From 1f57b085f73bc069403b19545d39edc1fb7462c4 Mon Sep 17 00:00:00 2001
From: Talha SARI <sarit17@itu.edu.tr>
Date: Fri, 8 Dec 2023 18:36:31 +0300
Subject: [PATCH 1/3] fix AutoQueryEngine bug causing not use of
 qa_prompt_template its given (#177)

---
 autollm/__init__.py             |  2 +-
 autollm/auto/query_engine.py    | 19 ++++++++++++-------
 autollm/auto/service_context.py |  5 ++++-
 3 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/autollm/__init__.py b/autollm/__init__.py
index 7c32b9d8..d7c61a62 100644
--- a/autollm/__init__.py
+++ b/autollm/__init__.py
@@ -4,7 +4,7 @@
 and vector databases, along with various utility functions.
 """
 
-__version__ = '0.1.3'
+__version__ = '0.1.4'
 __author__ = 'safevideo'
 __license__ = 'AGPL-3.0'
 
diff --git a/autollm/auto/query_engine.py b/autollm/auto/query_engine.py
index 959fed8e..964847b0 100644
--- a/autollm/auto/query_engine.py
+++ b/autollm/auto/query_engine.py
@@ -3,7 +3,7 @@
 from llama_index import Document, ServiceContext, VectorStoreIndex
 from llama_index.embeddings.utils import EmbedType
 from llama_index.indices.query.base import BaseQueryEngine
-from llama_index.prompts.base import PromptTemplate
+from llama_index.prompts.base import BasePromptTemplate, PromptTemplate
 from llama_index.prompts.prompt_type import PromptType
 from llama_index.response_synthesizers import get_response_synthesizer
 from llama_index.schema import BaseNode
@@ -24,11 +24,11 @@ def create_query_engine(
         llm_api_base: Optional[str] = None,
         # service_context_params
         system_prompt: str = None,
-        query_wrapper_prompt: str = None,
+        query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
         enable_cost_calculator: bool = True,
         embed_model: Union[str, EmbedType] = "default",  # ["default", "local"]
         chunk_size: Optional[int] = 512,
-        chunk_overlap: Optional[int] = 200,
+        chunk_overlap: Optional[int] = 100,
         context_window: Optional[int] = None,
         enable_title_extractor: bool = False,
         enable_summary_extractor: bool = False,
@@ -61,7 +61,7 @@ def create_query_engine(
         llm_temperature (float): The temperature to use for the LLM.
         llm_api_base (str): The API base to use for the LLM.
         system_prompt (str): The system prompt to use for the query engine.
-        query_wrapper_prompt (str): The query wrapper prompt to use for the query engine.
+        query_wrapper_prompt (Union[str, BasePromptTemplate]): The query wrapper prompt to use for the query engine.
         enable_cost_calculator (bool): Flag to enable cost calculator logging.
         embed_model (Union[str, EmbedType]): The embedding model to use for generating embeddings. "default" for OpenAI,
                                             "local" for HuggingFace or use full identifier (e.g., local:intfloat/multilingual-e5-large)
@@ -133,10 +133,15 @@ def create_query_engine(
         refine_prompt_template = PromptTemplate(refine_prompt, prompt_type=PromptType.REFINE)
     else:
         refine_prompt_template = None
+
+    # Convert query_wrapper_prompt to PromptTemplate if it is a string
+    if isinstance(query_wrapper_prompt, str):
+        query_wrapper_prompt = PromptTemplate(template=query_wrapper_prompt)
     response_synthesizer = get_response_synthesizer(
         service_context=service_context,
-        response_mode=response_mode,
+        text_qa_template=query_wrapper_prompt,
         refine_template=refine_prompt_template,
+        response_mode=response_mode,
         structured_answer_filtering=structured_answer_filtering)
 
     return vector_store_index.as_query_engine(
@@ -213,7 +218,7 @@ def from_defaults(
             llm_temperature: float = 0.1,
             # service_context_params
             system_prompt: str = None,
-            query_wrapper_prompt: str = None,
+            query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
             enable_cost_calculator: bool = True,
             embed_model: Union[str, EmbedType] = "default",  # ["default", "local"]
             chunk_size: Optional[int] = 512,
@@ -246,7 +251,7 @@ def from_defaults(
             llm_temperature (float): The temperature to use for the LLM.
             llm_api_base (str): The API base to use for the LLM.
             system_prompt (str): The system prompt to use for the query engine.
-            query_wrapper_prompt (str): The query wrapper prompt to use for the query engine.
+            query_wrapper_prompt (Union[str, BasePromptTemplate]): The query wrapper prompt to use for the query engine.
             enable_cost_calculator (bool): Flag to enable cost calculator logging.
             embed_model (Union[str, EmbedType]): The embedding model to use for generating embeddings. "default" for OpenAI,
                                                 "local" for HuggingFace or use full identifier (e.g., local:intfloat/multilingual-e5-large)
diff --git a/autollm/auto/service_context.py b/autollm/auto/service_context.py
index ff45e794..9aad5294 100644
--- a/autollm/auto/service_context.py
+++ b/autollm/auto/service_context.py
@@ -65,11 +65,14 @@ def from_defaults(
         """
         if not system_prompt and not query_wrapper_prompt:
             system_prompt, query_wrapper_prompt = set_default_prompt_template()
-        # Convert system_prompt to ChatPromptTemplate if it is a string
+        # Convert query_wrapper_prompt to PromptTemplate if it is a string
         if isinstance(query_wrapper_prompt, str):
             query_wrapper_prompt = PromptTemplate(template=query_wrapper_prompt)
 
         callback_manager: CallbackManager = kwargs.get('callback_manager', CallbackManager())
+        kwargs.pop(
+            'callback_manager', None)  # Make sure callback_manager is not passed to ServiceContext twice
+
         if enable_cost_calculator:
             llm_model_name = llm.metadata.model_name if not "default" else "gpt-3.5-turbo"
             callback_manager.add_handler(CostCalculatingHandler(model_name=llm_model_name, verbose=True))

From 0407ac65fcab58c05f8a09876a06f703f6973be2 Mon Sep 17 00:00:00 2001
From: Talha SARI <sarit17@itu.edu.tr>
Date: Fri, 22 Dec 2023 15:14:12 +0300
Subject: [PATCH 2/3] feature: implement auto embedding (#181)

* implement auto embedding

* bump litellm

* add autoEmbedding to autoQuery pipeline

* fix tests

* bump llama-index
---
 autollm/auto/embedding.py       | 98 +++++++++++++++++++++++++++++++++
 autollm/auto/llm.py             |  4 +-
 autollm/auto/query_engine.py    | 19 ++++---
 autollm/auto/service_context.py |  2 +-
 requirements.txt                |  4 +-
 tests/config.yaml               |  2 +-
 tests/test_auto_lite_llm.py     |  4 +-
 7 files changed, 117 insertions(+), 16 deletions(-)
 create mode 100644 autollm/auto/embedding.py

diff --git a/autollm/auto/embedding.py b/autollm/auto/embedding.py
new file mode 100644
index 00000000..f95bc965
--- /dev/null
+++ b/autollm/auto/embedding.py
@@ -0,0 +1,98 @@
+import asyncio
+from typing import Any, List
+
+from litellm import embedding as lite_embedding
+from llama_index.bridge.pydantic import Field
+from llama_index.embeddings.base import BaseEmbedding, Embedding
+
+
+class AutoEmbedding(BaseEmbedding):
+    """
+    Custom embedding class for flexible and efficient text embedding.
+
+    This class interfaces with the LiteLLM library to use its embedding functionality, making it compatible
+    with a wide range of LLM models.
+    """
+
+    # Define the model attribute using Pydantic's Field
+    model: str = Field(default="text-embedding-ada-002", description="The name of the embedding model.")
+
+    def __init__(self, model: str, **kwargs: Any) -> None:
+        """
+        Initialize the AutoEmbedding with a specific model.
+
+        Args:
+            model (str): ID of the embedding model to use.
+            **kwargs (Any): Additional keyword arguments.
+        """
+        super().__init__(**kwargs)
+        self.model = model  # Set the model ID for embedding
+
+    def _get_query_embedding(self, query: str) -> Embedding:
+        """
+        Synchronously get the embedding for a query string.
+
+        Args:
+            query (str): The query text to embed.
+
+        Returns:
+            Embedding: The embedding vector.
+        """
+        response = lite_embedding(model=self.model, input=[query])
+        return self._parse_embedding_response(response)
+
+    async def _aget_query_embedding(self, query: str) -> Embedding:
+        """
+        Asynchronously get the embedding for a query string.
+
+        Args:
+            query (str): The query text to embed.
+
+        Returns:
+            Embedding: The embedding vector.
+        """
+        response = await asyncio.to_thread(lite_embedding, model=self.model, input=[query])
+        return self._parse_embedding_response(response)
+
+    def _get_text_embedding(self, text: str) -> Embedding:
+        """
+        Synchronously get the embedding for a text string.
+
+        Args:
+            text (str): The text to embed.
+
+        Returns:
+            Embedding: The embedding vector.
+        """
+        return self._get_query_embedding(text)
+
+    async def _aget_text_embedding(self, text: str) -> Embedding:
+        """
+        Asynchronously get the embedding for a text string.
+
+        Args:
+            text (str): The text to embed.
+
+        Returns:
+            Embedding: The embedding vector.
+        """
+        return await self._aget_query_embedding(text)
+
+    def _parse_embedding_response(self, response):
+        """
+        Parse the embedding response from LiteLLM and extract the embedding data.
+
+        Args:
+            response: The response object from LiteLLM's embedding function.
+
+        Returns:
+            List[float]: The extracted embedding list.
+        """
+        try:
+            if 'data' in response and len(response['data']) > 0 and 'embedding' in response['data'][0]:
+                return response['data'][0]['embedding']
+            else:
+                raise ValueError("Invalid response structure from embedding function.")
+        except (TypeError, KeyError, IndexError) as e:
+            # Handle any parsing errors
+            raise ValueError(f"Error parsing embedding response: {e}")
diff --git a/autollm/auto/llm.py b/autollm/auto/llm.py
index 1616b501..36e323ed 100644
--- a/autollm/auto/llm.py
+++ b/autollm/auto/llm.py
@@ -1,7 +1,7 @@
 from typing import Optional
 
 from llama_index.llms import LiteLLM
-from llama_index.llms.base import LLM
+from llama_index.llms.base import BaseLLM
 
 
 class AutoLiteLLM:
@@ -14,7 +14,7 @@ def from_defaults(
             model: str = "gpt-3.5-turbo",
             max_tokens: Optional[int] = 256,
             temperature: float = 0.1,
-            api_base: Optional[str] = None) -> LLM:
+            api_base: Optional[str] = None) -> BaseLLM:
         """
         Create any LLM by model name. Check https://docs.litellm.ai/docs/providers for a list of
         supported models.
diff --git a/autollm/auto/query_engine.py b/autollm/auto/query_engine.py
index 964847b0..70953037 100644
--- a/autollm/auto/query_engine.py
+++ b/autollm/auto/query_engine.py
@@ -8,6 +8,7 @@
 from llama_index.response_synthesizers import get_response_synthesizer
 from llama_index.schema import BaseNode
 
+from autollm.auto.embedding import AutoEmbedding
 from autollm.auto.llm import AutoLiteLLM
 from autollm.auto.service_context import AutoServiceContext
 from autollm.auto.vector_store_index import AutoVectorStoreIndex
@@ -26,7 +27,7 @@ def create_query_engine(
         system_prompt: str = None,
         query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
         enable_cost_calculator: bool = True,
-        embed_model: Union[str, EmbedType] = "default",  # ["default", "local"]
+        embed_model: Optional[str] = "text-embedding-ada-002",
         chunk_size: Optional[int] = 512,
         chunk_overlap: Optional[int] = 100,
         context_window: Optional[int] = None,
@@ -106,9 +107,12 @@ def create_query_engine(
 
     llm = AutoLiteLLM.from_defaults(
         model=llm_model, api_base=llm_api_base, max_tokens=llm_max_tokens, temperature=llm_temperature)
+
+    embedding = AutoEmbedding(model=embed_model)
+
     service_context = AutoServiceContext.from_defaults(
         llm=llm,
-        embed_model=embed_model,
+        embed_model=embedding,
         system_prompt=system_prompt,
         query_wrapper_prompt=query_wrapper_prompt,
         enable_cost_calculator=enable_cost_calculator,
@@ -173,7 +177,7 @@ class AutoQueryEngine:
         system_prompt=None,
         query_wrapper_prompt=None,
         enable_cost_calculator=True,
-        embed_model="default",  # ["default", "local"]
+        embed_model="text-embedding-ada-002",
         chunk_size=512,
         chunk_overlap=None,
         context_window=None,
@@ -212,15 +216,15 @@ def from_defaults(
             documents: Optional[Sequence[Document]] = None,
             nodes: Optional[Sequence[BaseNode]] = None,
             # llm_params
-            llm_model: str = "gpt-3.5-turbo",
+            llm_model: Optional[str] = "gpt-3.5-turbo",
             llm_api_base: Optional[str] = None,
             llm_max_tokens: Optional[int] = None,
-            llm_temperature: float = 0.1,
+            llm_temperature: Optional[float] = 0.1,
             # service_context_params
             system_prompt: str = None,
             query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
             enable_cost_calculator: bool = True,
-            embed_model: Union[str, EmbedType] = "default",  # ["default", "local"]
+            embed_model: Optional[str] = "text-embedding-ada-002",
             chunk_size: Optional[int] = 512,
             chunk_overlap: Optional[int] = 200,
             context_window: Optional[int] = None,
@@ -253,8 +257,7 @@ def from_defaults(
             system_prompt (str): The system prompt to use for the query engine.
             query_wrapper_prompt (Union[str, BasePromptTemplate]): The query wrapper prompt to use for the query engine.
             enable_cost_calculator (bool): Flag to enable cost calculator logging.
-            embed_model (Union[str, EmbedType]): The embedding model to use for generating embeddings. "default" for OpenAI,
-                                                "local" for HuggingFace or use full identifier (e.g., local:intfloat/multilingual-e5-large)
+            embed_model (Union[str, EmbedType]): The embedding model to use for generating embeddings.
             chunk_size (int): The token chunk size for each chunk.
             chunk_overlap (int): The token overlap between each chunk.
             context_window (int): The maximum context size that will get sent to the LLM.
diff --git a/autollm/auto/service_context.py b/autollm/auto/service_context.py
index 9aad5294..0a36db28 100644
--- a/autollm/auto/service_context.py
+++ b/autollm/auto/service_context.py
@@ -32,7 +32,7 @@ def from_defaults(
             query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
             enable_cost_calculator: bool = False,
             chunk_size: Optional[int] = 512,
-            chunk_overlap: Optional[int] = 200,
+            chunk_overlap: Optional[int] = 100,
             context_window: Optional[int] = None,
             enable_title_extractor: bool = False,
             enable_summary_extractor: bool = False,
diff --git a/requirements.txt b/requirements.txt
index c57b1ddf..293458fb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
-llama-index==0.9.10
-litellm==1.1.1
+llama-index==0.9.21
+litellm==1.15.6
 uvicorn
 fastapi
 python-dotenv
diff --git a/tests/config.yaml b/tests/config.yaml
index a939feb8..df3fd16e 100644
--- a/tests/config.yaml
+++ b/tests/config.yaml
@@ -6,7 +6,7 @@ tasks:
     llm_temperature: 0.1
     system_prompt: "You are a friendly chatbot that can summarize documents.:"  # System prompt for this task
     enable_cost_calculator: true
-    embed_model: "default"
+    embed_model: "text-embedding-ada-002"
     chunk_size: 512
     chunk_overlap: 64
     context_window: 2048
diff --git a/tests/test_auto_lite_llm.py b/tests/test_auto_lite_llm.py
index 737d46e1..9c3461c1 100644
--- a/tests/test_auto_lite_llm.py
+++ b/tests/test_auto_lite_llm.py
@@ -1,5 +1,5 @@
 from llama_index import Document, ServiceContext, VectorStoreIndex
-from llama_index.llms.base import LLM
+from llama_index.llms.base import BaseLLM
 from llama_index.query_engine import BaseQueryEngine
 
 from autollm.auto.llm import AutoLiteLLM
@@ -11,7 +11,7 @@ def test_auto_lite_llm():
     llm = AutoLiteLLM.from_defaults(model="gpt-3.5-turbo")
 
     # Check if the llm is an instance of LLM
-    assert isinstance(llm, LLM)
+    assert isinstance(llm, BaseLLM)
 
     service_context = ServiceContext.from_defaults(llm=llm)
 

From 2ec1b21d32471da390f99cbe2409399185857ba9 Mon Sep 17 00:00:00 2001
From: Talha SARI <sarit17@itu.edu.tr>
Date: Fri, 22 Dec 2023 19:25:53 +0300
Subject: [PATCH 3/3] refactor and enhance Lancedb URI Handling in
 AutoVectorStoreIndex (#182)

* bump autollm

* add incerement method to lancedb

* add validate and setup method for lancedb_uri

* some fixes

* fix functionality of exist_ok

* remove accidental lancedb dir

* more fixes

* remove deprecated parameters & methods
---
 .gitignore                         |   1 +
 autollm/__init__.py                |   2 +-
 autollm/auto/query_engine.py       |  85 +++---------------------
 autollm/auto/vector_store_index.py | 100 +++++++++++++++++++++++++++--
 autollm/utils/document_reading.py  |  19 +-----
 autollm/utils/env_utils.py         |  16 +++++
 6 files changed, 123 insertions(+), 100 deletions(-)

diff --git a/.gitignore b/.gitignore
index 170f1a07..665bb5be 100644
--- a/.gitignore
+++ b/.gitignore
@@ -170,3 +170,4 @@ storage/
 # vscode settings
 .vscode
 .lancedb
+lancedb/
diff --git a/autollm/__init__.py b/autollm/__init__.py
index d7c61a62..763b64ba 100644
--- a/autollm/__init__.py
+++ b/autollm/__init__.py
@@ -4,7 +4,7 @@
 and vector databases, along with various utility functions.
 """
 
-__version__ = '0.1.4'
+__version__ = '0.1.5'
 __author__ = 'safevideo'
 __license__ = 'AGPL-3.0'
 
diff --git a/autollm/auto/query_engine.py b/autollm/auto/query_engine.py
index 70953037..eb3235c1 100644
--- a/autollm/auto/query_engine.py
+++ b/autollm/auto/query_engine.py
@@ -45,11 +45,8 @@ def create_query_engine(
         vector_store_type: str = "LanceDBVectorStore",
         lancedb_uri: str = "./.lancedb",
         lancedb_table_name: str = "vectors",
-        # Deprecated parameters
-        llm_params: dict = None,
-        vector_store_params: dict = None,
-        service_context_params: dict = None,
-        query_engine_params: dict = None,
+        exist_ok: bool = False,
+        overwrite_existing: bool = False,
         **vector_store_kwargs) -> BaseQueryEngine:
     """
     Create a query engine from parameters.
@@ -84,27 +81,6 @@ def create_query_engine(
     Returns:
         A llama_index.BaseQueryEngine instance.
     """
-    # Check for deprecated parameters
-    if llm_params is not None:
-        raise ValueError(
-            "llm_params is deprecated. Instead of llm_params={'llm_model': 'model_name', ...}, "
-            "use llm_model='model_name', llm_api_base='api_base', llm_max_tokens=1028, llm_temperature=0.1 directly as arguments."
-        )
-    if vector_store_params is not None:
-        raise ValueError(
-            "vector_store_params is deprecated. Instead of vector_store_params={'vector_store_type': 'type', ...}, "
-            "use vector_store_type='type', lancedb_uri='uri', lancedb_table_name='table', enable_metadata_extraction=True directly as arguments."
-        )
-    if service_context_params is not None:
-        raise ValueError(
-            "service_context_params is deprecated. Use the explicit parameters like system_prompt='prompt', "
-            "query_wrapper_prompt='wrapper', enable_cost_calculator=True, embed_model='model', chunk_size=512, "
-            "chunk_overlap=..., context_window=... directly as arguments.")
-    if query_engine_params is not None:
-        raise ValueError(
-            "query_engine_params is deprecated. Instead of query_engine_params={'similarity_top_k': 5, ...}, "
-            "use similarity_top_k=5 directly as an argument.")
-
     llm = AutoLiteLLM.from_defaults(
         model=llm_model, api_base=llm_api_base, max_tokens=llm_max_tokens, temperature=llm_temperature)
 
@@ -132,6 +108,8 @@ def create_query_engine(
         documents=documents,
         nodes=nodes,
         service_context=service_context,
+        exist_ok=exist_ok,
+        overwrite_existing=overwrite_existing,
         **vector_store_kwargs)
     if refine_prompt is not None:
         refine_prompt_template = PromptTemplate(refine_prompt, prompt_type=PromptType.REFINE)
@@ -187,7 +165,6 @@ class AutoQueryEngine:
         vector_store_type="LanceDBVectorStore",
         lancedb_uri="./.lancedb",
         lancedb_table_name="vectors",
-        enable_metadata_extraction=False,
         **vector_store_kwargs)
     )
     ```
@@ -237,12 +214,8 @@ def from_defaults(
             vector_store_type: str = "LanceDBVectorStore",
             lancedb_uri: str = "./.lancedb",
             lancedb_table_name: str = "vectors",
-            enable_metadata_extraction: bool = False,
-            # Deprecated parameters
-            llm_params: dict = None,
-            vector_store_params: dict = None,
-            service_context_params: dict = None,
-            query_engine_params: dict = None,
+            exist_ok: bool = False,
+            overwrite_existing: bool = False,
             **vector_store_kwargs) -> BaseQueryEngine:
         """
         Create an AutoQueryEngine from default parameters.
@@ -272,6 +245,8 @@ def from_defaults(
             vector_store_type (str): The vector store type to use for the query engine.
             lancedb_uri (str): The URI to use for the LanceDB vector store.
             lancedb_table_name (str): The table name to use for the LanceDB vector store.
+            exist_ok (bool): Flag to allow overwriting an existing vector store.
+            overwrite_existing (bool): Flag to allow overwriting an existing vector store.
 
             Returns:
                 A llama_index.BaseQueryEngine instance.
@@ -302,50 +277,10 @@ def from_defaults(
             vector_store_type=vector_store_type,
             lancedb_uri=lancedb_uri,
             lancedb_table_name=lancedb_table_name,
-            enable_metadata_extraction=enable_metadata_extraction,
-            # Deprecated parameters
-            llm_params=llm_params,
-            vector_store_params=vector_store_params,
-            service_context_params=service_context_params,
-            query_engine_params=query_engine_params,
+            exist_ok=exist_ok,
+            overwrite_existing=overwrite_existing,
             **vector_store_kwargs)
 
-    @staticmethod
-    def from_parameters(
-            documents: Sequence[Document] = None,
-            system_prompt: str = None,
-            query_wrapper_prompt: str = None,
-            enable_cost_calculator: bool = True,
-            embed_model: Union[str, EmbedType] = "default",  # ["default", "local"]
-            llm_params: dict = None,
-            vector_store_params: dict = None,
-            service_context_params: dict = None,
-            query_engine_params: dict = None) -> BaseQueryEngine:
-        """
-        DEPRECATED. Use AutoQueryEngine.from_defaults instead.
-
-        Create an AutoQueryEngine from parameters.
-
-        Parameters:
-            documents (Sequence[Document]): Sequence of llama_index.Document instances.
-            system_prompt (str): The system prompt to use for the query engine.
-            query_wrapper_prompt (str): The query wrapper prompt to use for the query engine.
-            enable_cost_calculator (bool): Flag to enable cost calculator logging.
-            embed_model (Union[str, EmbedType]): The embedding model to use for generating embeddings. "default" for OpenAI,
-                                                "local" for HuggingFace or use full identifier (e.g., local:intfloat/multilingual-e5-large)
-            llm_params (dict): Parameters for the LLM.
-            vector_store_params (dict): Parameters for the vector store.
-            service_context_params (dict): Parameters for the service context.
-            query_engine_params (dict): Parameters for the query engine.
-
-        Returns:
-            A llama_index.BaseQueryEngine instance.
-        """
-
-        # TODO: Remove this method in the next release
-        raise ValueError(
-            "AutoQueryEngine.from_parameters is deprecated. Use AutoQueryEngine.from_defaults instead.")
-
     @staticmethod
     def from_config(
             config_file_path: str,
diff --git a/autollm/auto/vector_store_index.py b/autollm/auto/vector_store_index.py
index cef7628f..28b3ab96 100644
--- a/autollm/auto/vector_store_index.py
+++ b/autollm/auto/vector_store_index.py
@@ -1,8 +1,13 @@
+import os
+import shutil
 from typing import Optional, Sequence
 
 from llama_index import Document, ServiceContext, StorageContext, VectorStoreIndex
 from llama_index.schema import BaseNode
 
+from autollm.utils.env_utils import on_rm_error
+from autollm.utils.logging import logger
+
 
 def import_vector_store_class(vector_store_class_name: str):
     """
@@ -25,24 +30,33 @@ class name and additional parameters.
     @staticmethod
     def from_defaults(
             vector_store_type: str = "LanceDBVectorStore",
-            lancedb_uri: str = "./.lancedb",
+            lancedb_uri: str = None,
             lancedb_table_name: str = "vectors",
             documents: Optional[Sequence[Document]] = None,
             nodes: Optional[Sequence[BaseNode]] = None,
             service_context: Optional[ServiceContext] = None,
+            exist_ok: bool = False,
+            overwrite_existing: bool = False,
             **kwargs) -> VectorStoreIndex:
         """
-        Initializes a Vector Store index from Vector Store type and additional parameters.
+        Initializes a Vector Store index from Vector Store type and additional parameters. Handles lancedb
+        path and document management according to specified behaviors.
 
         Parameters:
-            vector_store_type (str): The class name of the vector store (e.g., 'LanceDBVectorStore', 'SimpleVectorStore'..)
+            vector_store_type (str): The class name of the vector store.
+            lancedb_uri (str): The URI for the LanceDB vector store.
+            lancedb_table_name (str): The table name for the LanceDB vector store.
             documents (Optional[Sequence[Document]]): Documents to initialize the vector store index from.
-            nodes (Optional[Sequence[BaseNode]]): Nodes to initialize the vector store index from.
-            service_context (Optional[ServiceContext]): Service context to initialize the vector store index from.
-            **kwargs: Additional parameters for initializing the vector store
+            service_context (Optional[ServiceContext]): Service context for initialization.
+            exist_ok (bool): If True, allows adding to an existing database.
+            overwrite_existing (bool): If True, allows overwriting an existing database.
+            **kwargs: Additional parameters for initialization.
 
         Returns:
-            index (VectorStoreIndex): The initialized Vector Store index instance for given vector store type and parameter set.
+            VectorStoreIndex: The initialized Vector Store index instance.
+
+        Raises:
+            ValueError: For invalid parameter combinations or missing information.
         """
         if documents is None and nodes is None and vector_store_type == "SimpleVectorStore":
             raise ValueError("documents or nodes must be provided for SimpleVectorStore")
@@ -55,6 +69,12 @@ def from_defaults(
 
         # If LanceDBVectorStore, use lancedb_uri and lancedb_table_name
         if vector_store_type == "LanceDBVectorStore":
+            lancedb_uri = AutoVectorStoreIndex._validate_and_setup_lancedb_uri(
+                lancedb_uri=lancedb_uri,
+                documents=documents,
+                exist_ok=exist_ok,
+                overwrite_existing=overwrite_existing)
+
             vector_store = VectorStoreClass(uri=lancedb_uri, table_name=lancedb_table_name, **kwargs)
         else:
             vector_store = VectorStoreClass(**kwargs)
@@ -82,3 +102,69 @@ def from_defaults(
                 show_progress=True)
 
         return index
+
+    @staticmethod
+    def _validate_and_setup_lancedb_uri(lancedb_uri, documents, exist_ok, overwrite_existing):
+        """
+        Validates and sets up the lancedb_uri based on the given parameters.
+
+        Parameters:
+            lancedb_uri (str): The URI for the LanceDB vector store.
+            documents (Sequence[Document]): Documents to initialize the vector store index from.
+            exist_ok (bool): Flag to allow adding to an existing database.
+            overwrite_existing (bool): Flag to allow overwriting an existing database.
+
+        Returns:
+            str: The validated and potentially modified lancedb_uri.
+        """
+        default_lancedb_uri = "./lancedb/db"
+
+        # Scenario 0: Handle no lancedb uri and no documents provided
+        if not documents and not lancedb_uri:
+            raise ValueError(
+                "A lancedb uri is required to connect to a database. Please provide a lancedb uri.")
+
+        # Scenario 1: Handle lancedb_uri given but no documents provided
+        if not documents and lancedb_uri:
+            # Check if the database exists
+            db_exists = os.path.exists(lancedb_uri)
+            if not db_exists:
+                raise ValueError(
+                    f"No existing database found at {lancedb_uri}. Please provide a valid lancedb uri.")
+
+        # Scenario 2: Handle no lancedb uri but documents provided
+        if documents and not lancedb_uri:
+            lancedb_uri = default_lancedb_uri
+            lancedb_uri = AutoVectorStoreIndex._increment_lancedb_uri(lancedb_uri)
+            logger.info(
+                f"A new database is being created at {lancedb_uri}. Please provide a lancedb path to use an existing database."
+            )
+
+        # Scenario 3: Handle lancedb uri given and documents provided
+        if documents and lancedb_uri:
+            db_exists = os.path.exists(lancedb_uri)
+            if exist_ok and overwrite_existing:
+                if db_exists:
+                    shutil.rmtree(lancedb_uri)
+                    logger.info(f"Overwriting existing database at {lancedb_uri}.")
+            elif not exist_ok and overwrite_existing:
+                raise ValueError("Cannot overwrite existing database without exist_ok set to True.")
+            elif db_exists:
+                if not exist_ok:
+                    lancedb_uri = AutoVectorStoreIndex._increment_lancedb_uri(lancedb_uri)
+                    logger.info(f"Existing database found. Creating a new database at {lancedb_uri}.")
+                    logger.info(
+                        "Please use exist_ok=True to add to the existing database and overwrite_existing=True to overwrite the existing database."
+                    )
+                else:
+                    logger.info(f"Adding documents to existing database at {lancedb_uri}.")
+
+        return lancedb_uri
+
+    @staticmethod
+    def _increment_lancedb_uri(base_uri: str) -> str:
+        """Increment the lancedb uri to create a new database."""
+        i = 1
+        while os.path.exists(f"{base_uri}_{i}"):
+            i += 1
+        return f"{base_uri}_{i}"
diff --git a/autollm/utils/document_reading.py b/autollm/utils/document_reading.py
index f6685cb3..888899dc 100644
--- a/autollm/utils/document_reading.py
+++ b/autollm/utils/document_reading.py
@@ -1,12 +1,11 @@
-import os
 import shutil
-import stat
 from pathlib import Path
-from typing import Callable, List, Optional, Sequence, Tuple
+from typing import List, Optional, Sequence
 
 from llama_index.readers.file.base import SimpleDirectoryReader
 from llama_index.schema import Document
 
+from autollm.utils.env_utils import on_rm_error
 from autollm.utils.git_utils import clone_or_pull_repository
 from autollm.utils.logging import logger
 from autollm.utils.markdown_reader import MarkdownReader
@@ -65,20 +64,6 @@ def read_files_as_documents(
     return documents
 
 
-# From http://stackoverflow.com/a/4829285/548792
-def on_rm_error(func: Callable, path: str, exc_info: Tuple):
-    """
-    Error handler for `shutil.rmtree` to handle permission errors.
-
-    Parameters:
-        func (Callable): The function that raised the error.
-        path (str): The path to the file or directory which couldn't be removed.
-        exc_info (Tuple): Exception information returned by sys.exc_info().
-    """
-    os.chmod(path, stat.S_IWRITE)
-    os.unlink(path)
-
-
 def read_github_repo_as_documents(
         git_repo_url: str,
         relative_folder_path: Optional[str] = None,
diff --git a/autollm/utils/env_utils.py b/autollm/utils/env_utils.py
index 5499c9a1..60f43c7a 100644
--- a/autollm/utils/env_utils.py
+++ b/autollm/utils/env_utils.py
@@ -1,5 +1,7 @@
 import os
+import stat
 from pathlib import Path
+from typing import Callable, Tuple
 
 import yaml
 from dotenv import load_dotenv
@@ -55,3 +57,17 @@ def load_config_and_dotenv(config_file_path: str, env_file_path: str = None) ->
         config = yaml.safe_load(f)
 
     return config
+
+
+# From http://stackoverflow.com/a/4829285/548792
+def on_rm_error(func: Callable, path: str, exc_info: Tuple):
+    """
+    Error handler for `shutil.rmtree` to handle permission errors.
+
+    Parameters:
+        func (Callable): The function that raised the error.
+        path (str): The path to the file or directory which couldn't be removed.
+        exc_info (Tuple): Exception information returned by sys.exc_info().
+    """
+    os.chmod(path, stat.S_IWRITE)
+    os.unlink(path)