import {LLMProvidersGrid} from '@site/src/components/SupportedGrids';

# Models

Hindsight uses several machine learning models for different tasks.

## Overview

- **LLM** — Fact extraction, reasoning, and generation. Provider-specific, fully configurable.
- **Embedding** — Vector representations for semantic search. Default: `BAAI/bge-small-en-v1.5`.
- **Cross-Encoder** — Reranking search results. Default: `cross-encoder/ms-marco-MiniLM-L-6-v2`.

Embedding and cross-encoder models are downloaded automatically from HuggingFace on first run.

---

## LLM

Used for fact extraction, entity resolution, mental model consolidation, and answer synthesis.

**Supported providers:**

<LLMProvidersGrid />

Also supports **any OpenAI-compatible API** (e.g., Azure OpenAI, Together AI, Fireworks) and **100+ providers via LiteLLM** (e.g., AWS Bedrock, Azure OpenAI, Together AI).

:::tip OpenAI-Compatible Providers
Hindsight works with any provider that exposes an OpenAI-compatible API (e.g., Azure OpenAI). Simply set `HINDSIGHT_API_LLM_PROVIDER=openai` and configure `HINDSIGHT_API_LLM_BASE_URL` to point to your provider's endpoint.

See [Configuration](./configuration#llm-provider) for setup examples.
:::

:::tip AWS Bedrock
Set `HINDSIGHT_API_LLM_PROVIDER=bedrock` to use AWS Bedrock models directly. Model names use Bedrock model IDs (e.g., `us.amazon.nova-2-lite-v1:0`). No API key is required — authentication uses AWS credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_REGION_NAME`) or IAM roles.

See [Configuration](./configuration#llm-provider) for setup examples.
:::

:::tip LiteLLM Provider (Azure, Together AI, and more)
Set `HINDSIGHT_API_LLM_PROVIDER=litellm` to use any model supported by [LiteLLM](https://docs.litellm.ai/docs/providers), including **Azure OpenAI**, **Together AI**, **Fireworks AI**, and many more. Model names use LiteLLM's provider prefix format (e.g., `azure/gpt-4o`).

See [Configuration](./configuration#llm-provider) for setup examples.
:::

### Benchmarks

Not sure which model to use? The **[Model Leaderboard](https://benchmarks.hindsight.vectorize.io/)** benchmarks models across accuracy, speed, cost, and reliability for retain, reflect, and observation consolidation so you can pick the right trade-off for your use case.

[![Model Leaderboard](/img/leaderboard.png)](https://benchmarks.hindsight.vectorize.io/)

### Tested Models

The following models have been tested and verified to work correctly with Hindsight:

| Provider | Model |
|----------|-------|
| **OpenAI** | `gpt-5.2` |
| **OpenAI** | `gpt-5` |
| **OpenAI** | `gpt-5-mini` |
| **OpenAI** | `gpt-5-nano` |
| **OpenAI** | `gpt-4.1-mini` |
| **OpenAI** | `gpt-4.1-nano` |
| **OpenAI** | `gpt-4o-mini` |
| **Anthropic** | `claude-sonnet-4-20250514` |
| **Anthropic** | `claude-3-5-sonnet-20241022` |
| **Gemini** | `gemini-3-pro-preview` |
| **Gemini** | `gemini-2.5-flash` |
| **Gemini** | `gemini-2.5-flash-lite` |
| **Groq** | `openai/gpt-oss-120b` |
| **Groq** | `openai/gpt-oss-20b` |

### Provider Default Models

Each provider has a recommended default model that's used when `HINDSIGHT_API_LLM_MODEL` is not explicitly set. This makes configuration simpler - just specify the provider and get a sensible default:

| Provider | Default Model |
|----------|--------------|
| `openai` | `gpt-4o-mini` |
| `anthropic` | `claude-haiku-4-5-20251001` |
| `gemini` | `gemini-2.5-flash` |
| `groq` | `openai/gpt-oss-120b` |
| `minimax` | `MiniMax-M2.7` |
| `ollama` | `gemma3:12b` |
| `lmstudio` | `local-model` |
| `vertexai` | `gemini-2.0-flash-001` |
| `openai-codex` | `gpt-5.2-codex` |
| `claude-code` | `claude-sonnet-4-5-20250929` |
| `bedrock` | `us.amazon.nova-2-lite-v1:0` |
| `volcano` | `doubao-pro-32k` |
| `litellm` | `gpt-4o-mini` |

**Example:** Setting just the provider uses its default model:
```bash
# Uses claude-haiku-4-5-20251001 automatically
export HINDSIGHT_API_LLM_PROVIDER=anthropic
export HINDSIGHT_API_LLM_API_KEY=sk-ant-xxxxxxxxxxxx
```

You can override the default by explicitly setting `HINDSIGHT_API_LLM_MODEL`:
```bash
# Override to use Sonnet instead
export HINDSIGHT_API_LLM_PROVIDER=anthropic
export HINDSIGHT_API_LLM_API_KEY=sk-ant-xxxxxxxxxxxx
export HINDSIGHT_API_LLM_MODEL=claude-sonnet-4-5-20250929
```

This also applies to per-operation overrides:
```bash
# Global: OpenAI gpt-4o-mini (default)
export HINDSIGHT_API_LLM_PROVIDER=openai

# Retain: Anthropic claude-haiku-4-5-20251001 (default)
export HINDSIGHT_API_RETAIN_LLM_PROVIDER=anthropic
```

### Using Other Models

Other LLM models not listed above may work with Hindsight, but they must support **at least 65,000 output tokens** to ensure reliable fact extraction. If you need support for a specific model that doesn't meet this requirement, please [open an issue](https://github.com/hindsight-ai/hindsight/issues) to request an exception.

:::tip Models with Limited Output Tokens
If your model only supports 32k or fewer output tokens (e.g., some older models), you can reduce the retain completion token limit:

```bash
# For models that support 32k output tokens
export HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS=32000

# For models that support 16k output tokens
export HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS=16000
```

**Important:** `HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS` must be greater than `HINDSIGHT_API_RETAIN_CHUNK_SIZE` (default: 3000). The system will validate this on startup and provide an error message if the configuration is invalid.
:::

### Configuration

```bash
# Groq (recommended)
export HINDSIGHT_API_LLM_PROVIDER=groq
export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
export HINDSIGHT_API_LLM_MODEL=openai/gpt-oss-20b

# OpenAI
export HINDSIGHT_API_LLM_PROVIDER=openai
export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx
export HINDSIGHT_API_LLM_MODEL=gpt-4o

# Gemini
export HINDSIGHT_API_LLM_PROVIDER=gemini
export HINDSIGHT_API_LLM_API_KEY=xxxxxxxxxxxx
export HINDSIGHT_API_LLM_MODEL=gemini-2.0-flash

# Anthropic
export HINDSIGHT_API_LLM_PROVIDER=anthropic
export HINDSIGHT_API_LLM_API_KEY=sk-ant-xxxxxxxxxxxx
export HINDSIGHT_API_LLM_MODEL=claude-sonnet-4-20250514

# Ollama (local)
export HINDSIGHT_API_LLM_PROVIDER=ollama
export HINDSIGHT_API_LLM_BASE_URL=http://localhost:11434/v1
export HINDSIGHT_API_LLM_MODEL=llama3

# LM Studio (local)
export HINDSIGHT_API_LLM_PROVIDER=lmstudio
export HINDSIGHT_API_LLM_BASE_URL=http://localhost:1234/v1
export HINDSIGHT_API_LLM_MODEL=your-local-model

# MiniMax (1M context window)
export HINDSIGHT_API_LLM_PROVIDER=minimax
export HINDSIGHT_API_LLM_API_KEY=your-minimax-api-key
export HINDSIGHT_API_LLM_MODEL=MiniMax-M2.7

# Vertex AI (Google Cloud)
export HINDSIGHT_API_LLM_PROVIDER=vertexai
export HINDSIGHT_API_LLM_MODEL=gemini-2.0-flash-001
export HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=your-gcp-project-id
# Optional: region (default: us-central1)
# export HINDSIGHT_API_LLM_VERTEXAI_REGION=us-central1
# Optional: service account key (otherwise uses ADC)
# export HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/path/to/key.json
```

**Note:** The LLM is the primary bottleneck for retain operations. See [Performance](./performance) for optimization strategies.

---

### OpenAI Codex Setup (ChatGPT Plus/Pro)

Use your ChatGPT Plus or Pro subscription for Hindsight without separate OpenAI Platform API costs.

**Prerequisites:**
- Active ChatGPT Plus or Pro subscription
- Node.js/npm installed (for Codex CLI)

**Setup Steps:**

1. **Install Codex CLI:**
   ```bash
   npm install -g @openai/codex
   ```

2. **Login with ChatGPT credentials:**
   ```bash
   codex auth login
   ```
   This opens a browser window to authenticate with your ChatGPT account and saves OAuth tokens to `~/.codex/auth.json`.

3. **Verify authentication:**
   ```bash
   ls ~/.codex/auth.json  # Should show the auth file exists
   ```

4. **Configure Hindsight:**
   ```bash
   export HINDSIGHT_API_LLM_PROVIDER=openai-codex
   # export HINDSIGHT_API_LLM_MODEL=gpt-5.1-codex  # defaults to gpt-5.2-codex
   # No API key needed - reads from ~/.codex/auth.json automatically
   ```

5. **Start Hindsight:**
   ```bash
   hindsight-api
   ```

You can use any model supported by OpenAI Codex CLI

**Important Notes:**
- OAuth tokens are stored in `~/.codex/auth.json`
- Tokens refresh automatically when needed
- Usage is billed to your ChatGPT subscription (not separate API costs)
- For personal development use only (see ChatGPT Terms of Service)

---

### Claude Code Setup (Claude Pro/Max)

Use your Claude Pro or Max subscription for Hindsight without separate Anthropic API costs.


:::warning Terms of Service Notice

This integration uses the Claude Agent SDK with your personal Claude Pro/Max subscription
credentials. You must be logged into Claude Code on your own machine before using this provider.

**Please be aware:**

- Anthropic's [Agent SDK documentation](https://docs.claude.com/en/api/agent-sdk/overview)
  states that third-party developers should not offer claude.ai login or rate limits for
  their products. Hindsight does **not** perform any login on your behalf — it uses
  credentials you've already authenticated via `claude auth login`.
- In January 2026, Anthropic [enforced restrictions](https://paddo.dev/blog/anthropic-walled-garden-crackdown/)
  against third-party tools using Claude subscription OAuth tokens. Those restrictions
  targeted tools that **spoofed the Claude Code client identity** — Hindsight uses the
  official Claude Agent SDK instead.
- This provider is intended for **local, personal development use only**. Do not use it
  in production deployments or shared environments.
- Anthropic's terms may change. If you want guaranteed compliance, use the `anthropic`
  provider with an API key instead.
- Usage counts against your Claude Pro/Max subscription limits.

For production or team use, we recommend using `HINDSIGHT_API_LLM_PROVIDER=anthropic` with
an API key from the [Anthropic Console](https://console.anthropic.com/).

:::


**Prerequisites:**
- Active Claude Pro or Max subscription
- Claude Code CLI installed

**Setup Steps:**

1. **Install Claude Code CLI:**
   ```bash
   npm install -g @anthropics/claude-code
   # Or via Homebrew
   brew install anthropics/claude-code/claude-code
   ```

2. **Login with Claude credentials:**
   ```bash
   claude auth login
   ```
   This opens a browser window to authenticate with your Claude account. Authentication is automatically managed by the Claude Agent SDK.

3. **Verify authentication:**
   ```bash
   claude --version
   # Should show version without errors
   ```

4. **Configure Hindsight:**
   ```bash
   export HINDSIGHT_API_LLM_PROVIDER=claude-code
   # No API key needed - uses claude auth login credentials
   ```

5. **Start Hindsight:**
   ```bash
   hindsight-api
   ```

You can use any model supported by Claude Code CLI.

**Important Notes:**
- Authentication handled by Claude Agent SDK (uses bundled CLI)
- Credentials managed securely by Claude Code
- Usage billed to your Claude subscription (not separate API costs)
- For personal development use only (see Claude Terms of Service)


---

### Vertex AI Setup (Google Cloud)

Google Cloud's Vertex AI provides access to Gemini models via the native Google GenAI SDK.

**Prerequisites:**
- GCP project with Vertex AI API enabled
- IAM role `roles/aiplatform.user` for your credentials

**Environment Variables:**

| Variable | Description | Required |
|----------|-------------|----------|
| `HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID` | Your GCP project ID | Yes |
| `HINDSIGHT_API_LLM_VERTEXAI_REGION` | GCP region (e.g., `us-central1`) | No (default: `us-central1`) |
| `HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY` | Path to service account JSON key file | No (uses ADC if not set) |

**Authentication Methods:**

1. **Application Default Credentials (ADC)** - Recommended for development
   ```bash
   # Setup ADC
   gcloud auth application-default login

   # Configure Hindsight
   export HINDSIGHT_API_LLM_PROVIDER=vertexai
   export HINDSIGHT_API_LLM_MODEL=gemini-2.0-flash-001
   export HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=your-project-id
   ```

2. **Service Account Key** - Recommended for production
   ```bash
   # Create service account and download key
   gcloud iam service-accounts create hindsight-api
   gcloud projects add-iam-policy-binding your-project-id \
     --member="serviceAccount:hindsight-api@your-project-id.iam.gserviceaccount.com" \
     --role="roles/aiplatform.user"
   gcloud iam service-accounts keys create key.json \
     --iam-account=hindsight-api@your-project-id.iam.gserviceaccount.com

   # Configure Hindsight
   export HINDSIGHT_API_LLM_PROVIDER=vertexai
   export HINDSIGHT_API_LLM_MODEL=gemini-2.0-flash-001
   export HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=your-project-id
   export HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/path/to/key.json
   ```

**Notes:**
- Model names can optionally include the `google/` prefix (e.g., `google/gemini-2.0-flash-001`) — it will be stripped automatically
- The native SDK handles token refresh automatically
- Uses service account credentials if provided, otherwise falls back to ADC

---

## Embedding Model

Converts text into dense vector representations for semantic similarity search.

**Default:** `BAAI/bge-small-en-v1.5` (384 dimensions, ~130MB)

### Supported Providers

| Provider | Description | Best For |
|----------|-------------|----------|
| `local` | SentenceTransformers (default) | Development, low latency |
| `openai` | OpenAI embeddings API | Production, high quality |
| `cohere` | Cohere embeddings API | Production, multilingual |
| `tei` | HuggingFace Text Embeddings Inference | Production, self-hosted |
| `litellm` | LiteLLM proxy (unified gateway) | Multi-provider setups |

### Local Models

| Model | Dimensions | Use Case |
|-------|------------|----------|
| `BAAI/bge-small-en-v1.5` | 384 | Default, fast, good quality |
| `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` | 384 | Multilingual (50+ languages) |

### OpenAI Models

| Model | Dimensions | Use Case |
|-------|------------|----------|
| `text-embedding-3-small` | 1536 | Default OpenAI, cost-effective |
| `text-embedding-3-large` | 3072 | Higher quality, more expensive |
| `text-embedding-ada-002` | 1536 | Legacy model |

### Cohere Models

| Model | Dimensions | Use Case |
|-------|------------|----------|
| `embed-english-v3.0` | 1024 | English text |
| `embed-multilingual-v3.0` | 1024 | 100+ languages |

:::warning Embedding Dimensions
Hindsight automatically detects the embedding dimension at startup and adjusts the database schema. Once memories are stored, you cannot change dimensions without losing data.
:::

**Configuration Examples:**

```bash
# Local provider (default)
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=local
export HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=BAAI/bge-small-en-v1.5

# OpenAI
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxxxxxxxxxxx
export HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL=text-embedding-3-small

# Cohere
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=cohere
export HINDSIGHT_API_COHERE_API_KEY=your-api-key
export HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL=embed-english-v3.0

# TEI (self-hosted)
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=tei
export HINDSIGHT_API_EMBEDDINGS_TEI_URL=http://localhost:8080

# LiteLLM proxy
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=litellm
export HINDSIGHT_API_LITELLM_API_BASE=http://localhost:4000
export HINDSIGHT_API_EMBEDDINGS_LITELLM_MODEL=text-embedding-3-small
```

See [Configuration](./configuration#embeddings) for all options including Azure OpenAI and custom endpoints.

---

## Cross-Encoder (Reranker)

Reranks initial search results to improve precision.

**Default:** `cross-encoder/ms-marco-MiniLM-L-6-v2` (~85MB)

### Supported Providers

| Provider | Description | Best For |
|----------|-------------|----------|
| `local` | SentenceTransformers CrossEncoder (default) | Development, low latency |
| `cohere` | Cohere rerank API | Production, high quality |
| `zeroentropy` | ZeroEntropy rerank API (zerank-2) | Production, state-of-the-art accuracy |
| `tei` | HuggingFace Text Embeddings Inference | Production, self-hosted |
| `flashrank` | FlashRank (lightweight, fast) | Resource-constrained environments |
| `litellm` | LiteLLM proxy (unified gateway) | Multi-provider setups |
| `litellm-sdk` | LiteLLM SDK (direct API, no proxy) | Multi-provider, simpler setup |
| `rrf` | RRF-only (no neural reranking) | Testing, minimal resources |

### Local Models

| Model | Use Case |
|-------|----------|
| `cross-encoder/ms-marco-MiniLM-L-6-v2` | Default, fast |
| `cross-encoder/ms-marco-MiniLM-L-12-v2` | Higher accuracy |
| `cross-encoder/mmarco-mMiniLMv2-L12-H384-v1` | Multilingual |

### Cohere Models

| Model | Use Case |
|-------|----------|
| `rerank-english-v3.0` | English text |
| `rerank-multilingual-v3.0` | 100+ languages |

### ZeroEntropy Models

| Model | Use Case |
|-------|----------|
| `zerank-2` | Flagship multilingual reranker (default) |
| `zerank-2-small` | Faster, lighter variant |

### LiteLLM Supported Providers

LiteLLM supports multiple reranking providers via the `/rerank` endpoint:

| Provider | Model Example |
|----------|---------------|
| Cohere | `cohere/rerank-english-v3.0` |
| Together AI | `together_ai/...` |
| Voyage AI | `voyage/rerank-2` |
| Jina AI | `jina_ai/...` |
| AWS Bedrock | `bedrock/...` |

**Configuration Examples:**

```bash
# Local provider (default)
export HINDSIGHT_API_RERANKER_PROVIDER=local
export HINDSIGHT_API_RERANKER_LOCAL_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2

# Cohere
export HINDSIGHT_API_RERANKER_PROVIDER=cohere
export HINDSIGHT_API_COHERE_API_KEY=your-api-key
export HINDSIGHT_API_RERANKER_COHERE_MODEL=rerank-english-v3.0

# ZeroEntropy (state-of-the-art accuracy)
export HINDSIGHT_API_RERANKER_PROVIDER=zeroentropy
export HINDSIGHT_API_RERANKER_ZEROENTROPY_API_KEY=your-api-key
export HINDSIGHT_API_RERANKER_ZEROENTROPY_MODEL=zerank-2  # default, can omit

# TEI (self-hosted)
export HINDSIGHT_API_RERANKER_PROVIDER=tei
export HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8081

# FlashRank (lightweight)
export HINDSIGHT_API_RERANKER_PROVIDER=flashrank

# LiteLLM proxy
export HINDSIGHT_API_RERANKER_PROVIDER=litellm
export HINDSIGHT_API_LITELLM_API_BASE=http://localhost:4000
export HINDSIGHT_API_RERANKER_LITELLM_MODEL=cohere/rerank-english-v3.0

# RRF-only (no neural reranking)
export HINDSIGHT_API_RERANKER_PROVIDER=rrf
```

See [Configuration](./configuration#reranker) for all options including Azure-hosted endpoints and batch settings.
