You can build a local semantic code-search index in Zoo Code with Ollama’s BGE-M3 embeddings and Qdrant. The documented workflow parses code into meaningful blocks, embeds them, stores vectors in Qdrant, and makes them searchable through Zoo Code’s codebase_search tool. It is not a proven “lightning-fast” setup: no controlled end-to-end benchmark establishes indexing time or search latency, and the verified instructions below apply to Zoo Code rather than every Roo Code release or Cline.
How the indexing pipeline works
Zoo Code’s indexing guide describes four stages: Tree-sitter parses supported source files into semantic blocks such as functions, classes, and methods; an embedding provider converts those blocks into vectors; Qdrant stores the vectors; and Zoo Code’s codebase_search tool retrieves relevant code in response to a natural-language query. See Zoo Code’s Codebase Indexing documentation.
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In this setup, Ollama runs BGE-M3 as the embedding provider and Qdrant holds the index. Search can return code snippets, file paths, line numbers, similarity scores, and navigation links. It is semantic retrieval, not a replacement for exact text search when you already know a symbol or string.
What is verified for Zoo Code, Roo Code, and Cline
The concrete setup documented here is for Zoo Code. A Lawrence Berkeley National Laboratory CBorg page’s search-result description identifies Zoo Code as formerly RooCode, but that does not establish that every Roo release has the same indexing interface. The page is available at CBorg’s Zoo Code guide.
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Whether current Cline builds support this exact built-in indexing workflow is not established here. Do not assume the Zoo Code settings and tools exist in Cline; check the current documentation for the specific editor and version you use before following editor-specific steps.
Set up local Qdrant and Ollama
1. Start Qdrant
For a local Docker instance, run the command from Zoo Code’s guide. It publishes Qdrant on port 6333 and persists its data in the named Docker volume qdrant_data:
docker run -d
--name qdrant
--restart unless-stopped
-p 6333:6333
-v qdrant_data:/qdrant/storage
qdrant/qdrantThe local endpoint to enter in Zoo Code is http://localhost:6333. If you use hosted Qdrant instead, enter the endpoint supplied by that deployment and provide its API key if required. With hosted Qdrant, vectors are stored remotely rather than in your local Qdrant instance. The Qdrant documentation covers the database more broadly.
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2. Download BGE-M3 in Ollama
Install and start Ollama, then pull the model:
ollama pull bge-m3Ollama’s model listing documents this model and its embedding API; the typical local base URL used in Zoo Code is http://localhost:11434. Check Ollama’s BGE-M3 listing for the current model details.
3. Configure Zoo Code and start indexing
- Open Zoo Code’s codebase-indexing settings panel.
- Set the embedder provider to
Ollama, the base URL tohttp://localhost:11434, and the model tobge-m3. - Enter the Qdrant URL:
http://localhost:6333for the local Docker instance, or your hosted endpoint. Add a Qdrant API key only if your deployment requires one. - Save the settings and start indexing. The panel reports indexing, indexed, and error states.
Zoo Code documents a 1 MB maximum file size and says Tree-sitter-supported languages get the best results. Files excluded by ignore patterns will not be part of the useful search corpus, so review your project’s .gitignore and .rooignore rules if expected files are missing.
Check the index and tune search
After indexing finishes, try a question grounded in your project, such as “How is user authentication handled?” Other useful examples are “Database connection setup” and “API endpoint definitions.” These queries test whether the index retrieves relevant code by meaning rather than by an exact phrase.
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Zoo Code documents a default search-score threshold of 0.4. Lowering it admits more candidates, which can include weaker matches; raising it narrows results and may exclude useful code. Treat the default as a starting point and adjust it against queries whose expected files you can verify.
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Understand dimensions before changing models
Ollama lists BGE-M3’s embedding length as 1024 dimensions. Zoo Code warns that changing to a model with a different vector dimension requires a full re-index. If the existing Qdrant collection contains vectors of a different dimension, clear the old index before rebuilding; Zoo Code’s clear-index function deletes the collection data and local file cache, and that deletion is irreversible. Back up or otherwise preserve anything you need before clearing it.
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The Ollama listing also gives BGE-M3 an 8K context window and a default package size of 1.2 GB. Those figures describe model capacity and package size; they are not hardware requirements, indexing-speed estimates, or evidence that this configuration will outperform another embedding model for code search.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What “lightning-fast” can realistically mean
The available product documentation does not provide a controlled end-to-end benchmark for Zoo Code, Ollama BGE-M3, and Qdrant. It gives no reliable duration for indexing or search latency, and Zoo Code notes that large projects can take longer, including codebases with 10,000 or more files. Actual results depend on factors such as repository size, file filtering, local compute, model loading, and where Qdrant runs; these are practical considerations, not published measurements for this stack.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallMeasure your own workload: record how long a first index takes, how long subsequent searches take, and whether results are relevant for representative questions. The model’s context window, package size, and vector dimension do not substitute for those measurements.
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Privacy and deployment choices
Zoo Code says parsing happens locally, file permissions and ignore patterns are respected, and only small code chunks—stated as 100–1000 characters—are sent to the embedding provider. It also says vectors remain in the selected Qdrant instance and describes local Ollama plus local Qdrant as an offline path. These are Zoo Code’s documented privacy and behavior claims, not an independent network-traffic audit.
With local Ollama and local Qdrant, the documented components can run on your machine. Choosing hosted Qdrant changes the location of vector storage and requires using that service’s endpoint; consult its deployment documentation and your organization’s data-handling requirements before indexing sensitive code.
Quick Recap
Troubleshoot common problems
- Qdrant connection error: Confirm that the Qdrant process or container is running and that Zoo Code has the correct endpoint. For the local Docker example, that is
http://localhost:6333. - Ollama or model connection error: Confirm Ollama is running, BGE-M3 has been pulled, and the configured base URL is
http://localhost:11434. - Expected files are absent: Check
.gitignoreand.rooignorepatterns and ensure the files are within the documented 1 MB limit. - Indexing is slow: Large repositories can take longer. Review which files are eligible for indexing; the documentation does not establish a universal indexing-time target.
- Index is stuck or corrupted: Zoo Code advises clearing and rebuilding the index. Clearing removes the Qdrant collection data and local file cache, so treat it as an irreversible reset.
- Results are too broad or too sparse: Adjust the search-score threshold from its documented default of
0.4and test again with queries whose relevant files you know.
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