LangChain Integration

pip install langchain-moorcheh

from langchain_moorcheh import MoorchehVectorStore
from moorcheh_sdk import MoorchehClient

import logging
import os
from uuid import uuid4
import asyncio
from typing import Any, List, Optional, Literal, Tuple, Type, TypeVar, Sequence
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from google.colab import userdata

MOORCHEH_API_KEY = os.environ['MOORCHEH_API_KEY']

namespace = "your_namespace_name"
namespace_type = "text" # or vector
store = MoorchehVectorStore(
            api_key=MOORCHEH_API_KEY,
            namespace=namespace,
            namespace_type=namespace_type
        )

document_1 = Document(
    page_content="Brewed a fresh cup of Ethiopian coffee and paired it with a warm croissant.",
    metadata={"source": "blog"},
)

document_2 = Document(
    page_content="Tomorrow's weather will be sunny with light winds, reaching a high of 78°F.",
    metadata={"source": "news"},
)

document_3 = Document(
    page_content="Experimenting with LangChain for an AI-powered note-taking assistant!",
    metadata={"source": "tweet"},
)

document_4 = Document(
    page_content="Local bakery donates 500 loaves of bread to the community food bank.",
    metadata={"source": "news"},
)

document_5 = Document(
    page_content="That concert last night was absolutely unforgettable—what a performance!",
    metadata={"source": "tweet"},
)

document_6 = Document(
    page_content="Check out our latest article: 5 ways to boost productivity while working from home.",
    metadata={"source": "website"},
)

document_7 = Document(
    page_content="The ultimate guide to mastering homemade pizza dough.",
    metadata={"source": "website"},
)

document_8 = Document(
    page_content="LangGraph just made multi-agent workflows way easier—seriously impressive!",
    metadata={"source": "tweet"},
)

document_9 = Document(
    page_content="Oil prices rose 3% today after unexpected supply cuts from major exporters.",
    metadata={"source": "news"},
)

document_10 = Document(
    page_content="I really hope this post doesn't vanish into the digital void…",
    metadata={"source": "tweet"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]

uuids = [str(uuid4()) for _ in range(len(documents))]

store.add_documents(documents=documents, ids=uuids)

store.delete(ids=["chunk_id_here"])

query = "Give me a brief summary of the provided documents"
answer = store.generative_answer(query, ai_model = "anthropic.claude-3-7-sonnet-20250219-v1:0")
print(answer)

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
store = MoorchehVectorStore(
    api_key=MOORCHEH_API_KEY,
    namespace=namespace,
    namespace_type=namespace_type,
    embedding=embeddings
)

# Search for similar documents
results = store.similarity_search("coffee and breakfast", k=3)
for doc in results:
    print(doc.page_content)
    print(doc.metadata)
    print("---")

# Search with scores
results = store.similarity_search_with_score("weather forecast", k=3)
for doc, score in results:
    print(f"Score: {score}")
    print(f"Content: {doc.page_content}")
    print("---")

try:
    store.add_documents(documents=documents, ids=uuids)
    print("Documents added successfully")
except Exception as e:
    print(f"Error adding documents: {e}")

import logging
logging.basicConfig(level=logging.DEBUG)