"""Redis vector store."""
from __future__ import annotations
from typing import Any, Iterable, List, Optional, Tuple, Union, cast
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from redisvl.index import SearchIndex # type: ignore[import]
from redisvl.query import RangeQuery, VectorQuery # type: ignore[import]
from redisvl.query.filter import FilterExpression # type: ignore[import]
from redisvl.redis.utils import buffer_to_array, convert_bytes # type: ignore[import]
from redisvl.schema import StorageType # type: ignore[import]
from langchain_redis.config import RedisConfig
from langchain_redis.version import __lib_name__
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
[docs]
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices."""
if len(X) == 0 or len(Y) == 0:
return np.array([])
X = np.array(X)
Y = np.array(Y)
if X.shape[1] != Y.shape[1]:
raise ValueError(
f"Number of columns in X and Y must be the same. X has shape {X.shape} "
f"and Y has shape {Y.shape}."
)
try:
import simsimd as simd # type: ignore
X = np.array(X, dtype=np.float32)
Y = np.array(Y, dtype=np.float32)
Z = 1 - simd.cdist(X, Y, metric="cosine")
if isinstance(Z, float):
return np.array([Z])
return np.array(Z)
except ImportError:
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
# Ignore divide by zero errors run time warnings as those are handled below.
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
[docs]
def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: List[np.ndarray],
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to the query AND diversity
among selected documents.
Args:
query_embedding: Embedding of the query text.
embedding_list: List of embeddings to select from.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results, where 0 corresponds to
maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
k: Number of results to return. Defaults to 4.
Returns:
List of indices of selected embeddings.
Example:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
import numpy as np
embeddings = OpenAIEmbeddings()
vector_store = RedisVectorStore(
index_name="langchain-demo",
embedding=embeddings,
redis_url="redis://localhost:6379",
)
query = "What is the capital of France?"
query_embedding = embeddings.embed_query(query)
# Assuming you have a list of document embeddings
doc_embeddings = [embeddings.embed_query(doc) for doc in documents]
selected_indices = vector_store.maximal_marginal_relevance(
query_embedding=np.array(query_embedding),
embedding_list=[np.array(emb) for emb in doc_embeddings],
lambda_mult=0.5,
k=2
)
for idx in selected_indices:
print(f"Selected document: {documents[idx]}")
"""
if min(k, len(embedding_list)) <= 0:
return []
if query_embedding.ndim == 1:
query_embedding = np.expand_dims(query_embedding, axis=0)
similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
most_similar = int(np.argmax(similarity_to_query))
idxs = [most_similar]
selected = np.array([embedding_list[most_similar]])
while len(idxs) < min(k, len(embedding_list)):
best_score = -np.inf
idx_to_add = -1
similarity_to_selected = cosine_similarity(embedding_list, selected)
for i, query_score in enumerate(similarity_to_query):
if i in idxs:
continue
redundant_score = max(similarity_to_selected[i])
equation_score = (
lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idxs
[docs]
class RedisVectorStore(VectorStore):
"""Redis vector store integration.
Setup:
Install ``langchain-redis`` and running the Redis docker container.
.. code-block:: bash
pip install -qU langchain-redis
docker run -p 6379:6379 redis/redis-stack-server:latest
Key init args — indexing params:
index_name: str
Name of the index to create.
embedding: Embeddings
Embedding function to use.
distance_metric: str
Distance metric to use for similarity search. Default is "COSINE".
indexing_algorithm: str
Indexing algorithm to use. Default is "FLAT".
vector_datatype: str
Data type of the vector. Default is "FLOAT32".
Key init args — client params:
redis_url: Optional[str]
URL of the Redis instance to connect to.
redis_client: Optional[Redis]
Pre-existing Redis connection.
Instantiate:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
vector_store = RedisVectorStore(
index_name="langchain-demo",
embedding=OpenAIEmbeddings(),
redis_url="redis://localhost:6379",
)
You can also connect to an existing Redis instance by passing in a
pre-existing Redis connection via the redis_client argument.
Instantiate from existing connection:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
from redis import Redis
redis_client = Redis.from_url("redis://localhost:6379")
store = RedisVectorStore(
embedding=OpenAIEmbeddings(),
index_name="langchain-demo",
redis_client=redis_client
)
Add Documents:
.. code-block:: python
from langchain_core.documents import Document
document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="bar", metadata={"foo": "baz"})
document_3 = Document(page_content="to be deleted")
documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)
Delete Documents:
.. code-block:: python
vector_store.delete(ids=["3"])
Search:
.. code-block:: python
results = vector_store.similarity_search(query="foo", k=1)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* foo [{'baz': 'bar'}]
Search with filter:
.. code-block:: python
from redisvl.query.filter import Tag
results = vector_store.similarity_search(
query="foo",
k=1,
filter=Tag("baz") == "bar"
)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* foo [{'baz': 'bar'}]
Search with score:
.. code-block:: python
results = vector_store.similarity_search_with_score(query="foo", k=1)
for doc, score in results:
print(f"* [SIM={score:.3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.916] foo [{'baz': 'bar'}]
Use as Retriever:
.. code-block:: python
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.get_relevant_documents("foo")
.. code-block:: python
[Document(page_content='foo', metadata={'baz': 'bar'})]
"""
[docs]
def __init__(
self,
embeddings: Embeddings,
config: Optional[RedisConfig] = None,
**kwargs: Any,
):
self.config = config or RedisConfig(**kwargs)
self._embeddings = embeddings
if self.config.embedding_dimensions is None:
self.config.embedding_dimensions = len(
self._embeddings.embed_query(
"The quick brown fox jumps over the lazy dog"
)
)
if self.config.index_schema:
self._index = SearchIndex(
self.config.index_schema, self.config.redis(), lib_name=__lib_name__
)
self._index.create(overwrite=False)
elif self.config.schema_path:
self._index = SearchIndex.from_yaml(
self.config.schema_path, lib_name=__lib_name__
)
self._index.set_client(self.config.redis())
self._index.create(overwrite=False)
elif self.config.from_existing and self.config.index_name:
self._index = SearchIndex.from_existing(
self.config.index_name, self.config.redis(), lib_name=__lib_name__
)
self._index.create(overwrite=False)
else:
# Set the default separator for tag fields where separator is not defined
modified_metadata_schema = []
if self.config.metadata_schema is not None:
for field in self.config.metadata_schema:
if field["type"] == "tag":
if "attrs" not in field or "separator" not in field["attrs"]:
modified_field = field.copy()
modified_field.setdefault("attrs", {})["separator"] = (
self.config.default_tag_separator
)
modified_metadata_schema.append(modified_field)
else:
modified_metadata_schema.append(field)
else:
modified_metadata_schema.append(field)
self._index = SearchIndex.from_dict(
{
"index": {
"name": self.config.index_name,
"prefix": f"{self.config.key_prefix}",
"storage_type": self.config.storage_type,
},
"fields": [
{"name": self.config.content_field, "type": "text"},
{
"name": self.config.embedding_field,
"type": "vector",
"attrs": {
"dims": self.config.embedding_dimensions,
"distance_metric": self.config.distance_metric,
"algorithm": self.config.indexing_algorithm,
"datatype": self.config.vector_datatype,
},
},
*modified_metadata_schema,
],
},
lib_name=__lib_name__,
)
self._index.set_client(self.config.redis())
self._index.create(overwrite=False)
@property
def index(self) -> SearchIndex:
return self._index
@property
def embeddings(self) -> Embeddings:
return self._embeddings
@property
def key_prefix(self) -> Optional[str]:
return self.config.key_prefix
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
keys: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Add text documents to the vector store.
Args:
texts: Iterable of strings to add to the vector store.
metadatas: Optional list of metadata dicts associated with the texts.
keys: Optional list of keys to associate with the documents.
**kwargs: Additional keyword arguments:
- ids: Optional list of ids to associate with the documents.
- refresh_indices: Whether to refresh the Redis indices
after adding the texts. Defaults to True.
- create_index_if_not_exists: Whether to create the Redis
index if it doesn't already exist. Defaults to True.
- batch_size: Optional. Number of texts to add to the
index at a time. Defaults to 1000.
Returns:
List of ids from adding the texts into the vector store.
Example:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
vector_store = RedisVectorStore(
index_name="langchain-demo",
embedding=OpenAIEmbeddings(),
redis_url="redis://localhost:6379",
)
texts = [
"The quick brown fox jumps over the lazy dog",
"Hello world",
"Machine learning is fascinating"
]
metadatas = [
{"source": "book", "page": 1},
{"source": "greeting", "language": "english"},
{"source": "article", "topic": "AI"}
]
ids = vector_store.add_texts(
texts=texts,
metadatas=metadatas,
batch_size=2
)
print(f"Added documents with ids: {ids}")
Note:
- If `metadatas` is provided, it must have the same length as `texts`.
- If `keys` is provided, it must have the same length as `texts`.
- The `batch_size` parameter can be used to control the number of
documents added in each batch, which can be useful for managing
memory usage when adding a large number of documents.
"""
# Convert texts to a list if it's not already
texts_list = list(texts)
# Embed the documents in bulk
embeddings = self._embeddings.embed_documents(texts_list)
datas = [
{
self.config.content_field: text,
self.config.embedding_field: embedding
if self.config.storage_type == StorageType.JSON.value
else np.array(embedding, dtype=np.float32).tobytes(),
**{
field_name: (
self.config.default_tag_separator.join(metadata[field_name])
if isinstance(metadata.get(field_name), list)
else metadata.get(field_name)
)
for field_name in metadata
},
}
for text, embedding, metadata in zip(
texts_list, embeddings, metadatas or [{}] * len(texts_list)
)
]
result = (
self._index.load(
datas, keys=[f"{self.config.key_prefix}:{key}" for key in keys]
)
if keys
else self._index.load(datas)
)
return list(result) if result is not None else []
[docs]
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
config: Optional[RedisConfig] = None,
keys: Optional[List[str]] = None,
return_keys: bool = False,
**kwargs: Any,
) -> RedisVectorStore:
"""Create a RedisVectorStore from a list of texts.
Args:
texts: List of texts to add to the vector store.
embedding: Embedding function to use for encoding the texts.
metadatas: Optional list of metadata dicts associated with the texts.
config: Optional RedisConfig object. If not provided, one will be created
from kwargs.
keys: Optional list of keys to associate with the documents.
return_keys: Whether to return the keys of the added documents.
**kwargs: Additional keyword arguments to pass to RedisConfig if config is
not provided.
Commonly used kwargs include:
- index_name: Name of the Redis index to create.
- redis_url: URL of the Redis instance to connect to.
- distance_metric: Distance metric to use for similarity search.
Default is "COSINE".
- indexing_algorithm: Indexing algorithm to use. Default is "FLAT".
Returns:
RedisVectorStore: A new RedisVectorStore instance with the texts added.
Example:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
texts = [
"The quick brown fox jumps over the lazy dog",
"Hello world",
"Machine learning is fascinating"
]
metadatas = [
{"source": "book", "page": 1},
{"source": "greeting", "language": "english"},
{"source": "article", "topic": "AI"}
]
embeddings = OpenAIEmbeddings()
vector_store = RedisVectorStore.from_texts(
texts=texts,
embedding=embeddings,
metadatas=metadatas,
index_name="langchain-demo",
redis_url="redis://localhost:6379",
distance_metric="COSINE"
)
# Now you can use the vector_store for similarity search
results = vector_store.similarity_search("AI and machine learning", k=1)
print(results[0].page_content)
Note:
- This method creates a new RedisVectorStore instance and adds the
provided texts to it.
- If `metadatas` is provided, it must have the same length as `texts`.
- If `keys` is provided, it must have the same length as `texts`.
- The `return_keys` parameter determines whether the method returns just the
RedisVectorStore instance or a tuple of (RedisVectorStore, List[str]) where
the second element is the list of keys for the added documents.
"""
config = config or RedisConfig.from_kwargs(**kwargs)
if metadatas is None:
metadatas = [{} for _ in range(len(texts))]
vector_store = cls(embeddings=embedding, config=config, **kwargs)
out_keys = vector_store.add_texts(texts, metadatas, keys) # type: ignore
if return_keys:
return cast(RedisVectorStore, (vector_store, out_keys))
else:
return vector_store
[docs]
@classmethod
def from_documents(
cls,
documents: List[Document],
embedding: Embeddings,
config: Optional[RedisConfig] = None,
return_keys: bool = False,
**kwargs: Any,
) -> RedisVectorStore:
"""Create a RedisVectorStore from a list of Documents.
Args:
documents: List of Document objects to add to the vector store.
embedding: Embeddings object to use for encoding the documents.
config: Optional RedisConfig object. If not provided, one will be
created from kwargs.
return_keys: Whether to return the keys of the added documents.
**kwargs: Additional keyword arguments to pass to RedisConfig if config
is not provided.
Common kwargs include:
- index_name: Name of the Redis index to create.
- redis_url: URL of the Redis instance to connect to.
- distance_metric: Distance metric to use for similarity search.
Default is "COSINE".
- indexing_algorithm: Indexing algorithm to use. Default is "FLAT".
Returns:
RedisVectorStore: A new RedisVectorStore instance with the documents added.
Example:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document
documents = [
Document(
page_content="The quick brown fox",
metadata={"animal": "fox"}
),
Document(
page_content="jumps over the lazy dog",
metadata={"animal": "dog"}
)
]
embeddings = OpenAIEmbeddings()
vector_store = RedisVectorStore.from_documents(
documents=documents,
embedding=embeddings,
index_name="animal-docs",
redis_url="redis://localhost:6379"
)
# Now you can use the vector_store for similarity search
results = vector_store.similarity_search("quick animal", k=1)
print(results[0].page_content)
Note:
- This method creates a new RedisVectorStore instance and adds the provided
documents to it.
- The method extracts the text content and metadata from
each Document object.
- If a RedisConfig object is not provided, one will be created using
the additional kwargs passed to this method.
- The embedding function is used to convert the document text into vector
representations for efficient similarity search.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
config = config or RedisConfig.from_kwargs(**kwargs)
return cls.from_texts(
texts=texts,
embedding=embedding,
metadatas=metadatas,
config=config,
return_keys=return_keys,
**kwargs,
)
[docs]
@classmethod
def from_existing_index(
cls,
index_name: str,
embedding: Embeddings,
**kwargs: Any,
) -> RedisVectorStore:
"""Create a RedisVectorStore from an existing Redis Search Index.
This method allows you to connect to an already existing index in Redis,
which can be useful for continuing work with previously created indexes
or for connecting to indexes created outside of this client.
Args:
index_name: Name of the existing index to use.
embedding: Embedding function to use for encoding queries.
**kwargs: Additional keyword arguments to pass to RedisConfig.
Common kwargs include:
- redis_url: URL of the Redis instance to connect to.
- redis_client: Pre-existing Redis client to use.
- vector_query_field: Name of the field containing the vector
representations.
- content_field: Name of the field containing the document content.
Returns:
RedisVectorStore: A new RedisVectorStore instance connected to the
existing index.
Example:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
from redis import Redis
embeddings = OpenAIEmbeddings()
# Connect to an existing index
vector_store = RedisVectorStore.from_existing_index(
index_name="my-existing-index",
embedding=embeddings,
redis_url="redis://localhost:6379",
vector_query_field="embedding",
content_field="text"
)
# Now you can use the vector_store for similarity search
results = vector_store.similarity_search("AI and machine learning", k=1)
print(results[0].page_content)
Note:
- This method assumes that the index already exists in Redis.
- The embedding function provided should be compatible with the embeddings
stored in the existing index.
- If you're using custom field names for vectors or content in your
existing index, make sure to specify them using `vector_query_field` and
`content_field` respectively.
- This method is useful for scenarios where you want to reuse an
existing index, such as when the index was created by another process
or when you want to use the same index across different sessions
or applications.
"""
config = RedisConfig.from_kwargs(**kwargs)
config.index_name = index_name
config.from_existing = True
return RedisVectorStore(embedding, config=config, **kwargs)
[docs]
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete ids from the vector store.
Args:
ids: Optional list of ids of the documents to delete.
**kwargs: Additional keyword arguments (not used in the
current implementation).
Returns:
Optional[bool]: True if one or more keys are deleted,
False otherwise
Example:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
vector_store = RedisVectorStore(
index_name="langchain-demo",
embedding=OpenAIEmbeddings(),
redis_url="redis://localhost:6379",
)
# Assuming documents with these ids exist in the store
ids_to_delete = ["doc1", "doc2", "doc3"]
result = vector_store.delete(ids=ids_to_delete)
if result:
print("Documents were succesfully deleted")
else:
print("No Documents were deleted")
Note:
- If `ids` is None or an empty list, the method returns False.
- If the number of actually deleted keys differs from the number of keys
submitted for deletion the method returns False
- The method uses the `drop_keys` functionality from RedisVL to delete
the keys from Redis.
- Keys are constructed by prefixing each id with the `key_prefix` specified
in the configuration.
"""
if ids and len(ids) > 0:
keys = [f"{self.config.key_prefix}:{id}" for id in ids]
return self._index.drop_keys(keys) == len(ids)
else:
return False
[docs]
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[FilterExpression] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Optional filter expression to apply.
**kwargs: Other keyword arguments:
- return_metadata: Whether to return metadata. Defaults to True.
- distance_threshold: Optional distance threshold for filtering results.
- return_all: Whether to return all data in the Hash/JSON including
non-indexed fields
Returns:
List of Documents most similar to the query vector.
"""
return_metadata = kwargs.get("return_metadata", True)
distance_threshold = kwargs.get("distance_threshold")
return_all = kwargs.get("return_all", False)
# Determine the fields to return based on the return_metadata flag
if not return_all:
return_fields = [self.config.content_field]
if return_metadata:
return_fields += [
field.name
for field in self._index.schema.fields.values()
if field.name
not in [self.config.embedding_field, self.config.content_field]
]
else:
return_fields = []
if distance_threshold is None:
results = self._index.query(
VectorQuery(
vector=embedding,
vector_field_name=self.config.embedding_field,
return_fields=return_fields,
num_results=k,
filter_expression=filter,
)
)
else:
results = self._index.query(
RangeQuery(
vector=embedding,
vector_field_name=self.config.embedding_field,
return_fields=return_fields,
num_results=k,
filter_expression=filter,
distance_threshold=distance_threshold,
)
)
if not return_all:
return [
Document(
page_content=doc[self.config.content_field],
metadata=(
{
field.name: doc[field.name]
for field in self._index.schema.fields.values()
if field.name
not in [
self.config.embedding_field,
self.config.content_field,
]
}
if return_metadata
else {}
),
)
for doc in results
]
else:
if self.config.storage_type == StorageType.HASH.value:
# Fetch full hash data for each document
if not results:
full_docs = []
else:
with self._index.client.pipeline(transaction=False) as pipe:
for doc in results:
pipe.hgetall(doc["id"])
full_docs = convert_bytes(pipe.execute())
return [
Document(
page_content=doc[self.config.content_field],
metadata={
k: v
for k, v in doc.items()
if k != self.config.content_field
},
)
for doc in full_docs
]
else:
# Fetch full JSON data for each document
if not results:
full_docs = []
else:
with self._index.client.json().pipeline(transaction=False) as pipe:
for doc in results:
pipe.get(doc["id"], ".")
full_docs = pipe.execute()
return [
Document(
page_content=doc[self.config.content_field],
metadata={
k: v
for k, v in doc.items()
if k != self.config.content_field
},
)
for doc in full_docs
if doc is not None # Handle potential missing documents
]
[docs]
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[FilterExpression] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Optional filter expression to apply.
**kwargs: Other keyword arguments to pass to the search function.
Returns:
List of Documents most similar to the query.
"""
embedding = self._embeddings.embed_query(query)
return self.similarity_search_by_vector(embedding, k, filter, **kwargs)
[docs]
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[FilterExpression] = None,
**kwargs: Any,
) -> Union[List[Tuple[Document, float]], List[Tuple[Document, float, np.ndarray]]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Optional filter expression to apply.
**kwargs: Other keyword arguments:
with_vectors: Whether to return document vectors. Defaults to False.
return_metadata: Whether to return metadata. Defaults to True.
distance_threshold: Optional distance threshold for filtering results.
Returns:
List of tuples of Documents most similar to the query vector, score, and
optionally the document vector.
"""
with_vectors = kwargs.get("with_vectors", False)
return_metadata = kwargs.get("return_metadata", True)
distance_threshold = kwargs.get("distance_threshold")
return_all = kwargs.get("return_all", False)
if not return_all:
return_fields = [self.config.content_field]
if return_metadata:
return_fields += [
field.name
for field in self._index.schema.fields.values()
if field.name
not in [self.config.embedding_field, self.config.content_field]
]
if with_vectors:
return_fields.append(self.config.embedding_field)
else:
return_fields = []
if distance_threshold is None:
results = self._index.query(
VectorQuery(
vector=embedding,
vector_field_name=self.config.embedding_field,
return_fields=return_fields,
num_results=k,
filter_expression=filter,
)
)
else:
results = self._index.query(
RangeQuery(
vector=embedding,
vector_field_name=self.config.embedding_field,
return_fields=return_fields,
num_results=k,
filter_expression=filter,
distance_threshold=distance_threshold,
)
)
if not return_all:
if with_vectors:
# Extract the document ids
doc_ids = [doc["id"] for doc in results]
# Retrieve the documents from the storage
docs_from_storage = self._index._storage.get(
self._index.client, doc_ids
)
# Create a dictionary mapping document ids to their embeddings
doc_embeddings_dict = {
doc_id: doc[self.config.embedding_field]
if self.config.storage_type == StorageType.JSON.value
else buffer_to_array(
doc[self.config.embedding_field],
dtype=self.config.vector_datatype,
)
for doc_id, doc in zip(doc_ids, docs_from_storage)
}
# Prepare the results with embeddings
docs_with_scores = [
(
Document(
page_content=doc[self.config.content_field],
metadata=(
{
field.name: doc[field.name]
for field in self._index.schema.fields.values()
if field.name
not in [
self.config.embedding_field,
self.config.content_field,
"id",
]
}
if return_metadata
else {}
),
),
float(doc["vector_distance"]),
doc_embeddings_dict[doc[self.config.id_field]],
)
for doc in results
]
else:
# Prepare the results without embeddings
docs_with_scores = [
( # type: ignore[misc]
Document(
page_content=doc[self.config.content_field],
metadata=(
{
field.name: doc[field.name]
for field in self._index.schema.fields.values()
if field.name
not in [
self.config.embedding_field,
self.config.content_field,
"id",
]
}
if return_metadata
else {}
),
),
float(doc["vector_distance"]),
)
for doc in results
]
else:
if self.config.storage_type == StorageType.HASH.value:
# Fetch full hash data for each document
pipe = self._index.client.pipeline()
for doc in results:
pipe.hgetall(doc["id"])
full_docs = convert_bytes(pipe.execute())
if with_vectors:
docs_with_scores = [
(
Document(
page_content=doc[self.config.content_field],
metadata={
k: v
for k, v in doc.items()
if k != self.config.content_field
},
),
float(result.get("vector_distance", 0)),
buffer_to_array(
doc.get(self.config.embedding_field),
dtype=self.config.vector_datatype,
),
)
for doc, result in zip(full_docs, results)
]
else:
docs_with_scores = [
cast( # type: ignore[misc]
Union[
Tuple[Document, float],
Tuple[Document, float, np.ndarray],
],
(
Document(
page_content=doc[self.config.content_field],
metadata={
k: v
for k, v in doc.items()
if k != self.config.content_field
},
),
float(result.get("vector_distance", 0)),
),
)
for doc, result in zip(full_docs, results)
]
else:
# Fetch full JSON data for each document
doc_ids = [doc["id"] for doc in results]
full_docs = self._index.client.json().mget(doc_ids, ".")
if with_vectors:
docs_with_scores = [
(
Document(
page_content=doc[self.config.content_field],
metadata={
k: v
for k, v in doc.items()
if k != self.config.content_field
},
),
float(result.get("vector_distance", 0)),
doc.get(self.config.embedding_field),
)
for doc, result in zip(full_docs, results)
]
else:
docs_with_scores = [
cast( # type: ignore[misc]
Union[
Tuple[Document, float],
Tuple[Document, float, np.ndarray],
],
(
Document(
page_content=doc[self.config.content_field],
metadata={
k: v
for k, v in doc.items()
if k != self.config.content_field
},
),
float(result.get("vector_distance", 0)),
),
)
for doc, result in zip(full_docs, results)
]
return docs_with_scores
[docs]
def similarity_search_with_score( # type: ignore[override]
self,
query: str,
k: int = 4,
filter: Optional[FilterExpression] = None,
**kwargs: Any,
) -> Union[List[Tuple[Document, float]], List[Tuple[Document, float, np.ndarray]]]:
"""Return documents most similar to query string, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Optional filter expression to apply to the query.
**kwargs: Other keyword arguments to pass to the search function:
- custom_query: Optional callable that can be used
to customize the query.
- doc_builder: Optional callable to customize Document creation.
- return_metadata: Whether to return metadata. Defaults to True.
- distance_threshold: Optional distance threshold for filtering results.
- return_all: Whether to return all data in the Hash/JSON including
non-indexed fields. Defaults to False.
Returns:
List of tuples of (Document, score) most similar to the query.
Example:
.. code-block:: python
from langchain_redis import RedisVectorStore
from langchain_openai import OpenAIEmbeddings
vector_store = RedisVectorStore(
index_name="langchain-demo",
embedding=OpenAIEmbeddings(),
redis_url="redis://localhost:6379",
)
results = vector_store.similarity_search_with_score(
"What is machine learning?",
k=2,
filter=None
)
for doc, score in results:
print(f"Score: {score}")
print(f"Content: {doc.page_content}")
print(f"Metadata: {doc.metadata}\n")
Note:
- The method returns scores along with documents. Lower scores indicate
higher similarity.
- The actual search is performed using the vector representation of the
query, which is why an embedding function must be provided during
initialization.
- The `filter` parameter allows for additional filtering of results
based on metadata.
- If `return_all` is set to True, all fields stored in Redis will be
returned, which may include non-indexed fields.
"""
embedding = self._embeddings.embed_query(query)
return self.similarity_search_with_score_by_vector(
embedding,
k,
filter,
**kwargs,
)
[docs]
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.
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.
**kwargs: Other keyword arguments to pass to the search function.
Returns:
List of Documents selected by maximal marginal relevance.
"""
# Fetch top fetch_k documents based on similarity to the embedding
docs_scores_embeddings = self.similarity_search_with_score_by_vector(
embedding, k=fetch_k, with_vectors=True, **kwargs
)
# Extract documents and embeddings
documents = []
embeddings = []
for item in docs_scores_embeddings:
if len(item) == 3:
doc, _, emb = item
documents.append(doc)
embeddings.append(emb)
elif len(item) == 2:
doc, _ = item
documents.append(doc)
# Perform MMR on the embeddings
if embeddings:
mmr_selected = maximal_marginal_relevance(
np.array(embedding),
embeddings,
k=min(k, len(documents)),
lambda_mult=lambda_mult,
)
# Return the selected documents based on MMR
return [documents[i] for i in mmr_selected]
else:
return []
[docs]
def max_marginal_relevance_search(
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
among selected documents.
Args:
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.
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.
**kwargs: Other keyword arguments to pass to the search function.
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self.embeddings.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
query_embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
)
