Github Lmdat Llm App Contribute to lmdat llm app development by creating an account on github. Contribute to lmdat llm app development by creating an account on github.
Github Ggude Llm App A curated collection of awesome llm apps built with rag, ai agents, multi agent teams, mcp, voice agents, and more. this repository features llm apps that use models from openai , anthropic, google, xai and open source models like qwen or llama that you can run locally on your computer. # from pydantic import basemodel, field from langchain core.pydantic v1 import basemodel, field from typing import type from langchain.tools import basetool from langchain core.messages import aimessage, humanmessage, systemmessage from langchain core.prompts import chatprompttemplate, messagesplaceholder, prompttemplate from langchain.chains.retrieval import create retrieval chain from langchain.chains.history aware retriever import create history aware retriever from langchain.chains bine documents import create stuff documents chain # from langchain.chains.retrieval qa.base import retrievalqa from langchain core.output parsers import stroutputparser from langchain core.runnables import runnablepassthrough from langchain.chains.sql database.query import create sql query chain from langchain community.document loaders import webbaseloader from langchain.text splitter import recursivecharactertextsplitter, charactertextsplitter from langchain chroma import chroma from langchain huggingface import huggingfaceembeddings from langchain.embeddings import cachebackedembeddings from langchain.storage import localfilestore from langchain groq import chatgroq from finance.stock import fetch stock price from finance.gold import sjc gold price from datetime import datetime from ai.rag db import singletonragdb ff8, singletonragdb pntt, singletonragdb bds from ai.llm import singletonchatllm from database import get db from ai.prompt templates import answer sql prompt, write sql prompt, sql few shot prompt import config import os import re import json from dotenv import load dotenv, find dotenv load dotenv (find dotenv ()) # define tool functions def get current time tool (*args, **kwargs) > str: return datetime.now ().strftime ("%h:%m") def get sjc gold price tool (*args, **kwargs): return {'price': sjc gold price ()} def get rag qa tool (query, **kwargs): root path = config.app root path rag db name = kwargs.get ('db name', 'chromadb ff8') from url = kwargs.get ('from url', false) # db dir = f" {root path} ragdb {rag db name.lower ()}db ff8" db dir = os.path.join (root path, 'ragdb', f" {rag db name}") embedding model = os.getenv ('hf embedding model name') if from url == false: if 'ff8' in rag db name: ragdb = singletonragdb ff8 (db dir, embedding model) elif 'pntt' in rag db name: ragdb = singletonragdb pntt (db dir, embedding model) else: ragdb = singletonragdb bds (db dir, embedding model) retriever = ragdb.get db ().as retriever (search type='similarity') else: ragdb = get data from url tool (rag db name) retriever = ragdb.as retriever (search type='similarity') # retriever = ragdb.get db ().as retriever (search type='similarity', search kwargs= {'k': 3}) # retriever = ragdb.get db ().as retriever (search type='similarity score threshold', # search kwargs= {'score threshold': 0.4}) # retriever = ragdb.get db ().as retriever (search type='similarity') # retriever = ragdb.get db ().as retriever (search type='mmr') chatllm = singletonchatllm (llm name=os.getenv ('chat llm name')) llm = chatllm.get llm () # # contextual question prompt # # helps ai figure out that it should refomulate the question based on the chat history # aware retriever system messages = [ # "given a chat history and the latest user question ", # "which might be referenced context in the chat history, ", # "produce a standalone question which can be understood without the chat history.", # "do not answer the question, just reformulate it if needed and otherwise return it as is.". It provides you ready to deploy llm (large language model) app templates. you can test them on your own machine and deploy on cloud (gcp, aws, azure, render, ) or on premises. Contribute to lmdat llm app development by creating an account on github.
Github Pathwaycom Llm App Ready To Run Cloud Templates For Rag Ai It provides you ready to deploy llm (large language model) app templates. you can test them on your own machine and deploy on cloud (gcp, aws, azure, render, ) or on premises. Contribute to lmdat llm app development by creating an account on github. The llm searches for relevant pages, reads them, and synthesizes an answer with citations. answers can take different forms depending on the question — a markdown page, a comparison table, a slide deck (marp), a chart (matplotlib), a canvas. the important insight: good answers can be filed back into the wiki as new pages. In this post, we’ll cover five major steps to building your own llm app, the emerging architecture of today’s llm apps, and problem areas that you can start exploring today. Insights: lmdat llm app pulse contributors community standards commits code frequency dependency graph network forks. Introduction lmdeploy is a toolkit for compressing, deploying, and serving llm, developed by the mmrazor and mmdeploy teams. it has the following core features:.
Intro Llm Github The llm searches for relevant pages, reads them, and synthesizes an answer with citations. answers can take different forms depending on the question — a markdown page, a comparison table, a slide deck (marp), a chart (matplotlib), a canvas. the important insight: good answers can be filed back into the wiki as new pages. In this post, we’ll cover five major steps to building your own llm app, the emerging architecture of today’s llm apps, and problem areas that you can start exploring today. Insights: lmdat llm app pulse contributors community standards commits code frequency dependency graph network forks. Introduction lmdeploy is a toolkit for compressing, deploying, and serving llm, developed by the mmrazor and mmdeploy teams. it has the following core features:.
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