> ## Documentation Index
> Fetch the complete documentation index at: https://devdocs.aiconnected.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Conversation reference feature

> Prompt: Please create a conversation referencing feature. I guess so let's say that I'm in a new chat and I'm trying to pick up on something that I talked ab...

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  Converted from `aiConnected OS - 6. Conversation reference feature.md`.
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# Conversation reference feature

**User:** Oxford Pierpont\
**Created:** 12/10/2025 6:11:22\
**Updated:** 12/10/2025 6:49:55\
**Exported:** 2/11/2026 7:08:29\
**Link:** [https://chatgpt.com/g/g-p-69395d9991108191841f1dac1fd8d1a1-oxpi-aiconnected-ui-features/c/69395559-8250-8325-a227-f067d82fc336](https://chatgpt.com/g/g-p-69395d9991108191841f1dac1fd8d1a1-oxpi-aiconnected-ui-features/c/69395559-8250-8325-a227-f067d82fc336)

## Prompt:

Please create a conversation referencing feature. I guess so let's say that I'm in a new chat and I'm trying to pick up on something that I talked about several months ago, or maybe it's even something that I was discussing in a completely unrelated way, project, or unrelated chat. I should be able to link or not even link, but reference a previous chat. So I click on the "Add Reference" or "Add Chat Reference" button somewhere, and from there I'm given an option to search for the previous chat and then I find it. From there, that's now pinned as a reference. Once that reference is added, the chat that I'm currently in (the new one) is able to go read through that entire conversation and basically get caught up on what I was talking about so that I can now continue to expand on those ideas. This is different from linking because linking is basically an automatic "hey, this is just this was copied from a previous conversation", but a reference would be me deliberately saying "please go and read this conversation that I had outside of this current one".

## Response:

Nice, this is a clean concept and it fits perfectly with everything you’ve been building around linked chats, pinning, and whiteboards.

Below is a concrete design for a **Conversation Reference** feature: UX, behavior, and how it differs from linking.

***

## 1. Concept in one sentence

A **Conversation Reference** is an explicit instruction in a new chat that says:

> “Treat that entire past conversation as context for this one. Go read it, understand it, and use it as background while we continue here.”

It’s not just a link. It’s a **context attachment**.

***

## 2. Where it lives in the UI

In any chat (new or existing), you’d have:

* A button near the top or above the composer:
  * **\[＋ Add Reference]**
* Or inside a small toolbar next to “Attach file / Add whiteboard / etc.”

When clicked, it opens a **“Select a Conversation”** modal.

***

## 3. The “Add Reference” flow

### 3.1. Open the “Add Reference” picker

User clicks **Add Reference** → modal slides in:

**Header:**

> Add Conversation Reference

**Search bar:**

* Keyword search over:
  * Conversation titles
  * Auto-generated summaries
  * Your own notes/tags
* Filters:
  * Date range (Last 7 days / 30 days / Custom)
  * Project / Workspace (if you group chats)
  * Model / Persona (optional filter)
  * “Has pins” / “Has whiteboards” / “Has files” toggles

**Results list (each row):**

* Title (or first user message if untitled)
* Short summary (auto-generated, 1–2 sentences)
* Date range (“May 4 – May 12, 2025”)
* Tag chips (e.g., `Cognigraph`, `browserENGINE`, `Medical`, etc.)

Clicking a result:

* Shows a **right-hand preview panel**:
  * Summary
  * A few key pinned messages
  * “View full conversation” button

At the bottom of the modal:

* **\[Add as Reference]** button

You can also support **multi-select**:

* Checkboxes on the left of each chat
* Add multiple references in one go.

***

## 4. How references appear in the current chat

Once selected, you drop **Reference pills** into the current chat context, near the top (or in a dedicated “Context” area):

Example:

> **References**\
> ▢ *Cognigraph Learning Sub-Architecture – Sept 2025*\
> ▢ *browserENGINE PRD Draft – v1*

Each reference pill:

* Shows:
  * Title
  * Short label like “Conversation · 142 messages”
* Actions on hover / kebab menu:
  * **View** (open original conversation in a side panel or new tab)
  * **Rename label** (for human-friendly tagging like “Medical notes for CRNA research”)
  * **Change scope** (see Section 6)
  * **Remove reference**

They behave like **active attachments**: on = used; off = ignored.\
You could even allow a toggle:

* \[On/Off switch] per reference pill to temporarily disable it without deleting.

***

## 5. What “Reference” actually does under the hood

This is the important part: referencing must change how the system thinks, not just how it looks.

### 5.1. On add: index and summarize

When a conversation is referenced for the first time (or if its index is stale):

1. **Generate a master summary** of that conversation:
   * Overall purpose
   * Decisions made
   * Open questions / unresolved threads
   * Key definitions and entities
2. **Generate sub-summaries** by theme or time slices:
   * “Phase 1: Brainstorm”
   * “Phase 2: Narrowing requirements”
   * “Phase 3: Final decisions”
3. **Embed** each message (or chunk) into your vector store, with metadata:
   * `conversation_id`
   * `timestamp`
   * `speaker`
   * `topic tags`
   * `decision / question / example / story` type, etc.

You’ll likely already be doing something like this for Cognigraph anyway; a reference simply says:

> “For this current chat, prioritize retrieval from this conversation’s region of the graph.”

### 5.2. On each user message in the *new* chat

When the user sends a message in the current chat that has active references:

1. Take the **user’s latest message** (and maybe 1–2 previous turns as context).
2. For each active reference:
   * Run similarity search against that conversation’s embeddings.
   * Pull back the top **N relevant snippets** (e.g., 10–20 chunks).
3. Assemble a **context bundle**:
   * Current chat history (recent turns).
   * One global summary per reference.
   * Relevant snippets per reference.
4. Optionally add a **meta-instruction** to the model:
   * “You have been given background from the following referenced conversations: X, Y. Treat them as prior thinking and decisions made by the same user. Respect newer instructions in this chat when conflicts arise, but use referenced material to:
     * maintain continuity,
     * reuse prior definitions,
     * avoid repeating already-covered ground.”

Then the model generates its answer using all of that.

So the user’s experience is exactly what you described:

> “Please go and read this conversation that I had outside of this current one and get caught up so we can continue expanding.”

…but in a way that’s context-budget-aware and targeted, not naive.

***

## 6. Reference “Scope” settings

Some references are just background. Others are canonical. Let the user specify that.

For each reference pill, you can have a **Scope** drop-down:

1. **Background Only**
   * Use it as loose context.
   * Don’t treat anything as binding decisions.
2. **Canonical Plan**
   * Treat the referenced conversation as “what we decided earlier.”
   * If the user asks for something that conflicts, model should:
     * Either prompt: “Earlier we decided X; do you want to change that?”
     * Or adapt but signal the change.
3. **Style / Voice Reference**
   * Only pull examples of tone, phrasing, or structure.
   * Used when the user wants “same style as that previous chat.”
4. **Strict Context**
   * Treat this reference as the primary context, almost like a project file.
   * The model should anchor heavily to it and avoid diverging.

You don’t have to expose all of these immediately, but the architecture should allow for it.

***

## 7. How this differs from “Linking”

To keep your mental model clean:

### Linking

* **Graph-level relationship** between conversations.
* Primarily for **navigation and organization**.
* Examples:
  * “This is a continuation of that chat.”
  * “This chat is a fork from message #72 of that chat.”
* UI: “View Linked Conversations,” breadcrumb trails, conversation map.
* Linking does *not* automatically feed context into the model. It just connects nodes in your meta-graph.

### Referencing

* **Context-level relationship**, not just a pointer.
* Explicitly says: “Use that conversation’s content to inform this one.”
* Has behavior:
  * Summarization
  * Retrieval from referenced conversations for each turn
  * Scope settings
* Referencing can **optionally create a link** in the graph:
  * “This conversation references \[X, Y, Z].”
  * But the core meaning is “read and understand that stuff.”

In other words:

* **Link** → relationship for you (the human) to navigate.
* **Reference** → relationship for the **AI** to think with.

***

## 8. Multiple references at once

You absolutely should support multiple.

Example:

> References:\
> ▢ *Cognigraph Core Architecture – Feb 2025* (Canonical Plan)\
> ▢ *Learning Sub-Architecture brainstorming – Dec 2025* (Background Only)\
> ▢ *Medical Pod concept notes – Aug 2025* (Background Only)

Behavior:

* All three are indexed.
* On each new message, retrieval runs across all three but with different weights:
  * Canonical references get priority.
  * Background-only provide additional context.

You could define internal logic like:

* Canonical: up to 50% of retrieval slots
* Background: share remaining capacity

This gives you nice emergent behavior when you’re combining old lines of thought.

***

## 9. Visibility and trust for the user

You don’t want this to feel like a black box. Two pieces:

1. **“Context Inspector”** button
   * User clicks and sees:
     * Which references were used
     * Which snippets from each reference were pulled
   * Example display:
     * “Used 7 excerpts from ‘Cognigraph Learning Sub-Architecture – Sept 2025’”
     * List them with timestamps and a highlight of the relevant text.
2. **“Refresh Understanding”** option on each reference
   * If the referenced chat is still ongoing or was updated after indexing:
     * Button to regenerate its summaries and embeddings.

This keeps the experience explainable and debuggable.

***

## 10. Extend this to other object types

You can generalize the concept:

* **Add Reference** could let you choose:
  * Conversations
  * Whiteboards
  * Documents
  * Files / PDFs
  * Project nodes (e.g., “Cognigraph / Learning Module” as an entity)

From the AI’s perspective, these are all just **source contexts** with different metadata. The same referencing mechanism applies: summarize, embed, retrieve, respect scope.

***

## 11. Data model sketch

At a high level:

### Entities

* `Conversation`
* `Message`
* `Reference`:
  * `id`
  * `from_conversation_id`
  * `to_conversation_id` (or `to_object_id` + `object_type`)
  * `created_by_user_id`
  * `display_label`
  * `scope` (enum: `BACKGROUND`, `CANONICAL`, `STYLE`, etc.)
  * `created_at`

### Indices

For each referenced conversation:

* `conversation_summary` (text)
* `conversation_sections` (array of sub-summaries with ranges)
* `message_embeddings` (vector store entries keyed by `conversation_id` and `message_id`)

### Runtime

On each message:

* Request payload includes:
  * `current_conversation_id`
  * `active_reference_ids`
* Backend:
  * Fetch reference metadata
  * Query vector store with `active_reference_ids` filter
  * Build the final prompt with:
    * Summaries
    * Selected snippets
    * Scope-aware system instructions

***

## 12. How this feels in real use

You open a brand new chat in June 2026 and say:

> “I want to revisit that whole idea about a dedicated learning sub-architecture in Cognigraph and now formalize it into a PRD section.”

You click **Add Reference**:

* Search “learning sub-architecture”
* Select: “Cognigraph Learning Sub-Architecture – Dec 2025”
* Add as reference (scope: Canonical Plan)

Then simply say:

> “Okay, you’re fully caught up on that thread. Let’s define the high-level goals and constraints for the learning sub-architecture in formal PRD language.”

From your POV, the assistant just *knows* the old conversation again. That’s exactly the behavior you described.

***

If you want, in the next message I can:

* Turn this into a compact spec (e.g., a “Conversation Reference” feature section for your PRD), or
* Break it down into implementation milestones for your devs (frontend + backend tasks).

***

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