Chronologue supports embedding memory traces using OpenAI’s text-embedding-3-small model. This allows textual memory (e.g., reflections, goals, observations) to be projected into a 1536-dimensional vector space suitable for semantic search, clustering, or similarity-based ranking. The primary embedding logic is defined in modules/embed_memory_traces.py.

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Embedding Format Standard

Each memory trace receives an additional field: 
{
  "id": "trace_014",
  "type": "observation",
  "content": "Checked all incubator temps and reset incubator #3.",
  "timestamp": "2025-04-17T09:03:00",
  "embedding": [0.013, -0.022, ..., 0.005]  // Length 1536
}
Embeddings are stored inline in the JSON and written to *_embedded.json files in the output directory.

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Key Functions and Parameters for Customization

get_openai_embedding(text: str) -> List[float]
  • Calls OpenAI’s API with the “text-embedding-3-small” model
  • Returns a 1536-dim vector
  • Customize: switch to “text-embedding-3-large” if higher fidelity is needed
embed_trace(trace: Dict) -> List[float]
  • Extracts content and returns its embedding
  • Customize: embed title, notes, or concatenated fields for richer signals
embed_memory_traces(traces: List[Dict], overwrite: bool = False) -> List[Dict]
  • Embeds all traces, skipping if existing embeddings are present (unless overwrite=True)
  • Customize: add logging, batch embedding, or progress bars

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File IO and CLI Behavior

This script performs batch embedding of memory trace files:
  1. Loads all .json files in data/conversation/raw/
  2. Embeds each trace (if needed)
  3. Writes updated traces to data/conversation/embedding/ with _embedded.json suffix
Run with: python modules/core/embeddings.py

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Applications: Ranking and Clustering

Embedding memory traces enables advanced behavior:

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Ranking

  • Use cosine similarity or dot product to compare a new query (embedded) to existing memory traces
  • Top-k nearest traces can condition LLM reasoning (retrieval-augmented CoT)
  • Tools: FAISS, ScaNN, PyTorch cosine similarity
  • Embeddings can be ranked not only by semantic similarity but also by temporal proximity, allowing agents to prioritize contextually recent or chronologically relevant events
  • Combine semantic and temporal scores (e.g., weighted average or custom kernel) for better personalization and relevance

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Clustering for Organization

  • Group related reflections or observations
  • Visualize user themes, summarize weeks, and build agendas with calendar themes
  • Import Calendar — convert .ics files into JSON memory traces to embed calendar events alongside natural language memories
  • Event Retrieval — use embeddings from memory traces to prioritize, filter, or retrieve relevant calendar events during planning or summarization