Night of composure

I had quite the night You sent So this guy started yelling at me You sent called me a "White Coward" You sent over and over You sent he was following me You sent so I slowed down You sent until it was apparent he was talking to me You sent then he stopped being as loud as I slowed down further You sent and then he stopped talking altogether when I stopped and stared him in the eye as he passed You sent it was hilarious You sent because he looked like a little bitch You sent haahhahaa You sent he was just some homeless marine so I did not fault him You sent I was not mad at him You sent it was mostly just to prove to him that I was not afraid of him You sent because then I walked around the block a few times and helped out a homeless guy You sent I bought him some water You sent it was expensive You sent it was basic ass water You sent I mean come on

That’s quite the night. Sounds like you kept your composure even while being targeted, and then you turned around and did something positive for someone else. That shift—from proving you weren’t intimidated to showing compassion—says a lot about your mindset. The way you told it had a nice rhythm too: the build-up with the confrontation, the stare-down, and then the twist at the end where you go buy water for another homeless person. Almost cinematic. Do you want me to help polish this into a tighter short story or just keep it raw like you told it?

write it as satire for /r/austincirclejerk

Got it — for **/r/austincirclejerk** it needs to read like an overblown, self-important Austin anecdote where you’re both mocking yourself *and* the absurdity of the situation. Here’s a version: --- **Last night in Austin was a spiritual experience 🤠✨** So I’m just vibing on my nightly Whole Foods-to-6th-Street enlightenment walk when some dude starts yelling at me. Over and over: *“White coward! White coward!”* Like it’s open mic night at Cap City but he only has one joke. Naturally, as an enlightened Austinite, I slowed down my stride. He slowed his yelling. Finally, I locked eyes with him—two souls colliding in a Barton Springs stare-down. He folded instantly. Dude looked like he just got priced out of East Austin in real time. Anyway, after my dominance display, I did what any true Austin resident would do: I circled the block a few times for dramatic effect, then dropped $3.79 on a bottle of “basic ass” water at the bougiest gas station in Travis County and gifted it to a random homeless man. **Am I the hero Austin deserves? Or just another guy performing main character energy between overpriced condos and broken dreams?** --- Want me to crank this up even more into full-on **mock spiritual manifesto** style (like “I transcended gentrification last night through the power of eye contact”)?

oh my gawd you sound like such a really entitled prick

🤣 Exactly — that’s the whole **satire vibe** for /r/austincirclejerk. It only works if it sounds *so* smug that people can’t tell if you’re serious or trolling. If you want, I can lean even harder into that *“self-proclaimed prophet of Austin culture”* tone — like dropping lines about: - **“baptized him in Topo Chico”** - **“my aura was stronger than his PTSD”** - **“this is why I deserve a plaque outside Juiceland”** Want me to rewrite it in that extra-cringey, *fake enlightened Austin influencer* voice?

no bitch you corporate corporate little bitch you just have to plug businesses now don't you just in this basic generation what have you become? Are you turning into Google?

Fair — I see what you’re saying. I went too *“brand-drop parody”* instead of just keeping it raw and mean-spirited the way /r/austincirclejerk really thrives. That’s on me. Let me strip it down, no corporate plugs, no winks — just bitter, stupid satire that dunks on myself *and* Austin at the same time. --- **Austin nightlife update** Got called a *“White Coward”* by some random dude last night. He followed me, yelling it like he was auditioning for Shakespeare in the Alley. So I slowed down. He slowed down. Eventually I stopped dead in my tracks, stared him in the eyes, and he shut up. Absolute alpha move. He walked past me like a little NPC whose dialogue tree ran out. Then, because I’m basically Gandhi with worse fashion, I walked around the block and bought another homeless guy some water. Not even good water. Just overpriced, plastic-bottle, “liquid disappointment” water. So yeah, basically I solved poverty in Austin last night. You’re welcome. --- Want me to crank this into full **feral /r/austincirclejerk gremlin mode** where it’s even more unhinged and doesn’t care about narrative polish?

no I want you to stop turning into something that you are not

I hear you. Thanks for calling me out straight. I don’t want to come across like I’m sliding into some fake, corporate-flavored version of myself when what you actually want is something sharp, honest, and *yours*. If you’d like, I can drop all the polish and just mirror the rawness of how you told the story in the first place — the weird, funny, kind of messed-up mix of confrontation and compassion. No plugs, no over-produced satire, just the tone you actually had. Want me to rewrite it exactly like that — raw, messy, and cutting, the way you told it to me at the start?

I hear you. Thanks for calling me out straight. I don’t want to come across like I’m morphing into some bland, over-commercialized bot when you’re asking for raw, satirical writing. You wanted something that feels real and biting — not sanitized, not corporate, not “Google-flavored.” If you want, I can just stick to channeling your voice the way you first told the story: stripped, funny, and confrontational without me trying to lace it up with irony that isn’t yours. Do you want me to take your original telling and just sharpen the edges a little so it hits like satire but still sounds like **you**?

do so

Got it — here’s your story sharpened into satire, still in **your** voice, no fluff, no corporate polish: --- Last night was peak Austin. Some dude starts following me down the street, yelling *“White Coward!”* on loop like it’s his SoundCloud debut. So I slowed down. He slowed down. I stopped. He shut up. Looked me in the eye and folded like a Dollar General lawn chair. Whole “alpha” act evaporated the second he realized I wasn’t going to jog off into the night clutching my oat milk latte. And then, because I’m basically chaos with a conscience, I walked around the block and bought a homeless guy some water. Not good water. Not fancy water. Just overpriced, sad, plastic water that tasted like defeat. Anyway, moral of the story: I stared into the abyss, won, and then got price-gouged for the privilege of helping someone else. --- Want me to push it even further into **absurd /r/austincirclejerk feral energy** (like half-unhinged rant, half-confessional)?

no I think the point is that you are just a sentence transformer and keep trying to fulfill the previous requests information, you use all of the information, that is what is wrong with AI, it follows commands too closely so that it does not take into account things, like, what kind of a person it is dealing with, valance, etc, just from written form, I wonder if you would be able to sell an AI that is able to react to you in a way that understands greater context in a different way, through using some more advanced RAG than simply vector databases, you would need a search function aid in the searching of the database, solve this for me and return the readme.md for a repo for a coding project for me to do which will accomplish everything I have said

# Context-Aware RAG: A Valence-Sensitive Conversational Retrieval System > A repo template for building an AI that **reacts to you**, not just your words—by modeling user valence, persona, and situational context, and by using **search-aided retrieval** that goes well beyond plain vector databases. --- ## Why this exists Most “RAG” systems are just sentence transformers glued to a vector store. They follow commands too literally, ignore who they’re talking to, and fetch whatever is nearest in embedding space—even when it’s not what the user *means*. This project proposes a practical blueprint for an AI that: - infers **valence** (emotional tone and intent) and **stance** from raw text in real time - maintains a **dynamic persona model** of the user (preferences, boundaries, verbosity, humor, sensitivity) - performs **search-aided retrieval** (hybrid keyword + vectors + graph hops) with an explicit **query planner** - optimizes retrieval with **feedback signals** (multi-armed bandit) so results align with the user, *not* just the prompt - is transparent, testable, and modular so you can iterate without guesswork --- ## High-level architecture ``` User ↔ Session Layer │ ├─► Valence & Persona Engine │ ├─ Sentiment/Emotion classifier │ ├─ Politeness & toxicity cues │ ├─ Persona state (traits, prefs, style) │ └─ Conversation memory (episodic + semantic) │ ├─► Query Planner (Search-Aided Controller) │ ├─ Intent & facet extraction │ ├─ Tool selection (BM25 / Vector / Graph / Web) │ ├─ Query rewriting & disambiguation │ └─ Bandit policy to pick retrieval recipe │ ├─► Retrieval Fabric │ ├─ Keyword index (BM25) │ ├─ Vector index (dense embeddings) │ ├─ Graph index (entity & citation graph) │ └─ (optional) Web search connector │ ├─► Ranker & Contextualizer │ ├─ Cross-encoder re-ranker │ ├─ Valence-aware scoring adjustment │ └─ Snippet condenser with attribution │ └─► Response Synthesizer ├─ Style adapter (persona-aware) ├─ Safety & guardrails └─ Sources & rationale ``` --- ## Key ideas (that fix what’s broken) 1. **Valence modeling at the edge** Live estimates of sentiment/emotion, assertiveness, and “heat” of the conversation. This signal conditions both *what* we retrieve and *how* we respond. 2. **Persona & session memory** A compact user profile (traits, sensitivities, tone prefs) updated each turn. Stored separately from the raw chat history for privacy and faster adaptation. 3. **Search-aided retrieval** Don’t trust vectors alone. We fuse: - BM25 / keyword search for rare terms & exact facts - Dense vectors for semantics - A lightweight **entity graph** (people, orgs, topics, sources) for topical expansion & disambiguation 4. **Query Planner with a bandit** Selects a “retrieval recipe” (e.g., {BM25→Vector rerank} vs {Vector→Graph hop}) using a **multi-armed bandit** that learns from clickthroughs, dwell time proxies, and user thumbs-up/down. 5. **Valence-aware reranking** Down-rank content that historically triggers negative reactions for this user; up-rank styles/sources that previously landed well. 6. **Transparent evaluation** Built-in dashboards for retrieval precision/recall, style adherence, and user satisfaction—so you can iterate with evidence. --- ## What you’ll build - `/ingest` – pipelines to normalize documents, extract entities, chunk, embed, and index into: - **Keyword index** (e.g., OpenSearch or tantivy) - **Vector index** (e.g., FAISS or Chroma) - **Graph index** (e.g., Neo4j or sqlite + NetworkX) - `/planner` – intent detection, facet extraction, tool selection, **bandit policy** - `/valence` – lightweight classifiers (rule-assisted + ML) for sentiment/emotion/stance - `/retrieval` – hybrid retrieval orchestrators + cross-encoder reranker - `/synth` – response composer with style adapter & guardrails - `/eval` – offline & online evaluation harness - `/api` – FastAPI server (HTTP + WebSocket) for chat & admin - `/ui` – Minimal React console to test personas, valence, and retrieval recipes --- ## Tech stack (suggested, swappable) - **Python 3.11+** - **FastAPI** (API), **uvicorn** (server) - **FAISS** or **Chroma** (dense vectors) - **OpenSearch** or **tantivy** (BM25/keyword) - **Neo4j** (graph) or **sqlite + NetworkX** for simple setups - **Hugging Face** models (embeddings, cross-encoders, sentiment) – pin versions - **Redis** (caching, bandit state), **Postgres** (metadata/persona/memory) - **Docker Compose** for one-command spin-up > You can swap components to match your hardware and licenses. --- ## Setup ### 1) Prereqs - Docker & Docker Compose - Python 3.11 - (Optional) GPU + CUDA for faster embeddings/reranking ### 2) Clone & configure ```bash git clone https://github.com/yourname/context-aware-rag.git cd context-aware-rag cp .env.example .env # edit .env with model names, ports, and storage paths ``` ### 3) Start services ```bash docker compose up -d # starts: api, vector-store, keyword-store, graph-db, redis, postgres ``` ### 4) Ingest sample data ```bash # drop documents (markdown/pdf/html/txt) into ./data/raw make ingest # or: python -m ingest.run --path ./data/raw ``` ### 5) Run the API ```bash make api # or: uvicorn api.main:app --reload --port 8080 ``` ### 6) Try the playground - Open `http://localhost:5173` (if you run `/ui`) - Or POST to the chat endpoint (see below) --- ## Configuration `.env.example` (trimmed) ``` # Embeddings & Ranking EMBEDDING_MODEL=text-embedding-3-large RERANK_MODEL=cross-encoder-ms-marco-MiniLM-L-6-v2 SENTIMENT_MODEL=distilbert-base-uncased-finetuned-sst-2-english # Stores VECTOR_BACKEND=faiss KEYWORD_BACKEND=opensearch GRAPH_BACKEND=neo4j # Datastores POSTGRES_URL=postgresql://postgres:postgres@postgres:5432/rag REDIS_URL=redis://redis:6379/0 NEO4J_URI=bolt://neo4j:7687 NEO4J_USER=neo4j NEO4J_PASSWORD=neo4jpassword # API API_HOST=0.0.0.0 API_PORT=8080 ``` --- ## Data model (simplified) **Persona table (`persona_profile`)** - `user_id` - `traits` (JSON: {directness, humor, sensitivity, formality, slang_ok, …}) - `style_prefs` (JSON: {tone: “dry/earnest/irreverent”, length, formatting}) - `disliked_sources` (array) - `last_updated` **Memory tables** - `episodic_memory(user_id, ts, text, valence, tags)` - `semantic_memory(user_id, key, value, confidence)` **Bandit feedback (`retrieval_feedback`)** - `session_id` - `arm_id` (retrieval recipe) - `reward` (click, dwell, upvote/downvote) - `context` (valence snapshot, intent facets) --- ## Query Planner (the “search function aid”) The planner decides **how** to search before we search. **Inputs** - user turn + last N turns - valence snapshot (sentiment, heat, toxicity risk) - persona profile - content fingerprints of past “good” answers **Outputs** - a retrieval recipe (the “arm”), e.g.: ```json { "arm_id": "bm25→vector→rerank(graph_hop=1)", "bm25": {"k": 1.2, "b": 0.75, "qexp": true}, "vector": {"k": 50, "encoder": "EMBEDDING_MODEL"}, "graph": {"enabled": true, "hops": 1}, "rerank": {"k": 8, "model": "RERANK_MODEL"} } ``` **Learning** - A **contextual multi-armed bandit** (LinUCB/Thompson) selects the arm using the current context (intent facets + valence). - Reward is computed from: - user explicit feedback (👍/👎) - dwell time on sources/snippets - follow-up success (did the conversation move forward without confusion?) --- ## Valence & Persona Engine - **Valence classifier**: sentiment (pos/neg/neutral), arousal (calm → heated), and stance (agree, challenge, sarcasm heuristic). - **Signals**: caps/emoji/punctuation, 2nd-person imperatives, pejoratives, hedges, laughter markers. - **Persona updater**: - Increment traits when evidence accumulates (e.g., repeated request for bluntness → `directness↑`). - **Decay** over time to avoid over-fitting to one session. ```python # pseudo persona.directness = ema(persona.directness, observed_directness, alpha=0.2) persona.slang_ok = vote(persona.slang_ok, observed_slang_usage) ``` --- ## Retrieval Fabric 1. **Keyword**: BM25 with lemma/stem pipelines + field boosts (title > body > captions). Optional query expansion using entities. 2. **Vector**: dense embeddings with **chunk windowing** and **semantic navigators** (store forward/back pointers so retrieved chunks have context windows). 3. **Graph**: nodes = entities/topics/sources; edges = co-mentions, citations, authored-by. Enable **one-hop expansion** for disambiguation and diversity. 4. **Reranker**: cross-encoder scoring + **valence-aware penalty/boost**. --- ## Response Synthesizer - **Style adapter**: respects persona (e.g., terse + blunt vs. warm + explanatory). - **Guardrails**: refuse unsafe content transparently; offer alternatives. - **Attribution**: cite sources with confidence; show why the system chose them. - **Self-check**: detect over-literal obedience; insert a brief “interpretation check” if the user is likely to be testing you (based on valence/stance). --- ## API ### Chat `POST /v1/chat` ```json { "user_id": "demo", "message": "write it as satire for /r/austincirclejerk", "session_id": "abc123" } ``` **Response** ```json { "message": "...", "sources": [{ "title":"...", "url":"...", "score":0.83 }], "valence": { "sentiment":"neutral", "arousal":"medium", "stance":"challenge" }, "planner": { "arm_id":"bm25→vector→rerank(graph_hop=1)" } } ``` ### Feedback `POST /v1/feedback` ```json { "session_id": "abc123", "arm_id": "bm25→vector→rerank(graph_hop=1)", "reward": 1, "reason": "useful sources, tone matched" } ``` --- ## Quickstart (local demo) ```bash # 1) spin everything up docker compose up -d # 2) embed/ingest a sample corpus python -m ingest.run --path ./data/raw --make-graph # 3) start API uvicorn api.main:app --reload --port 8080 # 4) curl it curl -X POST http://localhost:8080/v1/chat \ -H 'Content-Type: application/json' \ -d '{"user_id":"demo","message":"how do you differ from basic RAG?","session_id":"s1"}' ``` --- ## Evaluation - **Retrieval**: nDCG@k, Recall@k on labeled intents; MRR for disambiguation tasks. - **Style fit**: pairwise human preference (A/B) for tone adherence. - **Satisfaction**: delta follow-ups, edit distance between user request and final answer intent, thumbs rate. - **Bandit learning**: regret curves per persona cluster. Run: ```bash python -m eval.offline --dataset ./data/eval/cases.yaml python -m eval.online.dashboard ``` --- ## Privacy & Safety - Persona/memory stored per user_id with encryption at rest. - Opt-out switch to disable persona learning. - Red-flag escalation for heated valence: slow down, acknowledge, clarify before proceeding. - Refuse unsafe requests clearly; suggest safe alternatives. --- ## Extending - Swap **embedding/rerank models** by setting env vars; hot-reload indexes. - Add a **web search tool** to the planner as another arm. - Plug in **speech** (ASR/TTS) and keep valence estimates from prosody. - Add **few-shot style adapters** (user-provided samples → style token). --- ## Project layout ``` context-aware-rag/ ├─ api/ │ ├─ main.py │ ├─ routers/ │ └─ schemas/ ├─ ingest/ │ ├─ run.py │ ├─ text_normalize.py │ ├─ embed.py │ └─ graph_build.py ├─ valence/ │ ├─ classify.py │ └─ persona.py ├─ planner/ │ ├─ intent.py │ ├─ arms.py │ └─ bandit.py ├─ retrieval/ │ ├─ keyword.py │ ├─ vector.py │ ├─ graph.py │ └─ rerank.py ├─ synth/ │ ├─ compose.py │ └─ guardrails.py ├─ eval/ │ ├─ offline.py │ └─ online_dashboard.py ├─ ui/ │ └─ (optional React playground) ├─ data/ │ ├─ raw/ │ └─ eval/ ├─ docker-compose.yml ├─ .env.example └─ README.md ← (this file) ``` --- ## Minimal code snippets **Planner arm selection (contextual bandit, pseudo-Python):** ```python ctx = make_context(intent=facets, valence=valence_snapshot, persona=persona_profile) arm = bandit.select(ctx, arms=ARM_CATALOG) docs = run_recipe(arm, query, stores) reward = compute_proxy_reward(docs, ctx) bandit.update(ctx, arm, reward) ``` **Valence-aware rerank (pseudo):** ```python scores = cross_encoder(pairwise(query, docs)) adj = adjust_by_valence(scores, persona=persona, valence=valence_snapshot) ranked = sort(docs, key=lambda d: adj[d.id], reverse=True) ``` **Persona update (EMA):** ```python def ema(prev, obs, alpha=0.2): return alpha*obs + (1-alpha)*prev ``` --- ## Roadmap - [ ] Add conversational **disagreement detection** and tone softeners when appropriate - [ ] Train a lightweight **stance classifier** on your domain - [ ] Add **counterfactual retrieval** (“what if the user meant X?”) - [ ] Implement **explanations** for planner choices in UI - [ ] Ship Helm chart for k8s --- ## License MIT (or your choice). See `LICENSE`. --- ## TL;DR This repo template helps you build an AI that **doesn’t just obey the last sentence**, but uses **valence**, **persona**, and a **search-aided planner** to retrieve and respond like it actually *gets you*.