Add serialized object to retriever start callback (#7074)

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langchain/callbacks/base.py

@@ -168,10 +168,12 @@ class CallbackManagerMixin:
 
     def on_retriever_start(
         self,
+        serialized: Dict[str, Any],
         query: str,
         *,
         run_id: UUID,
         parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Any:
         """Run when Retriever starts running."""
@@ -421,6 +423,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
 
     async def on_retriever_start(
         self,
+        serialized: Dict[str, Any],
         query: str,
         *,
         run_id: UUID,

langchain/callbacks/manager.py

@@ -1196,6 +1196,7 @@ class CallbackManager(BaseCallbackManager):
 
     def on_retriever_start(
         self,
+        serialized: Dict[str, Any],
         query: str,
         run_id: Optional[UUID] = None,
         parent_run_id: Optional[UUID] = None,
@@ -1209,6 +1210,7 @@ class CallbackManager(BaseCallbackManager):
             self.handlers,
             "on_retriever_start",
             "ignore_retriever",
+            serialized,
             query,
             run_id=run_id,
             parent_run_id=self.parent_run_id,
@@ -1463,6 +1465,7 @@ class AsyncCallbackManager(BaseCallbackManager):
 
     async def on_retriever_start(
         self,
+        serialized: Dict[str, Any],
         query: str,
         run_id: Optional[UUID] = None,
         parent_run_id: Optional[UUID] = None,
@@ -1476,6 +1479,7 @@ class AsyncCallbackManager(BaseCallbackManager):
             self.handlers,
             "on_retriever_start",
             "ignore_retriever",
+            serialized,
             query,
             run_id=run_id,
             parent_run_id=self.parent_run_id,

langchain/callbacks/tracers/base.py

@@ -312,6 +312,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
 
     def on_retriever_start(
         self,
+        serialized: Dict[str, Any],
         query: str,
         *,
         run_id: UUID,
@@ -326,6 +327,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
             id=run_id,
             name="Retriever",
             parent_run_id=parent_run_id,
+            serialized=serialized,
             inputs={"query": query},
             extra=kwargs,
             events=[{"name": "start", "time": start_time}],

langchain/retrievers/azure_cognitive_search.py

@@ -7,7 +7,7 @@ from typing import Dict, List, Optional
 
 import aiohttp
 import requests
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -17,7 +17,7 @@ from langchain.schema import BaseRetriever, Document
 from langchain.utils import get_from_dict_or_env
 
 
-class AzureCognitiveSearchRetriever(BaseRetriever, BaseModel):
+class AzureCognitiveSearchRetriever(BaseRetriever):
     """Wrapper around Azure Cognitive Search."""
 
     service_name: str = ""

langchain/retrievers/chatgpt_plugin_retriever.py

@@ -4,7 +4,6 @@ from typing import List, Optional
 
 import aiohttp
 import requests
-from pydantic import BaseModel
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -13,7 +12,7 @@ from langchain.callbacks.manager import (
 from langchain.schema import BaseRetriever, Document
 
 
-class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
+class ChatGPTPluginRetriever(BaseRetriever):
     url: str
     bearer_token: str
     top_k: int = 3

langchain/retrievers/contextual_compression.py

@@ -2,8 +2,6 @@
 
 from typing import Any, List
 
-from pydantic import BaseModel, Extra
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -14,7 +12,7 @@ from langchain.retrievers.document_compressors.base import (
 from langchain.schema import BaseRetriever, Document
 
 
-class ContextualCompressionRetriever(BaseRetriever, BaseModel):
+class ContextualCompressionRetriever(BaseRetriever):
     """Retriever that wraps a base retriever and compresses the results."""
 
     base_compressor: BaseDocumentCompressor
@@ -26,7 +24,6 @@ class ContextualCompressionRetriever(BaseRetriever, BaseModel):
     class Config:
         """Configuration for this pydantic object."""
 
-        extra = Extra.forbid
         arbitrary_types_allowed = True
 
     def _get_relevant_documents(

langchain/retrievers/databerry.py

@@ -17,16 +17,6 @@ class DataberryRetriever(BaseRetriever):
     top_k: Optional[int]
     api_key: Optional[str]
 
-    def __init__(
-        self,
-        datastore_url: str,
-        top_k: Optional[int] = None,
-        api_key: Optional[str] = None,
-    ):
-        self.datastore_url = datastore_url
-        self.api_key = api_key
-        self.top_k = top_k
-
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:

langchain/retrievers/docarray.py

@@ -2,7 +2,6 @@ from enum import Enum
 from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
-from pydantic import BaseModel
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -20,7 +19,7 @@ class SearchType(str, Enum):
     mmr = "mmr"
 
 
-class DocArrayRetriever(BaseRetriever, BaseModel):
+class DocArrayRetriever(BaseRetriever):
     """
     Retriever class for DocArray Document Indices.
 

langchain/retrievers/elastic_search_bm25.py

@@ -40,9 +40,8 @@ class ElasticSearchBM25Retriever(BaseRetriever):
     https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
     """
 
-    def __init__(self, client: Any, index_name: str):
+    client: Any
-        self.client = client
+    index_name: str
-        self.index_name = index_name
 
     @classmethod
     def create(
@@ -75,7 +74,7 @@ class ElasticSearchBM25Retriever(BaseRetriever):
 
         # Create the index with the specified settings and mappings
         es.indices.create(index=index_name, mappings=mappings, settings=settings)
-        return cls(es, index_name)
+        return cls(client=es, index_name=index_name)
 
     def add_texts(
         self,

langchain/retrievers/kendra.py

@@ -1,7 +1,7 @@
 import re
 from typing import Any, Dict, List, Literal, Optional
 
-from pydantic import BaseModel, Extra
+from pydantic import BaseModel, Extra, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -179,37 +179,34 @@ class AmazonKendraRetriever(BaseRetriever):
 
     """
 
-    def __init__(
+    index_id: str
-        self,
+    region_name: Optional[str] = None
-        index_id: str,
+    credentials_profile_name: Optional[str] = None
-        region_name: Optional[str] = None,
+    top_k: int = 3
-        credentials_profile_name: Optional[str] = None,
+    attribute_filter: Optional[Dict] = None
-        top_k: int = 3,
+    client: Any
-        attribute_filter: Optional[Dict] = None,
-        client: Optional[Any] = None,
-    ):
-        self.index_id = index_id
-        self.top_k = top_k
-        self.attribute_filter = attribute_filter
 
-        if client is not None:
+    @root_validator(pre=True)
-            self.client = client
+    def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]:
-            return
+        if values["client"] is not None:
+            return values
 
         try:
             import boto3
 
-            if credentials_profile_name is not None:
+            if values["credentials_profile_name"] is not None:
-                session = boto3.Session(profile_name=credentials_profile_name)
+                session = boto3.Session(profile_name=values["credentials_profile_name"])
             else:
                 # use default credentials
                 session = boto3.Session()
 
             client_params = {}
-            if region_name is not None:
+            if values["region_name"] is not None:
-                client_params["region_name"] = region_name
+                client_params["region_name"] = values["region_name"]
 
-            self.client = session.client("kendra", **client_params)
+            values["client"] = session.client("kendra", **client_params)
+
+            return values
         except ImportError:
             raise ModuleNotFoundError(
                 "Could not import boto3 python package. "

langchain/retrievers/knn.py

@@ -8,7 +8,6 @@ import concurrent.futures
 from typing import Any, List, Optional
 
 import numpy as np
-from pydantic import BaseModel
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -33,7 +32,7 @@ def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
         return np.array(list(executor.map(embeddings.embed_query, contexts)))
 
 
-class KNNRetriever(BaseRetriever, BaseModel):
+class KNNRetriever(BaseRetriever):
     """KNN Retriever."""
 
     embeddings: Embeddings

langchain/retrievers/llama_index.py

@@ -1,6 +1,6 @@
 from typing import Any, Dict, List, cast
 
-from pydantic import BaseModel, Field
+from pydantic import Field
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -9,7 +9,7 @@ from langchain.callbacks.manager import (
 from langchain.schema import BaseRetriever, Document
 
 
-class LlamaIndexRetriever(BaseRetriever, BaseModel):
+class LlamaIndexRetriever(BaseRetriever):
     """Question-answering with sources over an LlamaIndex data structure."""
 
     index: Any
@@ -45,7 +45,7 @@ class LlamaIndexRetriever(BaseRetriever, BaseModel):
         raise NotImplementedError("LlamaIndexRetriever does not support async")
 
 
-class LlamaIndexGraphRetriever(BaseRetriever, BaseModel):
+class LlamaIndexGraphRetriever(BaseRetriever):
     """Question-answering with sources over an LlamaIndex graph data structure."""
 
     graph: Any

langchain/retrievers/merger_retriever.py

@@ -15,18 +15,7 @@ class MergerRetriever(BaseRetriever):
         retrievers: A list of retrievers to merge.
     """
 
-    def __init__(
+    retrievers: List[BaseRetriever]
-        self,
-        retrievers: List[BaseRetriever],
-    ):
-        """
-        Initialize the MergerRetriever class.
-
-        Args:
-            retrievers: A list of retrievers to merge.
-        """
-
-        self.retrievers = retrievers
 
     def _get_relevant_documents(
         self,

langchain/retrievers/metal.py

@@ -1,5 +1,7 @@
 from typing import Any, List, Optional
 
+from pydantic import root_validator
+
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -10,16 +12,26 @@ from langchain.schema import BaseRetriever, Document
 class MetalRetriever(BaseRetriever):
     """Retriever that uses the Metal API."""
 
-    def __init__(self, client: Any, params: Optional[dict] = None):
+    client: Any
+
+    params: Optional[dict] = None
+
+    @root_validator(pre=True)
+    def validate_client(cls, values: dict) -> dict:
+        """Validate that the client is of the correct type."""
         from metal_sdk.metal import Metal
 
-        if not isinstance(client, Metal):
+        if "client" in values:
-            raise ValueError(
+            client = values["client"]
-                "Got unexpected client, should be of type metal_sdk.metal.Metal. "
+            if not isinstance(client, Metal):
-                f"Instead, got {type(client)}"
+                raise ValueError(
-            )
+                    "Got unexpected client, should be of type metal_sdk.metal.Metal. "
-        self.client: Metal = client
+                    f"Instead, got {type(client)}"
-        self.params = params or {}
+                )
+
+        values["params"] = values.get("params", {})
+
+        return values
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun

langchain/retrievers/milvus.py

@@ -2,6 +2,8 @@
 import warnings
 from typing import Any, Dict, List, Optional
 
+from pydantic import root_validator
+
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -16,21 +18,28 @@ from langchain.vectorstores.milvus import Milvus
 class MilvusRetriever(BaseRetriever):
     """Retriever that uses the Milvus API."""
 
-    def __init__(
+    embedding_function: Embeddings
-        self,
+    collection_name: str = "LangChainCollection"
-        embedding_function: Embeddings,
+    connection_args: Optional[Dict[str, Any]] = None
-        collection_name: str = "LangChainCollection",
+    consistency_level: str = "Session"
-        connection_args: Optional[Dict[str, Any]] = None,
+    search_params: Optional[dict] = None
-        consistency_level: str = "Session",
+
-        search_params: Optional[dict] = None,
+    store: Milvus
-    ):
+    retriever: BaseRetriever
-        self.store = Milvus(
+
-            embedding_function,
+    @root_validator(pre=True)
-            collection_name,
+    def create_retriever(cls, values: Dict) -> Dict:
-            connection_args,
+        """Create the Milvus store and retriever."""
-            consistency_level,
+        values["store"] = Milvus(
+            values["embedding_function"],
+            values["collection_name"],
+            values["connection_args"],
+            values["consistency_level"],
         )
-        self.retriever = self.store.as_retriever(search_kwargs={"param": search_params})
+        values["retriever"] = values["store"].as_retriever(
+            search_kwargs={"param": values["search_params"]}
+        )
+        return values
 
     def add_texts(
         self, texts: List[str], metadatas: Optional[List[dict]] = None

langchain/retrievers/multi_query.py

@@ -47,28 +47,10 @@ class MultiQueryRetriever(BaseRetriever):
     """Given a user query, use an LLM to write a set of queries.
     Retrieve docs for each query. Rake the unique union of all retrieved docs."""
 
-    def __init__(
+    retriever: BaseRetriever
-        self,
+    llm_chain: LLMChain
-        retriever: BaseRetriever,
+    verbose: bool = True
-        llm_chain: LLMChain,
+    parser_key: str = "lines"
-        verbose: bool = True,
-        parser_key: str = "lines",
-    ) -> None:
-        """Initialize MultiQueryRetriever.
-
-        Args:
-            retriever: retriever to query documents from
-            llm_chain: llm_chain for query generation
-            verbose: show the queries that we generated to the user
-            parser_key: attribute name for the parsed output
-
-        Returns:
-            MultiQueryRetriever
-        """
-        self.retriever = retriever
-        self.llm_chain = llm_chain
-        self.verbose = verbose
-        self.parser_key = parser_key
 
     @classmethod
     def from_llm(

langchain/retrievers/pinecone_hybrid_search.py

@@ -3,7 +3,7 @@
 import hashlib
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -98,7 +98,7 @@ def create_index(
         index.upsert(vectors)
 
 
-class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
+class PineconeHybridSearchRetriever(BaseRetriever):
     embeddings: Embeddings
     """description"""
     sparse_encoder: Any

langchain/retrievers/remote_retriever.py

@@ -2,7 +2,6 @@ from typing import List, Optional
 
 import aiohttp
 import requests
-from pydantic import BaseModel
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -11,7 +10,7 @@ from langchain.callbacks.manager import (
 from langchain.schema import BaseRetriever, Document
 
 
-class RemoteLangChainRetriever(BaseRetriever, BaseModel):
+class RemoteLangChainRetriever(BaseRetriever):
     url: str
     headers: Optional[dict] = None
     input_key: str = "message"

langchain/retrievers/svm.py

@@ -8,7 +8,6 @@ import concurrent.futures
 from typing import Any, List, Optional
 
 import numpy as np
-from pydantic import BaseModel
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -32,7 +31,7 @@ def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
         return np.array(list(executor.map(embeddings.embed_query, contexts)))
 
 
-class SVMRetriever(BaseRetriever, BaseModel):
+class SVMRetriever(BaseRetriever):
     """SVM Retriever."""
 
     embeddings: Embeddings

langchain/retrievers/tfidf.py

@@ -7,8 +7,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, Iterable, List, Optional
 
-from pydantic import BaseModel
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -16,7 +14,7 @@ from langchain.callbacks.manager import (
 from langchain.schema import BaseRetriever, Document
 
 
-class TFIDFRetriever(BaseRetriever, BaseModel):
+class TFIDFRetriever(BaseRetriever):
     vectorizer: Any
     docs: List[Document]
     tfidf_array: Any

langchain/retrievers/time_weighted_retriever.py

@@ -4,7 +4,7 @@ import datetime
 from copy import deepcopy
 from typing import Any, Dict, List, Optional, Tuple
 
-from pydantic import BaseModel, Field
+from pydantic import Field
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -19,7 +19,7 @@ def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> f
     return (time - ref_time).total_seconds() / 3600
 
 
-class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
+class TimeWeightedVectorStoreRetriever(BaseRetriever):
     """Retriever combining embedding similarity with recency."""
 
     vectorstore: VectorStore

langchain/retrievers/vespa_retriever.py

@@ -18,29 +18,13 @@ if TYPE_CHECKING:
 class VespaRetriever(BaseRetriever):
     """Retriever that uses the Vespa."""
 
-    def __init__(
+    app: Vespa
-        self,
+    body: Dict
-        app: Vespa,
+    content_field: str
-        body: Dict,
+    metadata_fields: Sequence[str]
-        content_field: str,
-        metadata_fields: Optional[Sequence[str]] = None,
-    ):
-        """
-
-        Args:
-            app: Vespa client.
-            body: query body.
-            content_field: result field with document contents.
-            metadata_fields: result fields to include in document metadata.
-
-        """
-        self._application = app
-        self._query_body = body
-        self._content_field = content_field
-        self._metadata_fields = metadata_fields or ()
 
     def _query(self, body: Dict) -> List[Document]:
-        response = self._application.query(body)
+        response = self.app.query(body)
 
         if not str(response.status_code).startswith("2"):
             raise RuntimeError(
@@ -55,11 +39,11 @@ class VespaRetriever(BaseRetriever):
 
         docs = []
         for child in response.hits:
-            page_content = child["fields"].pop(self._content_field, "")
+            page_content = child["fields"].pop(self.content_field, "")
-            if self._metadata_fields == "*":
+            if self.metadata_fields == "*":
                 metadata = child["fields"]
             else:
-                metadata = {mf: child["fields"].get(mf) for mf in self._metadata_fields}
+                metadata = {mf: child["fields"].get(mf) for mf in self.metadata_fields}
             metadata["id"] = child["id"]
             docs.append(Document(page_content=page_content, metadata=metadata))
         return docs
@@ -67,7 +51,7 @@ class VespaRetriever(BaseRetriever):
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:
-        body = self._query_body.copy()
+        body = self.body.copy()
         body["query"] = query
         return self._query(body)
 
@@ -79,7 +63,7 @@ class VespaRetriever(BaseRetriever):
     def get_relevant_documents_with_filter(
         self, query: str, *, _filter: Optional[str] = None
     ) -> List[Document]:
-        body = self._query_body.copy()
+        body = self.body.copy()
         _filter = f" and {_filter}" if _filter else ""
         body["yql"] = body["yql"] + _filter
         body["query"] = query
@@ -139,4 +123,9 @@ class VespaRetriever(BaseRetriever):
         body["yql"] = yql
         if k:
             body["hits"] = k
-        return cls(app, body, content_field, metadata_fields=metadata_fields)
+        return cls(
+            app=app,
+            body=body,
+            content_field=content_field,
+            metadata_fields=metadata_fields,
+        )

langchain/retrievers/weaviate_hybrid_search.py

@@ -2,10 +2,10 @@
 
 from __future__ import annotations
 
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, cast
 from uuid import uuid4
 
-from pydantic import Extra
+from pydantic import root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -16,16 +16,19 @@ from langchain.schema import BaseRetriever
 
 
 class WeaviateHybridSearchRetriever(BaseRetriever):
-    def __init__(
+    client: Any
-        self,
+    index_name: str
-        client: Any,
+    text_key: str
-        index_name: str,
+    alpha: float = 0.5
-        text_key: str,
+    k: int = 4
-        alpha: float = 0.5,
+    attributes: List[str]
-        k: int = 4,
+    create_schema_if_missing: bool = True
-        attributes: Optional[List[str]] = None,
+
-        create_schema_if_missing: bool = True,
+    @root_validator(pre=True)
-    ):
+    def validate_client(
+        cls,
+        values: Dict[str, Any],
+    ) -> Dict[str, Any]:
         try:
             import weaviate
         except ImportError:
@@ -33,36 +36,31 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
                 "Could not import weaviate python package. "
                 "Please install it with `pip install weaviate-client`."
             )
-        if not isinstance(client, weaviate.Client):
+        if not isinstance(values["client"], weaviate.Client):
+            client = values["client"]
             raise ValueError(
                 f"client should be an instance of weaviate.Client, got {type(client)}"
             )
-        self._client = client
+        if values["attributes"] is None:
-        self.k = k
+            values["attributes"] = []
-        self.alpha = alpha
-        self._index_name = index_name
-        self._text_key = text_key
-        self._query_attrs = [self._text_key]
-        if attributes is not None:
-            self._query_attrs.extend(attributes)
 
-        if create_schema_if_missing:
+        cast(List, values["attributes"]).append(values["text_key"])
-            self._create_schema_if_missing()
 
-    def _create_schema_if_missing(self) -> None:
+        if values["create_schema_if_missing"]:
-        class_obj = {
+            class_obj = {
-            "class": self._index_name,
+                "class": values["index_name"],
-            "properties": [{"name": self._text_key, "dataType": ["text"]}],
+                "properties": [{"name": values["text_key"], "dataType": ["text"]}],
-            "vectorizer": "text2vec-openai",
+                "vectorizer": "text2vec-openai",
-        }
+            }
 
-        if not self._client.schema.exists(self._index_name):
+            if not values["client"].schema.exists(values["index_name"]):
-            self._client.schema.create_class(class_obj)
+                values["client"].schema.create_class(class_obj)
+
+        return values
 
     class Config:
         """Configuration for this pydantic object."""
 
-        extra = Extra.forbid
         arbitrary_types_allowed = True
 
     # added text_key
@@ -70,11 +68,11 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
         """Upload documents to Weaviate."""
         from weaviate.util import get_valid_uuid
 
-        with self._client.batch as batch:
+        with self.client.batch as batch:
             ids = []
             for i, doc in enumerate(docs):
                 metadata = doc.metadata or {}
-                data_properties = {self._text_key: doc.page_content, **metadata}
+                data_properties = {self.text_key: doc.page_content, **metadata}
 
                 # If the UUID of one of the objects already exists
                 # then the existing objectwill be replaced by the new object.
@@ -83,7 +81,7 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
                 else:
                     _id = get_valid_uuid(uuid4())
 
-                batch.add_data_object(data_properties, self._index_name, _id)
+                batch.add_data_object(data_properties, self.index_name, _id)
                 ids.append(_id)
         return ids
 
@@ -95,7 +93,7 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
         where_filter: Optional[Dict[str, object]] = None,
     ) -> List[Document]:
         """Look up similar documents in Weaviate."""
-        query_obj = self._client.query.get(self._index_name, self._query_attrs)
+        query_obj = self.client.query.get(self.index_name, self.attributes)
         if where_filter:
             query_obj = query_obj.with_where(where_filter)
 
@@ -105,8 +103,8 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
 
         docs = []
 
-        for res in result["data"]["Get"][self._index_name]:
+        for res in result["data"]["Get"][self.index_name]:
-            text = res.pop(self._text_key)
+            text = res.pop(self.text_key)
             docs.append(Document(page_content=text, metadata=res))
         return docs
 

langchain/retrievers/zep.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from pydantic import root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -27,13 +29,14 @@ class ZepRetriever(BaseRetriever):
     https://docs.getzep.com/deployment/quickstart/
     """
 
-    def __init__(
+    zep_client: Any
-        self,
+
-        session_id: str,
+    session_id: str
-        url: str,
+
-        api_key: Optional[str] = None,
+    top_k: Optional[int]
-        top_k: Optional[int] = None,
+
-    ):
+    @root_validator(pre=True)
+    def create_client(cls, values: dict) -> dict:
         try:
             from zep_python import ZepClient
         except ImportError:
@@ -41,10 +44,11 @@ class ZepRetriever(BaseRetriever):
                 "Could not import zep-python package. "
                 "Please install it with `pip install zep-python`."
             )
-
+        values["zep_client"] = values.get(
-        self.zep_client = ZepClient(base_url=url, api_key=api_key)
+            "zep_client",
-        self.session_id = session_id
+            ZepClient(base_url=values["url"], api_key=values.get("api_key")),
-        self.top_k = top_k
+        )
+        return values
 
     def _search_result_to_doc(
         self, results: List[MemorySearchResult]

langchain/retrievers/zilliz.py

@@ -2,6 +2,8 @@
 import warnings
 from typing import Any, Dict, List, Optional
 
+from pydantic import root_validator
+
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -16,21 +18,27 @@ from langchain.vectorstores.zilliz import Zilliz
 class ZillizRetriever(BaseRetriever):
     """Retriever that uses the Zilliz API."""
 
-    def __init__(
+    embedding_function: Embeddings
-        self,
+    collection_name: str = "LangChainCollection"
-        embedding_function: Embeddings,
+    connection_args: Optional[Dict[str, Any]] = None
-        collection_name: str = "LangChainCollection",
+    consistency_level: str = "Session"
-        connection_args: Optional[Dict[str, Any]] = None,
+    search_params: Optional[dict] = None
-        consistency_level: str = "Session",
+
-        search_params: Optional[dict] = None,
+    store: Zilliz
-    ):
+    retriever: BaseRetriever
-        self.store = Zilliz(
+
-            embedding_function,
+    @root_validator(pre=True)
-            collection_name,
+    def create_client(cls, values: dict) -> dict:
-            connection_args,
+        values["store"] = Zilliz(
-            consistency_level,
+            values["embedding_function"],
+            values["collection_name"],
+            values["connection_args"],
+            values["consistency_level"],
         )
-        self.retriever = self.store.as_retriever(search_kwargs={"param": search_params})
+        values["retriever"] = values["store"].as_retriever(
+            search_kwargs={"param": values["search_params"]}
+        )
+        return values
 
     def add_texts(
         self, texts: List[str], metadatas: Optional[List[dict]] = None

langchain/schema/retriever.py

@@ -5,6 +5,8 @@ from abc import ABC, abstractmethod
 from inspect import signature
 from typing import TYPE_CHECKING, Any, List
 
+from langchain.load.dump import dumpd
+from langchain.load.serializable import Serializable
 from langchain.schema.document import Document
 
 if TYPE_CHECKING:
@@ -15,7 +17,7 @@ if TYPE_CHECKING:
     )
 
 
-class BaseRetriever(ABC):
+class BaseRetriever(Serializable, ABC):
     """Abstract base class for a Document retrieval system.
 
     A retrieval system is defined as something that can take string queries and return
@@ -46,6 +48,11 @@ class BaseRetriever(ABC):
                     raise NotImplementedError
     """  # noqa: E501
 
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+
     _new_arg_supported: bool = False
     _expects_other_args: bool = False
 
@@ -81,7 +88,9 @@ class BaseRetriever(ABC):
         parameters = signature(cls._get_relevant_documents).parameters
         cls._new_arg_supported = parameters.get("run_manager") is not None
         # If a V1 retriever broke the interface and expects additional arguments
-        cls._expects_other_args = (not cls._new_arg_supported) and len(parameters) > 2
+        cls._expects_other_args = (
+            len(set(parameters.keys()) - {"self", "query", "run_manager"}) > 0
+        )
 
     @abstractmethod
     def _get_relevant_documents(
@@ -123,6 +132,7 @@ class BaseRetriever(ABC):
             callbacks, None, verbose=kwargs.get("verbose", False)
         )
         run_manager = callback_manager.on_retriever_start(
+            dumpd(self),
             query,
             **kwargs,
         )
@@ -160,6 +170,7 @@ class BaseRetriever(ABC):
             callbacks, None, verbose=kwargs.get("verbose", False)
         )
         run_manager = await callback_manager.on_retriever_start(
+            dumpd(self),
             query,
             **kwargs,
         )

langchain/utilities/arxiv.py

@@ -3,7 +3,7 @@ import logging
 import os
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import BaseModel, root_validator
 
 from langchain.schema import Document
 
@@ -40,11 +40,6 @@ class ArxivAPIWrapper(BaseModel):
     load_all_available_meta: bool = False
     doc_content_chars_max: Optional[int] = 4000
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that the python package exists in environment."""

langchain/utilities/pupmed.py

@@ -5,7 +5,7 @@ import urllib.error
 import urllib.request
 from typing import List
 
-from pydantic import BaseModel, Extra
+from pydantic import BaseModel
 
 from langchain.schema import Document
 
@@ -42,11 +42,6 @@ class PubMedAPIWrapper(BaseModel):
     load_all_available_meta: bool = False
     email: str = "your_email@example.com"
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-
     def run(self, query: str) -> str:
         """
         Run PubMed search and get the article meta information.

langchain/utilities/wikipedia.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import BaseModel, root_validator
 
 from langchain.schema import Document
 
@@ -27,11 +27,6 @@ class WikipediaAPIWrapper(BaseModel):
     load_all_available_meta: bool = False
     doc_content_chars_max: int = 4000
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that the python package exists in environment."""

tests/integration_tests/retrievers/test_merger_retriever.py

@@ -24,7 +24,7 @@ def test_merger_retriever_get_relevant_docs() -> None:
     )
 
     # The Lord of the Retrievers.
-    lotr = MergerRetriever([retriever_a, retriever_b])
+    lotr = MergerRetriever(retrievers=[retriever_a, retriever_b])
 
     actual = lotr.get_relevant_documents("Tell me about the Celtics")
     assert len(actual) == 2

tests/unit_tests/callbacks/test_callback_manager.py

@@ -146,7 +146,7 @@ def test_ignore_retriever() -> None:
     handler1 = FakeCallbackHandler(ignore_retriever_=True)
     handler2 = FakeCallbackHandler()
     manager = CallbackManager(handlers=[handler1, handler2])
-    run_manager = manager.on_retriever_start("")
+    run_manager = manager.on_retriever_start({}, "")
     run_manager.on_retriever_end([])
     run_manager.on_retriever_error(Exception())
     assert handler1.starts == 0

tests/unit_tests/retrievers/test_base.py

@@ -142,8 +142,7 @@ async def test_fake_retriever_v1_with_kwargs_upgrade_async(
 
 
 class FakeRetrieverV2(BaseRetriever):
-    def __init__(self, throw_error: bool = False) -> None:
+    throw_error: bool = False
-        self.throw_error = throw_error
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun | None