Recall Without an LLM Key

A minimal guide to running the full remember → recall round trip with no LLM API key anywhere: GLiNER2 extracts the graph and writes the chunk summaries, and fastembed embeds them on CPU. Use it when you have no key to spend, when the data cannot leave the machine, or when you want a smoke test that proves the pipeline itself does not depend on an LLM.

Before You Start

Complete Quickstart to understand basic operations
Install the two extras this script needs — GLiNER for extraction, fastembed for embeddings:
pip install "cognee[gliner]" "fastembed<=0.8.0"
Allow for the first-run downloads: the GLiNER model is about 800 MB and the bge-small-en-v1.5 embedding model about 130 MB, both cached after the first run
No LLM API key is needed, and none is used — the script removes LLM_API_KEY and OPENAI_API_KEY from the environment before importing cognee
Read Local Setup (No API Key) for the .env form of local provider configuration, and Embedding Providers to swap in a different fastembed model with its matching dimensions

Code in Action

import asyncio
import os

# Make sure no key leaks in from the shell: the point is to prove the pipeline
# runs without one.
for var in ("LLM_API_KEY", "OPENAI_API_KEY"):
    os.environ.pop(var, None)

os.environ.update(
    {
        "GRAPH_EXTRACTOR": "gliner",
        "EMBEDDING_PROVIDER": "fastembed",
        "EMBEDDING_MODEL": "BAAI/bge-small-en-v1.5",
        "EMBEDDING_DIMENSIONS": "384",
        "EMBEDDING_MAX_COMPLETION_TOKENS": "512",
        # Per-turn feedback analysis is an LLM call; without it recall is LLM-free.
        "AUTO_FEEDBACK": "false",
    }
)

import cognee  # noqa: E402  (environment must be set before the import)
from cognee import SearchType  # noqa: E402

TEXT = (
    "Marie Curie was born in Warsaw and worked at the University of Paris. "
    "She won the Nobel Prize in Physics in 1903 with Pierre Curie and Henri Becquerel."
)


async def main():
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

    # remember() runs add + cognify and then improve(). Without session_ids,
    # improve() only runs the default enrichment (triplet/vector indexing) —
    # embeddings, no LLM — so it is safe to leave self_improvement on.
    await cognee.remember(TEXT, dataset_name="no_llm")

    # No query_type: on the gliner backend this is CHUNKS.
    results = await cognee.recall("Where was Marie Curie born?", datasets=["no_llm"], top_k=3)
    print(f"\ndefault ({results[0].search_type}): {len(results)} result(s)")
    for item in results:
        print("  -", item.text.replace("\n", " | "))

    # The GLiNER-built summaries are searchable too.
    results = await cognee.recall(
        "Where was Marie Curie born?",
        query_type=SearchType.SUMMARIES,
        datasets=["no_llm"],
        top_k=3,
    )
    print(f"\nSUMMARIES: {len(results)} result(s)")
    for item in results:
        print("  -", item.text.replace("\n", " | "))


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

What Just Happened

Step 1: Clear Any Inherited API Key

# Make sure no key leaks in from the shell: the point is to prove the pipeline
# runs without one.
for var in ("LLM_API_KEY", "OPENAI_API_KEY"):
    os.environ.pop(var, None)

Cognee reads its LLM key from the environment, so a key exported in your shell or picked up from a .env file would quietly make this run an ordinary LLM run. Popping both variables first is what makes the result meaningful: everything after this line has no LLM to call.

Step 2: Select the Local Extractor and Embedder

os.environ.update(
    {
        "GRAPH_EXTRACTOR": "gliner",
        "EMBEDDING_PROVIDER": "fastembed",
        "EMBEDDING_MODEL": "BAAI/bge-small-en-v1.5",
        "EMBEDDING_DIMENSIONS": "384",
        "EMBEDDING_MAX_COMPLETION_TOKENS": "512",
        # Per-turn feedback analysis is an LLM call; without it recall is LLM-free.
        "AUTO_FEEDBACK": "false",
    }
)

import cognee  # noqa: E402  (environment must be set before the import)
from cognee import SearchType  # noqa: E402

GRAPH_EXTRACTOR=gliner swaps cognify’s LLM task list for the GLiNER one, which extracts entities and relationships and writes each chunk’s summary locally instead of prompting a model. Both halves have to be local: leaving EMBEDDING_PROVIDER at its default would send embedding requests to OpenAI and fail without a key. EMBEDDING_DIMENSIONS and EMBEDDING_MAX_COMPLETION_TOKENS describe bge-small-en-v1.5 — 384-dimensional vectors and a 512-token input limit — so cognee sizes its vector collections and chunks correctly. The whole block runs before import cognee because cognee reads this configuration at import time, hence the # noqa: E402 markers on the imports.

Step 3: Remember the Text Locally

await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)

# remember() runs add + cognify and then improve(). Without session_ids,
# improve() only runs the default enrichment (triplet/vector indexing) —
# embeddings, no LLM — so it is safe to leave self_improvement on.
await cognee.remember(TEXT, dataset_name="no_llm")

Pruning first clears any vector collections left over from a previous embedding model — their dimensions would not match the 384 configured above. remember() then ingests the text, builds the graph with GLiNER, and embeds the results with fastembed. Because no LLM task is in the pipeline, cognee also skips the first-run LLM connection probe, so the missing key never becomes an error.

Step 4: Recall With the Default Search Type

# No query_type: on the gliner backend this is CHUNKS.
results = await cognee.recall("Where was Marie Curie born?", datasets=["no_llm"], top_k=3)
print(f"\ndefault ({results[0].search_type}): {len(results)} result(s)")
for item in results:
    print("  -", item.text.replace("\n", " | "))

With no query_type, recall() normally answers with an LLM-written completion. When no usable LLM key is configured it falls back to SearchType.CHUNKS instead — a pure vector search that returns the matching text chunks — which is why the call works here at all. The printed search_type on each result reports which type actually ran.

Step 5: Search the GLiNER Summaries

# The GLiNER-built summaries are searchable too.
results = await cognee.recall(
    "Where was Marie Curie born?",
    query_type=SearchType.SUMMARIES,
    datasets=["no_llm"],
    top_k=3,
)
print(f"\nSUMMARIES: {len(results)} result(s)")
for item in results:
    print("  -", item.text.replace("\n", " | "))

SearchType.SUMMARIES searches the per-chunk summaries rather than the raw chunks. On this run those summaries were written by GLiNER from the extracted entities and relationships, not by an LLM, and they are indexed and retrievable like any other summary. Both CHUNKS and SUMMARIES are retrieval-only search types, so requesting one explicitly stays LLM-free.

What Still Needs an LLM

The graph, the summaries, and the two searches above run entirely on local models. Two things still do not:

Completion search types. Anything ending in _COMPLETION — GRAPH_COMPLETION, RAG_COMPLETION, HYBRID_COMPLETION — retrieves context and then asks an LLM to write the answer. Requesting one without a key fails; see Search Basics for the full list of types and what each returns.
Per-turn feedback analysis. AUTO_FEEDBACK is disabled above because session feedback is itself an LLM call. For the same reason, remember() here is left without session_ids, so improve() only runs the default triplet and vector enrichment.

Local Setup (No API Key)

The .env form of local provider configuration, plus local-run troubleshooting.

Custom GLiNER Extraction

Pass your own entity and relation labels, and measure what extraction dropped.

Embedding Providers

Pick a different fastembed model and its matching dimensions.

Search Basics

Every search type, and which ones need an LLM to answer.

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

OSS

Before You Start

Code in Action

What Just Happened

Step 1: Clear Any Inherited API Key

Step 2: Select the Local Extractor and Embedder

Step 3: Remember the Text Locally

Step 4: Recall With the Default Search Type

Step 5: Search the GLiNER Summaries

What Still Needs an LLM

Local Setup (No API Key)

Custom GLiNER Extraction

Embedding Providers

Search Basics

​Before You Start

​Code in Action

​What Just Happened

​Step 1: Clear Any Inherited API Key

​Step 2: Select the Local Extractor and Embedder

​Step 3: Remember the Text Locally

​Step 4: Recall With the Default Search Type

​Step 5: Search the GLiNER Summaries

​What Still Needs an LLM

Local Setup (No API Key)

Custom GLiNER Extraction

Embedding Providers

Search Basics

Before You Start

Code in Action

What Just Happened

Step 1: Clear Any Inherited API Key

Step 2: Select the Local Extractor and Embedder

Step 3: Remember the Text Locally

Step 4: Recall With the Default Search Type

Step 5: Search the GLiNER Summaries

What Still Needs an LLM