blog

docs/blog/posts/introduction.md

@@ -0,0 +1,178 @@
+---
+draft: False 
+date: 2023-09-11
+tags:
+  - Introduction
+---
+
+# Bridging Language Models with Python using Instructor, Pydantic, and OpenAI's Function Calls
+
+Language models have seen significant growth. Using them effectively often requires complex frameworks. This post discusses how Instructor simplifies this process using Pydantic.
+
+## The Problem with Existing LLM Frameworks
+
+Current frameworks for Language Learning Models (LLMs) have complex setups. Developers find it hard to control interactions with language models. Some frameworks require complex JSON Schema setups.
+
+## The OpenAI Function Calling Game-Changer
+
+OpenAI's Function Calling feature provides a constrained interaction model. However, it has its own complexities, mostly around JSON Schema.
+
+## Why Pydantic?
+
+Instructor uses Pydantic to simplify the interaction between the programmer and the language model.
+
+- **Widespread Adoption**: Pydantic is a popular tool among Python developers.
+- **Simplicity**: Pydantic allows model definition in Python.
+- **Framework Compatibility**: Many Python frameworks already use Pydantic.
+
+```python
+import pydantic
+import instructor
+import openai
+
+# Enables the response_model
+instructor.patch()
+
+class UserDetail(pydantic.BaseModel):
+    name: str
+    age: int
+    
+    def introduce(self):
+        return f"Hello I'm {self.name} and I'm {self.age} years old"
+
+user: UserDetail = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    response_model=UserDetail,
+    messages=[
+        {"role": "user", "content": "Extract Jason is 25 years old"},
+    ]
+)
+```
+
+## Simplifying Validation Flow with Pydantic
+
+Pydantic validators simplify features like re-asking or self-critique. This makes these tasks less complex compared to other frameworks.
+
+```python
+from typing_extensions import Annotated
+from pydantic import BaseModel, BeforeValidator
+from instructor import llm_validator, patch
+
+import openai
+
+class QuestionAnswerNoEvil(BaseModel):
+    question: str
+    answer: Annotated[
+        str,
+        BeforeValidator(
+            llm_validator("don't say objectionable things")
+        ),
+    ]
+```
+
+## The Modular Approach
+
+Pydantic allows for modular output schemas. This leads to more organized code.
+
+### Composition of Schemas
+```python
+class UserDetails(BaseModel):
+    name: str
+    age: int
+
+class UserWithAddress(UserDetails):
+    address: str
+```
+
+### Defining Relationships
+```python
+class UserDetail(BaseModel):
+    id: int
+    age: int
+    name: str
+    friends: List[int]
+
+class UserRelationships(BaseModel):
+    users: List[UserDetail]
+```
+
+### Using Enums
+```python
+from enum import Enum, auto
+
+class Role(Enum):
+    PRINCIPAL = auto()
+    TEACHER = auto()
+    STUDENT = auto()
+    OTHER = auto()
+
+class UserDetail(BaseModel):
+    age: int
+    name: str
+    role: Role
+```
+
+### Flexible Schemas
+```python
+from typing import List
+
+class Property(BaseModel):
+    key: str
+    value: str
+
+class UserDetail(BaseModel):
+    age: int
+    name: str
+    properties: List[Property]
+```
+
+### Chain of Thought
+```python
+class TimeRange(BaseModel):
+    chain_of_thought: str
+    start_time: int
+    end_time: int
+
+class UserDetail(BaseModel):
+    id: int
+    age: int
+    name: str
+    work_time: TimeRange
+    leisure_time: TimeRange
+```
+
+## Language Models as Microservices
+
+The architecture resembles FastAPI. Most code can be written as Python functions that use Pydantic objects. This eliminates the need for prompt chains.
+
+### FastAPI Stub
+
+```python
+app = FastAPI()
+
+@app.get("/user/{user_id}", response_model=UserDetails)
+async def get_user(user_id: int) -> UserDetails:
+    return UserDetails(...)
+```
+
+### Using Instructor as a Function
+
+```python
+def extract_user(str) -> UserDetails:
+    return openai.ChatCompletion(
+           response_model=UserDetails,
+           messages=[...]
+    )
+```
+
+### Response Modeling
+```python
+class MaybeUser(BaseModel):
+    result: Optional[UserDetail]
+    error: bool
+    message: Optional[str]
+```
+
+## Conclusion
+
+Instructor, with Pydantic, simplifies interaction with language models. It is usable for both experienced and new developers.

mkdocs.yml

@@ -72,6 +72,21 @@ nav:
       - 'MultiTask': 'api_multitask.md'
       - "Introduction: Writing Prompts": "writing-prompts.md"
       - "Prompting Templates": "chat-completion.md"
+  - Blog:
+    - "blog/index.md"
+plugins:
+  - group:
+      enabled: !ENV CI
+      plugins:
+        - optimize
+        - minify
+  - blog:
+      enabled: !ENV CI
+      blog_dir: "blog"
+      blog_toc: true
+      post_dir: blog/posts
+      post_date_format: yyyy/MM/dd
+      post_url_format: "{date}/{slug}"
 extra:
   analytics:
     provider: google