docs: restructure navigation and fix code formatting (#1191)

Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ivan Leo <[email protected]>

docs/blog/index.md

@@ -47,14 +47,13 @@ If you want to get updates on new features and tips on how to use Instructor, yo
 
 ## Integrations and Tools
 
-- [Ollama Integration](../hub/ollama.md)
-- [llama-cpp-python Integration](../hub/llama-cpp-python.md)
-- [Anyscale Integration](../hub/anyscale.md)
-- [Together Compute Integration](../hub/together.md)
+- [Ollama Integration](../integrations/ollama.md)
+- [llama-cpp-python Integration](../integrations/llama-cpp-python.md)
+- [Together Compute Integration](../integrations/together.md)
 - [Extracting Data into Pandas DataFrame using GPT-3.5 Turbo](../hub/pandas_df.md)
 - [Implementing Streaming Partial Responses with Field-Level Streaming](../hub/partial_streaming.md)
 
 ## Media and Resources
 
 - [Course: Structured Outputs with Instructor](https://www.wandb.courses/courses/steering-language-models?x=1)
-- [Keynote: Pydantic is All You Need](posts/aisummit-2023.md)
+- [Keynote: Pydantic is All You Need](posts/aisummit-2023.md)

docs/blog/posts/best_framework.md

@@ -32,7 +32,7 @@ from pydantic import BaseModel
 import instructor
 
 class User(BaseModel):
-    name: str 
+    name: str
     age: int
 
 client = instructor.from_openai(openai.OpenAI())
@@ -42,7 +42,7 @@ user = client.chat.completions.create(
     response_model=User, # (1)!
     messages=[
         {
-            "role": "user", 
+            "role": "user",
             "content": "Extract the user's name and age from this: John is 25 years old"
         }
     ]
@@ -63,14 +63,14 @@ Other features on instructor, in and out of the llibrary are:
 2. Ability to use [Pydantic's validation context](../../concepts/reask_validation.md)
 3. [Parallel Tool Calling](../../concepts/parallel.md) with correct types
 4. Streaming [Partial](../../concepts/partial.md) and [Iterable](../../concepts/iterable.md) data.
-5. Returning [Primitive](../../concepts/types.md) Types and [Unions](../../concepts/unions.md) as well! 
-6. Lots, and Lots of [Cookbooks](../../examples/index.md), [Tutorials](../../tutorials/1-introduction.ipynb), Documentation and even [instructor hub](../../hub/index.md)
+5. Returning [Primitive](../../concepts/types.md) Types and [Unions](../../concepts/unions.md) as well!
+6. Lots, and Lots of [Cookbooks](../../examples/index.md), [Tutorials](../../tutorials/1-introduction.ipynb), Documentation and even [instructor hub](../../integrations/index.md)
 
 ## Instructor's Broad Applicability
 
 One of the key strengths of Instructor is that it's designed as a lightweight patch over the official OpenAI Python SDK. This means it can be easily integrated not just with OpenAI's hosted API service, but with any provider or platform that exposes an interface compatible with the OpenAI SDK.
 
-For example, providers like [Anyscale](../../hub/anyscale.md), [Together](../../hub/together.md), [Ollama](../../hub/ollama.md), [Groq](../../hub/groq.md), and [llama-cpp-python](../../hub/llama-cpp-python.md) all either use or mimic the OpenAI Python SDK under the hood. With Instructor's zero-overhead patching approach, teams can immediately start deriving structured data outputs from any of these providers. There's no need for custom integration work.
+For example, providers like [Together](../../integrations/together.md), [Ollama](../../integrations/ollama.md), [Groq](../../integrations/groq.md), and [llama-cpp-python](../../integrations/llama-cpp-python.md) all either use or mimic the OpenAI Python SDK under the hood. With Instructor's zero-overhead patching approach, teams can immediately start deriving structured data outputs from any of these providers. There's no need for custom integration work.
 
 ## Direct access to the messages array
 
@@ -84,4 +84,4 @@ This incremental, zero-overhead adoption path makes Instructor perfect for sprin
 
 And if you decide Instructor isn't a good fit after all, removing it is as simple as not applying the patch! The familiarity and flexibility of working directly with the OpenAI SDK is a core strength.
 
-Instructor solves the "string hellll" of unstructured LLM outputs. It allows teams to easily realize the full potential of tools like GPTs by mapping their text to type-safe, validated data structures. If you're looking to get more structured value out of LLMs, give Instructor a try!
+Instructor solves the "string hellll" of unstructured LLM outputs. It allows teams to easily realize the full potential of tools like GPTs by mapping their text to type-safe, validated data structures. If you're looking to get more structured value out of LLMs, give Instructor a try!

docs/blog/posts/introducing-structured-outputs.md

@@ -41,7 +41,7 @@ In this article, we'll show how `instructor` addresses many of these challenges
 
 ### Limited Validation and Retry Logic
 
-Validation is crucial for building reliable and effective applications. We want to catch errors in real time using `Pydantic` [validators](/concepts/reask_validation/) in order to allow our LLM to correct its responses on the fly.
+Validation is crucial for building reliable and effective applications. We want to catch errors in real time using `Pydantic` [validators](../../concepts/reask_validation.md) in order to allow our LLM to correct its responses on the fly.
 
 Let's see an example of a simple validator below which ensures user names are always in uppercase.
 
@@ -192,12 +192,13 @@ This built-in retry logic allows for targetted correction to the generated respo
 
 ### Real-time Streaming Validation
 
-A common use-case is to define a single schema and extract multiple instances of it. With `instructor`, doing this is relatively straightforward by using [our `create_iterable` method](/concepts/lists/).
+A common use-case is to define a single schema and extract multiple instances of it. With `instructor`, doing this is relatively straightforward by using [our `create_iterable` method](../../concepts/lists.md).
 
 ```python
 import instructor
 import openai
 from pydantic import BaseModel
+```
 
 client = instructor.from_openai(openai.OpenAI(), mode=instructor.Mode.TOOLS_STRICT)
 
@@ -228,7 +229,7 @@ for user in users:
     #> name='John' age=10
 ```
 
-Other times, we might also want to stream out information as it's dynamically generated into some sort of frontend component With `instructor`, you'll be able to do just that [using the `create_partial` method](/concepts/partial/).
+Other times, we might also want to stream out information as it's dynamically generated into some sort of frontend component With `instructor`, you'll be able to do just that [using the `create_partial` method](../../concepts/partial.md).
 
 ```python
 import instructor
@@ -375,4 +376,4 @@ While OpenAI's Structured Outputs shows promise, it has key limitations. The sys
 
 If you're interested in Structured Outputs, `instructor` addresses these critical issues. It provides automatic retries, real-time input validation, and multi-provider integration, allowing developers to more effectively implement Structured Outputs in their AI projects.
 
-if you haven't given `instructor` a shot, try it today!
+if you haven't given `instructor` a shot, try it today!

docs/blog/posts/open_source.md

@@ -17,11 +17,11 @@ tags:
 - API Integration
 ---
 
-# Structured Output for Open Source and Local LLMs 
+# Structured Output for Open Source and Local LLMs
 
 Instructor has expanded its capabilities for language models. It started with API interactions via the OpenAI SDK, using [Pydantic](https://pydantic-docs.helpmanual.io/) for structured data validation. Now, Instructor supports multiple models and platforms.
 
-The integration of [JSON mode](../../concepts/patching.md#json-mode) improved adaptability to vision models and open source alternatives. This allows support for models from [GPT](https://openai.com/api/) and [Mistral](https://mistral.ai) to models on [Ollama](https://ollama.ai) and [Hugging Face](https://huggingface.co/models), using [llama-cpp-python](../../hub/llama-cpp-python.md).
+The integration of [JSON mode](../../concepts/patching.md#json-mode) improved adaptability to vision models and open source alternatives. This allows support for models from [GPT](https://openai.com/api/) and [Mistral](https://mistral.ai) to models on [Ollama](https://ollama.ai) and [Hugging Face](https://huggingface.co/models), using [llama-cpp-python](../../integrations/llama-cpp-python.md).
 
 Instructor now works with cloud-based APIs and local models for structured data extraction. Developers can refer to our guide on [Patching](../../concepts/patching.md) for information on using JSON mode with different models.
 
@@ -40,7 +40,7 @@ OpenAI clients offer functionalities for different needs. We explore clients int
 
 ### Ollama: A New Frontier for Local Models
 
-Ollama enables structured outputs with local models using JSON schema. See our [Ollama documentation](../../hub/ollama.md) for details.
+Ollama enables structured outputs with local models using JSON schema. See our [Ollama documentation](../../integrations/ollama.md) for details.
 
 For setup and features, refer to the documentation. The [Ollama website](https://ollama.ai/download) provides resources, models, and support.
 
@@ -68,6 +68,7 @@ client = instructor.from_openai(
     mode=instructor.Mode.JSON,
 )
 
+
 user = client.chat.completions.create(
     model="llama2",
     messages=[
@@ -93,7 +94,6 @@ Example of using llama-cpp-python for structured outputs:
 ```python
 import llama_cpp
 import instructor
-
 from llama_cpp.llama_speculative import LlamaPromptLookupDecoding
 from pydantic import BaseModel
 
@@ -111,9 +111,10 @@ llama = llama_cpp.Llama(
 
 create = instructor.patch(
     create=llama.create_chat_completion_openai_v1,
-    mode=instructor.Mode.JSON_SCHEMA, 
+    mode=instructor.Mode.JSON_SCHEMA,
 )
 
+
 class UserDetail(BaseModel):
     name: str
     age: int
@@ -131,72 +132,32 @@ user = create(
 
 print(user)
 #> name='Jason' age=30
-"""
 ```
 
 ## Alternative Providers
 
-### Anyscale
-
-Anyscale's Mistral model, as detailed in our [Anyscale documentation](../../hub/anyscale.md) and on [Anyscale's official documentation](https://docs.anyscale.com/), introduces the ability to obtain structured outputs using JSON schema.
-
-```bash
-export ANYSCALE_API_KEY="your-api-key"
-```
-
-```python
-import os
-from openai import OpenAI
-from pydantic import BaseModel
-import instructor
-
-
-class UserDetails(BaseModel):
-    name: str
-    age: int
-
-
-# enables `response_model` in create call
-client = instructor.from_openai(
-    OpenAI(
-        base_url="https://api.endpoints.anyscale.com/v1",
-        api_key=os.environ["ANYSCALE_API_KEY"],
-    ),
-    # This uses Anyscale's json schema output mode
-    mode=instructor.Mode.JSON_SCHEMA,
-)
-
-resp = client.chat.completions.create(
-    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
-    messages=[
-        {"role": "system", "content": "You are a world class extractor"},
-        {"role": "user", "content": 'Extract the following entities: "Jason is 20"'},
-    ],
-    response_model=UserDetails,
-)
-print(resp)
-#> name='Jason' age=20
-```
-
 ### Groq
 
-Groq's platform, detailed further in our [Groq documentation](../../hub/groq.md) and on [Groq's official documentation](https://groq.com/), offers a unique approach to processing with its tensor architecture. This innovation significantly enhances the performance of structured output processing.
+Groq's platform, detailed further in our [Groq documentation](../../integrations/groq.md) and on [Groq's official documentation](https://groq.com/), offers a unique approach to processing with its tensor architecture. This innovation significantly enhances the performance of structured output processing.
 
 ```bash
 export GROQ_API_KEY="your-api-key"
 ```
 
 ```python
 import os
-import instructor
-import groq
 from pydantic import BaseModel
 
-client = qrog.Groq(
+import groq
+import instructor
+
+
+client = groq.Groq(
     api_key=os.environ.get("GROQ_API_KEY"),
 )
 
-# By default, the patch function will patch the ChatCompletion.create and ChatCompletion.create methods to support the response_model parameter
+# By default, the patch function will patch the ChatCompletion.create and ChatCompletion.create methods
+# to support the response_model parameter
 client = instructor.from_openai(client, mode=instructor.Mode.MD_JSON)
 
 
@@ -216,24 +177,26 @@ user: UserExtract = client.chat.completions.create(
 )
 
 assert isinstance(user, UserExtract), "Should be instance of UserExtract"
+
 print(user)
 #> name='jason' age=25
-"""
 ```
 
 ### Together AI
 
-Together AI, when combined with Instructor, offers a seamless experience for developers looking to leverage structured outputs in their applications. For more details, refer to our [Together AI documentation](../hub/together.md) and explore the [patching guide](../concepts/patching.md) to enhance your applications.
+Together AI, when combined with Instructor, offers a seamless experience for developers looking to leverage structured outputs in their applications. For more details, refer to our [Together AI documentation](../../integrations/together.md) and explore the [patching guide](../../concepts/patching.md) to enhance your applications.
 
 ```bash
 export TOGETHER_API_KEY="your-api-key"
 ```
 
 ```python
 import os
-import openai
 from pydantic import BaseModel
+
 import instructor
+import openai
+
 
 client = openai.OpenAI(
     base_url="https://api.together.xyz/v1",
@@ -242,6 +205,7 @@ client = openai.OpenAI(
 
 client = instructor.from_openai(client, mode=instructor.Mode.TOOLS)
 
+
 class UserExtract(BaseModel):
     name: str
     age: int
@@ -256,29 +220,33 @@ user: UserExtract = client.chat.completions.create(
 )
 
 assert isinstance(user, UserExtract), "Should be instance of UserExtract"
-print(user)
 
+print(user)
 #> name='jason' age=25
 ```
 
 ### Mistral
 
-For those interested in exploring the capabilities of Mistral Large with Instructor, we highly recommend checking out our comprehensive guide on [Mistral Large](../../hub/mistral.md).
+For those interested in exploring the capabilities of Mistral Large with Instructor, we highly recommend checking out our comprehensive guide on [Mistral Large](../../integrations/mistral.md).
 
 ```python
 import instructor
-
 from pydantic import BaseModel
 from mistralai.client import MistralClient
 
+
 client = MistralClient()
 
-patched_chat = instructor.from_openai(create=client.chat, mode=instructor.Mode.MISTRAL_TOOLS)
+patched_chat = instructor.from_openai(
+    create=client.chat, mode=instructor.Mode.MISTRAL_TOOLS
+)
+
 
 class UserDetails(BaseModel):
     name: str
     age: int
 
+
 resp = patched_chat(
     model="mistral-large-latest",
     response_model=UserDetails,
@@ -289,6 +257,7 @@ resp = patched_chat(
         },
     ],
 )
+
 print(resp)
 #> name='Jason' age=20
-```
+```

docs/blog/posts/pairwise-llm-judge.md

@@ -64,7 +64,7 @@ Next, we'll create a function that uses our LLM to judge the relevance between a
 ```python
 def judge_relevance(question: str, text: str) -> Judgment:
     return client.chat.create(
-        model="gpt-4o-mini",
+        model="gpt-4",
         messages=[
             {
                 "role": "system",
@@ -102,8 +102,7 @@ def judge_relevance(question: str, text: str) -> Judgment:
                     {{text}}
                     </text>
                 """
-            },
-            },
+            }
         ],
         response_model=Judgment,
         context={"question": question, "text": text},
@@ -134,7 +133,7 @@ if __name__ == "__main__":
             score += 1
 
     print(f"Score: {score}/{len(test_pairs)}")
-    # > Score 9/10
+    #> Score 9/10
 ```
 
 This test loop runs the judge on each pair and compares the result to a predetermined similarity value, calculating an overall score.

docs/blog/posts/pydantic-is-still-all-you-need.md

@@ -39,7 +39,7 @@ Pydantic, combined with function calling, offers a superior alternative for stru
 - Validators to improve system reliability
 - Cleaner, more maintainable code
 
-For more details on how Pydantic enhances data validation, check out our [Data Validation with Pydantic](../concepts/models.md) guide.
+For more details on how Pydantic enhances data validation, check out our [Data Validation with Pydantic](../../concepts/models.md) guide.
 
 And here's the kicker: nothing's really changed in the past year. The core API is still just:
 
@@ -63,7 +63,7 @@ Since last year:
 - Built a version in Rust
 - Seen 40% month-over-month growth in the Python library
 
-We now support [Ollama](../../hub/ollama.md), [llama-cpp-python](../../hub/llama-cpp-python.md), [Anthropic](../../hub/anthropic.md), [Cohere](../../hub/cohere.md), [Google](../../hub/google.md), [Vertex AI](../../hub/vertexai.md), and more. As long as language models support function calling capabilities, this API will remain standard.
+We now support [Ollama](../../integrations/ollama.md), [llama-cpp-python](../../integrations/llama-cpp-python.md), [Anthropic](../../integrations/anthropic.md), [Cohere](../../integrations/cohere.md), [Google](../../integrations/google.md), [Vertex AI](../../integrations/vertex.md), and more. As long as language models support function calling capabilities, this API will remain standard.
 
 ## Key Features
 
@@ -123,4 +123,4 @@ Pydantic is still all you need for effective structured outputs with LLMs. It's
 
 As we continue to refine AI language models, keeping these principles in mind will lead to more robust, maintainable, and powerful applications. The future of AI isn't just about what the models can do, but how seamlessly we can integrate them into our existing software ecosystems.
 
-For more advanced use cases and integrations, check out our [examples](../../examples/index.md) section, which covers various LLM providers and specialized implementations.
+For more advanced use cases and integrations, check out our [examples](../../examples/index.md) section, which covers various LLM providers and specialized implementations.