semantic search api for documents

Here’s a practical design for a semantic search API for documents.

Core idea

Instead of matching keywords only, semantic search:

1. Breaks documents into chunks
2. Converts chunks into embeddings
3. Stores embeddings in a vector database
4. On query, embeds the query and finds the most similar chunks/documents

---

Typical API endpoints

1. Ingest documents

POST /documents

Uploads a document and indexes it for search.

Request

{
  "title": "Employee Handbook",
  "content": "Full document text here...",
  "metadata": {
    "department": "HR",
    "author": "Jane"
  }
}

Response

{
  "document_id": "doc_123",
  "status": "indexed"
}

---

2. Search documents

POST /search

Searches semantically across indexed documents.

Request

{
  "query": "What is the vacation policy?",
  "top_k": 5,
  "filters": {
    "department": "HR"
  }
}

Response

{
  "query": "What is the vacation policy?",
  "results": [
    {
      "document_id": "doc_123",
      "chunk_id": "chunk_9",
      "title": "Employee Handbook",
      "score": 0.92,
      "snippet": "Employees accrue 20 days of paid vacation..."
    }
  ]
}

---

3. Get a document

GET /documents/{document_id}

Returns document metadata and optionally full text.

---

4. Delete a document

DELETE /documents/{document_id}

Removes document and its vectors from the index.

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Suggested architecture

Components

• API server: FastAPI / Flask / Node.js
• Embedding model: OpenAI embeddings, sentence-transformers, etc.
• Vector database: Pinecone, Weaviate, Qdrant, Milvus, FAISS
• Document storage: PostgreSQL, S3, Elasticsearch, or plain DB
• Chunking service: splits long docs into searchable segments

Indexing flow

1. Receive document
2. Extract text
3. Chunk text
4. Generate embeddings for each chunk
5. Store chunks + embeddings + metadata

Search flow

1. Receive query
2. Generate query embedding
3. Vector similarity search
4. Apply metadata filters
5. Return top matches

---

Example FastAPI shape

from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional, Dict

app = FastAPI()

class DocumentCreate(BaseModel):
    title: str
    content: str
    metadata: Optional[Dict] = {}

class SearchRequest(BaseModel):
    query: str
    top_k: int = 5
    filters: Optional[Dict] = {}

@app.post("/documents")
def add_document(doc: DocumentCreate):
    # chunk -> embed -> store
    return {"document_id": "doc_123", "status": "indexed"}

@app.post("/search")
def search(req: SearchRequest):
    # embed query -> vector search
    return {
        "query": req.query,
        "results": []
    }

---

Useful features to include

• Filters: by date, department, file type, tags
• Hybrid search: combine keyword + vector search
• Reranking: re-score top results with a cross-encoder/LLM
• Pagination
• Access control: per-user/per-role document permissions
• Chunk previews: return matching snippet
• Highlighting: show matched semantic passage

---

If you want a production-ready schema

I can provide:

1. a full OpenAPI spec
2. a FastAPI implementation
3. a Node.js/Express implementation
4. a design using Postgres + pgvector
5. a design using Pinecone/Qdrant

If you want, I can generate a complete API spec next.