Together ai support, blog post (#366)

Co-authored-by: Hassan El Mghari <hassan4709@gmail.com>

examples/patching/anyscale.py

@@ -0,0 +1,37 @@
+import os
+import instructor
+
+from openai import OpenAI
+from pydantic import BaseModel
+
+
+# By default, the patch function will patch the ChatCompletion.create and ChatCompletion.acreate methods. to support response_model parameter
+client = instructor.patch(
+    OpenAI(
+        base_url="https://api.endpoints.anyscale.com/v1",
+        api_key=os.environ["ANYSCALE_API_KEY"],
+    ),
+    mode=instructor.Mode.JSON_SCHEMA,
+)
+
+
+# Now, we can use the response_model parameter using only a base model
+# rather than having to use the OpenAISchema class
+class UserExtract(BaseModel):
+    name: str
+    age: int
+
+
+user: UserExtract = client.chat.completions.create(
+    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    response_model=UserExtract,
+    messages=[
+        {"role": "user", "content": "Extract jason is 25 years old"},
+    ],
+)  # type: ignore
+
+print(user)
+{
+    "name": "Jason",
+    "age": 25,
+}

examples/patching/oai.py

@@ -1,3 +1,4 @@
+import os
 import instructor
 
 from openai import OpenAI
@@ -5,7 +6,10 @@ from pydantic import BaseModel
 
 
 # By default, the patch function will patch the ChatCompletion.create and ChatCompletion.acreate methods. to support response_model parameter
-client = instructor.patch(OpenAI())
+client = instructor.patch(
+    OpenAI(),
+    mode=instructor.Mode.TOOLS,
+)
 
 
 # Now, we can use the response_model parameter using only a base model

examples/patching/together.py

@@ -0,0 +1,35 @@
+import os
+import openai
+from pydantic import BaseModel
+import instructor
+
+client = openai.OpenAI(
+    base_url="https://api.together.xyz/v1",
+    api_key=os.environ["TOGETHER_API_KEY"],
+)
+
+
+# By default, the patch function will patch the ChatCompletion.create and ChatCompletion.acreate methods. to support response_model parameter
+client = instructor.patch(client, mode=instructor.Mode.TOOLS)
+
+
+# Now, we can use the response_model parameter using only a base model
+# rather than having to use the OpenAISchema class
+class UserExtract(BaseModel):
+    name: str
+    age: int
+
+
+user: UserExtract = client.chat.completions.create(
+    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    response_model=UserExtract,
+    messages=[
+        {"role": "user", "content": "Extract jason is 25 years old"},
+    ],
+)  # type: ignore
+
+print(user.model_dump_json(indent=2))
+{
+    "name": "Jason",
+    "age": 25,
+}