Replaced llm_validator in pre made agents

agency_swarm/__init__.py

@@ -5,3 +5,4 @@
 from .util import set_openai_client
 from .util import get_openai_client
 from .util.streaming import AgencyEventHandler
+from .util.validators import llm_validator

agency_swarm/agents/Devid/Devid.py

@@ -2,7 +2,7 @@
 import re
 from agency_swarm.agents import Agent
 from agency_swarm.tools import FileSearch
-from instructor import llm_validator
+from agency_swarm import llm_validator
 
 
 class Devid(Agent):

agency_swarm/agents/Devid/tools/ChangeFile.py

@@ -2,12 +2,11 @@
 from enum import Enum
 from typing import Literal, Optional, List
 
-from instructor import OpenAISchema
-from pydantic import Field, model_validator, field_validator
+from pydantic import Field, model_validator, field_validator, BaseModel
 
 from agency_swarm import BaseTool
 
-class LineChange(OpenAISchema):
+class LineChange(BaseModel):
     """
     Line changes to be made.
     """

agency_swarm/agents/Devid/tools/FileWriter.py

@@ -3,7 +3,7 @@
 import json
 
 import os
-from instructor import llm_validator, OpenAISchema
+from agency_swarm import llm_validator
 
 from agency_swarm import get_openai_client
 from agency_swarm.tools import BaseTool

agency_swarm/agents/Devid/tools/util/format_file_deps.py

@@ -1,5 +1,4 @@
-from instructor import OpenAISchema
-from pydantic import Field
+from pydantic import Field, BaseModel
 from typing import List, Literal
 
 from agency_swarm import get_openai_client
@@ -13,11 +12,11 @@ def format_file_deps(v):
         with open(file, 'r') as f:
             content = f.read()
 
-        class Dependency(OpenAISchema):
+        class Dependency(BaseModel):
             type: Literal['class', 'function', 'import'] = Field(..., description="The type of the dependency.")
             name: str = Field(..., description="The name of the dependency, matching the import or definition.")
 
-        class Dependencies(OpenAISchema):
+        class Dependencies(BaseModel):
             dependencies: List[Dependency] = Field([], description="The dependencies extracted from the file.")
 
             def append_dependencies(self):
@@ -29,7 +28,7 @@ def append_dependencies(self):
                 result += f"File path: {file}\n"
                 result += f"Functions: {functions}\nClasses: {classes}\nImports: {imports}\nVariables: {variables}\n\n"
 
-        resp = client.chat.completions.create(
+        completion = client.beta.chat.completions.parse(
             messages=[
                 {
                     "role": "system",
@@ -42,9 +41,14 @@ def append_dependencies(self):
             ],
             model="gpt-3.5-turbo",
             temperature=0,
-            response_model=Dependencies
+            response_format=Dependencies
         )
 
-        resp.append_dependencies()
+        if completion.choices[0].message.refusal:
+            raise ValueError(completion.choices[0].message.refusal)
+
+        model = completion.choices[0].message.parsed
+
+        model.append_dependencies()
 
     return result

agency_swarm/util/__init__.py

@@ -1,4 +1,5 @@
 from .cli.create_agent_template import create_agent_template
 from .cli.import_agent import import_agent
 from .oai import set_openai_key, get_openai_client, set_openai_client
-from .files import determine_file_type
+from .files import determine_file_type
+from .validators import llm_validator

agency_swarm/util/validators.py

@@ -0,0 +1,94 @@
+from openai import OpenAI
+from typing import Callable
+from pydantic import Field
+from agency_swarm.tools import BaseTool
+
+class Validator(BaseTool):
+    """
+    Validate if an attribute is correct and if not,
+    return a new value with an error message
+    """
+
+    is_valid: bool = Field(
+        default=True,
+        description="Whether the attribute is valid based on the requirements",
+    )
+    reason: str = Field(
+        default=None,
+        description="The error message if the attribute is not valid, otherwise None",
+    )
+    fixed_value: str = Field(
+        default=None,
+        description="If the attribute is not valid, suggest a new value for the attribute",
+    )
+
+    def run(self):
+        pass
+
+def llm_validator(
+    statement: str,
+    client: OpenAI=None,
+    allow_override: bool = False,
+    model: str = "gpt-3.5-turbo",
+    temperature: float = 0,
+) -> Callable[[str], str]:
+    """
+    Create a validator that uses the LLM to validate an attribute
+
+    ## Usage
+
+    ```python
+    from agency_swarm import llm_validator
+    from pydantic import Field, field_validator
+
+    class User(BaseTool):
+        name: str = Annotated[str, llm_validator("The name must be a full name all lowercase")
+        age: int = Field(description="The age of the person")
+
+    try:
+        user = User(name="Jason Liu", age=20)
+    except ValidationError as e:
+        print(e)
+    ```
+
+    ```
+    1 validation error for User
+    name
+        The name is valid but not all lowercase (type=value_error.llm_validator)
+    ```
+
+    Note that there, the error message is written by the LLM, and the error type is `value_error.llm_validator`.
+
+    Parameters:
+        statement (str): The statement to validate
+        model (str): The LLM to use for validation (default: "gpt-3.5-turbo-0613")
+        temperature (float): The temperature to use for the LLM (default: 0)
+        openai_client (OpenAI): The OpenAI client to use (default: None)
+    """
+    def llm(v: str) -> str:
+        resp = client.beta.chat.completions.parse(
+            response_format=Validator,
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a world class validation model. Capable to determine if the following value is valid for the statement, if it is not, explain why and suggest a new value.",
+                },
+                {
+                    "role": "user",
+                    "content": f"Does `{v}` follow the rules: {statement}",
+                },
+            ],
+            model=model,
+            temperature=temperature,
+        )
+
+        # If the response is  not valid, return the reason, this could be used in
+        # the future to generate a better response, via reasking mechanism.
+        assert resp.is_valid, resp.reason
+
+        if allow_override and not resp.is_valid and resp.fixed_value is not None:
+            # If the value is not valid, but we allow override, return the fixed value
+            return resp.fixed_value
+        return v
+
+    return llm