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Add GPT4Vision Image loader (#1089)

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Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
This commit is contained in:
Sidharth Mohanty
2024-01-02 03:57:23 +05:30
committed by GitHub
parent 367d6b70e2
commit c62663f2e4
29 changed files with 291 additions and 714 deletions
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19
embedchain/vectordb/weaviate.py
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@@ -157,7 +157,6 @@ class WeaviateDB(BaseVectorDB):
documents: List[str],
metadatas: List[object],
ids: List[str],
skip_embedding: bool,
**kwargs: Optional[Dict[str, any]],
):
"""add data in vector database
@@ -169,14 +168,8 @@ class WeaviateDB(BaseVectorDB):
:type metadatas: List[object]
:param ids: ids of docs
:type ids: List[str]
:param skip_embedding: A boolean flag indicating if the embedding for the documents to be added is to be
generated or not
:type skip_embedding: bool
"""
print("Adding documents to Weaviate...")
if not skip_embedding:
embeddings = self.embedder.embedding_fn(documents)
embeddings = self.embedder.embedding_fn(documents)
self.client.batch.configure(batch_size=self.BATCH_SIZE, timeout_retries=3) # Configure batch
with self.client.batch as batch: # Initialize a batch process
for id, text, metadata, embedding in zip(ids, documents, metadatas, embeddings):
@@ -202,7 +195,6 @@ class WeaviateDB(BaseVectorDB):
input_query: List[str],
n_results: int,
where: Dict[str, any],
skip_embedding: bool,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
@@ -214,20 +206,13 @@ class WeaviateDB(BaseVectorDB):
:type n_results: int
:param where: Optional. to filter data
:type where: Dict[str, any]
:param skip_embedding: A boolean flag indicating if the embedding for the documents to be added is to be
generated or not
:type skip_embedding: bool
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
: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 not skip_embedding:
query_vector = self.embedder.embedding_fn([input_query])[0]
else:
query_vector = input_query
query_vector = self.embedder.embedding_fn([input_query])[0]
keys = set(where.keys() if where is not None else set())
data_fields = ["text"]