Code owners
Assign users and groups as approvers for specific file changes. Learn more.
env-variables.ts 12.43 KiB
import fs from "fs/promises";
import path from "path";
import { TOOL_SYSTEM_PROMPT_ENV_VAR, Tool } from "./tools";
import {
ModelConfig,
TemplateDataSource,
TemplateFramework,
TemplateType,
TemplateVectorDB,
} from "./types";
import { TSYSTEMS_LLMHUB_API_URL } from "./providers/llmhub";
export type EnvVar = {
name?: string;
description?: string;
value?: string;
};
const renderEnvVar = (envVars: EnvVar[]): string => {
return envVars.reduce(
(prev, env) =>
prev +
(env.description
? `# ${env.description.replaceAll("\n", "\n# ")}\n`
: "") +
(env.name
? env.value
? `${env.name}=${env.value}\n\n`
: `# ${env.name}=\n\n`
: ""),
"",
);
};
const getVectorDBEnvs = (
vectorDb?: TemplateVectorDB,
framework?: TemplateFramework,
): EnvVar[] => {
if (!vectorDb || !framework) {
return [];
}
switch (vectorDb) {
case "mongo":
return [
{
name: "MONGODB_URI",
description:
"For generating a connection URI, see https://www.mongodb.com/docs/manual/reference/connection-string/ \nThe MongoDB connection URI.",
},
{
name: "MONGODB_DATABASE",
},
{
name: "MONGODB_VECTORS",
},
{
name: "MONGODB_VECTOR_INDEX",
},
];
case "pg":
return [
{
name: "PG_CONNECTION_STRING",
description:
"For generating a connection URI, see https://docs.timescale.com/use-timescale/latest/services/create-a-service\nThe PostgreSQL connection string.",
},
];
case "pinecone": return [
{
name: "PINECONE_API_KEY",
description:
"Configuration for Pinecone vector store\nThe Pinecone API key.",
},
{
name: "PINECONE_ENVIRONMENT",
},
{
name: "PINECONE_INDEX_NAME",
},
];
case "milvus":
return [
{
name: "MILVUS_ADDRESS",
description:
"The address of the Milvus server. Eg: http://localhost:19530",
value: "http://localhost:19530",
},
{
name: "MILVUS_COLLECTION",
description:
"The name of the Milvus collection to store the vectors.",
value: "llamacollection",
},
{
name: "MILVUS_USERNAME",
description: "The username to access the Milvus server.",
},
{
name: "MILVUS_PASSWORD",
description: "The password to access the Milvus server.",
},
];
case "astra":
return [
{
name: "ASTRA_DB_APPLICATION_TOKEN",
description: "The generated app token for your Astra database",
},
{
name: "ASTRA_DB_ENDPOINT",
description: "The API endpoint for your Astra database",
},
{
name: "ASTRA_DB_COLLECTION",
description: "The name of the collection in your Astra database",
},
];
case "qdrant":
return [
{
name: "QDRANT_URL",
description:
"The qualified REST URL of the Qdrant server. Eg: http://localhost:6333",
},
{
name: "QDRANT_COLLECTION",
description: "The name of Qdrant collection to use.",
},
{
name: "QDRANT_API_KEY",
description:
"Optional API key for authenticating requests to Qdrant.",
},
];
case "llamacloud":
return [ {
name: "LLAMA_CLOUD_INDEX_NAME",
description:
"The name of the LlamaCloud index to use (part of the LlamaCloud project).",
value: "test",
},
{
name: "LLAMA_CLOUD_PROJECT_NAME",
description: "The name of the LlamaCloud project.",
value: "Default",
},
{
name: "LLAMA_CLOUD_BASE_URL",
description:
"The base URL for the LlamaCloud API. Only change this for non-production environments",
value: "https://api.cloud.llamaindex.ai",
},
];
case "chroma":
const envs = [
{
name: "CHROMA_COLLECTION",
description: "The name of the collection in your Chroma database",
},
{
name: "CHROMA_HOST",
description: "The API endpoint for your Chroma database",
},
{
name: "CHROMA_PORT",
description: "The port for your Chroma database",
},
];
// TS Version doesn't support config local storage path
if (framework === "fastapi") {
envs.push({
name: "CHROMA_PATH",
description: `The local path to the Chroma database.
Specify this if you are using a local Chroma database.
Otherwise, use CHROMA_HOST and CHROMA_PORT config above`,
});
}
return envs;
default:
return [];
}
};
const getModelEnvs = (modelConfig: ModelConfig): EnvVar[] => {
return [
{
name: "MODEL_PROVIDER",
description: "The provider for the AI models to use.",
value: modelConfig.provider,
},
{
name: "MODEL",
description: "The name of LLM model to use.",
value: modelConfig.model,
},
{
name: "EMBEDDING_MODEL",
description: "Name of the embedding model to use.",
value: modelConfig.embeddingModel,
},
{
name: "EMBEDDING_DIM",
description: "Dimension of the embedding model to use.",
value: modelConfig.dimensions.toString(),
}, {
name: "CONVERSATION_STARTERS",
description: "The questions to help users get started (multi-line).",
},
...(modelConfig.provider === "openai"
? [
{
name: "OPENAI_API_KEY",
description: "The OpenAI API key to use.",
value: modelConfig.apiKey,
},
{
name: "LLM_TEMPERATURE",
description: "Temperature for sampling from the model.",
},
{
name: "LLM_MAX_TOKENS",
description: "Maximum number of tokens to generate.",
},
]
: []),
...(modelConfig.provider === "anthropic"
? [
{
name: "ANTHROPIC_API_KEY",
description: "The Anthropic API key to use.",
value: modelConfig.apiKey,
},
]
: []),
...(modelConfig.provider === "groq"
? [
{
name: "GROQ_API_KEY",
description: "The Groq API key to use.",
value: modelConfig.apiKey,
},
]
: []),
...(modelConfig.provider === "gemini"
? [
{
name: "GOOGLE_API_KEY",
description: "The Google API key to use.",
value: modelConfig.apiKey,
},
]
: []),
...(modelConfig.provider === "ollama"
? [
{
name: "OLLAMA_BASE_URL",
description:
"The base URL for the Ollama API. Eg: http://127.0.0.1:11434",
},
]
: []),
...(modelConfig.provider === "mistral"
? [
{
name: "MISTRAL_API_KEY",
description: "The Mistral API key to use.",
value: modelConfig.apiKey,
},
]
: []),
...(modelConfig.provider === "t-systems"
? [
{
name: "T_SYSTEMS_LLMHUB_BASE_URL", description:
"The base URL for the T-Systems AI Foundation Model API. Eg: http://localhost:11434",
value: TSYSTEMS_LLMHUB_API_URL,
},
{
name: "T_SYSTEMS_LLMHUB_API_KEY",
description: "API Key for T-System's AI Foundation Model.",
value: modelConfig.apiKey,
},
]
: []),
];
};
const getFrameworkEnvs = (
framework: TemplateFramework,
port?: number,
): EnvVar[] => {
const sPort = port?.toString() || "8000";
const result: EnvVar[] = [
{
name: "FILESERVER_URL_PREFIX",
description:
"FILESERVER_URL_PREFIX is the URL prefix of the server storing the images generated by the interpreter.",
value:
framework === "nextjs"
? // FIXME: if we are using nextjs, port should be 3000
"http://localhost:3000/api/files"
: `http://localhost:${sPort}/api/files`,
},
];
if (framework === "fastapi") {
result.push(
...[
{
name: "APP_HOST",
description: "The address to start the backend app.",
value: "0.0.0.0",
},
{
name: "APP_PORT",
description: "The port to start the backend app.",
value: sPort,
},
],
);
}
return result;
};
const getEngineEnvs = (): EnvVar[] => {
return [
{
name: "TOP_K",
description:
"The number of similar embeddings to return when retrieving documents.",
value: "3",
},
{
name: "STREAM_TIMEOUT",
description:
"The time in milliseconds to wait for the stream to return a response.",
value: "60000",
},
];
};
const getToolEnvs = (tools?: Tool[]): EnvVar[] => {
if (!tools?.length) return [];
const toolEnvs: EnvVar[] = []; tools.forEach((tool) => {
if (tool.envVars?.length) {
toolEnvs.push(
// Don't include the system prompt env var here
// It should be handled separately by merging with the default system prompt
...tool.envVars.filter(
(env) => env.name !== TOOL_SYSTEM_PROMPT_ENV_VAR,
),
);
}
});
return toolEnvs;
};
const getSystemPromptEnv = (tools?: Tool[]): EnvVar => {
const defaultSystemPrompt =
"You are a helpful assistant who helps users with their questions.";
// build tool system prompt by merging all tool system prompts
let toolSystemPrompt = "";
tools?.forEach((tool) => {
const toolSystemPromptEnv = tool.envVars?.find(
(env) => env.name === TOOL_SYSTEM_PROMPT_ENV_VAR,
);
if (toolSystemPromptEnv) {
toolSystemPrompt += toolSystemPromptEnv.value + "\n";
}
});
const systemPrompt = toolSystemPrompt
? `\"${toolSystemPrompt}\"`
: defaultSystemPrompt;
return {
name: "SYSTEM_PROMPT",
description: "The system prompt for the AI model.",
value: systemPrompt,
};
};
const getTemplateEnvs = (template?: TemplateType): EnvVar[] => {
if (template === "multiagent") {
return [
{
name: "MESSAGE_QUEUE_PORT",
},
{
name: "CONTROL_PLANE_PORT",
},
{
name: "HUMAN_CONSUMER_PORT",
},
{
name: "AGENT_QUERY_ENGINE_PORT",
value: "8003",
},
{
name: "AGENT_QUERY_ENGINE_DESCRIPTION",
value: "Query information from the provided data",
},
{
name: "AGENT_DUMMY_PORT",
value: "8004",
},
];
} else {
return [];
}
};
export const createBackendEnvFile = async (
root: string,
opts: {
llamaCloudKey?: string;
vectorDb?: TemplateVectorDB;
modelConfig: ModelConfig;
framework: TemplateFramework;
dataSources?: TemplateDataSource[];
template?: TemplateType;
port?: number;
tools?: Tool[];
},
) => {
// Init env values
const envFileName = ".env";
const envVars: EnvVar[] = [
{
name: "LLAMA_CLOUD_API_KEY",
description: `The Llama Cloud API key.`,
value: opts.llamaCloudKey,
},
// Add model environment variables
...getModelEnvs(opts.modelConfig),
// Add engine environment variables
...getEngineEnvs(),
// Add vector database environment variables
...getVectorDBEnvs(opts.vectorDb, opts.framework),
...getFrameworkEnvs(opts.framework, opts.port),
...getToolEnvs(opts.tools),
// Add template environment variables
...getTemplateEnvs(opts.template),
getSystemPromptEnv(opts.tools),
];
// Render and write env file
const content = renderEnvVar(envVars);
await fs.writeFile(path.join(root, envFileName), content);
console.log(`Created '${envFileName}' file. Please check the settings.`);
};
export const createFrontendEnvFile = async (
root: string,
opts: {
customApiPath?: string;
},
) => {
const defaultFrontendEnvs = [
{
name: "NEXT_PUBLIC_CHAT_API",
description: "The backend API for chat endpoint.",
value: opts.customApiPath
? opts.customApiPath
: "http://localhost:8000/api/chat",
},
];
const content = renderEnvVar(defaultFrontendEnvs);
await fs.writeFile(path.join(root, ".env"), content);
};
