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Update genai_cookbook site content with new agent sample app #32
Update genai_cookbook site content with new agent sample app #32
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Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
Signed-off-by: Prithvi Kannan <prithvi.kannan@databricks.com>
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change this up since the new agent code does not provide a B_quality_iteration/
folder. instead we just advise users to edit their earlier notebooks and use mlflow to compare
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change this up since the new agent code does not provide a B_quality_iteration/ folder. instead we just advise users to edit their earlier notebooks and use mlflow to compare
@@ -24,9 +24,8 @@ You can find all of the sample code referenced throughout this section [here](ht | |||
3. Unity Catalog Schema where the output Delta Tables with the parsed/chunked documents and Vector Search indexes are stored, either: | |||
- Write access to an existing [Unity Catalog](https://docs.databricks.com/en/data-governance/unity-catalog/index.html) and [Schema](https://docs.databricks.com/en/data-governance/unity-catalog/index.html#the-unity-catalog-object-model) | |||
- Permissions to create a new Unity Catalog and Schema - the setup Notebook will do this for you | |||
4. A [**single-user**](https://docs.databricks.com/en/compute/configure.html#access-modes) cluster running [DBR 14.3+](https://docs.databricks.com/en/release-notes/runtime/index.html) with access to the internet |
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serverless works now
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LGTM, just a minor nit.
@@ -66,19 +66,17 @@ As a reminder, there are 3 types of potential fixes: | |||
--> | |||
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#### Instructions | |||
For all types, you will use the [`B_quality_iteration/02_evaluate_fixes`](https://github.com/databricks/genai-cookbook/blob/main/rag_app_sample_code/B_quality_iteration/02_evaluate_fixes.py) Notebook to evaluate the resulting chain versus your baseline configuration (at first, this is your POC) and pick a "winner". This notebook will help you pick the winning experiment and deploy it to the Review App or a production-ready, scalable REST API. | |||
Based on which type of fix you want to make, modify the [`00_global_config`](https://github.com/databricks/genai-cookbook/blob/v0.2.0/agent_app_sample_code/00_global_config.py), [`02_data_pipeline`](https://github.com/databricks/genai-cookbook/blob/v0.2.0/agent_app_sample_code/02_data_pipeline.py), or the [`03_agent_proof_of_concept`](https://github.com/databricks/genai-cookbook/blob/v0.2.0/agent_app_sample_code/03_agent_proof_of_concept.py). Use the [`05_evaluate_poc_quality`](https://github.com/databricks/genai-cookbook/blob/v0.2.0/agent_app_sample_code/05_evaluate_poc_quality.py) notebook to evaluate the resulting chain versus your baseline configuration (at first, this is your POC) and pick a "winner". This notebook will help you pick the winning experiment and deploy it to the Review App or a production-ready, scalable REST API. | |||
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1. Open the [`B_quality_iteration/02_evaluate_fixes`](https://github.com/databricks/genai-cookbook/blob/main/rag_app_sample_code/B_quality_iteration/02_evaluate_fixes.py) Notebook |
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Curious here, does this notebook also work with agents?
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i think it should in theory, but we felt that it was too heavy-weight for this cookbook. we are planning to drop the whole /rag_app_sample_code folder.
Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
/agent_app_sample_code
at tagv0.2.0
B_quality_iteration
folder to handhold iterationHow is this PR tested?