LLM Configuration Guide

Welcome! Whether you are a beginner newly exposed to AI or a veteran skilled with various APIs, this guide will help you set up Large Language Models (LLMs) quickly.

This project exposes a unified AI model access flow that supports official APIs, OpenAI-compatible platforms, and local models. Under the hood it is powered by LiteLLM, but most users only need to think in terms of picking a provider, adding an API key, and optionally choosing a primary model or channels. To cater to different experience levels, we provide a three-tier configuration hierarchy. Choose the method that fits you best.

If you are choosing a concrete provider, setting up GitHub Actions Secrets / Variables, troubleshooting a details.reason error, or rolling back an LLM configuration, start with the Provider Configuration Guide. It is the maintained reference for provider presets, Actions variable mapping, runtime capability-check boundaries, and common error handling.

Quick Navigation: Which section should you read?

1. [Beginners] "I just want to get the system running ASAP, keep it as simple as possible!" -> Go to Method 1: Simple Model Config
2. [Advanced Users] "I have several Keys, want to configure fallback models, and define custom Base URLs." -> Go to Method 2: Channels Mode Config
3. [Veterans] "I want complex load balancing, request routing, and enterprise-level high availability!" -> Go to Method 3: Advanced YAML Config
4. [Local Models] "I want to use Ollama local models!" -> Go to Example 4: Using Ollama Local Models
5. [Vision Models] "I want to extract stock codes from images!" -> Go to Vision Model Config

Method 1: Simple Model Config (For Beginners)

Goal: Just paste your API Key and the model name to start using it immediately. No need to mess with complex concepts.

If you only plan to use one single model, this is the fastest way. Open the .env file in the project's root directory (if it doesn't exist, copy .env.example and rename it to .env).

Anspire Open Example:

💡 Anspire Open: supports Chinese-optimized search and OpenAI-compatible model access using a shared key.

• The following values are configuration examples only; model availability depends on your account and Anspire console.
• This PR does not add a reproducible online smoke test for Anspire connectivity; please validate with the Web "Test connection" flow before relying on production traffic.

# Anspire Open API keys (multiple keys supported, separated by commas)
# Get your key at: https://open.anspire.cn/?share_code=QFBC0FYC
# When no higher-priority OpenAI-compatible source is set, this key is reused for Anspire search + LLM path (example fallback behavior only).
# Example model: Doubao-Seed-2.0-lite; example gateway: https://open-gateway.anspire.cn/v6
ANSPIRE_API_KEYS=sk-xxxxxxxxxxxxxxxx
# Optional: switch example model or gateway according to your Anspire account and official docs.
# ANSPIRE_LLM_MODEL=Doubao-Seed-2.0-pro
# ANSPIRE_LLM_BASE_URL=https://open-gateway.anspire.ai/v6

Example 1: Using a Third-party OpenAI-Compatible Platform (Highly Recommended)

Most third-party relay platforms and local API providers support the OpenAI interface format. As long as the platform provides an API Key and a Base URL, you can configure it easily using the following pattern:

# Fill in the API Key provided by your platform
OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx
# Fill in the platform's API Base URL (Very Important: Usually must end with /v1)
OPENAI_BASE_URL=https://api.siliconflow.cn/v1
# Fill in the specific model name (Very Important: You must add the "openai/" prefix so the system recognizes it)
LITELLM_MODEL=openai/deepseek-ai/DeepSeek-V3 

Example 2: Using the Official DeepSeek API

# Fill in the API Key requested from the official DeepSeek platform
DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxx

Compatibility note: with only this line, the system still defaults to deepseek/deepseek-chat and logs a migration warning. deepseek-chat / deepseek-reasoner still work for compatibility with old configs, but DeepSeek marks them deprecated after 2026/07/24. New configs should migrate through the Web quick channel or explicitly set LITELLM_MODEL=deepseek/deepseek-v4-flash for deepseek-v4-flash / deepseek-v4-pro.

Example 3: Using the Free Gemini API

# Fill in your Google Gemini Key
GEMINI_API_KEY=AIzac...

Example 4: Using Ollama Local Models

# Ollama requires no API Key; works after running ollama serve locally
OLLAMA_API_BASE=http://localhost:11434
LITELLM_MODEL=ollama/qwen3:8b

Important: Ollama must be configured with OLLAMA_API_BASE. Do not use OPENAI_BASE_URL, or the system will concatenate URLs incorrectly (e.g. 404, api/generate/api/show). For remote Ollama, set OLLAMA_API_BASE to the actual address (e.g. http://192.168.1.100:11434). Current dependency requirement is LiteLLM ≥1.80.10 (matches requirements.txt).

Congratulations! If you're a beginner, you can stop reading here and run the program! Want to test the connection? Open your terminal in the root directory and run: python test_env.py --llm

Method 2: Channels Mode Config (Advanced/Multi-model)

Goal: I have Keys from multiple different platforms and want to use them together. If my primary model fails or the network drops, I want it to automatically switch to fallback models.

Configure via Web UI directly: After starting the application, you can do this visually under System Settings -> AI Model -> AI Model Access in the Web UI.

New editor behavior: For DeepSeek, DashScope, and other OpenAI-compatible providers that expose /v1/models, the settings page can now fetch models directly from {base_url}/models and let you select multiple entries visually. The underlying storage format is still the existing comma-separated LLM_{CHANNEL}_MODELS=model1,model2 value. If a provider does not support /models, authentication fails, or the endpoint is temporarily unavailable, you can still type the model list manually and save normally.

First-run Setup Status

The backend exposes a read-only status endpoint at GET /api/v1/system/config/setup/status. It reports whether the minimum first-run pieces are present: primary LLM, Agent model inheritance/configuration, stock list, optional notification channel, and local storage. The endpoint only reads the saved .env plus the current process environment; it does not reload runtime config, write .env, test a real model, or create a database file. Frontend onboarding and later smoke-run flows can build on this endpoint incrementally.

Web channel editor: compatibility, migration, and rollback rules

• The preset provider / Base URL / sample models are form defaults only. What gets persisted is still exactly what you submit in LLM_{CHANNEL}_PROTOCOL, LLM_{CHANNEL}_BASE_URL, LLM_{CHANNEL}_MODELS, and LLM_{CHANNEL}_API_KEY(S); the editor does not silently rewrite them to a different provider name or URL.
• "Discover models" only calls {base_url}/models for OpenAI Compatible / DeepSeek channels, and the default "Test connection" action only sends one minimal chat completion request. Optional runtime capability checks must be explicitly selected by the user and send additional JSON / tools / stream / vision smoke requests; the result only represents a best-effort check for the current account, model, and endpoint at that moment. The returned stage / error_code / details / latency_ms / capability_results fields are for structured diagnostics only, are never persisted back into .env, and do not block saving.
• Runtime capability checks send real LLM requests and may incur token / image-input cost, RPM/TPM rate limiting, insufficient balance errors, or timeouts. A failed check may come from account permissions, model entitlement, endpoint region, balance, provider compatibility layers, or LiteLLM translation behavior; it does not prove that the provider globally lacks that capability. P3 does not include online smoke coverage for every real provider. Its compatibility basis is the repository dependency window litellm>=1.80.10,<1.82.7, LiteLLM completion() / OpenAI I/O format / streaming / exception mapping, and the OpenAI Chat Completions shapes for JSON mode, tool calling, streaming, and vision input.
• External references: LiteLLM Python SDK / OpenAI I/O format / streaming / exception mapping: https://docs.litellm.ai/; LiteLLM OpenAI-compatible routing: https://docs.litellm.ai/docs/providers/openai_compatible; OpenAI Chat Completions: https://platform.openai.com/docs/api-reference/chat/create; JSON mode: https://platform.openai.com/docs/guides/structured-outputs?api-mode=chat; tool calling: https://platform.openai.com/docs/guides/function-calling?api-mode=chat; streaming: https://platform.openai.com/docs/guides/streaming-responses?api-mode=chat; vision input: https://platform.openai.com/docs/guides/images-vision?api-mode=chat.
• Saving channels only updates the keys submitted in that save operation; there is no whole-config silent migration when you switch channel settings. The one deliberate cleanup is runtime model references: if LITELLM_MODEL, AGENT_LITELLM_MODEL, VISION_MODEL, or LITELLM_FALLBACK_MODELS point to models that no longer exist in the currently enabled channels, the editor clears/removes those stale references before saving so runtime calls do not keep targeting invalid models. Even when enabled channels expose no selectable models, stale managed-provider values without a matching legacy key are cleaned. cohere/*, google/*, and xai/* are kept as explicit direct-env compatibility examples for legacy retention behavior only, and are not a runtime availability guarantee.
• Backend consistency basis: runtime validation in SystemConfigService._validate_llm_runtime_selection (src/services/system_config_service.py) relies on _uses_direct_env_provider (src/config.py). Only gemini, vertex_ai, anthropic, openai, and deepseek are treated as managed key-backed providers; cohere, google, and xai are not in that allowlist, so they remain valid direct provider runtime entries.
• Rollback stays minimal: restore the previous channel model list and re-select the runtime models, or restore the previous LLM_*, LITELLM_MODEL, AGENT_LITELLM_MODEL, VISION_MODEL, and LLM_TEMPERATURE values from your desktop export / manual .env backup. No extra migration script is required.
• The current dependency window for this flow in the repository is litellm>=1.80.10,<1.82.7 (see requirements.txt). Regression coverage for it lives in tests/test_system_config_service.py, tests/test_system_config_api.py, and apps/dsa-web/src/components/settings/__tests__/LLMChannelEditor.test.tsx.

External provider model examples notice: cohere/*, google/*, and xai/* provider-prefixed values are included here only to describe current runtime retention behavior and are not a global availability guarantee. Specific model names in docs or tests are configuration-retention examples, not production recommendations. Check the provider's official model/API docs and validate against the repository dependency window litellm>=1.80.10,<1.82.7 before production use.

Rollback & compatibility evidence

• Scope and cleanup behavior under litellm>=1.80.10,<1.82.7: only runtime references (LITELLM_MODEL, AGENT_LITELLM_MODEL, VISION_MODEL, LITELLM_FALLBACK_MODELS) are sanitized during save; non-channel direct providers such as cohere/*, google/*, and xai/* are preserved.
• Rollback path: export desktop config, then restore the backup through POST /api/v1/system/config/import; or manually restore historical .env entries (LITELLM_*, AGENT_LITELLM_MODEL, VISION_MODEL, LLM_TEMPERATURE) and restart.
• Rollback evidence: tests/test_system_config_service.py::test_import_desktop_env_restores_runtime_models_after_cleanup covers restore from exported desktop backup after runtime cleanup.
• Direct-provider evidence: tests/test_system_config_service.py::SystemConfigServiceTestCase::test_validate_accepts_minimax_model_as_direct_env_provider, test_validate_accepts_cohere_model_as_direct_env_provider, test_validate_accepts_google_model_as_direct_env_provider, and test_validate_accepts_xai_model_as_direct_env_provider cover the preserved direct-provider behavior.
• Frontend regression commands: cd apps/dsa-web && npm run lint && npm run build && npm run test -- src/components/settings/__tests__/LLMChannelEditor.test.tsx.
• Recommended rollback sequence (including UI reload): export desktop backup, restore via POST /api/v1/system/config/import, then call GET /api/v1/system/config to refresh the settings page and verify LITELLM_MODEL / AGENT_LITELLM_MODEL / VISION_MODEL / LLM_TEMPERATURE before continuing.

Official references for provider presets / Base URLs / model naming

• OpenAI-compatible routing in LiteLLM: https://docs.litellm.ai/docs/providers/openai_compatible
• OpenAI official API docs: https://platform.openai.com/docs/api-reference/chat
• DeepSeek official API docs: https://api-docs.deepseek.com/
• Anspire Open: https://open.anspire.cn/?share_code=QFBC0FYC
• DashScope OpenAI-compatible mode: https://help.aliyun.com/zh/model-studio/compatibility-of-openai-with-dashscope
• Moonshot / Kimi official compatibility docs: https://platform.moonshot.ai/docs/guide/compatibility
• Anthropic official Messages API: https://docs.anthropic.com/en/api/messages
• Gemini official OpenAI compatibility docs: https://ai.google.dev/gemini-api/docs/openai
• Cohere official: https://docs.cohere.com/
• Cohere API reference: https://docs.cohere.com/reference/
• Cohere LiteLLM provider page: https://docs.litellm.ai/docs/providers/cohere
• Google Gemini API and model list: https://ai.google.dev/gemini-api/docs/openai, https://ai.google.dev/gemini-api/docs/models
• Google LiteLLM provider page: https://docs.litellm.ai/docs/providers/gemini
• xAI official: https://docs.x.ai/docs
• xAI LiteLLM provider page: https://docs.litellm.ai/docs/providers/xai
• Ollama API docs: https://github.com/ollama/ollama/blob/main/docs/api.md

If you prefer modifying files, configuring this in the .env file is also very smooth. It allows you to manage multiple platforms simultaneously. The rules are:

1. Declare your channels first: LLM_CHANNELS=channel_name_1,channel_name_2
2. Provide configurations for each channel (Note the uppercase): LLM_{CHANNEL_NAME}_XXX

Example: Configuring DeepSeek and a Third-party Relay with Fallbacks

# 1. Enable channel mode, declare two channels here: deepseek and aihubmix
LLM_CHANNELS=deepseek,aihubmix

# 2. Channel 1: Configure Official DeepSeek
LLM_DEEPSEEK_BASE_URL=https://api.deepseek.com
LLM_DEEPSEEK_API_KEY=sk-1111111111111
LLM_DEEPSEEK_MODELS=deepseek-v4-flash,deepseek-v4-pro

# 3. Channel 2: Configure a common relay/proxy API
LLM_AIHUBMIX_BASE_URL=https://api.aihubmix.com/v1
LLM_AIHUBMIX_API_KEY=sk-2222222222222
LLM_AIHUBMIX_MODELS=gpt-5.5,claude-sonnet-4-6

# 4. [Key Step] Specify the primary model and fallback list
# Set your primary model:
LITELLM_MODEL=deepseek/deepseek-v4-flash
# Optional: set an Agent-only primary model (empty = inherit the primary model)
AGENT_LITELLM_MODEL=deepseek/deepseek-v4-pro
# If the primary model crashes, try these fallbacks sequentially:
LITELLM_FALLBACK_MODELS=openai/gpt-5.4-mini,anthropic/claude-sonnet-4-6

Example: Ollama Channel Mode (Local Models, No API Key)

# 1. Enable channel mode, declare ollama channel
LLM_CHANNELS=ollama

# 2. Configure Ollama address (default local port 11434)
LLM_OLLAMA_BASE_URL=http://localhost:11434
LLM_OLLAMA_MODELS=qwen3:8b,llama3.2

# 3. Specify primary model
LITELLM_MODEL=ollama/qwen3:8b

MiniMax Model Naming in Channel Mode

• If you access MiniMax through an OpenAI-compatible channel, enter the model as minimax/<model-name> in the channel model list, for example minimax/MiniMax-M1.
• The Web settings page now keeps that value unchanged in Primary, Agent Primary, Fallback, and Vision selectors instead of rewriting it to openai/minimax/<model-name>.

Ask-Stock Agent / LiteLLM compatibility notes

• The ask-stock Agent follows the same three-tier runtime priority as the regular analyzer: LITELLM_CONFIG (LiteLLM YAML) > LLM_CHANNELS > legacy provider keys. Once an upper tier is valid and active, lower tiers are ignored for that request.
• In YAML mode, the Agent reuses LiteLLM model_list / model_name routing semantics directly. In channel mode, it first reads AGENT_LITELLM_MODEL; when that is empty it inherits LITELLM_MODEL, then continues through LITELLM_FALLBACK_MODELS.
• If you do not use YAML or Channels, leave AGENT_LITELLM_MODEL empty, and still rely on legacy provider env vars, the ask-stock Agent continues to inherit them: GEMINI_API_KEY + GEMINI_MODEL -> gemini/<model>, OPENAI_API_KEY + OPENAI_MODEL -> openai/<model>, and ANTHROPIC_API_KEY + ANTHROPIC_MODEL -> anthropic/<model>.
• This fix only improves two things: preserving the backend's real failure reason and returning a more specific diagnostic when no usable Agent LLM is configured. It does not silently delete, clear, migrate, or rewrite your existing GEMINI_*, OPENAI_*, ANTHROPIC_*, or LITELLM_* settings.
• If the current environment has no valid Agent model path at all, the ask-stock page still returns a failure and now surfaces the backend's real configuration diagnosis. As soon as you restore any valid model source, the flow recovers without running any migration step.
• The recommended forward path is still to configure LITELLM_MODEL / AGENT_LITELLM_MODEL explicitly or move to LLM_CHANNELS; legacy provider keys remain a compatibility fallback for older .env files, local macOS development, and existing deployments.

Kimi K2.6 Fixed-Temperature Compatibility Notes

• Moonshot officially documents Kimi as an OpenAI-compatible API, with https://api.moonshot.ai/v1 as the base URL: https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart
• LiteLLM officially requires the openai/ prefix for OpenAI-compatible model routing: https://docs.litellm.ai/docs/providers/openai_compatible
• Moonshot's compatibility docs distinguish two fixed values: thinking mode must use 1.0, while non-thinking mode must use 0.6; other values are rejected by the API: https://platform.moonshot.ai/docs/guide/compatibility#parameters-differences-in-request-body
• The current runtime dependency window in this repository is litellm>=1.80.10,<1.82.7 (see requirements.txt); this compatibility fix is regression-covered in that range across the main analyzer, market review, direct Agent LiteLLM calls, and the system-settings channel connectivity test path.
• This repository therefore normalizes kimi-k2.6 and kimi-k2.6-* right before dispatch based on the actual request mode: default / thinking requests use temperature=1.0; if your LiteLLM YAML route alias explicitly sets litellm_params.extra_body.thinking.type: disabled (or an equivalent non-thinking override), it automatically switches to temperature=0.6. Your saved LLM_TEMPERATURE value in .env or the Web settings is not rewritten.
• SystemConfigService only updates keys that you actually submit when saving from the Web settings page or importing a desktop .env; switching to Kimi does not silently clear, migrate, or rewrite an existing LLM_TEMPERATURE. The temporary 1.0/0.6 used for Kimi channel tests is request-scoped and is not persisted back into the config file.
• Non-Kimi primary models, non-Kimi fallbacks, and any request after switching away from Kimi still use your configured temperature. Existing configs do not need migration; changing the model restores the original behavior automatically.
• Repository-side compatibility coverage lives in tests/test_llm_channel_config.py, tests/test_market_analyzer_generate_text.py, tests/test_agent_pipeline.py, and tests/test_system_config_service.py.
• Minimal rollback: revert only the Kimi fixed-temperature change set; no separate LLM_TEMPERATURE migration is required.

Critical Warning: If you enable LLM_CHANNELS, any standard DEEPSEEK_API_KEY or OPENAI_API_KEY declared independently will be completely ignored. Use only one mode to prevent configuration conflicts. Docker note: If LITELLM_MODEL, LLM_CHANNELS, LLM_DEEPSEEK_MODELS, or related variables are explicitly passed through docker compose environment: or docker run -e, they will override the .env written by the Web settings page after a container restart. Update the deployment environment at the same time.

Method 3: Advanced YAML Config (Expert Setup)

Goal: I want maximum control and origin-level routing rules for enterprise-grade high availability.

This layer maps directly to the underlying LiteLLM routing capabilities, including high concurrency, automatic retries, and TPM/RPM-based load balancing.

1. Keep only one declaration line in your .env:
   envCopy

   LITELLM_CONFIG=./litellm_config.yaml
   

2. Create a litellm_config.yaml in the project root directory (you can refer to litellm_config.example.yaml).

Example litellm_config.yaml:

model_list:
  - model_name: my-smart-model
    litellm_params:
      model: deepseek/deepseek-v4-flash
      api_base: https://api.deepseek.com
      api_key: "os.environ/MY_CUSTOM_SECRET_KEY"  # Fetch from environment vars for security

  # Ollama local model (no api_key needed)
  - model_name: ollama/qwen3:8b
    litellm_params:
      model: ollama/qwen3:8b
      api_base: http://localhost:11434

Priority Rule: YAML is king! If YAML is configured, both Channels Mode and Simple Mode are entirely ignored. Hierarchy: YAML > Channels > Simple.

GitHub Actions Notes

The bundled daily_analysis.yml explicitly passes the common LLM runtime fields to the job environment:

• Runtime selection: LLM_CHANNELS, LITELLM_MODEL, LITELLM_FALLBACK_MODELS, AGENT_LITELLM_MODEL, VISION_MODEL, VISION_PROVIDER_PRIORITY, LLM_TEMPERATURE
• Multiple keys: GEMINI_API_KEYS, ANTHROPIC_API_KEYS, OPENAI_API_KEYS, DEEPSEEK_API_KEYS (the current workflow imports these from repository Secrets only, not from same-named Variables)
• Common channel names: primary, secondary, aihubmix, deepseek, dashscope, zhipu, moonshot, minimax, volcengine, siliconflow, openrouter, gemini, anthropic, openai, ollama

For example, if you set LLM_CHANNELS=primary,deepseek in GitHub Actions, also configure the corresponding LLM_PRIMARY_* and LLM_DEEPSEEK_* entries. The LLM_<NAME>_API_KEY / LLM_<NAME>_API_KEYS fields are also imported from repository Secrets only right now, so storing them in Variables will not work at runtime. If you use a custom channel name such as my_proxy, GitHub Actions must explicitly add matching LLM_MY_PROXY_* mappings in the workflow env: block. Local .env and Docker runs do not have this limitation.

Advanced Feature: Vision Model Config

Certain specific features in our system (like uploading a stock chart screenshot to extract the stock code) require models capable of computer vision. You need to assign a dedicated vision model in your .env.

# Specify your dedicated vision model name
VISION_MODEL=openai/gpt-5.5
# Make sure to provide its corresponding provider API KEY (e.g., OPENAI_API_KEY):
# OPENAI_API_KEY=xxx

Vision Fallback Mechanism: To prevent unexpected failures, the system has a built-in fallback strategy. If the primary vision model fails, it will attempt to use alternative vision-capable provider keys in the following order:

# Default fallback sequence:
VISION_PROVIDER_PRIORITY=gemini,anthropic,openai

Troubleshooting

Afraid you got the config wrong? Type the following commands in your terminal to diagnose:

• python test_env.py --config: Only verifies if the logic in your .env is structurally correct. (Provides instant results, no network calls, strictly checks for syntax omissions).
• python test_env.py --llm: Sends a real greeting to the LLM to test the actual endpoint. This thoroughly verifies if your network is working and if your account has sufficient balance.

Common Pitfalls

2. Ensure your Base URL ends with /v1.
3. Check if you forgot the openai/ prefix on the model name! | | Kimi K2.6 returns invalid temperature (it may say only 1.0 or 0.6 is allowed) | The model requires different fixed temperatures for thinking vs non-thinking mode, while older config or call paths may still pass 0.7. | After this fix, default / thinking kimi-k2.6 requests automatically use temperature=1.0; if you explicitly disable thinking in a LiteLLM YAML route, the request automatically uses 0.6 instead. Prefer openai/kimi-k2.6 with your Moonshot or relay OpenAI-compatible Base URL and API key. Non-Kimi fallbacks still keep your configured LLM_TEMPERATURE. | | Spins endlessly, eventually hits Timeout/ConnectionRefused | You are using restricted APIs (like Google/OpenAI) in a blocked region without a proxy, or your cloud server lacks external internet access. | Highly recommend using official regional APIs (like DeepSeek) or OpenAI-compatible relay platforms. Third-party platforms bypass these network constraints. | | Ollama returns 404, Could not get model info, or api/generate/api/show | Using OPENAI_BASE_URL for Ollama makes the system concatenate URLs incorrectly | Use OLLAMA_API_BASE=http://localhost:11434 or channel mode (LLM_CHANNELS=ollama + LLM_OLLAMA_BASE_URL) instead |

Veteran's Tip: If you enable Agent Mode (Deep-thinking & web-search), experience shows you should use a stronger model like deepseek-v4-pro. Trying to save money by using weak mini-models for agents will likely result in infinite loops or missed objectives.