Add embedding and vectorstore provider info as tags (#8027)

Example:
https://smith.langchain.com/public/bcd3714d-abba-4790-81c8-9b5718535867/r


The vectorstore implementations aren't super standardized yet, so just
adding an optional embeddings property to pass in.

langchain/callbacks/tracers/base.py

@@ -326,6 +326,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
         metadata: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> None:
@@ -346,6 +347,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
             start_time=start_time,
             execution_order=execution_order,
             child_execution_order=execution_order,
+            tags=tags,
             child_runs=[],
             run_type=RunTypeEnum.retriever,
         )

langchain/vectorstores/analyticdb.py

@@ -79,6 +79,10 @@ class AnalyticDB(VectorStore):
         self.engine = create_engine(self.connection_string, **_engine_args)
         self.create_collection()
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def _select_relevance_score_fn(self) -> Callable[[float], float]:
         return self._euclidean_relevance_score_fn
 

langchain/vectorstores/annoy.py

@@ -61,6 +61,11 @@ class Annoy(VectorStore):
         self.docstore = docstore
         self.index_to_docstore_id = index_to_docstore_id
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        # TODO: Accept embedding object directly
+        return None
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/atlas.py

@@ -46,7 +46,7 @@ class AtlasDB(VectorStore):
         Args:
             name (str): The name of your project. If the project already exists,
                 it will be loaded.
-            embedding_function (Optional[Callable]): An optional function used for
+            embedding_function (Optional[Embeddings]): An optional function used for
                 embedding your data. If None, data will be embedded with
                 Nomic's embed model.
             api_key (str): Your nomic API key
@@ -86,6 +86,10 @@ class AtlasDB(VectorStore):
         )
         self.project._latest_project_state()
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self._embedding_function
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/awadb.py

@@ -73,6 +73,12 @@ class AwaDB(VectorStore):
             self.table2embeddings[table_name] = embedding
         self.using_table_name = table_name
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        if self.using_table_name in self.table2embeddings:
+            return self.table2embeddings[self.using_table_name]
+        return None
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/azuresearch.py

@@ -191,6 +191,11 @@ class AzureSearch(VectorStore):
         self.semantic_configuration_name = semantic_configuration_name
         self.semantic_query_language = semantic_query_language
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        # TODO: Support embedding object directly
+        return None
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/base.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import asyncio
+import logging
 import math
 import warnings
 from abc import ABC, abstractmethod
@@ -31,6 +32,8 @@ from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever
 
+logger = logging.getLogger(__name__)
+
 VST = TypeVar("VST", bound="VectorStore")
 
 
@@ -55,6 +58,14 @@ class VectorStore(ABC):
             List of ids from adding the texts into the vectorstore.
         """
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        """Access the query embedding object if available."""
+        logger.debug(
+            f"{Embeddings.__name__} is not implemented for {self.__class__.__name__}"
+        )
+        return None
+
     def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
         """Delete by vector ID or other criteria.
 
@@ -435,8 +446,17 @@ class VectorStore(ABC):
         """Return VectorStore initialized from texts and embeddings."""
         raise NotImplementedError
 
+    def __get_retriever_tags(self) -> List[str]:
+        """Get tags for retriever."""
+        tags = [self.__class__.__name__]
+        if self.embeddings:
+            tags.append(self.embeddings.__class__.__name__)
+        return tags
+
     def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
-        return VectorStoreRetriever(vectorstore=self, **kwargs)
+        tags = kwargs.pop("tags", None) or []
+        tags.extend(self.__get_retriever_tags())
+        return VectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
 
 
 class VectorStoreRetriever(BaseRetriever):

langchain/vectorstores/cassandra.py

@@ -77,6 +77,10 @@ class Cassandra(VectorStore):
             primary_key_type="TEXT",
         )
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding
+
     def _select_relevance_score_fn(self) -> Callable[[float], float]:
         return self._cosine_relevance_score_fn
 

langchain/vectorstores/chroma.py

@@ -121,6 +121,10 @@ class Chroma(VectorStore):
         )
         self.override_relevance_score_fn = relevance_score_fn
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self._embedding_function
+
     @xor_args(("query_texts", "query_embeddings"))
     def __query_collection(
         self,

langchain/vectorstores/clickhouse.py

@@ -212,6 +212,10 @@ CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
         self.client.command("SET allow_experimental_annoy_index=1")
         self.client.command(self.schema)
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def escape_str(self, value: str) -> str:
         return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
 

langchain/vectorstores/deeplake.py

@@ -151,6 +151,10 @@ class DeepLake(VectorStore):
         self._embedding_function = embedding_function
         self._id_tensor_name = "ids" if "ids" in self.vectorstore.tensors() else "id"
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self._embedding_function
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/elastic_vector_search.py

@@ -153,6 +153,10 @@ class ElasticVectorSearch(VectorStore, ABC):
                 f"Your elasticsearch client string is mis-formatted. Got error: {e} "
             )
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embeddings
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/faiss.py

@@ -87,6 +87,11 @@ class FAISS(VectorStore):
                 )
             )
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        # TODO: Accept embeddings object directly
+        return None
+
     def __add(
         self,
         texts: Iterable[str],

langchain/vectorstores/hologres.py

@@ -148,6 +148,10 @@ class Hologres(VectorStore):
         self.create_vector_extension()
         self.create_table()
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def create_vector_extension(self) -> None:
         try:
             self.storage.create_vector_extension()

langchain/vectorstores/lancedb.py

@@ -51,6 +51,10 @@ class LanceDB(VectorStore):
         self._id_key = id_key
         self._text_key = text_key
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embedding
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/marqo.py

@@ -82,6 +82,10 @@ class Marqo(VectorStore):
 
         self._document_batch_size = 1024
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return None
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/matching_engine.py

@@ -84,6 +84,10 @@ class MatchingEngine(VectorStore):
         self.credentials = credentials
         self.gcs_bucket_name = gcs_bucket_name
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding
+
     def _validate_google_libraries_installation(self) -> None:
         """Validates that Google libraries that are needed are installed."""
         try:

langchain/vectorstores/milvus.py

@@ -164,6 +164,10 @@ class Milvus(VectorStore):
         # Initialize the vector store
         self._init()
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_func
+
     def _create_connection_alias(self, connection_args: dict) -> str:
         """Create the connection to the Milvus server."""
         from pymilvus import MilvusException, connections

langchain/vectorstores/mongodb_atlas.py

@@ -77,6 +77,10 @@ class MongoDBAtlasVectorSearch(VectorStore):
         self._text_key = text_key
         self._embedding_key = embedding_key
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embedding
+
     @classmethod
     def from_connection_string(
         cls,

langchain/vectorstores/myscale.py

@@ -115,7 +115,7 @@ class MyScale(VectorStore):
     ) -> None:
         """MyScale Wrapper to LangChain
 
-        embedding_function (Embeddings):
+        embedding (Embeddings):
         config (MyScaleSettings): Configuration to MyScale Client
         Other keyword arguments will pass into
             [clickhouse-connect](https://docs.myscale.com/)
@@ -175,7 +175,7 @@ class MyScale(VectorStore):
         self.dim = dim
         self.BS = "\\"
         self.must_escape = ("\\", "'")
-        self.embedding_function = embedding.embed_query
+        self._embeddings = embedding
         self.dist_order = "ASC" if self.config.metric in ["cosine", "l2"] else "DESC"
 
         # Create a connection to myscale
@@ -189,6 +189,10 @@ class MyScale(VectorStore):
         self.client.command("SET allow_experimental_object_type=1")
         self.client.command(schema_)
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embeddings
+
     def escape_str(self, value: str) -> str:
         return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
 
@@ -238,7 +242,7 @@ class MyScale(VectorStore):
         column_names = {
             colmap_["id"]: ids,
             colmap_["text"]: texts,
-            colmap_["vector"]: map(self.embedding_function, texts),
+            colmap_["vector"]: map(self._embeddings.embed_query, texts),
         }
         metadatas = metadatas or [{} for _ in texts]
         column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
@@ -269,7 +273,7 @@ class MyScale(VectorStore):
     @classmethod
     def from_texts(
         cls,
-        texts: List[str],
+        texts: Iterable[str],
         embedding: Embeddings,
         metadatas: Optional[List[Dict[Any, Any]]] = None,
         config: Optional[MyScaleSettings] = None,
@@ -280,8 +284,8 @@ class MyScale(VectorStore):
         """Create Myscale wrapper with existing texts
 
         Args:
-            embedding_function (Embeddings): Function to extract text embedding
             texts (Iterable[str]): List or tuple of strings to be added
+            embedding (Embeddings): Function to extract text embedding
             config (MyScaleSettings, Optional): Myscale configuration
             text_ids (Optional[Iterable], optional): IDs for the texts.
                                                      Defaults to None.
@@ -357,7 +361,7 @@ class MyScale(VectorStore):
             List[Document]: List of Documents
         """
         return self.similarity_search_by_vector(
-            self.embedding_function(query), k, where_str, **kwargs
+            self._embeddings.embed_query(query), k, where_str, **kwargs
         )
 
     def similarity_search_by_vector(
@@ -417,7 +421,7 @@ class MyScale(VectorStore):
             and cosine distance in float for each.
             Lower score represents more similarity.
         """
-        q_str = self._build_qstr(self.embedding_function(query), k, where_str)
+        q_str = self._build_qstr(self._embeddings.embed_query(query), k, where_str)
         try:
             return [
                 (

langchain/vectorstores/opensearch_vector_search.py

@@ -316,6 +316,10 @@ class OpenSearchVectorSearch(VectorStore):
         self.index_name = index_name
         self.client = _get_opensearch_client(opensearch_url, **kwargs)
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/pgembedding.py

@@ -135,6 +135,10 @@ class PGEmbedding(VectorStore):
         self.create_tables_if_not_exists()
         self.create_collection()
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def connect(self) -> sqlalchemy.engine.Connection:
         engine = sqlalchemy.create_engine(self.connection_string)
         conn = engine.connect()

langchain/vectorstores/pgvector.py

@@ -125,6 +125,10 @@ class PGVector(VectorStore):
         self.create_tables_if_not_exists()
         self.create_collection()
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def connect(self) -> sqlalchemy.engine.Connection:
         engine = sqlalchemy.create_engine(self.connection_string)
         conn = engine.connect()

langchain/vectorstores/pinecone.py

@@ -62,6 +62,11 @@ class Pinecone(VectorStore):
         self._namespace = namespace
         self.distance_strategy = distance_strategy
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        # TODO: Accept this object directly
+        return None
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/qdrant.py

@@ -123,7 +123,7 @@ class Qdrant(VectorStore):
                 "Use `embeddings` only."
             )
 
-        self.embeddings = embeddings
+        self._embeddings = embeddings
         self._embeddings_function = embedding_function
         self.client: qdrant_client.QdrantClient = client
         self.collection_name = collection_name
@@ -143,10 +143,14 @@ class Qdrant(VectorStore):
                 "Using `embeddings` as `embedding_function` which is deprecated"
             )
             self._embeddings_function = embeddings
-            self.embeddings = None
+            self._embeddings = None
 
         self.distance_strategy = distance_strategy.upper()
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self._embeddings
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/redis.py

@@ -161,6 +161,11 @@ class Redis(VectorStore):
         self.distance_metric = distance_metric
         self.relevance_score_fn = relevance_score_fn
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        # TODO: Accept embedding object directly
+        return None
+
     def _select_relevance_score_fn(self) -> Callable[[float], float]:
         if self.relevance_score_fn:
             return self.relevance_score_fn
@@ -601,7 +606,9 @@ class Redis(VectorStore):
         )
 
     def as_retriever(self, **kwargs: Any) -> RedisVectorStoreRetriever:
-        return RedisVectorStoreRetriever(vectorstore=self, **kwargs)
+        tags = kwargs.pop("tags", None) or []
+        tags.extend(self.__get_retriever_tags())
+        return RedisVectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
 
 
 class RedisVectorStoreRetriever(VectorStoreRetriever):

langchain/vectorstores/rocksetdb.py

@@ -83,6 +83,10 @@ class Rockset(VectorStore):
         self._text_key = text_key
         self._embedding_key = embedding_key
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embeddings
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/singlestoredb.py

@@ -213,6 +213,10 @@ class SingleStoreDB(VectorStore):
         )
         self._create_table()
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding
+
     def _select_relevance_score_fn(self) -> Callable[[float], float]:
         return self._max_inner_product_relevance_score_fn
 
@@ -441,7 +445,9 @@ class SingleStoreDB(VectorStore):
         return instance
 
     def as_retriever(self, **kwargs: Any) -> SingleStoreDBRetriever:
-        return SingleStoreDBRetriever(vectorstore=self, **kwargs)
+        tags = kwargs.pop("tags", None) or []
+        tags.extend(self.__get_retriever_tags())
+        return SingleStoreDBRetriever(vectorstore=self, **kwargs, tags=tags)
 
 
 class SingleStoreDBRetriever(VectorStoreRetriever):

langchain/vectorstores/sklearn.py

@@ -163,6 +163,10 @@ class SKLearnVectorStore(VectorStore):
         if self._persist_path is not None and os.path.isfile(self._persist_path):
             self._load()
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embedding_function
+
     def persist(self) -> None:
         if self._serializer is None:
             raise SKLearnVectorStoreException(

langchain/vectorstores/starrocks.py

@@ -207,6 +207,10 @@ CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
     def escape_str(self, value: str) -> str:
         return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
         ks = ",".join(column_names)
         embed_tuple_index = tuple(column_names).index(

langchain/vectorstores/supabase.py

@@ -67,6 +67,10 @@ class SupabaseVectorStore(VectorStore):
         self.table_name = table_name or "documents"
         self.query_name = query_name or "match_documents"
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embedding
+
     def add_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/tair.py

@@ -51,6 +51,10 @@ class Tair(VectorStore):
         self.metadata_key = metadata_key
         self.search_params = search_params
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self.embedding_function
+
     def create_index_if_not_exist(
         self,
         dim: int,

langchain/vectorstores/tigris.py

@@ -28,6 +28,10 @@ class Tigris(VectorStore):
         self._embed_fn = embeddings
         self._vector_store = TigrisVectorStore(client.get_search(), index_name)
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embed_fn
+
     @property
     def search_index(self) -> TigrisVectorStore:
         return self._vector_store

langchain/vectorstores/typesense.py

@@ -81,6 +81,10 @@ class Typesense(VectorStore):
     def _collection(self) -> Collection:
         return self._typesense_client.collections[self._typesense_collection_name]
 
+    @property
+    def embeddings(self) -> Embeddings:
+        return self._embedding
+
     def _prep_texts(
         self,
         texts: Iterable[str],

langchain/vectorstores/vectara.py

@@ -61,6 +61,10 @@ class Vectara(VectorStore):
         adapter = requests.adapters.HTTPAdapter(max_retries=3)
         self._session.mount("http://", adapter)
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return None
+
     def _get_post_headers(self) -> dict:
         """Returns headers that should be attached to each post request."""
         return {
@@ -402,7 +406,9 @@ class Vectara(VectorStore):
         return vectara
 
     def as_retriever(self, **kwargs: Any) -> VectaraRetriever:
-        return VectaraRetriever(vectorstore=self, **kwargs)
+        tags = kwargs.pop("tags", None) or []
+        tags.extend(self.__get_retriever_tags())
+        return VectaraRetriever(vectorstore=self, **kwargs, tags=tags)
 
 
 class VectaraRetriever(VectorStoreRetriever):

langchain/vectorstores/weaviate.py

@@ -118,6 +118,10 @@ class Weaviate(VectorStore):
         if attributes is not None:
             self._query_attrs.extend(attributes)
 
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self._embedding
+
     def _select_relevance_score_fn(self) -> Callable[[float], float]:
         return (
             self.relevance_score_fn