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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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16
embedchain/vectordb/elasticsearch.py
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@@ -114,7 +114,6 @@ class ElasticsearchDB(BaseVectorDB):
documents: List[str],
metadatas: List[object],
ids: List[str],
skip_embedding: bool,
**kwargs: Optional[Dict[str, any]],
) -> Any:
"""
@@ -127,12 +126,9 @@ class ElasticsearchDB(BaseVectorDB):
:type metadatas: List[object]
:param ids: ids of docs
:type ids: List[str]
:param skip_embedding: Optional. If True, then the input_query is assumed to be already embedded.
:type skip_embedding: bool
"""
if not skip_embedding:
embeddings = self.embedder.embedding_fn(documents)
embeddings = self.embedder.embedding_fn(documents)
for chunk in chunks(
list(zip(ids, documents, metadatas, embeddings)), self.BATCH_SIZE, desc="Inserting batches in elasticsearch"
@@ -161,7 +157,6 @@ class ElasticsearchDB(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]]:
@@ -174,8 +169,6 @@ class ElasticsearchDB(BaseVectorDB):
:type n_results: int
:param where: Optional. to filter data
:type where: Dict[str, any]
:param skip_embedding: Optional. If True, then the input_query is assumed to be already embedded.
:type skip_embedding: bool
:return: The context of the document that matched your query, url of the source, doc_id
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
@@ -183,11 +176,8 @@ class ElasticsearchDB(BaseVectorDB):
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 skip_embedding:
query_vector = input_query
else:
input_query_vector = self.embedder.embedding_fn(input_query)
query_vector = input_query_vector[0]
input_query_vector = self.embedder.embedding_fn(input_query)
query_vector = input_query_vector[0]
# `https://www.elastic.co/guide/en/elasticsearch/reference/7.17/query-dsl-script-score-query.html`
query = {