mirror of
https://github.com/hwchase17/langchain.git
synced 2025-07-09 14:35:50 +00:00
fixed formatting
This commit is contained in:
parent
47a6b4d674
commit
90fd840fb1
@ -311,9 +311,9 @@ class Vectara(VectorStore):
|
|||||||
|
|
||||||
metadatas = []
|
metadatas = []
|
||||||
for x in responses:
|
for x in responses:
|
||||||
md = { m["name"]: m["value"] for m in x["metadata"] }
|
md = {m["name"]: m["value"] for m in x["metadata"]}
|
||||||
doc_num = x['documentIndex']
|
doc_num = x["documentIndex"]
|
||||||
doc_md = { m["name"]: m["value"] for m in documents[doc_num]['metadata'] }
|
doc_md = {m["name"]: m["value"] for m in documents[doc_num]["metadata"]}
|
||||||
md.update(doc_md)
|
md.update(doc_md)
|
||||||
metadatas.append(md)
|
metadatas.append(md)
|
||||||
|
|
||||||
@ -325,7 +325,7 @@ class Vectara(VectorStore):
|
|||||||
),
|
),
|
||||||
x["score"],
|
x["score"],
|
||||||
)
|
)
|
||||||
for x,md in zip(responses,metadatas)
|
for x, md in zip(responses, metadatas)
|
||||||
]
|
]
|
||||||
|
|
||||||
return docs
|
return docs
|
||||||
|
@ -14,6 +14,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
|||||||
# VECTARA_API_KEY, VECTARA_CORPUS_ID and VECTARA_CUSTOMER_ID
|
# VECTARA_API_KEY, VECTARA_CORPUS_ID and VECTARA_CUSTOMER_ID
|
||||||
#
|
#
|
||||||
|
|
||||||
|
|
||||||
def get_abbr(s: str) -> str:
|
def get_abbr(s: str) -> str:
|
||||||
words = s.split(" ") # Split the string into words
|
words = s.split(" ") # Split the string into words
|
||||||
first_letters = [word[0] for word in words] # Extract the first letter of each word
|
first_letters = [word[0] for word in words] # Extract the first letter of each word
|
||||||
@ -51,9 +52,9 @@ def test_vectara_add_documents() -> None:
|
|||||||
)
|
)
|
||||||
assert len(output1) == 2
|
assert len(output1) == 2
|
||||||
assert output1[0].page_content == "large language model"
|
assert output1[0].page_content == "large language model"
|
||||||
assert output1[0].metadata['abbr'] == "llm"
|
assert output1[0].metadata["abbr"] == "llm"
|
||||||
assert output1[1].page_content == "information retrieval"
|
assert output1[1].page_content == "information retrieval"
|
||||||
assert output1[1].metadata['abbr'] == "ir"
|
assert output1[1].metadata["abbr"] == "ir"
|
||||||
|
|
||||||
# test with metadata filter (doc level)
|
# test with metadata filter (doc level)
|
||||||
# since the query does not match test_num=1 directly we get RAG as the matching result
|
# since the query does not match test_num=1 directly we get RAG as the matching result
|
||||||
@ -65,11 +66,12 @@ def test_vectara_add_documents() -> None:
|
|||||||
)
|
)
|
||||||
assert len(output2) == 1
|
assert len(output2) == 1
|
||||||
assert output2[0].page_content == "retrieval augmented generation"
|
assert output2[0].page_content == "retrieval augmented generation"
|
||||||
assert output2[0].metadata['abbr'] == "rag"
|
assert output2[0].metadata["abbr"] == "rag"
|
||||||
|
|
||||||
for doc_id in doc_ids:
|
for doc_id in doc_ids:
|
||||||
docsearch._delete_doc(doc_id)
|
docsearch._delete_doc(doc_id)
|
||||||
|
|
||||||
|
|
||||||
def test_vectara_from_files() -> None:
|
def test_vectara_from_files() -> None:
|
||||||
"""Test end to end construction and search."""
|
"""Test end to end construction and search."""
|
||||||
|
|
||||||
|
Loading…
Reference in New Issue
Block a user