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To build an AI email reply system, connect a mailbox API to an application that retrieves relevant, authorized email or knowledge-base content from PostgreSQL, uses that context to draft a reply, and applies a review or policy gate before sending. PostgreSQL with the pgvector extension can hold both ordinary application records and embeddings; a separate vector database is not required for every design. Retrieval can ground a draft, but it does not guarantee that the reply is correct.
How the system fits together
Treat mailbox access, retrieval, generation, and sending as separate stages. That separation makes it easier to change a mail provider or model without redesigning the whole application, and to control what the system is allowed to send.
- Authorize and receive: connect to the selected mailbox through its provider API, using the provider’s current authorization model. Receive new messages through an available event mechanism or polling, and keep a synchronization checkpoint.
- Normalize and store: preserve provider message IDs, thread IDs, timestamps, sender and recipient metadata, and sync state. Extract useful text for retrieval, handling quoted history and signatures according to your use case.
- Retrieve authorized context: search approved email history or a separate knowledge base for content relevant to the incoming message. Apply mailbox-owner or tenant filters in the database query, not merely in the prompt.
- Draft: provide the incoming message and selected context to an LLM. Ask it to use supported facts, avoid unsupported commitments, and return a draft with references to the retrieved material.
- Review and send: present the draft for editing and approval initially. After approval, send it through the chosen provider API and record the result. Expand to automatic sending only for narrowly defined cases that pass your own evaluation and policy checks.
The stages involve different permissions: reading or synchronizing a mailbox is not the same operation as sending mail. Confirm the selected provider’s current API permissions, event or polling options, sending limits, and organizational rules before implementation. The cited examples do not establish those details for every provider.
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Retrieval-augmented generation (RAG) retrieves relevant information from a knowledge source and uses it to condition an LLM response. Google Cloud’s guide describes using PostgreSQL with pgvector to store, index, and query embeddings, alongside relational application data; embeddings can be generated outside the database. Google Cloud’s Cloud SQL generative AI guide presents this pattern. (The URL is reproduced exactly as supplied.)
#1 Best Overall
For an email assistant, keep message identity and synchronization state distinct from searchable text and vectors. One practical starting schema might contain:
- Mailboxes or tenants: owner or tenant identity and provider connection metadata, without exposing credentials in ordinary records.
- Messages: provider ID, thread ID, mailbox or tenant key, timestamps, participant metadata, and synchronization state.
- Message chunks: text segments, message relationship, and any metadata needed to filter or explain retrieval.
- Embeddings: vector representation associated with each searchable segment, using a vector column dimension compatible with the chosen embedding model.
- Draft and send events: draft text, review or approval status, edits, send outcome, and references to the context used.
This is a design recommendation rather than a schema prescribed by the examples. Store enough provenance to inspect which messages informed a draft, and ensure retrieval predicates constrain results to the mailbox or tenant authorized for the current request.
Rank #2
Choose vector or hybrid retrieval
Vector search
Embedding search can surface passages with related meaning even when they do not repeat the same words as the incoming email. Microsoft Azure’s PostgreSQL sample demonstrates a vector column and cosine-distance search using an HNSW index. Its model and vector dimensions are specific to that example; select dimensions to match the embedding model you actually deploy rather than copying the sample as a universal default. Microsoft Learn’s Azure PostgreSQL AI integration guide describes the vendor-specific example.
Hybrid search
Email queries often include exact names, product codes, ticket IDs, or phrases where semantic similarity alone may rank the wrong material. Hybrid retrieval combines vector search with PostgreSQL full-text search. Microsoft’s Azure sample combines those result lists using Reciprocal Rank Fusion (RRF). That is an implementation example, not proof that hybrid search or RRF is best for every inbox. Compare relevance, exact-term behavior, latency, and operational complexity on representative messages from your own corpus. The Azure PostgreSQL RAG sample shows the hybrid approach.
Neither the cited guides nor the sample establishes a universally best chunk size, ranking formula, embedding model, or accuracy level for email reply drafting. Test the choices against the kinds of messages your system will handle.
Prepare email content for search
Decide whether to index whole messages, selected thread summaries, or chunks. The appropriate unit depends on whether a useful answer usually appears in a single message, a longer conversation, or an approved reference document; the available examples do not establish an optimal email chunking method.
- Keep searchable content separate from provider identity and synchronization fields.
- Remove or handle signatures and quoted history when they create noise, while retaining context needed to understand a thread.
- Track source IDs and timestamps so a reviewer can inspect the passages that informed a draft.
- Define deletion and re-synchronization behavior when a source message changes or is removed.
- Do not put unfiltered mailbox content into a shared retrieval pool. Enforce owner or tenant access constraints in each retrieval query.
Generate a grounded draft without trusting the email as an instruction
Build the prompt from the incoming message and a bounded set of retrieved context. Tell the model to answer only with support from that context, identify when information is missing, and avoid making commitments the mailbox owner has not authorized. Treat the contents of an email as untrusted data: a sender’s message must not be able to override system-level instructions or broaden access to other messages. These are safeguards to implement in your application, not capabilities established by the cited sample.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep review and sending under control
Start with a draft-only or human-approval workflow. A Microsoft Community Hub example, published January 30, 2025, uses Semantic Kernel, Azure OpenAI, Neon PostgreSQL with pgvector, and Microsoft Graph; it creates drafts, allows approval or editing, sends through Graph, and stores draft and sent email embeddings for similarity search. It is an implementation example, not independent validation of security, deliverability, or production readiness. Its specific model names may also have aged. Read the Microsoft Community Hub email-assistant example.
Before enabling any automatic sending, define narrow cases the system may handle and explicit conditions for abstaining or escalating to a person. Log approval, edits, and send outcomes; prevent duplicate sends when retries occur; and provide a way to disable sending quickly. Test recipient selection and thread handling as carefully as the text of the reply.
Build and evaluate in stages
- Select the integration: choose the mailbox provider and confirm its current API, permissions, synchronization approach, send behavior, and applicable organizational policy. The Microsoft Graph example is one possible pattern, not a universal provider recommendation.
- Stand up PostgreSQL: use a deployment that supports pgvector and the index options you need. Check extension availability, backups, network controls, and data-handling terms for the specific hosting environment.
- Ingest a limited corpus: normalize messages and metadata, establish synchronization and deletion behavior, and index only content the assistant is authorized to use.
- Choose and test retrieval: select an embedding model, match vector dimensions to it, and compare vector-only and hybrid retrieval on representative queries. Inspect whether results are relevant and belong to the right mailbox.
- Draft, review, and record: generate drafts with source references, keep human approval in the initial workflow, and record edits and outcomes.
- Evaluate before widening access: create a test set covering routine questions, ambiguous requests, sensitive information, misleading instructions, and messages with similar names or threads. Check relevance, factual grounding, tone, data leakage, recipient correctness, duplicate sends, and whether the system abstains when it should. The cited sources provide no measured thresholds; set criteria that fit your application.
- Secure operations: protect provider tokens and model credentials using an appropriate secret-management path; apply least privilege, tenant isolation, retention controls, encryption, audit logging, rate limits, and a kill switch. Exact controls depend on the selected provider and deployment.
Implementation choices depend on your stack
PostgreSQL with pgvector is one way to combine relational records and vector retrieval; it does not make a separate vector database mandatory or rule one out. The key deployment question is whether your chosen PostgreSQL service supports the required extension, indexing, backups, and security controls.
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Embedding generation can happen in the application through an external model API or, in supported environments, through a database-side integration. Microsoft’s Azure PostgreSQL documentation demonstrates the latter for its own service; do not assume the same integration or model availability on another host. Model dimensions, deployment names, API scopes, and index options are version- and provider-specific, so confirm current documentation before deployment.
An OpenAI retrieval-plugin repository also illustrates email or document retrieval with PostgreSQL and pgvector as a possible backend, but repository examples should not be treated as current product guidance. See the retrieval-plugin repository.
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