Lancedb Integration (#1411)

2024-06-21 21:29:22 +05:30
parent f6ddd5ffc5
commit 48b24f6f12
16 changed files with 963 additions and 12 deletions
--- a/docs/components/vector-databases/lancedb.mdx
+++ b/docs/components/vector-databases/lancedb.mdx
@@ -0,0 +1,48 @@
+---
+title: LanceDB
+---
+
+## Install Embedchain with LanceDB
+
+Install Embedchain, LanceDB and  related dependencies using the following command:
+
+```bash
+pip install "embedchain[lancedb]"
+```
+
+LanceDB is a developer-friendly, open source database for AI. From hyper scalable vector search and advanced retrieval for RAG, to streaming training data and interactive exploration of large scale AI datasets.
+In order to use LanceDB as vector database, not need to set any key for local use. 
+
+<CodeGroup>
+```python main.py
+import os
+from embedchain import App
+
+# set OPENAI_API_KEY as env variable
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+# Create Embedchain App and set config
+app = App.from_config(config={
+    "vectordb": {
+        "provider": "lancedb",
+            "config": {
+                "collection_name": "lancedb-index"
+            }
+        }
+    }
+)
+
+# Add data source and start queryin
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# query continuously
+while(True):
+    question = input("Enter question: ")
+    if question in ['q', 'exit', 'quit']:
+        break
+    answer = app.query(question)
+    print(answer)
+```
+
+</CodeGroup>
+<Snippet file="missing-vector-db-tip.mdx" />
--- a/embedchain/config/vectordb/lancedb.py
+++ b/embedchain/config/vectordb/lancedb.py
@@ -0,0 +1,33 @@
+from typing import Optional
+
+from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class LanceDBConfig(BaseVectorDbConfig):
+    def __init__(
+        self,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        host: Optional[str] = None,
+        port: Optional[str] = None,
+        allow_reset=True,
+    ):
+        """
+        Initializes a configuration class instance for LanceDB.
+
+        :param collection_name: Default name for the collection, defaults to None
+        :type collection_name: Optional[str], optional
+        :param dir: Path to the database directory, where the database is stored, defaults to None
+        :type dir: Optional[str], optional
+        :param host: Database connection remote host. Use this if you run Embedchain as a client, defaults to None
+        :type host: Optional[str], optional
+        :param port: Database connection remote port. Use this if you run Embedchain as a client, defaults to None
+        :type port: Optional[str], optional
+        :param allow_reset: Resets the database. defaults to False
+        :type allow_reset: bool
+        """
+
+        self.allow_reset = allow_reset
+        super().__init__(collection_name=collection_name, dir=dir, host=host, port=port)
--- a/embedchain/embedchain.py
+++ b/embedchain/embedchain.py
@@ -6,7 +6,9 @@ from typing import Any, Optional, Union
 from dotenv import load_dotenv
 from langchain.docstore.document import Document

-from embedchain.cache import adapt, get_gptcache_session, gptcache_data_convert, gptcache_update_cache_callback
+from embedchain.cache import (adapt, get_gptcache_session,
+                              gptcache_data_convert,
+                              gptcache_update_cache_callback)
 from embedchain.chunkers.base_chunker import BaseChunker
 from embedchain.config import AddConfig, BaseLlmConfig, ChunkerConfig
 from embedchain.config.base_app_config import BaseAppConfig
@@ -16,7 +18,8 @@ from embedchain.embedder.base import BaseEmbedder
 from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.llm.base import BaseLlm
 from embedchain.loaders.base_loader import BaseLoader
-from embedchain.models.data_type import DataType, DirectDataType, IndirectDataType, SpecialDataType
+from embedchain.models.data_type import (DataType, DirectDataType,
+                                         IndirectDataType, SpecialDataType)
 from embedchain.utils.misc import detect_datatype, is_valid_json_string
 from embedchain.vectordb.base import BaseVectorDB

--- a/embedchain/factory.py
+++ b/embedchain/factory.py
@@ -91,6 +91,7 @@ class VectorDBFactory:
        "chroma": "embedchain.vectordb.chroma.ChromaDB",
        "elasticsearch": "embedchain.vectordb.elasticsearch.ElasticsearchDB",
        "opensearch": "embedchain.vectordb.opensearch.OpenSearchDB",
+        "lancedb": "embedchain.vectordb.lancedb.LanceDB",
        "pinecone": "embedchain.vectordb.pinecone.PineconeDB",
        "qdrant": "embedchain.vectordb.qdrant.QdrantDB",
        "weaviate": "embedchain.vectordb.weaviate.WeaviateDB",
@@ -100,6 +101,7 @@ class VectorDBFactory:
        "chroma": "embedchain.config.vectordb.chroma.ChromaDbConfig",
        "elasticsearch": "embedchain.config.vectordb.elasticsearch.ElasticsearchDBConfig",
        "opensearch": "embedchain.config.vectordb.opensearch.OpenSearchDBConfig",
+        "lancedb": "embedchain.config.vectordb.lancedb.LanceDBConfig",
        "pinecone": "embedchain.config.vectordb.pinecone.PineconeDBConfig",
        "qdrant": "embedchain.config.vectordb.qdrant.QdrantDBConfig",
        "weaviate": "embedchain.config.vectordb.weaviate.WeaviateDBConfig",
--- a/embedchain/llm/base.py
+++ b/embedchain/llm/base.py
@@ -5,7 +5,9 @@ from typing import Any, Optional
 from langchain.schema import BaseMessage as LCBaseMessage

 from embedchain.config import BaseLlmConfig
-from embedchain.config.llm.base import DEFAULT_PROMPT, DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE, DOCS_SITE_PROMPT_TEMPLATE
+from embedchain.config.llm.base import (DEFAULT_PROMPT,
+                                        DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE,
+                                        DOCS_SITE_PROMPT_TEMPLATE)
 from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.memory.base import ChatHistory
 from embedchain.memory.message import ChatMessage
--- a/embedchain/llm/jina.py
+++ b/embedchain/llm/jina.py
@@ -35,7 +35,8 @@ class JinaLlm(BaseLlm):
        if config.top_p:
            kwargs["model_kwargs"]["top_p"] = config.top_p
        if config.stream:
-            from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+            from langchain.callbacks.streaming_stdout import \
+                StreamingStdOutCallbackHandler

            chat = JinaChat(**kwargs, streaming=config.stream, callbacks=[StreamingStdOutCallbackHandler()])
        else:
--- a/embedchain/loaders/audio.py
+++ b/embedchain/loaders/audio.py
@@ -1,6 +1,8 @@
-import os
 import hashlib
+import os
+
 import validators
+
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader

--- a/embedchain/loaders/unstructured_file.py
+++ b/embedchain/loaders/unstructured_file.py
@@ -11,7 +11,8 @@ class UnstructuredLoader(BaseLoader):
        """Load data from an Unstructured file."""
        try:
            import unstructured  # noqa: F401
-            from langchain_community.document_loaders import UnstructuredFileLoader
+            from langchain_community.document_loaders import \
+                UnstructuredFileLoader
        except ImportError:
            raise ImportError(
                'Unstructured file requires extra dependencies. Install with `pip install "unstructured[local-inference, all-docs]"`'  # noqa: E501
--- a/embedchain/utils/misc.py
+++ b/embedchain/utils/misc.py
@@ -446,7 +446,7 @@ def validate_config(config_data):
            },
            Optional("vectordb"): {
                Optional("provider"): Or(
-                    "chroma", "elasticsearch", "opensearch", "pinecone", "qdrant", "weaviate", "zilliz"
+                    "chroma", "elasticsearch", "opensearch", "lancedb", "pinecone", "qdrant", "weaviate", "zilliz"
                ),
                Optional("config"): object,  # TODO: add particular config schema for each provider
            },
--- a/embedchain/vectordb/lancedb.py
+++ b/embedchain/vectordb/lancedb.py
@@ -0,0 +1,307 @@
+from typing import Any, Dict, List, Optional, Union
+
+import pyarrow as pa
+
+try:
+    import lancedb
+except ImportError:
+    raise ImportError('LanceDB is required. Install with pip install "embedchain[lancedb]"') from None
+
+from embedchain.config.vectordb.lancedb import LanceDBConfig
+from embedchain.helpers.json_serializable import register_deserializable
+from embedchain.vectordb.base import BaseVectorDB
+
+
+@register_deserializable
+class LanceDB(BaseVectorDB):
+    """
+    LanceDB as vector database
+    """
+
+    BATCH_SIZE = 100
+
+    def __init__(
+        self,
+        config: Optional[LanceDBConfig] = None,
+    ):
+        """LanceDB as vector database.
+
+        :param config: LanceDB database config, defaults to None
+        :type config: LanceDBConfig, optional
+        """
+        if config:
+            self.config = config
+        else:
+            self.config = LanceDBConfig()
+
+        self.client = lancedb.connect(self.config.dir or "~/.lancedb")
+        self.embedder_check = True
+
+        super().__init__(config=self.config)
+
+    def _initialize(self):
+        """
+        This method is needed because `embedder` attribute needs to be set externally before it can be initialized.
+        """
+        if not self.embedder:
+            raise ValueError(
+                "Embedder not set. Please set an embedder with `_set_embedder()` function before initialization."
+            )
+        else:
+            # check embedder function is working or not
+            try:
+                self.embedder.embedding_fn("Hello LanceDB")
+            except Exception:
+                self.embedder_check = False
+
+        self._get_or_create_collection(self.config.collection_name)
+
+    def _get_or_create_db(self):
+        """
+        Called during initialization
+        """
+        return self.client
+
+    def _generate_where_clause(self, where: Dict[str, any]) -> str:
+        """
+        This method generate where clause using dictionary containing attributes and their values
+        """
+
+        where_filters = ""
+
+        if len(list(where.keys())) == 1:
+            where_filters = f"{list(where.keys())[0]} = {list(where.values())[0]}"
+            return where_filters
+
+        where_items = list(where.items())
+        where_count = len(where_items)
+
+        for i, (key, value) in enumerate(where_items, start=1):
+            condition = f"{key} = {value} AND "
+            where_filters += condition
+
+            if i == where_count:
+                condition = f"{key} = {value}"
+                where_filters += condition
+
+        return where_filters
+
+    def _get_or_create_collection(self, table_name: str, reset=False):
+        """
+        Get or create a named collection.
+
+        :param name: Name of the collection
+        :type name: str
+        :return: Created collection
+        :rtype: Collection
+        """
+        if not self.embedder_check:
+            schema = pa.schema(
+                [
+                    pa.field("doc", pa.string()),
+                    pa.field("metadata", pa.string()),
+                    pa.field("id", pa.string()),
+                ]
+            )
+
+        else:
+            schema = pa.schema(
+                [
+                    pa.field("vector", pa.list_(pa.float32(), list_size=self.embedder.vector_dimension)),
+                    pa.field("doc", pa.string()),
+                    pa.field("metadata", pa.string()),
+                    pa.field("id", pa.string()),
+                ]
+            )
+
+        if not reset:
+            if table_name not in self.client.table_names():
+                self.collection = self.client.create_table(table_name, schema=schema)
+
+        else:
+            self.client.drop_table(table_name)
+            self.collection = self.client.create_table(table_name, schema=schema)
+
+        self.collection = self.client[table_name]
+
+        return self.collection
+
+    def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
+        """
+        Get existing doc ids present in vector database
+
+        :param ids: list of doc ids to check for existence
+        :type ids: List[str]
+        :param where: Optional. to filter data
+        :type where: Dict[str, Any]
+        :param limit: Optional. maximum number of documents
+        :type limit: Optional[int]
+        :return: Existing documents.
+        :rtype: List[str]
+        """
+        if limit is not None:
+            max_limit = limit
+        else:
+            max_limit = 3
+        results = {"ids": [], "metadatas": []}
+
+        where_clause = {}
+        if where:
+            where_clause = self._generate_where_clause(where)
+
+        if ids is not None:
+            records = (
+                self.collection.to_lance().scanner(filter=f"id IN {tuple(ids)}", columns=["id"]).to_table().to_pydict()
+            )
+            for id in records["id"]:
+                if where is not None:
+                    result = (
+                        self.collection.search(query=id, vector_column_name="id")
+                        .where(where_clause)
+                        .limit(max_limit)
+                        .to_list()
+                    )
+                else:
+                    result = self.collection.search(query=id, vector_column_name="id").limit(max_limit).to_list()
+                results["ids"] = [r["id"] for r in result]
+                results["metadatas"] = [r["metadata"] for r in result]
+
+        return results
+
+    def add(
+        self,
+        documents: List[str],
+        metadatas: List[object],
+        ids: List[str],
+    ) -> Any:
+        """
+        Add vectors to lancedb database
+
+        :param documents: Documents
+        :type documents: List[str]
+        :param metadatas: Metadatas
+        :type metadatas: List[object]
+        :param ids: ids
+        :type ids: List[str]
+        """
+        data = []
+        to_ingest = list(zip(documents, metadatas, ids))
+
+        if not self.embedder_check:
+            for doc, meta, id in to_ingest:
+                temp = {}
+                temp["doc"] = doc
+                temp["metadata"] = str(meta)
+                temp["id"] = id
+                data.append(temp)
+        else:
+            for doc, meta, id in to_ingest:
+                temp = {}
+                temp["doc"] = doc
+                temp["vector"] = self.embedder.embedding_fn([doc])[0]
+                temp["metadata"] = str(meta)
+                temp["id"] = id
+                data.append(temp)
+
+        self.collection.add(data=data)
+
+    def _format_result(self, results) -> list:
+        """
+        Format LanceDB results
+
+        :param results: LanceDB query results to format.
+        :type results: QueryResult
+        :return: Formatted results
+        :rtype: list[tuple[Document, float]]
+        """
+        return results.tolist()
+
+    def query(
+        self,
+        input_query: str,
+        n_results: int = 3,
+        where: Optional[dict[str, any]] = None,
+        raw_filter: Optional[dict[str, any]] = None,
+        citations: bool = False,
+        **kwargs: Optional[dict[str, any]],
+    ) -> Union[list[tuple[str, dict]], list[str]]:
+        """
+        Query contents from vector database based on vector similarity
+
+        :param input_query: query string
+        :type input_query: str
+        :param n_results: no of similar documents to fetch from database
+        :type n_results: int
+        :param where: to filter data
+        :type where: dict[str, Any]
+        :param raw_filter: Raw filter to apply
+        :type raw_filter: dict[str, Any]
+        :param citations: we use citations boolean param to return context along with the answer.
+        :type citations: bool, default is False.
+        :raises InvalidDimensionException: Dimensions do not match.
+        :return: The content of the document that matched your query,
+        along with url of the source and doc_id (if citations flag is true)
+        :rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
+        """
+        if where and raw_filter:
+            raise ValueError("Both `where` and `raw_filter` cannot be used together.")
+        try:
+            query_embedding = self.embedder.embedding_fn(input_query)[0]
+            result = self.collection.search(query_embedding).limit(n_results).to_list()
+        except Exception as e:
+            e.message()
+
+        results_formatted = result
+
+        contexts = []
+        for result in results_formatted:
+            if citations:
+                metadata = result["metadata"]
+                contexts.append((result["doc"], metadata))
+            else:
+                contexts.append(result["doc"])
+        return contexts
+
+    def set_collection_name(self, name: str):
+        """
+        Set the name of the collection. A collection is an isolated space for vectors.
+
+        :param name: Name of the collection.
+        :type name: str
+        """
+        if not isinstance(name, str):
+            raise TypeError("Collection name must be a string")
+        self.config.collection_name = name
+        self._get_or_create_collection(self.config.collection_name)
+
+    def count(self) -> int:
+        """
+        Count number of documents/chunks embedded in the database.
+
+        :return: number of documents
+        :rtype: int
+        """
+        return self.collection.count_rows()
+
+    def delete(self, where):
+        return self.collection.delete(where=where)
+
+    def reset(self):
+        """
+        Resets the database. Deletes all embeddings irreversibly.
+        """
+        # Delete all data from the collection and recreate collection
+        if self.config.allow_reset:
+            try:
+                self._get_or_create_collection(self.config.collection_name, reset=True)
+            except ValueError:
+                raise ValueError(
+                    "For safety reasons, resetting is disabled. "
+                    "Please enable it by setting `allow_reset=True` in your LanceDbConfig"
+                ) from None
+        # Recreate
+        else:
+            print(
+                "For safety reasons, resetting is disabled. "
+                "Please enable it by setting `allow_reset=True` in your LanceDbConfig"
+            )
--- a/notebooks/lancedb.ipynb
+++ b/notebooks/lancedb.ipynb
@@ -0,0 +1,146 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b02n_zJ_hl3d"
+      },
+      "source": [
+        "## Cookbook for using LanceDB with Embedchain"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gyJ6ui2vhtMY"
+      },
+      "source": [
+        "### Step-1: Install embedchain package"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-NbXjAdlh0vJ"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install embedchain lancedb"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nGnpSYAAh2bQ"
+      },
+      "source": [
+        "### Step-2: Set environment variables needed for LanceDB\n",
+        "\n",
+        "You can find this env variable on your [OpenAI](https://platform.openai.com/account/api-keys)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0fBdQ9GAiRvK"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from embedchain import App\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = \"sk-xxx\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PGt6uPLIi1CS"
+      },
+      "source": [
+        "### Step-3 Create embedchain app and define your config"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Amzxk3m-i3tD"
+      },
+      "outputs": [],
+      "source": [
+        "app = App.from_config(config={\n",
+        "    \"vectordb\": {\n",
+        "        \"provider\": \"lancedb\",\n",
+        "            \"config\": {\n",
+        "                \"collection_name\": \"lancedb-index\"\n",
+        "            }\n",
+        "        }\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XNXv4yZwi7ef"
+      },
+      "source": [
+        "### Step-4: Add data sources to your app"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Sn_0rx9QjIY9"
+      },
+      "outputs": [],
+      "source": [
+        "app.add(\"https://www.forbes.com/profile/elon-musk\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_7W6fDeAjMAP"
+      },
+      "source": [
+        "### Step-5: All set. Now start asking questions related to your data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cvIK7dWRjN_f"
+      },
+      "outputs": [],
+      "source": [
+        "while(True):\n",
+        "    question = input(\"Enter question: \")\n",
+        "    if question in ['q', 'exit', 'quit']:\n",
+        "        break\n",
+        "    answer = app.query(question)\n",
+        "    print(answer)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.11.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.

 [[package]]
 name = "aiohttp"
@@ -1142,6 +1142,17 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"

+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+optional = true
+python-versions = ">=3.5"
+files = [
+    {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
+    {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
+]
+
 [[package]]
 name = "deprecated"
 version = "1.2.14"
@@ -1159,6 +1170,20 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]

+[[package]]
+name = "deprecation"
+version = "2.1.0"
+description = "A library to handle automated deprecations"
+optional = true
+python-versions = "*"
+files = [
+    {file = "deprecation-2.1.0-py2.py3-none-any.whl", hash = "sha256:a10811591210e1fb0e768a8c25517cabeabcba6f0bf96564f8ff45189f90b14a"},
+    {file = "deprecation-2.1.0.tar.gz", hash = "sha256:72b3bde64e5d778694b0cf68178aed03d15e15477116add3fb773e581f9518ff"},
+]
+
+[package.dependencies]
+packaging = "*"
+
 [[package]]
 name = "discord"
 version = "2.3.2"
@@ -2785,6 +2810,42 @@ websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
 [package.extras]
 adal = ["adal (>=1.0.2)"]

+[[package]]
+name = "lancedb"
+version = "0.6.13"
+description = "lancedb"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "lancedb-0.6.13-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:4667353ca7fa187e94cb0ca4c5f9577d65eb5160f6f3fe9e57902d86312c3869"},
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+]
+
+[package.dependencies]
+attrs = ">=21.3.0"
+cachetools = "*"
+deprecation = "*"
+overrides = ">=0.7"
+pydantic = ">=1.10"
+pylance = "0.10.12"
+ratelimiter = ">=1.0,<2.0"
+requests = ">=2.31.0"
+retry = ">=0.9.2"
+semver = "*"
+tqdm = ">=4.27.0"
+
+[package.extras]
+azure = ["adlfs (>=2024.2.0)"]
+clip = ["open-clip", "pillow", "torch"]
+dev = ["pre-commit", "ruff"]
+docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
+embeddings = ["awscli (>=1.29.57)", "boto3 (>=1.28.57)", "botocore (>=1.31.57)", "cohere", "google-generativeai", "huggingface-hub", "instructorembedding", "open-clip-torch", "openai (>=1.6.1)", "pillow", "sentence-transformers", "torch"]
+tests = ["aiohttp", "boto3", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "pytest-asyncio", "pytest-mock", "pytz", "tantivy"]
+
 [[package]]
 name = "langchain"
 version = "0.1.20"
@@ -4781,6 +4842,65 @@ all = ["apache-bookkeeper-client (>=4.16.1)", "fastavro (>=1.9.2)", "grpcio (>=1
 avro = ["fastavro (>=1.9.2)"]
 functions = ["apache-bookkeeper-client (>=4.16.1)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]

+[[package]]
+name = "py"
+version = "1.11.0"
+description = "library with cross-python path, ini-parsing, io, code, log facilities"
+optional = true
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"},
+    {file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"},
+]
+
+[[package]]
+name = "pyarrow"
+version = "15.0.0"
+description = "Python library for Apache Arrow"
+optional = true
+python-versions = ">=3.8"
+files = [
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+    {file = "pyarrow-15.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:001fca027738c5f6be0b7a3159cc7ba16a5c52486db18160909a0831b063c4e4"},
+    {file = "pyarrow-15.0.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:d1c48648f64aec09accf44140dccb92f4f94394b8d79976c426a5b79b11d4fa7"},
+    {file = "pyarrow-15.0.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:972a0141be402bb18e3201448c8ae62958c9c7923dfaa3b3d4530c835ac81aed"},
+    {file = "pyarrow-15.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:f01fc5cf49081426429127aa2d427d9d98e1cb94a32cb961d583a70b7c4504e6"},
+    {file = "pyarrow-15.0.0.tar.gz", hash = "sha256:876858f549d540898f927eba4ef77cd549ad8d24baa3207cf1b72e5788b50e83"},
+]
+
+[package.dependencies]
+numpy = ">=1.16.6,<2"
+
 [[package]]
 name = "pyasn1"
 version = "0.6.0"
@@ -5019,6 +5139,32 @@ dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pyte
 docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
 tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]

+[[package]]
+name = "pylance"
+version = "0.10.12"
+description = "python wrapper for Lance columnar format"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "pylance-0.10.12-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:30cbcca078edeb37e11ae86cf9287d81ce6c0c07ba77239284b369a4b361497b"},
+    {file = "pylance-0.10.12-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:e558163ff6035d518706cc66848497219ccc755e2972b8f3b1706a3e1fd800fd"},
+    {file = "pylance-0.10.12-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75afb39f71d7f12429f9b4d380eb6cf6aed179ae5a1c5d16cc768373a1521f87"},
+    {file = "pylance-0.10.12-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:3de391dfc3a99bdb245fd1e27ef242be769a94853f802ef57f246e9a21358d32"},
+    {file = "pylance-0.10.12-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:34a5278b90f4cbcf21261353976127aa2ffbbd7d068810f0a2b0c1aa0334022a"},
+    {file = "pylance-0.10.12-cp38-abi3-win_amd64.whl", hash = "sha256:6cef5975d513097fd2c22692296c9a5a138928f38d02cd34ab63a7369abc1463"},
+]
+
+[package.dependencies]
+numpy = ">=1.22"
+pyarrow = ">=12,<15.0.1"
+
+[package.extras]
+benchmarks = ["pytest-benchmark"]
+dev = ["ruff (==0.2.2)"]
+ray = ["ray[data]"]
+tests = ["boto3", "datasets", "duckdb", "h5py (<3.11)", "ml-dtypes", "pandas", "pillow", "polars[pandas,pyarrow]", "pytest", "tensorflow", "tqdm"]
+torch = ["torch"]
+
 [[package]]
 name = "pymilvus"
 version = "2.4.3"
@@ -5540,6 +5686,20 @@ urllib3 = ">=1.26.14,<3"
 [package.extras]
 fastembed = ["fastembed (==0.2.6)"]

+[[package]]
+name = "ratelimiter"
+version = "1.2.0.post0"
+description = "Simple python rate limiting object"
+optional = true
+python-versions = "*"
+files = [
+    {file = "ratelimiter-1.2.0.post0-py3-none-any.whl", hash = "sha256:a52be07bc0bb0b3674b4b304550f10c769bbb00fead3072e035904474259809f"},
+    {file = "ratelimiter-1.2.0.post0.tar.gz", hash = "sha256:5c395dcabdbbde2e5178ef3f89b568a3066454a6ddc223b76473dac22f89b4f7"},
+]
+
+[package.extras]
+test = ["pytest (>=3.0)", "pytest-asyncio"]
+
 [[package]]
 name = "regex"
 version = "2024.5.15"
@@ -5720,6 +5880,21 @@ urllib3 = ">=1.25.10,<3.0"
 [package.extras]
 tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=7.0.0)", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "tomli", "tomli-w", "types-requests"]

+[[package]]
+name = "retry"
+version = "0.9.2"
+description = "Easy to use retry decorator."
+optional = true
+python-versions = "*"
+files = [
+    {file = "retry-0.9.2-py2.py3-none-any.whl", hash = "sha256:ccddf89761fa2c726ab29391837d4327f819ea14d244c232a1d24c67a2f98606"},
+    {file = "retry-0.9.2.tar.gz", hash = "sha256:f8bfa8b99b69c4506d6f5bd3b0aabf77f98cdb17f3c9fc3f5ca820033336fba4"},
+]
+
+[package.dependencies]
+decorator = ">=3.4.2"
+py = ">=1.4.26,<2.0.0"
+
 [[package]]
 name = "rich"
 version = "13.7.1"
@@ -6018,6 +6193,17 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pyde
 doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
 test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]

+[[package]]
+name = "semver"
+version = "3.0.2"
+description = "Python helper for Semantic Versioning (https://semver.org)"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "semver-3.0.2-py3-none-any.whl", hash = "sha256:b1ea4686fe70b981f85359eda33199d60c53964284e0cfb4977d243e37cf4bf4"},
+    {file = "semver-3.0.2.tar.gz", hash = "sha256:6253adb39c70f6e51afed2fa7152bcd414c411286088fb4b9effb133885ab4cc"},
+]
+
 [[package]]
 name = "sentence-transformers"
 version = "2.7.0"
@@ -7716,6 +7902,7 @@ gmail = ["google-api-core", "google-api-python-client", "google-auth", "google-a
 google = ["google-generativeai"]
 googledrive = ["google-api-python-client", "google-auth-httplib2", "google-auth-oauthlib"]
 huggingface-hub = ["huggingface_hub"]
+lancedb = ["lancedb"]
 llama2 = ["replicate"]
 milvus = ["pymilvus"]
 mistralai = ["langchain-mistralai"]
@@ -7738,4 +7925,4 @@ youtube = ["youtube-transcript-api", "yt_dlp"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<=3.13"
-content-hash = "cb7d2794bc2f54e05b2f870843eccc4342f3f2a6531eaa50a3d0d77b358ac4d5"
+content-hash = "f9e6357bd1b5f407368d3d52c3f728e12f41fe7d4836e321ec2e10413f58e8a1"
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -122,6 +122,7 @@ slack-sdk = { version = "3.21.3", optional = true }
 clarifai = { version = "^10.0.1", optional = true }
 cohere = { version = "^5.3", optional = true }
 together = { version = "^0.2.8", optional = true }
+lancedb = { version = "^0.6.2", optional = true }
 weaviate-client = { version = "^3.24.1", optional = true }
 docx2txt = { version = "^0.8", optional = true }
 qdrant-client = { version = "^1.6.3", optional = true }
@@ -173,6 +174,7 @@ pytest-asyncio = "^0.21.1"
 [tool.poetry.extras]
 streamlit = ["streamlit"]
 opensource = ["sentence-transformers", "torch", "gpt4all"]
+lancedb = ["lancedb"]
 elasticsearch = ["elasticsearch"]
 opensearch = ["opensearch-py"]
 poe = ["fastapi-poe"]
--- a/tests/chunkers/test_chunkers.py
+++ b/tests/chunkers/test_chunkers.py
@@ -1,3 +1,4 @@
+from embedchain.chunkers.audio import AudioChunker
 from embedchain.chunkers.common_chunker import CommonChunker
 from embedchain.chunkers.discourse import DiscourseChunker
 from embedchain.chunkers.docs_site import DocsSiteChunker
@@ -19,7 +20,6 @@ from embedchain.chunkers.text import TextChunker
 from embedchain.chunkers.web_page import WebPageChunker
 from embedchain.chunkers.xml import XmlChunker
 from embedchain.chunkers.youtube_video import YoutubeVideoChunker
-from embedchain.chunkers.audio import AudioChunker
 from embedchain.config.add_config import ChunkerConfig

 chunker_config = ChunkerConfig(chunk_size=500, chunk_overlap=0, length_function=len)
--- a/tests/loaders/test_audio.py
+++ b/tests/loaders/test_audio.py
@@ -1,11 +1,13 @@
+import hashlib
 import os
 import sys
-import hashlib
-import pytest
 from unittest.mock import mock_open, patch

+import pytest
+
 if sys.version_info > (3, 10):  # as `match` statement was introduced in python 3.10
    from deepgram import PrerecordedOptions
+
    from embedchain.loaders.audio import AudioLoader


--- a/tests/vectordb/test_lancedb.py
+++ b/tests/vectordb/test_lancedb.py
@@ -0,0 +1,215 @@
+import os
+import shutil
+
+import pytest
+
+from embedchain import App
+from embedchain.config import AppConfig
+from embedchain.config.vectordb.lancedb import LanceDBConfig
+from embedchain.vectordb.lancedb import LanceDB
+
+os.environ["OPENAI_API_KEY"] = "test-api-key"
+
+
+@pytest.fixture
+def lancedb():
+    return LanceDB(config=LanceDBConfig(dir="test-db", collection_name="test-coll"))
+
+
+@pytest.fixture
+def app_with_settings():
+    lancedb_config = LanceDBConfig(allow_reset=True, dir="test-db-reset")
+    lancedb = LanceDB(config=lancedb_config)
+    app_config = AppConfig(collect_metrics=False)
+    return App(config=app_config, db=lancedb)
+
+
+@pytest.fixture(scope="session", autouse=True)
+def cleanup_db():
+    yield
+    try:
+        shutil.rmtree("test-db.lance")
+        shutil.rmtree("test-db-reset.lance")
+    except OSError as e:
+        print("Error: %s - %s." % (e.filename, e.strerror))
+
+
+def test_lancedb_duplicates_throw_warning(caplog):
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    app.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+    app.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+    assert "Insert of existing doc ID: 0" not in caplog.text
+    assert "Add of existing doc ID: 0" not in caplog.text
+    app.db.reset()
+
+
+def test_lancedb_duplicates_collections_no_warning(caplog):
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    app.set_collection_name("test_collection_1")
+    app.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+    app.set_collection_name("test_collection_2")
+    app.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+    assert "Insert of existing doc ID: 0" not in caplog.text
+    assert "Add of existing doc ID: 0" not in caplog.text
+    app.db.reset()
+    app.set_collection_name("test_collection_1")
+    app.db.reset()
+
+
+def test_lancedb_collection_init_with_default_collection():
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    assert app.db.collection.name == "embedchain_store"
+
+
+def test_lancedb_collection_init_with_custom_collection():
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    app.set_collection_name(name="test_collection")
+    assert app.db.collection.name == "test_collection"
+
+
+def test_lancedb_collection_set_collection_name():
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    app.set_collection_name("test_collection")
+    assert app.db.collection.name == "test_collection"
+
+
+def test_lancedb_collection_changes_encapsulated():
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    app.set_collection_name("test_collection_1")
+    assert app.db.count() == 0
+    app.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+    assert app.db.count() == 1
+
+    app.set_collection_name("test_collection_2")
+    assert app.db.count() == 0
+
+    app.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+    app.set_collection_name("test_collection_1")
+    assert app.db.count() == 1
+    app.db.reset()
+    app.set_collection_name("test_collection_2")
+    app.db.reset()
+
+
+def test_lancedb_collection_collections_are_persistent():
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    app.set_collection_name("test_collection_1")
+    app.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+    del app
+
+    db = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app = App(config=AppConfig(collect_metrics=False), db=db)
+    app.set_collection_name("test_collection_1")
+    assert app.db.count() == 1
+
+    app.db.reset()
+
+
+def test_lancedb_collection_parallel_collections():
+    db1 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db", collection_name="test_collection_1"))
+    app1 = App(
+        config=AppConfig(collect_metrics=False),
+        db=db1,
+    )
+    db2 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db", collection_name="test_collection_2"))
+    app2 = App(
+        config=AppConfig(collect_metrics=False),
+        db=db2,
+    )
+
+    # cleanup if any previous tests failed or were interrupted
+    app1.db.reset()
+    app2.db.reset()
+
+    app1.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+
+    assert app1.db.count() == 1
+    assert app2.db.count() == 0
+
+    app1.db.add(ids=["1", "2"], documents=["doc1", "doc2"], metadatas=["test", "test"])
+    app2.db.add(ids=["0"], documents=["doc1"], metadatas=["test"])
+
+    app1.set_collection_name("test_collection_2")
+    assert app1.db.count() == 1
+    app2.set_collection_name("test_collection_1")
+    assert app2.db.count() == 3
+
+    # cleanup
+    app1.db.reset()
+    app2.db.reset()
+
+
+def test_lancedb_collection_ids_share_collections():
+    db1 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app1 = App(config=AppConfig(collect_metrics=False), db=db1)
+    app1.set_collection_name("one_collection")
+    db2 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app2 = App(config=AppConfig(collect_metrics=False), db=db2)
+    app2.set_collection_name("one_collection")
+
+    # cleanup
+    app1.db.reset()
+    app2.db.reset()
+
+    app1.db.add(ids=["0", "1"], documents=["doc1", "doc2"], metadatas=["test", "test"])
+    app2.db.add(ids=["2"], documents=["doc3"], metadatas=["test"])
+
+    assert app1.db.count() == 2
+    assert app2.db.count() == 3
+
+    # cleanup
+    app1.db.reset()
+    app2.db.reset()
+
+
+def test_lancedb_collection_reset():
+    db1 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app1 = App(config=AppConfig(collect_metrics=False), db=db1)
+    app1.set_collection_name("one_collection")
+    db2 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app2 = App(config=AppConfig(collect_metrics=False), db=db2)
+    app2.set_collection_name("two_collection")
+    db3 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app3 = App(config=AppConfig(collect_metrics=False), db=db3)
+    app3.set_collection_name("three_collection")
+    db4 = LanceDB(config=LanceDBConfig(allow_reset=True, dir="test-db"))
+    app4 = App(config=AppConfig(collect_metrics=False), db=db4)
+    app4.set_collection_name("four_collection")
+
+    # cleanup if any previous tests failed or were interrupted
+    app1.db.reset()
+    app2.db.reset()
+    app3.db.reset()
+    app4.db.reset()
+
+    app1.db.add(ids=["1"], documents=["doc1"], metadatas=["test"])
+    app2.db.add(ids=["2"], documents=["doc2"], metadatas=["test"])
+    app3.db.add(ids=["3"], documents=["doc3"], metadatas=["test"])
+    app4.db.add(ids=["4"], documents=["doc4"], metadatas=["test"])
+
+    app1.db.reset()
+
+    assert app1.db.count() == 0
+    assert app2.db.count() == 1
+    assert app3.db.count() == 1
+    assert app4.db.count() == 1
+
+    # cleanup
+    app2.db.reset()
+    app3.db.reset()
+    app4.db.reset()
+
+
+def generate_embeddings(dummy_embed, embed_size):
+    generated_embedding = []
+    for i in range(embed_size):
+        generated_embedding.append(dummy_embed)
+
+    return generated_embedding