Introduce Llama stack distributions (#22)

* Add distribution CLI scaffolding

* More progress towards `llama distribution install`

* getting closer to a distro definition, distro install + configure works

* Distribution server now functioning

* read existing configuration, save enums properly

* Remove inference uvicorn server entrypoint and llama inference CLI command

* updated dependency and client model name

* Improved exception handling

* local imports for faster cli

* undo a typo, add a passthrough distribution

* implement full-passthrough in the server

* add safety adapters, configuration handling, server + clients

* cleanup, moving stuff to common, nuke utils

* Add a Path() wrapper at the earliest place

* fixes

* Bring agentic system api to toolchain

Add adapter dependencies and resolve adapters using a topological sort

* refactor to reduce size of `agentic_system`

* move straggler files and fix some important existing bugs

* ApiSurface -> Api

* refactor a method out

* Adapter -> Provider

* Make each inference provider into its own subdirectory

* installation fixes

* Rename Distribution -> DistributionSpec, simplify RemoteProviders

* dict key instead of attr

* update inference config to take model and not model_dir

* Fix passthrough streaming, send headers properly not part of body :facepalm

* update safety to use model sku ids and not model dirs

* Update cli_reference.md

* minor fixes

* add DistributionConfig, fix a bug in model download

* Make install + start scripts do proper configuration automatically

* Update CLI_reference

* Nuke fp8_requirements, fold fbgemm into common requirements

* Update README, add newline between API surface configurations

* Refactor download functionality out of the Command so can be reused

* Add `llama model download` alias for `llama download`

* Show message about checksum file so users can check themselves

* Simpler intro statements

* get ollama working

* Reduce a bunch of dependencies from toolchain

Some improvements to the distribution install script

* Avoid using `conda run` since it buffers everything

* update dependencies and rely on LLAMA_TOOLCHAIN_DIR for dev purposes

* add validation for configuration input

* resort imports

* make optional subclasses default to yes for configuration

* Remove additional_pip_packages; move deps to providers

* for inline make 8b model the default

* Add scripts to MANIFEST

* allow installing from test.pypi.org

* Fix #2 to help with testing packages

* Must install llama-models at that same version first

* fix PIP_ARGS

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Hardik Shah <hjshah@meta.com>

llama_toolchain/agentic_system/meta_reference/system_prompt.py

@@ -0,0 +1,152 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import json
+from datetime import datetime
+from typing import List
+
+from llama_toolchain.inference.api import (
+    BuiltinTool,
+    Message,
+    SystemMessage,
+    ToolDefinition,
+)
+
+from .tools.builtin import SingleMessageBuiltinTool
+
+
+def get_agentic_prefix_messages(
+    builtin_tools: List[SingleMessageBuiltinTool], custom_tools: List[ToolDefinition]
+) -> List[Message]:
+    messages = []
+    content = ""
+    if builtin_tools:
+        content += "Environment: ipython\n"
+
+        tool_str = ", ".join(
+            [
+                t.get_name()
+                for t in builtin_tools
+                if t.get_name() != BuiltinTool.code_interpreter.value
+            ]
+        )
+        if tool_str:
+            content += f"Tools: {tool_str}\n"
+
+    current_date = datetime.now()
+    formatted_date = current_date.strftime("%d %B %Y")
+    date_str = f"""
+Cutting Knowledge Date: December 2023
+Today Date: {formatted_date}\n\n"""
+    content += date_str
+
+    if custom_tools:
+        custom_message = get_system_prompt_for_custom_tools(custom_tools)
+        content += custom_message
+
+    # TODO: Replace this hard coded message with instructions coming in the request
+    if False:
+        content += "You are a helpful Assistant."
+
+    messages.append(SystemMessage(content=content))
+    return messages
+
+
+def get_system_prompt_for_custom_tools(custom_tools: List[ToolDefinition]) -> str:
+    custom_tool_params = ""
+    for t in custom_tools:
+        custom_tool_params += get_instruction_string(t) + "\n"
+        custom_tool_params += get_parameters_string(t) + "\n\n"
+
+    content = f"""
+You have access to the following functions:
+
+{custom_tool_params}
+Think very carefully before calling functions.
+If you choose to call a function ONLY reply in the following format with no prefix or suffix:
+
+<function=example_function_name>{{"example_name": "example_value"}}</function>
+
+Reminder:
+- If looking for real time information use relevant functions before falling back to brave_search
+- Function calls MUST follow the specified format, start with <function= and end with </function>
+- Required parameters MUST be specified
+- Only call one function at a time
+- Put the entire function call reply on one line
+
+"""
+    return content
+
+
+def get_instruction_string(custom_tool_definition) -> str:
+    return f"Use the function '{custom_tool_definition.tool_name}' to '{custom_tool_definition.description}'"
+
+
+def get_parameters_string(custom_tool_definition) -> str:
+    return json.dumps(
+        {
+            "name": custom_tool_definition.tool_name,
+            "description": custom_tool_definition.description,
+            "parameters": {
+                name: definition.__dict__
+                for name, definition in custom_tool_definition.parameters.items()
+            },
+        }
+    )
+
+
+# NOTE: Unused right now
+def translate_custom_tool_definition_to_json(tool_def):
+    """Translates ToolDefinition to json as expected by model
+    eg. output for a function
+    {
+        "type": "function",
+        "function": {
+            "name": "conv_int",
+            "description": "Convert serialized fract24 integer into int value.",
+            "parameters": {
+                "type": "object",
+                "properties": [
+                    {
+                        "data": {
+                            "type": "object",
+                            "description": ""
+                        }
+                    }
+                ],
+                "required": ["data"]
+            }
+        }
+    }
+    """
+    assert isinstance(tool_def.tool_name, str)
+    func_def = {"type": "function", "function": {}}
+    func_def["function"]["name"] = tool_def.tool_name
+    func_def["function"]["description"] = tool_def.description or ""
+    if tool_def.parameters:
+        required = []
+        properties = []
+        for p_name, p_def in tool_def.parameters.items():
+            properties.append(
+                {
+                    p_name: {
+                        # TODO: see if this should not always be object
+                        "type": "object",
+                        "description": p_def.description or "",
+                    }
+                }
+            )
+            if p_def.required:
+                required.append(p_name)
+        func_def["function"]["parameters"] = {
+            "type": "object",
+            "properties": properties,
+            "required": required,
+        }
+    else:
+        func_def["function"]["parameters"] = {}
+
+    return json.dumps(func_def)