47be07f97f
* bump pg0 0.11.x and improve documentation * bump pg0 0.11.x and improve documentation * bump pg0 0.11.x and improve documentation * ci: test notebooks on ci * ci: test notebooks on ci * rm llms-full from repo * formatting * formatting
162 lines
4.3 KiB
Markdown
162 lines
4.3 KiB
Markdown
---
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sidebar_position: 1
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---
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# Hindsight Quickstart
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:::tip Run this notebook
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This recipe is available as an interactive Jupyter notebook.
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[**Open in GitHub →**](https://github.com/vectorize-io/hindsight-cookbook/blob/main/notebooks/01-quickstart.ipynb)
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:::
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This notebook covers the basics of using Hindsight:
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- **Retain**: Store information in memory
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- **Recall**: Retrieve memories matching a query
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- **Reflect**: Generate insights from memories
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## Prerequisites
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Make sure you have Hindsight running. The easiest way is via Docker:
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```bash
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export OPENAI_API_KEY=your-key
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docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
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-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
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-e HINDSIGHT_API_LLM_MODEL=o3-mini \
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-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
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ghcr.io/vectorize-io/hindsight:latest
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```
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- API: http://localhost:8888
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- UI: http://localhost:9999
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## Installation
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Install the Hindsight Python client:
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```python
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!pip install hindsight-client nest_asyncio python-dotenv -U
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```
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## Connect to Hindsight
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```python
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# Jupyter notebooks already run an asyncio event loop. The hindsight client
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# uses loop.run_until_complete() internally, but Python doesn't allow nested
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# event loops by default. nest_asyncio patches this to allow nesting.
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import nest_asyncio
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nest_asyncio.apply()
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import os
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from dotenv import load_dotenv
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# Load environment variables from .env file
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# Copy .env.example to .env and fill in your values
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load_dotenv()
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# Configuration (override with env vars if set)
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HINDSIGHT_API_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
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HINDSIGHT_UI_URL = os.getenv("HINDSIGHT_UI_URL", "http://localhost:9999")
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from hindsight_client import Hindsight
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client = Hindsight(base_url=HINDSIGHT_API_URL)
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```
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## Retain: Store Information
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The `retain` operation is used to push new memories into Hindsight. It tells Hindsight to _retain_ the information you pass in.
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Behind the scenes, the retain operation uses an LLM to extract key facts, temporal data, entities, and relationships.
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```python
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# Simple retain
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client.retain(
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bank_id="my-bank",
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content="Alice works at Google as a software engineer"
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)
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# View the stored document in the UI:
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print(f"View documents: {HINDSIGHT_UI_URL}/banks/my-bank?view=documents")
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```
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```python
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# Retain with context and timestamp
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client.retain(
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bank_id="my-bank",
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content="Alice got promoted to senior engineer",
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context="career update",
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timestamp="2025-06-15T10:00:00Z"
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)
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```
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## Recall: Retrieve Memories
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The `recall` operation retrieves memories matching a query. It performs 4 retrieval strategies in parallel:
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- **Semantic**: Vector similarity
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- **Keyword**: BM25 exact matching
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- **Graph**: Entity/temporal/causal links
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- **Temporal**: Time range filtering
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```python
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# Simple recall
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results = client.recall(bank_id="my-bank", query="What does Alice do?")
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print("Memories:")
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for r in results.results:
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print(f" - {r.text}")
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```
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```python
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# Temporal recall
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results = client.recall(bank_id="my-bank", query="What happened in June?")
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print("Memories:")
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for r in results.results:
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print(f" - {r.text}")
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```
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## Reflect: Generate Insights
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The `reflect` operation performs a more thorough analysis of existing memories. This allows the agent to form new connections between memories which are then persisted as opinions and/or observations.
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Example use cases:
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- An AI Project Manager reflecting on what risks need to be mitigated
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- A Sales Agent reflecting on why certain outreach messages have gotten responses
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- A Support Agent reflecting on opportunities where customers have unanswered questions
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```python
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response = client.reflect(bank_id="my-bank", query="What should I know about Alice?")
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print(response)
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```
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## Memory Types
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Hindsight organizes memory into four networks to mimic human memory:
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- **World**: Facts about the world ("The stove gets hot")
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- **Experiences**: Agent's own experiences ("I touched the stove and it really hurt")
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- **Opinion**: Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
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- **Observation**: Complex mental models derived by reflecting on facts and experiences
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## Cleanup
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Delete the bank created during this notebook:
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```python
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import requests
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response = requests.delete(f"{HINDSIGHT_API_URL}/v1/default/banks/my-bank")
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print(f"Deleted my-bank: {response.json()}")
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```
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