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ElasticKnnSearch rewrite - bug fix - return Document (#8180)

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Fixes: 
https://github.com/hwchase17/langchain/issues/7117
https://github.com/hwchase17/langchain/issues/5760

Adding back `create_index` , `add_texts`, `from_texts` to
ElasticKnnSearch

`from_texts` matches standard `from_texts` methods as quick start up
method

`knn_search` and `hybrid_result` return a list of [`Document()`,
`score`,]

# Test `from_texts` for quick start
```
# create new index using from_text

from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings

model_id = "sentence-transformers__all-distilroberta-v1" 
dims = 768
es_cloud_id = ""
es_user = ""
es_password = ""
test_index = "knn_test_index_305"

embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    #input_field=input_field,
    es_cloud_id=es_cloud_id,
    es_user=es_user,
    es_password=es_password,
)

# add texts and create class instance
texts = ["This is a test document", "This is another test document"]
knnvectorsearch = ElasticKnnSearch.from_texts(
    texts=texts,
    embedding=embeddings,
    index_name= test_index,
    vector_query_field='vector',
    query_field='text',
    model_id=model_id,
    dims=dims,
	es_cloud_id=es_cloud_id, 
	es_user=es_user, 
	es_password=es_password
)

# Test `add_texts` method
texts2 = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knnvectorsearch.add_texts(texts2)

query = "Hello"
knn_result = knnvectorsearch.knn_search(query = query, model_id= model_id, k=2)

hybrid_result = knnvectorsearch.knn_hybrid_search(query = query, model_id= model_id, k=2)

```

The  mapping is as follows:
```
{
  "knn_test_index_012": {
    "mappings": {
      "properties": {
        "text": {
          "type": "text"
        },
        "vector": {
          "type": "dense_vector",
          "dims": 768,
          "index": true,
          "similarity": "dot_product"
        }
      }
    }
  }
}
```

# Check response type
```
>>> hybrid_result
[(Document(page_content='Hello, world!', metadata={}), 0.94232327), (Document(page_content='I love Python.', metadata={}), 0.5321523)]

>>> hybrid_result[0]
(Document(page_content='Hello, world!', metadata={}), 0.94232327)

>>> hybrid_result[0][0]
Document(page_content='Hello, world!', metadata={})

>>> type(hybrid_result[0][0])
<class 'langchain.schema.document.Document'>
```

# Test with existing Index
```
from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings

## Initialize ElasticsearchEmbeddings
model_id = "sentence-transformers__all-distilroberta-v1" 
dims = 768
es_cloud_id = 
es_user = ""
es_password = ""
test_index = "knn_test_index_012"

embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    es_cloud_id=es_cloud_id,
    es_user=es_user,
    es_password=es_password,
)

## Initialize ElasticKnnSearch
knn_search = ElasticKnnSearch(
	es_cloud_id=es_cloud_id, 
	es_user=es_user, 
	es_password=es_password, 
	index_name= test_index, 
	embedding= embeddings
)


## Test adding vectors

### Test `add_texts` method when index created
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)

```

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
pull/8402/head
Jeff Vestal 1 year ago committed by GitHub
parent a221a9ced0
commit c7ff5f19a8
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1 changed files with 269 additions and 122 deletions
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391
libs/langchain/langchain/vectorstores/elastic_vector_search.py
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@ -339,11 +339,38 @@ class ElasticVectorSearch(VectorStore, ABC):
self.client.delete(index=self.index_name, id=id)
class ElasticKnnSearch(ElasticVectorSearch):
class ElasticKnnSearch(VectorStore, ABC):
"""
A class for performing k-Nearest Neighbors (k-NN) search on an Elasticsearch index.
The class is designed for a text search scenario where documents are text strings
and their embeddings are vector representations of those strings.
ElasticKnnSearch is a class for performing k-nearest neighbor
(k-NN) searches on text data using Elasticsearch.
This class is used to create an Elasticsearch index of text data that
can be searched using k-NN search. The text data is transformed into
vector embeddings using a provided embedding model, and these embeddings
are stored in the Elasticsearch index.
Attributes:
index_name (str): The name of the Elasticsearch index.
embedding (Embeddings): The embedding model to use for transforming text data
into vector embeddings.
es_connection (Elasticsearch, optional): An existing Elasticsearch connection.
es_cloud_id (str, optional): The Cloud ID of your Elasticsearch Service
deployment.
es_user (str, optional): The username for your Elasticsearch Service deployment.
es_password (str, optional): The password for your Elasticsearch Service
deployment.
vector_query_field (str, optional): The name of the field in the Elasticsearch
index that contains the vector embeddings.
query_field (str, optional): The name of the field in the Elasticsearch index
that contains the original text data.
Usage:
>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_content='Hello world!', metadata={}), 0.9)]
"""
def __init__(
@ -357,22 +384,6 @@ class ElasticKnnSearch(ElasticVectorSearch):
vector_query_field: Optional[str] = "vector",
query_field: Optional[str] = "text",
):
"""
Initializes an instance of the ElasticKnnSearch class and sets up the
Elasticsearch client.
Args:
index_name: The name of the Elasticsearch index.
embedding: An instance of the Embeddings class, used to generate vector
representations of text strings.
es_connection: An existing Elasticsearch connection.
es_cloud_id: The Cloud ID of the Elasticsearch instance. Required if
creating a new connection.
es_user: The username for the Elasticsearch instance. Required if
creating a new connection.
es_password: The password for the Elasticsearch instance. Required if
creating a new connection.
"""
try:
import elasticsearch
except ImportError:
@ -402,48 +413,10 @@ class ElasticKnnSearch(ElasticVectorSearch):
or valid credentials for creating a new connection."""
)
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
index_name: Optional[str] = None,
refresh_indices: bool = True,
es_connection: Optional["Elasticsearch"] = None,
es_cloud_id: Optional[str] = None,
es_user: Optional[str] = None,
es_password: Optional[str] = None,
**kwargs: Any,
) -> ElasticKnnSearch:
"""Construct ElasticKnnSearch wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Elasticsearch instance.
3. Adds the documents to the newly created Elasticsearch index.
This is intended to be a quick way to get started.
"""
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(
index_name,
embedding,
es_connection=es_connection,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
**kwargs,
)
vectorsearch.add_texts(
texts, metadatas=metadatas, refresh_indices=refresh_indices, ids=ids
)
return vectorsearch
@staticmethod
def _default_knn_mapping(dims: int) -> Dict:
"""Generates a default index mapping for kNN search."""
def _default_knn_mapping(
dims: int, similarity: Optional[str] = "dot_product"
) -> Dict:
return {
"properties": {
"text": {"type": "text"},
@ -451,7 +424,7 @@ class ElasticKnnSearch(ElasticVectorSearch):
"type": "dense_vector",
"dims": dims,
"index": True,
"similarity": "dot_product",
"similarity": similarity,
},
}
}
@ -490,6 +463,21 @@ class ElasticKnnSearch(ElasticVectorSearch):
return knn
def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""
Pass through to `knn_search`
"""
results = self.knn_search(query=query, k=k, **kwargs)
return [doc for doc, score in results]
def similarity_search_with_score(
self, query: str, k: int = 10, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Pass through to `knn_search including score`"""
return self.knn_search(query=query, k=k, **kwargs)
def knn_search(
self,
query: Optional[str] = None,
@ -501,51 +489,62 @@ class ElasticKnnSearch(ElasticVectorSearch):
fields: Optional[
Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
) -> Dict:
page_content: Optional[str] = "text",
) -> List[Tuple[Document, float]]:
"""
Performs a k-nearest neighbor (k-NN) search on the Elasticsearch index.
The search can be conducted using either a raw query vector or a model ID.
The method first generates
the body of the search query, which can be interpreted by Elasticsearch.
It then performs the k-NN
search on the Elasticsearch index and returns the results.
Perform a k-NN search on the Elasticsearch index.
Args:
query: The query or queries to be used for the search. Required if
`query_vector` is not provided.
k: The number of nearest neighbors to return. Defaults to 10.
query_vector: The query vector to be used for the search. Required if
`query` is not provided.
model_id: The ID of the model to use for generating the query vector, if
`query` is provided.
size: The number of search hits to return. Defaults to 10.
source: Whether to include the source of each hit in the results.
fields: The fields to include in the source of each hit. If None, all
fields are included.
vector_query_field: Field name to use in knn search if not default 'vector'
query (str, optional): The query text to search for.
k (int, optional): The number of nearest neighbors to return.
query_vector (List[float], optional): The query vector to search for.
model_id (str, optional): The ID of the model to use for transforming the
query text into a vector.
size (int, optional): The number of search results to return.
source (bool, optional): Whether to return the source of the search results.
fields (List[Mapping[str, Any]], optional): The fields to return in the
search results.
page_content (str, optional): The name of the field that contains the page
content.
Returns:
The search results.
Raises:
ValueError: If neither `query_vector` nor `model_id` is provided, or if
both are provided.
A list of tuples, where each tuple contains a Document object and a score.
"""
# if not source and (fields == None or page_content not in fields):
if not source and (
fields is None or not any(page_content in field for field in fields)
):
raise ValueError("If source=False `page_content` field must be in `fields`")
knn_query_body = self._default_knn_query(
query_vector=query_vector, query=query, model_id=model_id, k=k
)
# Perform the kNN search on the Elasticsearch index and return the results.
res = self.client.search(
response = self.client.search(
index=self.index_name,
knn=knn_query_body,
size=size,
source=source,
fields=fields,
)
return dict(res)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"][page_content]
if source
else hit["fields"][page_content][0],
metadata=hit["fields"] if fields else {},
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
def knn_hybrid_search(
self,
@ -560,43 +559,38 @@ class ElasticKnnSearch(ElasticVectorSearch):
fields: Optional[
Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
) -> Dict[Any, Any]:
"""Performs a hybrid k-nearest neighbor (k-NN) and text-based search on the
Elasticsearch index.
The search can be conducted using either a raw query vector or a model ID.
The method first generates
the body of the k-NN search query and the text-based query, which can be
interpreted by Elasticsearch.
It then performs the hybrid search on the Elasticsearch index and returns the
results.
page_content: Optional[str] = "text",
) -> List[Tuple[Document, float]]:
"""
Perform a hybrid k-NN and text search on the Elasticsearch index.
Args:
query: The query or queries to be used for the search. Required if
`query_vector` is not provided.
k: The number of nearest neighbors to return. Defaults to 10.
query_vector: The query vector to be used for the search. Required if
`query` is not provided.
model_id: The ID of the model to use for generating the query vector, if
`query` is provided.
size: The number of search hits to return. Defaults to 10.
source: Whether to include the source of each hit in the results.
knn_boost: The boost factor for the k-NN part of the search.
query_boost: The boost factor for the text-based part of the search.
fields
The fields to include in the source of each hit. If None, all fields are
included. Defaults to None.
vector_query_field: Field name to use in knn search if not default 'vector'
query_field: Field name to use in search if not default 'text'
query (str, optional): The query text to search for.
k (int, optional): The number of nearest neighbors to return.
query_vector (List[float], optional): The query vector to search for.
model_id (str, optional): The ID of the model to use for transforming the
query text into a vector.
size (int, optional): The number of search results to return.
source (bool, optional): Whether to return the source of the search results.
knn_boost (float, optional): The boost value to apply to the k-NN search
results.
query_boost (float, optional): The boost value to apply to the text search
results.
fields (List[Mapping[str, Any]], optional): The fields to return in the
search results.
page_content (str, optional): The name of the field that contains the page
content.
Returns:
The search results.
Raises:
ValueError: If neither `query_vector` nor `model_id` is provided, or if
both are provided.
A list of tuples, where each tuple contains a Document object and a score.
"""
# if not source and (fields == None or page_content not in fields):
if not source and (
fields is None or not any(page_content in field for field in fields)
):
raise ValueError("If source=False `page_content` field must be in `fields`")
knn_query_body = self._default_knn_query(
query_vector=query_vector, query=query, model_id=model_id, k=k
)
@ -610,7 +604,7 @@ class ElasticKnnSearch(ElasticVectorSearch):
}
# Perform the hybrid search on the Elasticsearch index and return the results.
res = self.client.search(
response = self.client.search(
index=self.index_name,
query=match_query_body,
knn=knn_query_body,
@ -618,4 +612,157 @@ class ElasticKnnSearch(ElasticVectorSearch):
size=size,
source=source,
)
return dict(res)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"][page_content]
if source
else hit["fields"][page_content][0],
metadata=hit["fields"] if fields else {},
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
def create_knn_index(self, mapping: Dict) -> None:
"""
Create a new k-NN index in Elasticsearch.
Args:
mapping (Dict): The mapping to use for the new index.
Returns:
None
"""
self.client.indices.create(index=self.index_name, mappings=mapping)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
model_id: Optional[str] = None,
refresh_indices: bool = False,
**kwargs: Any,
) -> List[str]:
"""
Add a list of texts to the Elasticsearch index.
Args:
texts (Iterable[str]): The texts to add to the index.
metadatas (List[Dict[Any, Any]], optional): A list of metadata dictionaries
to associate with the texts.
model_id (str, optional): The ID of the model to use for transforming the
texts into vectors.
refresh_indices (bool, optional): Whether to refresh the Elasticsearch
indices after adding the texts.
**kwargs: Arbitrary keyword arguments.
Returns:
A list of IDs for the added texts.
"""
# Check if the index exists.
if not self.client.indices.exists(index=self.index_name):
dims = kwargs.get("dims")
if dims is None:
raise ValueError("ElasticKnnSearch requires 'dims' parameter")
similarity = kwargs.get("similarity")
optional_args = {}
if similarity is not None:
optional_args["similarity"] = similarity
mapping = self._default_knn_mapping(dims=dims, **optional_args)
self.create_knn_index(mapping)
embeddings = self.embedding.embed_documents(list(texts))
# body = []
body: List[Mapping[str, Any]] = []
for text, vector in zip(texts, embeddings):
body.extend(
[
{"index": {"_index": self.index_name}},
{"text": text, "vector": vector},
]
)
responses = self.client.bulk(operations=body)
ids = [
item["index"]["_id"]
for item in responses["items"]
if item["index"]["result"] == "created"
]
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> ElasticKnnSearch:
"""
Create a new ElasticKnnSearch instance and add a list of texts to the
Elasticsearch index.
Args:
texts (List[str]): The texts to add to the index.
embedding (Embeddings): The embedding model to use for transforming the
texts into vectors.
metadatas (List[Dict[Any, Any]], optional): A list of metadata dictionaries
to associate with the texts.
**kwargs: Arbitrary keyword arguments.
Returns:
A new ElasticKnnSearch instance.
"""
index_name = kwargs.get("index_name", str(uuid.uuid4()))
es_connection = kwargs.get("es_connection")
es_cloud_id = kwargs.get("es_cloud_id")
es_user = kwargs.get("es_user")
es_password = kwargs.get("es_password")
vector_query_field = kwargs.get("vector_query_field", "vector")
query_field = kwargs.get("query_field", "text")
model_id = kwargs.get("model_id")
dims = kwargs.get("dims")
if dims is None:
raise ValueError("ElasticKnnSearch requires 'dims' parameter")
optional_args = {}
if vector_query_field is not None:
optional_args["vector_query_field"] = vector_query_field
if query_field is not None:
optional_args["query_field"] = query_field
knnvectorsearch = cls(
index_name=index_name,
embedding=embedding,
es_connection=es_connection,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
**optional_args,
)
# Encode the provided texts and add them to the newly created index.
knnvectorsearch.add_texts(texts, model_id=model_id, dims=dims, **optional_args)
return knnvectorsearch

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