LangChain

pip install langchain langchain-openai langchain-ceramic

export CERAMIC_API_KEY="your-api-key"

export OPENAI_API_KEY="your-api-key"

from langchain_ceramic import CeramicSearch
from langchain_openai import ChatOpenAI
from langchain.agents import create_agent

# Initialize the Ceramic search tool and retrieve a maximum of five results
ceramic_search = CeramicSearch(max_results=5)

# Initialize the agent with the Ceramic search tool
agent = create_agent(
    model=ChatOpenAI(model="gpt-5.5"), 
    tools=[ceramic_search],
    system_prompt="You are a helpful research assistant. Use web search to find accurate, up-to-date information."
)

# Generate a response using natural language queries
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Tell me about California rental laws."}]}
)
print(result["messages"][-1].content)

from langchain_ceramic import CeramicSearchRetriever
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

# Initialize the LLM and Ceramic Search Retriever
llm = ChatOpenAI(model="gpt-5.5")
retriever = CeramicSearchRetriever(k=5)

# Convert the natural language query to keywords before retrieval
keyword_prompt = PromptTemplate.from_template(
    """
    Rewrite the following question as a 2-8 word keyword query for a lexical search engine.
    
    Rules:
    - Extract specific entities, topics, locations, and dates
    - Replace conversational phrasing with concrete keywords
    - Do not include uninformative words such as articles (the, a, an). Avoid prepositions (on, about, in, for, of, at, by, with) unless they are within established phrases or names (United States of America, Into the Wild).
    - Include relevant synonyms explicitly when terminology is ambiguous
    - Keep word order meaningful (`house cat` and `cat house` return different results)
    - Good keyword query examples:
        - "2026 Super Bowl halftime performer"
        - "climate change effects global warming impact"
        - "beginner investing strategies stocks bonds basics"
    
    Return only the keyword query with no explanation.

    Question: {query}
    """
)
keyword_chain = keyword_prompt | llm | StrOutputParser()

# Format the prompt with the query and retrieved search context
answer_prompt = ChatPromptTemplate.from_template(
    "Answer the query based on the provided context.\n\nQuery: {query}\n\nContext: {context}"
)

# Create the complete chain, which involves keyword_chain and passes the formatted prompt to the LLM
# RunnablePassthrough() preserves the natural language query for the answer prompt
chain = (
    {"query": RunnablePassthrough(), "context": keyword_chain | retriever}
    | answer_prompt
    | llm
    | StrOutputParser()
)

# Generate the response
answer = chain.invoke("What are the latest AI chip export restrictions?")
print(answer)

docs = await retriever.ainvoke("California rental laws")