From 3033c6b964d077a8ab13538913bdf1e73b162983 Mon Sep 17 00:00:00 2001 From: cs0lar <62176855+cs0lar@users.noreply.github.com> Date: Mon, 24 Apr 2023 19:50:55 +0100 Subject: [PATCH] fixes #1214 (#3003) ### Background Continuing to implement all the interface methods defined by the `VectorStore` class. This PR pertains to implementation of the `max_marginal_relevance_search_by_vector` method. ### Changes - a `max_marginal_relevance_search_by_vector` method implementation has been added in `weaviate.py` - tests have been added to the the new method - vcr cassettes have been added for the weaviate tests ### Test Plan Added tests for the `max_marginal_relevance_search_by_vector` implementation ### Change Safety - [x] I have added tests to cover my changes --- langchain/vectorstores/annoy.py | 32 +- langchain/vectorstores/base.py | 42 +- langchain/vectorstores/chroma.py | 17 +- langchain/vectorstores/deeplake.py | 35 +- langchain/vectorstores/faiss.py | 27 +- langchain/vectorstores/milvus.py | 13 +- langchain/vectorstores/qdrant.py | 10 +- langchain/vectorstores/supabase.py | 21 +- langchain/vectorstores/weaviate.py | 43 +- ...te.test_max_marginal_relevance_search.yaml | 693 +++++++++--------- ...x_marginal_relevance_search_by_vector.yaml | 557 ++++++++++++++ ....test_similarity_search_with_metadata.yaml | 646 ++++++++-------- ...st_similarity_search_without_metadata.yaml | 645 ++++++++-------- .../vectorstores/test_weaviate.py | 29 + 14 files changed, 1784 insertions(+), 1026 deletions(-) create mode 100644 tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search_by_vector.yaml diff --git a/langchain/vectorstores/annoy.py b/langchain/vectorstores/annoy.py index 3a3ea156..538f75c2 100644 --- a/langchain/vectorstores/annoy.py +++ b/langchain/vectorstores/annoy.py @@ -201,7 +201,12 @@ class Annoy(VectorStore): return [doc for doc, _ in docs_and_scores] def max_marginal_relevance_search_by_vector( - self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -212,6 +217,10 @@ class Annoy(VectorStore): embedding: Embedding to look up documents similar to. fetch_k: Number of Documents to fetch to pass to MMR algorithm. k: Number of Documents to return. Defaults to 4. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. @@ -221,7 +230,10 @@ class Annoy(VectorStore): ) embeddings = [self.index.get_item_vector(i) for i in idxs] mmr_selected = maximal_marginal_relevance( - np.array([embedding], dtype=np.float32), embeddings, k=k + np.array([embedding], dtype=np.float32), + embeddings, + k=k, + lambda_mult=lambda_mult, ) # ignore the -1's if not enough docs are returned/indexed selected_indices = [idxs[i] for i in mmr_selected if i != -1] @@ -236,7 +248,12 @@ class Annoy(VectorStore): return docs def max_marginal_relevance_search( - self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -247,12 +264,17 @@ class Annoy(VectorStore): query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ embedding = self.embedding_function(query) - docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k) + docs = self.max_marginal_relevance_search_by_vector( + embedding, k, fetch_k, lambda_mult=lambda_mult + ) return docs @classmethod diff --git a/langchain/vectorstores/base.py b/langchain/vectorstores/base.py index bba941c0..db7ff43c 100644 --- a/langchain/vectorstores/base.py +++ b/langchain/vectorstores/base.py @@ -153,7 +153,12 @@ class VectorStore(ABC): return await asyncio.get_event_loop().run_in_executor(None, func) def max_marginal_relevance_search( - self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -164,25 +169,40 @@ class VectorStore(ABC): query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ raise NotImplementedError async def amax_marginal_relevance_search( - self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance.""" # This is a temporary workaround to make the similarity search # asynchronous. The proper solution is to make the similarity search # asynchronous in the vector store implementations. - func = partial(self.max_marginal_relevance_search, query, k, fetch_k, **kwargs) + func = partial( + self.max_marginal_relevance_search, query, k, fetch_k, lambda_mult, **kwargs + ) return await asyncio.get_event_loop().run_in_executor(None, func) def max_marginal_relevance_search_by_vector( - self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -193,14 +213,22 @@ class VectorStore(ABC): embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ raise NotImplementedError async def amax_marginal_relevance_search_by_vector( - self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance.""" raise NotImplementedError diff --git a/langchain/vectorstores/chroma.py b/langchain/vectorstores/chroma.py index 7d29dbe5..1068963c 100644 --- a/langchain/vectorstores/chroma.py +++ b/langchain/vectorstores/chroma.py @@ -198,6 +198,7 @@ class Chroma(VectorStore): embedding: List[float], k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: @@ -208,6 +209,10 @@ class Chroma(VectorStore): embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List of Documents selected by maximal marginal relevance. @@ -220,7 +225,10 @@ class Chroma(VectorStore): include=["metadatas", "documents", "distances", "embeddings"], ) mmr_selected = maximal_marginal_relevance( - np.array(embedding, dtype=np.float32), results["embeddings"][0], k=k + np.array(embedding, dtype=np.float32), + results["embeddings"][0], + k=k, + lambda_mult=lambda_mult, ) candidates = _results_to_docs(results) @@ -233,6 +241,7 @@ class Chroma(VectorStore): query: str, k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: @@ -243,6 +252,10 @@ class Chroma(VectorStore): query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List of Documents selected by maximal marginal relevance. @@ -254,7 +267,7 @@ class Chroma(VectorStore): embedding = self._embedding_function.embed_query(query) docs = self.max_marginal_relevance_search_by_vector( - embedding, k, fetch_k, filter + embedding, k, fetch_k, lambda_mul=lambda_mult, filter=filter ) return docs diff --git a/langchain/vectorstores/deeplake.py b/langchain/vectorstores/deeplake.py index 2f3f9970..05cf573e 100644 --- a/langchain/vectorstores/deeplake.py +++ b/langchain/vectorstores/deeplake.py @@ -315,8 +315,12 @@ class DeepLake(VectorStore): view = view[indices] if use_maximal_marginal_relevance: + lambda_mult = kwargs.get("lambda_mult", 0.5) indices = maximal_marginal_relevance( - query_emb, embeddings[indices], k=min(k, len(indices)) + query_emb, + embeddings[indices], + k=min(k, len(indices)), + lambda_mult=lambda_mult, ) view = view[indices] scores = [scores[i] for i in indices] @@ -406,7 +410,12 @@ class DeepLake(VectorStore): ) def max_marginal_relevance_search_by_vector( - self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity @@ -415,6 +424,10 @@ class DeepLake(VectorStore): embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ @@ -423,10 +436,16 @@ class DeepLake(VectorStore): k=k, fetch_k=fetch_k, use_maximal_marginal_relevance=True, + lambda_mult=lambda_mult, ) def max_marginal_relevance_search( - self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity @@ -435,6 +454,10 @@ class DeepLake(VectorStore): query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ @@ -443,7 +466,11 @@ class DeepLake(VectorStore): "For MMR search, you must specify an embedding function on" "creation." ) return self.search( - query=query, k=k, fetch_k=fetch_k, use_maximal_marginal_relevance=True + query=query, + k=k, + fetch_k=fetch_k, + use_maximal_marginal_relevance=True, + lambda_mult=lambda_mult, ) @classmethod diff --git a/langchain/vectorstores/faiss.py b/langchain/vectorstores/faiss.py index f391caed..a606174a 100644 --- a/langchain/vectorstores/faiss.py +++ b/langchain/vectorstores/faiss.py @@ -227,7 +227,12 @@ class FAISS(VectorStore): return [doc for doc, _ in docs_and_scores] def max_marginal_relevance_search_by_vector( - self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -238,7 +243,10 @@ class FAISS(VectorStore): embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ @@ -246,7 +254,10 @@ class FAISS(VectorStore): # -1 happens when not enough docs are returned. embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1] mmr_selected = maximal_marginal_relevance( - np.array([embedding], dtype=np.float32), embeddings, k=k + np.array([embedding], dtype=np.float32), + embeddings, + k=k, + lambda_mult=lambda_mult, ) selected_indices = [indices[0][i] for i in mmr_selected] docs = [] @@ -266,6 +277,7 @@ class FAISS(VectorStore): query: str, k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -277,12 +289,17 @@ class FAISS(VectorStore): query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ embedding = self.embedding_function(query) - docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k) + docs = self.max_marginal_relevance_search_by_vector( + embedding, k, fetch_k, lambda_mult=lambda_mult + ) return docs def merge_from(self, target: FAISS) -> None: diff --git a/langchain/vectorstores/milvus.py b/langchain/vectorstores/milvus.py index ab3b66de..f3c1c65a 100644 --- a/langchain/vectorstores/milvus.py +++ b/langchain/vectorstores/milvus.py @@ -619,6 +619,7 @@ class Milvus(VectorStore): query: str, k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, @@ -631,6 +632,10 @@ class Milvus(VectorStore): k (int, optional): How many results to give. Defaults to 4. fetch_k (int, optional): Total results to select k from. Defaults to 20. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5 param (dict, optional): The search params for the specified index. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. @@ -652,6 +657,7 @@ class Milvus(VectorStore): embedding=embedding, k=k, fetch_k=fetch_k, + lambda_mult=lambda_mult, param=param, expr=expr, timeout=timeout, @@ -663,6 +669,7 @@ class Milvus(VectorStore): embedding: list[float], k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, @@ -675,6 +682,10 @@ class Milvus(VectorStore): k (int, optional): How many results to give. Defaults to 4. fetch_k (int, optional): Total results to select k from. Defaults to 20. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5 param (dict, optional): The search params for the specified index. Defaults to None. expr (str, optional): Filtering expression. Defaults to None. @@ -730,7 +741,7 @@ class Milvus(VectorStore): # Get the new order of results. new_ordering = maximal_marginal_relevance( - np.array(embedding), ordered_result_embeddings, k=k + np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult ) # Reorder the values and return. diff --git a/langchain/vectorstores/qdrant.py b/langchain/vectorstores/qdrant.py index b46f38cd..0c0c2e19 100644 --- a/langchain/vectorstores/qdrant.py +++ b/langchain/vectorstores/qdrant.py @@ -151,6 +151,7 @@ class Qdrant(VectorStore): query: str, k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -163,7 +164,10 @@ class Qdrant(VectorStore): k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ @@ -176,7 +180,9 @@ class Qdrant(VectorStore): limit=fetch_k, ) embeddings = [result.vector for result in results] - mmr_selected = maximal_marginal_relevance(embedding, embeddings, k=k) + mmr_selected = maximal_marginal_relevance( + embedding, embeddings, k=k, lambda_mult=lambda_mult + ) return [ self._document_from_scored_point( results[i], self.content_payload_key, self.metadata_payload_key diff --git a/langchain/vectorstores/supabase.py b/langchain/vectorstores/supabase.py index d7227dbe..d6d5b027 100644 --- a/langchain/vectorstores/supabase.py +++ b/langchain/vectorstores/supabase.py @@ -236,6 +236,7 @@ class SupabaseVectorStore(VectorStore): embedding: List[float], k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -247,7 +248,10 @@ class SupabaseVectorStore(VectorStore): embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ @@ -259,7 +263,10 @@ class SupabaseVectorStore(VectorStore): matched_embeddings = [doc_tuple[2] for doc_tuple in result] mmr_selected = maximal_marginal_relevance( - np.array([embedding], dtype=np.float32), matched_embeddings, k=k + np.array([embedding], dtype=np.float32), + matched_embeddings, + k=k, + lambda_mult=lambda_mult, ) filtered_documents = [matched_documents[i] for i in mmr_selected] @@ -271,6 +278,7 @@ class SupabaseVectorStore(VectorStore): query: str, k: int = 4, fetch_k: int = 20, + lambda_mult: float = 0.5, **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -282,7 +290,10 @@ class SupabaseVectorStore(VectorStore): query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. - + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. @@ -318,5 +329,7 @@ class SupabaseVectorStore(VectorStore): $$;``` """ embedding = self._embedding.embed_documents([query]) - docs = self.max_marginal_relevance_search_by_vector(embedding[0], k, fetch_k) + docs = self.max_marginal_relevance_search_by_vector( + embedding[0], k, fetch_k, lambda_mult=lambda_mult + ) return docs diff --git a/langchain/vectorstores/weaviate.py b/langchain/vectorstores/weaviate.py index 4211ec7e..dd5e79ca 100644 --- a/langchain/vectorstores/weaviate.py +++ b/langchain/vectorstores/weaviate.py @@ -135,7 +135,12 @@ class Weaviate(VectorStore): return docs def max_marginal_relevance_search( - self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, ) -> List[Document]: """Return docs selected using the maximal marginal relevance. @@ -146,12 +151,14 @@ class Weaviate(VectorStore): query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. """ - lambda_mult = kwargs.get("lambda_mult", 0.5) - if self._embedding is not None: embedding = self._embedding.embed_query(query) else: @@ -159,6 +166,35 @@ class Weaviate(VectorStore): "max_marginal_relevance_search requires a suitable Embeddings object" ) + return self.max_marginal_relevance_search_by_vector( + embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs + ) + + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + + Returns: + List of Documents selected by maximal marginal relevance. + """ vector = {"vector": embedding} query_obj = self._client.query.get(self._index_name, self._query_attrs) results = ( @@ -180,6 +216,7 @@ class Weaviate(VectorStore): payload[idx].pop("_additional") meta = payload[idx] docs.append(Document(page_content=text, metadata=meta)) + return docs @classmethod diff --git a/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml b/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml index dc54553a..4b461af6 100644 --- a/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml +++ b/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml @@ -121,231 +121,231 @@ interactions: M0fjN39o9dRD5r2dU9//+PS4PfsM2yiIR+/1pKvepC2Vd3VY8vBwDcH+FDlzv/VJ54OQUDjfR3Q4 KswcG29nwPV2ebAw9Z7ix79hu4y2hLinh8kf65EiuNtHtqW5aopLPgt/+vDHJ8w/vt7OghPz6/cs pXrvFZDLbEMs36clLZywBksjFsN41pQjAxNgdRzvLPjqcfb1Y+GBu4SCHa4Rjx9XCa2ViyA/P2Dq - sVmsxnS2Jp7tP83JfkkKIsZYEvs1Pctvvk+wLZYGfr8r7I+owSEsQrr7PwAAAP//pJ1Lr6PAlqXn - 9StKd0pfgTEQQc14m5cJXsa21GoBtjFgjA1EACHVfy/hc7vVgxp1DzOVx2nHY6+1vr3N2fKhQWu/ - IjEMJr5BakdqnQ7hIPz6LQHLR3gghmYy0I0tgMyXbw879DmUcONVAafVdrN8yv7y868bT4zBysiL - BRVm8JEN1jRZwbfrpSI68sSR1aSZb+mhArLCPYif1BMdN734W393Bm5B6bs04IV9nX79rGbjcV9A - 7UVCbtOp4M9/+ZGn477OEaBDfQmgtKxn5EnuJ6GHY1BBRs4CdHQvaJibNa+gvPgqcjceh51ZvsMI - FFXwVPcYkJh0mbjdn79+9m69CBLc+PRf/hiiQrr89AFp1/o9zGoncPKmv/jHT+b006SQLR7PXz9K - 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2023 19:25:56 GMT + - Mon, 24 Apr 2023 17:46:26 GMT Server: - cloudflare Transfer-Encoding: @@ -365,7 +365,7 @@ interactions: openai-organization: - user-iy0qn7phyookv8vra62ulvxe openai-processing-ms: - - '298' + - '362' openai-version: - '2020-10-01' strict-transport-security: @@ -375,9 +375,9 @@ interactions: x-ratelimit-remaining-requests: - '58' x-ratelimit-reset-requests: - - 1.666s + - 1.302s x-request-id: - - 05aa395b3d571732ad5050891d1e0e1e + - 0d03d8038beee517067eba903c658f2e status: code: 200 message: OK diff --git a/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_without_metadata.yaml b/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_without_metadata.yaml index 15695091..d2a51e7a 100644 --- a/tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_without_metadata.yaml +++ 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a/tests/integration_tests/vectorstores/test_weaviate.py +++ b/tests/integration_tests/vectorstores/test_weaviate.py @@ -88,3 +88,32 @@ class TestWeaviate: Document(page_content="foo", metadata={"page": 0}), Document(page_content="bar", metadata={"page": 1}), ] + + @pytest.mark.vcr(ignore_localhost=True) + def test_max_marginal_relevance_search_by_vector( + self, weaviate_url: str, embedding_openai: OpenAIEmbeddings + ) -> None: + """Test end to end construction and MRR search by vector.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + + docsearch = Weaviate.from_texts( + texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url + ) + foo_embedding = embedding_openai.embed_query("foo") + + # if lambda=1 the algorithm should be equivalent to standard ranking + standard_ranking = docsearch.similarity_search("foo", k=2) + output = docsearch.max_marginal_relevance_search_by_vector( + foo_embedding, k=2, fetch_k=3, lambda_mult=1.0 + ) + assert output == standard_ranking + + # if lambda=0 the algorithm should favour maximal diversity + output = docsearch.max_marginal_relevance_search_by_vector( + foo_embedding, k=2, fetch_k=3, lambda_mult=0.0 + ) + assert output == [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="bar", metadata={"page": 1}), + ]