docs: parea blog

docs/blog/.authors.yml

@@ -12,4 +12,9 @@ authors:
   anmol:
     name: Anmol Jawandha
     description: Contributor
-    avatar: https://pbs.twimg.com/profile_images/1248544843556466693/PgxUIeBs_400x400.jpg
+    avatar: https://pbs.twimg.com/profile_images/1248544843556466693/PgxUIeBs_400x400.jpg
+  joschkabraun:
+    name: Joschka Braun
+    description: Contributor
+    avatar: https://pbs.twimg.com/profile_images/1601251353531224065/PYpqKsjL_400x400.jpg
+    url: https://twitter.com/joschkabraun

docs/blog/posts/parea.md

@@ -0,0 +1,128 @@
+---
+draft: False
+date: 2024-02-18
+tags:
+  - parea
+authors:
+  - jxnl
+  - joschkabraun
+---
+
+# Best Support with Parea
+
+[Parea](https://www.parea.ai) is a platform that enables teams to monitor, collaborate, test & label for LLM applications. In this blog we will explore how Parea can be used to enhance the OpenAI client alongside `instructor` and debug + improve instructor calls.
+
+<!-- more -->
+
+## Parea
+
+In order to use Parea, you first need to create an account and a [Parea API key](https://docs.parea.ai/api-reference/authentication).
+
+Next, you will need to install the Parea SDK:
+
+```
+pip install -U parea-ai instructor
+```
+
+[//]: # (If you want to pull this example down from [instructor-hub]&#40;../../hub/index.md&#41; you can use the following command:)
+
+[//]: # ()
+[//]: # (```bash)
+
+[//]: # (instructor hub pull --slug batch_classification_langsmith --py > batch_classification_langsmith.py)
+
+[//]: # (```)
+
+In this example we'll use the `wrap_openai` function to wrap the OpenAI client with Parea. This will allow us to use Parea's observability and monitoring features with the OpenAI client. Then we'll use `instructor` to patch the client with the `TOOLS` mode. This will allow us to use `instructor` to add additional functionality to the client. We'll use `instructor` to write emails with URLs from the `instructor` docs.
+
+```python
+import os
+
+import instructor
+import requests
+from dotenv import load_dotenv
+from openai import OpenAI
+from pydantic import BaseModel, field_validator, Field
+import re
+from parea import Parea
+
+load_dotenv()
+
+client = OpenAI()
+
+p = Parea(api_key=os.getenv("PAREA_API_KEY"))
+p.wrap_openai_client(client, "instructor")
+
+client = instructor.from_openai(client)
+
+
+class Email(BaseModel):
+    subject: str
+    body: str = Field(
+        ...,
+        description="Email body, Should contain links to instructor documentation. ",
+    )
+
+    @field_validator("body")
+    def check_urls(cls, v):
+        urls = re.findall(r"https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+", v)
+        errors = []
+        for url in urls:
+            if not url.startswith("https://python.useinstructor.com"):
+                errors.append(
+                    f"URL {url} is not from useinstructor.com, Only include URLs that include use instructor.com. "
+                )
+            response = requests.get(url)
+            if response.status_code != 200:
+                errors.append(
+                    f"URL {url} returned status code {response.status_code}. Only include valid URLs that exist."
+                )
+            elif "404" in response.text:
+                errors.append(
+                    f"URL {url} contained '404' in the body. Only include valid URLs that exist."
+                )
+        if errors:
+            raise ValueError("\n".join(errors))
+        return 
+
+
+def main():
+    email = client.messages.create(
+        model="gpt-3.5-turbo",
+        max_tokens=1024,
+        max_retries=3,
+        messages=[
+            {
+                "role": "user",
+                "content": "I'm responding to a student's question. Here is the link to the documentation: {{doc_link1}} and {{doc_link2}}",
+            }
+        ],
+        # Parea supports templated prompts with {{...}} syntax
+        template_inputs={
+            "doc_link1": "https://python.useinstructor.com/docs/tutorial/tutorial-1",
+            "doc_link2": "https://jxnl.github.io/docs/tutorial/tutorial-2",
+        },
+        response_model=Email,
+    )
+    print(email)
+
+
+if __name__ == "__main__":
+    main()
+```
+
+If you follow what we've done, Parea has wrapped the client and proceeded to write emails with links from the instructors docs. This is a simple example of how you can use Parea to enhance the OpenAI client. You can use Parea to monitor and observe the client, and use `instructor` to add additional functionality to the client.
+
+To take a look at trace of this execution checkout the screenshot below. Noticeable:
+
+- left sidebar: all related LLM calls are grouped under a trace called `instructor`
+- middle section: the root trace visualizes the `templated_inputs` as inputs and the created `Email` object as output
+- bottom of right sidebar: any validation errors are captured and tracked as score for the trace which enables visualizing them in dashboards and filtering by them on tables
+
+![](./img/parea/trace.png)
+
+## Label Responses for Fine-Tuning
+
+Sometimes you may want to let subject-matter experts (SMEs) label responses to use them for fine-tuning. Parea provides a way to do this via an annotation queue. Editing raw JSON objects to correct tool use & function calling responses can be error-prone, esp. for non-devs. For that purpose, Parea has a so-called [Form Mode](https://docs.parea.ai/manual-review/overview#labeling-function-calling-tool-use-responses) which allows the user to safely fill-out a form instead of editing the JSON object. The labeled data can then be exported and used for fine-tuning.
+
+![Form Mode](img/parea/form-mode.gif)