LangChain

Installation

pip install langchain-mengram

MengramRetriever

Subclasses BaseRetriever from LangChain. Searches across all 3 memory types and returns Document objects.

from langchain_mengram import MengramRetriever

retriever = MengramRetriever(
    api_key="om-your-key",
    user_id="alice",
    top_k=5,
    memory_types=["semantic", "episodic", "procedural"],
)

# Use as any LangChain retriever
docs = retriever.invoke("deployment issues")
for doc in docs:
    print(doc.page_content)
    print(doc.metadata)  # {{"source": "mengram", "memory_type": "semantic", ...}}

Use in a chain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

retriever = MengramRetriever(api_key="om-...")

prompt = ChatPromptTemplate.from_template(
    "Context from memory:\n{{context}}\n\nQuestion: {{question}}"
)

chain = (
    {{"context": retriever, "question": RunnablePassthrough()}}
    | prompt
    | ChatOpenAI(model="gpt-4o")
    | StrOutputParser()
)

answer = chain.invoke("What deployment stack am I using?")

Cognitive Profile

from langchain_mengram import get_mengram_profile

# Get a system prompt string
prompt = get_mengram_profile(api_key="om-...", user_id="alice")

Parameters

Parameter	Type	Default	Description
api_key	str	required	Mengram API key
user_id	str	"default"	User to search
api_url	str	"https://mengram.io"	API base URL
top_k	int	5	Max results per type
memory_types	list	all 3	Which types to search