Nlweb LLM Providers

NLWeb LLM & Embedding Providers

Before writing code

Fetch live docs:

1. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-providers.md for the canonical provider list and config schema.
2. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/config/config_llm.yaml for the exact model IDs and env-var names currently shipped.
3. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/config/config_embedding.yaml for embedding defaults.
4. Inspect AskAgent/python/llm_providers/<provider>.py for the SDK calls the provider class makes.
5. Web-search the latest release notes — new providers and models get added often.

Conceptual Architecture

Mixed-Mode = Many Small LLM Calls

NLWeb's pipeline doesn't make one big LLM call per query. It makes many small calls: decontextualize the query, detect Schema.org item type, route to a tool, rank results, optionally summarize/generate. Each call has a strict <returnStruc> JSON schema in prompts.xml. Cost and latency are dominated by the number of calls, not the size of any single one.

High / Low Tier Model Selection

config_llm.yaml defines a high model and a low model per provider:

providers:
  openai:
    high: gpt-4.1
    low: gpt-4.1-mini
    api_key_env: OPENAI_API_KEY

The codebase decides which tier to use per call site — e.g., decontextualization is "low", final generate is "high". The exact assignment lives in core/ modules and the ModelRouter subsystem.

The Default Provider

Out of the box, NLWeb's preferred_endpoint (in config_llm.yaml) is azure_openai with gpt-4.1 / gpt-4.1-mini. Most users override this in .env or by editing the YAML.

All Supported LLM Providers

(Verify the live config_llm.yaml for current models and key names.)

Embedding Providers

Set preferred_provider in config_embedding.yaml. This must match what you used at ingest time — the most common NLWeb bug is changing the embedding provider after data is loaded, then getting empty results.

ModelRouter

NLWeb's ModelRouter/ subsystem is a cost/quality router that picks the right model tier (high vs low) per call site. It's still evolving — verify whether it's active in your release.

Why So Many Providers?

R.V. Guha's design goal: NLWeb should run on whatever LLM stack the site operator already has. A Snowflake customer uses Cortex; an Azure shop uses Azure OpenAI; a privacy-conscious deployment uses Ollama on prem. The provider abstraction is intentional.

Implementation Guidance

Switching the Primary LLM Provider

In config_llm.yaml:

preferred_endpoint: anthropic

providers:
  anthropic:
    high: claude-3-7-sonnet-latest
    low: claude-3-5-haiku-latest
    api_key_env: ANTHROPIC_API_KEY

Set ANTHROPIC_API_KEY in .env. Restart the server.

Running Locally with Ollama (Offline)

Install Ollama, pull a model:

ollama pull llama3.1:8b
ollama pull nomic-embed-text

In config_llm.yaml:

preferred_endpoint: ollama
providers:
  ollama:
    high: llama3.1:8b
    low: llama3.1:8b
    base_url: http://localhost:11434

In config_embedding.yaml:

preferred_provider: ollama
providers:
  ollama:
    model: nomic-embed-text
    dim: 768

Important: re-ingest after switching embedding provider — old vectors are now wrong-dim.

Adding a Custom Provider

1. Subclass the base class in llm_providers/ (look at openai.py or anthropic.py as templates).
2. Implement the required methods (typically complete() returning JSON-conformant output for the <returnStruc> schemas, plus optional streaming).
3. Register in the provider factory (verify exact location — usually a registry in core/llm.py).
4. Add an entry in config_llm.yaml.
5. Test against a known-good <returnStruc> prompt before deploying.

Tuning Cost

• Use low tier for everything except the final generate (default behavior — verify).
• Set tool_selection_enabled: false in config_nlweb.yaml to skip the router call entirely.
• Disable who_endpoint_enabled to skip federated discovery.
• Pre-compute decontextualized_query client-side to skip that LLM call.

Switching Embedding Providers Safely

# 1. Stop serving traffic
# 2. Change config_embedding.yaml
# 3. Drop the index
python -m data_loading.db_load --only-delete delete-site <site>
# 4. Re-ingest
python -m data_loading.db_load <source> <site>
# 5. Restart

You cannot mix-and-match embedding providers across a single retrieval index. Vectors are not portable across providers.

Verifying Provider Wiring

nlweb check runs connectivity diagnostics for all configured providers. Use it before debugging "the model isn't responding" issues — the answer is usually a missing env var.

Provider Failure Modes

• OpenAI / Anthropic / Gemini 429s: rate limits. Add backoff in the provider class or reduce concurrency.
• Azure OpenAI 404 on deployment: the deployment_name in config doesn't match what's deployed in Azure. They're per-deployment, not per-model.
• Ollama "model not found": ollama pull <model> first.
• Snowflake Cortex authentication: requires the warehouse + role to have Cortex enabled.
• HuggingFace inference endpoint cold-start: first call takes 30-60s. Pre-warm.

Always re-fetch config_llm.yaml from the live repo — provider keys and model IDs change.