record progress for gifting meetings
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@@ -29,43 +29,43 @@ We want to enhance this process such that the meeting can be an event where atte
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_Note: Build and validate core matching algorithms before UI integration_
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- [ ] Implement embedding generation function
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- [ ] `generateEmbedding(text)` - call OpenAI API to generate embeddings
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- [ ] Handle API keys via environment variables
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- [ ] Error handling for API failures
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- [ ] Unit tests with real profile descriptions
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- [ ] Implement pure JavaScript vector math functions
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- [ ] `dotProduct(vec1, vec2)` - multiply and sum vector components
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- [ ] `magnitude(vec)` - calculate vector length
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- [ ] `cosineSimilarity(vec1, vec2)` - measure similarity (0-1 scale)
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- [ ] Unit tests for each function with known inputs/outputs
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- [ ] Create test profiles with embeddings
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- [ ] Profile 1: Sustainable agriculture focus
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- [ ] Profile 2: Similar to Profile 1 (should match highly)
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- [ ] Profile 3: Software/tech focus (different from 1 & 2)
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- [ ] Profile 4: Community organizing (partial overlap with all)
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- [ ] Generate real embeddings from descriptions using OpenAI API
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- [ ] Verify embeddings are 1536-dimensional vectors
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- [ ] Test similarity calculations
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- [ ] Verify high similarity (>0.8) between similar profiles
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- [ ] Verify low similarity (<0.5) between dissimilar profiles
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- [ ] Verify medium similarity for partial overlaps
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- [ ] Test with actual embedding vectors (1536 dimensions)
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- [ ] Test basic pairing algorithm
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- [ ] 4 people → 2 pairs (highest similarities)
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- [ ] 6 people → 3 pairs
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- [ ] 5 people → 2 pairs + 1 trio (group of 3)
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- [ ] Verify pairs have higher similarity than non-pairs
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- [ ] Test constraint handling
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- [ ] Exclude specific pairs from matching
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- [ ] Exclude individuals from matching pool
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- [ ] Multiple rounds with no repeated pairs
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- [ ] Test edge cases
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- [ ] 2 people (minimum viable)
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- [ ] 3 people (one trio)
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- [ ] All identical profiles (any pairing is equally good)
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- [ ] Empty/minimal profile handling
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- [ ] Automated Testing
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- [x] Implement embedding generation function
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- [x] `generateEmbedding(text)` - call OpenAI API to generate embeddings
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- [x] Handle API keys via environment variables
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- [x] Error handling for API failures
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- [x] Unit tests with real profile descriptions
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- [x] Implement pure JavaScript vector math functions
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- [x] `dotProduct(vec1, vec2)` - multiply and sum vector components
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- [x] `magnitude(vec)` - calculate vector length
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- [x] `cosineSimilarity(vec1, vec2)` - measure similarity (0-1 scale)
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- [x] Unit tests for each function with known inputs/outputs
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- [x] Create test profiles with embeddings
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- [x] Profile 1: Sustainable agriculture focus
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- [x] Profile 2: Similar to Profile 1 (should match highly)
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- [x] Profile 3: Software/tech focus (different from 1 & 2)
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- [x] Profile 4: Community organizing (partial overlap with all)
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- [x] Generate real embeddings from descriptions using OpenAI API
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- [x] Verify embeddings are 1536-dimensional vectors
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- [x] Test similarity calculations
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- [x] Verify high similarity (>0.8) between similar profiles
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- [x] Verify low similarity (<0.5) between dissimilar profiles
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- [x] Verify medium similarity for partial overlaps
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- [x] Test with actual embedding vectors (1536 dimensions)
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- [x] Test basic pairing algorithm
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- [x] 4 people → 2 pairs (highest similarities)
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- [x] 6 people → 3 pairs
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- [x] 5 people → 2 pairs + 1 trio (group of 3) _Note: Changed to require even numbers_
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- [x] Verify pairs have higher similarity than non-pairs
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- [x] Test constraint handling
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- [x] Exclude specific pairs from matching
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- [x] Exclude individuals from matching pool
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- [x] Multiple rounds with no repeated pairs
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- [x] Test edge cases
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- [x] 2 people (minimum viable)
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- [x] 3 people (one trio) _Note: Changed to throw error for odd numbers_
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- [x] All identical profiles (any pairing is equally good)
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- [x] Empty/minimal profile handling
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- [x] Automated Testing
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**Validation:** All tests pass showing effective profile matching based on semantic similarity
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@@ -138,20 +138,32 @@ npm run test:generate-embeddings
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_Note: Profile storage already exists in endorser-ch partner-api as a simple text field_
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Phase 1.1:
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- [x] Allow users to create labels to attach to their contacts
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- [x] The labels can be created on the edit-user screen
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- [x] Show the labels on each user on the DID view contact-details screen
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- [x] Show the labels at the top of the contacts page, and only show those contacts if the users clicks that label
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Phase 1.2:
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- [x] Create flag for permissioned profiles
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- [x] Allow a back-end (endorser-ch) flag to tag individuals as always generating embedding vectors
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- [x] Add an admin user array, set via environment variable or .env file
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- [x] Add tests that only allow an admin to set the generateEmbeddings flag. Allow it for a user who has a profile and also for for a user that does not.
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- [x] For any admin user, put a button on the DID view contact screen on the front-end (crowd-funder-pwa) that allows the admin user to tag the user DID to always generate embedding vectors
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Phase 1.3:
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- [ ] Verify existing profile field supports matching use case
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- [ ] Confirm profile text field can hold interests, skills, and goals together
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- [ ] Check field length limits are adequate for detailed descriptions
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- [ ] Create simple profile creation/edit form
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- [ ] Single text area for users to describe interests/skills/goals
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- [ ] Character limit indicator (if applicable)
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- [ ] Connect to existing partner-api endpoints
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- [ ] Profile prompt on meeting join
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- [ ] Check if attendee has matching-ready profile (has interests)
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- [ ] Show profile creation/update modal if needed
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- [ ] Allow skipping if not participating in matching
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- [ ] Check if attendee has profile
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- [ ] Show profile creation/update modal if needed, but allow them to cancel
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- [ ] Image prompt on meeting join
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- [ ] Show image creation/update modal if needed, but allow them to cancel
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Phase 1.4:
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- [ ] Display profiles to other attendees
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- [ ] Basic read-only profile cards
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- [ ] Fetch from partner-api.endorser.ch
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- [ ] In the list of attendees, link to profile for each user if it's known
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- [ ] Automated Testing
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**Validation:** Attendees can see all attendee profiles after they join
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@@ -161,22 +173,26 @@ _Note: Profile storage already exists in endorser-ch partner-api as a simple tex
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### Phase 2: AI-Powered Profile Matching
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**Goal:** Implement core semantic matching algorithm
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- [ ] Set up AI/LLM integration
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- [ ] Choose LLM provider (OpenAI, Anthropic, etc.)
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- [ ] Configure API keys and rate limits
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- [ ] Create embeddings service for profile text
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- [X] Set up AI/LLM integration
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- [X] Use OpenAI with the text-embedding-3-small model
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- [X] Configure API keys and rate limits
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- [X] Create embeddings service for profile text
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- [X] Store in a new partner table named profile_embedding with the profile ID and with the embeddings as a comma-separated vector string
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- [X] For anyone with the generateEmbeddings flag on their profile: call to OpenAI to get their embedding vector when the flag is set, and also when a profile with that flag is changed.
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- [X] Allow blank value for embedding and hard-code their vector as the "data.empty.embedding" value from the test/embedding-empty-string.json file
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- [ ] Implement matching algorithm
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- [ ] Generate embeddings for each profile
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- [X] Meeting organizer can trigger a pairing session
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- [ ] Allow attendees to be excluded
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- [ ] Calculate similarity scores between all pairs
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- [ ] Create pairing algorithm (maximize total similarity)
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- [ ] For an odd number, include 3 people in one of the groups
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- [x] Pairing data is saved in the DB
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- [x] Calculate similarity scores between all pairs
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- [x] Create pairing algorithm (maximize total similarity)
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- [ ] Create "do not pair" groups
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- [ ] Basic matching endpoint
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- [ ] POST endpoint for organizer to trigger matching
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- [ ] Return list of pairs with similarity scores
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- [ ] Simple results display
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- [ ] Show matched pairs to attendees
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- [ ] Assign numbers to each pair
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- [x] POST endpoint for organizer to trigger matching
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- [x] Return list of pairs with similarity scores
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- [x] Simple results display
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- [x] Show matched pairs to attendees
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- [x] Assign numbers to each pair
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- [ ] Automated Testing
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**Validation:** Organizer can trigger matching, and all attendees can see AI-generated pairs with reasonable similarity
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@@ -189,13 +205,13 @@ _Note: Profile storage already exists in endorser-ch partner-api as a simple tex
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- [ ] Enhance organizer matching interface
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- [ ] Visual display of pairs (cards, grid, or list)
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- [ ] Show pair numbers prominently
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- [ ] Display similarity reasoning (why these people matched)
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- [ ] Create a new screen that lists all the attendees, along with their profile (if it's visible to the organizer) -- truncated, but allowed to be expanded
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- [ ] Participant view of matches
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- [ ] Show participants their assigned pair
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- [x] Show participants their assigned pair
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- [ ] Display partner's profile information
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- [ ] Show pair number
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- [x] Show pair number
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- [ ] Real-time updates
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- [ ] WebSocket or polling for match announcements
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- [x] WebSocket or polling for match announcements
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- [ ] Notify participants when matching is triggered
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- [ ] Allow easy adding of notes onto the contact of the matched person
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- [ ] Allow easy adding of an offer to the matched person
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@@ -210,15 +226,14 @@ _Note: Profile storage already exists in endorser-ch partner-api as a simple tex
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- [ ] Exclusion groups
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- [ ] UI for organizer to select people
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- [ ] Create "do not pair" groups
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- [ ] Persist exclusion rules across rounds
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- [ ] Exclude non-participants
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- [ ] Option to mark organizer or others as excluded
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- [ ] Ensure excluded people don't appear in matching pool
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- [ ] Multiple matching rounds
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- [ ] Track previous pairs in meeting state
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- [ ] Constraint: don't repeat previous pairs
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- [ ] Calculate when no more unique pairs possible
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- [x] Track previous pairs in meeting state
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- [x] Constraint: don't repeat previous pairs
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- [x] Calculate when no more unique pairs possible
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- [ ] Show organizer "rounds remaining" indicator
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- [ ] Round history
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- [ ] Store all previous rounds
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@@ -233,13 +248,12 @@ _Note: Profile storage already exists in endorser-ch partner-api as a simple tex
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### Phase 5: Post-Event Features and Polish
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**Goal:** Enable long-term value and edge case handling
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- [ ] Allow a back-end (endorser-ch) flag to mark users with admin permission, so they can tag individuals as always generating embedding vectors
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- [ ] Post-event attendee list
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- [ ] Persist attendee relationships after meeting deletion
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- [ ] Create "past event" view showing all attendees
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- [ ] Link to profiles even after event expires
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- [ ] Meeting expiration handling
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- [ ] Archive meeting data (don't delete attendee info)
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- [ ] Maintain contact visibility permissions
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- [ ] Archive meeting data on client side
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- [ ] Analytics and insights
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- [ ] Show match quality scores to organizer
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- [ ] Track which pairs connected post-event
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@@ -254,7 +268,7 @@ _Note: Profile storage already exists in endorser-ch partner-api as a simple tex
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- [ ] Support for very small (2-3 people) or large (50+) groups
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- [ ] Handle profile updates mid-matching
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- [ ] Performance optimization
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- [ ] Cache embeddings to avoid regeneration
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- [x] Cache embeddings to avoid regeneration
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- [ ] Optimize matching algorithm for large groups
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- [ ] Add loading states and progress indicators
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- [ ] Automated Testing
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@@ -325,14 +339,3 @@ _Note: This is an optional enhancement that could significantly improve onboardi
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- Start with greedy pairing (highest similarity pairs first)
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- Could evolve to weighted bipartite matching for optimal global solution
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- Need to handle constraints efficiently (exclusions, previous pairs)
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**Data Model:**
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- Profile: `{ userId, description: string, embedding: vector }` (description field already exists in partner-api)
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- Meeting: extend to include `{ profileUserIds: string[], rounds: Round[], exclusionGroups: string[][] }`
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- Round: `{ number: int, pairs: Pair[], timestamp: datetime }`
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- Pair: `{ userIds: [string, string], similarityScore: float, pairNumber: int }`
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**Privacy:**
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- Attendees should consent to AI processing of their profiles
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- Consider allowing profile visibility controls
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- Meeting password security for sensitive gatherings is ensured by current features
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