mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
fixed formatting
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parent
47a6b4d674
commit
90fd840fb1
@ -311,9 +311,9 @@ class Vectara(VectorStore):
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metadatas = []
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for x in responses:
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md = { m["name"]: m["value"] for m in x["metadata"] }
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doc_num = x['documentIndex']
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doc_md = { m["name"]: m["value"] for m in documents[doc_num]['metadata'] }
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md = {m["name"]: m["value"] for m in x["metadata"]}
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doc_num = x["documentIndex"]
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doc_md = {m["name"]: m["value"] for m in documents[doc_num]["metadata"]}
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md.update(doc_md)
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metadatas.append(md)
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@ -325,7 +325,7 @@ class Vectara(VectorStore):
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),
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x["score"],
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)
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for x,md in zip(responses,metadatas)
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for x, md in zip(responses, metadatas)
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]
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return docs
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@ -14,6 +14,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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# VECTARA_API_KEY, VECTARA_CORPUS_ID and VECTARA_CUSTOMER_ID
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#
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def get_abbr(s: str) -> str:
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words = s.split(" ") # Split the string into words
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first_letters = [word[0] for word in words] # Extract the first letter of each word
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@ -51,9 +52,9 @@ def test_vectara_add_documents() -> None:
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)
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assert len(output1) == 2
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assert output1[0].page_content == "large language model"
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assert output1[0].metadata['abbr'] == "llm"
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assert output1[0].metadata["abbr"] == "llm"
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assert output1[1].page_content == "information retrieval"
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assert output1[1].metadata['abbr'] == "ir"
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assert output1[1].metadata["abbr"] == "ir"
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# test with metadata filter (doc level)
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# since the query does not match test_num=1 directly we get RAG as the matching result
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@ -65,11 +66,12 @@ def test_vectara_add_documents() -> None:
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)
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assert len(output2) == 1
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assert output2[0].page_content == "retrieval augmented generation"
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assert output2[0].metadata['abbr'] == "rag"
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assert output2[0].metadata["abbr"] == "rag"
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for doc_id in doc_ids:
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docsearch._delete_doc(doc_id)
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def test_vectara_from_files() -> None:
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"""Test end to end construction and search."""
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