[Feature] OpenAI Function Calling (#1224)
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UnMonsieur
@@ -68,125 +68,75 @@ llm:
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</CodeGroup>
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### Function Calling
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To enable [function calling](https://platform.openai.com/docs/guides/function-calling) in your application using embedchain and OpenAI, you need to pass functions into `OpenAILlm` class as an array of functions. Here are several ways in which you can achieve that:
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Embedchain supports OpenAI [Function calling](https://platform.openai.com/docs/guides/function-calling) with a single function. It accepts inputs in accordance with the [Langchain interface](https://python.langchain.com/docs/modules/model_io/chat/function_calling#legacy-args-functions-and-function_call).
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Examples:
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<Accordion title="Using Pydantic Models">
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<Accordion title="Pydantic Model">
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```python
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import os
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from embedchain import App
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from embedchain.llm.openai import OpenAILlm
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import requests
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from pydantic import BaseModel, Field, ValidationError, field_validator
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from pydantic import BaseModel
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os.environ["OPENAI_API_KEY"] = "sk-xxx"
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class multiply(BaseModel):
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"""Multiply two integers together."""
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class QA(BaseModel):
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"""
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A question and answer pair.
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"""
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question: str = Field(
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..., description="The question.", example="What is a mountain?"
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)
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answer: str = Field(
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..., description="The answer.", example="A mountain is a hill."
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)
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person_who_is_asking: str = Field(
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..., description="The person who is asking the question.", example="John"
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)
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@field_validator("question")
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def question_must_end_with_a_question_mark(cls, v):
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"""
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Validate that the question ends with a question mark.
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"""
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if not v.endswith("?"):
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raise ValueError("question must end with a question mark")
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return v
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@field_validator("answer")
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def answer_must_end_with_a_period(cls, v):
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"""
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Validate that the answer ends with a period.
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"""
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if not v.endswith("."):
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raise ValueError("answer must end with a period")
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return v
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llm = OpenAILlm(config=None,functions=[QA])
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app = App(llm=llm)
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result = app.query("Hey I am Sid. What is a mountain? A mountain is a hill.")
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print(result)
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a: int = Field(..., description="First integer")
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b: int = Field(..., description="Second integer")
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```
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</Accordion>
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</Accordion>
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<Accordion title="Using OpenAI JSON schema">
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<Accordion title="Python function">
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```python
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def multiply(a: int, b: int) -> int:
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"""Multiply two integers together.
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Args:
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a: First integer
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b: Second integer
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"""
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return a * b
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```
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</Accordion>
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<Accordion title="OpenAI tool dictionary">
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```python
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multiply = {
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"type": "function",
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"function": {
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"name": "multiply",
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"description": "Multiply two integers together.",
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"parameters": {
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"type": "object",
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"properties": {
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"a": {
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"description": "First integer",
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"type": "integer"
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},
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"b": {
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"description": "Second integer",
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"type": "integer"
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}
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},
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"required": [
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"a",
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"b"
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]
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}
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}
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}
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```
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</Accordion>
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With any of the previous inputs, the OpenAI LLM can be queried to provide the appropriate arguments for the function.
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```python
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import os
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from embedchain import App
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from embedchain.llm.openai import OpenAILlm
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import requests
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from pydantic import BaseModel, Field, ValidationError, field_validator
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os.environ["OPENAI_API_KEY"] = "sk-xxx"
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json_schema = {
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"name": "get_qa",
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"description": "A question and answer pair and the user who is asking the question.",
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"parameters": {
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"type": "object",
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"properties": {
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"question": {"type": "string", "description": "The question."},
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"answer": {"type": "string", "description": "The answer."},
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"person_who_is_asking": {
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"type": "string",
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"description": "The person who is asking the question.",
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}
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},
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"required": ["question", "answer", "person_who_is_asking"],
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},
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}
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llm = OpenAILlm(config=None,functions=[json_schema])
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llm = OpenAILlm(tools=multiply)
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app = App(llm=llm)
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result = app.query("Hey I am Sid. What is a mountain? A mountain is a hill.")
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print(result)
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```
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</Accordion>
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<Accordion title="Using actual python functions">
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```python
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import os
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from embedchain import App
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from embedchain.llm.openai import OpenAILlm
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import requests
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from pydantic import BaseModel, Field, ValidationError, field_validator
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os.environ["OPENAI_API_KEY"] = "sk-xxx"
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def find_info_of_pokemon(pokemon: str):
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"""
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Find the information of the given pokemon.
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Args:
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pokemon: The pokemon.
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"""
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req = requests.get(f"https://pokeapi.co/api/v2/pokemon/{pokemon}")
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if req.status_code == 404:
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raise ValueError("pokemon not found")
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return req.json()
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llm = OpenAILlm(config=None,functions=[find_info_of_pokemon])
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app = App(llm=llm)
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result = app.query("Tell me more about the pokemon pikachu.")
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print(result)
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result = app.query("What is the result of 125 multiplied by fifteen?")
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```
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</Accordion>
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## Google AI
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