57f154454d
* fix(recall): cap entity fanout in graph expansion to prevent slow queries On large banks, the entity co-occurrence self-join in _expand_combined() produces massive intermediate row counts when seeds reference high-fanout entities (e.g. an entity with 25K+ mentions). This causes recall latency to degrade significantly. Changes: - Replace unbounded entity self-join with LATERAL per-entity cap (graph_per_entity_limit, default 200), reducing intermediate rows from potentially millions to at most num_entities * 200 - Add ORDER BY unit_id DESC in LATERAL subquery for deterministic recency-biased sampling (rides the PK index, no extra sort) - Add timeout fallback (graph_expansion_timeout, default 10s) that drops entity expansion and falls back to semantic+causal only - Add composite index (entity_id, unit_id) on unit_entities for index-only scans in the LATERAL subquery - Merge 3 unmerged migration heads into one - Fix recall_perf.py dotenv override issue Unlike the approach in #895, this does NOT filter out hub entities entirely — all entities are kept but capped equally, preserving retrieval quality for queries about frequently-mentioned entities. Benchmarked on a 67K-unit bank (top entity = 25K mentions): - retrieval_graph: 0.337s → 0.055s (84% faster) - end-to-end recall: 0.912s → 0.519s (43% faster) * fix(tests): fix broken test_combined_scoring and test_reranking_proof_count - test_combined_scoring: replace MagicMock(spec=RetrievalResult) with real dataclass instances — MagicMock attributes returned nested mocks that failed on >= comparisons with int - test_reranking_proof_count: remove deleted `embedding` param from RetrievalResult constructor, use None for occurred_start/end to get neutral recency (datetime.now gave recency=1.0 which boosted scores) * refactor: rename config to link_expansion_ prefix, fix observation fanout - Rename GRAPH_PER_ENTITY_LIMIT → LINK_EXPANSION_PER_ENTITY_LIMIT and GRAPH_EXPANSION_TIMEOUT → LINK_EXPANSION_TIMEOUT to follow the convention that these are specific to the link_expansion graph retriever - Apply the same LATERAL per-entity cap to _expand_observations(), which had the same unbounded self-join through unit_entities * style: fix formatting in config.py |
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| pyproject.toml | ||
| README.md | ||
Hindsight API
Memory System for AI Agents — Temporal + Semantic + Entity Memory Architecture using PostgreSQL with pgvector.
Hindsight gives AI agents persistent memory that works like human memory: it stores facts, tracks entities and relationships, handles temporal reasoning ("what happened last spring?"), and forms opinions based on configurable disposition traits.
Installation
pip install hindsight-api
Quick Start
Run the Server
# Set your LLM provider
export HINDSIGHT_API_LLM_PROVIDER=openai
export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx
# Start the server (uses embedded PostgreSQL by default)
hindsight-api
The server starts at http://localhost:8888 with:
- REST API for memory operations
- MCP server at
/mcpfor tool-use integration
Use the Python API
from hindsight_api import MemoryEngine
# Create and initialize the memory engine
memory = MemoryEngine()
await memory.initialize()
# Create a memory bank for your agent
bank = await memory.create_memory_bank(
name="my-assistant",
background="A helpful coding assistant"
)
# Store a memory
await memory.retain(
memory_bank_id=bank.id,
content="The user prefers Python for data science projects"
)
# Recall memories
results = await memory.recall(
memory_bank_id=bank.id,
query="What programming language does the user prefer?"
)
# Reflect with reasoning
response = await memory.reflect(
memory_bank_id=bank.id,
query="Should I recommend Python or R for this ML project?"
)
CLI Options
hindsight-api --help
# Common options
hindsight-api --port 9000 # Custom port (default: 8888)
hindsight-api --host 127.0.0.1 # Bind to localhost only
hindsight-api --workers 4 # Multiple worker processes
hindsight-api --log-level debug # Verbose logging
Configuration
Configure via environment variables:
| Variable | Description | Default |
|---|---|---|
HINDSIGHT_API_DATABASE_URL |
PostgreSQL connection string | pg0 (embedded) |
HINDSIGHT_API_LLM_PROVIDER |
openai, anthropic, gemini, groq, ollama, lmstudio |
openai |
HINDSIGHT_API_LLM_API_KEY |
API key for LLM provider | - |
HINDSIGHT_API_LLM_MODEL |
Model name | gpt-4o-mini |
HINDSIGHT_API_HOST |
Server bind address | 0.0.0.0 |
HINDSIGHT_API_PORT |
Server port | 8888 |
Example with External PostgreSQL
export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
export HINDSIGHT_API_LLM_PROVIDER=groq
export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
hindsight-api
Docker
docker run --rm -it -p 8888:8888 \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight:latest
MCP Server
For local MCP integration without running the full API server:
hindsight-local-mcp
This runs a stdio-based MCP server that can be used directly with MCP-compatible clients.
Key Features
- Multi-Strategy Retrieval (TEMPR) — Semantic, keyword, graph, and temporal search combined with RRF fusion
- Entity Graph — Automatic entity extraction and relationship tracking
- Temporal Reasoning — Native support for time-based queries
- Disposition Traits — Configurable skepticism, literalism, and empathy influence opinion formation
- Three Memory Types — World facts, bank actions, and formed opinions with confidence scores
Documentation
Full documentation: https://hindsight.vectorize.io
License
Apache 2.0