basicmachines-co-basic-memory/docs/litellm-provider.md

LiteLLM Provider

Basic Memory can use the LiteLLM SDK for semantic search embeddings. This lets you keep Basic Memory's vector indexing and search behavior while routing embedding calls to OpenAI-compatible and provider-specific backends such as OpenAI, Azure OpenAI, Cohere, Bedrock, NVIDIA NIM, and other LiteLLM-supported embedding providers.

Use this page when you want to try a non-default embedding model, validate a provider, or tune LiteLLM-specific settings.

Quick Start

The default LiteLLM model is OpenAI text-embedding-3-small through the LiteLLM model string openai/text-embedding-3-small.

export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export OPENAI_API_KEY=sk-...

bm reindex --embeddings

Then use vector or hybrid search:

search_notes("login token flow", search_type="hybrid")

Basic Memory Options

All options can be set in config or as environment variables.

Config Field	Env Var	Default	Notes
semantic_search_enabled	BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED	Auto	Set to true to force vector/hybrid support on.
semantic_embedding_provider	BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER	fastembed	Set to litellm for the LiteLLM provider.
semantic_embedding_model	BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL	bge-small-en-v1.5	With litellm, the default is remapped to openai/text-embedding-3-small.
semantic_embedding_dimensions	BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS	Provider default	Required for non-default LiteLLM models because vector tables are dimensioned before the first API call.
semantic_embedding_forward_dimensions	BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS	Auto	Sends dimensions to LiteLLM only when supported. Auto is enabled for text-embedding-3 model strings.
semantic_embedding_document_input_type	BASIC_MEMORY_SEMANTIC_EMBEDDING_DOCUMENT_INPUT_TYPE	Auto	LiteLLM input_type for indexed notes/passages.
semantic_embedding_query_input_type	BASIC_MEMORY_SEMANTIC_EMBEDDING_QUERY_INPUT_TYPE	Auto	LiteLLM input_type for search queries.
semantic_embedding_batch_size	BASIC_MEMORY_SEMANTIC_EMBEDDING_BATCH_SIZE	2	Number of text chunks per provider request.
semantic_embedding_request_concurrency	BASIC_MEMORY_SEMANTIC_EMBEDDING_REQUEST_CONCURRENCY	4	Maximum concurrent LiteLLM embedding requests.
semantic_embedding_sync_batch_size	BASIC_MEMORY_SEMANTIC_EMBEDDING_SYNC_BATCH_SIZE	2	Number of prepared vector jobs flushed through the sync pipeline together.

Dimensions

Basic Memory needs the vector dimension before it can create SQLite or Postgres vector tables. The OpenAI default is known, so this works without an explicit dimension:

export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=openai/text-embedding-3-small

For every other LiteLLM model, set the dimension explicitly:

export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=cohere/embed-english-v3.0
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1024

For fixed-size models, semantic_embedding_dimensions is Basic Memory's local schema and validation size. For OpenAI/Azure text-embedding-3 models, LiteLLM can also forward dimensions as a provider-side reduced-output request. Basic Memory enables that automatically when the model string contains text-embedding-3.

If you use an Azure deployment alias such as azure/<deployment-name>, the model string may not reveal that the underlying model supports reduced output dimensions. Set this only when your deployment supports it:

export BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS=true

Asymmetric Models

Some embedding models use different request roles for indexed documents and search queries. Basic Memory automatically sets these for known LiteLLM families:

Model Family	Document input_type	Query input_type
Cohere v3 embeddings	search_document	search_query
NVIDIA NIM retrieval embeddings	passage	query

For any other asymmetric model, configure both roles explicitly:

export BASIC_MEMORY_SEMANTIC_EMBEDDING_DOCUMENT_INPUT_TYPE=passage
export BASIC_MEMORY_SEMANTIC_EMBEDDING_QUERY_INPUT_TYPE=query

Changing provider, model, dimensions, dimension-forwarding, or document/query roles changes the meaning of stored vectors. Rebuild embeddings after any of those changes:

bm reindex --embeddings

Provider Setup Examples

LiteLLM reads provider credentials from the environment. These are the examples covered by Basic Memory's live validation harness.

OpenAI Through LiteLLM

export OPENAI_API_KEY=sk-...
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=openai/text-embedding-3-small

Cohere v3

export COHERE_API_KEY=...
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=cohere/embed-english-v3.0
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1024

The provider auto-selects search_document for indexed chunks and search_query for search queries.

Azure OpenAI

export AZURE_API_KEY=...
export AZURE_API_BASE=https://<resource-name>.openai.azure.com
export AZURE_API_VERSION=2024-02-01

export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=azure/<deployment-name>
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1536

If your Azure deployment is a reduced-dimension text-embedding-3 deployment, set the dimension you want and enable forwarding:

export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=512
export BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS=true

NVIDIA NIM

export NVIDIA_NIM_API_KEY=...
# Optional when using a custom or self-hosted NIM endpoint:
export NVIDIA_NIM_API_BASE=https://integrate.api.nvidia.com/v1

export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=nvidia_nim/nvidia/embed-qa-4
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1024

The provider auto-selects passage for indexed chunks and query for search queries.

Testing LiteLLM Providers

Run the non-live LiteLLM unit and harness tests first:

uv run pytest tests/repository/test_litellm_provider.py \
  test-int/semantic/test_litellm_live_harness.py -q

Run the SQLite and Postgres vector identity regressions when changing model identity, role, or vector sync behavior:

uv run pytest \
  tests/repository/test_sqlite_vector_search_repository.py::test_sqlite_embedding_model_key_includes_litellm_role_settings \
  -q

BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest \
  tests/repository/test_postgres_search_repository.py::test_postgres_litellm_role_change_reembeds_existing_chunks \
  -q

The Postgres command uses testcontainers, so Docker must be running.

Live Provider Harness

The live harness makes real LiteLLM API calls and spends provider quota. It is opt-in by design:

export OPENAI_API_KEY=sk-...
export COHERE_API_KEY=...

just test-litellm-live

Built-in cases run when their API keys are present:

Case	Required Env Var	Validates
openai-text-embedding-3-small	OPENAI_API_KEY	OpenAI via LiteLLM, 1536 dimensions, normalized vectors, ranking sanity.
cohere-embed-english-v3	COHERE_API_KEY	Cohere v3 role handling, 1024 dimensions, normalized vectors, ranking sanity.

Add provider aliases or new backends with a custom cases file:

cat > /tmp/litellm-cases.json <<'JSON'
[
  {
    "name": "azure-text-embedding-3-small-512",
    "model": "azure/<deployment-name>",
    "dimensions": 512,
    "api_key_env": "AZURE_API_KEY",
    "forward_dimensions": true
  },
  {
    "name": "nvidia-embed-qa-4",
    "model": "nvidia_nim/nvidia/embed-qa-4",
    "dimensions": 1024,
    "api_key_env": "NVIDIA_NIM_API_KEY",
    "document_input_type": "passage",
    "query_input_type": "query"
  }
]
JSON

just test-litellm-live --cases-file /tmp/litellm-cases.json

For CI-style output:

just test-litellm-live --cases-file /tmp/litellm-cases.json --json

The harness embeds two documents and one query, validates dimension and vector normalization, checks that the authentication query ranks the authentication document above a distractor, and reports latency plus role/dimension settings.

Provider Reference

LiteLLM's own provider and embedding docs are the source of truth for current model strings and credential names:

• LiteLLM embedding models
• LiteLLM Azure OpenAI provider
• LiteLLM NVIDIA NIM provider