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app.py
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app.py
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from langchain import hub
from langchain.output_parsers import PydanticOutputParser
from langchain_core.output_parsers import StrOutputParser
from langchain.schema import Document
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.vectorstores import Chroma
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langgraph.graph import END, StateGraph
from typing import Dict, TypedDict
from langchain.prompts import PromptTemplate
import pprint
import streamlit as st
import yaml
import nest_asyncio
nest_asyncio.apply()
# load config
with open('config.yaml', 'r') as file:
config = yaml.safe_load(file)
openai_api_key = config["openai_api_key"]
openai_api_base = config["openai_api_base"]
google_api_key = config["google_api_key"]
tavily_api_key = config["tavily_api_key"]
run_local = config["run_local"]
local_llm = config["local_llm"]
models = config["models"]
doc_url = config["doc_url"]
loader = WebBaseLoader(doc_url)
loader.requests_per_second = 1
docs = loader.aload()
# print(len(docs))
# Split
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=500, chunk_overlap=100
)
all_splits = text_splitter.split_documents(docs)
# Embed and index
if run_local == 'Yes':
embeddings = GPT4AllEmbeddings()
elif models == 'openai':
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key, openai_api_base=openai_api_base)
else:
embeddings = GoogleGenerativeAIEmbeddings(
model="models/embedding-001", google_api_key=google_api_key
)
# Index
vectorstore = Chroma.from_documents(
documents=all_splits,
collection_name="rag-chroma",
embedding=embeddings,
)
retriever = vectorstore.as_retriever()
class GraphState(TypedDict):
"""
Represents the state of our graph.
Attributes:
keys: A dictionary where each key is a string.
"""
keys: Dict[str, any]
def retrieve(state):
"""
Retrieve documents
Args:
state (dict): The current graph state
Returns:
state (dict): New key added to state, documents,
that contains retrieved documents
"""
print("---RETRIEVE---")
state_dict = state["keys"]
question = state_dict["question"]
local = state_dict["local"]
documents = retriever.get_relevant_documents(question)
return {"keys": {"documents": documents, "local": local,
"question": question}}
def generate(state):
"""
Generate answer
Args:
state (dict): The current graph state
Returns:
state (dict): New key added to state, generation,
that contains LLM generation
"""
print("---GENERATE---")
state_dict = state["keys"]
question = state_dict["question"]
documents = state_dict["documents"]
# Prompt
prompt = hub.pull("rlm/rag-prompt")
# LLM Setup
if run_local == "Yes":
llm = ChatOllama(model=local_llm,
temperature=0)
elif models == "openai" :
llm = ChatOpenAI(
model="gpt-3.5-turbo", # gpt-4-0125-preview
temperature=0 ,
openai_api_key=openai_api_key
)
else:
llm = ChatGoogleGenerativeAI(model="gemini-pro",
google_api_key=google_api_key,
convert_system_message_to_human = True,
verbose = True,
)
# Post-processing
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# Chain
rag_chain = prompt | llm | StrOutputParser()
# Run
generation = rag_chain.invoke({"context": documents,
"question": question})
return {
"keys": {"documents": documents, "question": question,
"generation": generation}
}
def grade_documents(state):
"""
Determines whether the retrieved documents are relevant to the question.
Args:
state (dict): The current graph state
Returns:
state (dict): Updates documents key with relevant documents
"""
print("---CHECK RELEVANCE---")
state_dict = state["keys"]
question = state_dict["question"]
documents = state_dict["documents"]
local = state_dict["local"]
# LLM
if run_local == "Yes":
llm = ChatOllama(model=local_llm,
temperature=0)
elif models == "openai" :
llm = ChatOpenAI(
model="gpt-4-0125-preview",
temperature=0 ,
openai_api_key=openai_api_key
)
else:
llm = ChatGoogleGenerativeAI(model="gemini-pro",
google_api_key=google_api_key,
convert_system_message_to_human = True,
verbose = True,
)
# Data model
class grade(BaseModel):
"""Binary score for relevance check."""
score: str = Field(description="Relevance score 'yes' or 'no'")
# Set up a parser + inject instructions into the prompt template.
parser = PydanticOutputParser(pydantic_object=grade)
from langchain_core.output_parsers import JsonOutputParser
parser = JsonOutputParser(pydantic_object=grade)
prompt = PromptTemplate(
template="""You are a grader assessing relevance of a retrieved
document to a user question. \n
Here is the retrieved document: \n\n {context} \n\n
Here is the user question: {question} \n
If the document contains keywords related to the user question,
grade it as relevant. \n
It does not need to be a stringent test. The goal is to filter out
erroneous retrievals. \n
Give a binary score 'yes' or 'no' score to indicate whether the
document is relevant to the question. \n
Provide the binary score as a JSON with no premable or
explaination and use these instructons to format the output:
{format_instructions}""",
input_variables=["query"],
partial_variables={"format_instructions":
parser.get_format_instructions()},
)
chain = prompt | llm | parser
# Score
filtered_docs = []
search = "No" # Default do not opt for web search to supplement retrieval
for d in documents:
score = chain.invoke(
{
"question": question,
"context": d.page_content,
"format_instructions": parser.get_format_instructions(),
}
)
grade = score["score"]
if grade == "yes":
print("---GRADE: DOCUMENT RELEVANT---")
filtered_docs.append(d)
else:
print("---GRADE: DOCUMENT NOT RELEVANT---")
search = "Yes" # Perform web search
continue
return {
"keys": {
"documents": filtered_docs,
"question": question,
"local": local,
"run_web_search": search,
}
}
def transform_query(state):
"""
Transform the query to produce a better question.
Args:
state (dict): The current graph state
Returns:
state (dict): Updates question key with a re-phrased question
"""
print("---TRANSFORM QUERY---")
state_dict = state["keys"]
question = state_dict["question"]
documents = state_dict["documents"]
local = state_dict["local"]
# Create a prompt template with format instructions and the query
prompt = PromptTemplate(
template="""You are generating questions that is well optimized for
retrieval. \n
Look at the input and try to reason about the underlying sematic
intent / meaning. \n
Here is the initial question:
\n ------- \n
{question}
\n ------- \n
Provide an improved question without any premable, only respond
with the updated question: """,
input_variables=["question"],
)
# Grader
# LLM
if run_local == "Yes":
llm = ChatOllama(model=local_llm,
temperature=0)
elif models == "openai" :
llm = ChatOpenAI(
model="gpt-4-0125-preview",
temperature=0 ,
openai_api_key=openai_api_key
)
else:
llm = ChatGoogleGenerativeAI(model="gemini-pro",
google_api_key=google_api_key,
convert_system_message_to_human = True,
verbose = True,
)
# Prompt
chain = prompt | llm | StrOutputParser()
better_question = chain.invoke({"question": question})
return {
"keys": {"documents": documents, "question": better_question,
"local": local}
}
def web_search(state):
"""
Web search based on the re-phrased question using Tavily API.
Args:
state (dict): The current graph state
Returns:
state (dict): Web results appended to documents.
"""
print("---WEB SEARCH---")
state_dict = state["keys"]
question = state_dict["question"]
documents = state_dict["documents"]
local = state_dict["local"]
try:
tool = TavilySearchResults()
docs = tool.invoke({"query": question})
web_results = "\n".join([d["content"] for d in docs])
web_results = Document(page_content=web_results)
documents.append(web_results)
except Exception as error:
print(error)
return {"keys": {"documents": documents, "local": local,
"question": question}}
def decide_to_generate(state):
"""
Determines whether to generate an answer or re-generate a question
for web search.
Args:
state (dict): The current state of the agent, including all keys.
Returns:
str: Next node to call
"""
print("---DECIDE TO GENERATE---")
state_dict = state["keys"]
# question = state_dict["question"]
# filtered_documents = state_dict["documents"]
search = state_dict["run_web_search"]
if search == "Yes":
# All documents have been filtered check_relevance
# We will re-generate a new query
print("---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---")
return "transform_query"
else:
# We have relevant documents, so generate answer
print("---DECISION: GENERATE---")
return "generate"
workflow = StateGraph(GraphState)
# Define the nodes
workflow.add_node("retrieve", retrieve) # retrieve
workflow.add_node("grade_documents", grade_documents) # grade documents
workflow.add_node("generate", generate) # generatae
workflow.add_node("transform_query", transform_query) # transform_query
workflow.add_node("web_search", web_search) # web search
# Build graph
workflow.set_entry_point("retrieve")
workflow.add_edge("retrieve", "grade_documents")
workflow.add_conditional_edges(
"grade_documents",
decide_to_generate,
{
"transform_query": "transform_query",
"generate": "generate",
},
)
workflow.add_edge("transform_query", "web_search")
workflow.add_edge("web_search", "generate")
workflow.add_edge("generate", END)
# Compile
app = workflow.compile()
# Run
st.title("CRAG Ollama Chat")
st.text("A possible query: How is the attention mechanism implemented in code in the article?")
# User input
user_question = st.text_input("Please enter your question:")
# Explain how the different types of agent memory work?
if user_question:
inputs = {
"keys": {
"question": user_question,
"local": run_local,
}
}
# For the output of each node, create an st.expander UI component
for output in app.stream(inputs):
for key, value in output.items():
# Create expandable UI block with the node name
with st.expander(f"Node '{key}':"):
# detailed information of nodes in expander
st.text(pprint.pformat(value["keys"], indent=2, width=80, depth=None))
final_generation = value['keys'].get('generation', 'No final generation produced.')
st.subheader("Final Generation:")
st.write(final_generation)