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feat(ollama): use official ollama library (RooCodeInc#1859)
Co-authored-by: Saoud Rizwan <[email protected]>
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7 files changed

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‎package-lock.json‎

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‎package.json‎

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@@ -290,6 +290,7 @@
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"isbinaryfile": "^5.0.2",
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"mammoth": "^1.8.0",
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"monaco-vscode-textmate-theme-converter": "^0.1.7",
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"ollama": "^0.5.13",
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"open-graph-scraper": "^6.9.0",
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"openai": "^4.83.0",
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"os-name": "^6.0.0",

‎src/api/providers/ollama.ts‎

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@@ -1,40 +1,35 @@
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import { Anthropic } from "@anthropic-ai/sdk"
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import OpenAI from "openai"
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import { Message, Ollama } from "ollama"
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import { ApiHandler } from "../"
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import { ApiHandlerOptions, ModelInfo, openAiModelInfoSaneDefaults } from "../../shared/api"
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import { convertToOpenAiMessages } from "../transform/openai-format"
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import { convertToOllamaMessages } from "../transform/ollama-format"
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import { ApiStream } from "../transform/stream"
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export class OllamaHandler implements ApiHandler {
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private options: ApiHandlerOptions
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private client: OpenAI
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private client: Ollama
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constructor(options: ApiHandlerOptions) {
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this.options = options
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this.client = new OpenAI({
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baseURL: (this.options.ollamaBaseUrl || "http://localhost:11434") + "/v1",
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apiKey: "ollama",
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})
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this.client = new Ollama({ host: this.options.ollamaBaseUrl || "http://localhost:11434" })
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}
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async *createMessage(systemPrompt: string, messages: Anthropic.Messages.MessageParam[]): ApiStream {
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const openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [
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{ role: "system", content: systemPrompt },
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...convertToOpenAiMessages(messages),
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]
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const ollamaMessages: Message[] = [{ role: "system", content: systemPrompt }, ...convertToOllamaMessages(messages)]
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const stream = await this.client.chat.completions.create({
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const stream = await this.client.chat({
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model: this.getModel().id,
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messages: openAiMessages,
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temperature: 0,
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messages: ollamaMessages,
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stream: true,
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options: {
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num_ctx: Number(this.options.ollamaApiOptionsCtxNum) || 32768,
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},
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})
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for await (const chunk of stream) {
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const delta = chunk.choices[0]?.delta
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if (delta?.content) {
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if (typeof chunk.message.content === "string") {
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yield {
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type: "text",
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text: delta.content,
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text: chunk.message.content,
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}
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}
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}

‎src/api/transform/ollama-format.ts‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,109 @@
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import { Anthropic } from "@anthropic-ai/sdk"
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import { Message } from "ollama"
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export function convertToOllamaMessages(anthropicMessages: Anthropic.Messages.MessageParam[]): Message[] {
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const ollamaMessages: Message[] = []
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for (const anthropicMessage of anthropicMessages) {
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if (typeof anthropicMessage.content === "string") {
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ollamaMessages.push({
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role: anthropicMessage.role,
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content: anthropicMessage.content,
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})
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} else {
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if (anthropicMessage.role === "user") {
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const { nonToolMessages, toolMessages } = anthropicMessage.content.reduce<{
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nonToolMessages: (Anthropic.TextBlockParam | Anthropic.ImageBlockParam)[]
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toolMessages: Anthropic.ToolResultBlockParam[]
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}>(
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(acc, part) => {
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if (part.type === "tool_result") {
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acc.toolMessages.push(part)
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} else if (part.type === "text" || part.type === "image") {
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acc.nonToolMessages.push(part)
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}
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return acc
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},
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{ nonToolMessages: [], toolMessages: [] },
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)
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// Process tool result messages FIRST since they must follow the tool use messages
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let toolResultImages: string[] = []
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toolMessages.forEach((toolMessage) => {
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// The Anthropic SDK allows tool results to be a string or an array of text and image blocks, enabling rich and structured content. In contrast, the Ollama SDK only supports tool results as a single string, so we map the Anthropic tool result parts into one concatenated string to maintain compatibility.
34+
let content: string
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36+
if (typeof toolMessage.content === "string") {
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content = toolMessage.content
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} else {
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content =
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toolMessage.content
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?.map((part) => {
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if (part.type === "image") {
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toolResultImages.push(`data:${part.source.media_type};base64,${part.source.data}`)
44+
return "(see following user message for image)"
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}
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return part.text
47+
})
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.join("\n") ?? ""
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}
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ollamaMessages.push({
51+
role: "user",
52+
images: toolResultImages.length > 0 ? toolResultImages : undefined,
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content: content,
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})
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})
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// Process non-tool messages
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if (nonToolMessages.length > 0) {
59+
ollamaMessages.push({
60+
role: "user",
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content: nonToolMessages
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.map((part) => {
63+
if (part.type === "image") {
64+
return `data:${part.source.media_type};base64,${part.source.data}`
65+
}
66+
return part.text
67+
})
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.join("\n"),
69+
})
70+
}
71+
} else if (anthropicMessage.role === "assistant") {
72+
const { nonToolMessages, toolMessages } = anthropicMessage.content.reduce<{
73+
nonToolMessages: (Anthropic.TextBlockParam | Anthropic.ImageBlockParam)[]
74+
toolMessages: Anthropic.ToolUseBlockParam[]
75+
}>(
76+
(acc, part) => {
77+
if (part.type === "tool_use") {
78+
acc.toolMessages.push(part)
79+
} else if (part.type === "text" || part.type === "image") {
80+
acc.nonToolMessages.push(part)
81+
} // assistant cannot send tool_result messages
82+
return acc
83+
},
84+
{ nonToolMessages: [], toolMessages: [] },
85+
)
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87+
// Process non-tool messages
88+
let content: string = ""
89+
if (nonToolMessages.length > 0) {
90+
content = nonToolMessages
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.map((part) => {
92+
if (part.type === "image") {
93+
return "" // impossible as the assistant cannot send images
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}
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return part.text
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})
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.join("\n")
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}
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ollamaMessages.push({
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role: "assistant",
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content,
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})
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}
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}
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}
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return ollamaMessages
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}

‎src/core/webview/ClineProvider.ts‎

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@@ -79,6 +79,7 @@ type GlobalStateKey =
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| "openAiModelInfo"
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| "ollamaModelId"
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| "ollamaBaseUrl"
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| "ollamaApiOptionsCtxNum"
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| "lmStudioModelId"
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| "lmStudioBaseUrl"
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| "anthropicBaseUrl"
@@ -579,6 +580,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
579580
openAiModelInfo,
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ollamaModelId,
581582
ollamaBaseUrl,
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ollamaApiOptionsCtxNum,
582584
lmStudioModelId,
583585
lmStudioBaseUrl,
584586
anthropicBaseUrl,
@@ -624,6 +626,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
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await this.updateGlobalState("openAiModelInfo", openAiModelInfo)
625627
await this.updateGlobalState("ollamaModelId", ollamaModelId)
626628
await this.updateGlobalState("ollamaBaseUrl", ollamaBaseUrl)
629+
await this.updateGlobalState("ollamaApiOptionsCtxNum", ollamaApiOptionsCtxNum)
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await this.updateGlobalState("lmStudioModelId", lmStudioModelId)
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await this.updateGlobalState("lmStudioBaseUrl", lmStudioBaseUrl)
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await this.updateGlobalState("anthropicBaseUrl", anthropicBaseUrl)
@@ -1880,6 +1883,7 @@ Here is the project's README to help you get started:\n\n${mcpDetails.readmeCont
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openAiModelInfo,
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ollamaModelId,
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ollamaBaseUrl,
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ollamaApiOptionsCtxNum,
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lmStudioModelId,
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lmStudioBaseUrl,
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anthropicBaseUrl,
@@ -1938,6 +1942,7 @@ Here is the project's README to help you get started:\n\n${mcpDetails.readmeCont
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this.getGlobalState("openAiModelInfo") as Promise<ModelInfo | undefined>,
19391943
this.getGlobalState("ollamaModelId") as Promise<string | undefined>,
19401944
this.getGlobalState("ollamaBaseUrl") as Promise<string | undefined>,
1945+
this.getGlobalState("ollamaApiOptionsCtxNum") as Promise<string | undefined>,
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this.getGlobalState("lmStudioModelId") as Promise<string | undefined>,
19421947
this.getGlobalState("lmStudioBaseUrl") as Promise<string | undefined>,
19431948
this.getGlobalState("anthropicBaseUrl") as Promise<string | undefined>,
@@ -2019,6 +2024,7 @@ Here is the project's README to help you get started:\n\n${mcpDetails.readmeCont
20192024
openAiModelInfo,
20202025
ollamaModelId,
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ollamaBaseUrl,
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ollamaApiOptionsCtxNum,
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lmStudioModelId,
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lmStudioBaseUrl,
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anthropicBaseUrl,

‎src/shared/api.ts‎

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@@ -44,6 +44,7 @@ export interface ApiHandlerOptions {
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openAiModelInfo?: ModelInfo
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ollamaModelId?: string
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ollamaBaseUrl?: string
47+
ollamaApiOptionsCtxNum?: string
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lmStudioModelId?: string
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lmStudioBaseUrl?: string
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geminiApiKey?: string

‎webview-ui/src/components/settings/ApiOptions.tsx‎

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@@ -1133,6 +1133,13 @@ const ApiOptions = ({ showModelOptions, apiErrorMessage, modelIdErrorMessage, is
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placeholder={"e.g. llama3.1"}>
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<span style={{ fontWeight: 500 }}>Model ID</span>
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</VSCodeTextField>
1136+
<VSCodeTextField
1137+
value={apiConfiguration?.ollamaApiOptionsCtxNum || "32768"}
1138+
style={{ width: "100%" }}
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onInput={handleInputChange("ollamaApiOptionsCtxNum")}
1140+
placeholder={"e.g. 32768"}>
1141+
<span style={{ fontWeight: 500 }}>Model Context Window</span>
1142+
</VSCodeTextField>
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{ollamaModels.length > 0 && (
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<VSCodeRadioGroup
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value={

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