diff --git a/projects/Basic-RAG/BasicRAG_stuff.py b/projects/Basic-RAG/BasicRAG_stuff.py
index 4bfafc3..63edeab 100644
--- a/projects/Basic-RAG/BasicRAG_stuff.py
+++ b/projects/Basic-RAG/BasicRAG_stuff.py
@@ -1,6 +1,10 @@
-from grag.grag.rag import BasicRAG
+from grag.components.multivec_retriever import Retriever
+from grag.components.vectordb.deeplake_client import DeepLakeClient
+from grag.rag.basic_rag import BasicRAG
 
-rag = BasicRAG(doc_chain="stuff")
+client = DeepLakeClient(collection_name="test")
+retriever = Retriever(vectordb=client)
+rag = BasicRAG(doc_chain="stuff", retriever=retriever)
 
 if __name__ == "__main__":
     while True:
diff --git a/projects/Retriver-GUI/retriever_app.py b/projects/Retriver-GUI/retriever_app.py
index f55c0c6..9f4198c 100644
--- a/projects/Retriver-GUI/retriever_app.py
+++ b/projects/Retriver-GUI/retriever_app.py
@@ -46,7 +46,7 @@ def render_search_results(self):
                     st.write(result.metadata)
 
     def check_connection(self):
-        response = self.app.retriever.client.test_connection()
+        response = self.app.retriever.vectordb.test_connection()
         if response:
             return True
         else:
@@ -55,14 +55,14 @@ def check_connection(self):
     def render_stats(self):
         st.write(f'''
         **Chroma Client Details:** \n
-            Host Address    : {self.app.retriever.client.host}:{self.app.retriever.client.port} \n
-            Collection Name : {self.app.retriever.client.collection_name} \n
-            Embeddings Type : {self.app.retriever.client.embedding_type} \n
-            Embeddings Model: {self.app.retriever.client.embedding_model} \n
-            Number of docs  : {self.app.retriever.client.collection.count()} \n
+            Host Address    : {self.app.retriever.vectordb.host}:{self.app.retriever.vectordb.port} \n
+            Collection Name : {self.app.retriever.vectordb.collection_name} \n
+            Embeddings Type : {self.app.retriever.vectordb.embedding_type} \n
+            Embeddings Model: {self.app.retriever.vectordb.embedding_model} \n
+            Number of docs  : {self.app.retriever.vectordb.collection.count()} \n
         ''')
         if st.button('Check Connection'):
-            response = self.app.retriever.client.test_connection()
+            response = self.app.retriever.vectordb.test_connection()
             if response:
                 st.write(':green[Connection Active]')
             else:
diff --git a/pyproject.toml b/pyproject.toml
index 897ab02..b97ba19 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,6 +42,7 @@ dependencies = [
     "huggingface_hub>=0.20.2",
     "pydantic>=2.5.0",
     "rouge-score>=0.1.2",
+    "deeplake>=3.8.27"
 ]
 
 [project.urls]
@@ -101,9 +102,13 @@ exclude_lines = [
 [tool.ruff]
 line-length = 88
 indent-width = 4
+extend-exclude = ["tests", "others"]
 
 [tool.ruff.lint]
 select = ["E4", "E7", "E9", "F", "I", "D"]
+ignore = ["D104"]
+exclude = ["__about__.py"]
+
 
 [tool.ruff.format]
 quote-style = "double"
diff --git a/src/config.ini b/src/config.ini
index 74ab6c4..c2938f9 100644
--- a/src/config.ini
+++ b/src/config.ini
@@ -14,6 +14,12 @@ n_gpu_layers_cpp : -1
 std_out : True
 base_dir : ${root:root_path}/models
 
+[deeplake]
+collection_name : arxiv
+embedding_type : instructor-embedding
+embedding_model : hkunlp/instructor-xl
+store_path : ${data:data_path}/vectordb
+
 [chroma]
 host : localhost
 port : 8000
@@ -25,6 +31,14 @@ embedding_model : hkunlp/instructor-xl
 store_path : ${data:data_path}/vectordb
 allow_reset : True
 
+[deeplake]
+collection_name : arxiv
+# embedding_type : sentence-transformers
+# embedding_model : "all-mpnet-base-v2"
+embedding_type : instructor-embedding
+embedding_model : hkunlp/instructor-xl
+store_path : ${data:data_path}/vectordb
+
 [text_splitter]
 chunk_size : 5000
 chunk_overlap : 400
@@ -54,4 +68,4 @@ data_path : ${root:root_path}/data
 root_path : /home/ubuntu/volume_2k/Capstone_5
 
 [quantize]
-llama_cpp_path : ${root:root_path}
\ No newline at end of file
+llama_cpp_path : ${root:root_path}
diff --git a/src/grag/components/embedding.py b/src/grag/components/embedding.py
index 7a9d249..eeb0f82 100644
--- a/src/grag/components/embedding.py
+++ b/src/grag/components/embedding.py
@@ -1,3 +1,9 @@
+"""Class for embedding.
+
+This module provides:
+- Embedding
+"""
+
 from langchain_community.embeddings import HuggingFaceInstructEmbeddings
 from langchain_community.embeddings.sentence_transformer import (
     SentenceTransformerEmbeddings,
@@ -6,6 +12,7 @@
 
 class Embedding:
     """A class for vector embeddings.
+
     Supports:
         huggingface sentence transformers -> model_type = 'sentence-transformers'
         huggingface instructor embeddings -> model_type = 'instructor-embedding'
@@ -17,6 +24,7 @@ class Embedding:
     """
 
     def __init__(self, embedding_type: str, embedding_model: str):
+        """Initialize the embedding with embedding_type and embedding_model."""
         self.embedding_type = embedding_type
         self.embedding_model = embedding_model
         match self.embedding_type:
diff --git a/src/grag/components/llm.py b/src/grag/components/llm.py
index 20db968..6e7296c 100644
--- a/src/grag/components/llm.py
+++ b/src/grag/components/llm.py
@@ -1,3 +1,5 @@
+"""Class for LLM."""
+
 import os
 from pathlib import Path
 
@@ -50,6 +52,7 @@ def __init__(
         quantization=llm_conf["quantization"],
         pipeline=llm_conf["pipeline"],
     ):
+        """Initialize the LLM class using the given parameters."""
         self.base_dir = Path(base_dir)
         self._model_name = model_name
         self.quantization = quantization
diff --git a/src/grag/components/multivec_retriever.py b/src/grag/components/multivec_retriever.py
index 18ed752..9fd8664 100644
--- a/src/grag/components/multivec_retriever.py
+++ b/src/grag/components/multivec_retriever.py
@@ -1,10 +1,17 @@
+"""Class for retriever.
+
+This module provides:
+- Retriever
+"""
+
 import asyncio
 import uuid
-from typing import List
+from typing import Any, Dict, List, Optional
 
-from grag.components.chroma_client import ChromaClient
 from grag.components.text_splitter import TextSplitter
 from grag.components.utils import get_config
+from grag.components.vectordb.base import VectorDB
+from grag.components.vectordb.deeplake_client import DeepLakeClient
 from langchain.retrievers.multi_vector import MultiVectorRetriever
 from langchain.storage import LocalFileStore
 from langchain_core.documents import Document
@@ -13,14 +20,16 @@
 
 
 class Retriever:
-    """A class for multi vector retriever, it connects to a vector database and a local file store.
-    It is used to return most similar chunks from a vector store but has the additional funcationality
-    to return a linked document, chunk, etc.
+    """A class for multi vector retriever.
+
+    It connects to a vector database and a local file store.
+    It is used to return most similar chunks from a vector store but has the additional functionality to return a
+    linked document, chunk, etc.
 
     Attributes:
         store_path: Path to the local file store
         id_key: A key prefix for identifying documents
-        client: ChromaClient class instance from components.chroma_client
+        vectordb: ChromaClient class instance from components.client
         store: langchain.storage.LocalFileStore object, stores the key value pairs of document id and parent file
         retriever: langchain.retrievers.multi_vector.MultiVectorRetriever class instance, langchain's multi-vector retriever
         splitter: TextSplitter class instance from components.text_splitter
@@ -31,12 +40,16 @@ class Retriever:
 
     def __init__(
         self,
+        vectordb: Optional[VectorDB] = None,
         store_path: str = multivec_retriever_conf["store_path"],
         id_key: str = multivec_retriever_conf["id_key"],
         namespace: str = multivec_retriever_conf["namespace"],
         top_k=1,
+        client_kwargs: Optional[Dict[str, Any]] = None,
     ):
-        """Args:
+        """Initialize the Retriever.
+
+        Args:
         store_path: Path to the local file store, defaults to argument from config file
         id_key: A key prefix for identifying documents, defaults to argument from config file
         namespace: A namespace for producing unique id, defaults to argument from congig file
@@ -45,10 +58,16 @@ def __init__(
         self.store_path = store_path
         self.id_key = id_key
         self.namespace = uuid.UUID(namespace)
-        self.client = ChromaClient()
+        if vectordb is None:
+            if client_kwargs is not None:
+                self.vectordb = DeepLakeClient(**client_kwargs)
+            else:
+                self.vectordb = DeepLakeClient()
+        else:
+            self.vectordb = vectordb
         self.store = LocalFileStore(self.store_path)
         self.retriever = MultiVectorRetriever(
-            vectorstore=self.client.langchain_chroma,
+            vectorstore=self.vectordb.langchain_client,
             byte_store=self.store,
             id_key=self.id_key,
         )
@@ -58,6 +77,7 @@ def __init__(
 
     def id_gen(self, doc: Document) -> str:
         """Takes a document and returns a unique id (uuid5) using the namespace and document source.
+
         This ensures that a  single document always gets the same unique id.
 
         Args:
@@ -81,7 +101,9 @@ def gen_doc_ids(self, docs: List[Document]) -> List[str]:
         return [self.id_gen(doc) for doc in docs]
 
     def split_docs(self, docs: List[Document]) -> List[Document]:
-        """Takes a list of documents and splits them into smaller chunks using TextSplitter from compoenents.text_splitter
+        """Takes a list of documents and splits them into smaller chunks.
+
+        Using TextSplitter from components.text_splitter
         Also adds the unique parent document id into metadata
 
         Args:
@@ -101,8 +123,7 @@ def split_docs(self, docs: List[Document]) -> List[Document]:
         return chunks
 
     def add_docs(self, docs: List[Document]):
-        """Takes a list of documents, splits them using the split_docs method and then adds them into the vector database
-        and adds the parent document into the file store.
+        """Adds given documents into the vector database also adds the parent document into the file store.
 
         Args:
             docs: List of langchain_core.documents.Document
@@ -113,12 +134,11 @@ def add_docs(self, docs: List[Document]):
         """
         chunks = self.split_docs(docs)
         doc_ids = self.gen_doc_ids(docs)
-        self.client.add_docs(chunks)
+        self.vectordb.add_docs(chunks)
         self.retriever.docstore.mset(list(zip(doc_ids, docs)))
 
     async def aadd_docs(self, docs: List[Document]):
-        """Takes a list of documents, splits them using the split_docs method and then adds them into the vector database
-        and adds the parent document into the file store.
+        """Adds given documents into the vector database also adds the parent document into the file store.
 
         Args:
             docs: List of langchain_core.documents.Document
@@ -129,11 +149,11 @@ async def aadd_docs(self, docs: List[Document]):
         """
         chunks = self.split_docs(docs)
         doc_ids = self.gen_doc_ids(docs)
-        await asyncio.run(self.client.aadd_docs(chunks))
+        await asyncio.run(self.vectordb.aadd_docs(chunks))
         self.retriever.docstore.mset(list(zip(doc_ids)))
 
     def get_chunk(self, query: str, with_score=False, top_k=None):
-        """Returns the most (cosine) similar chunks from the vector database.
+        """Returns the most similar chunks from the vector database.
 
         Args:
             query: A query string
@@ -144,14 +164,8 @@ def get_chunk(self, query: str, with_score=False, top_k=None):
             list of Documents
 
         """
-        if with_score:
-            return self.client.langchain_chroma.similarity_search_with_relevance_scores(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
-        else:
-            return self.client.langchain_chroma.similarity_search(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
+        _top_k = top_k if top_k else self.retriever.search_kwargs["k"]
+        return self.vectordb.get_chunk(query=query, top_k=_top_k, with_score=with_score)
 
     async def aget_chunk(self, query: str, with_score=False, top_k=None):
         """Returns the most (cosine) similar chunks from the vector database, asynchronously.
@@ -165,14 +179,10 @@ async def aget_chunk(self, query: str, with_score=False, top_k=None):
             list of Documents
 
         """
-        if with_score:
-            return await self.client.langchain_chroma.asimilarity_search_with_relevance_scores(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
-        else:
-            return await self.client.langchain_chroma.asimilarity_search(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
+        _top_k = top_k if top_k else self.retriever.search_kwargs["k"]
+        return await self.vectordb.aget_chunk(
+            query=query, top_k=_top_k, with_score=with_score
+        )
 
     def get_doc(self, query: str):
         """Returns the parent document of the most (cosine) similar chunk from the vector database.
diff --git a/src/grag/components/parse_pdf.py b/src/grag/components/parse_pdf.py
index d918c93..dc30f8a 100644
--- a/src/grag/components/parse_pdf.py
+++ b/src/grag/components/parse_pdf.py
@@ -1,3 +1,9 @@
+"""Classes for parsing files.
+
+This module provides:
+- ParsePDF
+"""
+
 from langchain_core.documents import Document
 from unstructured.partition.pdf import partition_pdf
 
@@ -32,7 +38,7 @@ def __init__(
         add_captions_to_blocks=parser_conf["add_captions_to_blocks"],
         table_as_html=parser_conf["table_as_html"],
     ):
-        # Instantialize instance variables with parameters
+        """Initialize instance variables with parameters."""
         self.strategy = strategy
         if extract_images:  # by default always extract Table
             self.extract_image_block_types = [
@@ -72,7 +78,8 @@ def partition(self, path: str):
 
     def classify(self, partitions):
         """Classifies the partitioned elements into Text, Tables, and Images list in a dictionary.
-        Add captions for each element (if available).
+
+        Also adds captions for each element (if available).
 
         Parameters:
             partitions (list): The list of partitioned elements from the PDF document.
@@ -117,6 +124,8 @@ def classify(self, partitions):
         return classified_elements
 
     def text_concat(self, elements) -> str:
+        """Context aware concatenates all elements into a single string."""
+        full_text = ""
         for current_element, next_element in zip(elements, elements[1:]):
             curr_type = current_element.category
             next_type = next_element.category
diff --git a/src/grag/components/prompt.py b/src/grag/components/prompt.py
index ecefa71..4364c06 100644
--- a/src/grag/components/prompt.py
+++ b/src/grag/components/prompt.py
@@ -1,3 +1,10 @@
+"""Classes for prompts.
+
+This module provides:
+- Prompt - for generic prompts
+- FewShotPrompt - for few-shot prompts
+"""
+
 import json
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
@@ -13,6 +20,20 @@
 
 
 class Prompt(BaseModel):
+    """A class for generic prompts.
+
+    Attributes:
+        name (str): The prompt name (Optional, defaults to "custom_prompt")
+        llm_type (str): The type of llm, llama2, etc (Optional, defaults to "None")
+        task (str): The task (Optional, defaults to QA)
+        source (str): The source of the prompt (Optional, defaults to "NoSource")
+        doc_chain (str): The doc chain for the prompt ("stuff", "refine") (Optional, defaults to "stuff")
+        language (str): The language of the prompt (Optional, defaults to "en")
+        filepath (str): The filepath of the prompt (Optional)
+        input_keys (List[str]): The input keys for the prompt
+    template (str): The template for the prompt
+    """
+
     name: str = Field(default="custom_prompt")
     llm_type: str = Field(default="None")
     task: str = Field(default="QA")
@@ -27,6 +48,7 @@ class Prompt(BaseModel):
     @field_validator("input_keys")
     @classmethod
     def validate_input_keys(cls, v) -> List[str]:
+        """Validate the input_keys field."""
         if v is None or v == []:
             raise ValueError("input_keys cannot be empty")
         return v
@@ -34,6 +56,7 @@ def validate_input_keys(cls, v) -> List[str]:
     @field_validator("doc_chain")
     @classmethod
     def validate_doc_chain(cls, v: str) -> str:
+        """Validate the doc_chain field."""
         if v not in SUPPORTED_DOC_CHAINS:
             raise ValueError(
                 f"The provided doc_chain, {v} is not supported, supported doc_chains are {SUPPORTED_DOC_CHAINS}"
@@ -43,6 +66,7 @@ def validate_doc_chain(cls, v: str) -> str:
     @field_validator("task")
     @classmethod
     def validate_task(cls, v: str) -> str:
+        """Validate the task field."""
         if v not in SUPPORTED_TASKS:
             raise ValueError(
                 f"The provided task, {v} is not supported, supported tasks are {SUPPORTED_TASKS}"
@@ -53,6 +77,7 @@ def validate_task(cls, v: str) -> str:
     # def load_template(self):
     #     self.prompt = ChatPromptTemplate.from_template(self.template)
     def __init__(self, **kwargs):
+        """Initialize the prompt."""
         super().__init__(**kwargs)
         self.prompt = PromptTemplate(
             input_variables=self.input_keys, template=self.template
@@ -61,6 +86,7 @@ def __init__(self, **kwargs):
     def save(
         self, filepath: Union[Path, str, None], overwrite=False
     ) -> Union[None, ValueError]:
+        """Saves the prompt class into a json file."""
         dump = self.model_dump_json(indent=2, exclude_defaults=True, exclude_none=True)
         if filepath is None:
             filepath = f"{self.name}.json"
@@ -74,17 +100,37 @@ def save(
 
     @classmethod
     def load(cls, filepath: Union[Path, str]):
+        """Loads a json file and returns a Prompt class."""
         with open(f"{filepath}", "r") as f:
             prompt_json = json.load(f)
         _prompt = cls(**prompt_json)
         _prompt.filepath = str(filepath)
         return _prompt
 
-    def format(self, **kwargs):
+    def format(self, **kwargs) -> str:
+        """Formats the prompt with provided keys and returns a string."""
         return self.prompt.format(**kwargs)
 
 
 class FewShotPrompt(Prompt):
+    """A class for generic prompts.
+
+    Attributes:
+        name (str): The prompt name (Optional, defaults to "custom_prompt") (Parent Class)
+        llm_type (str): The type of llm, llama2, etc (Optional, defaults to "None") (Parent Class)
+        task (str): The task (Optional, defaults to QA) (Parent Class)
+        source (str): The source of the prompt (Optional, defaults to "NoSource") (Parent Class)
+        doc_chain (str): The doc chain for the prompt ("stuff", "refine") (Optional, defaults to "stuff") (Parent Class)
+        language (str): The language of the prompt (Optional, defaults to "en") (Parent Class)
+        filepath (str): The filepath of the prompt (Optional) (Parent Class)
+        input_keys (List[str]): The input keys for the prompt (Parent Class)
+        input_keys (List[str]): The output keys for the prompt
+        prefix (str): The template prefix for the prompt
+        suffix (str): The template suffix for the prompt
+        example_template (str): The template for formatting the examples
+        examples (List[Dict[str, Any]]): The list of examples, each example is a dictionary with respective keys
+    """
+
     output_keys: List[str]
     examples: List[Dict[str, Any]]
     prefix: str
@@ -95,6 +141,7 @@ class FewShotPrompt(Prompt):
     )
 
     def __init__(self, **kwargs):
+        """Initialize the prompt."""
         super().__init__(**kwargs)
         eg_formatter = PromptTemplate(
             input_vars=self.input_keys + self.output_keys,
@@ -111,6 +158,7 @@ def __init__(self, **kwargs):
     @field_validator("output_keys")
     @classmethod
     def validate_output_keys(cls, v) -> List[str]:
+        """Validate the output_keys field."""
         if v is None or v == []:
             raise ValueError("output_keys cannot be empty")
         return v
@@ -118,6 +166,7 @@ def validate_output_keys(cls, v) -> List[str]:
     @field_validator("examples")
     @classmethod
     def validate_examples(cls, v) -> List[Dict[str, Any]]:
+        """Validate the examples field."""
         if v is None or v == []:
             raise ValueError("examples cannot be empty")
         for eg in v:
diff --git a/src/grag/components/text_splitter.py b/src/grag/components/text_splitter.py
index cff3c7c..d04c9a5 100644
--- a/src/grag/components/text_splitter.py
+++ b/src/grag/components/text_splitter.py
@@ -1,3 +1,9 @@
+"""Class for splitting/chunking text.
+
+This module provides:
+- TextSplitter
+"""
+
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 
 from .utils import get_config
@@ -7,10 +13,23 @@
 
 # %%
 class TextSplitter:
-    def __init__(self):
+    """Class for recursively chunking text, it prioritizes '/n/n then '/n' and so on.
+
+    Attributes:
+        chunk_size: maximum size of chunk
+        chunk_overlap: chunk overlap size
+    """
+
+    def __init__(
+        self,
+        chunk_size: int = text_splitter_conf["chunk_size"],
+        chunk_overlap: int = text_splitter_conf["chunk_overlap"],
+    ):
+        """Initialize TextSplitter."""
         self.text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=int(text_splitter_conf["chunk_size"]),
-            chunk_overlap=int(text_splitter_conf["chunk_overlap"]),
+            chunk_size=int(chunk_size),
+            chunk_overlap=int(chunk_overlap),
             length_function=len,
             is_separator_regex=False,
         )
+        """Initialize TextSplitter using chunk_size and chunk_overlap"""
diff --git a/src/grag/components/utils.py b/src/grag/components/utils.py
index cb64258..2e34dc9 100644
--- a/src/grag/components/utils.py
+++ b/src/grag/components/utils.py
@@ -1,3 +1,12 @@
+"""Utils functions.
+
+This module provides:
+- stuff_docs: concats langchain documents into string
+- load_prompt: loads json prompt to langchain prompt
+- find_config_path: finds the path of the 'config.ini' file by traversing up the directory tree from the current path.
+- get_config: retrieves and parses the configuration settings from the 'config.ini' file.
+"""
+
 import json
 import os
 import textwrap
@@ -10,7 +19,9 @@
 
 
 def stuff_docs(docs: List[Document]) -> str:
-    """Args:
+    r"""Concatenates langchain documents into a string using '\n\n' seperator.
+
+    Args:
         docs: List of langchain_core.documents.Document
 
     Returns:
@@ -20,8 +31,7 @@ def stuff_docs(docs: List[Document]) -> str:
 
 
 def reformat_text_with_line_breaks(input_text, max_width=110):
-    """Reformat the given text to ensure each line does not exceed a specific width,
-    preserving existing line breaks.
+    """Reformat the given text to ensure each line does not exceed a specific width, preserving existing line breaks.
 
     Args:
     input_text (str): The text to be reformatted.
@@ -62,7 +72,7 @@ def display_llm_output_and_sources(response_from_llm):
 
 
 def load_prompt(json_file: str | os.PathLike, return_input_vars=False):
-    """Loads a prompt template from json file and returns a langchain ChatPromptTemplate
+    """Loads a prompt template from json file and returns a langchain ChatPromptTemplate.
 
     Args:
         json_file: path to the prompt template json file.
diff --git a/src/grag/components/vectordb/__init__.py b/src/grag/components/vectordb/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/grag/components/vectordb/base.py b/src/grag/components/vectordb/base.py
new file mode 100644
index 0000000..b0b0623
--- /dev/null
+++ b/src/grag/components/vectordb/base.py
@@ -0,0 +1,85 @@
+"""Abstract base class for vector database clients.
+
+This module provides:
+- VectorDB
+"""
+
+from abc import ABC, abstractmethod
+from typing import List, Tuple, Union
+
+from langchain_community.vectorstores.utils import filter_complex_metadata
+from langchain_core.documents import Document
+
+
+class VectorDB(ABC):
+    """Abstract base class for vector database clients."""
+
+    @abstractmethod
+    def __len__(self) -> int:
+        """Number of chunks in the vector database."""
+        ...
+
+    @abstractmethod
+    def delete(self) -> None:
+        """Delete all chunks in the vector database."""
+
+    @abstractmethod
+    def add_docs(self, docs: List[Document], verbose: bool = True) -> None:
+        """Adds documents to the vector database.
+
+        Args:
+            docs: List of Documents
+            verbose: Show progress bar
+
+        Returns:
+            None
+        """
+        ...
+
+    @abstractmethod
+    async def aadd_docs(self, docs: List[Document], verbose: bool = True) -> None:
+        """Adds documents to the vector database (asynchronous).
+
+        Args:
+            docs: List of Documents
+            verbose: Show progress bar
+
+        Returns:
+            None
+        """
+        ...
+
+    @abstractmethod
+    def get_chunk(
+        self, query: str, with_score: bool = False, top_k: int = None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most similar chunks from the vector database.
+
+        Args:
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
+
+        Returns:
+            list of Documents
+        """
+        ...
+
+    @abstractmethod
+    async def aget_chunk(
+        self, query: str, with_score: bool = False, top_k: int = None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most similar chunks from the vector database (asynchronous).
+
+        Args:
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
+
+        Returns:
+            list of Documents
+        """
+        ...
+
+    def _filter_metadata(self, docs: List[Document]) -> List[Document]:
+        return filter_complex_metadata(docs, allowed_types=self.allowed_metadata_types)
diff --git a/src/grag/components/chroma_client.py b/src/grag/components/vectordb/chroma_client.py
similarity index 53%
rename from src/grag/components/chroma_client.py
rename to src/grag/components/vectordb/chroma_client.py
index 7efd7c3..cac8ab3 100644
--- a/src/grag/components/chroma_client.py
+++ b/src/grag/components/vectordb/chroma_client.py
@@ -1,10 +1,16 @@
-from typing import List
+"""Class for Chroma vector database.
+
+This module provides:
+- ChromaClient
+"""
+
+from typing import List, Tuple, Union
 
 import chromadb
 from grag.components.embedding import Embedding
 from grag.components.utils import get_config
+from grag.components.vectordb.base import VectorDB
 from langchain_community.vectorstores import Chroma
-from langchain_community.vectorstores.utils import filter_complex_metadata
 from langchain_core.documents import Document
 from tqdm import tqdm
 from tqdm.asyncio import tqdm as atqdm
@@ -12,7 +18,7 @@
 chroma_conf = get_config()["chroma"]
 
 
-class ChromaClient:
+class ChromaClient(VectorDB):
     """A class for connecting to a hosted Chroma Vectorstore collection.
 
     Attributes:
@@ -24,15 +30,15 @@ class ChromaClient:
             name of the collection in the Chroma Vectorstore, each ChromaClient connects to a single collection
         embedding_type : str
             type of embedding used, supported 'sentence-transformers' and 'instructor-embedding'
-        embedding_modelname : str
+        embedding_model : str
             model name of embedding used, should correspond to the embedding_type
         embedding_function
             a function of the embedding model, derived from the embedding_type and embedding_modelname
-        chroma_client
+        client: chromadb.HttpClient
             Chroma API for client
         collection
             Chroma API for the collection
-        langchain_chroma
+        langchain_client: langchain_community.vectorstores.Chroma
             LangChain wrapper for Chroma collection
     """
 
@@ -44,7 +50,9 @@ def __init__(
         embedding_type=chroma_conf["embedding_type"],
         embedding_model=chroma_conf["embedding_model"],
     ):
-        """Args:
+        """Initialize a ChromaClient object.
+
+        Args:
         host: IP Address of hosted Chroma Vectorstore, defaults to argument from config file
         port: port address of hosted Chroma Vectorstore, defaults to argument from config file
         collection_name: name of the collection in the Chroma Vectorstore, defaults to argument from config file
@@ -61,19 +69,35 @@ def __init__(
             embedding_model=self.embedding_model, embedding_type=self.embedding_type
         ).embedding_function
 
-        self.chroma_client = chromadb.HttpClient(host=self.host, port=self.port)
-        self.collection = self.chroma_client.get_or_create_collection(
+        self.client = chromadb.HttpClient(host=self.host, port=self.port)
+        self.collection = self.client.get_or_create_collection(
             name=self.collection_name
         )
-        self.langchain_chroma = Chroma(
-            client=self.chroma_client,
+        self.langchain_client = Chroma(
+            client=self.client,
             collection_name=self.collection_name,
             embedding_function=self.embedding_function,
         )
         self.allowed_metadata_types = (str, int, float, bool)
 
-    def test_connection(self, verbose=True):
-        """Tests connection with Chroma Vectorstore
+    def __len__(self) -> int:
+        """Count the number of chunks in the database."""
+        return self.collection.count()
+
+    def delete(self) -> None:
+        """Delete all the chunks in the database collection."""
+        self.client.delete_collection(self.collection_name)
+        self.collection = self.client.get_or_create_collection(
+            name=self.collection_name
+        )
+        self.langchain_client = Chroma(
+            client=self.client,
+            collection_name=self.collection_name,
+            embedding_function=self.embedding_function,
+        )
+
+    def test_connection(self, verbose=True) -> int:
+        """Tests connection with Chroma Vectorstore.
 
         Args:
             verbose: if True, prints connection status
@@ -81,7 +105,7 @@ def test_connection(self, verbose=True):
         Returns:
             A random integer if connection is alive else None
         """
-        response = self.chroma_client.heartbeat()
+        response = self.client.heartbeat()
         if verbose:
             if response:
                 print(f"Connection to {self.host}/{self.port} is alive..")
@@ -89,8 +113,24 @@ def test_connection(self, verbose=True):
                 print(f"Connection to {self.host}/{self.port} is not alive !!")
         return response
 
-    async def aadd_docs(self, docs: List[Document], verbose=True):
-        """Asynchronously adds documents to chroma vectorstore
+    def add_docs(self, docs: List[Document], verbose=True) -> None:
+        """Adds documents to chroma vectorstore.
+
+        Args:
+            docs: List of Documents
+            verbose: Show progress bar
+
+        Returns:
+            None
+        """
+        docs = self._filter_metadata(docs)
+        for doc in (
+            tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs
+        ):
+            _id = self.langchain_client.add_documents([doc])
+
+    async def aadd_docs(self, docs: List[Document], verbose=True) -> None:
+        """Asynchronously adds documents to chroma vectorstore.
 
         Args:
             docs: List of Documents
@@ -100,37 +140,59 @@ async def aadd_docs(self, docs: List[Document], verbose=True):
             None
         """
         docs = self._filter_metadata(docs)
-        # tasks = [self.langchain_chroma.aadd_documents([doc]) for doc in docs]
-        # if verbose:
-        #     await tqdm_asyncio.gather(*tasks, desc=f'Adding to {self.collection_name}')
-        # else:
-        #     await asyncio.gather(*tasks)
         if verbose:
             for doc in atqdm(
                 docs,
                 desc=f"Adding documents to {self.collection_name}",
                 total=len(docs),
             ):
-                await self.langchain_chroma.aadd_documents([doc])
+                await self.langchain_client.aadd_documents([doc])
         else:
             for doc in docs:
-                await self.langchain_chroma.aadd_documents([doc])
+                await self.langchain_client.aadd_documents([doc])
 
-    def add_docs(self, docs: List[Document], verbose=True):
-        """Adds documents to chroma vectorstore
+    def get_chunk(
+        self, query: str, with_score: bool = False, top_k: int = None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most similar chunks from the chroma database.
 
         Args:
-            docs: List of Documents
-            verbose: Show progress bar
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
 
         Returns:
-            None
+            list of Documents
+
         """
-        docs = self._filter_metadata(docs)
-        for doc in (
-            tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs
-        ):
-            _id = self.langchain_chroma.add_documents([doc])
+        if with_score:
+            return self.langchain_client.similarity_search_with_relevance_scores(
+                query=query, k=top_k if top_k else 1
+            )
+        else:
+            return self.langchain_client.similarity_search(
+                query=query, k=top_k if top_k else 1
+            )
+
+    async def aget_chunk(
+        self, query: str, with_score=False, top_k=None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most (cosine) similar chunks from the vector database, asynchronously.
+
+        Args:
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
+
+        Returns:
+            list of Documents
 
-    def _filter_metadata(self, docs: List[Document]):
-        return filter_complex_metadata(docs, allowed_types=self.allowed_metadata_types)
+        """
+        if with_score:
+            return await self.langchain_client.asimilarity_search_with_relevance_scores(
+                query=query, k=top_k if top_k else 1
+            )
+        else:
+            return await self.langchain_client.asimilarity_search(
+                query=query, k=top_k if top_k else 1
+            )
diff --git a/src/grag/components/vectordb/deeplake_client.py b/src/grag/components/vectordb/deeplake_client.py
new file mode 100644
index 0000000..f0d5ba5
--- /dev/null
+++ b/src/grag/components/vectordb/deeplake_client.py
@@ -0,0 +1,159 @@
+"""Class for DeepLake vector database.
+
+This module provides:
+- DeepLakeClient
+"""
+
+from pathlib import Path
+from typing import List, Tuple, Union
+
+from grag.components.embedding import Embedding
+from grag.components.utils import get_config
+from grag.components.vectordb.base import VectorDB
+from langchain_community.vectorstores import DeepLake
+from langchain_core.documents import Document
+from tqdm import tqdm
+from tqdm.asyncio import tqdm as atqdm
+
+deeplake_conf = get_config()["deeplake"]
+
+
+class DeepLakeClient(VectorDB):
+    """A class for connecting to a DeepLake Vectorstore.
+
+    Attributes:
+        store_path : str, Path
+            The path to store the DeepLake vectorstore.
+        embedding_type : str
+            type of embedding used, supported 'sentence-transformers' and 'instructor-embedding'
+        embedding_model : str
+            model name of embedding used, should correspond to the embedding_type
+        embedding_function
+            a function of the embedding model, derived from the embedding_type and embedding_modelname
+        client: deeplake.core.vectorstore.VectorStore
+            DeepLake API
+        collection
+            Chroma API for the collection
+        langchain_client: langchain_community.vectorstores.DeepLake
+            LangChain wrapper for DeepLake API
+    """
+
+    def __init__(
+        self,
+        collection_name: str = deeplake_conf["collection_name"],
+        store_path: Union[str, Path] = deeplake_conf["store_path"],
+        embedding_type: str = deeplake_conf["embedding_type"],
+        embedding_model: str = deeplake_conf["embedding_model"],
+        read_only: bool = False,
+    ):
+        """Initialize DeepLake client object."""
+        self.store_path = Path(store_path)
+        self.collection_name = collection_name
+        self.read_only = read_only
+        self.embedding_type: str = embedding_type
+        self.embedding_model: str = embedding_model
+
+        self.embedding_function = Embedding(
+            embedding_model=self.embedding_model, embedding_type=self.embedding_type
+        ).embedding_function
+
+        # self.client = VectorStore(path=self.store_path / self.collection_name)
+        self.langchain_client = DeepLake(
+            dataset_path=str(self.store_path / self.collection_name),
+            embedding=self.embedding_function,
+            read_only=self.read_only,
+        )
+        self.client = self.langchain_client.vectorstore
+        self.allowed_metadata_types = (str, int, float, bool)
+
+    def __len__(self) -> int:
+        """Number of chunks in the vector database."""
+        return self.client.__len__()
+
+    def delete(self) -> None:
+        """Delete all chunks in the vector database."""
+        self.client.delete(delete_all=True)
+
+    def add_docs(self, docs: List[Document], verbose=True) -> None:
+        """Adds documents to deeplake vectorstore.
+
+        Args:
+            docs: List of Documents
+            verbose: Show progress bar
+
+        Returns:
+            None
+        """
+        docs = self._filter_metadata(docs)
+        for doc in (
+            tqdm(docs, desc=f"Adding to {self.collection_name}:") if verbose else docs
+        ):
+            _id = self.langchain_client.add_documents([doc])
+
+    async def aadd_docs(self, docs: List[Document], verbose=True) -> None:
+        """Asynchronously adds documents to chroma vectorstore.
+
+        Args:
+            docs: List of Documents
+            verbose: Show progress bar
+
+        Returns:
+            None
+        """
+        docs = self._filter_metadata(docs)
+        if verbose:
+            for doc in atqdm(
+                docs,
+                desc=f"Adding documents to {self.collection_name}",
+                total=len(docs),
+            ):
+                await self.langchain_client.aadd_documents([doc])
+        else:
+            for doc in docs:
+                await self.langchain_client.aadd_documents([doc])
+
+    def get_chunk(
+        self, query: str, with_score: bool = False, top_k: int = None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most similar chunks from the deeplake database.
+
+        Args:
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
+
+        Returns:
+            list of Documents
+
+        """
+        if with_score:
+            return self.langchain_client.similarity_search_with_score(
+                query=query, k=top_k if top_k else 1
+            )
+        else:
+            return self.langchain_client.similarity_search(
+                query=query, k=top_k if top_k else 1
+            )
+
+    async def aget_chunk(
+        self, query: str, with_score=False, top_k=None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most similar chunks from the deeplake database, asynchronously.
+
+        Args:
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
+
+        Returns:
+            list of Documents
+
+        """
+        if with_score:
+            return await self.langchain_client.asimilarity_search_with_score(
+                query=query, k=top_k if top_k else 1
+            )
+        else:
+            return await self.langchain_client.asimilarity_search(
+                query=query, k=top_k if top_k else 1
+            )
diff --git a/src/grag/rag/basic_rag.py b/src/grag/rag/basic_rag.py
index a99ecdd..da461b6 100644
--- a/src/grag/rag/basic_rag.py
+++ b/src/grag/rag/basic_rag.py
@@ -1,5 +1,11 @@
+"""Class for Basic RAG.
+
+This module provides:
+- BasicRAG
+"""
+
 import json
-from typing import List, Union
+from typing import List, Optional, Union
 
 from grag import prompts
 from grag.components.llm import LLM
@@ -13,8 +19,20 @@
 
 
 class BasicRAG:
+    """Class for Basis RAG.
+
+    Attributes:
+        model_name (str): Name of the llm model
+        doc_chain (str): Name of the document chain, ("stuff", "refine"), defaults to "stuff"
+        task (str): Name of task, defaults to "QA"
+        llm_kwargs (dict): Keyword arguments for LLM class
+        retriever_kwargs (dict): Keyword arguments for Retriever class
+        custom_prompt (Prompt): Prompt, defaults to None
+    """
+
     def __init__(
         self,
+        retriever: Optional[Retriever] = None,
         model_name=None,
         doc_chain="stuff",
         task="QA",
@@ -22,10 +40,13 @@ def __init__(
         retriever_kwargs=None,
         custom_prompt: Union[Prompt, FewShotPrompt, None] = None,
     ):
-        if retriever_kwargs is None:
-            self.retriever = Retriever()
+        if retriever is None:
+            if retriever_kwargs is None:
+                self.retriever = Retriever()
+            else:
+                self.retriever = Retriever(**retriever_kwargs)
         else:
-            self.retriever = Retriever(**retriever_kwargs)
+            self.retriever = retriever
 
         if llm_kwargs is None:
             self.llm_ = LLM()
@@ -54,6 +75,7 @@ def __init__(
 
     @property
     def model_name(self):
+        """Return the name of the model."""
         return self._model_name
 
     @model_name.setter
@@ -67,6 +89,7 @@ def model_name(self, value):
 
     @property
     def doc_chain(self):
+        """Returns the doc_chain."""
         return self._doc_chain
 
     @doc_chain.setter
@@ -86,6 +109,7 @@ def doc_chain(self, value):
 
     @property
     def task(self):
+        """Returns the task."""
         return self._task
 
     @task.setter
@@ -99,6 +123,7 @@ def task(self, value):
         self.prompt_matcher()
 
     def prompt_matcher(self):
+        """Matches relvant prompt using model, task and doc_chain."""
         matcher_path = self.prompt_path.joinpath("matcher.json")
         with open(f"{matcher_path}", "r") as f:
             matcher_dict = json.load(f)
@@ -122,7 +147,9 @@ def prompt_matcher(self):
 
     @staticmethod
     def stuff_docs(docs: List[Document]) -> str:
-        """Args:
+        r"""Concatenates docs into a string seperated by '\n\n'.
+
+        Args:
             docs: List of langchain_core.documents.Document
 
         Returns:
@@ -132,6 +159,8 @@ def stuff_docs(docs: List[Document]) -> str:
 
     @staticmethod
     def output_parser(call_func):
+        """Decorator to format llm output."""
+
         def output_parser_wrapper(*args, **kwargs):
             response, sources = call_func(*args, **kwargs)
             if conf["llm"]["std_out"] == "False":
@@ -146,6 +175,7 @@ def output_parser_wrapper(*args, **kwargs):
 
     @output_parser
     def stuff_call(self, query: str):
+        """Call function for stuff chain."""
         retrieved_docs = self.retriever.get_chunk(query)
         context = self.stuff_docs(retrieved_docs)
         prompt = self.main_prompt.format(context=context, question=query)
@@ -155,6 +185,7 @@ def stuff_call(self, query: str):
 
     @output_parser
     def refine_call(self, query: str):
+        """Call function for refine chain."""
         retrieved_docs = self.retriever.get_chunk(query)
         sources = [doc.metadata["source"] for doc in retrieved_docs]
         responses = []
@@ -176,6 +207,7 @@ def refine_call(self, query: str):
         return responses, sources
 
     def __call__(self, query: str):
+        """Call function for the class."""
         if self.doc_chain == "stuff":
             return self.stuff_call(query)
         elif self.doc_chain == "refine":
diff --git a/src/tests/components/multivec_retriever_test.py b/src/tests/components/multivec_retriever_test.py
index 3ccb3fb..14dad0b 100644
--- a/src/tests/components/multivec_retriever_test.py
+++ b/src/tests/components/multivec_retriever_test.py
@@ -1,3 +1,92 @@
+import json
+
+from grag.components.multivec_retriever import Retriever
+from langchain_core.documents import Document
+
+retriever = Retriever()  # pass test collection
+
+doc = Document(page_content="Hello worlds", metadata={"source": "bars"})
+
+
+def test_retriver_id_gen():
+    doc = Document(page_content="Hello world", metadata={"source": "bar"})
+    id_ = retriever.id_gen(doc)
+    assert isinstance(id, str)
+    assert len(id_) == 32
+    doc.page_content = doc.page_content + 'ABC'
+    id_1 = retriever.id_gen(doc)
+    assert id_ == id_1
+    doc.metadata["source"] = "bars"
+    id_1 = retriever.id_gen(doc)
+    assert id_ != id_1
+
+
+def test_retriever_gen_doc_ids():
+    docs = [Document(page_content="Hello world", metadata={"source": "bar"}),
+            Document(page_content="Hello", metadata={"source": "foo"})]
+    ids = retriever.gen_doc_ids(docs)
+    assert len(ids) == len(docs)
+    assert all(isinstance(id, str) for id in ids)
+
+
+def test_retriever_split_docs():
+    pass
+
+
+def test_retriever_split_docs():
+    pass
+
+
+def test_retriever_add_docs():
+    # small enough docs to not split.
+    docs = [Document(page_content=
+                     """And so on this rainbow day, with storms all around them, and blue sky
+                 above, they rode only as far as the valley. But from there, before they
+                 turned to go back, the monuments appeared close, and they loomed
+                 grandly with the background of purple bank and creamy cloud and shafts
+                 of golden lightning. They seemed like sentinels--guardians of a great
+                 and beautiful love born under their lofty heights, in the lonely
+                 silence of day, in the star-thrown shadow of night. They were like that
+                 love. And they held Lucy and Slone, calling every day, giving a
+                 nameless and tranquil content, binding them true to love, true to the
+                 sage and the open, true to that wild upland home""", metadata={"source": "test_doc_1"}),
+            Document(page_content=
+                     """Slone and Lucy never rode down so far as the stately monuments, though
+                     these held memories as hauntingly sweet as others were poignantly
+                     bitter. Lucy never rode the King again. But Slone rode him, learned to
+                     love him. And Lucy did not race any more. When Slone tried to stir in
+                     her the old spirit all the response he got was a wistful shake of head
+                     or a laugh that hid the truth or an excuse that the strain on her
+                     ankles from Joel Creech's lasso had never mended. The girl was
+                     unutterably happy, but it was possible that she would never race a
+                     horse again.""", metadata={"source": "test_doc_2"}),
+            Document(page_content=
+                     """Bostil wanted to be alone, to welcome the King, to lead him back to the
+                 home corral, perhaps to hide from all eyes the change and the uplift
+                 that would forever keep him from wronging another man.
+             
+                 The late rains came and like magic, in a few days, the sage grew green
+                 and lustrous and fresh, the gray turning to purple.
+             
+                 Every morning the sun rose white and hot in a blue and cloudless sky.
+                 And then soon the horizon line showed creamy clouds that rose and
+                 spread and darkened. Every afternoon storms hung along the ramparts and
+                 rainbows curved down beautiful and ethereal. The dim blackness of the
+                 storm-clouds was split to the blinding zigzag of lightning, and the
+                 thunder rolled and boomed, like the Colorado in flood.""", metadata={"source": "test_doc_3"})
+            ]
+    ids = retriever.gen_doc_ids(docs)
+    retriever.add_docs(docs)
+    retrieved = retriever.store.mget(ids)
+    assert len(retrieved) == len(ids)
+    for i, doc in enumerate(docs):
+        retrieved_doc = json.loads(retrieved[i].decode())
+        assert doc.metadata == retrieved_doc.metadata
+
+
+def test_retriever_aadd_docs():
+    pass
+
 # # add code folder to sys path
 # import os
 # from pathlib import Path
diff --git a/src/tests/components/vectordb/__init__.py b/src/tests/components/vectordb/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/tests/components/chroma_client_test.py b/src/tests/components/vectordb/chroma_client_test.py
similarity index 59%
rename from src/tests/components/chroma_client_test.py
rename to src/tests/components/vectordb/chroma_client_test.py
index 1596dd3..c491dfd 100644
--- a/src/tests/components/chroma_client_test.py
+++ b/src/tests/components/vectordb/chroma_client_test.py
@@ -1,12 +1,13 @@
 import asyncio
 
-from grag.components.chroma_client import ChromaClient
+import pytest
+from grag.components.vectordb.chroma_client import ChromaClient
 from langchain_core.documents import Document
 
 
 def test_chroma_connection():
-    client = ChromaClient()
-    response = client.test_connection()
+    chroma_client = ChromaClient()
+    response = chroma_client.test_connection()
     assert isinstance(response, int)
 
 
@@ -45,14 +46,12 @@ def test_chroma_add_docs():
     storm-clouds was split to the blinding zigzag of lightning, and the
     thunder rolled and boomed, like the Colorado in flood.""",
     ]
-    client = ChromaClient(collection_name="test")
-    if client.collection.count() > 0:
-        client.chroma_client.delete_collection("test")
-    client = ChromaClient(collection_name="test")
+    chroma_client = ChromaClient(collection_name="test")
+    if len(chroma_client) > 0:
+        chroma_client.delete()
     docs = [Document(page_content=doc) for doc in docs]
-    client.add_docs(docs)
-    collection_count = client.collection.count()
-    assert collection_count == len(docs)
+    chroma_client.add_docs(docs)
+    assert len(chroma_client) == len(docs)
 
 
 def test_chroma_aadd_docs():
@@ -90,11 +89,60 @@ def test_chroma_aadd_docs():
     storm-clouds was split to the blinding zigzag of lightning, and the
     thunder rolled and boomed, like the Colorado in flood.""",
     ]
-    client = ChromaClient(collection_name="test")
-    if client.collection.count() > 0:
-        client.chroma_client.delete_collection("test")
-    client = ChromaClient(collection_name="test")
+    chroma_client = ChromaClient(collection_name="test")
+    if len(chroma_client) > 0:
+        chroma_client.delete()
     docs = [Document(page_content=doc) for doc in docs]
     loop = asyncio.get_event_loop()
-    loop.run_until_complete(client.aadd_docs(docs))
-    assert client.collection.count() == len(docs)
+    loop.run_until_complete(chroma_client.aadd_docs(docs))
+    assert len(chroma_client) == len(docs)
+
+
+chrome_get_chunk_params = [(1, False), (1, True), (2, False), (2, True)]
+
+
+@pytest.mark.parametrize("top_k,with_score", chrome_get_chunk_params)
+def test_chroma_get_chunk(top_k, with_score):
+    query = """Slone and Lucy never rode down so far as the stately monuments, though
+    these held memories as hauntingly sweet as others were poignantly
+    bitter. Lucy never rode the King again. But Slone rode him, learned to
+    love him. And Lucy did not race any more. When Slone tried to stir in
+    her the old spirit all the response he got was a wistful shake of head
+    or a laugh that hid the truth or an excuse that the strain on her
+    ankles from Joel Creech's lasso had never mended. The girl was
+    unutterably happy, but it was possible that she would never race a
+    horse again."""
+    chroma_client = ChromaClient(collection_name="test")
+    retrieved_chunks = chroma_client.get_chunk(
+        query=query, top_k=top_k, with_score=with_score
+    )
+    assert len(retrieved_chunks) == top_k
+    if with_score:
+        assert all(isinstance(doc[0], Document) for doc in retrieved_chunks)
+        assert all(isinstance(doc[1], float) for doc in retrieved_chunks)
+    else:
+        assert all(isinstance(doc, Document) for doc in retrieved_chunks)
+
+
+@pytest.mark.parametrize("top_k,with_score", chrome_get_chunk_params)
+def test_chroma_aget_chunk(top_k, with_score):
+    query = """Slone and Lucy never rode down so far as the stately monuments, though
+    these held memories as hauntingly sweet as others were poignantly
+    bitter. Lucy never rode the King again. But Slone rode him, learned to
+    love him. And Lucy did not race any more. When Slone tried to stir in
+    her the old spirit all the response he got was a wistful shake of head
+    or a laugh that hid the truth or an excuse that the strain on her
+    ankles from Joel Creech's lasso had never mended. The girl was
+    unutterably happy, but it was possible that she would never race a
+    horse again."""
+    chroma_client = ChromaClient(collection_name="test")
+    loop = asyncio.get_event_loop()
+    retrieved_chunks = loop.run_until_complete(
+        chroma_client.aget_chunk(query=query, top_k=top_k, with_score=with_score)
+    )
+    assert len(retrieved_chunks) == top_k
+    if with_score:
+        assert all(isinstance(doc[0], Document) for doc in retrieved_chunks)
+        assert all(isinstance(doc[1], float) for doc in retrieved_chunks)
+    else:
+        assert all(isinstance(doc, Document) for doc in retrieved_chunks)
diff --git a/src/tests/components/vectordb/deeplake_client_test.py b/src/tests/components/vectordb/deeplake_client_test.py
new file mode 100644
index 0000000..cea5e61
--- /dev/null
+++ b/src/tests/components/vectordb/deeplake_client_test.py
@@ -0,0 +1,146 @@
+import asyncio
+
+import pytest
+from grag.components.vectordb.deeplake_client import DeepLakeClient
+from langchain_core.documents import Document
+
+
+def test_deeplake_add_docs():
+    docs = [
+        """And so on this rainbow day, with storms all around them, and blue sky
+    above, they rode only as far as the valley. But from there, before they
+    turned to go back, the monuments appeared close, and they loomed
+    grandly with the background of purple bank and creamy cloud and shafts
+    of golden lightning. They seemed like sentinels--guardians of a great
+    and beautiful love born under their lofty heights, in the lonely
+    silence of day, in the star-thrown shadow of night. They were like that
+    love. And they held Lucy and Slone, calling every day, giving a
+    nameless and tranquil content, binding them true to love, true to the
+    sage and the open, true to that wild upland home.""",
+        """Slone and Lucy never rode down so far as the stately monuments, though
+    these held memories as hauntingly sweet as others were poignantly
+    bitter. Lucy never rode the King again. But Slone rode him, learned to
+    love him. And Lucy did not race any more. When Slone tried to stir in
+    her the old spirit all the response he got was a wistful shake of head
+    or a laugh that hid the truth or an excuse that the strain on her
+    ankles from Joel Creech's lasso had never mended. The girl was
+    unutterably happy, but it was possible that she would never race a
+    horse again.""",
+        """Bostil wanted to be alone, to welcome the King, to lead him back to the
+    home corral, perhaps to hide from all eyes the change and the uplift
+    that would forever keep him from wronging another man.
+
+    The late rains came and like magic, in a few days, the sage grew green
+    and lustrous and fresh, the gray turning to purple.
+
+    Every morning the sun rose white and hot in a blue and cloudless sky.
+    And then soon the horizon line showed creamy clouds that rose and
+    spread and darkened. Every afternoon storms hung along the ramparts and
+    rainbows curved down beautiful and ethereal. The dim blackness of the
+    storm-clouds was split to the blinding zigzag of lightning, and the
+    thunder rolled and boomed, like the Colorado in flood.""",
+    ]
+    deeplake_client = DeepLakeClient(collection_name="test")
+    if len(deeplake_client) > 0:
+        deeplake_client.delete()
+    docs = [Document(page_content=doc) for doc in docs]
+    deeplake_client.add_docs(docs)
+    assert len(deeplake_client) == len(docs)
+    del deeplake_client
+
+
+def test_chroma_aadd_docs():
+    docs = [
+        """And so on this rainbow day, with storms all around them, and blue sky
+    above, they rode only as far as the valley. But from there, before they
+    turned to go back, the monuments appeared close, and they loomed
+    grandly with the background of purple bank and creamy cloud and shafts
+    of golden lightning. They seemed like sentinels--guardians of a great
+    and beautiful love born under their lofty heights, in the lonely
+    silence of day, in the star-thrown shadow of night. They were like that
+    love. And they held Lucy and Slone, calling every day, giving a
+    nameless and tranquil content, binding them true to love, true to the
+    sage and the open, true to that wild upland home.""",
+        """Slone and Lucy never rode down so far as the stately monuments, though
+    these held memories as hauntingly sweet as others were poignantly
+    bitter. Lucy never rode the King again. But Slone rode him, learned to
+    love him. And Lucy did not race any more. When Slone tried to stir in
+    her the old spirit all the response he got was a wistful shake of head
+    or a laugh that hid the truth or an excuse that the strain on her
+    ankles from Joel Creech's lasso had never mended. The girl was
+    unutterably happy, but it was possible that she would never race a
+    horse again.""",
+        """Bostil wanted to be alone, to welcome the King, to lead him back to the
+    home corral, perhaps to hide from all eyes the change and the uplift
+    that would forever keep him from wronging another man.
+
+    The late rains came and like magic, in a few days, the sage grew green
+    and lustrous and fresh, the gray turning to purple.
+
+    Every morning the sun rose white and hot in a blue and cloudless sky.
+    And then soon the horizon line showed creamy clouds that rose and
+    spread and darkened. Every afternoon storms hung along the ramparts and
+    rainbows curved down beautiful and ethereal. The dim blackness of the
+    storm-clouds was split to the blinding zigzag of lightning, and the
+    thunder rolled and boomed, like the Colorado in flood.""",
+    ]
+    deeplake_client = DeepLakeClient(collection_name="test")
+    if len(deeplake_client) > 0:
+        deeplake_client.delete()
+    docs = [Document(page_content=doc) for doc in docs]
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(deeplake_client.aadd_docs(docs))
+    assert len(deeplake_client) == len(docs)
+    del deeplake_client
+
+
+deeplake_get_chunk_params = [(1, False), (1, True), (2, False), (2, True)]
+
+
+@pytest.mark.parametrize("top_k,with_score", deeplake_get_chunk_params)
+def test_deeplake_get_chunk(top_k, with_score):
+    query = """Slone and Lucy never rode down so far as the stately monuments, though
+    these held memories as hauntingly sweet as others were poignantly
+    bitter. Lucy never rode the King again. But Slone rode him, learned to
+    love him. And Lucy did not race any more. When Slone tried to stir in
+    her the old spirit all the response he got was a wistful shake of head
+    or a laugh that hid the truth or an excuse that the strain on her
+    ankles from Joel Creech's lasso had never mended. The girl was
+    unutterably happy, but it was possible that she would never race a
+    horse again."""
+    deeplake_client = DeepLakeClient(collection_name="test", read_only=True)
+    retrieved_chunks = deeplake_client.get_chunk(
+        query=query, top_k=top_k, with_score=with_score
+    )
+    assert len(retrieved_chunks) == top_k
+    if with_score:
+        assert all(isinstance(doc[0], Document) for doc in retrieved_chunks)
+        assert all(isinstance(doc[1], float) for doc in retrieved_chunks)
+    else:
+        assert all(isinstance(doc, Document) for doc in retrieved_chunks)
+    del deeplake_client
+
+
+@pytest.mark.parametrize("top_k,with_score", deeplake_get_chunk_params)
+def test_deeplake_aget_chunk(top_k, with_score):
+    query = """Slone and Lucy never rode down so far as the stately monuments, though
+    these held memories as hauntingly sweet as others were poignantly
+    bitter. Lucy never rode the King again. But Slone rode him, learned to
+    love him. And Lucy did not race any more. When Slone tried to stir in
+    her the old spirit all the response he got was a wistful shake of head
+    or a laugh that hid the truth or an excuse that the strain on her
+    ankles from Joel Creech's lasso had never mended. The girl was
+    unutterably happy, but it was possible that she would never race a
+    horse again."""
+    deeplake_client = DeepLakeClient(collection_name="test", read_only=True)
+    loop = asyncio.get_event_loop()
+    retrieved_chunks = loop.run_until_complete(
+        deeplake_client.aget_chunk(query=query, top_k=top_k, with_score=with_score)
+    )
+    assert len(retrieved_chunks) == top_k
+    if with_score:
+        assert all(isinstance(doc[0], Document) for doc in retrieved_chunks)
+        assert all(isinstance(doc[1], float) for doc in retrieved_chunks)
+    else:
+        assert all(isinstance(doc, Document) for doc in retrieved_chunks)
+    del deeplake_client
diff --git a/src/tests/rag/basic_rag_test.py b/src/tests/rag/basic_rag_test.py
index 06db25e..2249028 100644
--- a/src/tests/rag/basic_rag_test.py
+++ b/src/tests/rag/basic_rag_test.py
@@ -1,4 +1,4 @@
-from typing import List, Text
+from typing import Text, List
 
 from grag.rag.basic_rag import BasicRAG