Ingestion pipeline

Capture every new post / edit / media event.

Preprocess: clean, language detection, extract metadata (timestamp, subreddit, post-id, reply-to, upvotes).

Media routing: send images to a vision pipeline, videos to frame extractor → video model, audio to speech pipeline.

Short-term context cache (hot memory)

Keep most recent N posts / conversation turns directly in the LLM prompt (sliding window).

Example: last 2–5 posts or last X minutes of activity. This is cheap and low-latency.

Vector DB (semantic memory)

Create embeddings for each item (post, comment, image caption, condensed transcript).

Store vectors + metadata in a vector DB (FAISS / Milvus / Pinecone / Weaviate).

Use semantic retrieval (k-nearest neighbors) to fetch relevant history for a given query.

Memory condensation / hierarchical store

Periodic background condensing job (not "background" for you to wait on — it's part of infra) that:

Clusters older posts by topic/time.

Summarizes each cluster into compact human-readable memory (e.g., 50–200 tokens).

Replaces many raw items with one condensed memory entry (but keep originals for full audit).

Keeps vector DB with both raw recent vectors and long-term condensed vectors.

Versioning + edits

Store canonical entity per post-id. When edited:

Replace text and update embedding (and optionally keep a diffs log).

Keep a “last-modified” and optionally small delta log so retrieval can prioritize latest content.

For the LLM: retrieval returns the canonical (latest) content. If you want to show an edit history, return both.

Retriever + RAG

On query, retrieve:

Hot cache (recent posts).

k-nearest semantic memories (raw + condensed).

Topical summaries (precomputed).

Feed these into the LLM in a structured prompt: short-term context first, high-relevance raw items next, condensed long-term memories after.

Multimodal perception (vision / audio)

Vision: SAM-style segmentation + CLIP embeddings + a dedicated classifier (or fine-tuned ViT) for fine detail queries. For video, use frame sampling + temporal model (I3D / SlowFast or transformer-based video encoders) to produce embeddings or structured facts.

Audio: VAD → transcription (Whisper or wav2vec2 variants) → embed transcript; also extract audio embeddings for speaker/tonal cues if needed.

Store all modality embeddings in the same vector DB with modality tag.

Neural compression & learned selectors

Use an LLM to create semantic pointers — short summaries that are more effective than naive truncation.

Keep high-value items uncompressed (high karma, many replies), compress low-value ones.