Update external client check + google (#193)

.changeset/eleven-needles-fry.md

@@ -0,0 +1,5 @@
+---
+"@instructor-ai/instructor": minor
+---
+
+update peer deps + remove baseUrl check on generic client type guard

.github/workflows/test-pr.yml

@@ -22,7 +22,7 @@ jobs:
       ANYSCALE_API_KEY: ${{ secrets.ANYSCALE_API_KEY }}
       TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-
+      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
     steps:
       - uses: actions/checkout@v3
         with:

.github/workflows/test.yml

@@ -14,7 +14,8 @@ jobs:
       ANYSCALE_API_KEY: ${{ secrets.ANYSCALE_API_KEY }}
       ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
       TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
-
+      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
+
     steps:
       - uses: actions/checkout@v3
         with:

.tool-versions

@@ -1,3 +1,3 @@
 nodejs 20.9.0
-bun 1.0.15
+bun 1.1.43
 python 3.12.1

package.json

@@ -59,7 +59,7 @@
     "zod": ">=3.23.8"
   },
   "devDependencies": {
-    "@anthropic-ai/sdk": "0.29.2",
+    "@anthropic-ai/sdk": "0.33.1",
     "@changesets/changelog-github": "^0.5.0",
     "@changesets/cli": "^2.27.1",
     "@ianvs/prettier-plugin-sort-imports": "4.1.0",
@@ -75,8 +75,8 @@
     "eslint-plugin-only-warn": "^1.1.0",
     "eslint-plugin-prettier": "^5.1.2",
     "husky": "^8.0.3",
-    "llm-polyglot": "2.2.0",
-    "openai": "4.68.1",
+    "llm-polyglot": "2.5.0",
+    "openai": "4.78.1",
     "prettier": "latest",
     "ts-inference-check": "^0.3.0",
     "tsup": "^8.0.1",

src/instructor.ts

@@ -475,7 +475,6 @@ function isGenericClient(client: any): client is GenericClient {
   return (
     typeof client === "object" &&
     client !== null &&
-    "baseURL" in client &&
     "chat" in client &&
     typeof client.chat === "object" &&
     "completions" in client.chat &&

tests/google.test.ts

@@ -0,0 +1,295 @@
+import Instructor from "@/index"
+import { omit } from "@/lib"
+import { describe, expect, test } from "bun:test"
+import { createLLMClient } from "llm-polyglot"
+import z from "zod"
+
+const googleClient = createLLMClient({
+  provider: "google",
+  apiKey: process.env.GEMINI_API_KEY
+})
+
+describe("LLMClient google Provider - mode: TOOLS", () => {
+  const instructor = Instructor({
+    client: googleClient,
+    mode: "TOOLS"
+  })
+
+  test("basic completion", async () => {
+    const completion = await instructor.chat.completions.create({
+      model: "gemini-1.5-flash-latest",
+      max_tokens: 1000,
+      messages: [
+        {
+          role: "user",
+          content: "My name is Dimitri Kennedy."
+        }
+      ],
+      response_model: {
+        name: "extract_name",
+        schema: z.object({
+          name: z.string()
+        })
+      }
+    })
+
+    expect(omit(["_meta"], completion)).toEqual({ name: "Dimitri Kennedy" })
+  })
+
+  test("complex schema", async () => {
+    const completion = await instructor.chat.completions.create({
+      model: "gemini-1.5-flash-latest",
+      max_tokens: 1000,
+      messages: [
+        {
+          role: "user",
+          content: `User Data Submission:
+
+            First Name: John
+            Last Name: Doe
+            Contact Details:
+            Email: [email protected]
+            Phone Number: 555-1234
+            Job History:
+            
+            Company Name: Acme Corp
+            Role: Software Engineer
+            Years: 5
+            Company Name: Globex Inc.
+            Role: Lead Developer
+            Years: 3
+            Skills:
+            
+            Programming
+            Leadership
+            Communication
+            `
+        }
+      ],
+      response_model: {
+        name: "process_user_data",
+        schema: z.object({
+          story: z
+            .string()
+            .describe("A long and mostly made up story about the user - minimum 500 words"),
+          userDetails: z.object({
+            firstName: z.string(),
+            lastName: z.string(),
+            contactDetails: z.object({
+              email: z.string(),
+              phoneNumber: z.string().optional()
+            })
+          }),
+          jobHistory: z.array(
+            z.object({
+              companyName: z.string(),
+              role: z.string(),
+              years: z.number().optional()
+            })
+          ),
+          skills: z.array(z.string())
+        })
+      }
+    })
+
+    expect(omit(["_meta", "story"], completion)).toEqual({
+      userDetails: {
+        firstName: "John",
+        lastName: "Doe",
+        contactDetails: {
+          email: "[email protected]",
+          phoneNumber: "555-1234"
+        }
+      },
+      jobHistory: [
+        {
+          companyName: "Acme Corp",
+          role: "Software Engineer",
+          years: 5
+        },
+        {
+          companyName: "Globex Inc.",
+          role: "Lead Developer",
+          years: 3
+        }
+      ],
+      skills: ["Programming", "Leadership", "Communication"]
+    })
+  })
+})
+
+describe("LLMClient google Provider - mode: TOOLS - stream", () => {
+  const instructor = Instructor({
+    client: googleClient,
+    mode: "TOOLS"
+  })
+
+  test("basic completion", async () => {
+    const completion = await instructor.chat.completions.create({
+      model: "gemini-1.5-flash-latest",
+      stream: true,
+      max_tokens: 1000,
+      messages: [
+        {
+          role: "user",
+          content: "My name is Dimitri Kennedy."
+        }
+      ],
+      response_model: {
+        name: "extract_name",
+        schema: z.object({
+          name: z.string()
+        })
+      }
+    })
+
+    let final = {}
+
+    for await (const result of completion) {
+      final = result
+    }
+
+    //@ts-expect-error ignore for testing
+    expect(omit(["_meta"], final)).toEqual({ name: "Dimitri Kennedy" })
+  })
+
+  test("complex schema", async () => {
+    const completion = await instructor.chat.completions.create({
+      model: "gemini-1.5-flash-latest",
+      max_tokens: 1000,
+      stream: true,
+      messages: [
+        {
+          role: "user",
+          content: `User Data Submission:
+
+            First Name: John
+            Last Name: Doe
+            Contact Details:
+            Email: [email protected]
+            Phone Number: 555-1234
+            Job History:
+            
+            Company Name: Acme Corp
+            Role: Software Engineer
+            Years: 5
+            Company Name: Globex Inc.
+            Role: Lead Developer
+            Years: 3
+            Skills:
+            
+            Programming
+            Leadership
+            Communication
+            `
+        }
+      ],
+      response_model: {
+        name: "process_user_data",
+        schema: z.object({
+          story: z
+            .string()
+            .describe("A long and mostly made up story about the user - minimum 500 words"),
+          userDetails: z.object({
+            firstName: z.string(),
+            lastName: z.string(),
+            contactDetails: z.object({
+              email: z.string(),
+              phoneNumber: z.string().optional()
+            })
+          }),
+          jobHistory: z.array(
+            z.object({
+              companyName: z.string(),
+              role: z.string(),
+              years: z.number().optional()
+            })
+          ),
+          skills: z.array(z.string())
+        })
+      }
+    })
+
+    let final = {}
+
+    for await (const result of completion) {
+      final = result
+    }
+
+    //@ts-expect-error ignore for testing
+    expect(omit(["_meta", "story"], final)).toEqual({
+      userDetails: {
+        firstName: "John",
+        lastName: "Doe",
+        contactDetails: {
+          email: "[email protected]",
+          phoneNumber: "555-1234"
+        }
+      },
+      jobHistory: [
+        {
+          companyName: "Acme Corp",
+          role: "Software Engineer",
+          years: 5
+        },
+        {
+          companyName: "Globex Inc.",
+          role: "Lead Developer",
+          years: 3
+        }
+      ],
+      skills: ["Programming", "Leadership", "Communication"]
+    })
+  })
+})
+
+describe("LLMClient google Provider - Grounding", () => {
+  test("grounding with structured output", async () => {
+    const completion = await googleClient.chat.completions.create({
+      model: "gemini-1.5-flash-latest",
+      messages: [
+        {
+          role: "user",
+          content:
+            "You are a food critic. Review the most popular Italian restaurants in Boston's North End, focusing on their traditional dishes."
+        }
+      ],
+      groundingThreshold: 0.7,
+      max_tokens: 1000,
+      stream: false
+    })
+
+    expect(completion.choices?.[0]?.message?.content).toBeTruthy()
+  })
+
+  test("grounding with different thresholds", async () => {
+    const highThreshold = await googleClient.chat.completions.create({
+      model: "gemini-1.5-flash-latest",
+      messages: [
+        {
+          role: "user",
+          content: "What are the current top-rated Italian restaurants in Boston's North End?"
+        }
+      ],
+      groundingThreshold: 0.9,
+      max_tokens: 1000
+    })
+
+    // Lower threshold for more general responses
+    const lowThreshold = await googleClient.chat.completions.create({
+      model: "gemini-1.5-flash-latest",
+      messages: [
+        {
+          role: "user",
+          content: "What are the current top-rated Italian restaurants in Boston's North End?"
+        }
+      ],
+      groundingThreshold: 0.5,
+      max_tokens: 1000
+    })
+
+    expect(highThreshold.choices?.[0]?.message?.content).not.toBe(
+      lowThreshold.choices?.[0]?.message?.content
+    )
+  })
+})