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Moved ToolPromptFormat and jinja templates to llama_models.llama3.api

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This commit is contained in:
Hardik Shah 2024-08-23 14:58:52 -07:00
parent ab8193c88c
commit 5655266d58
13 changed files with 21 additions and 388 deletions
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2
llama_toolchain/agentic_system/client.py
View file

@ -17,6 +17,7 @@ from llama_models.llama3.api.datatypes import (
BuiltinTool,
SamplingParams,
ToolParamDefinition,
ToolPromptFormat,
UserMessage,
)
from termcolor import cprint
@ -32,7 +33,6 @@ from .api import (
AgenticSystemToolDefinition,
AgenticSystemTurnCreateRequest,
AgenticSystemTurnResponseStreamChunk,
ToolPromptFormat,
)

4
llama_toolchain/agentic_system/meta_reference/agent_instance.py
View file

@ -10,6 +10,8 @@ import uuid
from datetime import datetime
from typing import AsyncGenerator, List, Optional
from llama_models.llama3.api.datatypes import ToolPromptFormat
from termcolor import cprint
from llama_toolchain.agentic_system.api.datatypes import (
@ -26,12 +28,10 @@ from llama_toolchain.agentic_system.api.datatypes import (
ShieldCallStep,
StepType,
ToolExecutionStep,
ToolPromptFormat,
Turn,
)
from llama_toolchain.inference.api import ChatCompletionRequest, Inference
from llama_toolchain.inference.api.datatypes import (
Attachment,
BuiltinTool,

8
llama_toolchain/agentic_system/utils.py
View file

@ -7,7 +7,12 @@
import uuid
from typing import Any, List, Optional
from llama_models.llama3.api.datatypes import BuiltinTool, Message, SamplingParams
from llama_models.llama3.api.datatypes import (
BuiltinTool,
Message,
SamplingParams,
ToolPromptFormat,
)
from llama_toolchain.agentic_system.api import (
AgenticSystemCreateRequest,
@ -15,7 +20,6 @@ from llama_toolchain.agentic_system.api import (
AgenticSystemSessionCreateRequest,
AgenticSystemToolDefinition,
)
from llama_toolchain.agentic_system.api.datatypes import ToolPromptFormat
from llama_toolchain.agentic_system.client import AgenticSystemClient
from llama_toolchain.agentic_system.meta_reference.execute_with_custom_tools import (

26
llama_toolchain/common/prompt_templates/base.py
View file

@ -1,26 +0,0 @@
from dataclasses import dataclass
from typing import Any, Dict, List
from jinja2 import Template
@dataclass
class PromptTemplate:
template: str
data: Dict[str, Any]
def render(self):
template = Template(self.template)
return template.render(self.data)
class PromptTemplateGeneratorBase:
"""
Base class for prompt template generators.
"""
def gen(self, *args, **kwargs) -> PromptTemplate:
raise NotImplementedError()
def data_examples(self) -> List[Any]:
raise NotImplementedError()

206
llama_toolchain/common/prompt_templates/system_prompts.py
View file

@ -1,206 +0,0 @@
import textwrap
from datetime import datetime
from typing import Any, Dict, List
from llama_models.llama3.api.datatypes import (
BuiltinTool,
ToolDefinition,
ToolParamDefinition,
)
from .base import PromptTemplate, PromptTemplateGeneratorBase
class SystemDefaultGenerator(PromptTemplateGeneratorBase):
def gen(self, *args, **kwargs) -> PromptTemplate:
template_str = textwrap.dedent(
"""
Cutting Knowledge Date: December 2023
Today Date: {{ today }}
"""
)
return PromptTemplate(
template_str.lstrip("\n"),
{"today": datetime.now().strftime("%d %B %Y")},
)
def data_examples(self) -> List[Any]:
return [None]
class BuiltinToolGenerator(PromptTemplateGeneratorBase):
def _tool_breakdown(self, tools: List[ToolDefinition]):
builtin_tools, custom_tools = [], []
for dfn in tools:
if isinstance(dfn.tool_name, BuiltinTool):
builtin_tools.append(dfn)
else:
custom_tools.append(dfn)
return builtin_tools, custom_tools
def gen(self, tools: List[ToolDefinition]) -> PromptTemplate:
builtin_tools, custom_tools = self._tool_breakdown(tools)
data = []
template_str = textwrap.dedent(
"""
{% if builtin_tools or custom_tools -%}
Environment: ipython
{% endif -%}
{% set builtin_tools = builtin_tools | reject('equalto', 'code_interpreter') | list -%}
{% if builtin_tools -%}
Tools: {{ builtin_tools | join(", ") | trim -}}
{% endif %}
"""
)
return PromptTemplate(
template_str.lstrip("\n"),
{
"builtin_tools": [t.tool_name.value for t in builtin_tools],
"custom_tools": custom_tools,
},
)
def data_examples(self) -> List[List[ToolDefinition]]:
return [
# builtin tools
[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
ToolDefinition(tool_name=BuiltinTool.brave_search),
ToolDefinition(tool_name=BuiltinTool.wolfram_alpha),
],
# only code interpretor
[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
],
]
class JsonCustomToolGenerator(PromptTemplateGeneratorBase):
def gen(self, custom_tools: List[ToolDefinition]) -> PromptTemplate:
template_str = textwrap.dedent(
"""
Answer the user's question by making use of the following functions if needed.
If none of the function can be used, please say so.
Here is a list of functions in JSON format:
{% for t in custom_tools -%}
{# manually setting up JSON because jinja sorts keys in unexpected ways -#}
{%- set tname = t.tool_name -%}
{%- set tdesc = t.description -%}
{%- set tparams = t.parameters -%}
{%- set required_params = [] -%}
{%- for name, param in tparams.items() if param.required == true -%}
{%- set _ = required_params.append(name) -%}
{%- endfor -%}
{
"type": "function",
"function": {
"name": "{{tname}}",
"description": "{{tdesc}}",
"parameters": {
"type": "object",
"properties": [
{%- for name, param in tparams.items() %}
{
"{{name}}": {
"type": "object",
"description": "{{param.description}}"
}
}{% if not loop.last %},{% endif %}
{%- endfor %}
],
"required": {{ required_params | tojson }}
}
}
}
{% endfor %}
Return function calls in JSON format.
"""
)
return PromptTemplate(
template_str.lstrip("\n"),
{"custom_tools": [t.model_dump() for t in custom_tools]},
)
def data_examples(self) -> List[List[ToolDefinition]]:
return [
[
ToolDefinition(
tool_name="trending_songs",
description="Returns the trending songs on a Music site",
parameters={
"n": ToolParamDefinition(
param_type="int",
description="The number of songs to return",
required=True,
),
"genre": ToolParamDefinition(
param_type="str",
description="The genre of the songs to return",
required=False,
),
},
),
]
]
class FunctionTagCustomToolGenerator(PromptTemplateGeneratorBase):
def gen(self, custom_tools: List[ToolDefinition]) -> PromptTemplate:
template_str = textwrap.dedent(
"""
You have access to the following functions:
{% for t in custom_tools %}
{#- manually setting up JSON because jinja sorts keys in unexpected ways -#}
{%- set tname = t.tool_name -%}
{%- set tdesc = t.description -%}
{%- set tparams = t.parameters | tojson -%}
Use the function '{{ tname }}' to '{{ tdesc }}':
{"name": "{{tname}}", "description": "{{tdesc}}", "parameters": {{tparams}}}
{% endfor -%}
Think very carefully before calling functions.
If a 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 PromptTemplate(
template_str.lstrip("\n"),
{"custom_tools": [t.model_dump() for t in custom_tools]},
)
def data_examples(self) -> List[List[ToolDefinition]]:
return [
[
ToolDefinition(
tool_name="trending_songs",
description="Returns the trending songs on a Music site",
parameters={
"n": ToolParamDefinition(
param_type="int",
description="The number of songs to return",
required=True,
),
"genre": ToolParamDefinition(
param_type="str",
description="The genre of the songs to return",
required=False,
),
},
),
]
]

35
llama_toolchain/inference/api/datatypes.py
View file

@ -15,41 +15,6 @@ from typing_extensions import Annotated
from llama_models.llama3.api.datatypes import * # noqa: F403
@json_schema_type
class ToolChoice(Enum):
auto = "auto"
required = "required"
@json_schema_type
class ToolPromptFormat(Enum):
"""This Enum refers to the prompt format for calling zero shot tools
`json` --
Refers to the json format for calling tools.
The json format takes the form like
{
"type": "function",
"function" : {
"name": "function_name",
"description": "function_description",
"parameters": {...}
}
}
`function_tag` --
This is an example of how you could define
your own user defined format for making tool calls.
The function_tag format looks like this,
<function=function_name>(parameters)</function>
The detailed prompts for each of these formats are defined in `system_prompt.py`
"""
json = "json"
function_tag = "function_tag"
class LogProbConfig(BaseModel):
top_k: Optional[int] = 0

6
llama_toolchain/inference/api/endpoints.py
View file

@ -7,7 +7,7 @@
from .datatypes import * # noqa: F403
from typing import Optional, Protocol
from llama_models.llama3.api.datatypes import ToolDefinition
from llama_models.llama3.api.datatypes import ToolDefinition, ToolPromptFormat
# this dependency is annoying and we need a forked up version anyway
from llama_models.schema_utils import webmethod
@ -16,7 +16,7 @@ from llama_models.schema_utils import webmethod
@json_schema_type
class CompletionRequest(BaseModel):
model: str
content: InterleavedTextAttachment
content: InterleavedTextMedia
sampling_params: Optional[SamplingParams] = SamplingParams()
stream: Optional[bool] = False
@ -41,7 +41,7 @@ class CompletionResponseStreamChunk(BaseModel):
@json_schema_type
class BatchCompletionRequest(BaseModel):
model: str
content_batch: List[InterleavedTextAttachment]
content_batch: List[InterleavedTextMedia]
sampling_params: Optional[SamplingParams] = SamplingParams()
logprobs: Optional[LogProbConfig] = None

4
llama_toolchain/inference/meta_reference/config.py
View file

@ -11,10 +11,10 @@ from llama_models.datatypes import ModelFamily
from llama_models.schema_utils import json_schema_type
from llama_models.sku_list import all_registered_models
from llama_toolchain.inference.api import QuantizationConfig
from pydantic import BaseModel, Field, field_validator
from llama_toolchain.inference.api import QuantizationConfig
@json_schema_type
class MetaReferenceImplConfig(BaseModel):

3
llama_toolchain/inference/prepare_messages.py
View file

@ -1,7 +1,8 @@
import textwrap
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_toolchain.inference.api import * # noqa: F403
from llama_toolchain.common.prompt_templates.system_prompts import (
from llama_models.llama3.prompt_templates import (
BuiltinToolGenerator,
FunctionTagCustomToolGenerator,
JsonCustomToolGenerator,

2
tests/test_e2e.py
View file

@ -15,7 +15,7 @@ from llama_toolchain.agentic_system.event_logger import EventLogger, LogEvent
from llama_toolchain.agentic_system.utils import get_agent_system_instance
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_toolchain.agentic_system.api.datatypes import StepType, ToolPromptFormat
from llama_toolchain.agentic_system.api.datatypes import StepType
from llama_toolchain.tools.custom.datatypes import CustomTool
from tests.example_custom_tool import GetBoilingPointTool

6
tests/test_inference.py
View file

@ -14,13 +14,11 @@ from llama_models.llama3.api.datatypes import (
SystemMessage,
ToolDefinition,
ToolParamDefinition,
ToolPromptFormat,
ToolResponseMessage,
UserMessage,
)
from llama_toolchain.inference.api.datatypes import (
ChatCompletionResponseEventType,
ToolPromptFormat,
)
from llama_toolchain.inference.api.datatypes import ChatCompletionResponseEventType
from llama_toolchain.inference.api.endpoints import ChatCompletionRequest
from llama_toolchain.inference.meta_reference.config import MetaReferenceImplConfig

6
tests/test_ollama_inference.py
View file

@ -10,13 +10,11 @@ from llama_models.llama3.api.datatypes import (
SystemMessage,
ToolDefinition,
ToolParamDefinition,
ToolPromptFormat,
ToolResponseMessage,
UserMessage,
)
from llama_toolchain.inference.api.datatypes import (
ChatCompletionResponseEventType,
ToolPromptFormat,
)
from llama_toolchain.inference.api.datatypes import ChatCompletionResponseEventType
from llama_toolchain.inference.api.endpoints import ChatCompletionRequest
from llama_toolchain.inference.ollama.config import OllamaImplConfig
from llama_toolchain.inference.ollama.ollama import get_provider_impl

101
tests/test_prompt_templates.py
View file

@ -1,101 +0,0 @@
import textwrap
import unittest
from datetime import datetime
from llama_toolchain.common.prompt_templates.system_prompts import (
BuiltinToolGenerator,
FunctionTagCustomToolGenerator,
JsonCustomToolGenerator,
SystemDefaultGenerator,
)
class PromptTemplateTests(unittest.TestCase):
def check_generator_output(self, generator, expected_text):
example = generator.data_examples()[0]
pt = generator.gen(example)
text = pt.render()
# print(text) # debugging
self.assertEqual(text, expected_text)
def test_system_default(self):
generator = SystemDefaultGenerator()
today = datetime.now().strftime("%d %B %Y")
expected_text = f"Cutting Knowledge Date: December 2023\nToday Date: {today}"
self.check_generator_output(generator, expected_text)
def test_system_builtin_only(self):
generator = BuiltinToolGenerator()
expected_text = textwrap.dedent(
"""
Environment: ipython
Tools: brave_search, wolfram_alpha
"""
)
self.check_generator_output(generator, expected_text.strip("\n"))
def test_system_custom_only(self):
self.maxDiff = None
generator = JsonCustomToolGenerator()
expected_text = textwrap.dedent(
"""
Answer the user's question by making use of the following functions if needed.
If none of the function can be used, please say so.
Here is a list of functions in JSON format:
{
"type": "function",
"function": {
"name": "trending_songs",
"description": "Returns the trending songs on a Music site",
"parameters": {
"type": "object",
"properties": [
{
"n": {
"type": "object",
"description": "The number of songs to return"
}
},
{
"genre": {
"type": "object",
"description": "The genre of the songs to return"
}
}
],
"required": ["n"]
}
}
}
Return function calls in JSON format.
"""
)
self.check_generator_output(generator, expected_text.strip("\n"))
def test_system_custom_function_tag(self):
self.maxDiff = None
generator = FunctionTagCustomToolGenerator()
expected_text = textwrap.dedent(
"""
You have access to the following functions:
Use the function 'trending_songs' to 'Returns the trending songs on a Music site':
{"name": "trending_songs", "description": "Returns the trending songs on a Music site", "parameters": {"genre": {"description": "The genre of the songs to return", "param_type": "str", "required": false}, "n": {"description": "The number of songs to return", "param_type": "int", "required": true}}}
Think very carefully before calling functions.
If a 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
"""
)
self.check_generator_output(generator, expected_text.strip("\n"))