| Name | kobai-sdk JSON |
| Version |
0.2.10
JSON |
| download |
| home_page | None |
| Summary | A package that enables interaction with a Kobai tenant. |
| upload_time | 2025-07-09 21:02:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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| keywords |
kobai
databricks
notebook
genie
|
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# Kobai SDK for Python (Alpha)
Alpha: This SDK is not currently supported for production use while we stabilize the interface.
The Kobai SDK for Python includes functionality to accelerate development with Python on the Kobai Semantic Layer. It does not cover all Kobai Studio features, but rather focuses on integrating a Kobai tenant with data science and AI activities on the backend.
## Getting Started
This exercise demonstrates using the Kobai SDK to create a Databricks "Genie" Data Room environment, enabling users to interact with Kobai data in an AI Chat interface.
1. Please install Kobai SDK for Python via `pip install kobai-sdk`, gather some configuration details of the Kobai instance and tenant to connect to, and instantiate `TenantClient`:
```python
from kobai import tenant_client, spark_client, databricks_client
schema = 'main.demo'
uri = 'https://demo.kobai.io'
tenant_id = '1'
tenant_name = 'My Demo Tenant'
k = tenant_client.TenantClient(tenant_name, tenant_id, uri, schema)
```
2. Authenticate with the Kobai instance:
```python
client_id = 'your_Entra_app_id_here'
tenant_id = 'your_Entra_directory_id_here'
k.authenticate(client_id, tenant_id)
```
3. Initialize a Spark client using your current `SparkSession`, and generate semantically-rich SQL views describing this Kobai tenant:
```python
k.spark_init_session(spark)
k.spark_generate_genie_views()
```
4. Initialize a Databricks API client using your Notebook context, and create a Genie Data Rooms environment for this Kobai tenant.
```python
notebook_context = dbutils.notebook.entry_point.getDbutils().notebook().getContext()
sql_warehouse = '8834d98a8agffa76'
k.databricks_init_notebook(notebook_context, sql_warehouse)
k.databricks_build_genie()
```
## AI Functionality
The Kobai SDK enables users to ask follow-up questions based on the results of previous queries. This functionality currently supports models hosted on Databricks and Azure OpenAI.
#### Prerequisites
Before asking a follow-up question, ensure that you have instantiated the TenantClient and completed the authentication process.
#### Steps to Ask a Follow-Up Question
1. List Questions: Retrieve the questionId or questionName. You can list all questions or filter by domain.
```python
k.list_questions() # List all questions
k.list_domains() # To get the domain labels
k.list_questions(domain_label="LegoCollecting") # List questions by domain
```
2. Ask a Question: Use either the questionId or questionName to submit your query.
```python
question_json = k.run_question_remote(2927) # By questionId
kobai_query_name = "Set ownership"
question_json = k.run_question_remote(k.get_question_id(kobai_query_name)) # By questionName
```
3. Ask a Follow-Up Question: Based on the initial results, you can ask a follow-up question using either Azure OpenAI, Databricks or a user-provided chat model.
#### Using Azure OpenAI
###### Authentication Methods:
1. ApiKey
```python
from kobai import ai_query, llm_config
import json
followup_question = "Which owner owns the most sets?"
llm_config = llm_config.LLMConfig(endpoint="https://kobaipoc.openai.azure.com/", api_key="YOUR_API_KEY", deployment="gpt-4o-mini", llm_provider="azure_openai")
output = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, llm_config=llm_config)
print(output)
```
2. Azure Active Directory Authentication
Ensure that the logged-in tenant has access to Azure OpenAI.
In case of databricks notebook, the logged in service principal should have access to Azure OpenAI.
```python
from kobai import ai_query, llm_config
import json
followup_question = "Which owner owns the most sets?"
llm_config = llm_config.LLMConfig(endpoint="https://kobaipoc.openai.azure.com/", deployment="gpt-4o-mini", llm_provider="azure_openai")
llm_config.get_azure_ad_token()
output = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, llm_config=llm_config)
print(output)
```
#### Using Databricks (Default Configuration)
```python
from kobai import ai_query, llm_config
import json
followup_question = "Which owner owns the most sets?"
llm_config = llm_config.LLMConfig()
output = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, llm_config=llm_config)
print(output)
```
#### User Provided Chat Model
```python
from kobai import ai_query, llm_config
import json
from langchain_openai import AzureChatOpenAI
followup_question = "Which owner owns the most sets?"
llm_config = llm_config.LLMConfig(debug=True)
chat_model = AzureChatOpenAI(
azure_endpoint="https://kobaipoc.openai.azure.com/", azure_deployment="gpt-4o-mini",
api_key = "YOUR_API_KEY",
openai_api_version="2024-02-15-preview",
temperature=0.5,
max_tokens=150,)
output = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, override_model=chat_model, llm_config=llm_config)
print(output)
```
## Limitations
This version of the SDK is limited to use in certain contexts, as described below:
- Authentication is limited to MS Entra AD.
- Functionality limited to Databricks Notebook environments at this time.
Raw data
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"name": "kobai-sdk",
"maintainer": null,
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"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "kobai, databricks, notebook, genie",
"author": null,
"author_email": "Ryan Oattes <ryan@kobai.io>",
"download_url": "https://files.pythonhosted.org/packages/60/64/88cc093d82e685146ccd8b590bfa8cd2a106b86069d6fc584115fccdbb89/kobai_sdk-0.2.10.tar.gz",
"platform": null,
"description": "# Kobai SDK for Python (Alpha)\n\nAlpha: This SDK is not currently supported for production use while we stabilize the interface.\n\nThe Kobai SDK for Python includes functionality to accelerate development with Python on the Kobai Semantic Layer. It does not cover all Kobai Studio features, but rather focuses on integrating a Kobai tenant with data science and AI activities on the backend.\n\n## Getting Started\n\nThis exercise demonstrates using the Kobai SDK to create a Databricks \"Genie\" Data Room environment, enabling users to interact with Kobai data in an AI Chat interface.\n\n1. Please install Kobai SDK for Python via `pip install kobai-sdk`, gather some configuration details of the Kobai instance and tenant to connect to, and instantiate `TenantClient`:\n\n```python\nfrom kobai import tenant_client, spark_client, databricks_client\n\nschema = 'main.demo'\nuri = 'https://demo.kobai.io'\ntenant_id = '1'\ntenant_name = 'My Demo Tenant'\n\nk = tenant_client.TenantClient(tenant_name, tenant_id, uri, schema)\n```\n\n2. Authenticate with the Kobai instance:\n\n```python\nclient_id = 'your_Entra_app_id_here'\ntenant_id = 'your_Entra_directory_id_here'\n\nk.authenticate(client_id, tenant_id)\n```\n\n3. Initialize a Spark client using your current `SparkSession`, and generate semantically-rich SQL views describing this Kobai tenant:\n\n```python\nk.spark_init_session(spark)\nk.spark_generate_genie_views()\n```\n\n4. Initialize a Databricks API client using your Notebook context, and create a Genie Data Rooms environment for this Kobai tenant.\n\n```python\nnotebook_context = dbutils.notebook.entry_point.getDbutils().notebook().getContext()\nsql_warehouse = '8834d98a8agffa76'\n\nk.databricks_init_notebook(notebook_context, sql_warehouse)\nk.databricks_build_genie()\n```\n\n## AI Functionality\nThe Kobai SDK enables users to ask follow-up questions based on the results of previous queries. This functionality currently supports models hosted on Databricks and Azure OpenAI. \n\n#### Prerequisites\nBefore asking a follow-up question, ensure that you have instantiated the TenantClient and completed the authentication process.\n\n#### Steps to Ask a Follow-Up Question\n\n1. List Questions: Retrieve the questionId or questionName. You can list all questions or filter by domain.\n\n```python\nk.list_questions() # List all questions\nk.list_domains() # To get the domain labels\nk.list_questions(domain_label=\"LegoCollecting\") # List questions by domain\n```\n\n2. Ask a Question: Use either the questionId or questionName to submit your query.\n\n```python\nquestion_json = k.run_question_remote(2927) # By questionId\nkobai_query_name = \"Set ownership\"\nquestion_json = k.run_question_remote(k.get_question_id(kobai_query_name)) # By questionName\n```\n\n3. Ask a Follow-Up Question: Based on the initial results, you can ask a follow-up question using either Azure OpenAI, Databricks or a user-provided chat model.\n\n#### Using Azure OpenAI\n\n###### Authentication Methods:\n\n1. ApiKey\n\n```python\nfrom kobai import ai_query, llm_config\nimport json\n\nfollowup_question = \"Which owner owns the most sets?\"\n\nllm_config = llm_config.LLMConfig(endpoint=\"https://kobaipoc.openai.azure.com/\", api_key=\"YOUR_API_KEY\", deployment=\"gpt-4o-mini\", llm_provider=\"azure_openai\")\n\noutput = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, llm_config=llm_config)\nprint(output)\n```\n\n2. Azure Active Directory Authentication\n\nEnsure that the logged-in tenant has access to Azure OpenAI.\nIn case of databricks notebook, the logged in service principal should have access to Azure OpenAI.\n\n```python\nfrom kobai import ai_query, llm_config\nimport json\n\nfollowup_question = \"Which owner owns the most sets?\"\n\nllm_config = llm_config.LLMConfig(endpoint=\"https://kobaipoc.openai.azure.com/\", deployment=\"gpt-4o-mini\", llm_provider=\"azure_openai\")\nllm_config.get_azure_ad_token()\n\noutput = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, llm_config=llm_config)\nprint(output)\n```\n\n#### Using Databricks (Default Configuration)\n\n```python\nfrom kobai import ai_query, llm_config\nimport json\n\nfollowup_question = \"Which owner owns the most sets?\"\n\nllm_config = llm_config.LLMConfig()\n\noutput = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, llm_config=llm_config)\nprint(output)\n```\n\n#### User Provided Chat Model\n\n```python\nfrom kobai import ai_query, llm_config\nimport json\nfrom langchain_openai import AzureChatOpenAI\n\nfollowup_question = \"Which owner owns the most sets?\"\n\nllm_config = llm_config.LLMConfig(debug=True)\n\nchat_model = AzureChatOpenAI(\nazure_endpoint=\"https://kobaipoc.openai.azure.com/\", azure_deployment=\"gpt-4o-mini\",\napi_key = \"YOUR_API_KEY\",\nopenai_api_version=\"2024-02-15-preview\",\ntemperature=0.5, \nmax_tokens=150,)\n\noutput = ai_query.followup_question(followup_question, json.dumps(question_json), kobai_query_name, override_model=chat_model, llm_config=llm_config)\nprint(output)\n```\n\n## Limitations\n\nThis version of the SDK is limited to use in certain contexts, as described below:\n\n- Authentication is limited to MS Entra AD.\n- Functionality limited to Databricks Notebook environments at this time.\n",
"bugtrack_url": null,
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For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n \n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. 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