: Recall From the Local Graph