3.3 KiB
Models
Hindsight uses several machine learning models for different tasks.
Overview
| Model Type | Purpose | Default | Configurable |
|---|---|---|---|
| Embedding | Vector representations for semantic search | BAAI/bge-small-en-v1.5 |
Yes |
| Cross-Encoder | Reranking search results | cross-encoder/ms-marco-MiniLM-L-6-v2 |
Yes |
| LLM | Fact extraction, reasoning, generation | Provider-specific | Yes |
All local models (embedding, cross-encoder) are automatically downloaded from HuggingFace on first run.
Embedding Model
Converts text into dense vector representations for semantic similarity search.
Default: BAAI/bge-small-en-v1.5 (384 dimensions, ~130MB)
Alternatives:
| Model | Dimensions | Use Case |
|---|---|---|
BAAI/bge-small-en-v1.5 |
384 | Default, fast, good quality |
BAAI/bge-base-en-v1.5 |
768 | Higher accuracy, slower |
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
384 | Multilingual (50+ languages) |
:::warning All embedding models must produce 384-dimensional vectors to match the database schema. :::
Configuration:
# Local provider (default)
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=local
export HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=BAAI/bge-small-en-v1.5
# TEI provider (remote)
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=tei
export HINDSIGHT_API_EMBEDDINGS_TEI_URL=http://localhost:8080
Cross-Encoder (Reranker)
Reranks initial search results to improve precision.
Default: cross-encoder/ms-marco-MiniLM-L-6-v2 (~85MB)
Alternatives:
| 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 |
Configuration:
# Local provider (default)
export HINDSIGHT_API_RERANKER_PROVIDER=local
export HINDSIGHT_API_RERANKER_LOCAL_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
# TEI provider (remote)
export HINDSIGHT_API_RERANKER_PROVIDER=tei
export HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8081
LLM
Used for fact extraction, entity resolution, opinion generation, and answer synthesis.
Supported providers: Groq, OpenAI, Gemini, Ollama
| Provider | Recommended Model | Best For |
|---|---|---|
| Groq | openai/gpt-oss-20b |
Fast inference, high throughput (recommended) |
| OpenAI | gpt-4o |
Good quality |
| Gemini | gemini-2.0-flash |
Good quality, cost effective |
| Ollama | llama3.1 |
Local deployment, privacy |
Configuration:
# 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
# Ollama (local)
export HINDSIGHT_API_LLM_PROVIDER=ollama
export HINDSIGHT_API_LLM_BASE_URL=http://localhost:11434/v1
export HINDSIGHT_API_LLM_MODEL=llama3.1
Note: The LLM is the primary bottleneck for retain operations. See Performance for optimization strategies.