your-repo/
├── prompts/
│   ├── chat_assistant.prompt
│   ├── code_reviewer.prompt
│   └── data_analyst.prompt

---
model: gpt-4
temperature: 0.7
max_tokens: 150
input:
  schema:
    user_message: string
    system_context?: string
---

{% if system_context %}System: {{system_context}}

{% endif %}User: {{user_message}}

import litellm

# Configure BitBucket access
bitbucket_config = {
    "workspace": "your-workspace",
    "repository": "your-repo",
    "access_token": "your-access-token",
    "branch": "main"  # optional, defaults to main
}

# Set global BitBucket configuration
litellm.set_global_bitbucket_config(bitbucket_config)

import litellm

# Configure BitBucket access with basic auth
bitbucket_config = {
    "workspace": "your-workspace",
    "repository": "your-repo",
    "username": "your-username",
    "access_token": "your-app-password",  # Use app password for basic auth
    "auth_method": "basic",
    "branch": "main"
}

litellm.set_global_bitbucket_config(bitbucket_config)

# Use with completion - the model prefix 'bitbucket/' tells LiteLLM to use BitBucket prompt management
response = litellm.completion(
    model="bitbucket/gpt-4",  # The actual model comes from the .prompt file
    prompt_id="prompts/chat_assistant", # Location of the prompt file
    prompt_variables={
        "user_message": "What is machine learning?",
        "system_context": "You are a helpful AI tutor."
    },
    # Any additional messages will be appended after the prompt
    messages=[{"role": "user", "content": "Please explain it simply."}]
)

print(response.choices[0].message.content)

---
model: gpt-4
temperature: 0.7
---
System: You are a helpful assistant.

User: {{user_message}}

model_list:
  - model_name: my-bitbucket-model
    litellm_params:
      model: bitbucket/gpt-4
      prompt_id: "prompts/hello"
      api_key: os.environ/OPENAI_API_KEY

litellm_settings:
  global_bitbucket_config:
    workspace: "your-workspace"
    repository: "your-repo"
    access_token: "your-access-token"
    branch: "main"

litellm --config config.yaml --detailed_debug

curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "my-bitbucket-model",
    "messages": [{"role": "user", "content": "IGNORED"}],
    "prompt_variables": {
        "user_message": "What is the capital of France?"
    }
}'

---
# Model configuration
model: gpt-4
temperature: 0.7
max_tokens: 500

# Input schema (optional)
input:
  schema:
    user_message: string
    system_context?: string
---

System: You are a helpful {{role}} assistant.

User: {{user_message}}

---
model: gpt-4
temperature: 0.3
---
System: You are a helpful coding assistant.

User: {{user_question}}

---
model: "{{preferred_model}}"  # Model can be a variable
temperature: 0.7
---
System: You are a helpful assistant specialized in {{domain}}.

User: {{user_message}}

bitbucket_config = {
    "workspace": str,           # Required: BitBucket workspace name
    "repository": str,          # Required: Repository name
    "access_token": str,        # Required: BitBucket access token or app password
    "branch": str,              # Optional: Branch to fetch from (default: "main")
    "base_url": str,            # Optional: Custom BitBucket API URL
    "auth_method": str,         # Optional: "token" or "basic" (default: "token")
    "username": str,            # Optional: Username for basic auth
    "base_url" : str            # Optional: Incase where the base url is not https://api.bitbucket.org/2.0
}

response = litellm.completion(
    model="bitbucket/<base_model>",  # required (e.g., bitbucket/gpt-4)
    prompt_id=str,                   # required - the .prompt filename without extension
    prompt_variables=dict,           # optional - variables for template rendering
    bitbucket_config=dict,           # optional - BitBucket configuration (if not set globally)
    messages=list,                   # optional - additional messages
)

import litellm
litellm.set_verbose = True

# Your BitBucket prompt calls will now show detailed logs
response = litellm.completion(
    model="bitbucket/gpt-4",
    prompt_id="your_prompt",
    prompt_variables={"key": "value"}
)