chore: import upstream snapshot with attribution

examples/codegen-from-schema/create_fastapi_app.py

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+import json
+import datetime
+from pathlib import Path
+from jinja2 import Template
+import re
+from datamodel_code_generator import InputFileType, generate
+from pydantic import BaseModel
+
+APP_TEMPLATE_STR = '''# generated by instructor-codegen:
+#   timestamp: {{timestamp}}
+#   task_name: {{task_name}}
+#   api_path: {{api_path}}
+#   json_schema_path: {{json_schema_path}}
+
+from fastapi import FastAPI
+from pydantic import BaseModel
+from jinja2 import Template
+from models import {{title}}
+
+import openai
+import instructor
+
+instructor.from_openai()
+
+app = FastAPI()
+
+class TemplateVariables(BaseModel):
+{% for var in jinja_vars %}
+    {{var.strip()}}: str
+{% endfor %}
+
+class RequestSchema(BaseModel):
+    template_variables: TemplateVariables
+    model: str
+    temperature: int
+
+PROMPT_TEMPLATE = Template("""{{prompt_template}}""".strip())
+
+@app.post("{{api_path}}", response_model={{title}})
+async def {{task_name}}(input: RequestSchema) -> {{title}}:
+    rendered_prompt = PROMPT_TEMPLATE.render(**input.template_variables.model_dump())
+    return await openai.ChatCompletion.acreate(
+        model=input.model,
+        temperature=input.temperature,
+        response_model={{title}},
+        messages=[
+            {"role": "user", "content": rendered_prompt}
+        ]
+    ) # type: ignore
+'''
+
+
+class TemplateVariables(BaseModel):
+    biography: str
+
+
+def load_json_schema(json_schema_path: str) -> dict:
+    try:
+        with open(json_schema_path) as f:
+            return json.load(f)
+    except Exception as e:
+        raise ValueError(f"Failed to load JSON schema: {e}") from e
+
+
+def generate_pydantic_model(json_schema_path: str):
+    input_path = Path(json_schema_path)
+    output_path = Path("./models.py")
+    generate(
+        input_=input_path, input_file_type=InputFileType.JsonSchema, output=output_path
+    )
+
+
+def extract_jinja_vars(prompt_template: str) -> list:
+    return re.findall(r"\{\{(.*?)\}\}", prompt_template)
+
+
+def render_app_template(template_str: str, **kwargs) -> str:
+    app_template = Template(template_str)
+    return app_template.render(**kwargs)
+
+
+def create_app(
+    api_path: str, task_name: str, json_schema_path: str, prompt_template: str
+) -> str:
+    if not api_path.startswith("/"):
+        api_path = "/" + api_path
+
+    schema = load_json_schema(json_schema_path)
+    title = schema["title"]
+    generate_pydantic_model(json_schema_path)
+
+    jinja_vars = extract_jinja_vars(prompt_template)
+
+    return render_app_template(
+        APP_TEMPLATE_STR,
+        timestamp=datetime.datetime.now().isoformat(),
+        task_name=task_name,
+        api_path=api_path,
+        json_schema_path=json_schema_path,
+        title=title,
+        jinja_vars=jinja_vars,
+        prompt_template=prompt_template,
+    )
+
+
+if __name__ == "__main__":
+    try:
+        fastapi_code = create_app(
+            api_path="/api/v1/extract_person",
+            task_name="extract_person",
+            json_schema_path="./input.json",
+            prompt_template="Extract the person from the following: {{biography}}",
+        )
+
+        with open("./run.py", "w") as f:
+            f.write(fastapi_code)
+
+        print("FastAPI application generated and saved to './run.py'")
+
+    except Exception as e:
+        print(f"An error occurred: {e}")

examples/codegen-from-schema/input.json

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+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "type": "object",
+  "title": "ExtractPerson",
+  "properties": {
+    "name": {
+      "type": "string"
+    },
+    "age": {
+      "type": "integer"
+    },
+    "phoneNumbers": {
+      "type": "array",
+      "items": {
+        "type": "object",
+        "properties": {
+          "type": {
+            "type": "string",
+            "enum": ["home", "work", "mobile"]
+          },
+          "number": {
+            "type": "string"
+          }
+        },
+        "required": ["type", "number"]
+      }
+    }
+  },
+  "required": ["name", "age", "phoneNumbers"]
+}

examples/codegen-from-schema/models.py

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+# generated by datamodel-codegen:
+#   filename:  input.json
+#   timestamp: 2023-09-10T00:33:42+00:00
+
+from __future__ import annotations
+
+from enum import Enum
+
+from pydantic import BaseModel
+
+
+class Type(Enum):
+    home = "home"
+    work = "work"
+    mobile = "mobile"
+
+
+class PhoneNumber(BaseModel):
+    type: Type
+    number: str
+
+
+class ExtractPerson(BaseModel):
+    name: str
+    age: int
+    phoneNumbers: list[PhoneNumber]

examples/codegen-from-schema/readme.md

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+# FastAPI Code Generator
+
+## Overview
+
+Generates FastAPI application code from API path, task name, JSON schema path, and Jinja2 prompt template. Also creates a `models.py` file for Pydantic models.
+
+## Dependencies
+
+- FastAPI
+- Pydantic
+- Jinja2
+- datamodel-code-generator
+
+## Functions
+
+### `create_app(api_path: str, task_name: str, json_schema_path: str, prompt_template: str) -> str`
+
+Main function to generate FastAPI application code.
+
+## Usage
+
+Run the script with required parameters.
+
+Example:
+
+```python
+fastapi_code = create_app(
+    api_path="/api/v1/extract_person",
+    task_name="extract_person",
+    json_schema_path="./input.json",
+    prompt_template="Extract the person from the following: {{biography}}",
+)
+```
+
+Outputs FastAPI application code to `./run.py` and a Pydantic model to `./models.py`.

examples/codegen-from-schema/run.py

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+# This file was generated by instructor
+#   timestamp: 2023-09-09T20:33:42.572627
+#   task_name: extract_person
+#   api_path: /api/v1/extract_person
+#   json_schema_path: ./input.json
+
+import instructor
+
+from fastapi import FastAPI
+from pydantic import BaseModel
+from jinja2 import Template
+from models import ExtractPerson
+from openai import AsyncOpenAI
+
+aclient = instructor.apatch(AsyncOpenAI())
+
+app = FastAPI()
+
+
+class TemplateVariables(BaseModel):
+    biography: str
+
+
+class RequestSchema(BaseModel):
+    template_variables: TemplateVariables
+    model: str
+    temperature: int
+
+
+PROMPT_TEMPLATE = Template(
+    """Extract the person from the following: {{biography}}""".strip()
+)
+
+
+@app.post("/api/v1/extract_person", response_model=ExtractPerson)
+async def extract_person(input: RequestSchema) -> ExtractPerson:
+    rendered_prompt = PROMPT_TEMPLATE.render(**input.template_variables.model_dump())
+    return await aclient.chat.completions.create(
+        model=input.model,
+        temperature=input.temperature,
+        response_model=ExtractPerson,
+        messages=[{"role": "user", "content": rendered_prompt}],
+    )  # type: ignore