Use langchain-astradb package

docs/modules/ROOT/pages/dev-environment.adoc

@@ -148,7 +148,7 @@ To install the `load_dotenv` package, run `pip install python-dotenv`.
 ----
 import os
 from dotenv import load_dotenv
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 
 load_dotenv()
@@ -159,7 +159,7 @@ OPEN_AI_API_KEY = os.environ.get("OPENAI_API_KEY")
 ASTRA_DB_COLLECTION = os.environ.get("ASTRA_DB_COLLECTION")
 
 embedding = OpenAIEmbeddings()
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     embedding=embedding,
     collection_name="test",
     token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],

docs/modules/ROOT/pages/migration.adoc

@@ -40,7 +40,7 @@ import os
 from datasets import load_dataset
 from dotenv import load_dotenv
 from langchain_community.document_loaders import PyPDFDirectoryLoader
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_core import Document
 
@@ -52,7 +52,7 @@ OPEN_AI_API_KEY = os.environ.get("OPENAI_API_KEY")
 ASTRA_DB_COLLECTION = os.environ.get("ASTRA_DB_COLLECTION")
 
 embedding = OpenAIEmbeddings()
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     embedding=embedding,
     collection_name="test3",
     token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],

docs/modules/ROOT/pages/quickstart.adoc

@@ -118,7 +118,7 @@ With your environment set up, you're ready to create a RAG workflow in Python.
 import os
 from dotenv import load_dotenv
 from datasets import load_dataset
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_core import Document
 from langchain.prompts import ChatPromptTemplate
@@ -145,7 +145,7 @@ ASTRA_DB_COLLECTION = os.environ.get("ASTRA_DB_COLLECTION")
 [source,python]
 ----
 embedding = OpenAIEmbeddings()
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     embedding=embedding,
     collection_name="test",
     token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
@@ -244,7 +244,7 @@ Python::
 import os
 from dotenv import load_dotenv
 from datasets import load_dataset
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_core import Document
 from langchain_core.prompts import ChatPromptTemplate
@@ -260,7 +260,7 @@ OPEN_AI_API_KEY = os.environ.get("OPENAI_API_KEY")
 ASTRA_DB_COLLECTION = os.environ.get("ASTRA_DB_COLLECTION")
 
 embedding = OpenAIEmbeddings()
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     embedding=embedding,
     collection_name="test",
     token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],

docs/modules/default-architecture/pages/retrieval.adoc

@@ -16,7 +16,7 @@ Python::
 ----
 import os
 from dotenv import load_dotenv
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import RunnablePassthrough
@@ -27,7 +27,7 @@ load_dotenv()
 
 OPEN_AI_API_KEY = os.environ["OPENAI_API_KEY"]
 
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     embedding=OpenAIEmbeddings(openai_api_key=OPEN_AI_API_KEY),
     collection_name="test",
     token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],

docs/modules/default-architecture/pages/storing.adoc

@@ -13,15 +13,15 @@ This code embeds the loaded `Documents` from the xref:splitting.adoc[] example a
 ----
 import os
 from dotenv import load_dotenv
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 
 load_dotenv()
 
 ASTRA_DB_COLLECTION = os.environ.get("ASTRA_DB_COLLECTION")
 
 embedding = OpenAIEmbeddings()
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     embedding=embedding,
     collection_name="test",
     token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],

docs/modules/examples/pages/advanced-rag.adoc

@@ -39,7 +39,7 @@ from langchain_openai import OpenAIEmbeddings
 from langchain_community.retrievers import ParentDocumentRetriever
 from langchain_community.storage import InMemoryStore
 from langchain.text_splitter import TokenTextSplitter
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 ----
 +
 . The two advanced RAG techniques require different dependencies.
@@ -156,7 +156,7 @@ model = ChatOpenAI(openai_api_key=openai_api_key, model_name="gpt-3.5-turbo", te
 embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
 
 # Initialize a vector store for storing the child chunks
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     collection_name=collection_name,
     embedding=embedding,
     token=astra_token,

docs/modules/examples/pages/flare.adoc

@@ -36,7 +36,7 @@ pip install ragstack-ai
 ----
 import os
 from dotenv import load_dotenv
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_community.document_loaders import TextLoader
 from langchain.globals import set_verbose
@@ -53,7 +53,7 @@ from langchain.chains.flare.base import QuestionGeneratorChain
 [source,python]
 ----
 embedding = OpenAIEmbeddings()
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
         collection_name=collection,
         embedding=embedding,
         token=astra_token,

docs/modules/examples/pages/langchain-evaluation.adoc

@@ -38,7 +38,7 @@ pip install ragstack-ai langchain[openai]
 import os
 from dotenv import load_dotenv
 from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_community.document_loaders import TextLoader
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.evaluation import EvaluatorType
@@ -55,7 +55,7 @@ from langchain.chains import RetrievalQA
 [source,python]
 ----
 embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     collection_name=collection,
     embedding=embedding,
     token=astra_token,

docs/modules/examples/pages/langchain-unstructured-astra.adoc

@@ -57,7 +57,7 @@ import os
 import requests
 
 from dotenv import load_dotenv
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_core.documents import Document
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import PromptTemplate
@@ -144,7 +144,7 @@ See the Colab notebook linked at the top of this page for a more detailed invest
 +
 [source,python]
 ----
-astra_db_store = AstraDB(
+astra_db_store = AstraDBVectorStore(
     collection_name="langchain_unstructured",
     embedding=OpenAIEmbeddings(),
     token=os.getenv("ASTRA_DB_APPLICATION_TOKEN"),
@@ -236,7 +236,7 @@ import requests
 
 from dotenv import load_dotenv
 from langchain_community.document_loaders import unstructured
-from langchain_community.vectorstores import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain_core.documents import Document
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import PromptTemplate
@@ -269,7 +269,7 @@ elements = unstructured.get_elements_from_api(
     pdf_infer_table_structure=True,
 )
 
-astra_db_store = AstraDB(
+astra_db_store = AstraDBVectorStore(
     collection_name="langchain_unstructured",
     embedding=OpenAIEmbeddings(),
     token=os.getenv("ASTRA_DB_APPLICATION_TOKEN"),

docs/modules/examples/pages/nvidia_embeddings.adoc

@@ -69,9 +69,9 @@ embedding = NVIDIAEmbeddings(
 [source,python]
 ----
 import os
-from langchain.vectorstores.astradb import AstraDB
+from langchain_astradb import AstraDBVectorStore
 
-vstore = AstraDB(
+vstore = AstraDBVectorStore(
     collection_name=collection,
     embedding=embedding,
     token=os.getenv("ASTRA_DB_APPLICATION_TOKEN"),
@@ -194,7 +194,7 @@ vstore.delete_collection()
 ----
 from datasets import load_dataset
 from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
-from langchain.vectorstores.astradb import AstraDB
+from langchain_astradb import AstraDBVectorStore
 from langchain.schema import Document
 from langchain.prompts import ChatPromptTemplate
 from langchain.chat_models import ChatOpenAI
@@ -210,7 +210,7 @@ embedding = NVIDIAEmbeddings(nvidia_api_key=nvidia_api_key, model="nvolveqa_40k"
 collection_name = "test"
 astra_token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
 astra_api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
-vstore = AstraDB(collection_name=collection_name, embedding=embedding, 
+vstore = AstraDBVectorStore(collection_name=collection_name, embedding=embedding,
                  token=astra_token, api_endpoint=astra_api_endpoint)
 print("Astra vector store configured")
 

evaluation/langchain_trulens_full.ipynb

@@ -108,10 +108,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores.astradb import AstraDB\n",
+    "from langchain_astradb import AstraDBVectorStore\n",
     "import os\n",
     "\n",
-    "astra_db_vstore = AstraDB(\n",
+    "astra_db_vstore = AstraDBVectorStore(\n",
     "    collection_name=astra_db_collection_name,\n",
     "    embedding=open_ai_embeddings,\n",
     "    token=os.getenv(\"ASTRA_DB_TOKEN\"),\n",

evaluation/tru_shared.py

@@ -17,7 +17,7 @@
 
 from langchain_community.chat_models import AzureChatOpenAI
 from langchain_community.embeddings import AzureOpenAIEmbeddings
-from langchain.vectorstores.astradb import AstraDB
+from langchain_astradb import AstraDBVectorStore as LangChainAstraDBVectorStore
 
 # this code assumes the following env vars exist in a .env file:
 #
@@ -171,7 +171,7 @@ def get_azure_embeddings_model(framework: Framework):
 
 def get_astra_vector_store(framework: Framework, collection_name: str):
     if framework == Framework.LANG_CHAIN:
-        return AstraDB(
+        return LangChainAstraDBVectorStore(
             collection_name=collection_name,
             embedding=get_azure_embeddings_model(framework),
             token=os.getenv("ASTRA_DB_TOKEN"),

examples/notebooks/FLARE.ipynb

@@ -139,13 +139,13 @@
     }
    ],
    "source": [
-    "from langchain.vectorstores.astradb import AstraDB\n",
+    "from langchain_astradb import AstraDBVectorStore\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "import os\n",
     "\n",
     "# Configure your embedding model and vector store\n",
     "embedding = OpenAIEmbeddings()\n",
-    "vstore = AstraDB(\n",
+    "vstore = AstraDBVectorStore(\n",
     "    collection_name=collection,\n",
     "    embedding=embedding,\n",
     "    token=os.environ[\"ASTRA_DB_TOKEN\"],\n",
@@ -270,33 +270,33 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new FlareChain chain...\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3mCurrent Response: \u001b[0m\n",
+      "\u001B[1m> Entering new FlareChain chain...\u001B[0m\n",
+      "\u001B[36;1m\u001B[1;3mCurrent Response: \u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
+      "\u001B[32;1m\u001B[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
       "\n",
       ">>> CONTEXT: \n",
       ">>> USER INPUT: Who is Luchesi in relation to Antonio?\n",
-      ">>> RESPONSE: \u001b[0m\n",
+      ">>> RESPONSE: \u001B[0m\n",
       "\n",
       "\n",
-      "\u001b[1m> Entering new QuestionGeneratorChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new QuestionGeneratorChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
+      "\u001B[32;1m\u001B[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
       "\n",
       ">>> USER INPUT: Who is Luchesi in relation to Antonio?\n",
       ">>> EXISTING PARTIAL RESPONSE:  \n",
       "Luchesi is a rival of Antonio's. He is a wealthy merchant who is jealous of Antonio's success. FINISHED\n",
       "\n",
-      "The question to which the answer is the term/entity/phrase \" jealous of Antonio\" is:\u001b[0m\n",
+      "The question to which the answer is the term/entity/phrase \" jealous of Antonio\" is:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mGenerated Questions: ['Why is Luchesi considered a rival of Antonio?']\u001b[0m\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n",
+      "\u001B[33;1m\u001B[1;3mGenerated Questions: ['Why is Luchesi considered a rival of Antonio?']\u001B[0m\n",
       "\n",
       "\n",
-      "\u001b[1m> Entering new _OpenAIResponseChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new _OpenAIResponseChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
+      "\u001B[32;1m\u001B[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
       "\n",
       ">>> CONTEXT: The thousand injuries of Fortunato I had borne as I best could, but\n",
       "when he ventured upon insult, I vowed revenge.  You, who so well know\n",
@@ -620,11 +620,11 @@
       "him who clamoured.  I re-echoed--I aided--I surpassed them in volume\n",
       "and in strength.  I did this, and the clamourer grew still.\n",
       ">>> USER INPUT: Who is Luchesi in relation to Antonio?\n",
-      ">>> RESPONSE: \u001b[0m\n",
+      ">>> RESPONSE: \u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

examples/notebooks/advancedRAG.ipynb

@@ -270,15 +270,15 @@
     "from langchain.retrievers import ParentDocumentRetriever\n",
     "from langchain.storage import InMemoryStore\n",
     "from langchain.text_splitter import TokenTextSplitter\n",
-    "from langchain.vectorstores.astradb import AstraDB\n",
+    "from langchain_astradb import AstraDBVectorStore\n",
     "import os\n",
     "\n",
     "# Initialize the models\n",
     "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0.1)\n",
     "embedding = OpenAIEmbeddings()\n",
     "\n",
     "# Initialize a vector store for storing the child chunks\n",
-    "vstore = AstraDB(\n",
+    "vstore = AstraDBVectorStore(\n",
     "    collection_name=\"advancedRAG\",\n",
     "    embedding=embedding,\n",
     "    token=os.environ[\"ASTRA_DB_TOKEN\"],\n",

examples/notebooks/astradb.ipynb

@@ -133,7 +133,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import AstraDB"
+    "from langchain_astradb import AstraDBVectorStore"
    ]
   },
   {
@@ -195,7 +195,7 @@
    },
    "outputs": [],
    "source": [
-    "vstore = AstraDB(\n",
+    "vstore = AstraDBVectorStore(\n",
     "    embedding=embe,\n",
     "    collection_name=ASTRA_DB_COLLECTION,\n",
     "    api_endpoint=os.environ[\"ASTRA_DB_ENDPOINT\"],\n",

examples/notebooks/langchain-unstructured-astra.ipynb

@@ -357,10 +357,10 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.vectorstores import AstraDB\n",
+    "from langchain_astradb import AstraDBVectorStore\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
-    "astra_db_store = AstraDB(\n",
+    "astra_db_store = AstraDBVectorStore(\n",
     "    collection_name=\"langchain_unstructured\",\n",
     "    embedding=OpenAIEmbeddings(),\n",
     "    token=os.getenv(\"ASTRA_DB_TOKEN\"),\n",

examples/notebooks/langchain_evaluation.ipynb

@@ -124,12 +124,12 @@
    ],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores.astradb import AstraDB\n",
+    "from langchain_astradb import AstraDBVectorStore\n",
     "import os\n",
     "\n",
     "# Configure your embedding model and vector store\n",
     "embedding = OpenAIEmbeddings()\n",
-    "vstore = AstraDB(\n",
+    "vstore = AstraDBVectorStore(\n",
     "    collection_name=\"lc\",\n",
     "    embedding=embedding,\n",
     "    token=os.getenv(\"ASTRA_DB_TOKEN\"),\n",