RAG102.py

from langchain.vectorstores import Pinecone

text_field = "text"
index_name = '<>'

# switch back to normal index for langchain
index = pc.Index(index_name)

vectorstore = Pinecone(
    index, embed.embed_query, text_field
)

query = "who was Benito Mussolini?"

vectorstore.similarity_search(
    query,  # our search query
    k=3  # return 3 most relevant docs
)

#####################################################

from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

# completion llm
llm = ChatOpenAI(
    openai_api_key=OPENAI_API_KEY,
    model_name='gpt-3.5-turbo',
    temperature=0.0
)

qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

qa.run(query)
######################################################### with source link
from langchain.chains import RetrievalQAWithSourcesChain

qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

qa_with_sources(query)

######################################################### agent
from langchain.agents import Tool
from langchain.agents import AgentType
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from langchain.agents import load_tools
from langchain.llms import OpenAI
memory = ConversationBufferMemory(memory_key="chat_history")

from langchain.agents import Tool

tools = [
    Tool(
        name='Knowledge Base',
        func=qa.run,
        description=(
            'use this tool when answering general knowledge queries to get '
            'more information about the topic'
        )
    )
]

# conversational memory
from langchain.chains.conversation.memory import ConversationBufferWindowMemory

conversational_memory = ConversationBufferWindowMemory(
    memory_key='chat_history',
    k=5,
    return_messages=True
)

from langchain.agents import initialize_agent

agent = initialize_agent(
    agent='chat-conversational-react-description',
    tools=tools,
    llm=llm,
    verbose=True,
    max_iterations=3,
    early_stopping_method='generate',
    memory=conversational_memory
)

agent("how many records used in the dataset in the paper?")


#############################
from langchain.agents import tool
@tool
def coolest_guy(text: str) -> str:
    '''Returns the name of the coolest guy in the universe'''
    return "cc"
tools = load_tools(["wikipedia","llm-math"], llm=llm) 
tools = tools +[coolest_guy]

from langchain.agents import initialize_agent
agent = initialize_agent(tools, 
                         llm, 
                         agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, 
                         verbose=True)