How do I set up a document analysis model for meeting archive search across months of transcripts?

To set up a document analysis model for meeting archive search across months of transcripts, you want a pipeline that can:

1. ingest transcripts consistently
2. index them for search
3. analyze and extract structure
4. support question answering / retrieval across the archive

Here’s a practical setup.

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1) Define the use case clearly

Decide what “search” means for you:

• Keyword search: find exact terms
• Semantic search: find conceptually related passages
• Question answering: “When did we decide on the launch date?”
• Summarization: “What were the main decisions in Q2 meetings?”
• Trend analysis: repeated topics, action items, risks, owners

For meeting archives, most teams need:

• transcript-level search
• speaker-aware retrieval
• timestamped citations
• action item / decision extraction

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2) Prepare the transcript data

Your transcripts should ideally be normalized into a consistent JSON structure like:

{
  "meeting_id": "m-2024-01-12",
  "date": "2024-01-12",
  "title": "Weekly Product Sync",
  "speakers": [
    {"name": "Alice"},
    {"name": "Bob"}
  ],
  "segments": [
    {
      "speaker": "Alice",
      "start_time": "00:03:12",
      "end_time": "00:03:45",
      "text": "We need to finalize the launch checklist."
    }
  ]
}

Clean and standardize

• remove filler metadata
• fix speaker labels if possible
• split into chunks of 200–800 tokens
• preserve timestamps and meeting IDs
• keep date, team, project, and participants as metadata

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3) Choose an indexing strategy

For archive search, use hybrid retrieval:

A. Full-text index

Use Elasticsearch/OpenSearch or similar for:

• exact keyword match
• filters by date, meeting, speaker, team

B. Vector index

Use embeddings for semantic retrieval:

• convert transcript chunks into embeddings
• store in a vector database like:
  • Pinecone
  • Weaviate
  • Milvus
  • FAISS
  • pgvector

Why hybrid?

Because users often ask:

• “Where did we mention SOC 2?”
• “What did we decide about pricing?”
• “Find the meeting where Maya proposed the redesign.”

Keyword search handles exact terms; embeddings handle paraphrases.

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4) Chunk the transcripts properly

Don’t embed whole meetings as one document. Instead chunk by:

• speaker turns
• topic boundaries
• time windows
• paragraph blocks

A good chunk should:

• be semantically coherent
• include surrounding context
• carry metadata

Example chunk:

{
  "chunk_id": "m-2024-01-12-03",
  "meeting_id": "m-2024-01-12",
  "date": "2024-01-12",
  "speaker": "Alice",
  "start_time": "00:03:12",
  "end_time": "00:03:45",
  "text": "We need to finalize the launch checklist before Friday.",
  "tags": ["launch", "action_item"]
}

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5) Add document analysis layers

Beyond search, add NLP extraction jobs.

Useful extracted fields

• topics
• decisions
• action items
• owners
• risks / blockers
• mentioned projects
• dates / deadlines
• sentiment / urgency

You can do this with:

• rules + regex for dates and names
• classical NLP for entities
• LLM-based extraction for decisions/action items

Example extracted record:

{
  "meeting_id": "m-2024-01-12",
  "decisions": [
    "Launch moved to Feb 2"
  ],
  "action_items": [
    {"owner": "Bob", "task": "Update release notes", "due": "2024-01-19"}
  ]
}

This makes search much more valuable.

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6) Build retrieval + answer generation

A good user-facing system usually works like this:

1. user asks a question
2. system retrieves relevant transcript chunks
3. optionally reranks results
4. LLM summarizes or answers using only retrieved chunks
5. return citations with meeting date/time

Example query:

“When did we decide to delay the launch?”

System flow:

• retrieve chunks mentioning “launch,” “delay,” “decision”
• rerank by relevance
• answer: “In the Jan 12 weekly sync, Alice said the launch should move to Feb 2 because QA wasn’t complete.”
• show timestamped references

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7) Add metadata filters

Meeting archives become much easier to search if you index metadata:

• date range
• meeting title
• participants
• department / team
• project / product
• meeting type

Example filters:

• only meetings in Q3
• only product meetings
• only meetings with Alice and Bob
• only transcripts mentioning “pricing”

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8) Handle long-term archive growth

For months of transcripts, plan for:

• incremental ingestion
• re-embedding only changed documents
• nightly sync jobs
• deduplication of repeated recordings
• versioning of transcript corrections

If transcripts are updated after speech-to-text correction, keep:

• original transcript
• corrected transcript
• model-generated summaries
• extraction versions

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9) Recommended architecture

A simple robust architecture:

• Storage: S3 / GCS / Azure Blob for raw transcripts
• Metadata DB: Postgres
• Search index: OpenSearch / Elasticsearch
• Embeddings DB: pgvector / Pinecone / Weaviate
• Processing pipeline:
  • transcript normalization
  • chunking
  • embedding generation
  • entity/action-item extraction
  • index update
• Query service:
  • hybrid retrieval
  • reranking
  • LLM answer generation

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10) Example implementation stack

If you want a practical modern stack:

• Python
• LangChain or LlamaIndex for orchestration
• OpenSearch for keyword search
• pgvector or Pinecone for semantic search
• OpenAI embeddings or another embedding model
• LLM for summarization/extraction
• FastAPI for API layer

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11) Evaluate search quality

Test with real questions:

• “Where did we discuss enterprise pricing?”
• “Which meeting assigned the migration task?”
• “What did we decide about onboarding?”
• “Show all mentions of the April release delay.”

Measure:

• retrieval precision/recall
• answer faithfulness
• citation accuracy
• latency
• coverage over older transcripts

Create a small labeled set of:

• queries
• expected meetings/chunks
• expected answers

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12) Practical best practices

• preserve timestamps in every chunk
• never rely on embeddings alone
• use hybrid retrieval
• include meeting metadata
• store both raw and cleaned transcripts
• extract action items and decisions
• return citations to build trust
• periodically re-index as transcript quality improves

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Minimal architecture to start with

If you want the simplest workable setup:

1. Put transcripts in JSON
2. Chunk by speaker turn
3. Generate embeddings for each chunk
4. Store chunks + metadata in Postgres/pgvector
5. Add full-text search
6. Build a query endpoint that retrieves top chunks and feeds them to an LLM
7. Return answers with meeting/date/timestamp citations

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If you want, I can also give you:

• a sample database schema
• a Python ingestion pipeline
• a LangChain/LlamaIndex example
• or a recommended architecture for your specific stack