### Project Setup and Dependency Installation

Source: https://github.com/marcoeg/bauplan/blob/main/06-near-real-time/README.md

Commands to clone the example repository and install necessary Python dependencies using pip.

```bash
git clone https://github.com/BauplanLabs/wap_with_bauplan_and_prefect.git
cd wap_with_bauplan_and_prefect
pip install -r requirements.txt
```

--------------------------------

### Get Branches Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Example of how to list available branches using the Bauplan client.

```python
import bauplan
client = bauplan.Client()

for branch in client.get_branches():
    print(branch.name, branch.hash)
```

--------------------------------

### Get Branch Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Example of how to retrieve a branch using the Bauplan client.

```python
import bauplan
client = bauplan.Client()

branch = client.get_branch('my_branch_name')
print(branch.hash)
```

--------------------------------

### Define a Bauplan Input Model with Pushdown

Source: https://github.com/marcoeg/bauplan/blob/main/01-quick-start/README.md

Example of defining an input model in Bauplan that scans an existing table with column selection and filter pushdown for pickup time.

```python
@bauplan.model()
def trips():
    # Scans 'taxi_fhvhv' table with column selection and filter pushdown on pickup time
    pass
```

--------------------------------

### Setup: Clone Repository

Source: https://github.com/marcoeg/bauplan/blob/main/08-playlist-recomendations-mongodb/README.md

Clones the example repository for the recommender system project.

```bash
git clone https://github.com/BauplanLabs/wap_with_bauplan_and_prefect.git
cd wap_with_bauplan_and_prefect
```

--------------------------------

### Project Setup and Execution Commands

Source: https://github.com/marcoeg/bauplan/blob/main/10-pdf-analysis-with-openai/README.md

Provides bash commands for setting up the project, installing dependencies, configuring environment variables, and running the Prefect pipeline.

```bash
git clone https://github.com/BauplanLabs/wap_with_bauplan_and_prefect.git
cd wap_with_bauplan_and_prefect
pip install bauplan prefect PyPDF2 pandas openai

export BAUPLAN_API_KEY=<your-api-key>
export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>
export OPENAI_API_KEY=<your-openai-api-key>

prefect server start
prefect deploy -f pdf_analysis_pipeline.py
prefect run -n pdf-analysis-pipeline
bauplan logs
```

--------------------------------

### Setup Commands for Bauplan and Prefect Project

Source: https://github.com/marcoeg/bauplan/blob/main/13-data-products/README.md

Bash commands for setting up the project environment, including cloning the repository, installing dependencies, and configuring environment variables for Bauplan API key and AWS credentials.

```bash
git clone https://github.com/BauplanLabs/wap_with_bauplan_and_prefect.git
cd wap_with_bauplan_and_prefect
pip install bauplan prefect pandas
export BAUPLAN_API_KEY=<your-api-key>
export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>
```

--------------------------------

### Define a Bauplan Join Model

Source: https://github.com/marcoeg/bauplan/blob/main/01-quick-start/README.md

Example of a Bauplan model that joins two tables and combines chunks. This model specifies its execution environment and dependencies.

```python
@bauplan.model()
@bauplan.python(python_version='3.11')
def trips_and_zones(trips, zones):
    # Joins trips data with zones table based on PULocationID and LocationID
    # Combines chunks post-join
    pass
```

--------------------------------

### Setup: Install Dependencies

Source: https://github.com/marcoeg/bauplan/blob/main/08-playlist-recomendations-mongodb/README.md

Installs the necessary Python libraries for the project, including bauplan, pymongo, sentence-transformers, and pandas.

```bash
pip install bauplan pymongo sentence-transformers pandas
```

--------------------------------

### Project Setup and Configuration Commands

Source: https://github.com/marcoeg/bauplan/blob/main/09-unstructured-to-structured-with-llm/README.md

Provides essential bash commands for setting up the project, including cloning the repository, installing dependencies, and configuring environment variables for API keys and AWS credentials.

```bash
git clone https://github.com/BauplanLabs/wap_with_bauplan_and_prefect.git
cd wap_with_bauplan_and_prefect
pip install -r requirements.txt

export BAUPLAN_API_KEY=<your-api-key>
export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>
export LLM_API_KEY=<your-llm-api-key>
```

--------------------------------

### Project Setup and Configuration

Source: https://github.com/marcoeg/bauplan/blob/main/07-entity-matching-with-llm/README.md

Provides bash commands for setting up the project, including cloning the repository, installing dependencies, and configuring necessary environment variables for Bauplan, AWS, and LLM API keys.

```bash
git clone https://github.com/BauplanLabs/wap_with_bauplan_and_prefect.git
cd wap_with_bauplan_and_prefect
pip install -r requirements.txt

export BAUPLAN_API_KEY=<your-api-key>
export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>
export LLM_API_KEY=<your-llm-api-key>
```

--------------------------------

### Define a Python Bauplan Model

Source: https://github.com/marcoeg/bauplan/blob/main/01-quick-start/README.md

Example of defining a Python model for data transformation using Bauplan. This model specifies its Python environment and dependencies, including pandas and numpy.

```python
@bauplan.model()
@bauplan.python(python_version='3.11', packages=['pandas==1.5.3', 'numpy==1.23.2'])
def normalized_taxi_trips(trips_and_zones):
    # Convert Arrow to Pandas
    # Apply timestamp filters
    # Remove trips with 0 or excessive miles
    # Log-transform trip_miles
    pass
```

--------------------------------

### Get Tables Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Illustrates how to retrieve a list of tables and views from a specified branch using the `get_tables` method. The example shows iterating through the results and printing table names and kinds.

```python
import bauplan
client = bauplan.Client()

for table in client.get_tables('my_ref_or_branch_name'):
    print(table.name, table.kind)
```

--------------------------------

### Run Bauplan Pipeline

Source: https://github.com/marcoeg/bauplan/blob/main/01-quick-start/README.md

Commands to create a branch, checkout, and run the Bauplan pipeline. These commands orchestrate the execution of the defined data transformation DAG.

```bash
bauplan branch create <username>.<branch_name>
bauplan checkout <username>.<branch_name>
bauplan run
```

--------------------------------

### Get Table Data Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Demonstrates how to fetch table data and metadata using the `get_table` method. It shows how to iterate through table fields and access records.

```python
import bauplan
client = bauplan.Client()

table = client.get_table(
    table='my_table_name',
    ref='my_ref_or_branch_name',
)

for c in table.fields:
    print(c.name, c.required, c.type)

print(table.records)
```

--------------------------------

### Get Tag Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Provides an example of how to retrieve a specific tag using its name with the `get_tag` method and access its hash.

```python
import bauplan
client = bauplan.Client()

tag = client.get_tag('my_tag_name')
print(tag.hash)
```

--------------------------------

### Clone Example Repository

Source: https://github.com/marcoeg/bauplan/blob/main/11-RAG-service-support-agent/README.md

Clones the example repository for the RAG pipeline using Bauplan and Prefect.

```bash
git clone https://github.com/BauplanLabs/wap_with_bauplan_and_prefect.git
cd wap_with_bauplan_and_prefect
```

--------------------------------

### Revert Table Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Example of how to revert a table to a previous state using the Bauplan client.

```python
import bauplan
client = bauplan.Client()

assert client.revert_table(
    table='my_table_name',
    source_ref='my_ref_or_branch_name',
    into_branch='main',
)
```

--------------------------------

### Shortened Trips and Zones Model

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

A concise definition for the 'trips_and_zones' model using Bauplan and Python, suitable for quick setup or examples.

```python
@bauplan.model() @bauplan.python('3.11') def trips_and_zones(...):
```

--------------------------------

### Prefect Pipeline Deployment and Execution

Source: https://github.com/marcoeg/bauplan/blob/main/06-near-real-time/README.md

Commands to start the Prefect server, deploy the defined pipeline, and then run an instance of the pipeline.

```bash
prefect server start
prefect deploy -f nrt_pipeline.py
prefect run -n nrt-pipeline
```

--------------------------------

### Python Example: Query to Generator

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Demonstrates how to use the `query_to_generator` method of the Bauplan Client to execute a SQL query and iterate over the results.

```python
import bauplan
client = bauplan.Client()

res = client.query_to_generator(
    query='SELECT c1 FROM my_table',
    ref='my_ref_or_branch_name',
)
for row in res:
    # do logic

```

--------------------------------

### Install Dependencies

Source: https://github.com/marcoeg/bauplan/blob/main/11-RAG-service-support-agent/README.md

Installs the necessary Python libraries for the RAG pipeline, including Bauplan, Prefect, sentence-transformers, pandas, faiss-cpu, and openai.

```bash
pip install bauplan prefect sentence-transformers pandas faiss-cpu openai
```

--------------------------------

### Delete Table Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Example of how to delete a table using the Bauplan client.

```python
import bauplan
client = bauplan.Client()

assert client.delete_table(
    table='my_table_name',
    branch='my_branch_name',
)
```

--------------------------------

### Example Usage of query_to_parquet_file

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Demonstrates how to use the `query_to_parquet_file` function to export query results to a Parquet file.

```python
import bauplan
client = bauplan.Client()

client.query_to_parquet_file(
    path='./my.parquet',
    query='SELECT c1 FROM my_table',
    ref='my_ref_or_branch_name',
)
```

--------------------------------

### Delete Namespace Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Example of how to delete a namespace using the Bauplan client.

```python
import bauplan
client = bauplan.Client()

assert client.delete_namespace(
    namespace='my_namespace_name',
    branch='my_branch_name',
)
```

--------------------------------

### Python Example: Query to JSON File

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Illustrates how to use the `query_to_json_file` method of the Bauplan Client to export query results to a JSON file.

```python
import bauplan
client = bauplan.Client()

client.query_to_json_file(
    path='./my.json',
    query='SELECT c1 FROM my_table',
    ref='my_ref_or_branch_name',
)

```

--------------------------------

### Match Products with LLM

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Matches products from two lists by iterating through inputs, formatting prompts, and calling an OpenAI client. This is suitable for tasks requiring semantic matching or comparison.

```python
def match_with_llm(_product_a_list, _product_b_list, _llm_client):
    # loop over inputs, format prompts, call OpenAI
    pass
```

--------------------------------

### Python Environment with DuckDB 0.10.3

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Environment_Setups.md

Sets up a Python 3.11 environment with DuckDB version 0.10.3. This is beneficial for efficient in-process analytical data management.

```python
@bauplan.python('3.11', pip={'duckdb': '0.10.3'})
```

--------------------------------

### Check Namespace Existence Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Shows how to verify the existence of a namespace within a specific ref or branch using the `has_namespace` method.

```python
import bauplan
client = bauplan.Client()

assert client.has_namespace(
    namespace='my_namespace_name',
    ref='my_ref_or_branch_name',
)
```

--------------------------------

### Create Public S3 Bucket

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Creates an S3 bucket and applies a public read policy. It checks for existing buckets before creation and uses a predefined bucket policy.

```python
import json

def create_public_bucket(s3_client, bucket_name: str):
    if not does_bucket_exist(s3_client, bucket_name):
        s3_client.create_bucket(Bucket=bucket_name)
    bucket_policy = {...}
    s3_client.put_bucket_policy(Bucket=bucket_name, Policy=json.dumps(bucket_policy))
```

--------------------------------

### Create Tag in Bauplan

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Provides an example of creating a new tag in Bauplan, referencing a specific branch or ref. This is useful for marking specific points in the project history.

```python
import bauplan
client = bauplan.Client()

assert client.create_tag(
    tag='my_tag',
    from_ref='my_ref_or_branch_name',
)
```

--------------------------------

### Serialized Walmart Products with Pandas

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

A placeholder function for serializing Walmart product data. It uses Pandas and is defined within the Bauplan framework.

```python
@bauplan.python('3.11', pip={'pandas': '2.2.0'}) @bauplan.model() def serialized_walmart_products(...)
```

--------------------------------

### Python Environment with DuckDB and OpenAI

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Environment_Setups.md

Establishes a Python 3.11 environment with DuckDB (1.0.0) and OpenAI (1.57.2). This configuration supports data analysis integrated with AI/ML workflows.

```python
@bauplan.python('3.11', pip={'duckdb': '1.0.0', 'openai': '1.57.2'})
```

--------------------------------

### Generate Tip Predictions with Pandas and Scikit-learn

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Generates tip predictions using a pre-trained linear regression model loaded via bauplan.store. Requires pandas and scikit-learn.

```python
@bauplan.model(materialization_strategy='REPLACE')
@bauplan.python('3.11', pip={'scikit-learn': '1.3.2', 'pandas': '2.1.0'})
def tip_predictions(data=bauplan.Model('train_regression_model')):
    from bauplan.store import load_obj
    reg = load_obj("regression")
    test_set = data.to_pandas()
    y_hat = reg.predict(test_set[['log_trip_miles', 'base_passenger_fare', 'trip_time']])
    prediction_df = test_set.copy()
    prediction_df['predictions'] = y_hat
    return prediction_df
```

--------------------------------

### Monitoring Prefect and Bauplan

Source: https://github.com/marcoeg/bauplan/blob/main/06-near-real-time/README.md

Instructions on how to access the Prefect UI for monitoring and how to view Bauplan logs.

```bash
prefect ui
bauplan logs
```

--------------------------------

### Prefect Pipeline Deployment and Execution

Source: https://github.com/marcoeg/bauplan/blob/main/07-entity-matching-with-llm/README.md

Demonstrates how to deploy and run a Prefect pipeline. It includes commands to start the Prefect server, deploy the defined flow, and execute the pipeline.

```bash
prefect server start
prefect deploy -f llm_pipeline.py
prefect run -n llm-pipeline
```

--------------------------------

### Plan Table Creation in Bauplan

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Illustrates planning a table creation from S3 data using bauplan. This involves providing the table name, S3 search URI, and optional parameters like branch and partitioning.

```python
import bauplan
client = bauplan.Client()

plan_state = client.plan_table_creation(
    table='my_table_name',
    search_uri='s3://path/to/my/files/*.parquet',
    ref='my_ref_or_branch_name',
)
if plan_state.error:
    plan_error_action(...)
success_action(plan_state.plan)
```

--------------------------------

### Running the Pipeline: Deploy

Source: https://github.com/marcoeg/bauplan/blob/main/08-playlist-recomendations-mongodb/README.md

Deploys the Prefect recommender pipeline. This command makes the pipeline available for execution within the Prefect environment.

```bash
prefect deploy -f recommender_pipeline.py
```

--------------------------------

### Delete Tag Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Example of how to delete a tag using the Bauplan client.

```python
import bauplan
client = bauplan.Client()

assert client.delete_tag('my_tag_name')
```

--------------------------------

### Running Prefect Pipelines

Source: https://github.com/marcoeg/bauplan/blob/main/13-data-products/README.md

Bash commands to manage and run Prefect pipelines. This includes starting the Prefect server, deploying a pipeline defined in a Python file, and executing a deployed pipeline.

```bash
prefect server start
prefect deploy -f dataproduct_pipeline.py
prefect run -n dataproduct-pipeline
```

--------------------------------

### Bauplan Client Initialization and Namespace Retrieval

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Demonstrates how to initialize the Bauplan client and retrieve a specific namespace using its name and a reference. Includes error handling for non-existent namespaces or refs.

```python
import bauplan
client = bauplan.Client()

namespace = client.get_namespace(
    namespace='my_namespace_name',
    ref='my_ref_or_branch_name',
)
```

--------------------------------

### Simple Bauplan Model Definition

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

A basic example of defining a Bauplan model that takes a table as input and returns it, serving as a template for transformations.

```python
@bauplan.model()
def my_model(data=bauplan.Model('some_table')):
    # Transformation logic here
    return data
```

--------------------------------

### Bauplan Client Initialization and Query

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Demonstrates how to initialize the bauplan client and perform a query, returning the result as an Arrow Table, which can then be converted to a pandas DataFrame.

```python
import bauplan
client = bauplan.Client()

# query the table and return result set as an arrow Table
my_table = client.query('SELECT sum(trips) trips FROM travel_table', branch_name='main')

# efficiently cast the table to a pandas DataFrame
df = my_table.to_pandas()
```

--------------------------------

### Python Environment with DuckDB 1.0.0

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Environment_Setups.md

Configures a Python 3.11 environment with DuckDB version 1.0.0. This setup is recommended for advanced analytical queries and data processing.

```python
@bauplan.python('3.11', pip={'duckdb': '1.0.0'})
```

--------------------------------

### Materialization Strategy Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Demonstrates setting a materialization strategy for a Bauplan model, specifying 'REPLACE' to overwrite existing data. This is often used with data warehousing or batch processing.

```arrow
@bauplan.model(materialization_strategy='REPLACE')
```

--------------------------------

### Prefect Pipeline Deployment and Execution

Source: https://github.com/marcoeg/bauplan/blob/main/09-unstructured-to-structured-with-llm/README.md

Demonstrates how to deploy and run the Prefect pipeline. It includes starting the Prefect server, deploying the defined flow, and executing the pipeline, along with commands for monitoring logs.

```bash
prefect server start
prefect deploy -f llm_tabular_pipeline.py
prefect run -n llm-tabular-pipeline

# Monitoring
# Access Prefect UI: http://localhost:4200
# Check Bauplan logs:
bauplan logs
```

--------------------------------

### Python Environment with Pandas 2.2.0

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Environment_Setups.md

Configures a Python 3.11 environment with Pandas version 2.2.0. This setup is suitable for projects that require the latest features or bug fixes in Pandas.

```python
@bauplan.python('3.11', pip={'pandas': '2.2.0'})
```

--------------------------------

### Create Table in Bauplan

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Illustrates the process of creating a table in Bauplan from S3 data. It shows how to specify the table name, the S3 URI for data, and optionally the branch and partitioning.

```python
import bauplan
client = bauplan.Client()

table = client.create_table(
    table='my_table_name',
    search_uri='s3://path/to/my/files/*.parquet',
    ref='my_ref_or_branch_name',
)
```

--------------------------------

### Get Item Recommendations

Source: https://github.com/marcoeg/bauplan/blob/main/08-playlist-recomendations-mongodb/README.md

This Python code snippet demonstrates how to get recommendations for an item by querying a MongoDB collection using pre-computed embeddings. It utilizes the SentenceTransformer library to generate embeddings and a custom `query_recommendations` function. Ensure MongoDB URI is set in environment variables.

```python
from sentence_transformers import SentenceTransformer
import os

sample_embedding = SentenceTransformer('all-MiniLM-L6-v2').encode("Sample item description").tolist()
results = query_recommendations(
    mongodb_uri=os.getenv("MONGODB_URI"),
    db_name="recommender_db",
    collection_name="items",
    embedding=sample_embedding,
    top_k=5
)
for result in results:
    print(result['name'], result['description'])
```

--------------------------------

### Product LLM Matches with DuckDB and OpenAI

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

A placeholder function for matching products using LLM, likely involving DuckDB for data querying and OpenAI for LLM capabilities. It's configured for internet access.

```python
@bauplan.python('3.11', pip={'duckdb': '1.0.0', 'openai': '1.57.2'}) @bauplan.model(materialization_strategy='REPLACE', internet_access=True) def product_llm_matches(...)
```

--------------------------------

### Evaluate Regression Model

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Calculates the score (e.g., R-squared) of a trained regression model on test data.

```python
reg.score(X_test, y_test)
```

--------------------------------

### Bauplan Client Parameters

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Defines the configuration parameters for initializing the Bauplan client, including API keys, branches, namespaces, caching, debugging, and endpoint configurations.

```APIDOC
Parameters:

- profile: `str | None = None`
  (optional) The Bauplan config profile name to use to determine `api_key`.
- api_key: `str | None = None`
  (optional) Your unique Bauplan API key; mutually exclusive with `profile`. If not provided, fetch precedence is 1) environment `BAUPLAN_API_KEY` 2) `.bauplan/config.yml`.
- branch: `str | None = None`
  (optional) The default branch to use for queries and runs. If not provided, `active_branch` from the profile is used.
- namespace: `str | None = None`
  (optional) The default namespace to use for queries and runs.
- cache: `'on' | 'off' | None = None`
  (optional) Whether to enable or disable caching for all requests.
- debug: `bool | None = None`
  (optional) Whether to enable or disable debug mode for all requests.
- verbose: `bool | None = None`
  (optional) Whether to enable or disable verbose mode for all requests.
- args: `dict[str, str] | None = None`
  (optional) Additional arguments to pass to all requests.
- api_endpoint: `str | None = None`
  (optional) The Bauplan API endpoint to use. If not provided, fetch precedence is 1) environment `BAUPLAN_API_ENDPOINT` 2) `.bauplan/config.yml`.
- catalog_endpoint: `str | None = None`
  (optional) The Bauplan catalog endpoint to use. If not provided, fetch precedence is 1) environment `BAUPLAN_CATALOG_ENDPOINT` 2) `.bauplan/config.yml`.
- catalog_max_records: `int | None = None`
  (optional) The maximum number of records to fetch, per page, from the catalog.
- client_timeout: `int | None = None`
  (optional) The client timeout in seconds for all requests.
- env: `str | None = None`
  (optional) The environment to use for all requests. Default: `'prod'`.
- config_file_path: `str | Path | None = None`
  (optional) The path to the Bauplan config file to use. If not provided, fetch precedence is 1) environment `BAUPLAN_CONFIG_PATH` 2) `~/.bauplan/config.yml`.
  *Note*: Based on our previous discussion, if you’re using a `.env` file to set `BAUPLAN_CONFIG_PATH`, ensure it’s in the correct project directory to avoid loading a default `~/.bauplan/config.yml`.
- user_session_token: `str | None = None`
  (optional) Your unique Bauplan user session token.
- feature_flags: `dict[str, Any] | None = None`
  (optional) Feature flags to enable experimental features.
```

--------------------------------

### Check Branch Existence Example

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Demonstrates how to check if a branch exists using the `has_branch` method, asserting its presence.

```python
import bauplan
client = bauplan.Client()

assert client.has_branch('my_branch_name')
```

--------------------------------

### Running the Pipeline: Run Locally

Source: https://github.com/marcoeg/bauplan/blob/main/08-playlist-recomendations-mongodb/README.md

Executes the Prefect recommender pipeline locally. This is useful for testing and development before deploying to a remote environment.

```bash
prefect run -n recommender-pipeline
```

--------------------------------

### Convert Arrow to Pandas DataFrame

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Converts an Arrow DataFrame to a Pandas DataFrame. This is a common operation for data manipulation.

```python
df = data.to_pandas()
```

--------------------------------

### Load Model Object using bauplan.store

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Retrieves a previously saved model object from storage using the bauplan.store.load_obj function.

```python
from bauplan.store import load_obj
reg = load_obj("regression")
```

--------------------------------

### Bauplan Client Authentication Methods

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Module_Documentation.md

Illustrates various ways to authenticate the bauplan client, prioritizing API key over profile configuration, and showing direct specification of API key or profile.

```python
# by default, authenticate from BAUPLAN_API_KEY >> BAUPLAN_PROFILE >> ~/.bauplan/config.yml
client = bauplan.Client()
# client used ~/.bauplan/config.yml profile 'default'

os.environ['BAUPLAN_PROFILE'] = "someprofile"
client = bauplan.Client()
# >> client now uses profile 'someprofile'

os.environ['BAUPLAN_API_KEY'] = "mykey"
client = bauplan.Client()
# >> client now authenticates with api_key value "mykey", because api key > profile

# specify authentication directly - this supercedes BAUPLAN_API_KEY in the environment
client = bauplan.Client(api_key='MY_KEY')

# specify a profile from ~/.bauplan/config.yml - this supercedes BAUPLAN_PROFILE in the environment
client = bauplan.Client(profile='default')
```

--------------------------------

### Train Linear Regression Model

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Trains a linear regression model using provided training data (X_train, y_train).

```python
reg = LinearRegression().fit(X_train, y_train)
```

--------------------------------

### Setup: Configure Environment Variables

Source: https://github.com/marcoeg/bauplan/blob/main/08-playlist-recomendations-mongodb/README.md

Sets up essential environment variables for API keys and database URIs. These include Bauplan API key, AWS credentials, and MongoDB Atlas URI.

```bash
export BAUPLAN_API_KEY=<your-api-key>
export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>
export MONGODB_URI=<your-mongodb-atlas-uri>
```

--------------------------------

### Prepare Training Dataset with Pandas and Scikit-learn

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

Prepares a training dataset by dropping null values, applying log transformation to trip miles, and scaling features using StandardScaler. Requires pandas and scikit-learn.

```python
@bauplan.model()
@bauplan.python('3.10', pip={'pandas': '1.5.3', 'scikit-learn': '1.3.2'})
def training_dataset(data=bauplan.Model('clean_taxi_trips')):
    df = data.to_pandas()
    df = df.dropna()
    df['log_trip_miles'] = np.log10(df['trip_miles'])
    scaler = StandardScaler()
    scaled_features = scaler.fit_transform(df[['log_trip_miles', 'base_passenger_fare', 'trip_time']])
    scaled_df = pd.DataFrame(scaled_features, columns=['log_trip_miles', 'base_passenger_fare', 'trip_time'])
    return scaled_df
```

--------------------------------

### Shortened Taxi FHVHV Model

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

A compact definition for the 'taxi_fhvhv' model in Bauplan, demonstrating filter pushdown and column pruning for performance.

```python
trips=bauplan.Model('taxi_fhvhv', columns=[...], filter='...')
```

--------------------------------

### Configure Environment Variables

Source: https://github.com/marcoeg/bauplan/blob/main/11-RAG-service-support-agent/README.md

Sets up essential environment variables for Bauplan API key, AWS credentials, OpenAI API key, and optionally a vector database URL.

```bash
export BAUPLAN_API_KEY=<your-api-key>
export AWS_ACCESS_KEY_ID=<your-access-key>
export AWS_SECRET_ACCESS_KEY=<your-secret-key>
export OPENAI_API_KEY=<your-openai-api-key>
export VECTOR_DB_URL=<your-vector-db-url>  # Optional, depending on vector DB
```

--------------------------------

### Serialized Amazon Products with Pandas

Source: https://github.com/marcoeg/bauplan/blob/main/Documentation/Code_Examples.md

A placeholder function for serializing Amazon product data. It uses Pandas and is defined within the Bauplan framework.

```python
@bauplan.python('3.11', pip={'pandas': '2.2.0'}) @bauplan.model() def serialized_amazon_products(...)
```