chore: opensearch embedding model dimension configurability#248
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📝 WalkthroughWalkthroughDocumentation updates standardize Plane's OpenSearch embedding configuration by replacing Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes Poem
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akhil-vamshi-konam
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Actionable comments posted: 1
🧹 Nitpick comments (1)
docs/self-hosting/govern/plane-ai.md (1)
291-291: Clarify conditionalOPENSEARCH_ML_MODEL_IDwording in dimension-change guidance.This sentence reads as if
OPENSEARCH_ML_MODEL_IDis always user-configured; for auto-deployment setups, that can be misleading. Consider phrasing it as “if set / if using existing model ID.”✏️ Suggested wording tweak
-If you update the model or manually override the dimension size by setting `OPENSEARCH_EMBEDDING_DIMENSION`, you must recreate your search indices so they adopt the new dimension size, then reindex and revectorize your workspace. Ensure that the model associated with your `OPENSEARCH_ML_MODEL_ID` and your `EMBEDDING_MODEL` configuration share this same dimension size. +If you update the model or manually override the dimension size by setting `OPENSEARCH_EMBEDDING_DIMENSION`, you must recreate your search indices so they adopt the new dimension size, then reindex and revectorize your workspace. Ensure your `EMBEDDING_MODEL` and configured embedding model deployment (for example, `OPENSEARCH_ML_MODEL_ID` when you use an existing model ID) share the same dimension size.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@docs/self-hosting/govern/plane-ai.md` at line 291, Reword the guidance sentence to clarify that the OPENSEARCH_ML_MODEL_ID is conditional (only relevant if the user has set or is using an existing model ID) — update the text that mentions OPENSEARCH_EMBEDDING_DIMENSION, OPENSEARCH_ML_MODEL_ID, and EMBEDDING_MODEL to say something like “if set / if using an existing model ID” so readers running auto-deployments aren’t misled that they must always configure OPENSEARCH_ML_MODEL_ID; ensure the sentence still instructs to recreate indices and reindex/revectorize when the embedding dimension changes.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@docs/self-hosting/govern/environment-variables.md`:
- Around line 236-238: Update the three environment variable descriptions for
BR_AWS_ACCESS_KEY_ID, BR_AWS_SECRET_ACCESS_KEY, and BR_AWS_REGION so they
reference Amazon Bedrock or "Bedrock embedding" generically instead of saying
"Bedrock Titan embedding"; edit the text for each entry to read something like
"AWS access key ID for Amazon Bedrock embedding" / "AWS secret access key for
Amazon Bedrock embedding" / "AWS region for Amazon Bedrock embedding" so it
correctly indicates the credentials apply to all Bedrock embedding models.
---
Nitpick comments:
In `@docs/self-hosting/govern/plane-ai.md`:
- Line 291: Reword the guidance sentence to clarify that the
OPENSEARCH_ML_MODEL_ID is conditional (only relevant if the user has set or is
using an existing model ID) — update the text that mentions
OPENSEARCH_EMBEDDING_DIMENSION, OPENSEARCH_ML_MODEL_ID, and EMBEDDING_MODEL to
say something like “if set / if using an existing model ID” so readers running
auto-deployments aren’t misled that they must always configure
OPENSEARCH_ML_MODEL_ID; ensure the sentence still instructs to recreate indices
and reindex/revectorize when the embedding dimension changes.
ℹ️ Review info
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Run ID: 87149e28-33be-467a-8e2a-079a6f88379d
📒 Files selected for processing (3)
docs/self-hosting/govern/aws-opensearch-embedding.mddocs/self-hosting/govern/environment-variables.mddocs/self-hosting/govern/plane-ai.md
| | **BR_AWS_ACCESS_KEY_ID** | AWS access key ID for Bedrock Titan embedding | Conditional | | ||
| | **BR_AWS_SECRET_ACCESS_KEY** | AWS secret access key for Bedrock Titan embedding | Conditional | | ||
| | **BR_AWS_REGION** | AWS region for Bedrock Titan embedding | Conditional | |
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🧩 Analysis chain
🌐 Web query:
In AWS Bedrock, do the same AWS credentials (access key, secret key, session token, region) apply for invoking both Amazon Titan embedding models and Cohere embedding models, with model ID being the main selector?
💡 Result:
Yes, the same AWS credentials (access key, secret key, session token, and region) apply for invoking both Amazon Titan embedding models and Cohere embedding models in AWS Bedrock. The primary difference is the model ID specified in the InvokeModel API call: e.g., "amazon.titan-embed-text-v2:0" for Titan and "cohere.embed-english-v3" for Cohere. Both use the Bedrock Runtime service (bedrock-runtime) with standard AWS SigV4 authentication via IAM permissions like bedrock:InvokeModel.
Citations:
- 1: https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-runtime_example_bedrock-runtime_InvokeModelWithResponseStream_TitanTextEmbeddings_section.html
- 2: https://docs.cohere.com/docs/amazon-bedrock
- 3: https://docs.aws.amazon.com/code-library/latest/ug/bedrock-runtime_example_bedrock-runtime_InvokeModelWithResponseStream_TitanTextEmbeddings_section.html
- 4: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html
- 5: https://docs.aws.amazon.com/bedrock/latest/userguide/inference-prereq.html
- 6: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html
- 7: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-embed-v3.html
- 8: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-embed-v4.html
🏁 Script executed:
# Check if the file exists and examine lines around 236-238 and 316-318
wc -l docs/self-hosting/govern/environment-variables.mdRepository: makeplane/developer-docs
Length of output: 121
🏁 Script executed:
# View lines 230-245 to see the context of lines 236-238
sed -n '230,245p' docs/self-hosting/govern/environment-variables.mdRepository: makeplane/developer-docs
Length of output: 1734
🏁 Script executed:
# View lines 310-325 to see the context of lines 316-318
sed -n '310,325p' docs/self-hosting/govern/environment-variables.mdRepository: makeplane/developer-docs
Length of output: 3987
Update wording to indicate Bedrock credentials apply to all embedding models, not just Titan.
Lines 236-238 describe AWS credentials as "for Bedrock Titan embedding," but these same credentials work with any Bedrock embedding model (Titan, Cohere, etc.). The model ID is the selector, not the credentials. Change descriptions to reference "Amazon Bedrock" or "Bedrock embedding" generically to prevent confusion about model support.
Note: Lines 316-318 already use appropriate generic wording ("Amazon Bedrock integration") and do not require changes.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@docs/self-hosting/govern/environment-variables.md` around lines 236 - 238,
Update the three environment variable descriptions for BR_AWS_ACCESS_KEY_ID,
BR_AWS_SECRET_ACCESS_KEY, and BR_AWS_REGION so they reference Amazon Bedrock or
"Bedrock embedding" generically instead of saying "Bedrock Titan embedding";
edit the text for each entry to read something like "AWS access key ID for
Amazon Bedrock embedding" / "AWS secret access key for Amazon Bedrock embedding"
/ "AWS region for Amazon Bedrock embedding" so it correctly indicates the
credentials apply to all Bedrock embedding models.
2 participants
Description
This PR updates self-hosting docs for Plane AI embeddings and OpenSearch so they match the current configuration model:
Type of Change
Screenshots and Media (if applicable)
Test Scenarios
References
Summary by CodeRabbit
Release Notes