Adding the Neo4j Vector Retriever

In this optional challenge, add the movie plots vector retriever to the movie trailer agent you created previously.

To complete the code you will need to update the movie trailer agent to:

Create the Neo4jVector from the moviePlots vector index.
Create the RetrievalQA chain using the Neo4jVector as the retriever.
Update the tools to use the RetrievalQA chain.

Here is the code for the movie trailer agent

python

from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain import hub
from langchain_community.tools import YouTubeSearchTool

llm = ChatOpenAI(openai_api_key="sk-...")

prompt = PromptTemplate(
    template="""
    You are a movie expert. You find movies from a genre or plot.

    Chat History:{chat_history}
    Question:{input}
    """,
    input_variables=["chat_history", "input"],
)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

chat_chain = LLMChain(llm=llm, prompt=prompt, memory=memory)

youtube = YouTubeSearchTool()

tools = [
    Tool.from_function(
        name="Movie Chat",
        description="For when you need to chat about movies. The question will be a string. Return a string.",
        func=chat_chain.run,
        return_direct=True,
    ),
    Tool.from_function(
        name="Movie Trailer Search",
        description="Use when needing to find a movie trailer. The question will include the word 'trailer'. Return a link to a YouTube video.",
        func=youtube.run,
        return_direct=True,
    ),
]

agent_prompt = hub.pull("hwchase17/react-chat")
agent = create_react_agent(llm, tools, agent_prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    max_interations=3,
    verbose=True,
    handle_parse_errors=True,
)

while True:
    q = input("> ")
    response = agent_executor.invoke({"input": q})
    print(response["output"])

Here is the code for the movie plots vector retriever

python

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores.neo4j_vector import Neo4jVector

OPENAI_API_KEY = "sk-..."

chat_llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY)

embedding_provider = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

movie_plot_vector = Neo4jVector.from_existing_index(
    embedding_provider,
    url="bolt://localhost:7687",
    username="neo4j",
    password="pleaseletmein",
    index_name="moviePlots",
    embedding_node_property="embedding",
    text_node_property="plot",
)

plot_retriever = RetrievalQA.from_llm(
    llm=chat_llm,
    retriever=movie_plot_vector.as_retriever()
)

result = plot_retriever.invoke(
    {"query": "A movie where a mission to the moon goes wrong"}
)

print(result)

Running a RetrievalQA chain from a tool

Tools expect a single query input and a single output key.

The RetrievalQA chain returns multiple output keys.

As a result, the agent’s tool executor cannot call the RetrievalQA chain directly e.g. using func=retrievalQA.run.

You could wrap the RetrievalQA chain in a function that takes a single string input, format the results and return a single string.

python

# Create a function to invoke the retriever
def run_retriever(query):
    results = plot_retriever.invoke({"query":query})
    return str(results)


# Append the tool to the tools array
tools = [
    # ...
    Tool.from_function(
        ...
        func=run_retriever,
        ...
    )
]

Click to reveal the solution

There is no right or wrong way to complete this challenge. Here is one potential solution.

python

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, RetrievalQA
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain import hub
from langchain_community.tools import YouTubeSearchTool
from langchain_community.vectorstores.neo4j_vector import Neo4jVector

OPENAI_API_KEY = "sk-..."

llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY)
embedding_provider = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

prompt = PromptTemplate(
    template="""
    You are a movie expert. You find movies from a genre or plot.

    Chat History: {chat_history}
    Question: {input}
    """,
    input_variables=["chat_history", "input"]
)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

chat_chain = LLMChain(llm=llm, prompt=prompt, memory=memory)

youtube = YouTubeSearchTool()

movie_plot_vector = Neo4jVector.from_existing_index(
    embedding_provider,
    url="bolt://localhost:7687",
    username="neo4j",
    password="pleaseletmein",
    index_name="moviePlots",
    embedding_node_property="embedding",
    text_node_property="plot",
)

plot_retriever = RetrievalQA.from_llm(
    llm=llm,
    retriever=movie_plot_vector.as_retriever(),
    verbose=True,
    return_source_documents=True
)

def run_retriever(query):
    results = plot_retriever.invoke({"query":query})
    # format the results
    movies = '\n'.join([doc.metadata["title"] + " - " + doc.page_content for doc in results["source_documents"]])
    return movies

tools = [
    Tool.from_function(
        name="Movie Chat",
        description="For when you need to chat about movies. The question will be a string. Return a string.",
        func=chat_chain.run,
        return_direct=True
    ),
    Tool.from_function(
        name="Movie Trailer Search",
        description="Use when needing to find a movie trailer. The question will include the word 'trailer'. Return a link to a YouTube video.",
        func=youtube.run,
        return_direct=True
    ),
    Tool.from_function(
        name="Movie Plot Search",
        description="For when you need to compare a plot to a movie. The question will be a string. Return a string.",
        func=run_retriever,
        return_direct=True
    )
]

agent_prompt = hub.pull("hwchase17/react-chat")
agent = create_react_agent(llm, tools, agent_prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    max_interations=3,
    verbose=True,
    handle_parse_errors=True)

while True:
    q = input("> ")
    response = agent_executor.invoke({"input": q})
    print(response["output"])

Summary

In this optional challenge, you added the movie plots vector retriever to the movie trailer agent you created previously.

In the next module, you will learn how to use an LLM to generate Cypher and improve the responses of an LLM.

Summary

In this optional challenge, you added the movie plots vector retriever to the movie trailer agent you created previously.

In the next module, you will learn how to use an LLM to generate Cypher and improve the responses of an LLM.

Neo4j & LLM Fundamentals

Neo4j and Generative AI

Vectors & Semantic Search

Introduction to Langchain

Using LLMs for Query Generation

Adding the Neo4j Vector Retriever

Summary

Summary

Chatbot