tutorial126

tutorial125/main.py

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+from langchain_openai import ChatOpenAI
+import asyncio
+from browser_use import Agent
+from browser_use.controller.service import Controller
+import streamlit as st 
+
+llm=ChatOpenAI(model="gpt-4o")
+st.title("CHAT WITH ANY WEBSITE")
+input=st.text_input("enter your question")
+
+async def searchWeb(input):
+    agent=Agent(task=input,llm=llm,controller=Controller(keep_open=False,headless=False))
+    result=await agent.run()
+    return result
+
+if input is not None:
+    btn=st.button("submit")
+    if btn:
+        result=asyncio.run(searchWeb(input))
+        st.write(result.final_result())

tutorial125/qsn.txt

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+find best reviewed whey protein from healthkart.com and return the results with deatils
+find top 10 mobiles under 25000 from amazon.in based on review

tutorial126/app.py

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+import chromadb
+from llama_index.core import SimpleDirectoryReader,VectorStoreIndex,StorageContext
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.vector_stores.chroma import ChromaVectorStore
+from openai import OpenAI
+import streamlit as st 
+client=OpenAI()
+st.title("AYURDEVA WITH AI")
+query=st.text_input("ask your question")
+db=chromadb.PersistentClient(path="./db")
+cc=db.get_or_create_collection("tutorial126")
+vs=ChromaVectorStore(chroma_collection=cc)
+index=VectorStoreIndex.from_vector_store(vector_store=vs)
+if query is not None:
+    btn=st.button("submit")
+    if btn:
+        retriver=index.as_retriever(similarity_top_k=5)
+        nodes=retriver.retrieve(query)
+        docs=""
+        for node in nodes:
+            docs=docs+node.text
+        #st.write(docs)
+        system_prompt=f"""you are an intelligent ai assistant who has expertise in ayurvedic can write nice and well crafted articles ,use only {docs} as context to write answer the question asked by user, dont use anything else other than this{docs},make it professional writing,detailed use headers and bullets"""
+        response=client.chat.completions.create(
+            model="gpt-4o",
+            messages=[
+                {"role":"system","content":system_prompt},
+                {"role":"user","content":query}
+            ]
+        )
+        st.write(response.choices[0].message.content)

tutorial126/genData.py

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+from langchain_community.document_loaders import AsyncChromiumLoader
+from langchain_community.document_transformers import BeautifulSoupTransformer
+from langchain_openai import ChatOpenAI
+from langchain_ollama import ChatOllama
+from langchain.prompts import PromptTemplate
+import io,sys,time
+with io.open("urls.csv","r",encoding="utf-8")as f1:
+    data=f1.read()
+    f1.close()
+
+#llm=ChatOllama(model="llama3.2:latest")
+llm=ChatOpenAI(model="gpt-4o")
+template=""" for the given {text} extract every information related to {name} , dont miss anything related to {name} ,dont shorten the content try to use as it is"""
+prompt=PromptTemplate.from_template(template)
+
+lines=data.split("\n")
+with io.open("ragdoc.txt","w",encoding="utf-8")as f1:
+    for line in lines:
+        url=line.split(",")[1]
+        name=line.split(",")[0]
+        loader=AsyncChromiumLoader([url],user_agent="MyAppUserAgent")
+        htmldocs=loader.load()
+        print(htmldocs)
+        bs_transformer=BeautifulSoupTransformer()
+        docs_transformed=bs_transformer.transform_documents(htmldocs,tags_to_extract=["div"])
+        chain=prompt|llm
+        response=chain.invoke({"name":name,"text":docs_transformed})
+        print(name)
+        #print(response.content)
+        f1.write(str(response.content)+"\n\n")
+        time.sleep(20)
+    f1.close()

tutorial126/genIndex.py

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+import chromadb
+from llama_index.core import SimpleDirectoryReader,VectorStoreIndex,StorageContext
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.vector_stores.chroma import ChromaVectorStore
+
+embed_model=OpenAIEmbedding()
+loader=SimpleDirectoryReader(input_files=["/Users/roni/Documents/GitHub/LLMtutorial/tutorial126/ragdoc.txt"])
+docs=loader.load_data()
+db=chromadb.PersistentClient(path="./db")
+cc=db.get_or_create_collection("tutorial126")
+vs=ChromaVectorStore(chroma_collection=cc)
+sc=StorageContext.from_defaults(vector_store=vs)
+index=VectorStoreIndex.from_documents(docs,storage_context=sc,embed_model=embed_model)

tutorial126/genUrl.py

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+import requests,json,io 
+import string
+alphabets=list(string.ascii_lowercase)
+with io.open("urls.csv","w",encoding="utf-8")as f1:
+    for alphabet in alphabets:
+        url=f"https://www.1mg.com/pharmacy_api_gateway/v4/ayurvedas/by_alphabet?alphabet={alphabet}&page=1&per_page=50"
+        response=requests.get(url)
+        items=response.json()["data"]["schema"]["itemListElement"]
+        for item in items:
+            f1.write(item["name"]+","+item["url"]+"\n")
+    f1.close()

tutorial126/prompt.txt

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+f"""you are an intelligent ai assistant who has expertise in ayurvedic can write nice and well crafted articles ,use only {docs} as context to write answer the question asked by user, dont use anything else other than this{docs},make it professional writing,detailed use headers and bullets"""