forked from Archives/langchain
Add kwargs to VectorStore.maximum_marginal_relevance (#2921)
Same as similarity_search, allows child classes to add vector store-specific args (this was technically already happening in couple places but now typing is correct).
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@ -118,7 +118,7 @@ class VectorStore(ABC):
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return await asyncio.get_event_loop().run_in_executor(None, func)
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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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@ -136,18 +136,18 @@ class VectorStore(ABC):
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raise NotImplementedError
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async def amax_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance."""
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# This is a temporary workaround to make the similarity search
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# asynchronous. The proper solution is to make the similarity search
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# asynchronous in the vector store implementations.
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func = partial(self.max_marginal_relevance_search, query, k, fetch_k)
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func = partial(self.max_marginal_relevance_search, query, k, fetch_k, **kwargs)
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return await asyncio.get_event_loop().run_in_executor(None, func)
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def max_marginal_relevance_search_by_vector(
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self, embedding: List[float], k: int = 4, fetch_k: int = 20
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self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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@ -165,7 +165,7 @@ class VectorStore(ABC):
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raise NotImplementedError
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async def amax_marginal_relevance_search_by_vector(
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self, embedding: List[float], k: int = 4, fetch_k: int = 20
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self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance."""
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raise NotImplementedError
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@ -193,6 +193,7 @@ class Chroma(VectorStore):
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k: int = 4,
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fetch_k: int = 20,
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filter: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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@ -227,6 +228,7 @@ class Chroma(VectorStore):
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k: int = 4,
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fetch_k: int = 20,
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filter: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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@ -391,7 +391,7 @@ class DeepLake(VectorStore):
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)
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def max_marginal_relevance_search_by_vector(
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self, embedding: List[float], k: int = 4, fetch_k: int = 20
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self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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@ -411,7 +411,7 @@ class DeepLake(VectorStore):
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)
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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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self, query: str, k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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@ -208,7 +208,7 @@ class FAISS(VectorStore):
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return [doc for doc, _ in docs_and_scores]
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def max_marginal_relevance_search_by_vector(
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self, embedding: List[float], k: int = 4, fetch_k: int = 20
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self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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@ -243,7 +243,11 @@ class FAISS(VectorStore):
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return docs
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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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@ -147,7 +147,11 @@ class Qdrant(VectorStore):
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]
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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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