Working Examples!

Author: hellovai

fastapi-starter/README.md

@@ -13,7 +13,7 @@ Ensure your `settings.json` has:
 1. Run `poetry install`
 2. Run `poetry shell`
 3. Open up vscode command palette (command + shift + p, and select the .venv folder that was created in this directory as the interpreter)
-4. Run `uvicorn fastapi_starter.app:app --reload`
+4. Run `uvicorn fast_api_starter.app:app --reload`
 5. Curl the streaming endpoint:
    ```
    curl -X GET -H "Content-Type: application/json" http://localhost:8000/extract_resume

fastapi-starter/baml_example_app.py

@@ -10,23 +10,18 @@ class Education {
   year int
 }
 
-function ExtractResume {
-  input string
-  output Resume
-}
-
-impl<llm, ExtractResume> version1 {
+function ExtractResume(raw_text: string) -> Resume {
   client GPT4
   prompt #"
     Parse the following resume and return a structured representation of the data in the schema below.
 
     Resume:
     ---
-    {#input}
+    {{raw_text}}
     ---
 
     Output JSON format (only include these fields, and no others):
-    {#print_type(output)}
+    {{ ctx.output_format(prefix=null) }}
 
     Output JSON:
   "#

fastapi-starter/baml_src/example_1.baml

@@ -6,23 +6,42 @@ enum Category {
     Question
 }
 
-function ClassifyMessage { 
-  input (message: string, message_date: string)
-  output Category[]
+class Message {
+  role Role
+  content string
 }
 
-impl<llm, ClassifyMessage> level1 {
+enum Role {
+  Customer
+  Assistant
+}
+
+template_string PrintMessage(msg: Message, prefix: string?) #"
+  {{ _.chat('user' if msg.role == "Customer" else 'assistant') }}
+  {% if prefix %}
+  {{ prefix }}
+  {% endif %}
+  {{ msg.content }}
+"#
+
+function ClassifyMessage(convo: Message[]) -> Category[] {
   client GPT4
   prompt #"
-    Classify the following INPUT into following:
-    {#print_enum(Category)}
+    {# 
+      Prompts are auto-dedented and trimmed.
+      We use JINJA for our prompt syntax
+      (but we added some static analysis to make sure it's valid!)
+    #}
+
+    {{ ctx.output_format(prefix="Classify with the following json:") }}
 
-    INPUT
-    ---
-    date: {#input.message_date}
-    message: {#input.message}
-    ---
+    {% for c in convo %}
+    {{ PrintMessage(c, 
+      'This is the message to classify:' if loop.last and convo|length > 1 else null
+    ) }}
+    {% endfor %}
 
+    {{ _.chat('assistant') }}
     JSON array of categories that match:
   "#
 }

fastapi-starter/baml_src/example_2.baml

@@ -43,7 +43,7 @@ async def extract_resume():
     - Wrote code in Python and Java
     """
     async def stream_resume(resume):
-        async with b.ExtractResume.stream(resume) as stream:
+        async with b.ExtractResume.stream(raw_text=resume) as stream:
             async for chunk in stream.parsed_stream:
                 print(chunk.delta)
                 if chunk.is_parseable:

fastapi-starter/fast_api_starter/app.py

@@ -13,7 +13,7 @@ packages = [
 python = "^3.11"
 fastapi = "^0.110.0"
 uvicorn = {extras = ["standard"], version = "^0.29.0"}
-baml = "0.17.1"
+baml = "^0.19.0"
 
 
 [build-system]

fastapi-starter/poetry.lock

@@ -1,11 +1,16 @@
 export const dynamic = 'force-dynamic'
 
 import b from '../../../baml_client'
+import { Role } from '../../../baml_client/types';
 
 export async function POST(request: Request) {
     const result = await b.ClassifyMessage({
-        message: "I would like to cancel my order!",
-        message_date: "2021-01-01T00:00:00Z",
+        convo: [
+            {
+                role: Role.Customer,
+                content: "I want to cancel my subscription"
+            }
+        ]
     });
 
     return Response.json(result);

fastapi-starter/pyproject.toml

@@ -10,23 +10,18 @@ class Education {
   year int
 }
 
-function ExtractResume {
-  input string
-  output Resume
-}
-
-impl<llm, ExtractResume> version1 {
+function ExtractResume(raw_text: string) -> Resume {
   client GPT4
   prompt #"
     Parse the following resume and return a structured representation of the data in the schema below.
 
     Resume:
     ---
-    {#input}
+    {{raw_text}}
     ---
 
     Output JSON format (only include these fields, and no others):
-    {#print_type(output)}
+    {{ ctx.output_format(prefix=null) }}
 
     Output JSON:
   "#

nextjs-starter/app/api/example_baml/route.ts

@@ -6,23 +6,42 @@ enum Category {
     Question
 }
 
-function ClassifyMessage { 
-  input (message: string, message_date: string)
-  output Category[]
+class Message {
+  role Role
+  content string
 }
 
-impl<llm, ClassifyMessage> level1 {
+enum Role {
+  Customer
+  Assistant
+}
+
+template_string PrintMessage(msg: Message, prefix: string?) #"
+  {{ _.chat('user' if msg.role == "Customer" else 'assistant') }}
+  {% if prefix %}
+  {{ prefix }}
+  {% endif %}
+  {{ msg.content }}
+"#
+
+function ClassifyMessage(convo: Message[]) -> Category[] {
   client GPT4
   prompt #"
-    Classify the following INPUT into following:
-    {#print_enum(Category)}
+    {# 
+      Prompts are auto-dedented and trimmed.
+      We use JINJA for our prompt syntax
+      (but we added some static analysis to make sure it's valid!)
+    #}
+
+    {{ ctx.output_format(prefix="Classify with the following json:") }}
 
-    INPUT
-    ---
-    date: {#input.message_date}
-    message: {#input.message}
-    ---
+    {% for c in convo %}
+    {{ PrintMessage(c, 
+      'This is the message to classify:' if loop.last and convo|length > 1 else null
+    ) }}
+    {% endfor %}
 
+    {{ _.chat('assistant') }}
     JSON array of categories that match:
   "#
 }