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allow llmClient to be optionally passed in #352
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kamath
merged 3 commits into
browserbase:evals/add-ollama
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arihanv:arihanv/expose-llm-client
Jan 3, 2025
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--- | ||
"@browserbasehq/stagehand": minor | ||
--- | ||
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exposed llmClient in stagehand constructor |
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import { type ConstructorParams, type LogLine, Stagehand } from "../lib"; | ||
import { z } from "zod"; | ||
import { OllamaClient } from "./external_clients/ollama"; | ||
|
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const StagehandConfig: ConstructorParams = { | ||
env: "BROWSERBASE", | ||
apiKey: process.env.BROWSERBASE_API_KEY, | ||
projectId: process.env.BROWSERBASE_PROJECT_ID, | ||
verbose: 1, | ||
llmClient: new OllamaClient( | ||
(message: LogLine) => | ||
console.log(`[stagehand::${message.category}] ${message.message}`), | ||
false, | ||
undefined, | ||
"llama3.2", | ||
), | ||
debugDom: true, | ||
}; | ||
|
||
async function example() { | ||
const stagehand = new Stagehand(StagehandConfig); | ||
|
||
await stagehand.init(); | ||
await stagehand.page.goto("https://news.ycombinator.com"); | ||
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const headlines = await stagehand.page.extract({ | ||
instruction: "Extract only 3 stories from the Hacker News homepage.", | ||
schema: z.object({ | ||
stories: z.array( | ||
z.object({ | ||
title: z.string(), | ||
url: z.string(), | ||
points: z.number(), | ||
}), | ||
).length(3), | ||
}), | ||
}); | ||
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console.log(headlines); | ||
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await stagehand.close(); | ||
} | ||
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||
(async () => { | ||
await example(); | ||
})(); |
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import OpenAI, { type ClientOptions } from "openai"; | ||
import { zodResponseFormat } from "openai/helpers/zod"; | ||
import type { LLMCache } from "../../lib/cache/LLMCache"; | ||
import { validateZodSchema } from "../../lib/utils"; | ||
import { | ||
type ChatCompletionOptions, | ||
type ChatMessage, | ||
LLMClient, | ||
} from "../../lib/llm/LLMClient"; | ||
import type { LogLine } from "../../types/log"; | ||
import type { AvailableModel } from "../../types/model"; | ||
import type { | ||
ChatCompletion, | ||
ChatCompletionAssistantMessageParam, | ||
ChatCompletionContentPartImage, | ||
ChatCompletionContentPartText, | ||
ChatCompletionCreateParamsNonStreaming, | ||
ChatCompletionMessageParam, | ||
ChatCompletionSystemMessageParam, | ||
ChatCompletionUserMessageParam, | ||
} from "openai/resources/chat"; | ||
|
||
export class OllamaClient extends LLMClient { | ||
public type = "ollama" as const; | ||
private client: OpenAI; | ||
private cache: LLMCache | undefined; | ||
public logger: (message: LogLine) => void; | ||
private enableCaching: boolean; | ||
public clientOptions: ClientOptions; | ||
|
||
constructor( | ||
logger: (message: LogLine) => void, | ||
enableCaching = false, | ||
cache: LLMCache | undefined, | ||
modelName: "llama3.2", | ||
clientOptions?: ClientOptions, | ||
) { | ||
super(modelName as AvailableModel); | ||
this.client = new OpenAI({ | ||
...clientOptions, | ||
baseURL: clientOptions?.baseURL || "http://localhost:11434/v1", | ||
apiKey: "ollama", | ||
}); | ||
this.logger = logger; | ||
this.cache = cache; | ||
this.enableCaching = enableCaching; | ||
this.modelName = modelName as AvailableModel; | ||
} | ||
|
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async createChatCompletion<T = ChatCompletion>( | ||
options: ChatCompletionOptions, | ||
retries = 3, | ||
): Promise<T> { | ||
const { image, requestId, ...optionsWithoutImageAndRequestId } = options; | ||
|
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// TODO: Implement vision support | ||
if (image) { | ||
throw new Error( | ||
"Image provided. Vision is not currently supported for Ollama", | ||
); | ||
} | ||
|
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this.logger({ | ||
category: "ollama", | ||
message: "creating chat completion", | ||
level: 1, | ||
auxiliary: { | ||
options: { | ||
value: JSON.stringify({ | ||
...optionsWithoutImageAndRequestId, | ||
requestId, | ||
}), | ||
type: "object", | ||
}, | ||
modelName: { | ||
value: this.modelName, | ||
type: "string", | ||
}, | ||
}, | ||
}); | ||
|
||
const cacheOptions = { | ||
model: this.modelName, | ||
messages: options.messages, | ||
temperature: options.temperature, | ||
top_p: options.top_p, | ||
frequency_penalty: options.frequency_penalty, | ||
presence_penalty: options.presence_penalty, | ||
image: image, | ||
response_model: options.response_model, | ||
}; | ||
|
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if (options.image) { | ||
const screenshotMessage: ChatMessage = { | ||
role: "user", | ||
content: [ | ||
{ | ||
type: "image_url", | ||
image_url: { | ||
url: `data:image/jpeg;base64,${options.image.buffer.toString("base64")}`, | ||
}, | ||
}, | ||
...(options.image.description | ||
? [{ type: "text", text: options.image.description }] | ||
: []), | ||
], | ||
}; | ||
|
||
options.messages.push(screenshotMessage); | ||
} | ||
|
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if (this.enableCaching && this.cache) { | ||
const cachedResponse = await this.cache.get<T>( | ||
cacheOptions, | ||
options.requestId, | ||
); | ||
|
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if (cachedResponse) { | ||
this.logger({ | ||
category: "llm_cache", | ||
message: "LLM cache hit - returning cached response", | ||
level: 1, | ||
auxiliary: { | ||
requestId: { | ||
value: options.requestId, | ||
type: "string", | ||
}, | ||
cachedResponse: { | ||
value: JSON.stringify(cachedResponse), | ||
type: "object", | ||
}, | ||
}, | ||
}); | ||
return cachedResponse; | ||
} | ||
|
||
this.logger({ | ||
category: "llm_cache", | ||
message: "LLM cache miss - no cached response found", | ||
level: 1, | ||
auxiliary: { | ||
requestId: { | ||
value: options.requestId, | ||
type: "string", | ||
}, | ||
}, | ||
}); | ||
} | ||
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let responseFormat = undefined; | ||
if (options.response_model) { | ||
responseFormat = zodResponseFormat( | ||
options.response_model.schema, | ||
options.response_model.name, | ||
); | ||
} | ||
|
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/* eslint-disable */ | ||
// Remove unsupported options | ||
const { response_model, ...ollamaOptions } = { | ||
...optionsWithoutImageAndRequestId, | ||
model: this.modelName, | ||
}; | ||
|
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this.logger({ | ||
category: "ollama", | ||
message: "creating chat completion", | ||
level: 1, | ||
auxiliary: { | ||
ollamaOptions: { | ||
value: JSON.stringify(ollamaOptions), | ||
type: "object", | ||
}, | ||
}, | ||
}); | ||
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const formattedMessages: ChatCompletionMessageParam[] = | ||
options.messages.map((message) => { | ||
if (Array.isArray(message.content)) { | ||
const contentParts = message.content.map((content) => { | ||
if ("image_url" in content) { | ||
const imageContent: ChatCompletionContentPartImage = { | ||
image_url: { | ||
url: content.image_url.url, | ||
}, | ||
type: "image_url", | ||
}; | ||
return imageContent; | ||
} else { | ||
const textContent: ChatCompletionContentPartText = { | ||
text: content.text, | ||
type: "text", | ||
}; | ||
return textContent; | ||
} | ||
}); | ||
|
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if (message.role === "system") { | ||
const formattedMessage: ChatCompletionSystemMessageParam = { | ||
...message, | ||
role: "system", | ||
content: contentParts.filter( | ||
(content): content is ChatCompletionContentPartText => | ||
content.type === "text", | ||
), | ||
}; | ||
return formattedMessage; | ||
} else if (message.role === "user") { | ||
const formattedMessage: ChatCompletionUserMessageParam = { | ||
...message, | ||
role: "user", | ||
content: contentParts, | ||
}; | ||
return formattedMessage; | ||
} else { | ||
const formattedMessage: ChatCompletionAssistantMessageParam = { | ||
...message, | ||
role: "assistant", | ||
content: contentParts.filter( | ||
(content): content is ChatCompletionContentPartText => | ||
content.type === "text", | ||
), | ||
}; | ||
return formattedMessage; | ||
} | ||
} | ||
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const formattedMessage: ChatCompletionUserMessageParam = { | ||
role: "user", | ||
content: message.content, | ||
}; | ||
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return formattedMessage; | ||
}); | ||
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const body: ChatCompletionCreateParamsNonStreaming = { | ||
...ollamaOptions, | ||
model: this.modelName, | ||
messages: formattedMessages, | ||
response_format: responseFormat, | ||
stream: false, | ||
tools: options.tools?.filter((tool) => "function" in tool), // ensure only OpenAI compatibletools are used | ||
}; | ||
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const response = await this.client.chat.completions.create(body); | ||
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this.logger({ | ||
category: "ollama", | ||
message: "response", | ||
level: 1, | ||
auxiliary: { | ||
response: { | ||
value: JSON.stringify(response), | ||
type: "object", | ||
}, | ||
requestId: { | ||
value: requestId, | ||
type: "string", | ||
}, | ||
}, | ||
}); | ||
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if (options.response_model) { | ||
const extractedData = response.choices[0].message.content; | ||
const parsedData = JSON.parse(extractedData); | ||
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if (!validateZodSchema(options.response_model.schema, parsedData)) { | ||
if (retries > 0) { | ||
return this.createChatCompletion(options, retries - 1); | ||
} | ||
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throw new Error("Invalid response schema"); | ||
} | ||
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if (this.enableCaching) { | ||
this.cache.set( | ||
cacheOptions, | ||
{ | ||
...parsedData, | ||
}, | ||
options.requestId, | ||
); | ||
} | ||
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return parsedData; | ||
} | ||
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if (this.enableCaching) { | ||
this.logger({ | ||
category: "llm_cache", | ||
message: "caching response", | ||
level: 1, | ||
auxiliary: { | ||
requestId: { | ||
value: options.requestId, | ||
type: "string", | ||
}, | ||
cacheOptions: { | ||
value: JSON.stringify(cacheOptions), | ||
type: "object", | ||
}, | ||
response: { | ||
value: JSON.stringify(response), | ||
type: "object", | ||
}, | ||
}, | ||
}); | ||
this.cache.set(cacheOptions, response, options.requestId); | ||
} | ||
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return response as T; | ||
} | ||
} |
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for future PR: we should make
LLMClient
default inherit logger from Stagehand so we don't have to do thisThere was a problem hiding this comment.
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#352