참고소스 수정본

참고/instructor-main/examples/resolving-complex-entities/run.py

@@ -0,0 +1,120 @@
+from graphviz import Digraph
+from pydantic import BaseModel, Field
+
+import instructor
+from openai import OpenAI
+
+client = OpenAI()
+
+# Patch openai to use instructor
+# allows for response_model
+instructor.from_openai()
+
+
+class Property(BaseModel):
+    key: str
+    value: str
+    resolved_absolute_value: str
+
+
+class Entity(BaseModel):
+    id: int = Field(
+        ...,
+        description="Unique identifier for the entity, used for deduplication, design a scheme allows multiple entities",
+    )
+    subquote_string: list[str] = Field(
+        ...,
+        description="Correctly resolved value of the entity, if the entity is a reference to another entity, this should be the id of the referenced entity, include a few more words before and after the value to allow for some context to be used in the resolution",
+    )
+    entity_title: str
+    properties: list[Property] = Field(
+        ..., description="List of properties of the entity"
+    )
+    dependencies: list[int] = Field(
+        ...,
+        description="List of entity ids that this entity depends  or relies on to resolve it",
+    )
+
+
+class DocumentExtraction(BaseModel):
+    entities: list[Entity] = Field(
+        ...,
+        description="Body of the answer, each fact should be its separate object with a body and a list of sources",
+    )
+
+
+def ask_ai(content) -> DocumentExtraction:
+    resp: DocumentExtraction = client.chat.completions.create(
+        model="gpt-4",
+        response_model=DocumentExtraction,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a perfect entity resolution system that extracts facts from the document. Extract and resolve a list of entities from the following document:",
+            },
+            {
+                "role": "user",
+                "content": content,
+            },
+        ],
+    )  # type: ignore
+    return resp
+
+
+def generate_html_label(entity: Entity) -> str:
+    rows = [
+        f"<tr><td>{prop.key}</td><td>{prop.resolved_absolute_value}</td></tr>"
+        for prop in entity.properties
+    ]
+    table_rows = "".join(rows)
+    return f"""<
+    <table border="0" cellborder="1" cellspacing="0">
+    <tr><td colspan="2"><b>{entity.entity_title}</b></td></tr>
+    {table_rows}
+    </table>>"""
+
+
+def generate_graph(data: DocumentExtraction):
+    dot = Digraph(comment="Entity Graph", node_attr={"shape": "plaintext"})
+
+    # Add nodes
+    for entity in data.entities:
+        label = generate_html_label(entity)
+        dot.node(str(entity.id), label)
+
+    # Add edges
+    for entity in data.entities:
+        for dep_id in entity.dependencies:
+            dot.edge(str(entity.id), str(dep_id))
+
+    # Render graph
+    dot.render("entity.gz", view=True)
+
+
+content = """
+Sample Legal Contract
+Agreement Contract
+
+This Agreement is made and entered into on 2020-01-01 by and between Company A ("the Client") and Company B ("the Service Provider").
+
+Article 1: Scope of Work
+
+The Service Provider will deliver the software product to the Client 30 days after the agreement date.
+
+Article 2: Payment Terms
+
+The total payment for the service is $50,000.
+An initial payment of $10,000 will be made within 7 days of the the signed date.
+The final payment will be due 45 days after [SignDate].
+
+Article 3: Confidentiality
+
+The parties agree not to disclose any confidential information received from the other party for 3 months after the final payment date.
+
+Article 4: Termination
+
+The contract can be terminated with a 30-day notice, unless there are outstanding obligations that must be fulfilled after the [DeliveryDate].
+"""
+
+model = ask_ai(content)
+generate_graph(model)