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Text-to-Query agenti s MongoDB a LangGraphem

Apoorva Joshi

Senior AI/ML Developer Advocate, MongoDB

Výsledný produkt!

Sada nástrojů agenta

from langchain_mongodb.agent_toolkit.toolkit import MongoDBDatabaseToolkit

# Initialize the MongoDB database toolkit
toolkit = MongoDBDatabaseToolkit(db=db, llm=llm)

# Extract the tools from the toolkit
tools = toolkit.get_tools()

Prompt engineering

from langchain_mongodb.agent_toolkit import MONGODB_AGENT_SYSTEM_PROMPT

# Create a templated prompt for the LLM
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", MONGODB_AGENT_SYSTEM_PROMPT),
        ("system", "Do not re-run tools unless absolutely necessary. If you are not
                    able to get enough information using the tools, reply with
                    I DON'T KNOW. You have access to the following tools: {tool_names}."),
        MessagesPlaceholder(variable_name="messages"),
    ]
)

Orchestrace agenta

Paměť

Co dál?

Více o MongoDB a agentech Text-to-Query

Více o LangChain a LangGraph

Kurz DataCamp: Vývoj LLM aplikací s LangChain
Kurz DataCamp: Návrh agentních systémů s LangGraph

Gratulujeme!

Text-to-Query agenti s MongoDB a LangGraphem