---
title: "Ollama Local Workflow: Generation, Vectors, and Retrieval"
description: "Run local LLMs with Ollama: test generation endpoints, embedding dimensions, vector consistency, and ChatCrystal integration boundaries offline."
image: "https://zengliangyi.com/og/blog/en/ollama-local-workflow.png"
---

Running models locally can reduce material sent to remote services, but first establish that the selected model is local. Ollama also supports cloud models; a localhost service address alone does not prove every inference happens on the machine.

This article explains how to verify one note through the Ollama adapter in [ChatCrystal](/en/projects/chatcrystal) at commit [`7642768`](https://github.com/ZengLiangYi/ChatCrystal/tree/764276814e5c5a33ecbfeae63754f74b9261a3a8). It offers no unmeasured hardware speeds, model rankings, or promise of zero cost. Local operation still uses download bandwidth, storage, memory, and electricity.

## Install and identify the local service

Follow the [official Ollama quickstart](https://docs.ollama.com/quickstart) for your system and select a local model. Check:



```bash
ollama   --version  
  ollama   list
```



In a terminal environment without a running service, start `ollama serve`. If the port is occupied, identify its owner and inspect the actual API response. An occupied port alone does not establish a working Ollama service.

The ChatCrystal 0.6.1 npm entry also requires Node.js 24 or later. This is an application requirement, not an Ollama installation requirement. See the [getting-started guide](/en/blog/chatcrystal-getting-started) for installation and desktop-interface differences.

## Prepare models for separate tasks

These names are examples from the application’s fixed-version defaults, not a quality ranking:



```bash
ollama   pull   qwen2.5:7b  
  ollama   pull   nomic-embed-text  
  ollama   list
```



The first serves generation; the second serves embeddings. Download size and runtime resources depend on version, quantization, context, and concurrency. Disk capacity for files does not establish sufficient inference memory. Test short input before actual summary lengths. If resources are insufficient, choose a model suited to the machine and recheck its task capabilities.

## Test generation and vectors independently

The [official compatibility reference](https://docs.ollama.com/api/openai-compatibility) describes compatible endpoints and their supported scope. Test generation through the path used by ChatCrystal’s adapter:



```bash
curl   --fail-with-body   http://localhost:11434/v1/chat/completions   \  
    -H   'Content-Type: application/json'   \  
    -d   '{"model":"qwen2.5:7b","messages":[{"role":"user","content":"Explain note indexing in one sentence."}],"stream":false}'
```



Test vectors separately:



```bash
curl   --fail-with-body   http://localhost:11434/v1/embeddings   \  
    -H   'Content-Type: application/json'   \  
    -d   '{"model":"nomic-embed-text","input":"Note indexing test"}'
```



Inspect HTTP status and response shape: generation should contain assistant text; embeddings should contain a numeric vector inside `data`. Generation success does not establish vector availability, and a compatible endpoint does not give every model all features. These commands perform local inference; this article reports no measured performance from them.

## Connect ChatCrystal

With the default local connection:



```bash
crystal   config   set   llm.provider   ollama  
  crystal   config   set   llm.baseURL   http://localhost:11434  
  crystal   config   set   llm.model   qwen2.5:7b  
  crystal   config   set   embedding.provider   ollama  
  crystal   config   set   embedding.baseURL   http://localhost:11434  
  crystal   config   set   embedding.model   nomic-embed-text  
  crystal   config   get  
  crystal   config   test
```



The [application adapter](https://github.com/ZengLiangYi/ChatCrystal/blob/764276814e5c5a33ecbfeae63754f74b9261a3a8/server/src/services/providers.ts) appends `/v1` to the Ollama address, so supply the service root here. If vectors already exist, some configuration changes require confirmation and reindexing. Follow the [model configuration and rebuilding steps](/en/blog/llm-and-embedding-separation); a successful save does not establish a completed rebuild.

`config test` reports the two connection paths separately. Passing short tests can still be followed by failure on long summaries or structured output. Retain the actual error, input length, and model information for diagnosis.

## Verify one real note

Import a known conversation, select a record outside the demo data with reusable experience, generate its note, inspect the content, and read `embedding_status` from note JSON. The experience gate can legitimately filter some conversations; vector creation can fail independently after a successful summary.

Once status is `done`, test a query that should find the note and another with no relevant answer. See [recovering a debugging solution](/en/blog/recover-debugging-notes). Then increase input and concurrency, recording load time, response time, failure rate, and peak resources. Distinguish cold loading from steady inference.

## Verify offline behavior and data boundaries

Once required local models are downloaded, no cloud model is selected, and dependent services are available, test the full workflow without network access. A successful short local chat does not establish that import, summaries, relation discovery, and queries all work offline.

Inspect the actual LLM and embedding configurations together. Combining local and remote backends can still send material along the remote path. The application database and vector index also require care; see [local-first data boundaries](/en/blog/local-first-ai-data-boundaries).

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##  From writing to code

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[Visit my GitHub](https://github.com/ZengLiangYi)

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