Ollama Search Integration: Beyond Coding in 2026
Ollama plus Obsidian plus search API builds a private, grounded personal assistant. Architecture for personal knowledge base with web access.
Most Ollama usage in 2026 stays within coding assistance -- autocomplete, code review, local copilot. The underexplored use case is a personal knowledge assistant: Ollama running a local model connected to your notes (Obsidian, Logseq) plus a search API for real-time web data. This gives you a private, grounded assistant that knows your context and can verify claims against live sources.
Why Ollama alone is not enough
A local LLM running on Ollama (Llama 3.1, Mistral, Qwen) has two gaps: it has no access to your personal knowledge, and it has no access to current web data. Ask it about your project notes and it hallucinates. Ask it about today's news and it generates plausible fiction from training data. Fixing both gaps turns Ollama from a toy into a tool.
Architecture overview
Three components: (1) Ollama running a capable local model like Llama 3.1 70B or Qwen 2.5 32B, (2) a vector store indexing your Obsidian vault for personal context retrieval, (3) a search API for live web grounding. The flow: your question hits the orchestrator, which decides whether to pull from notes, search the web, or both, then feeds the context to the local model for synthesis.
Setting up the search layer
import requests, os, json
import ollama
SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
def web_search(query, count=5):
"""Search the web for current information."""
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": SCAVIO_KEY},
json={"query": query, "num_results": count}
)
results = resp.json()["results"]
return "\n".join([
f"- {r['title']}: {r['description']}" for r in results
])
def ask_ollama(prompt, context=""):
"""Query local Ollama with optional context."""
full_prompt = prompt
if context:
full_prompt = (
f"Use this context to answer accurately. "
f"If the context does not contain the answer, say so.\n\n"
f"Context:\n{context}\n\nQuestion: {prompt}"
)
response = ollama.chat(
model="llama3.1:70b",
messages=[{"role": "user", "content": full_prompt}]
)
return response["message"]["content"]Adding Obsidian vault as context
from pathlib import Path
import chromadb
# Index your Obsidian vault into ChromaDB for retrieval
VAULT_PATH = Path.home() / "Documents" / "ObsidianVault"
client = chromadb.PersistentClient(path=str(Path.home() / ".local" / "kb-index"))
collection = client.get_or_create_collection("obsidian_notes")
def index_vault():
"""Index all markdown files in your Obsidian vault."""
notes = list(VAULT_PATH.glob("**/*.md"))
for note in notes:
content = note.read_text(encoding="utf-8")
# Chunk by paragraphs for better retrieval
chunks = [c.strip() for c in content.split("\n\n") if len(c.strip()) > 50]
for i, chunk in enumerate(chunks):
doc_id = f"{note.stem}_{i}"
collection.upsert(
ids=[doc_id],
documents=[chunk],
metadatas=[{"source": str(note), "chunk": i}]
)
print(f"Indexed {len(notes)} notes into vector store")
def search_notes(query, n_results=3):
"""Retrieve relevant notes from your vault."""
results = collection.query(query_texts=[query], n_results=n_results)
docs = results["documents"][0]
sources = [m["source"] for m in results["metadatas"][0]]
return "\n\n".join([
f"[From: {s}]\n{d}" for s, d in zip(sources, docs)
])
index_vault()The orchestrator: notes + web + LLM
def personal_assistant(question):
"""Route question to notes, web, or both, then synthesize."""
# Step 1: Check if personal notes have relevant context
note_context = search_notes(question)
# Step 2: Get live web data for grounding
web_context = web_search(question)
# Step 3: Combine and send to local LLM
combined_context = (
f"=== Your Notes ===\n{note_context}\n\n"
f"=== Web Search Results ===\n{web_context}"
)
answer = ask_ollama(question, context=combined_context)
return {
"answer": answer,
"sources": {
"notes": note_context[:200],
"web": web_context[:200]
}
}
# Example: question that needs both personal context and live data
result = personal_assistant(
"What were my notes on competitor pricing, "
"and what are their current prices?"
)
print(result["answer"])What this setup costs
- Ollama: free, runs on your hardware. Llama 3.1 70B needs 40GB+ VRAM or runs quantized on 16GB
- ChromaDB: free, local storage
- Scavio search: 250 free queries/month, $30/month for 7K credits. At 20 questions/day, that is ~600/month -- well within the paid tier
- Total: $30/month for a private, grounded personal assistant with no data leaving your machine (except search queries)
Privacy architecture
Your notes never leave your machine. The vector index is local (ChromaDB on disk). The LLM runs locally via Ollama. The only external call is the search API query, which sends your search terms but not your personal data. If you are researching competitive intelligence, your notes about competitors stay private while the web search fills in current public data.
Beyond the basics
# Add a daily briefing that combines notes + live data
def morning_briefing(topics):
"""Generate a daily briefing from notes and web."""
briefing = []
for topic in topics:
note_ctx = search_notes(topic)
web_ctx = web_search(f"{topic} latest news 2026", count=3)
summary = ask_ollama(
f"Brief summary of what is new regarding: {topic}. "
f"Compare with my previous notes.",
context=f"Notes:\n{note_ctx}\n\nWeb:\n{web_ctx}"
)
briefing.append(f"## {topic}\n{summary}")
return "\n\n".join(briefing)
topics = ["AI agent frameworks", "search API market", "MCP protocol"]
print(morning_briefing(topics))
# 3 topics x 1 search = 3 credits = $0.015The shift from "Ollama for coding" to "Ollama as personal knowledge base" requires two additions: a vector store for your notes and a search API for live data. The coding use case is already well-served by cloud tools. The private knowledge assistant with web grounding is where local LLMs offer something cloud services cannot: complete control over your data with real-time information access.