[Feature] Improve github and youtube channel loader (#966)

Co-authored-by: Deven Patel <deven298@yahoo.com>

embedchain/chunkers/base_chunker.py

@@ -1,8 +1,5 @@
 import hashlib
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-from embedchain.config.add_config import ChunkerConfig
 from embedchain.helper.json_serializable import JSONSerializable
 from embedchain.models.data_type import DataType
 
@@ -10,15 +7,7 @@ from embedchain.models.data_type import DataType
 class BaseChunker(JSONSerializable):
     def __init__(self, text_splitter):
         """Initialize the chunker."""
-        if text_splitter is None:
-            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
-            self.text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size=config.chunk_size,
-                chunk_overlap=config.chunk_overlap,
-                length_function=config.length_function,
-            )
-        else:
-            self.text_splitter = text_splitter
+        self.text_splitter = text_splitter
         self.data_type = None
 
     def create_chunks(self, loader, src, app_id=None):

embedchain/chunkers/common_chunker.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helper.json_serializable import register_deserializable
+
+
+@register_deserializable
+class CommonChunker(BaseChunker):
+    """Common chunker for all loaders."""
+
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)

embedchain/data_formatter/data_formatter.py

@@ -116,8 +116,8 @@ class DataFormatter(JSONSerializable):
             DataType.SLACK: "embedchain.chunkers.slack.SlackChunker",
             DataType.DISCOURSE: "embedchain.chunkers.discourse.DiscourseChunker",
             DataType.SUBSTACK: "embedchain.chunkers.substack.SubstackChunker",
-            DataType.GITHUB: "embedchain.chunkers.base_chunker.BaseChunker",
-            DataType.YOUTUBE_CHANNEL: "embedchain.chunkers.base_chunker.BaseChunker",
+            DataType.GITHUB: "embedchain.chunkers.common_chunker.CommonChunker",
+            DataType.YOUTUBE_CHANNEL: "embedchain.chunkers.common_chunker.CommonChunker",
         }
 
         if data_type in chunker_classes:

embedchain/embedder/chroma_embeddings.py

@@ -1,104 +0,0 @@
-"""
-Note that this file is copied from Chroma repository. We will remove this file once the fix in
-ChromaDB's repository.
-"""
-
-from typing import Optional
-
-from chromadb.api.types import Documents, Embeddings
-
-
-class OpenAIEmbeddingFunction:
-    def __init__(
-        self,
-        api_key: Optional[str] = None,
-        model_name: str = "text-embedding-ada-002",
-        organization_id: Optional[str] = None,
-        api_base: Optional[str] = None,
-        api_type: Optional[str] = None,
-        api_version: Optional[str] = None,
-        deployment_id: Optional[str] = None,
-    ):
-        """
-        Initialize the OpenAIEmbeddingFunction.
-        Args:
-            api_key (str, optional): Your API key for the OpenAI API. If not
-                provided, it will raise an error to provide an OpenAI API key.
-            organization_id(str, optional): The OpenAI organization ID if applicable
-            model_name (str, optional): The name of the model to use for text
-                embeddings. Defaults to "text-embedding-ada-002".
-            api_base (str, optional): The base path for the API. If not provided,
-                it will use the base path for the OpenAI API. This can be used to
-                point to a different deployment, such as an Azure deployment.
-            api_type (str, optional): The type of the API deployment. This can be
-                used to specify a different deployment, such as 'azure'. If not
-                provided, it will use the default OpenAI deployment.
-            api_version (str, optional): The api version for the API. If not provided,
-                it will use the api version for the OpenAI API. This can be used to
-                point to a different deployment, such as an Azure deployment.
-            deployment_id (str, optional): Deployment ID for Azure OpenAI.
-
-        """
-        try:
-            import openai
-        except ImportError:
-            raise ValueError("The openai python package is not installed. Please install it with `pip install openai`")
-
-        if api_key is not None:
-            openai.api_key = api_key
-        # If the api key is still not set, raise an error
-        elif openai.api_key is None:
-            raise ValueError(
-                "Please provide an OpenAI API key. You can get one at https://platform.openai.com/account/api-keys"
-            )
-
-        if api_base is not None:
-            openai.api_base = api_base
-
-        if api_version is not None:
-            openai.api_version = api_version
-
-        self._api_type = api_type
-        if api_type is not None:
-            openai.api_type = api_type
-
-        if organization_id is not None:
-            openai.organization = organization_id
-
-        self._v1 = openai.__version__.startswith("1.")
-        if self._v1:
-            if api_type == "azure":
-                self._client = openai.AzureOpenAI(
-                    api_key=api_key, api_version=api_version, azure_endpoint=api_base
-                ).embeddings
-            else:
-                self._client = openai.OpenAI(api_key=api_key, base_url=api_base).embeddings
-        else:
-            self._client = openai.Embedding
-        self._model_name = model_name
-        self._deployment_id = deployment_id
-
-    def __call__(self, input: Documents) -> Embeddings:
-        # replace newlines, which can negatively affect performance.
-        input = [t.replace("\n", " ") for t in input]
-
-        # Call the OpenAI Embedding API
-        if self._v1:
-            embeddings = self._client.create(input=input, model=self._deployment_id or self._model_name).data
-
-            # Sort resulting embeddings by index
-            sorted_embeddings = sorted(embeddings, key=lambda e: e.index)  # type: ignore
-
-            # Return just the embeddings
-            return [result.embedding for result in sorted_embeddings]
-        else:
-            if self._api_type == "azure":
-                embeddings = self._client.create(input=input, engine=self._deployment_id or self._model_name)["data"]
-            else:
-                embeddings = self._client.create(input=input, model=self._model_name)["data"]
-
-            # Sort resulting embeddings by index
-            sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
-
-            # Return just the embeddings
-            return [result["embedding"] for result in sorted_embeddings]

embedchain/embedder/openai.py

@@ -1,18 +1,18 @@
 import os
 from typing import Optional
 
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
 from langchain.embeddings import OpenAIEmbeddings
 
 from embedchain.config import BaseEmbedderConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.models import VectorDimensions
 
-from .chroma_embeddings import OpenAIEmbeddingFunction
-
 
 class OpenAIEmbedder(BaseEmbedder):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config=config)
+
         if self.config.model is None:
             self.config.model = "text-embedding-ada-002"
 

embedchain/loaders/github.py

@@ -3,13 +3,40 @@ import hashlib
 import logging
 import os
 
+from tqdm import tqdm
+
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.loaders.json import JSONLoader
 from embedchain.loaders.mdx import MdxLoader
-from embedchain.loaders.unstructured_file import UnstructuredLoader
 from embedchain.utils import detect_datatype
 
 
+def _load_file_data(path):
+    data = []
+    data_content = []
+    try:
+        with open(path, "rb") as f:
+            content = f.read().decode("utf-8")
+    except Exception as e:
+        print(f"Error reading file {path}: {e}")
+        raise ValueError(f"Failed to read file {path}")
+
+    meta_data = {}
+    meta_data["url"] = path
+    data.append(
+        {
+            "content": content,
+            "meta_data": meta_data,
+        }
+    )
+    data_content.append(content)
+    doc_id = hashlib.sha256((" ".join(data_content) + path).encode()).hexdigest()
+    return {
+        "doc_id": doc_id,
+        "data": data,
+    }
+
+
 class GithubLoader(BaseLoader):
     def load_data(self, repo_url):
         """Load data from a git repo."""
@@ -22,7 +49,6 @@ class GithubLoader(BaseLoader):
 
         mdx_loader = MdxLoader()
         json_loader = JSONLoader()
-        unstructured_loader = UnstructuredLoader()
         data = []
         data_urls = []
 
@@ -49,18 +75,28 @@ class GithubLoader(BaseLoader):
             elif data_type == "json":
                 data = json_loader.load_data(file_path)
             else:
-                data = unstructured_loader.load_data(file_path)
+                data = _load_file_data(file_path)
 
             return data.get("data", [])
 
+        def _is_file_empty(file_path):
+            return os.path.getsize(file_path) == 0
+
+        def _is_whitelisted(file_path):
+            whitelisted_extensions = ["md", "txt", "html", "json", "py", "js", "jsx", "ts", "tsx", "mdx", "rst"]
+            _, file_extension = os.path.splitext(file_path)
+            return file_extension[1:] in whitelisted_extensions
+
         def _add_repo_files(repo_path: str):
-            with concurrent.futures.ThreadPoolExecutor() as executor:
+            with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
                 future_to_file = {
                     executor.submit(_load_file, os.path.join(root, filename)): os.path.join(root, filename)
                     for root, _, files in os.walk(repo_path)
                     for filename in files
-                }  # noqa: E501
-                for future in concurrent.futures.as_completed(future_to_file):
+                    if _is_whitelisted(os.path.join(root, filename))
+                    and not _is_file_empty(os.path.join(root, filename))  # noqa:E501
+                }
+                for future in tqdm(concurrent.futures.as_completed(future_to_file), total=len(future_to_file)):
                     file = future_to_file[future]
                     try:
                         results = future.result()
@@ -68,7 +104,7 @@ class GithubLoader(BaseLoader):
                             data.extend(results)
                             data_urls.extend([result.get("meta_data").get("url") for result in results])
                     except Exception as e:
-                        logging.error(f"Failed to process {file}: {e}")
+                        logging.warn(f"Failed to process {file}: {e}")
 
         source_hash = hashlib.sha256(repo_url.encode()).hexdigest()
         repo_path = f"/tmp/{source_hash}"

embedchain/loaders/sitemap.py

@@ -3,6 +3,7 @@ import hashlib
 import logging
 
 import requests
+from tqdm import tqdm
 
 try:
     from bs4 import BeautifulSoup
@@ -52,7 +53,7 @@ class SitemapLoader(BaseLoader):
 
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future_to_link = {executor.submit(load_link, link): link for link in links}
-            for future in concurrent.futures.as_completed(future_to_link):
+            for future in tqdm(concurrent.futures.as_completed(future_to_link), total=len(links), desc="Loading pages"):
                 link = future_to_link[future]
                 try:
                     data = future.result()

embedchain/loaders/substack.py

@@ -81,6 +81,6 @@ class SubstackLoader(BaseLoader):
             if data:
                 output.append({"content": data, "meta_data": {"url": link}})
             # TODO: allow users to configure this
-            time.sleep(0.4)  # added to avoid rate limiting
+            time.sleep(1.0)  # added to avoid rate limiting
 
         return {"doc_id": doc_id, "data": output}

embedchain/loaders/unstructured_file.py

@@ -1,11 +1,5 @@
 import hashlib
 
-try:
-    from langchain.document_loaders import UnstructuredFileLoader
-except ImportError:
-    raise ImportError(
-        'Unstructured file requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
-    ) from None
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
@@ -15,6 +9,13 @@ from embedchain.utils import clean_string
 class UnstructuredLoader(BaseLoader):
     def load_data(self, url):
         """Load data from a Unstructured file."""
+        try:
+            from langchain.document_loaders import UnstructuredFileLoader
+        except ImportError:
+            raise ImportError(
+                'Unstructured file requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'  # noqa: E501
+            ) from None
+
         loader = UnstructuredFileLoader(url)
         data = []
         all_content = []

embedchain/loaders/web_page.py

@@ -17,15 +17,17 @@ from embedchain.utils import clean_string
 
 @register_deserializable
 class WebPageLoader(BaseLoader):
+    # Shared session for all instances
+    _session = requests.Session()
+
     def load_data(self, url):
-        """Load data from a web page."""
-        response = requests.get(url)
+        """Load data from a web page using a shared requests session."""
+        response = self._session.get(url, timeout=30)
+        response.raise_for_status()
         data = response.content
         content = self._get_clean_content(data, url)
 
-        meta_data = {
-            "url": url,
-        }
+        meta_data = {"url": url}
 
         doc_id = hashlib.sha256((content + url).encode()).hexdigest()
         return {
@@ -86,3 +88,7 @@ class WebPageLoader(BaseLoader):
             )
 
         return content
+
+    @classmethod
+    def close_session(cls):
+        cls._session.close()

embedchain/loaders/youtube_channel.py

@@ -2,6 +2,8 @@ import concurrent.futures
 import hashlib
 import logging
 
+from tqdm import tqdm
+
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.loaders.youtube_video import YoutubeVideoLoader
 
@@ -48,11 +50,16 @@ class YoutubeChannelLoader(BaseLoader):
 
         def _add_youtube_channel():
             video_links = _get_yt_video_links()
+            logging.info("Loading videos from youtube channel...")
             with concurrent.futures.ThreadPoolExecutor() as executor:
+                # Submitting all tasks and storing the future object with the video link
                 future_to_video = {
                     executor.submit(_load_yt_video, video_link): video_link for video_link in video_links
-                }  # noqa: E501
-                for future in concurrent.futures.as_completed(future_to_video):
+                }
+
+                for future in tqdm(
+                    concurrent.futures.as_completed(future_to_video), total=len(video_links), desc="Processing videos"
+                ):
                     video = future_to_video[future]
                     try:
                         results = future.result()

embedchain/utils.py

@@ -5,7 +5,6 @@ import re
 import string
 from typing import Any
 
-from bs4 import BeautifulSoup
 from schema import Optional, Or, Schema
 
 from embedchain.models.data_type import DataType
@@ -16,6 +15,8 @@ def parse_content(content, type):
     if type not in implemented:
         raise ValueError(f"Parser type {type} not implemented. Please choose one of {implemented}")
 
+    from bs4 import BeautifulSoup
+
     soup = BeautifulSoup(content, type)
     original_size = len(str(soup.get_text()))
 
@@ -216,6 +217,10 @@ def detect_datatype(source: Any) -> DataType:
             logging.debug(f"Source of `{formatted_source}` detected as `csv`.")
             return DataType.CSV
 
+        if url.path.endswith(".mdx") or url.path.endswith(".md"):
+            logging.debug(f"Source of `{formatted_source}` detected as `mdx`.")
+            return DataType.MDX
+
         if url.path.endswith(".docx"):
             logging.debug(f"Source of `{formatted_source}` detected as `docx`.")
             return DataType.DOCX
@@ -292,6 +297,10 @@ def detect_datatype(source: Any) -> DataType:
             logging.debug(f"Source of `{formatted_source}` detected as `xml`.")
             return DataType.XML
 
+        if source.endswith(".mdx") or source.endswith(".md"):
+            logging.debug(f"Source of `{formatted_source}` detected as `mdx`.")
+            return DataType.MDX
+
         if source.endswith(".yaml"):
             with open(source, "r") as file:
                 yaml_content = yaml.safe_load(file)

embedchain/vectordb/chroma.py

@@ -3,6 +3,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 
 from chromadb import Collection, QueryResult
 from langchain.docstore.document import Document
+from tqdm import tqdm
 
 from embedchain.config import ChromaDbConfig
 from embedchain.helper.json_serializable import register_deserializable
@@ -157,12 +158,7 @@ class ChromaDB(BaseVectorDB):
                 " Ids size: {}".format(len(documents), len(metadatas), len(ids))
             )
 
-        for i in range(0, len(documents), self.BATCH_SIZE):
-            print(
-                "Inserting batches from {} to {} in vector database.".format(
-                    i, min(len(documents), i + self.BATCH_SIZE)
-                )
-            )
+        for i in tqdm(range(0, len(documents), self.BATCH_SIZE), desc="Inserting batches in chromadb"):
             if skip_embedding:
                 self.collection.add(
                     embeddings=embeddings[i : i + self.BATCH_SIZE],

embedchain/vectordb/opensearch.py

@@ -1,6 +1,9 @@
 import logging
+import time
 from typing import Dict, List, Optional, Set, Tuple, Union
 
+from tqdm import tqdm
+
 try:
     from opensearchpy import OpenSearch
     from opensearchpy.helpers import bulk
@@ -23,6 +26,8 @@ class OpenSearchDB(BaseVectorDB):
     OpenSearch as vector database
     """
 
+    BATCH_SIZE = 100
+
     def __init__(self, config: OpenSearchDBConfig):
         """OpenSearch as vector database.
 
@@ -131,19 +136,28 @@ class OpenSearchDB(BaseVectorDB):
         :type skip_embedding: bool
         """
 
-        docs = []
-        if not skip_embedding:
-            embeddings = self.embedder.embedding_fn(documents)
-        for id, text, metadata, embeddings in zip(ids, documents, metadatas, embeddings):
-            docs.append(
-                {
-                    "_index": self._get_index(),
-                    "_id": id,
-                    "_source": {"text": text, "metadata": metadata, "embeddings": embeddings},
-                }
-            )
-        bulk(self.client, docs)
-        self.client.indices.refresh(index=self._get_index())
+        for i in tqdm(range(0, len(documents), self.BATCH_SIZE), desc="Inserting batches in opensearch"):
+            if not skip_embedding:
+                embeddings = self.embedder.embedding_fn(documents[i : i + self.BATCH_SIZE])
+
+            docs = []
+            for id, text, metadata, embeddings in zip(
+                ids[i : i + self.BATCH_SIZE],
+                documents[i : i + self.BATCH_SIZE],
+                metadatas[i : i + self.BATCH_SIZE],
+                embeddings[i : i + self.BATCH_SIZE],
+            ):
+                docs.append(
+                    {
+                        "_index": self._get_index(),
+                        "_id": id,
+                        "_source": {"text": text, "metadata": metadata, "embeddings": embeddings},
+                    }
+                )
+            bulk(self.client, docs)
+            self.client.indices.refresh(index=self._get_index())
+            # Sleep for 0.1 seconds to avoid rate limiting
+            time.sleep(0.1)
 
     def query(
         self,

poetry.lock

@@ -337,26 +337,6 @@ description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
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 ]
@@ -764,13 +744,13 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.4.16"
+version = "0.4.17"
 description = "Chroma."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "chromadb-0.4.17-py3-none-any.whl", hash = "sha256:8cb88162bc6124441ba5a4b93819463a10e9aaafbe05a3286e876cbdc7a7e11d"},
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 ]
 
 [package.dependencies]
@@ -2070,7 +2050,7 @@ files = [
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     {file = "greenlet-3.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:73b2f1922a39d5d59cc0e597987300df3396b148a9bd10b76a058a2f2772fc04"},
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@@ -2080,6 +2060,7 @@ files = [
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@@ -2792,13 +2773,13 @@ adal = ["adal (>=1.0.2)"]
 
 [[package]]
 name = "langchain"
-version = "0.0.332"
+version = "0.0.336"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.0.332-py3-none-any.whl", hash = "sha256:4cbf183b8a385483907192efea2f55d34c0f0c441b0a02f41af1eeec4526677c"},
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+    {file = "langchain-0.0.336.tar.gz", hash = "sha256:2cbb992b0a6975948d35616386d088c2920b66023cb94eb4f4b25e097fa1374d"},
 ]
 
 [package.dependencies]
@@ -2807,7 +2788,7 @@ anyio = "<4.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.0.52,<0.1.0"
+langsmith = ">=0.0.63,<0.1.0"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -2816,17 +2797,17 @@ SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<9.0.0"
 
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 cli = ["typer (>=0.9.0,<0.10.0)"]
 cohere = ["cohere (>=4,<5)"]
 docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
 embeddings = ["sentence-transformers (>=2,<3)"]
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-openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.6.0)"]
+llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
+openai = ["openai (<2)", "tiktoken (>=0.3.2,<0.6.0)"]
 qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
 text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
@@ -2846,18 +2827,17 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.0.62"
+version = "0.0.65"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
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 ]
 
@@ -6892,30 +6791,30 @@ backoff = "*"
 beautifulsoup4 = "*"
 chardet = "*"
 dataclasses-json = "*"
-ebooklib = {version = "*", optional = true, markers = "extra == \"local-inference\""}
+ebooklib = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
 emoji = "*"
 filetype = "*"
 langdetect = "*"
 lxml = "*"
-markdown = {version = "*", optional = true, markers = "extra == \"local-inference\""}
-msg-parser = {version = "*", optional = true, markers = "extra == \"local-inference\""}
+markdown = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+msg-parser = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
 nltk = "*"
 numpy = "*"
-openpyxl = {version = "*", optional = true, markers = "extra == \"local-inference\""}
-pandas = {version = "*", optional = true, markers = "extra == \"local-inference\""}
-pdf2image = {version = "*", optional = true, markers = "extra == \"local-inference\""}
-"pdfminer.six" = {version = "*", optional = true, markers = "extra == \"local-inference\""}
-pypandoc = {version = "*", optional = true, markers = "extra == \"local-inference\""}
-python-docx = {version = ">=1.0.1", optional = true, markers = "extra == \"local-inference\""}
+openpyxl = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+pandas = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+pdf2image = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+"pdfminer.six" = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+pypandoc = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+python-docx = {version = ">=1.0.1", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
 python-iso639 = "*"
 python-magic = "*"
-python-pptx = {version = "<=0.6.21", optional = true, markers = "extra == \"local-inference\""}
+python-pptx = {version = "<=0.6.21", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
 rapidfuzz = "*"
 requests = "*"
 tabulate = "*"
-unstructured-inference = {version = "0.7.3", optional = true, markers = "extra == \"local-inference\""}
-"unstructured.pytesseract" = {version = ">=0.3.12", optional = true, markers = "extra == \"local-inference\""}
-xlrd = {version = "*", optional = true, markers = "extra == \"local-inference\""}
+unstructured-inference = {version = "0.7.3", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+"unstructured.pytesseract" = {version = ">=0.3.12", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
+xlrd = {version = "*", optional = true, markers = "extra == \"all-docs\" or extra == \"local-inference\""}
 
 [package.extras]
 airtable = ["pyairtable"]
@@ -7641,7 +7540,7 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [extras]
 cohere = ["cohere"]
 community = ["llama-hub"]
-dataloaders = ["beautifulsoup4", "docx2txt", "duckduckgo-search", "pypdf", "pytube", "sentence-transformers", "unstructured"]
+dataloaders = ["beautifulsoup4", "docx2txt", "duckduckgo-search", "pypdf", "pytube", "sentence-transformers", "unstructured", "youtube-transcript-api"]
 discord = ["discord"]
 elasticsearch = ["elasticsearch"]
 git = ["gitpython"]
@@ -7663,9 +7562,9 @@ streamlit = []
 vertexai = ["google-cloud-aiplatform"]
 weaviate = ["weaviate-client"]
 whatsapp = ["flask", "twilio"]
-youtube-channel = ["yt_dlp"]
+youtube = ["youtube-transcript-api", "yt_dlp"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "a7282080c7a4379bdc6f33dfe9cae7eb20764aae0176137ba5c7af7cdcc58ede"
+content-hash = "ea063cadfefd23d4c9b2a25c9096efe6bcedc367136d819ccb1fd2f510a91206"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "embedchain"
-version = "0.1.14"
+version = "0.1.17"
 description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data"
 authors = [
     "Taranjeet Singh <taranjeet@embedchain.ai>",
@@ -90,10 +90,10 @@ color = true
 [tool.poetry.dependencies]
 python = ">=3.9,<3.12"
 python-dotenv = "^1.0.0"
-langchain = "^0.0.332"
+langchain = "^0.0.336"
 requests = "^2.31.0"
 openai = ">=1.1.1"
-chromadb = "^0.4.16"
+chromadb = "^0.4.17"
 posthog = "^3.0.2"
 tiktoken = { version = "^0.4.0", optional = true }
 youtube-transcript-api = { version = "^0.6.1", optional = true }
@@ -120,7 +120,7 @@ weaviate-client = { version = "^3.24.1", optional = true }
 docx2txt = { version = "^0.8", optional = true }
 pinecone-client = { version = "^2.2.4", optional = true }
 qdrant-client = { version = "1.6.3", optional = true }
-unstructured = {extras = ["local-inference"], version = "^0.10.18", optional = true}
+unstructured = {extras = ["local-inference", "all-docs"], version = "^0.10.18", optional = true}
 pillow = { version = "10.0.1", optional = true }
 torchvision = { version = ">=0.15.1, !=0.15.2", optional = true }
 ftfy = { version = "6.1.1", optional = true }
@@ -169,7 +169,7 @@ huggingface_hub=["huggingface_hub"]
 cohere = ["cohere"]
 milvus = ["pymilvus"]
 dataloaders=[
-    "youtube-transcripts-api",
+    "youtube-transcript-api",
     "beautifulsoup4",
     "docx2txt",
     "duckduckgo-search",
@@ -193,9 +193,9 @@ json = ["llama-hub"]
 postgres = ["psycopg", "psycopg-binary", "psycopg-pool"]
 mysql = ["mysql-connector-python"]
 git = ["gitpython"]
-youtube_channel = [
+youtube = [
     "yt_dlp",
-    "youtube-transcripts-api",
+    "youtube-transcript-api",
 ]
 
 [tool.poetry.group.docs.dependencies]

tests/chunkers/test_chunkers.py

@@ -1,3 +1,4 @@
+from embedchain.chunkers.common_chunker import CommonChunker
 from embedchain.chunkers.discourse import DiscourseChunker
 from embedchain.chunkers.docs_site import DocsSiteChunker
 from embedchain.chunkers.docx_file import DocxFileChunker
@@ -39,6 +40,7 @@ chunker_common_config = {
     PostgresChunker: {"chunk_size": 1000, "chunk_overlap": 0, "length_function": len},
     SlackChunker: {"chunk_size": 1000, "chunk_overlap": 0, "length_function": len},
     DiscourseChunker: {"chunk_size": 1000, "chunk_overlap": 0, "length_function": len},
+    CommonChunker: {"chunk_size": 1000, "chunk_overlap": 0, "length_function": len},
 }
 
 

tests/loaders/test_web_page.py

@@ -27,7 +27,7 @@ def test_load_data(web_page_loader):
             </body>
         </html>
     """
-    with patch("embedchain.loaders.web_page.requests.get", return_value=mock_response):
+    with patch("embedchain.loaders.web_page.WebPageLoader._session.get", return_value=mock_response):
         result = web_page_loader.load_data(page_url)
 
     content = web_page_loader._get_clean_content(mock_response.content, page_url)