[bugfix] Fix issue when llm config is not defined (#763)

embedchain/config/llm/base_llm_config.py

@@ -67,7 +67,7 @@ class BaseLlmConfig(BaseConfig):
         deployment_name: Optional[str] = None,
         system_prompt: Optional[str] = None,
         where: Dict[str, Any] = None,
-        query_type: Optional[str] = None
+        query_type: Optional[str] = None,
     ):
         """
         Initializes a configuration class instance for the LLM.

embedchain/data_formatter/data_formatter.py

@@ -1,8 +1,8 @@
 from embedchain.chunkers.base_chunker import BaseChunker
 from embedchain.chunkers.docs_site import DocsSiteChunker
 from embedchain.chunkers.docx_file import DocxFileChunker
-from embedchain.chunkers.mdx import MdxChunker
 from embedchain.chunkers.images import ImagesChunker
+from embedchain.chunkers.mdx import MdxChunker
 from embedchain.chunkers.notion import NotionChunker
 from embedchain.chunkers.pdf_file import PdfFileChunker
 from embedchain.chunkers.qna_pair import QnaPairChunker

embedchain/embedchain.py

@@ -392,8 +392,13 @@ class EmbedChain(JSONSerializable):
         # Count before, to calculate a delta in the end.
         chunks_before_addition = self.db.count()
 
-        self.db.add(embeddings=embeddings_data.get("embeddings", None), documents=documents, metadatas=metadatas,
-                    ids=ids, skip_embedding = (chunker.data_type == DataType.IMAGES))
+        self.db.add(
+            embeddings=embeddings_data.get("embeddings", None),
+            documents=documents,
+            metadatas=metadatas,
+            ids=ids,
+            skip_embedding=(chunker.data_type == DataType.IMAGES),
+        )
         count_new_chunks = self.db.count() - chunks_before_addition
         print((f"Successfully saved {src} ({chunker.data_type}). New chunks count: {count_new_chunks}"))
         return list(documents), metadatas, ids, count_new_chunks
@@ -437,17 +442,18 @@ class EmbedChain(JSONSerializable):
         # We cannot query the database with the input query in case of an image search. This is because we need
         # to bring down both the image and text to the same dimension to be able to compare them.
         db_query = input_query
-        if config.query_type == "Images":
+        if hasattr(config, "query_type") and config.query_type == "Images":
             # We import the clip processor here to make sure the package is not dependent on clip dependency even if the
             # image dataset is not being used
             from embedchain.models.clip_processor import ClipProcessor
+
             db_query = ClipProcessor.get_text_features(query=input_query)
 
         contents = self.db.query(
             input_query=db_query,
             n_results=query_config.number_documents,
             where=where,
-            skip_embedding = (config.query_type == "Images")
+            skip_embedding=(hasattr(config, "query_type") and config.query_type == "Images"),
         )
 
         return contents

embedchain/llm/gpt4all.py

@@ -22,7 +22,7 @@ class GPT4ALLLlm(BaseLlm):
             from gpt4all import GPT4All
         except ModuleNotFoundError:
             raise ModuleNotFoundError(
-                "The GPT4All python package is not installed. Please install it with `pip install --upgrade embedchain[opensource]`" # noqa E501
+                "The GPT4All python package is not installed. Please install it with `pip install --upgrade embedchain[opensource]`"  # noqa E501
             ) from None
 
         return GPT4All(model_name=model)

embedchain/loaders/images.py

@@ -1,11 +1,11 @@
-import os
-import logging
 import hashlib
+import logging
+import os
+
 from embedchain.loaders.base_loader import BaseLoader
 
 
 class ImagesLoader(BaseLoader):
-
     def load_data(self, image_url):
         """
         Loads images from the supplied directory/file and applies CLIP model transformation to represent these images
@@ -15,6 +15,7 @@ class ImagesLoader(BaseLoader):
         """
         # load model and image preprocessing
         from embedchain.models.clip_processor import ClipProcessor
+
         model, preprocess = ClipProcessor.load_model()
         if os.path.isfile(image_url):
             data = [ClipProcessor.get_image_features(image_url, model, preprocess)]
@@ -28,8 +29,11 @@ class ImagesLoader(BaseLoader):
                     # Log the file that was not loaded
                     logging.exception("Failed to load the file {}. Exception {}".format(filepath, e))
         # Get the metadata like Size, Last Modified and Last Created timestamps
-        image_path_metadata = [str(os.path.getsize(image_url)), str(os.path.getmtime(image_url)),
-                               str(os.path.getctime(image_url))]
+        image_path_metadata = [
+            str(os.path.getsize(image_url)),
+            str(os.path.getmtime(image_url)),
+            str(os.path.getctime(image_url)),
+        ]
         doc_id = hashlib.sha256((" ".join(image_path_metadata) + image_url).encode()).hexdigest()
         return {
             "doc_id": doc_id,

embedchain/models/clip_processor.py

@@ -1,6 +1,6 @@
 try:
-    import torch
     import clip
+    import torch
     from PIL import Image, UnidentifiedImageError
 except ImportError:
     raise ImportError("Images requires extra dependencies. Install with `pip install embedchain[images]`") from None
@@ -39,14 +39,8 @@ class ClipProcessor:
             image_features /= image_features.norm(dim=-1, keepdim=True)
 
         image_features = image_features.cpu().detach().numpy().tolist()[0]
-        meta_data = {
-            "url": image_url
-        }
-        return {
-            "content": image_url,
-            "embedding": image_features,
-            "meta_data": meta_data
-        }
+        meta_data = {"url": image_url}
+        return {"content": image_url, "embedding": image_features, "meta_data": meta_data}
 
     @staticmethod
     def get_text_features(query):

embedchain/vectordb/chroma.py

@@ -115,8 +115,14 @@ class ChromaDB(BaseVectorDB):
     def get_advanced(self, where):
         return self.collection.get(where=where, limit=1)
 
-    def add(self, embeddings: List[List[float]], documents: List[str], metadatas: List[object],
-            ids: List[str], skip_embedding: bool) -> Any:
+    def add(
+        self,
+        embeddings: List[List[float]],
+        documents: List[str],
+        metadatas: List[object],
+        ids: List[str],
+        skip_embedding: bool,
+    ) -> Any:
         """
         Add vectors to chroma database
 
@@ -184,7 +190,7 @@ class ChromaDB(BaseVectorDB):
         except InvalidDimensionException as e:
             raise InvalidDimensionException(
                 e.message()
-                + ". This is commonly a side-effect when an embedding function, different from the one used to add the embeddings, is used to retrieve an embedding from the database." # noqa E501
+                + ". This is commonly a side-effect when an embedding function, different from the one used to add the embeddings, is used to retrieve an embedding from the database."  # noqa E501
             ) from None
         results_formatted = self._format_result(result)
         contents = [result[0].page_content for result in results_formatted]

embedchain/vectordb/elasticsearch.py

@@ -100,8 +100,14 @@ class ElasticsearchDB(BaseVectorDB):
         ids = [doc["_id"] for doc in docs]
         return {"ids": set(ids)}
 
-    def add(self, embeddings: List[List[float]], documents: List[str], metadatas: List[object],
-            ids: List[str], skip_embedding: bool) -> Any:
+    def add(
+        self,
+        embeddings: List[List[float]],
+        documents: List[str],
+        metadatas: List[object],
+        ids: List[str],
+        skip_embedding: bool,
+    ) -> Any:
         """
         add data in vector database
         :param documents: list of texts to add