Rename tools.py to extract_app.py

extract_app.py

@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+# Imports
+import asyncio
+import os
+import openai
+
+from typing import List, Optional
+from pydantic import BaseModel, Field
+
+from langchain.chains.openai_functions.extraction import create_extraction_chain_pydantic
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.utils.openai_functions import convert_pydantic_to_openai_function
+
+from dotenv import load_dotenv
+load_dotenv()
+
+openai.api_key = os.environ['OPENAI_API_KEY']
+
+
+# App
+
+# Pydantic is an easy way to define a schema
+class Person(BaseModel):
+    """Information about people to extract."""
+
+    name: str
+    age: Optional[int] = None
+
+# Main function to extract information
+def extract_information():
+    # Make sure to use a recent model that supports tools
+    llm = ChatOpenAI(model="gpt-3.5-turbo-1106")
+
+    return create_extraction_chain_pydantic(Person, llm)
+
+
+if __name__ == "__main__":
+    text = "My name is John and I am 20 years old. My name is sally and I am 30 years old."
+    chain = extract_information()
+    print(chain.invoke({"input": text})["text"])
+
+    async def extract_information_async(message: str):
+        return chain.invoke({"input": message})["text"]
+
+    async def main():
+        res = await extract_information_async(text)
+        print(res)
+
+    asyncio.run(main())

tools.py

@@ -1,73 +0,0 @@
-import io
-import os
-from openai import OpenAI
-from langchain.tools import StructuredTool, Tool
-from io import BytesIO
-import requests
-import json
-from io import BytesIO
-
-import chainlit as cl
-
-
-def get_image_name():
-    """
-    We need to keep track of images we generate, so we can reference them later
-    and display them correctly to our users.
-    """
-    image_count = cl.user_session.get("image_count")
-    if image_count is None:
-        image_count = 0
-    else:
-        image_count += 1
-
-    cl.user_session.set("image_count", image_count)
-
-    return f"image-{image_count}"
-
-
-def _generate_image(prompt: str):
-    """
-    This function is used to generate an image from a text prompt using
-    DALL-E 3.
-
-    We use the OpenAI API to generate the image, and then store it in our
-    user session so we can reference it later.
-    """
-    client = OpenAI()
-
-    response = client.images.generate(
-        model="dall-e-3",
-        prompt=prompt,
-        size="1024x1024",
-        quality="standard",
-        n=1,
-    )
-
-    image_payload = requests.get(response.data[0].url, stream=True)
-
-    image_bytes = BytesIO(image_payload.content)
-
-    print(type(image_bytes))
-
-    name = get_image_name()
-    cl.user_session.set(name, image_bytes.getvalue())
-    cl.user_session.set("generated_image", name)
-    return name
-
-
-def generate_image(prompt: str):
-    image_name = _generate_image(prompt)
-    return f"Here is {image_name}."
-
-
-# this is our tool - which is what allows our agent to generate images in the first place!
-# the `description` field is of utmost imporance as it is what the LLM "brain" uses to determine
-# which tool to use for a given input.
-generate_image_format = '{{"prompt": "prompt"}}'
-generate_image_tool = Tool.from_function(
-    func=generate_image,
-    name="GenerateImage",
-    description=f"Useful to create an image from a text prompt. Input should be a single string strictly in the following JSON format: {generate_image_format}",
-    return_direct=True,
-)