jorel
Version:
The easiest way to use LLMs, including streams, images, documents, tools and various agent scenarios.
239 lines (238 loc) • 10.2 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.AnthropicProvider = void 0;
const sdk_1 = require("@anthropic-ai/sdk");
const bedrock_sdk_1 = require("@anthropic-ai/bedrock-sdk");
const providers_1 = require("../../providers");
const convert_llm_message_1 = require("./convert-llm-message");
const shared_1 = require("../../shared");
const tools_1 = require("../../tools");
/** Provides access to OpenAI and other compatible services */
class AnthropicProvider {
constructor({ apiKey, bedrock, name } = {}) {
this.name = name || "anthropic";
if (bedrock) {
const region = bedrock.awsRegion || process.env.AWS_REGION;
const accessKeyId = bedrock.awsAccessKey || process.env.AWS_ACCESS_KEY_ID;
const secretAccessKey = bedrock.awsSecretKey || process.env.AWS_SECRET_ACCESS_KEY;
if (!region)
throw new Error("[AnthropicProvider]: Missing AWS region. Either pass it as config.region or set the AWS_REGION environment variable");
if (!accessKeyId)
throw new Error("[AnthropicProvider]: Missing AWS access key id. Either pass it as config.accessKeyId or set the AWS_ACCESS_KEY_ID environment variable");
if (!secretAccessKey)
throw new Error("[AnthropicProvider]: Missing AWS secret access key. Either pass it as config.secretAccessKey or set the AWS_SECRET_ACCESS_KEY environment variable");
this.client = new bedrock_sdk_1.AnthropicBedrock({
awsRegion: region,
awsAccessKey: accessKeyId,
awsSecretKey: secretAccessKey,
});
}
else {
const _apiKey = apiKey || process.env.ANTHROPIC_API_KEY;
if (!_apiKey)
throw new Error("[AnthropicProvider]: Missing API key. Either pass it as config.apiKey or set the ANTHROPIC_API_KEY environment variable");
this.client = new sdk_1.default({
apiKey: _apiKey,
});
}
}
async generateResponse(model, messages, config = {}) {
const start = Date.now();
const { chatMessages, systemMessage } = await (0, convert_llm_message_1.convertLlmMessagesToAnthropicMessages)(messages);
const temperature = config.temperature ?? undefined;
const response = await this.client.messages.create({
model,
messages: chatMessages,
temperature,
max_tokens: config.maxTokens || 4096,
system: systemMessage,
tool_choice: config.toolChoice === "none" || !config.tools || !config.tools.hasTools
? undefined
: config.toolChoice === "any"
? {
type: "auto",
disable_parallel_tool_use: config.tools?.allowParallelCalls,
}
: config.toolChoice === "required"
? {
type: "auto",
disable_parallel_tool_use: config.tools?.allowParallelCalls,
}
: config.toolChoice
? {
type: "tool",
name: config.toolChoice,
disable_parallel_tool_use: config.tools?.allowParallelCalls,
}
: undefined,
tools: config.toolChoice === "none"
? undefined
: config.tools?.asLlmFunctions?.map((tool) => ({
name: tool.function.name,
input_schema: {
...tool.function.parameters?.properties,
type: "object",
},
description: tool.function.description,
})),
});
const durationMs = Date.now() - start;
const inputTokens = response.usage.input_tokens;
const outputTokens = response.usage.output_tokens;
const content = response.content
.map((c) => (c.type === "text" ? c.text : ""))
.join("")
.trim();
const toolCalls = response.content
.filter((c) => c.type === "tool_use")
.map((c) => ({
id: (0, shared_1.generateUniqueId)(),
request: {
id: c.id,
function: {
name: c.name,
arguments: c.input && typeof c.input === "object" ? c.input : {},
},
},
approvalState: config.tools?.getTool(c.name)?.requiresConfirmation ? "requiresApproval" : "noApprovalRequired",
executionState: "pending",
result: null,
error: null,
}));
const provider = this.name;
return {
...(0, providers_1.generateAssistantMessage)(content, toolCalls),
meta: {
model,
provider,
temperature,
durationMs,
inputTokens,
outputTokens,
},
};
}
async *generateResponseStream(model, messages, config = {}) {
const start = Date.now();
const { chatMessages, systemMessage } = await (0, convert_llm_message_1.convertLlmMessagesToAnthropicMessages)(messages);
const temperature = config.temperature ?? undefined;
const responseStream = await this.client.messages.create({
model,
messages: chatMessages,
temperature,
max_tokens: config.maxTokens || 4096,
system: systemMessage,
stream: true,
tool_choice: config.toolChoice === "none" || !config.tools || !config.tools.hasTools
? undefined
: config.toolChoice === "any"
? {
type: "auto",
disable_parallel_tool_use: config.tools?.allowParallelCalls,
}
: config.toolChoice === "required"
? {
type: "auto",
disable_parallel_tool_use: config.tools?.allowParallelCalls,
}
: config.toolChoice
? {
type: "tool",
name: config.toolChoice,
disable_parallel_tool_use: config.tools?.allowParallelCalls,
}
: undefined,
tools: config.toolChoice === "none"
? undefined
: config.tools?.asLlmFunctions?.map((tool) => ({
name: tool.function.name,
input_schema: {
...tool.function.parameters?.properties,
type: "object",
},
description: tool.function.description,
})),
});
let inputTokens = undefined;
let outputTokens = undefined;
let content = "";
const _toolCalls = {};
for await (const chunk of responseStream) {
if (chunk.type === "content_block_delta") {
if (chunk.delta.type === "text_delta") {
content += chunk.delta.text;
yield { type: "chunk", content: chunk.delta.text };
}
}
if (chunk.type === "message_start") {
inputTokens = (inputTokens || 0) + chunk.message.usage.input_tokens;
outputTokens = (outputTokens || 0) + chunk.message.usage.output_tokens;
}
if (chunk.type === "content_block_start") {
if (chunk.content_block.type === "tool_use") {
_toolCalls[chunk.index] = { ...chunk.content_block, arguments: "" };
}
}
if (chunk.type === "content_block_delta") {
if (chunk.delta.type === "input_json_delta") {
const index = chunk.index;
const toolCall = _toolCalls[index];
toolCall.arguments += chunk.delta.partial_json;
}
}
if (chunk.type === "message_delta") {
outputTokens = (outputTokens || 0) + chunk.usage.output_tokens;
}
}
const durationMs = Date.now() - start;
const provider = this.name;
const meta = {
model,
provider,
temperature,
durationMs,
inputTokens,
outputTokens,
};
const toolCalls = Object.values(_toolCalls).map((c) => ({
id: (0, shared_1.generateUniqueId)(),
request: {
id: c.id,
function: {
name: c.name,
arguments: tools_1.LlmToolKit.deserialize(c.arguments),
},
},
approvalState: config.tools?.getTool(c.name)?.requiresConfirmation ? "requiresApproval" : "noApprovalRequired",
executionState: "pending",
result: null,
error: null,
}));
if (toolCalls && toolCalls.length > 0) {
yield {
type: "response",
role: "assistant_with_tools",
content,
toolCalls,
meta,
};
}
else {
yield {
type: "response",
role: "assistant",
content,
meta,
};
}
}
async getAvailableModels() {
const response = (await this.client.get("/v1/models"));
return response.data.map((model) => model.id);
}
// eslint-disable-next-line @typescript-eslint/no-unused-vars
async createEmbedding(model, text) {
throw new Error("Embeddings are not yet supported for Anthropic");
}
}
exports.AnthropicProvider = AnthropicProvider;