add _record function forLangtailPrompts and implicit completion flow

.github/workflows/main.yml

@@ -50,6 +50,7 @@ jobs:
       - name: run tests
         env:
           LANGTAIL_API_KEY: ${{secrets.LANGTAIL_API_KEY}}
+          OPENAI_API_KEY: ${{secrets.OPENAI_API_KEY}}
         run: pnpm test
 
   publish:

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Changelog
 
+## [Unreleased]
+
+- change return type of `build` to `Promise<IPromptObject>`
+- add `lt.completions.create` method to directly call openAI SDK with the output of `build` method
+
 # 0.3.1
 
 - fix next.js compatibility

README.md

@@ -162,18 +162,19 @@ const playgroundState = await lt.get({
 render your template and builds the final open AI compatible payload:
 
 ```ts
-const openAiBody = lt.build(playgroundState, {
+const preparedPrompt = lt.build(playgroundState, {
   stream: true,
   variables: {
     topic: "iron man",
   },
 })
 ```
 
-openAiBody now contains this object:
+preparedPrompt now contains this object:
 
 ```js
 {
+            "stream": true,
             "frequency_penalty": 0,
             "max_tokens": 800,
             "messages": [
@@ -194,9 +195,29 @@ Notice that your langtail template was replaced with a variable passed in. You c
 ```ts
 import OpenAI from "openai"
 
-const openai = new OpenAI()
+const openAI = new OpenAI()
 
-const joke = await openai.chat.completions.create(openAiBody)
+const jokeCompletion = await openAI.chat.completions.create(
+  preparedPrompt.toOpenAI(),
+)
 ```
 
 This way you are still using langtail prompts without exposing potentially sensitive data in your variables.
+In case you wish to use the proxyless feature and keep having your logs visible in langtail, use the `preparedPrompt` in the `lt.completions.create` call like this:
+
+### Proxyless with Langtail logs
+
+```ts
+const lt = new LangtailPrompts({
+  apiKey: "<LANGTAIL_API_KEY>",
+  openAIKey: "<OPENAI_API>", // required for the completions.create to work, you can also define it as env variable OPENAI_API_KEY
+})
+
+const preparedPrompt = lt.build(playgroundState, {
+  variables: {
+    topic: "iron man",
+  },
+})
+
+const jokeCompletion = await lt.completions.create(preparedPrompt) // results in an openAI ChatCompletion and you can see the request in langtail logs. Variables pass
+```

cspell.json

@@ -5,7 +5,10 @@
   "dictionaries": [],
   "words": [
     "Aragorn",
+    "datetime",
     "Langtail",
+    "logprobs",
+    "proxyless",
     "undici"
   ],
   "ignoreWords": [],

package.json

@@ -1,6 +1,6 @@
 {
   "name": "langtail",
-  "version": "0.3.1",
+  "version": "0.4.0-beta-0",
   "description": "",
   "main": "./dist/LangtailNode.js",
   "packageManager": "pnpm@8.15.6",
@@ -22,6 +22,7 @@
     "natural language processing",
     "gpt-3",
     "gpt-4",
+    "openrouter",
     "anthropic"
   ],
   "authors": [
@@ -54,17 +55,21 @@
       "require": "./dist/template.js",
       "import": "./dist/template.mjs",
       "types": "./dist/template.d.ts"
+    },
+    "./dist/dataSchema": {
+      "require": "./dist/dataSchema.js",
+      "import": "./dist/dataSchema.mjs",
+      "types": "./dist/dataSchema.d.ts"
     }
   },
   "files": [
     "dist"
   ],
   "dependencies": {
-    "@asteasolutions/zod-to-openapi": "^7.0.0",
     "@langtail/handlebars-evalless": "^0.1.1",
     "date-fns": "^3.6.0",
     "handlebars": "^4.7.8",
-    "openai": "^4.43.0",
+    "openai": "^4.46.1",
     "query-string": "^9.0.0",
     "zod": "^3.23.8"
   },
@@ -79,7 +84,8 @@
     "entryPoints": [
       "src/LangtailNode.ts",
       "src/template.ts",
-      "src/getOpenAIBody.ts"
+      "src/getOpenAIBody.ts",
+      "src/dataSchema.ts"
     ]
   }
 }

src/LangtailNode.spec.ts

@@ -2,7 +2,7 @@ import { LangtailNode, baseURL } from "./LangtailNode"
 import "dotenv/config"
 import { describe, expect, it } from "vitest"
 import nock from "nock"
-import { openAIStreamingResponseSchema } from "./dataSchema"
+import { ChatCompletionChunkSchema } from "./dataSchema"
 
 const lt = new LangtailNode()
 
@@ -24,13 +24,13 @@ describe("LangtailNode", () => {
     for await (const part of proxyCompletion) {
       partCount++
 
-      openAIStreamingResponseSchema.parse(part)
+      ChatCompletionChunkSchema.parse(part)
     }
 
     expect(partCount > 1).toBe(true)
   })
 
-  it("should not record", async (t) => {
+  it("should not record this completion in the logs", async (t) => {
     nock(baseURL) // nock works by intercepting requests at the network level, if open AI switches to undici we will need to intercept differently
       .post("/chat/completions")
       .reply(200, function (uri, req) {

src/LangtailNode.ts

@@ -22,6 +22,10 @@ export interface ILangtailExtraProps extends OpenAiBodyType {
   metadata?: Record<string, any>
 }
 
+export type ChatCompletionsCreateParams =
+  | (ChatCompletionCreateParamsStreaming & ILangtailExtraProps)
+  | (ChatCompletionCreateParamsNonStreaming & ILangtailExtraProps)
+
 export class LangtailNode {
   prompts: LangtailPrompts
   chat: {

src/LangtailPrompts.spec.ts

@@ -2,7 +2,7 @@ import "dotenv/config"
 import { describe, expect, it } from "vitest"
 
 import { LangtailPrompts } from "./LangtailPrompts"
-import { openAIStreamingResponseSchema } from "./dataSchema"
+import { ChatCompletionChunkSchema } from "./dataSchema"
 
 const lt = new LangtailPrompts({
   apiKey: process.env.LANGTAIL_API_KEY!,
@@ -17,12 +17,23 @@ describe(
       it("should return the correct path for project prompt", () => {
         const path = lt._createPromptPath({
           prompt: "prompt",
-          environment: "staging",
+          environment: "preview",
           version: "6vy19bmp",
         })
 
         expect(path).toBe(
-          "https://api.langtail.com/project-prompt/prompt/staging?v=6vy19bmp",
+          "https://api.langtail.com/project-prompt/prompt/preview?v=6vy19bmp",
+        )
+      })
+
+      it("staging with no version parameter", () => {
+        const path = lt._createPromptPath({
+          prompt: "prompt",
+          environment: "staging",
+        })
+
+        expect(path).toBe(
+          "https://api.langtail.com/project-prompt/prompt/staging",
         )
       })
 
@@ -35,23 +46,23 @@ describe(
 
         const path = ltProject._createPromptPath({
           prompt: "prompt",
-          environment: "staging",
+          environment: "preview",
           version: "6vy19bmp",
         })
 
         expect(path).toBe(
-          "https://api.langtail.com/some-workspace/ci-tests-project/prompt/staging?v=6vy19bmp",
+          "https://api.langtail.com/some-workspace/ci-tests-project/prompt/preview?v=6vy19bmp",
         )
 
         const pathForPromptConfig = ltProject._createPromptPath({
           prompt: "prompt",
-          environment: "staging",
+          environment: "preview",
           version: "6vy19bmp",
           configGet: true,
         })
 
         expect(pathForPromptConfig).toBe(
-          "https://api.langtail.com/some-workspace/ci-tests-project/prompt/staging?open-ai-completion-config-payload=true&v=6vy19bmp",
+          "https://api.langtail.com/some-workspace/ci-tests-project/prompt/preview?open-ai-completion-config-payload=true&v=6vy19bmp",
         )
       })
     })
@@ -98,11 +109,12 @@ describe(
           },
           stream: true,
         })
+
         let partCount = 0
         for await (const part of proxyCompletion) {
           partCount++
 
-          openAIStreamingResponseSchema.parse(part)
+          ChatCompletionChunkSchema.parse(part)
         }
 
         expect(partCount > 1).toBe(true)
@@ -153,17 +165,17 @@ describe(
 
     describe("build", () => {
       const ltLocal = new LangtailPrompts({
-        // baseURL: "https://api-staging.langtail.com",
+        // baseURL: "https://api-staging.langtail.com",// uncomment this line to test against local prompt api
         apiKey: process.env.LANGTAIL_API_KEY!,
       })
 
       it("should return the openAI body user can use with openai client", async () => {
-        const playgroundState = await ltLocal.get({
+        const prompt = await ltLocal.get({
           prompt: "optional-var-test",
           environment: "preview",
           version: "c8hrwdiz",
         })
-        expect(playgroundState).toMatchInlineSnapshot(`
+        expect(prompt).toMatchInlineSnapshot(`
           {
             "chatInput": {
               "optionalExtra": "",
@@ -195,15 +207,14 @@ describe(
           }
         `)
 
-        const openAiBody = ltLocal.build(playgroundState, {
+        const promptObj = ltLocal.build(prompt, {
           stream: true,
           variables: {
             optionalExtra: "This is an optional extra",
           },
-
         })
 
-        expect(openAiBody).toMatchInlineSnapshot(`
+        expect(promptObj.toOpenAI()).toMatchInlineSnapshot(`
           {
             "frequency_penalty": 0,
             "max_tokens": 800,
@@ -224,6 +235,31 @@ describe(
         `)
       })
     })
+
+    describe("completion proxyless use case", () => {
+      it("should return completion for ai clock prompt", async () => {
+        const ltLocal = new LangtailPrompts({
+          baseURL: "https://api-staging.langtail.com",
+          apiKey:
+            "lt-eyJ0eXAiOiJKV1QiLCJhbGciOiJFUzI1NiJ9.eyJpc3MiOiJsYW5ndGFpbC1hcGkiLCJzdWIiOiJjbG11cTVndW8wMDA0bDkwOHZvbjFvMjhmIiwianRpIjoiY2x1MThrczg0MDAwMTl1Y2JsOGFueHl5ZCIsInJhdGVMaW1pdCI6bnVsbCwiaWF0IjoxNzExMDI1Nzg5fQ.pXT-4CsIenb1VchGaSMxfn7ZBeQHdASWGSs-r7Ryk9uVrfgk7ju5bFDRHWY9N6ua42SrwTx75m5u6Un4wxONUQ",
+        })
+
+        const promptPlaygroundState = await ltLocal.get({
+          prompt: "ai-clock",
+          environment: "preview",
+          version: "yjxvsqwx",
+        })
+        const preparedPrompt = ltLocal.build(promptPlaygroundState, {
+          variables: {
+            time: "13:11",
+          },
+        })
+
+        const completion = await ltLocal.completions.create(preparedPrompt)
+
+        expect(completion.choices[0].message.content.length > 10).toBeTruthy()
+      })
+    })
   },
   { timeout: 20000 },
 )