Moorcheh Vector Store Demo¶

Install Required Packages¶

In [ ]:

!pip install llama_index
!pip install moorcheh_sdk

!pip install llama_index !pip install moorcheh_sdk

Import Required Libraries¶

In [ ]:

# demo.py

# --- Welcome to the Demo of the Moorcheh Vector Store ---
# --- Import the following packages --
import logging
import sys
import os
from moorcheh_sdk import MoorchehClient
from IPython.display import Markdown, display
from typing import Any, Callable, Dict, List, Optional, cast
from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    StorageContext,
    Settings,
)
from llama_index.core.base.embeddings.base_sparse import BaseSparseEmbedding
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.schema import BaseNode, MetadataMode, TextNode
from llama_index.core.vector_stores.types import (
    BasePydanticVectorStore,
    MetadataFilters,
    VectorStoreQuery,
    VectorStoreQueryMode,
    VectorStoreQueryResult,
)
from llama_index.core.vector_stores.utils import (
    DEFAULT_TEXT_KEY,
    legacy_metadata_dict_to_node,
    metadata_dict_to_node,
    node_to_metadata_dict,
)
from llama_index.core.vector_stores.types import (
    MetadataFilter,
    MetadataFilters,
    FilterOperator,
    FilterCondition,
)

# demo.py # --- Welcome to the Demo of the Moorcheh Vector Store --- # --- Import the following packages -- import logging import sys import os from moorcheh_sdk import MoorchehClient from IPython.display import Markdown, display from typing import Any, Callable, Dict, List, Optional, cast from llama_index.core import ( VectorStoreIndex, SimpleDirectoryReader, StorageContext, Settings, ) from llama_index.core.base.embeddings.base_sparse import BaseSparseEmbedding from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.schema import BaseNode, MetadataMode, TextNode from llama_index.core.vector_stores.types import ( BasePydanticVectorStore, MetadataFilters, VectorStoreQuery, VectorStoreQueryMode, VectorStoreQueryResult, ) from llama_index.core.vector_stores.utils import ( DEFAULT_TEXT_KEY, legacy_metadata_dict_to_node, metadata_dict_to_node, node_to_metadata_dict, ) from llama_index.core.vector_stores.types import ( MetadataFilter, MetadataFilters, FilterOperator, FilterCondition, )

Configure Logging¶

In [ ]:

# --- Logging Setup ---
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

# --- Logging Setup --- logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

Load Moorcheh API Key¶

In [ ]:

# --- Set the values of the API Keys in your Environment Variables ---
from google.colab import userdata

api_key = os.environ["MOORCHEH_API_KEY"] = userdata.get("MOORCHEH_API_KEY")

if "MOORCHEH_API_KEY" not in os.environ:
    raise EnvironmentError(f"Environment variable MOORCHEH_API_KEY is not set")

# --- Set the values of the API Keys in your Environment Variables --- from google.colab import userdata api_key = os.environ["MOORCHEH_API_KEY"] = userdata.get("MOORCHEH_API_KEY") if "MOORCHEH_API_KEY" not in os.environ: raise EnvironmentError(f"Environment variable MOORCHEH_API_KEY is not set")

Load and Chunk Documents¶

In [ ]:

# --- Load Documents ---
documents = SimpleDirectoryReader("./documents").load_data()

# --- Set chunk size and overlap ---
Settings.chunk_size = 1024
Settings.chunk_overlap = 20

# --- Load Documents --- documents = SimpleDirectoryReader("./documents").load_data() # --- Set chunk size and overlap --- Settings.chunk_size = 1024 Settings.chunk_overlap = 20

Initialize Vector Store and Create Index¶

In [ ]:

# --- Initialize the Moorcheh Vector Store ---
__all__ = ["MoorchehVectorStore"]

# Creates a Moorcheh Vector Store with the following parameters
# For text-based namespaces, set namespace_type to "text" and vector_dimension to None
# For vector-based namespaces, set namespace_type to "vector" and vector_dimension to the dimension of your uploaded vectors
vector_store = MoorchehVectorStore(
    api_key=api_key,
    namespace="llamaindex_moorcheh",
    namespace_type="text",
    vector_dimension=None,
    add_sparse_vector=False,
    batch_size=100,
)

# --- Create a Vector Store Index using the Vector Store and given Documents ---
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# --- Initialize the Moorcheh Vector Store --- __all__ = ["MoorchehVectorStore"] # Creates a Moorcheh Vector Store with the following parameters # For text-based namespaces, set namespace_type to "text" and vector_dimension to None # For vector-based namespaces, set namespace_type to "vector" and vector_dimension to the dimension of your uploaded vectors vector_store = MoorchehVectorStore( api_key=api_key, namespace="llamaindex_moorcheh", namespace_type="text", vector_dimension=None, add_sparse_vector=False, batch_size=100, ) # --- Create a Vector Store Index using the Vector Store and given Documents --- storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

Query the Vector Store¶

In [ ]:

# --- Generate Response ---
# --- Set Logging to DEBUG for more Detailed Outputs ---
query_engine = index.as_query_engine()
response = vector_store.generate_answer(
    query="Which company has had the highest revenue in 2025 and why?"
)
moorcheh_response = vector_store.get_generative_answer(
    query="Which company has had the highest revenue in 2025 and why?",
    ai_model="anthropic.claude-3-7-sonnet-20250219-v1:0",
)

display(Markdown(f"<b>{response}</b>"))
print(
    "\n\n================================\n\n",
    response,
    "\n\n================================\n\n",
)
print(
    "\n\n================================\n\n",
    moorcheh_response,
    "\n\n================================\n\n",
)

# --- Filters for Metadata ---
filter = MetadataFilters(
    filters=[
        MetadataFilter(
            key="file_path",
            value="insert the file path to the document here",
            operator=FilterOperator.EQ,
        )
    ],
    condition=FilterCondition.AND,
)

# --- Generate Response --- # --- Set Logging to DEBUG for more Detailed Outputs --- query_engine = index.as_query_engine() response = vector_store.generate_answer( query="Which company has had the highest revenue in 2025 and why?" ) moorcheh_response = vector_store.get_generative_answer( query="Which company has had the highest revenue in 2025 and why?", ai_model="anthropic.claude-3-7-sonnet-20250219-v1:0", ) display(Markdown(f"{response}")) print( "\n\n================================\n\n", response, "\n\n================================\n\n", ) print( "\n\n================================\n\n", moorcheh_response, "\n\n================================\n\n", ) # --- Filters for Metadata --- filter = MetadataFilters( filters=[ MetadataFilter( key="file_path", value="insert the file path to the document here", operator=FilterOperator.EQ, ) ], condition=FilterCondition.AND, )