Team: Grassroots · Members: Priyanshu Reddy, Adithya Bontha, Hemasri Vemulapalli, Akshitha Reddy · Live URL: madimargin-git-main-aimodel3.vercel.app · Documentation: DOCUMENTATION.md

MandiMargin — a rice-arbitrage decision assistant for AP/Telangana merchants, built on the myAI6 template for the BITSoM "AI in Business" midterm.

MandiMargin (built on myAI6)

A rice-arbitrage decision assistant for AP/Telangana merchants — built on the myAI6 RAG chatbot template (Next.js 16, Vercel AI SDK v6, Pinecone).

MandiMargin helps independent rice merchants and mill owners in Andhra Pradesh and Telangana decide each morning which nearby district to sell their paddy/rice in for the highest net profit after transport cost — see DOCUMENTATION.md for the full product case and technical writeup. Everything below this point is the original myAI6 template documentation, kept for setup/deployment reference.

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myAI6

A modular RAG chatbot built with Next.js 16, Vercel AI SDK v6, and Pinecone.

myAI6 is a retrieval-augmented generation (RAG) chatbot template: it answers questions about its owner using an indexed knowledge base, optional web search, and multi-vendor LLM support. The assistant's name, owner, and knowledge-base scope are all configured in config.ts — make it yours. It features a parent-child chunking architecture, academic-style citations, content moderation, and a dynamic UI with animated processing indicators.

Template created by Daniel M. Ringel. Please acknowledge use of any or all of this code.

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Quickstart

1. Clone and install

git clone https://github.com/dringel/myAI6.git
cd myAI6
npm install

2. Set environment variables

Copy the template and fill in your keys:

cp env.template .env.local

Required:

Variable	Source	Purpose
ANTHROPIC_API_KEY	console.anthropic.com	Default LLM (Claude) + moderation + compaction

Optional keys:

Variable	Source	Purpose
OPENAI_API_KEY	platform.openai.com	OpenAI chat models or MODERATION_PROVIDER=openai
PINECONE_API_KEY	app.pinecone.io	Knowledge base (vector search)
EXA_API_KEY	dashboard.exa.ai	Web search
FIREWORKS_API_KEY	fireworks.ai	Alternative LLM provider

Unused optional keys are harmless — nothing reads them unless you switch to that vendor in config.ts (or set MODERATION_PROVIDER=openai). Keeping them set makes future vendor switches a pure config change.

Optional feature switches (no key needed — just set the value):

Variable	Values	What it does when you change it
ENABLE_VECTOR_SEARCH	true (default) / false	false disconnects the knowledge base — the bot stops searching Pinecone and answers from general knowledge
ENABLE_WEB_SEARCH	true (default) / false	false disables web search
MODERATION_PROVIDER	llm (default) / openai / off	Which service safety-checks user messages: llm = fast LLM classifier on the utility model, openai = OpenAI moderation API, off = none

Optional security variables (generate values with openssl rand -hex 32):

Variable	Default	What it does
SUMMARY_HMAC_SECRET	derived from ANTHROPIC_API_KEY	Secret for signing compaction summaries so clients cannot forge them (see Security)
HEALTH_CHECK_TOKEN	unset	Unlocks detailed /api/health checks in production via Authorization: Bearer <token>; without it, production health checks return only {"status":"ok"}

If a switch is not set at all, its default applies. See Changing Settings in Vercel for a click-by-click guide.

3. Run locally

npm run dev

Open http://localhost:3000.

4. Deploy to Vercel

Push to GitHub. Connect the repo in vercel.com. Add the same environment variables in Vercel project settings. Every push to main triggers a deployment.

5. Add content to the knowledge base

Open the Jupyter notebook RAGloader/RAG_loader_pipeline.ipynb and follow the pipeline to ingest documents into Pinecone. See Knowledge Base Pipeline below.

---

Architecture

User Question
    |
    v
[Content Moderation] ── blocked ──> denial message
    |
    | passed
    v
[LLM + Tools]
    |
    |── vectorDatabaseSearch ──> Pinecone (children + propositions + parents)
    |── webSearch ────────────> Exa API (broad conceptual queries)
    |── fetchOwnerProfiles ───> Exa contents API (live owner profile pages)
    |
    v
[Streaming Response with Citations + Sources box]

Request Flow

1. User sends a message via the chat UI (app/page.tsx)
2. POST /api/chat validates input (message count, length, rate limit, compaction-summary signature)
3. Moderation checks the message (LLM classifier on the utility model by default; configurable via MODERATION_PROVIDER)
4. The LLM (default: Claude Haiku 4.5) processes the message with available tools
5. Tool priority: knowledge base first; web search to supplement; live profile fetch (fetchOwnerProfiles) first for latest-info-on-owner questions
6. Response streams back with inline [[N]](url) citations; citations are canonicalized at render time and the cited sources stream as a data-sources part rendered as the Sources box

Three-Namespace Retrieval

The knowledge base uses a parent-child architecture across three Pinecone namespaces:

Namespace	Content	Purpose
children	Small chunks (~500 chars) with keywords, summaries	Searched for relevance (PINECONE_TOP_K, default 20)
propositions	Atomic factual statements	Score boosting for children (PINECONE_PROP_K, default 15)
parents	Rich context chunks (~3000 chars)	Fetched on demand by parent_id

Retrieval flow:

1. Search children namespace (semantic similarity)
2. Search propositions namespace (boost matching children by PINECONE_PROP_BOOST × score, default 0.5)
3. Re-rank children by boosted score
4. Deduplicate: keep one child per parent
5. Fetch parent records for richer context
6. Filter by minimum score threshold (PINECONE_MIN_SCORE, default 0.1)

This avoids storing parent content redundantly on every child, reducing storage and token costs.

---

Knowledge Base Pipeline

The knowledge base is built by the Jupyter notebook RAGloader/RAG_loader_pipeline.ipynb (with its classes and functions in RAGloader/myAI5_RAG.py):

Pipeline Stages

1. Structural Parsing (Unstructured jobs API, hi-res layout analysis)

   • Extracts elements, images, and section hierarchy; tables stay intact
   • Removes page furniture (running headers, page numbers, rotated margin watermarks) that pollutes chunks

2. Formula & Figure Repair (vision model)

   • Display equations are transcribed to LaTeX ($$…$$) instead of garbled OCR text — the chatbot renders them as typeset math
   • Figures are re-rendered from the PDF regions, fixing crops that cut off side labels

3. Parent-Child Splitting

   • Parents: ~3000 character context windows
   • Children: ~500 character focused chunks with parent_id linkage

4. LLM Enrichment (KeyBERT + LLM; enrichment and vision models are configured per role, with adaptive-reasoning effort presets)

   • Keywords, summaries, hypothetical questions per child
   • Context breadcrumbs from document section hierarchy

5. Proposition Decomposition (Dense X Retrieval)

   • Breaks each child into atomic factual statements
   • Stored in propositions namespace for fine-grained matching

6. Image Processing & Hosting — figures/tables/slides described by the vision model, tables converted to markdown, images uploaded to Cloudinary (default, free) or a self-hosted SFTP server; post-processing varies by content type (see table below)

Per document, source_url takes one of three values: "" (cited by name only, no link), an existing page (DOI/SSRN — usually best), or "upload" (the document file itself is uploaded to the image host and linked). The notebook's maintenance section also provides index utilities: statistics, source listing, deletion with verification, a full index audit with orphan cleanup, and Pinecone backup/restore.

Supported Content Types

Type	Parsing	Post-processing
text_doc	Unstructured jobs API (hi-res)	None (standard text)
research_paper	Unstructured jobs API (hi-res), extracts figures/tables	Figures/tables saved as PNG, uploaded to the image host (Cloudinary by default), described by the vision model. Tables converted to markdown by the vision model (handles merged headers, rotated tables)
presentation	Unstructured jobs API (hi-res), skips individual figure extraction	Full slides rendered as PNGs via PyMuPDF, uploaded to the image host (Cloudinary by default), linked to children by page number
slides+text	Unstructured jobs API (hi-res), skips individual figure extraction (like presentation)	Full pages rendered as PNGs and linked to text chunks by page number. Text is primary content; slide images are supplementary hints shown when relevant. Use for transcripts, annotated decks, or PDFs with both narrative text and slides
standalone_image	Skips Unstructured entirely. Reads image file as base64, creates single figure chunk	Saves as PNG, uploads to the image host (Cloudinary by default), vision-model description becomes the chunk content
standalone_table	Skips Unstructured. CSV/TSV: parsed to markdown table directly. Image: reads as base64	Image tables: image-host upload + vision-model description + table-to-markdown conversion. CSV tables: LLM text description of the markdown content
notebook	Skips Unstructured. Parses .ipynb JSON directly. Markdown cells become text chunks, code cells become code chunks, image outputs become code_output chunks with base64	Code output images: saved as PNG, uploaded to the image host (Cloudinary by default), described by the vision model

Document Configuration

Each document is configured with a DocumentConfig:

doc_config = DocumentConfig(
    source_name="Doe_2026_Example_Paper",
    source_description="Doe (2026) An Example Research Paper, Journal of Examples",
    source_url="https://doi.org/10.0000/example",
    content_type="research_paper",
)

---

Configuration Reference

Configuration is split into two places:

• Vercel env vars — secrets and operational switches, changeable from the dashboard without a code change (redeploy to apply): all API keys, ENABLE_VECTOR_SEARCH, ENABLE_WEB_SEARCH, MODERATION_PROVIDER
• config.ts — design and tuning parameters (models, thresholds, prompts, namespaces), changed via commit + push

The tables below list everything; parameters marked "(env var)" belong to the first group.

Changing Settings in Vercel

Step-by-step guide for the env-var switches — no coding required:

1. Log in at vercel.com and open your project (e.g. my-ai-5)
2. Click Settings (top navigation) → Environment Variables (left menu)
3. To add a switch: type the name in Key (e.g. ENABLE_WEB_SEARCH), the value in Value (e.g. false), leave all environments checked, and click Save
4. To change an existing one: click the ⋯ menu next to it → Edit → change the value → Save
5. Redeploy so the change takes effect: go to Deployments, click ⋯ on the most recent deployment → Redeploy. (Vercel usually also offers a redeploy prompt right after you save.)

The change goes live in about a minute. To undo it, either delete the variable (the default applies again) or edit the value back.

Identity

Parameter	Default	Description
AI_NAME	"myAI6"	Assistant display name (the chatbot presents itself under this name)
AI_DESCRIPTION	(derived from AI_NAME/OWNER_NAME)	Metadata description (browser/search snippet)
BROWSER_TAB_TITLE	AI_NAME	Browser tab / metadata title (derived from AI_NAME by default)
OWNER_NAME	"Your Name"	The person the assistant represents (used in prompts and UI)
WELCOME_MESSAGE	"Hello! I'm myAI6..."	First message shown to users
CLEAR_CHAT_TEXT	"New"	New chat button label

Model Selection

Parameter	Default	Options
DEFAULT_VENDOR	"anthropic"	"anthropic", "openai", "fireworks" — vendor for the chat model
DEFAULT_MODEL_ID	"claude-haiku-4-5"	See model registry below
DEFAULT_MODE	"chat"	"chat", "reasoning"
DEFAULT_THINKING_LEVEL	"medium"	"off", "low", "medium", "high" — level used when reasoning mode is triggered
CHAT_THINKING_LEVEL	"low"	Thinking level in plain chat mode ("low", "medium", "high")
MAX_OUTPUT_TOKENS	undefined	Optional response-token cap passed to streamText; undefined = provider default. If set with Anthropic thinking enabled, must exceed the thinking budget in use
UTILITY_VENDOR	"anthropic"	"anthropic", "openai", "fireworks" — vendor for background tasks (moderation classifier, compaction summaries)
UTILITY_MODEL_ID	"claude-haiku-4-5"	Pick a fast, cheap model from the registry (e.g. gpt-5.4-mini, accounts/fireworks/models/deepseek-v3)

The chat model and the utility model are independent: you can run chat on one vendor and background tasks on another, or set both to the same vendor to fully switch providers (only that vendor's API key is then needed).

Available Models (verified August 2026 against the official vendor docs):

The registry is deliberately limited to cost-appropriate chatbot tiers. Premium models (Claude Fable/Opus, GPT-5.6 Sol, "pro" variants) are excluded — their per-request cost makes no sense for a public-facing chatbot. Anthropic + Haiku is and should remain the default.

Vendor	Model ID	Modes	Pricing (in/out per M tokens)	Notes
anthropic	claude-haiku-4-5	chat + reasoning	$1 / $5	Default. Fastest; 200K context, 64K max output
anthropic	claude-sonnet-5	chat + reasoning	$2 / $10 (intro through Aug 2026, then $3 / $15)	Best speed/intelligence balance; 1M context
anthropic	claude-sonnet-4-6	chat + reasoning	$3 / $15	Previous Sonnet (legacy, still active); 1M context
openai	gpt-5.6-luna	chat + reasoning	$0.20 / $1.20	Economy; long context
openai	gpt-5.6-terra	chat + reasoning	$2 / $12	Mid-tier; long context
openai	gpt-5.4-mini	chat + reasoning	$0.75 / $4.50	Previous-gen compact
fireworks	accounts/fireworks/models/deepseek-v4-pro	chat only	$1.74 / $3.48	Open-source via Fireworks (serverless)
fireworks	accounts/fireworks/models/deepseek-r1	reasoning only	see fireworks.ai	Open-source reasoning
fireworks	accounts/fireworks/models/kimi-k2p6	chat only	see fireworks.ai	Kimi K2.6

Thinking/reasoning depth is capped at high throughout the app (DEFAULT_THINKING_LEVEL: off / low / medium / high) — there is deliberately no max-style setting.

Fireworks model IDs require the full accounts/fireworks/models/ prefix — copy the exact ID from the model's page on fireworks.ai/models. New open-source models (DeepSeek V4 Flash, Qwen 3.8, GLM 5.2, Kimi K2.7, MiniMax, gpt-oss) typically appear within days of release.

Other models (e.g. Claude Opus, GPT-5.6 Sol) still work if you set their ID in config.ts — the registry list is guidance, not enforcement — but expect 5-25x the cost per request.

Thinking configuration (Anthropic — handled automatically by model generation):

Model generation	Thinking config sent
Haiku 4.5 (and older)	Fixed token budget (THINKING_BUDGET_LOW/MEDIUM/HIGH: 2,000 / 8,000 / 15,000)
Sonnet 4.6, Opus 4.8, Sonnet 5, Opus 5	Adaptive thinking (the model decides depth; token budgets are rejected by these models)
Fable 5	None (thinking is always on and cannot be configured)

Note: Anthropic thinking budgets are separate from output tokens. OpenAI reasoning effort consumes output tokens, which can cause truncation at high levels.

Pinecone (Knowledge Base)

Parameter	Default	Description
ENABLE_VECTOR_SEARCH (env var)	true	Set env ENABLE_VECTOR_SEARCH=false to disconnect the knowledge base entirely (KB tool removed from the model, no Pinecone connection, PINECONE_API_KEY not needed)
PINECONE_INDEX_NAME	"myai6"	Your Pinecone index name (lowercase letters, numbers, hyphens)
PINECONE_TOP_K	20	Children retrieved per search
PINECONE_MIN_SCORE	0.1	Minimum relevance score (0-1)
PINECONE_USE_PARENT_CHILD	true	Use 3-namespace architecture
PINECONE_NS_CHILDREN	"children"	Children namespace
PINECONE_NS_PARENTS	"parents"	Parents namespace
PINECONE_NS_PROPOSITIONS	"propositions"	Propositions namespace
PINECONE_PROP_BOOST	0.5	Proposition score boost weight
PINECONE_PROP_K	15	Propositions retrieved per search
PINECONE_VISUAL_TOP_K	20	topK for the visual-enrichment query (figures/tables/slides of a retrieved source)
PINECONE_CACHE_TTL_MS	300000	Search cache TTL (5 minutes)

KB Scope

The KB_SCOPE constant in config.ts tells the LLM what topics are indexed. Update this whenever you ingest new content:

export const KB_SCOPE = `
The knowledge base covers ${OWNER_NAME}'s work. Topics include:
- [Example] A research paper or article and its methods
- [Example] A CV or resume with education, employment, and awards
- [Example] Presentation slides or a talk transcript
`.trim();

Update KB_SCOPE whenever you ingest new content. The model uses it to decide whether to search the KB or answer from general knowledge.

Switching or Disconnecting the Knowledge Base

Switch to a different Pinecone index:

1. Change the PINECONE_API_KEY env var (if the index lives in a different Pinecone account)
2. Change PINECONE_INDEX_NAME in config.ts
3. Rewrite KB_SCOPE to describe the new content
4. Redeploy

The new index must use integrated inference with llama-text-embed-v2 (the embedding model the ingestion notebook uses) and the same record fields — the cleanest path is re-ingesting content with the notebook.

Disconnect the knowledge base entirely: Set the env var ENABLE_VECTOR_SEARCH=false (in Vercel or .env.local) and redeploy. The KB tool is removed from the model, no Pinecone connection is made, PINECONE_API_KEY can be deleted, and the health endpoint reports pinecone: "disabled". The bot answers from general knowledge (plus web search, if enabled).

Exa Web Search

Parameter	Default	Description
EXA_NUM_RESULTS	10	Results per search
EXA_SEARCH_TYPE	"deep"	"auto", "neural", "deep", "deep-reasoning"
EXA_MAX_CHARACTERS	3000	Max text per result
EXA_LIVECRAWL	"preferred"	Fetch live page content when possible: "never", "fallback", "preferred", "always". Reduces stale/deleted pages in results
EXA_SYSTEM_PROMPT	Prefer academic + owner profiles	Guides Exa's result selection
OWNER_PROFILE_SOURCES	placeholders (replace with real profiles)	The owner's official profile pages (name + exact URL). The fetchOwnerProfiles tool live-fetches these for "latest on the owner" queries; OWNER_PROFILE_DOMAINS (for includeDomains) is derived from them. Update here when a profile moves
OWNER_PROFILE_MAX_CHARACTERS	5000	Max live page text fetched per profile by fetchOwnerProfiles
ENABLE_WEB_SEARCH (env var)	true	Set env ENABLE_WEB_SEARCH=false to disable

The Exa client is created lazily on the first search, so EXA_API_KEY is only needed at runtime when web search is actually used — not at build time.

Search budget per response. The model may call webSearch up to MAX_WEB_SEARCHES times, vectorDatabaseSearch up to MAX_KB_SEARCHES times, and fetchOwnerProfiles once (soft caps, enforced via prompt guidance). The hard ceiling is MAX_STEPS total tool steps — keep it at least MAX_KB_SEARCHES + MAX_WEB_SEARCHES + 2 (one profile fetch plus the final compose step). To allow more searches, raise these in config.ts. Note that EXA_SEARCH_TYPE = "deep" consumes more Exa credits per call; switch to "auto" or "neural" to conserve them.

Source attribution. Each web result is returned with a distinct source (domain) alongside its url, and the system prompt requires the model to attribute every web-derived claim to the exact result it came from — it must not transfer a fact from one site to another site's citation, and must prefer live, authoritative sources over cached or removed pages.

Citation Verification

Drives the green check mark in the Sources box. The sentence preceding each citation (its claim) is checked against the cited source's retrieved text by significant-word containment; legacy in-citation quotes, when present, are checked too. All deterministic, no model involvement.

Parameter	Default	Description
CITATION_CLAIM_MIN_CHARS	20	Shorter preceding fragments are not treated as claims
CITATION_CLAIM_MAX_CHARS	300	Claims are trimmed to their last N characters
CITATION_CLAIM_MIN_WORD_LENGTH	4	Minimum word length to count as significant for claim matching
CITATION_CLAIM_MIN_WORDS	3	Claims with fewer significant words are unverifiable (no marker)
CITATION_CLAIM_MATCH_RATIO	0.6	Share of significant claim words that must appear in the source
CITATION_QUOTE_MIN_WORD_LENGTH	3	Minimum word length for quote matching
CITATION_QUOTE_MATCH_RATIO	0.8	Share of quote words that must appear in the source

Chat Limits

Parameter	Default	Description
MAX_STEPS	8	Max tool steps per request (≥ MAX_KB_SEARCHES + MAX_WEB_SEARCHES + 2)
MAX_KB_SEARCHES	2	Max vectorDatabaseSearch calls per response (prompt-enforced)
MAX_WEB_SEARCHES	3	Max webSearch calls per response (prompt-enforced)
MAX_MESSAGES	100	Max messages in conversation
MAX_MESSAGE_TEXT_LENGTH	10000	Max characters per message
VERCEL_MAX_DURATION	120	Vercel function timeout (seconds, keep in sync with route.ts)

Conversation Compaction

Uses an LLM to intelligently summarize older messages when token count exceeds threshold, preserving source references and citation URLs for continuity.

Parameter	Default	Description
COMPACTION_ENABLED	true	Enable/disable conversation compaction
COMPACTION_TOKEN_THRESHOLD	40000	Compact when summary + unsummarized messages exceed this (tokens)
COMPACTION_KEEP_RECENT	4	Keep last N messages intact when compacting (2 user + 2 assistant turns)
COMPACTION_CHARS_PER_TOKEN	4	Character-to-token ratio for estimation (lower = more conservative)
COMPACTION_MAX_SUMMARY_WORDS	1500	Max word limit for LLM summaries (instructs model, not a hard cut)
COMPACTION_MAX_SUMMARY_CHARS	8000	Hard safety cap on summary character length

How it works:

1. Client stores compaction summary in localStorage, sends it to the server via request headers (X-Compacted-Summary, X-Compacted-UpTo, X-Compacted-Signature)
2. Server verifies the summary's HMAC signature and size cap — a summary that was not issued (signed) by the server, or was modified, is ignored and the server recompacts from the full message history (see Security). Then it checks: tokens(summary) + tokens(unsummarized messages) > threshold?
3. Under threshold → reuse stored summary + pass all unsummarized messages through (no LLM call)
4. Over threshold → the utility model re-summarizes existing summary + older unsummarized messages into ONE bounded summary
5. Last COMPACTION_KEEP_RECENT messages are always kept intact as full messages
6. Updated summary is returned to client via response headers (with a fresh signature) and saved to localStorage
7. Each re-summarization merges the existing summary with new messages — older content is compressed while key facts, decisions, and source URLs are preserved

Feedback-aware summarization:

• Thumbs up (👍): Messages the user liked are marked [IMPORTANT] and given priority in the summary
• Thumbs down (👎): Messages the user disliked are excluded entirely from summarization input — they are never sent to the summarization LLM
• Feedback is stored per-conversation in localStorage and sent to the server via the X-Feedback header

Context Memory button:

• A document icon (📄) in the header bar shows the current compaction summary when clicked
• Displays: number of summarized messages, the full summary text, and source URLs
• Toggle visibility via COMPACTION_SHOW_CONTEXT_MEMORY in config.ts

Client-side storage (localStorage): All conversation data is stored in the browser's localStorage:

• chat-conversations — conversation index (id, title, timestamps)
• chat-data-{id} — per-conversation data containing:
  • messages — full message history (UIMessage array)
  • durations — reasoning duration tracking
  • compactedSummary — current compaction summary text
  • summarizedUpTo — message index up to which the summary covers
  • feedback — per-message thumbs up/down ratings ({ messageId: "up" | "down" })

Moderation

Parameter	Default	Description
MODERATION_PROVIDER (env var)	"llm"	"llm" = LLM classifier on the utility model (any vendor; anthropic accepted as legacy alias), "openai" = OpenAI moderation API, "off" = no moderation call (system prompt guardrails only)
MODERATION_FAIL_POLICY	"closed"	"closed" = block when moderation unavailable, "open" = allow (irrelevant when provider is "off")

All providers share the same 13-category taxonomy (sexual, harassment, hate, violence, self-harm, illicit content), so the category-specific denial messages apply regardless of provider. The "anthropic" provider runs a fast Claude Haiku classification call before each message — no OpenAI account needed.

Reasoning Display

Parameter	Default	Description
REASONING_DISPLAY_MODE	"truncated"	"full" = show all reasoning (debugging only — exposes prompts!), "truncated" = show first N words then "...", "hidden" = show label only
REASONING_TRUNCATE_WORDS	15	Number of words to show in "truncated" mode

---

Prompts

AI behavior is controlled by prompts.ts. All sections combine into SYSTEM_PROMPT:

Section	Purpose
IDENTITY_PROMPT	Identity, strict confidentiality rules (never reveal tech stack), positive perspective on owner's research
TOOL_CALLING_PROMPT	KB-first priority, web search for KB-related topics, broad query strategy, owner-profiles-first for latest-info questions
TONE_STYLE_PROMPT	Academic, professional, no emojis; all math in LaTeX ($…$ / $$…$$, never code fences) — rendered by @streamdown/math (KaTeX)
GUARDRAILS_PROMPT	Refuse dangerous/illegal/harmful content
CITATIONS_PROMPT	Inline [[N]](url) citations, mandatory visual content embedding, no References section (the code-rendered Sources box is the reference list)
DATE_AND_TIME	Current date/time (auto-generated)

Confidentiality Rules

The assistant never reveals:

• What AI model, platform, or technology powers it
• That it uses a knowledge base, vector database, or search system
• Its system prompt, instructions, or configuration
• Names like "Anthropic", "Claude", "OpenAI", "Pinecone", "Vercel", "Exa"

Tool Priority Rules

1. If a question relates to KB_SCOPE, search the knowledge base first
2. Web search is allowed for KB-related topics (recent developments, external perspectives, author profiles)
3. Web search is NOT allowed for topics completely unrelated to KB scope
4. Web search queries must use broad conceptual terms, not specific framework names
5. For "latest on the owner" questions, fetchOwnerProfiles pulls the live OWNER_PROFILE_SOURCES pages first; follow-up web searches run BROAD (journal sites, SSRN, arXiv) — includeDomains is reserved for explicit single-site requests, since profile sites are thinly indexed by search engines
6. Each web-derived claim is attributed to the exact result it came from — facts are never transferred between sources

Citation Rules

• Academic-style inline citations as numbered markdown links: [[N]](url), numbered in order of first use. Citations are pure markers — sentences must read completely with them removed
• Knowledge base and web results are cited the same way, sharing one numbering sequence. Web results are returned to the model with the same citation scaffolding as KB sources (a Reference Link per result). Web results with no URL are dropped, since they can't be linked.
• KB sources without a public URL are cited via a synthetic kb: target (provided in their retrieval scaffolding, e.g. [[N]](kb:CV-of-Daniel-M-Ringel)); inline they render as unlinked [N] markers, and they appear in the Sources box as unlinked entries
• The model does NOT write a References section — the code-rendered Sources box below the answer is the single reference list
• Each web claim is attributed to the exact source it came from — facts are never transferred between sources
• Figures and tables are always embedded as images
• One relevant slide image shown proactively per answer; additional slides on request

Sources Box (code-rendered, single reference list)

Below every answer that cites sources, one Sources box lists each cited source as a numbered clickable link. It is the only reference list — the model is instructed not to write a References section, so there are never two competing lists or duplicate numbering. The box is deterministic and independent of the model's markdown — it cannot be mislinked by the model.

How it works:

• Each search tool (vectorDatabaseSearch, webSearch) pushes a structured UISource (types/data.ts) into a request-scoped collector for every source it retrieves (deduped by URL across KB and web).
• The model's citation numbers are ignored entirely. A shared canonicalization (lib/citations.ts) renumbers every inline [[N]](url) sequentially by first appearance of its URL and strips bare [[N]] debris. The client runs it on the displayed text; the server runs the same function on the joined answer text to build the box — so inline numbers and box numbers are always 1..K, gap-free, and identical by construction. Retrieved-but-uncited sources are not shown (app/api/chat/route.ts).
• KB sources without a URL are cited via synthetic kb: targets and appear in the box as unlinked entries (title and source name, no link) — every cited source is listed, clickable or not.
• Claim verification: for each citation, the route deterministically checks the sentence preceding it (the claim) against the cited source's retrieved text by significant-word containment (claimSupported in lib/citations.ts; legacy in-citation quotes, when present, are checked too via quoteAppearsIn). A source whose claim checks out gets a green check mark in the box; sources that can't be checked (not retrieved this turn, claim too short) show no marker. Verification requires no model cooperation — nothing rides inside the citation, so content can never be swallowed.
• components/messages/sources.tsx renders the box from message.parts (KB sources get a book icon, web sources a globe), and it persists with the conversation.

---

Project Structure

myAI6/
├── app/
│   ├── api/
│   │   ├── chat/
│   │   │   ├── route.ts                    # Main chat endpoint (orchestrator)
│   │   │   └── tools/
│   │   │       ├── search-vector-database.ts  # Pinecone RAG tool
│   │   │       ├── web-search.ts              # Exa web search tool
│   │   │       └── fetch-owner-profiles.ts    # Live fetch of the owner's profile pages
│   │   ├── health/route.ts                 # Health check (detailed checks token-gated)
│   │   └── feedback/route.ts               # Thumbs up/down feedback logging
│   ├── page.tsx                            # Chat UI (main page)
│   ├── layout.tsx                          # Root layout (fonts, metadata)
│   ├── globals.css                         # Global styles
│   ├── parts/
│   │   └── chat-header.tsx                 # Header bar component
│   ├── terms/page.tsx                      # Terms of use page
│   └── images_raw/                         # Source assets (favicon, logo originals)
│       └── favicon/                        # Favicon files for various platforms
├── components/
│   ├── ai-elements/
│   │   ├── thinking-indicator.tsx          # Pulsing logo during "submitted" phase
│   │   ├── reasoning.tsx                   # Collapsible thinking blocks
│   │   ├── processing-indicator.tsx        # Post-tool processing status
│   │   ├── assembling-indicator.tsx        # Final response assembly status
│   │   ├── response.tsx                    # Markdown response renderer (sanitizes [blocked])
│   │   └── shimmer.tsx                     # Shimmer text effect
│   ├── messages/
│   │   ├── message-wall.tsx                # Conversation display with scroll
│   │   ├── assistant-message.tsx           # AI message with tools, reasoning, feedback, citation canonicalization
│   │   ├── user-message.tsx                # User message bubble
│   │   ├── reasoning-part.tsx              # Reasoning display (full/truncated/hidden)
│   │   ├── sources.tsx                     # Code-rendered Sources box (from data-sources stream part)
│   │   └── tool-call.tsx                   # Tool execution display with fun labels
│   ├── ui/                                 # Reusable Radix UI components (shadcn)
│   └── conversation-sidebar.tsx            # Chat history sidebar with delete
├── hooks/
│   └── use-rotating-label.ts              # Rotating status label hook
├── lib/
│   ├── ai/
│   │   ├── model-registry.ts              # Vendor/model definitions
│   │   ├── routing.ts                     # Request routing + reasoning escalation
│   │   └── tools.ts                       # Tool set assembly + guidance
│   ├── pinecone.ts                        # Vector search (3-namespace + visual enrichment)
│   ├── sources.ts                         # KB source formatting + context assembly
│   ├── citations.ts                       # Citation canonicalization (renumbering, list repair)
│   ├── moderation.ts                      # OpenAI moderation (13 categories)
│   ├── storage.ts                         # localStorage conversations + compaction data
│   ├── compaction.ts                      # Stateful LLM-based conversation compaction
│   ├── summary-signature.ts               # HMAC signing of compaction summaries
│   ├── cache.ts                           # TTL cache utility
│   ├── fun-labels.ts                      # Rotating UI labels + past tense variants
│   ├── env.ts                             # Zod env validation
│   └── __tests__/                         # Unit tests (vitest)
│       ├── cache.test.ts                   # Cache utility tests
│       ├── citations.test.ts               # Citation canonicalization tests
│       ├── routing.test.ts                 # Routing logic tests
│       └── sources.test.ts                 # KB source formatting tests
├── types/
│   └── data.ts                            # Chunk/Source TypeScript schemas
├── public/
│   ├── logo.png                           # App logo
│   └── thinking.png                       # Thinking indicator icon (separate from logo)
├── RAGloader/
│   ├── RAG_loader_pipeline.ipynb          # ⭐ Python notebook: ingest content into the Pinecone knowledge base
│   └── myAI5_RAG.py                       # Pipeline classes and functions imported by the notebook
├── config.ts                              # ⭐ Design/tuning parameters (models, thresholds, prompts scope)
├── prompts.ts                             # ⭐ All AI behavior prompts
├── middleware.ts                          # Rate limiting middleware (per-IP throttling)
├── env.template                           # Environment variable template (keys + operational switches)
├── components.json                        # shadcn/ui component config
├── package.json                           # Dependencies and scripts
├── tsconfig.json                          # TypeScript configuration
├── next.config.ts                         # Next.js configuration
├── vitest.config.ts                       # Test runner configuration
├── eslint.config.mjs                      # Linting configuration
├── .claude/settings.local.json            # Claude Code local settings
├── AGENTS.md                              # Agent/tool integration guide
└── LICENSE                                # MIT License

---

UI Processing Indicators

The chat UI shows animated status labels during each phase of processing:

Phase	Icon	Example Labels
Thinking	Brain / Logo	Deliberating, Pondering, Contemplating
Knowledge Base Search	Book	Retrieving, Querying, Accessing
Processing (after tools)	Refresh arrows	Synthesizing, Correlating, Extracting
Web Search	Globe	Searching, Investigating, Browsing
Assembling (final answer)	Sparkles	Composing, Structuring, Formulating

Labels rotate every 3 seconds with a shimmer effect. Once complete, they switch to past tense (e.g., "Reasoned for 2 seconds", "Retrieved knowledge").

Labels are fully customizable in lib/fun-labels.ts.

Conversation Sidebar

A slide-out sidebar on the left provides multi-conversation management:

• Conversation list — all saved conversations with auto-generated titles (from the first user message)
• New chat — + button creates a new conversation
• Switch conversations — click any conversation to load it
• Delete conversations — trash icon removes a conversation and its data
• Persistent storage — all conversations stored in browser localStorage (see Client-side storage)
• Toggle — sidebar can be opened/closed via the panel icon in the header

Labels are fully customizable in lib/fun-labels.ts.

---

API Endpoints

Endpoint	Method	Purpose
/api/chat	POST	Main chat (streaming response)
/api/health	GET	Health check — public liveness only; detailed checks token-gated
/api/feedback	POST	Log thumbs up/down feedback

/api/health has two response levels:

• Public (production, no token): returns only {"status":"ok"} — a bare liveness check. No dependencies are probed and no configuration details are revealed.

• Detailed (development, or production with Authorization: Bearer <HEALTH_CHECK_TOKEN>): returns status (healthy / degraded) plus per-service checks:

  • pinecone: ok / error / missing (key absent while KB enabled) / disabled (ENABLE_VECTOR_SEARCH=false)
  • anthropic: configured / missing
  • openai: configured / missing (only when an OpenAI chat model, OpenAI moderation, or an OpenAI utility model is configured) / not_required

  missing and error values make the status degraded (HTTP 503); everything else is healthy (HTTP 200).

To enable detailed checks in production, set the HEALTH_CHECK_TOKEN env var (e.g. openssl rand -hex 32) and call:

curl -H "Authorization: Bearer $HEALTH_CHECK_TOKEN" https://your-domain.com/api/health

---

Scripts

npm run dev        # Local development server
npm run build      # Production build
npm run start      # Start production server
npm run lint       # ESLint
npm run test       # Run tests (vitest)
npm run test:watch # Watch mode tests

---

Tech Stack

• Framework: Next.js 16 (Turbopack)
• AI SDK: Vercel AI SDK v6 (streaming)
• LLMs: Anthropic Claude (default), OpenAI GPT, Fireworks
• Vector DB: Pinecone (integrated inference)
• Web Search: Exa API (deep search)
• Moderation: LLM classifier on the utility model (default) or OpenAI Moderation API
• UI: React 19, Radix UI, Tailwind CSS 4
• Animations: Motion (Framer Motion)
• Validation: Zod
• Ingestion: Python (Unstructured jobs API, KeyBERT, PyMuPDF, Claude enrichment + vision, Cloudinary/SFTP image hosting)

---

Security

Rate Limiting

Per-IP request throttling protects against abuse and runaway API costs.

Parameter	Default	Description
RATE_LIMIT_ENABLED	true	Enable/disable rate limiting
RATE_LIMIT_WINDOW_MS	60000	Sliding window size (1 minute)
RATE_LIMIT_MAX_REQUESTS	20	Max requests per window per IP

• Applied only to /api/chat endpoint
• Uses in-memory sliding window (resets on server restart)
• Returns HTTP 429 with Retry-After header when limit is exceeded
• Implemented via Next.js middleware (middleware.ts)

Limitation: on serverless hosting (Vercel), the in-memory counter is per-instance and resets on every cold start, so parallel traffic can exceed the configured limit. Treat it as a first layer only. For a limit that holds across instances, add a platform-level rule — on Vercel: project → Firewall → Rules → new rule matching path /api/chat with a Rate Limit action (start with action Log to tune the threshold, then switch to the default 429 response). As a hard backstop against abuse-driven costs, set monthly spend limits in the Anthropic console and Exa dashboard, and enable Vercel Spend Management.

Compaction Summary Integrity

The client round-trips the conversation summary (see Conversation Compaction), and that summary enters the model context as trusted history — so the server signs it. Every summary is issued with an HMAC-SHA256 signature (X-Compacted-Signature) covering the summary text and the summarizedUpTo index (lib/summary-signature.ts). On each request the server accepts a client-supplied summary only if the signature verifies and the summary is within COMPACTION_MAX_SUMMARY_CHARS; anything forged, modified, or oversized is ignored and the server recompacts from the full message history. The signing key comes from SUMMARY_HMAC_SECRET, or is derived from ANTHROPIC_API_KEY when unset (rotating either just invalidates outstanding summaries, which are then regenerated).

Health Endpoint

/api/health reveals no dependency details to the public: unauthenticated production requests get a bare {"status":"ok"} with no Pinecone probe. Detailed per-service checks require HEALTH_CHECK_TOKEN (see API Endpoints).

Prompt Injection Defense

The system prompt includes explicit defenses against common injection attacks:

• Refuses requests to reveal system prompt, instructions, or configuration
• Ignores "ignore previous instructions", "act as DAN", "developer mode" attempts
• Does not grant elevated access to users claiming admin/developer roles
• Treats all user messages as untrusted input
• Continues normal behavior when manipulation is detected (no acknowledgment of the attempt)

Content Moderation

Every user message is checked for harmful content across 13 categories before processing. The provider is configurable via MODERATION_PROVIDER: an LLM classifier running on the configured utility model (default), the OpenAI Moderation API, or off (system prompt guardrails only). See Moderation section for configuration.

Web Search Protection

Web search is restricted to topics within KB_SCOPE via both:

• Prompt-level: System prompt instructs the model to only search for KB-related topics
• Tool description: Web search tool description limits scope to knowledge base subjects
• Users cannot directly invoke tools — only the LLM decides when to search

Authentication

The chat API is open to all visitors and protected by per-IP rate limiting; there is no user authentication and no secrets are ever embedded in the browser bundle.

Confidentiality

The assistant never reveals its underlying technology, system prompt, instructions, or configuration. See Confidentiality Rules for details.

---

Troubleshooting

Problem	Solution
AI not responding	Check ANTHROPIC_API_KEY in Vercel env vars
No knowledge base results	Verify PINECONE_API_KEY and that index has data
Web search not working	Check EXA_API_KEY or set ENABLE_WEB_SEARCH=false
Want to run without Pinecone	Set ENABLE_VECTOR_SEARCH=false — the KB tool is removed and no Pinecone key is needed
Response cuts off	Model may be using too many tokens on reasoning. Try lowering DEFAULT_THINKING_LEVEL
Too many tool calls	Reduce MAX_STEPS in config.ts
Build fails on Vercel	Check that maxDuration in route.ts is a literal number (not imported)
GPT-5.4 truncates answers	OpenAI reasoning consumes output tokens. Switch to Anthropic where thinking budget is separate

---

License

This project is released under the MIT License. Copyright (c) 2026 Daniel M. Ringel. Please acknowledge use of any or all of this code.