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Integrate Ollama for Embeddings

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Agent for Enterprise M&A Due Diligence

Format
Code-aware
Lines
9
Sections
4
Linked challenge
Agent for Enterprise M&A Due Diligence

Prompt source

Original prompt text with formatting preserved for inspection.

9 lines
4 sections
No variables
1 code block
Modify your LlamaIndex setup to explicitly use Ollama for generating embeddings (`OllamaEmbedding`). Describe the benefits of this approach (e.g., local control, cost savings). Ensure your `VectorStoreIndex` is built using these embeddings and demonstrate that your agent still performs accurately. Provide the updated configuration snippet.

```python
from llama_index.core import Settings
from llama_index.embeddings.ollama import OllamaEmbedding

# Configure OllamaEmbedding
Settings.embed_model = OllamaEmbedding(model_name="nomic-embed-text", base_url="http://localhost:11434")

# Re-initialize index or ensure existing index uses this setting
# ... (rebuild index if necessary or ensure it loads with new setting)
```

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.