A multimodal pipeline stitches together model serving, embedding storage, and a retrieval tool.
1. Serve the model locally → use vllm or ollama.
```bash
ollama serve multimodal-model:latest
```
2. Create a vector store for image embeddings → faiss works offline.
```python
import faiss, numpy as np
index = faiss.IndexFlatL2(768)
index.add(np.array(image_embeddings))
```
3. Add a simple HTTP tool that queries the store and returns top‑k results.
```bash
uvicorn retrieval_api:app --port 8000
```
4. Wire the tool into the LLM prompt chain (LangChain example).
```python
from langchain.agents import initialize_agent
agent = initialize_agent([retrieval_tool], llm, agent_type="zero-shot-react-description")
```
Gotcha: Ensure the embedding dimension of the vector store matches the model’s output; mismatches cause runtime errors.
Building a Multimodal AI Pipeline with a Local Stack
Learn the core steps to set up a multimodal AI pipeline using a local stack: serve the model, add a vector store, and connect a tool for image‑text retrieval – all with open‑source tools.
Trendzza Research Desk
Aug 31, 2026 · 1 min read
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