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app.py
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app.py
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import os
import json
import pprint
import secrets
import operator
import streamlit as st
from langchain import hub
from serpapi import GoogleSearch
from dotenv import load_dotenv
from typing import Annotated, Sequence, TypedDict, Dict
from langgraph.graph import END, StateGraph
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain.output_parsers.openai_tools import PydanticToolsParser
from langchain.prompts import PromptTemplate
from langchain.schema import Document
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.vectorstores import Chroma
from langchain_core.messages import BaseMessage, FunctionMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.runnables import RunnablePassthrough
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from postgres import create_record, update_record
from prompt import get_structure_template, get_content_generator_template
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings
## app imports
from st_frontend.frontend import main
from prompts.content_prompt import content_template
from prompts.structure_prompt import structure_template
from prompts.feedback_content_prompt import feedback_content_template
from prompts.faq_prompt import faq_template
### Uncomment import 'pdb' this to use debugger in the app
### Use this code in between any file or function to stop debugger at any point pdb.set_trace()
import pdb
## Used to load .env file
load_dotenv()
os.environ["LANGCHAIN_TRACING_V2"] = os.getenv("LANGCHAIN_TRACING_V2")
os.environ["LANGCHAIN_PROJECT"] = os.getenv("LANGCHAIN_PROJECT")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
os.environ["TAVILY_API_KEY"] = os.getenv("TAVILY_API_KEY")
class GraphState(TypedDict):
keys: Dict[str, any]
def create_collection(collection_name, question, urls):
print("---Got Results---")
docs = [WebBaseLoader(url).load() for url in urls]
docs_list = [item for sublist in docs for item in sublist]
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=1000, chunk_overlap=0
)
doc_splits = text_splitter.split_documents(docs_list)
print("---CREATING NEW DOCUMENTS---")
vectorstore = Chroma.from_documents(
documents=doc_splits,
collection_name=collection_name,
embedding=OpenAIEmbeddings(),
)
create_record(collection_name, urls)
print(f"Collection '{collection_name}' created successfully.")
return vectorstore.as_retriever()
def retrieve_documents(collection_name, question):
print("---RETRIEVING OLD DOCUMENTS---")
embedding_function = OpenAIEmbeddings()
vectorstore = Chroma(collection_name, embedding_function)
return vectorstore.as_retriever()
def retrieve(state):
print("---RETRIEVE---")
state_dict = state["keys"]
question = state_dict["question"]
primary_keyword = state_dict["primary_keyword"]
structure_prompt = state_dict["structure_prompt"]
urls = state_dict["selected_urls"]
step_to_execute = state_dict["step_to_execute"]
selected_keywords = state_dict["selected_keywords"]
if 'total_headings' in state_dict:
total_headings = state_dict['total_headings']
else:
total_headings = ''
if 'current_heading' in state_dict:
current_heading = state_dict['current_heading']
else:
current_heading = ''
if 'faq_prompt' in state_dict:
faq_prompt = state_dict['faq_prompt']
else:
faq_prompt = ''
if 'blog_prompt' in state_dict:
blog_prompt = state_dict['blog_prompt']
else:
blog_prompt = ''
if 'number_of_words_per_heading' in state_dict:
number_of_words_per_heading = state_dict['number_of_words_per_heading']
else:
number_of_words_per_heading = ''
if 'blog_content' in state_dict:
blog_content = state_dict['blog_content']
else:
blog_content = ''
if 'blog_title' in state_dict:
blog_title = state_dict["blog_title"]
else:
blog_title = ''
if 'blog' in state_dict:
blog = state_dict["blog"]
else:
blog = ''
if 'rephrase_context' in state_dict:
rephrase_context = state_dict["rephrase_context"]
else:
rephrase_context = ''
if 'rephrase' in state_dict:
rephrase = state_dict["rephrase"]
else:
rephrase = ''
if 'structure' in state_dict:
structure = state_dict["structure"]
else:
structure = ""
if 'heading' in state_dict:
heading = state_dict["heading"]
else:
heading = ""
if 'collection_key' in state_dict:
collection_key = state_dict["collection_key"]
retriever = retrieve_documents(collection_key, heading)
else:
collection_key = secrets.token_hex(12 // 2)
retriever = create_collection(collection_key, question, urls)
documents = retriever.get_relevant_documents(heading)
return { "keys":
{
"documents": documents,
"question": question,
'primary_keyword': primary_keyword,
"structure_prompt": structure_prompt,
"urls": urls,
"step_to_execute": step_to_execute,
"structure": structure,
"collection_key": collection_key,
"heading": heading,
"rephrase_context": rephrase_context,
"rephrase": rephrase,
"blog": blog,
"blog_title": blog_title,
"selected_keywords": selected_keywords,
"blog_content": blog_content,
"number_of_words_per_heading": number_of_words_per_heading,
"blog_prompt": blog_prompt,
"faq_prompt": faq_prompt,
"total_headings": total_headings,
"current_heading": current_heading
}
}
def generate(state):
blog_structure = {
"Blog_Structure_1":
{
"title": "TITLE",
"headings":
[
"HEADING 1",
"HEADING 2",
"HEADING 3",
"HEADING 4",
"HEADING 5",
"HEADING 6",
"HEADING 7",
"HEADING 8",
"HEADING 9",
"HEADING 10"
]
},
"Blog_Structure_2":
{
"title": "TITLE",
"headings":
[
"HEADING 1",
"HEADING 2",
"HEADING 3",
"HEADING 4",
"HEADING 5",
"HEADING 6",
"HEADING 7",
"HEADING 8",
"HEADING 9",
"HEADING 10"
]
},
"Blog_Structure_3":
{
"title": "TITLE",
"headings":
[
"HEADING 1",
"HEADING 2",
"HEADING 3",
"HEADING 4",
"HEADING 5",
"HEADING 6",
"HEADING 7",
"HEADING 8",
"HEADING 9",
"HEADING 10"
]
}
}
print("---GENERATE---")
state_dict = state["keys"]
question = state_dict["question"]
documents = state_dict["documents"]
primary_keyword = state_dict["primary_keyword"]
structure_prompt = state_dict["structure_prompt"]
urls = state_dict["urls"]
collection_key = state_dict["collection_key"]
step_to_execute = state_dict["step_to_execute"]
structure = state_dict["structure"]
heading = state_dict["heading"]
rephrase_context = state_dict["rephrase_context"]
rephrase = state_dict["rephrase"]
blog = state_dict["blog"]
blog_title = state_dict["blog_title"]
selected_keywords = state_dict['selected_keywords']
blog_content = state_dict['blog_content']
number_of_words_per_heading = state_dict['number_of_words_per_heading']
blog_prompt = state_dict['blog_prompt']
faq_prompt = state_dict['faq_prompt']
total_headings = state_dict['total_headings']
current_heading = state_dict['current_heading']
print(state_dict)
if step_to_execute == "Generate Structure":
heading = ''
template = structure_template()
prompt = PromptTemplate(template=template, input_variables=["documents", "question", "structure_prompt", "primary_keyword", "blog_structure", "selected_keywords"])
elif rephrase == True:
template = feedback_content_template()
prompt = PromptTemplate(template=template, input_variables=["documents", "structure", "primary_keyword", "refference_links", "rephrase_context", "blog", "structure_prompt"])
elif step_to_execute == "Generate Blog":
heading = state_dict["heading"]
template = content_template(blog_content)
prompt = PromptTemplate(template=template, input_variables=["documents", "structure", "primary_keyword", "number_of_words_per_heading", "refference_links", "heading", "blog_title", "selected_keywords", "blog_content", "blog_prompt", "total_headings", "current_heading"])
elif step_to_execute == "Generate Faq's":
template = faq_template()
prompt = PromptTemplate(template=template, input_variables=["documents", "primary_keyword", "selected_keywords", "faq_prompt"])
llm = ChatOpenAI(model_name="gpt-4-turbo-preview", temperature=0.7, streaming=True, max_tokens=4096, verbose=True)
# llm = ChatOpenAI(model_name="gpt-3.5-turbo-0125", temperature=0.7, streaming=True, max_tokens=4096, verbose=True)
# llm = ChatOllama(model="llama2:latest")
rag_chain = prompt | llm | StrOutputParser()
if step_to_execute == "Generate Structure":
generation = rag_chain.invoke(
{
"documents": documents,
"question": question,
"structure_prompt": structure_prompt,
"primary_keyword": primary_keyword,
"refference_links": urls,
"blog_structure": blog_structure,
"selected_keywords": selected_keywords
}
)
print("------- Structure Generated -------")
elif rephrase == True:
generation = rag_chain.invoke(
{
"documents": documents,
"primary_keyword": primary_keyword,
"refference_links": urls,
"structure": structure,
"heading": heading,
"blog": blog,
"blog_title": blog_title,
"rephrase_context": rephrase_context,
"structure_prompt": structure_prompt
}
)
print("------- Content Rephrased -------")
elif step_to_execute == "Generate Blog":
generation = rag_chain.invoke(
{
"documents": documents,
"primary_keyword": primary_keyword,
"refference_links": urls,
"structure": structure,
"heading": heading,
"blog": blog,
"blog_title": blog_title,
"selected_keywords": selected_keywords,
"blog_content": blog_content,
"number_of_words_per_heading": number_of_words_per_heading,
"blog_prompt": blog_prompt,
"total_headings": total_headings,
"current_heading": current_heading
}
)
print("------- Content Generated -------")
elif step_to_execute == "Generate Faq's":
generation = rag_chain.invoke(
{
"documents": documents,
"primary_keyword": primary_keyword,
"selected_keywords": selected_keywords,
"faq_prompt": faq_prompt,
}
)
print("------- Faq's Generated -------")
return { "keys":
{
"documents": documents,
"question": question,
'primary_keyword': primary_keyword,
"structure_prompt": structure_prompt,
"urls": urls,
"generation": generation,
"step_to_execute": step_to_execute,
"blog": generation,
"collection_key": collection_key,
"heading": heading
}
}
workflow = StateGraph(GraphState)
workflow.add_node("retrieve", retrieve)
workflow.add_node("generate", generate)
workflow.set_entry_point("retrieve")
workflow.add_edge("retrieve", "generate")
workflow.add_edge("generate", END)
app = workflow.compile()
if __name__ == "__main__":
main(app)