LibreChat/api/app/clients/agents/CustomAgent/initializeCustomAgent.js
Danny Avila e5336039fc
ci(backend-review.yml): add linter step to the backend review workflow (#625)
* ci(backend-review.yml): add linter step to the backend review workflow

* chore(backend-review.yml): remove prettier from lint-action configuration

* chore: apply new linting workflow

* chore(lint-staged.config.js): reorder lint-staged tasks for JavaScript and TypeScript files

* chore(eslint): update ignorePatterns in .eslintrc.js
chore(lint-action): remove prettier option in backend-review.yml
chore(package.json): add lint and lint:fix scripts

* chore(lint-staged.config.js): remove prettier --write command for js, jsx, ts, tsx files

* chore(titleConvo.js): remove unnecessary console.log statement
chore(titleConvo.js): add missing comma in options object

* chore: apply linting to all files

* chore(lint-staged.config.js): update lint-staged configuration to include prettier formatting
2023-07-14 09:36:49 -04:00

54 lines
1.5 KiB
JavaScript

const CustomAgent = require('./CustomAgent');
const { CustomOutputParser } = require('./outputParser');
const { AgentExecutor } = require('langchain/agents');
const { LLMChain } = require('langchain/chains');
const { BufferMemory, ChatMessageHistory } = require('langchain/memory');
const {
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
} = require('langchain/prompts');
const initializeCustomAgent = async ({
tools,
model,
pastMessages,
currentDateString,
...rest
}) => {
let prompt = CustomAgent.createPrompt(tools, { currentDateString, model: model.modelName });
const chatPrompt = ChatPromptTemplate.fromPromptMessages([
new SystemMessagePromptTemplate(prompt),
HumanMessagePromptTemplate.fromTemplate(`{chat_history}
Query: {input}
{agent_scratchpad}`),
]);
const outputParser = new CustomOutputParser({ tools });
const memory = new BufferMemory({
chatHistory: new ChatMessageHistory(pastMessages),
// returnMessages: true, // commenting this out retains memory
memoryKey: 'chat_history',
humanPrefix: 'User',
aiPrefix: 'Assistant',
inputKey: 'input',
outputKey: 'output',
});
const llmChain = new LLMChain({
prompt: chatPrompt,
llm: model,
});
const agent = new CustomAgent({
llmChain,
outputParser,
allowedTools: tools.map((tool) => tool.name),
});
return AgentExecutor.fromAgentAndTools({ agent, tools, memory, ...rest });
};
module.exports = initializeCustomAgent;