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docs/modules/indexes/retrievers/examples/azure-cognitive-search-retriever.ipynb

@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1edb9e6b",
+   "metadata": {},
+   "source": [
+    "# Azure Cognitive Search Retriever\n",
+    "\n",
+    "This notebook shows how to use Azure Cognitive Search (ACS) within LangChain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "074b0004",
+   "metadata": {},
+   "source": [
+    "## Set up Azure Cognitive Search\n",
+    "\n",
+    "To set up ACS, please follow the instrcutions [here](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).\n",
+    "\n",
+    "Please note\n",
+    "1. the name of your ACS service, \n",
+    "2. the name of your ACS index,\n",
+    "3. your API key.\n",
+    "\n",
+    "Your API key can be either Admin or Query key, but as we only read data it is recommended to use a Query key."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0474661d",
+   "metadata": {},
+   "source": [
+    "## Using the Azure Cognitive Search Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "39d6074e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain.retrievers import AzureCognitiveSearchRetriever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7243e6d",
+   "metadata": {},
+   "source": [
+    "Set Service Name, Index Name and API key as environment variables (alternatively, you can pass them as arguments to `AzureCognitiveSearchRetriever`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33fd23d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"AZURE_COGNITIVE_SEARCH_SERVICE_NAME\"] = \"<YOUR_ACS_SERVICE_NAME>\"\n",
+    "os.environ[\"AZURE_COGNITIVE_SEARCH_INDEX_NAME\"] =\"<YOUR_ACS_INDEX_NAME>\"\n",
+    "os.environ[\"AZURE_COGNITIVE_SEARCH_API_KEY\"] = \"<YOUR_API_KEY>\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057deaad",
+   "metadata": {},
+   "source": [
+    "Create the Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c18d0c4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = AzureCognitiveSearchRetriever(content_key=\"content\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e94ea104",
+   "metadata": {},
+   "source": [
+    "Now you can use retrieve documents from Azure Cognitive Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8b5794b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever.get_relevant_documents(\"what is langchain\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/retrievers/examples/chroma_self_query_retriever.ipynb

@@ -46,18 +46,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "bcbe04d9",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using embedded DuckDB without persistence: data will be transient\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "docs = [\n",
     "    Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"}),\n",
@@ -65,7 +57,7 @@
     "    Document(page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\", metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6}),\n",
     "    Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n",
     "    Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\"}),\n",
-    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": \"science fiction\", \"rating\": 9.9})\n",
+    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"director\": \"Andrei Tarkovsky\", \"genre\": \"science fiction\", \"rating\": 9.9})\n",
     "]\n",
     "vectorstore = Chroma.from_documents(\n",
     "    docs, embeddings\n",
@@ -302,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

docs/modules/indexes/retrievers/examples/self_query_retriever.ipynb

@@ -32,19 +32,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "3eb9c9a4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  from tqdm.autonotebook import tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -83,7 +74,7 @@
     "    Document(page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\", metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6}),\n",
     "    Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n",
     "    Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\"}),\n",
-    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": [\"science fiction\", \"thriller\"], \"rating\": 9.9})\n",
+    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"director\": \"Andrei Tarkovsky\", \"genre\": [\"science fiction\", \"thriller\"], \"rating\": 9.9})\n",
     "]\n",
     "vectorstore = Pinecone.from_documents(\n",
     "    docs, embeddings, index_name=\"langchain-self-retriever-demo\"\n",
@@ -320,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

docs/modules/indexes/retrievers/examples/vectorstore-retriever.ipynb

@@ -25,18 +25,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "9fbcc58f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exiting: Cleaning up .chroma directory\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain.text_splitter import CharacterTextSplitter\n",
     "from langchain.vectorstores import FAISS\n",
@@ -74,6 +66,7 @@
    "id": "79b783de",
    "metadata": {},
    "source": [
+    "## Maximum Marginal Relevance Retrieval\n",
     "By default, the vectorstore retriever uses similarity search. If the underlying vectorstore support maximum marginal relevance search, you can specify that as the search type."
    ]
   },
@@ -97,11 +90,42 @@
     "docs = retriever.get_relevant_documents(\"what did he say abotu ketanji brown jackson\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2d958271",
+   "metadata": {},
+   "source": [
+    "## Similarity Score Threshold Retrieval\n",
+    "\n",
+    "You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d4272ad8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = db.as_retriever(search_type=\"similarity_score_threshold\", search_kwargs={\"score_threshold\": .5})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "438e761d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = retriever.get_relevant_documents(\"what did he say abotu ketanji brown jackson\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c23b7698",
    "metadata": {},
    "source": [
+    "## Specifying top k\n",
     "You can also specify search kwargs like `k` to use when doing retrieval."
    ]
   },
@@ -171,7 +195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/modules/indexes/vectorstores/examples/docarray_hnsw.ipynb

@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2ce41f46-5711-4311-b04d-2fe233ac5b1b",
+   "metadata": {},
+   "source": [
+    "# DocArrayHnswSearch\n",
+    "\n",
+    ">[DocArrayHnswSearch](https://docs.docarray.org/user_guide/storing/index_hnswlib/) is a lightweight Document Index implementation provided by [Docarray](https://docs.docarray.org/) that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in [hnswlib](https://github.com/nmslib/hnswlib), and stores all other data in [SQLite](https://www.sqlite.org/index.html).\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `DocArrayHnswSearch`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee37d28",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ce1b8cb-dbf0-40c3-99ee-04f28143331b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install \"docarray[hnswlib]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "878f17df-100f-4854-9e87-472cf36d51f3",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Get an OpenAI token: https://platform.openai.com/account/api-keys\n",
+    "\n",
+    "# import os\n",
+    "# from getpass import getpass\n",
+    "\n",
+    "# OPENAI_API_KEY = getpass()\n",
+    "\n",
+    "# os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dbb6de2",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Using DocArrayHnswSearch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b757afef-ef0a-465d-8e8a-9aadb9c32b88",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import DocArrayHnswSearch\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "605e200e-e711-486b-b36e-cbe5dd2512d7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "documents = TextLoader('../../../state_of_the_union.txt').load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "db = DocArrayHnswSearch.from_documents(docs, embeddings, work_dir='hnswlib_store/', n_dim=1536)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed6f905b-4853-4a44-9730-614aa8e22b78",
+   "metadata": {},
+   "source": [
+    "## Similarity search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4d7e742f-2002-449d-a10e-16046890906c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0da9e26f-1fc2-48e6-95a7-f692c853bbd3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3febb987-e903-416f-af26-6897d84c8d61",
+   "metadata": {},
+   "source": [
+    "## Similarity search with score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "40764fdd-357d-475a-8152-5f1979d61a45",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "docs = db.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a479fc46-b299-4330-89b9-e9b5a218ea03",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={}),\n",
+       " 0.36962226)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4d3d4e97-5d2b-4571-8ff9-e3f6b6778714",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "# delete the dir\n",
+    "shutil.rmtree('hnswlib_store')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/vectorstores/examples/docarray_in_memory.ipynb

@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3afefb0-7e99-4912-a222-c6b186da11af",
+   "metadata": {},
+   "source": [
+    "# DocArrayInMemorySearch\n",
+    "\n",
+    ">[DocArrayInMemorySearch](https://docs.docarray.org/user_guide/storing/index_in_memory/) is a document index provided by [Docarray](https://docs.docarray.org/) that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `DocArrayInMemorySearch`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5031a3ec",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cd7391f-7759-4a21-952a-2ec972d818c6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install \"docarray\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6a40ad8-920e-4370-818d-3227e2f506ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Get an OpenAI token: https://platform.openai.com/account/api-keys\n",
+    "\n",
+    "# import os\n",
+    "# from getpass import getpass\n",
+    "\n",
+    "# OPENAI_API_KEY = getpass()\n",
+    "\n",
+    "# os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e49be085-ddf1-4028-8c0c-97836ce4a873",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import DocArrayInMemorySearch\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "38222aee-adc5-44c2-913c-97977b394cf5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "documents = TextLoader('../../../state_of_the_union.txt').load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "db = DocArrayInMemorySearch.from_documents(docs, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efbb6684-3846-4332-a624-ddd4d75844c1",
+   "metadata": {},
+   "source": [
+    "## Similarity search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aa28a7f8-41d0-4299-84eb-91d1576e8a63",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1eb16d2a-b466-456a-b412-5e74bb8523dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43896697-f99e-47b6-9117-47a25e9afa9c",
+   "metadata": {},
+   "source": [
+    "## Similarity search with score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8e9eef05-1516-469a-ad36-880c69aef7a9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "docs = db.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bd5fb0e4-2a94-4bb4-af8a-27327ecb1a7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={}),\n",
+       " 0.8154190158347903)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e5da522-ef0e-4a59-91ea-89e563f7b825",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/agents/agent.py

@@ -920,13 +920,15 @@ class AgentExecutor(Chain):
                 # See if tool should return directly
                 tool_return = self._get_tool_return(next_step_action)
                 if tool_return is not None:
-                    return self._return(tool_return, intermediate_steps)
+                    return self._return(
+                        tool_return, intermediate_steps, run_manager=run_manager
+                    )
             iterations += 1
             time_elapsed = time.time() - start_time
         output = self.agent.return_stopped_response(
             self.early_stopping_method, intermediate_steps, **inputs
         )
-        return self._return(output, intermediate_steps)
+        return self._return(output, intermediate_steps, run_manager=run_manager)
 
     async def _acall(
         self,
@@ -957,7 +959,11 @@ class AgentExecutor(Chain):
                         run_manager=run_manager,
                     )
                     if isinstance(next_step_output, AgentFinish):
-                        return await self._areturn(next_step_output, intermediate_steps)
+                        return await self._areturn(
+                            next_step_output,
+                            intermediate_steps,
+                            run_manager=run_manager,
+                        )
 
                     intermediate_steps.extend(next_step_output)
                     if len(next_step_output) == 1:
@@ -965,7 +971,9 @@ class AgentExecutor(Chain):
                         # See if tool should return directly
                         tool_return = self._get_tool_return(next_step_action)
                         if tool_return is not None:
-                            return await self._areturn(tool_return, intermediate_steps)
+                            return await self._areturn(
+                                tool_return, intermediate_steps, run_manager=run_manager
+                            )
 
                     iterations += 1
                     time_elapsed = time.time() - start_time
@@ -980,7 +988,9 @@ class AgentExecutor(Chain):
                 output = self.agent.return_stopped_response(
                     self.early_stopping_method, intermediate_steps, **inputs
                 )
-                return await self._areturn(output, intermediate_steps)
+                return await self._areturn(
+                    output, intermediate_steps, run_manager=run_manager
+                )
 
     def _get_tool_return(
         self, next_step_output: Tuple[AgentAction, str]

langchain/callbacks/manager.py

@@ -32,6 +32,11 @@ openai_callback_var: ContextVar[Optional[OpenAICallbackHandler]] = ContextVar(
 tracing_callback_var: ContextVar[Optional[LangChainTracer]] = ContextVar(  # noqa: E501
     "tracing_callback", default=None
 )
+tracing_v2_callback_var: ContextVar[
+    Optional[LangChainTracerV2]
+] = ContextVar(  # noqa: E501
+    "tracing_callback_v2", default=None
+)
 
 
 @contextmanager
@@ -727,6 +732,11 @@ def _configure(
         or tracer is not None
         or os.environ.get("LANGCHAIN_HANDLER") is not None
     )
+
+    tracer_v2 = tracing_v2_callback_var.get()
+    tracing_v2_enabled_ = (
+        os.environ.get("LANGCHAIN_TRACING_V2") is not None or tracer_v2 is not None
+    )
     tracer_session = os.environ.get("LANGCHAIN_SESSION")
     if tracer_session is None:
         tracer_session = "default"
@@ -746,6 +756,16 @@ def _configure(
                 handler = LangChainTracer()
                 handler.load_session(tracer_session)
                 callback_manager.add_handler(handler, True)
+        if tracing_v2_enabled_ and not any(
+            isinstance(handler, LangChainTracerV2)
+            for handler in callback_manager.handlers
+        ):
+            if tracer_v2:
+                callback_manager.add_handler(tracer_v2, True)
+            else:
+                handler = LangChainTracerV2()
+                handler.load_session(tracer_session)
+                callback_manager.add_handler(handler, True)
         if open_ai is not None and not any(
             isinstance(handler, OpenAICallbackHandler)
             for handler in callback_manager.handlers

langchain/embeddings/aleph_alpha.py

@@ -46,6 +46,8 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
     control_log_additive: Optional[bool] = True
     """Apply controls on prompt items by adding the log(control_factor) 
     to attention scores."""
+    aleph_alpha_api_key: Optional[str] = None
+    """API key for Aleph Alpha API."""
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:

langchain/experimental/client/tracing_datasets.ipynb

@@ -77,7 +77,9 @@
     "\n",
     "Datasets can be created in a number of ways, most often by collecting `Run`'s captured through the LangChain tracing API and converting a set of runs to a dataset.\n",
     "\n",
-    "The V2 tracing API is currently accessible using the `tracing_v2_enabled` context manager. Assuming the server was succesfully started above, running LangChain Agents, Chains, LLMs, and other primitives will then automatically capture traces. We'll start with a simple math example."
+    "The V2 tracing API is currently accessible using the `tracing_v2_enabled` context manager. Assuming the server was succesfully started above, running LangChain Agents, Chains, LLMs, and other primitives will then automatically capture traces. We'll start with a simple math example.\n",
+    "\n",
+    "**Note** You can also use the `LANGCHAIN_TRACING_V2` environment variable to enable tracing for all runs by default, regardless of whether or not those runs happen within the `tracing_v2_enabled` context manager (i.e. `os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"`)"
    ]
   },
   {
@@ -975,7 +977,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

langchain/retrievers/__init__.py

@@ -1,3 +1,4 @@
+from langchain.retrievers.azure_cognitive_search import AzureCognitiveSearchRetriever
 from langchain.retrievers.chatgpt_plugin_retriever import ChatGPTPluginRetriever
 from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain.retrievers.databerry import DataberryRetriever
@@ -31,5 +32,6 @@ __all__ = [
     "TimeWeightedVectorStoreRetriever",
     "VespaRetriever",
     "WeaviateHybridSearchRetriever",
+    "AzureCognitiveSearchRetriever",
     "WikipediaRetriever",
 ]

langchain/retrievers/azure_cognitive_search.py

@@ -0,0 +1,98 @@
+"""Retriever wrapper for Azure Cognitive Search."""
+from __future__ import annotations
+
+import json
+from typing import Dict, List, Optional
+
+import aiohttp
+import requests
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.schema import BaseRetriever, Document
+from langchain.utils import get_from_dict_or_env
+
+
+class AzureCognitiveSearchRetriever(BaseRetriever, BaseModel):
+    """Wrapper around Azure Cognitive Search."""
+
+    service_name: str = ""
+    """Name of Azure Cognitive Search service"""
+    index_name: str = ""
+    """Name of Index inside Azure Cognitive Search service"""
+    api_key: str = ""
+    """API Key. Both Admin and Query keys work, but for reading data it's
+    recommended to use a Query key."""
+    api_version: str = "2020-06-30"
+    """API version"""
+    aiosession: Optional[aiohttp.ClientSession] = None
+    """ClientSession, in case we want to reuse connection for better performance."""
+    content_key: str = "content"
+    """Key in a retrieved result to set as the Document page_content."""
+
+    class Config:
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that service name, index name and api key exists in environment."""
+        values["service_name"] = get_from_dict_or_env(
+            values, "service_name", "AZURE_COGNITIVE_SEARCH_SERVICE_NAME"
+        )
+        values["index_name"] = get_from_dict_or_env(
+            values, "index_name", "AZURE_COGNITIVE_SEARCH_INDEX_NAME"
+        )
+        values["api_key"] = get_from_dict_or_env(
+            values, "api_key", "AZURE_COGNITIVE_SEARCH_API_KEY"
+        )
+        return values
+
+    def _build_search_url(self, query: str) -> str:
+        base_url = f"https://{self.service_name}.search.windows.net/"
+        endpoint_path = f"indexes/{self.index_name}/docs?api-version={self.api_version}"
+        return base_url + endpoint_path + f"&search={query}"
+
+    @property
+    def _headers(self) -> Dict[str, str]:
+        return {
+            "Content-Type": "application/json",
+            "api-key": self.api_key,
+        }
+
+    def _search(self, query: str) -> List[dict]:
+        search_url = self._build_search_url(query)
+        response = requests.get(search_url, headers=self._headers)
+        if response.status_code != 200:
+            raise Exception(f"Error in search request: {response}")
+
+        return json.loads(response.text)["value"]
+
+    async def _asearch(self, query: str) -> List[dict]:
+        search_url = self._build_search_url(query)
+        if not self.aiosession:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(search_url, headers=self._headers) as response:
+                    response_json = await response.json()
+        else:
+            async with self.aiosession.get(
+                search_url, headers=self._headers
+            ) as response:
+                response_json = await response.json()
+
+        return response_json["value"]
+
+    def get_relevant_documents(self, query: str) -> List[Document]:
+        search_results = self._search(query)
+
+        return [
+            Document(page_content=result.pop(self.content_key), metadata=result)
+            for result in search_results
+        ]
+
+    async def aget_relevant_documents(self, query: str) -> List[Document]:
+        search_results = await self._asearch(query)
+
+        return [
+            Document(page_content=result.pop(self.content_key), metadata=result)
+            for result in search_results
+        ]

langchain/vectorstores/__init__.py

@@ -5,6 +5,7 @@ from langchain.vectorstores.atlas import AtlasDB
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.chroma import Chroma
 from langchain.vectorstores.deeplake import DeepLake
+from langchain.vectorstores.docarray import DocArrayHnswSearch, DocArrayInMemorySearch
 from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
 from langchain.vectorstores.faiss import FAISS
 from langchain.vectorstores.lancedb import LanceDB
@@ -40,4 +41,6 @@ __all__ = [
     "AnalyticDB",
     "Tair",
     "LanceDB",
+    "DocArrayHnswSearch",
+    "DocArrayInMemorySearch",
 ]

langchain/vectorstores/base.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import asyncio
+import warnings
 from abc import ABC, abstractmethod
 from functools import partial
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
@@ -116,6 +117,16 @@ class VectorStore(ABC):
         """Return docs and relevance scores in the range [0, 1].
 
         0 is dissimilar, 1 is most similar.
+
+        Args:
+            query: input text
+            k: Number of Documents to return. Defaults to 4.
+            **kwargs: kwargs to be passed to similarity search. Should include:
+                score_threshold: Optional, a floating point value between 0 to 1 to
+                    filter the resulting set of retrieved docs
+
+        Returns:
+            List of Tuples of (doc, similarity_score)
         """
         docs_and_similarities = self._similarity_search_with_relevance_scores(
             query, k=k, **kwargs
@@ -124,10 +135,23 @@ class VectorStore(ABC):
             similarity < 0.0 or similarity > 1.0
             for _, similarity in docs_and_similarities
         ):
-            raise ValueError(
+            warnings.warn(
                 "Relevance scores must be between"
                 f" 0 and 1, got {docs_and_similarities}"
             )
+
+        score_threshold = kwargs.get("score_threshold")
+        if score_threshold is not None:
+            docs_and_similarities = [
+                (doc, similarity)
+                for doc, similarity in docs_and_similarities
+                if similarity >= score_threshold
+            ]
+            if len(docs_and_similarities) == 0:
+                warnings.warn(
+                    f"No relevant docs were retrieved using the relevance score\
+                          threshold {score_threshold}"
+                )
         return docs_and_similarities
 
     def _similarity_search_with_relevance_scores(
@@ -324,13 +348,29 @@ class VectorStoreRetriever(BaseRetriever, BaseModel):
         """Validate search type."""
         if "search_type" in values:
             search_type = values["search_type"]
-            if search_type not in ("similarity", "mmr"):
+            if search_type not in ("similarity", "similarity_score_threshold", "mmr"):
                 raise ValueError(f"search_type of {search_type} not allowed.")
+            if search_type == "similarity_score_threshold":
+                score_threshold = values["search_kwargs"].get("score_threshold")
+                if (score_threshold is None) or (
+                    not isinstance(score_threshold, float)
+                ):
+                    raise ValueError(
+                        "`score_threshold` is not specified with a float value(0~1) "
+                        "in `search_kwargs`."
+                    )
         return values
 
     def get_relevant_documents(self, query: str) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
+        elif self.search_type == "similarity_score_threshold":
+            docs_and_similarities = (
+                self.vectorstore.similarity_search_with_relevance_scores(
+                    query, **self.search_kwargs
+                )
+            )
+            docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
             docs = self.vectorstore.max_marginal_relevance_search(
                 query, **self.search_kwargs

langchain/vectorstores/docarray/__init__.py

@@ -0,0 +1,7 @@
+from langchain.vectorstores.docarray.hnsw import DocArrayHnswSearch
+from langchain.vectorstores.docarray.in_memory import DocArrayInMemorySearch
+
+__all__ = [
+    "DocArrayHnswSearch",
+    "DocArrayInMemorySearch",
+]

langchain/vectorstores/docarray/base.py

@@ -0,0 +1,199 @@
+from abc import ABC
+from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
+
+import numpy as np
+from pydantic import Field
+
+from langchain.embeddings.base import Embeddings
+from langchain.schema import Document
+from langchain.vectorstores import VectorStore
+from langchain.vectorstores.utils import maximal_marginal_relevance
+
+if TYPE_CHECKING:
+    from docarray import BaseDoc
+    from docarray.index.abstract import BaseDocIndex
+
+
+def _check_docarray_import() -> None:
+    try:
+        import docarray
+
+        da_version = docarray.__version__.split(".")
+        if int(da_version[0]) == 0 and int(da_version[1]) <= 30:
+            raise ValueError(
+                f"To use the DocArrayHnswSearch VectorStore the docarray "
+                f"version >=0.31.0 is expected, received: {docarray.__version__}."
+                f"To upgrade, please run: `pip install -U docarray`."
+            )
+    except ImportError:
+        raise ImportError(
+            "Could not import docarray python package. "
+            'Please install it with `pip install "langchain[docarray]"`.'
+        )
+
+
+class DocArrayIndex(VectorStore, ABC):
+    def __init__(
+        self,
+        doc_index: "BaseDocIndex",
+        embedding: Embeddings,
+    ):
+        """Initialize a vector store from DocArray's DocIndex."""
+        self.doc_index = doc_index
+        self.embedding = embedding
+
+    @staticmethod
+    def _get_doc_cls(**embeddings_params: Any) -> Type["BaseDoc"]:
+        """Get docarray Document class describing the schema of DocIndex."""
+        from docarray import BaseDoc
+        from docarray.typing import NdArray
+
+        class DocArrayDoc(BaseDoc):
+            text: Optional[str]
+            embedding: Optional[NdArray] = Field(**embeddings_params)
+            metadata: Optional[dict]
+
+        return DocArrayDoc
+
+    @property
+    def doc_cls(self) -> Type["BaseDoc"]:
+        if self.doc_index._schema is None:
+            raise ValueError("doc_index expected to have non-null _schema attribute.")
+        return self.doc_index._schema
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        ids: List[str] = []
+        embeddings = self.embedding.embed_documents(list(texts))
+        for i, (t, e) in enumerate(zip(texts, embeddings)):
+            m = metadatas[i] if metadatas else {}
+            doc = self.doc_cls(text=t, embedding=e, metadata=m)
+            self.doc_index.index([doc])
+            ids.append(str(doc.id))
+
+        return ids
+
+    def similarity_search_with_score(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        """Return docs most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query and score for each.
+        """
+        query_embedding = self.embedding.embed_query(query)
+        query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore
+        docs, scores = self.doc_index.find(query_doc, search_field="embedding", limit=k)
+
+        result = [
+            (Document(page_content=doc.text, metadata=doc.metadata), score)
+            for doc, score in zip(docs, scores)
+        ]
+        return result
+
+    def similarity_search(
+        self, query: str, k: int = 4, **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.
+
+        Returns:
+            List of Documents most similar to the query.
+        """
+        results = self.similarity_search_with_score(query, k=k, **kwargs)
+        return [doc for doc, _ in results]
+
+    def _similarity_search_with_relevance_scores(
+        self,
+        query: str,
+        k: int = 4,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs and relevance scores, normalized on a scale from 0 to 1.
+
+        0 is dissimilar, 1 is most similar.
+        """
+        raise NotImplementedError
+
+    def similarity_search_by_vector(
+        self, embedding: List[float], k: int = 4, **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.
+
+        Returns:
+            List of Documents most similar to the query vector.
+        """
+
+        query_doc = self.doc_cls(embedding=embedding)  # type: ignore
+        docs = self.doc_index.find(
+            query_doc, search_field="embedding", limit=k
+        ).documents
+
+        result = [
+            Document(page_content=doc.text, metadata=doc.metadata) for doc in docs
+        ]
+        return result
+
+    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.
+            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.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        query_embedding = self.embedding.embed_query(query)
+        query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore
+
+        docs = self.doc_index.find(
+            query_doc, search_field="embedding", limit=fetch_k
+        ).documents
+
+        mmr_selected = maximal_marginal_relevance(
+            np.array(query_embedding), docs.embedding, k=k
+        )
+        results = [
+            Document(page_content=docs[idx].text, metadata=docs[idx].metadata)
+            for idx in mmr_selected
+        ]
+        return results

langchain/vectorstores/docarray/hnsw.py

@@ -0,0 +1,109 @@
+"""Wrapper around Hnswlib store."""
+from __future__ import annotations
+
+from typing import Any, List, Literal, Optional
+
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.docarray.base import (
+    DocArrayIndex,
+    _check_docarray_import,
+)
+
+
+class DocArrayHnswSearch(DocArrayIndex):
+    """Wrapper around HnswLib storage.
+
+    To use it, you should have the ``docarray[hnswlib]`` package with version >=0.31.0
+    installed. You can install it with `pip install "langchain[hnswlib]"`.
+    """
+
+    @classmethod
+    def from_params(
+        cls,
+        embedding: Embeddings,
+        work_dir: str,
+        n_dim: int,
+        dist_metric: Literal["cosine", "ip", "l2"] = "cosine",
+        max_elements: int = 1024,
+        index: bool = True,
+        ef_construction: int = 200,
+        ef: int = 10,
+        M: int = 16,
+        allow_replace_deleted: bool = True,
+        num_threads: int = 1,
+        **kwargs: Any,
+    ) -> DocArrayHnswSearch:
+        """Initialize DocArrayHnswSearch store.
+
+        Args:
+            embedding (Embeddings): Embedding function.
+            work_dir (str): path to the location where all the data will be stored.
+            n_dim (int): dimension of an embedding.
+            dist_metric (str): Distance metric for DocArrayHnswSearch can be one of:
+                "cosine", "ip", and "l2". Defaults to "cosine".
+            max_elements (int): Maximum number of vectors that can be stored.
+                Defaults to 1024.
+            index (bool): Whether an index should be built for this field.
+                Defaults to True.
+            ef_construction (int): defines a construction time/accuracy trade-off.
+                Defaults to 200.
+            ef (int): parameter controlling query time/accuracy trade-off.
+                Defaults to 10.
+            M (int): parameter that defines the maximum number of outgoing
+                connections in the graph. Defaults to 16.
+            allow_replace_deleted (bool): Enables replacing of deleted elements
+                with new added ones. Defaults to True.
+            num_threads (int): Sets the number of cpu threads to use. Defaults to 1.
+            **kwargs: Other keyword arguments to be passed to the get_doc_cls method.
+        """
+        _check_docarray_import()
+        from docarray.index import HnswDocumentIndex
+
+        doc_cls = cls._get_doc_cls(
+            dim=n_dim,
+            space=dist_metric,
+            max_elements=max_elements,
+            index=index,
+            ef_construction=ef_construction,
+            ef=ef,
+            M=M,
+            allow_replace_deleted=allow_replace_deleted,
+            num_threads=num_threads,
+            **kwargs,
+        )
+        doc_index = HnswDocumentIndex[doc_cls](work_dir=work_dir)  # type: ignore
+        return cls(doc_index, embedding)
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        work_dir: Optional[str] = None,
+        n_dim: Optional[int] = None,
+        **kwargs: Any,
+    ) -> DocArrayHnswSearch:
+        """Create an DocArrayHnswSearch store and insert data.
+
+
+        Args:
+            texts (List[str]): Text data.
+            embedding (Embeddings): Embedding function.
+            metadatas (Optional[List[dict]]): Metadata for each text if it exists.
+                Defaults to None.
+            work_dir (str): path to the location where all the data will be stored.
+            n_dim (int): dimension of an embedding.
+            **kwargs: Other keyword arguments to be passed to the __init__ method.
+
+        Returns:
+            DocArrayHnswSearch Vector Store
+        """
+        if work_dir is None:
+            raise ValueError("`work_dir` parameter has not been set.")
+        if n_dim is None:
+            raise ValueError("`n_dim` parameter has not been set.")
+
+        store = cls.from_params(embedding, work_dir, n_dim, **kwargs)
+        store.add_texts(texts=texts, metadatas=metadatas)
+        return store

langchain/vectorstores/docarray/in_memory.py

@@ -0,0 +1,69 @@
+"""Wrapper around in-memory storage."""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Literal, Optional
+
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.docarray.base import (
+    DocArrayIndex,
+    _check_docarray_import,
+)
+
+
+class DocArrayInMemorySearch(DocArrayIndex):
+    """Wrapper around in-memory storage for exact search.
+
+    To use it, you should have the ``docarray`` package with version >=0.31.0 installed.
+    You can install it with `pip install "langchain[in_memory_store]"`.
+    """
+
+    @classmethod
+    def from_params(
+        cls,
+        embedding: Embeddings,
+        metric: Literal[
+            "cosine_sim", "euclidian_dist", "sgeuclidean_dist"
+        ] = "cosine_sim",
+        **kwargs: Any,
+    ) -> DocArrayInMemorySearch:
+        """Initialize DocArrayInMemorySearch store.
+
+        Args:
+            embedding (Embeddings): Embedding function.
+            metric (str): metric for exact nearest-neighbor search.
+                Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
+                Defaults to "cosine_sim".
+            **kwargs: Other keyword arguments to be passed to the get_doc_cls method.
+        """
+        _check_docarray_import()
+        from docarray.index import InMemoryExactNNIndex
+
+        doc_cls = cls._get_doc_cls(space=metric, **kwargs)
+        doc_index = InMemoryExactNNIndex[doc_cls]()  # type: ignore
+        return cls(doc_index, embedding)
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[Dict[Any, Any]]] = None,
+        **kwargs: Any,
+    ) -> DocArrayInMemorySearch:
+        """Create an DocArrayInMemorySearch store and insert data.
+
+        Args:
+            texts (List[str]): Text data.
+            embedding (Embeddings): Embedding function.
+            metadatas (Optional[List[Dict[Any, Any]]]): Metadata for each text
+                if it exists. Defaults to None.
+            metric (str): metric for exact nearest-neighbor search.
+                Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
+                Defaults to "cosine_sim".
+
+        Returns:
+            DocArrayInMemorySearch Vector Store
+        """
+        store = cls.from_params(embedding, **kwargs)
+        store.add_texts(texts=texts, metadatas=metadatas)
+        return store

poetry.lock

@@ -883,14 +883,14 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"},
-    {file = "certifi-2022.12.7.tar.gz", hash = "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3"},
+    {file = "certifi-2023.5.7-py3-none-any.whl", hash = "sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716"},
+    {file = "certifi-2023.5.7.tar.gz", hash = "sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7"},
 ]
 
 [[package]]
@@ -1560,43 +1560,51 @@ wmi = ["wmi (>=1.5.1,<2.0.0)"]
 
 [[package]]
 name = "docarray"
-version = "0.21.0"
-description = "The data structure for unstructured data"
+version = "0.31.0"
+description = "The data structure for multimodal data"
 category = "main"
 optional = true
-python-versions = "*"
+python-versions = ">=3.7,<4.0"
 files = [
-    {file = "docarray-0.21.0.tar.gz", hash = "sha256:3c9f605123800c1b0cdf8c458be3fb19c05e9a81f723e51200ef531b02e689ee"},
+    {file = "docarray-0.31.0-py3-none-any.whl", hash = "sha256:3783e9bdcf0d59b17499660e54577f4e3d202545998afca9306ebcc09cf0e14e"},
+    {file = "docarray-0.31.0.tar.gz", hash = "sha256:a79d1ed70bd143b3e2a53ff90a62e4b3ce7231d5d237a2fab9b8311d7ae7d245"},
 ]
 
 [package.dependencies]
-jina-hubble-sdk = ">=0.24.0"
-numpy = "*"
-rich = ">=12.0.0"
+numpy = ">=1.17.3"
+orjson = ">=3.8.2"
+pydantic = ">=1.10.2"
+rich = ">=13.1.0"
+types-requests = ">=2.28.11.6"
+typing-inspect = ">=0.8.0"
 
 [package.extras]
-annlite = ["annlite"]
-benchmark = ["h5py", "matplotlib", "pandas", "seaborn"]
-common = ["Pillow", "fastapi", "lz4", "matplotlib", "protobuf (>=3.13.0)", "pydantic (>=1.9.0)", "requests", "uvicorn"]
-elasticsearch = ["elasticsearch (>=8.2.0)"]
-full = ["Pillow", "av", "fastapi", "grpcio (>=1.46.0,<1.48.1)", "grpcio-health-checking (>=1.46.0,<1.48.1)", "grpcio-reflection (>=1.46.0,<1.48.1)", "ipython", "lz4", "matplotlib", "protobuf (>=3.13.0)", "pydantic (>=1.9.0)", "requests", "scipy", "strawberry-graphql", "trimesh[easy]", "uvicorn"]
-milvus = ["pymilvus (>=2.1.0,<2.2.0)"]
-opensearch = ["opensearch-py (==2.0.1)"]
-qdrant = ["qdrant-client (>=0.10.3,<0.11.0)"]
-redis = ["redis (>=4.3.0)"]
-test = ["annlite", "black (==22.3.0)", "datasets", "elasticsearch (>=8.2.0)", "jina", "jupyterlab", "mock", "onnx", "onnxruntime", "opensearch-py (==2.0.1)", "paddlepaddle", "protobuf (>=3.13.0,<=3.20.0)", "pymilvus (==2.1.3)", "pytest", "pytest-cov (==3.0.0)", "pytest-custom_exit_code", "pytest-mock", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout", "redis (>=4.3.0)", "tensorflow (==2.7.0)", "torch (==1.9.0)", "torchvision (==0.10.0)", "transformers (>=4.16.2)", "weaviate-client (>=3.9.0,<3.10.0)"]
-weaviate = ["weaviate-client (>=3.9.0,<3.10.0)"]
+audio = ["pydub (>=0.25.1,<0.26.0)"]
+aws = ["smart-open[s3] (>=6.3.0)"]
+elasticsearch = ["elastic-transport (>=8.4.0,<9.0.0)", "elasticsearch (>=7.10.1)"]
+full = ["av (>=10.0.0)", "lz4 (>=1.0.0)", "pandas (>=1.1.0)", "pillow (>=9.3.0)", "protobuf (>=3.19.0)", "pydub (>=0.25.1,<0.26.0)", "trimesh[easy] (>=3.17.1)", "types-pillow (>=9.3.0.1)"]
+hnswlib = ["hnswlib (>=0.6.2)"]
+image = ["pillow (>=9.3.0)", "types-pillow (>=9.3.0.1)"]
+jac = ["jina-hubble-sdk (>=0.34.0)"]
+mesh = ["trimesh[easy] (>=3.17.1)"]
+pandas = ["pandas (>=1.1.0)"]
+proto = ["lz4 (>=1.0.0)", "protobuf (>=3.19.0)"]
+qdrant = ["qdrant-client (>=1.1.4)"]
+torch = ["torch (>=1.0.0)"]
+video = ["av (>=10.0.0)"]
+weaviate = ["weaviate-client (>=3.15)"]
+web = ["fastapi (>=0.87.0)"]
 
 [[package]]
 name = "docker"
-version = "6.0.1"
+version = "6.1.1"
 description = "A Python library for the Docker Engine API."
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "docker-6.0.1-py3-none-any.whl", hash = "sha256:dbcb3bd2fa80dca0788ed908218bf43972772009b881ed1e20dfc29a65e49782"},
-    {file = "docker-6.0.1.tar.gz", hash = "sha256:896c4282e5c7af5c45e8b683b0b0c33932974fe6e50fc6906a0a83616ab3da97"},
+    {file = "docker-6.1.1-py3-none-any.whl", hash = "sha256:8308b23d3d0982c74f7aa0a3abd774898c0c4fba006e9c3bde4f68354e470fe2"},
+    {file = "docker-6.1.1.tar.gz", hash = "sha256:5ec18b9c49d48ee145a5b5824bb126dc32fc77931e18444783fc07a7724badc0"},
 ]
 
 [package.dependencies]
@@ -2039,14 +2047,14 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.4.0"
+version = "2023.5.0"
 description = "File-system specification"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.4.0-py3-none-any.whl", hash = "sha256:f398de9b49b14e9d84d2c2d11b7b67121bc072fe97b930c4e5668ac3917d8307"},
-    {file = "fsspec-2023.4.0.tar.gz", hash = "sha256:bf064186cd8808f0b2f6517273339ba0a0c8fb1b7048991c28bc67f58b8b67cd"},
+    {file = "fsspec-2023.5.0-py3-none-any.whl", hash = "sha256:51a4ad01a5bb66fcc58036e288c0d53d3975a0df2a5dc59a93b59bade0391f2a"},
+    {file = "fsspec-2023.5.0.tar.gz", hash = "sha256:b3b56e00fb93ea321bc9e5d9cf6f8522a0198b20eb24e02774d329e9c6fb84ce"},
 ]
 
 [package.extras]
@@ -2099,26 +2107,24 @@ files = [
 
 [[package]]
 name = "google-api-core"
-version = "2.11.0"
+version = "2.8.2"
 description = "Google API client core library"
 category = "main"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.6"
 files = [
-    {file = "google-api-core-2.11.0.tar.gz", hash = "sha256:4b9bb5d5a380a0befa0573b302651b8a9a89262c1730e37bf423cec511804c22"},
-    {file = "google_api_core-2.11.0-py3-none-any.whl", hash = "sha256:ce222e27b0de0d7bc63eb043b956996d6dccab14cc3b690aaea91c9cc99dc16e"},
+    {file = "google-api-core-2.8.2.tar.gz", hash = "sha256:06f7244c640322b508b125903bb5701bebabce8832f85aba9335ec00b3d02edc"},
+    {file = "google_api_core-2.8.2-py3-none-any.whl", hash = "sha256:93c6a91ccac79079ac6bbf8b74ee75db970cc899278b97d53bc012f35908cf50"},
 ]
 
 [package.dependencies]
-google-auth = ">=2.14.1,<3.0dev"
+google-auth = ">=1.25.0,<3.0dev"
 googleapis-common-protos = ">=1.56.2,<2.0dev"
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+protobuf = ">=3.15.0,<5.0.0dev"
 requests = ">=2.18.0,<3.0.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)", "grpcio-status (>=1.49.1,<2.0dev)"]
-grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
-grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
+grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)"]
 
 [[package]]
 name = "google-api-python-client"
@@ -2232,32 +2238,32 @@ requests = "*"
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.59.0"
+version = "1.56.4"
 description = "Common protobufs used in Google APIs"
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "googleapis-common-protos-1.59.0.tar.gz", hash = "sha256:4168fcb568a826a52f23510412da405abd93f4d23ba544bb68d943b14ba3cb44"},
-    {file = "googleapis_common_protos-1.59.0-py2.py3-none-any.whl", hash = "sha256:b287dc48449d1d41af0c69f4ea26242b5ae4c3d7249a38b0984c86a4caffff1f"},
+    {file = "googleapis-common-protos-1.56.4.tar.gz", hash = "sha256:c25873c47279387cfdcbdafa36149887901d36202cb645a0e4f29686bf6e4417"},
+    {file = "googleapis_common_protos-1.56.4-py2.py3-none-any.whl", hash = "sha256:8eb2cbc91b69feaf23e32452a7ae60e791e09967d81d4fcc7fc388182d1bd394"},
 ]
 
 [package.dependencies]
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+protobuf = ">=3.15.0,<5.0.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.44.0,<2.0.0dev)"]
+grpc = ["grpcio (>=1.0.0,<2.0.0dev)"]
 
 [[package]]
 name = "gptcache"
-version = "0.1.21"
+version = "0.1.22"
 description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications."
 category = "main"
 optional = false
 python-versions = ">=3.8.1"
 files = [
-    {file = "gptcache-0.1.21-py3-none-any.whl", hash = "sha256:76322a19f96fe0576857072c62a7b29bc680dfde719d551d5bc1aedd0d8a5f34"},
-    {file = "gptcache-0.1.21.tar.gz", hash = "sha256:4c3eb750b972cc95681233358a7bbfd61233827e76ae81be47668552132a9002"},
+    {file = "gptcache-0.1.22-py3-none-any.whl", hash = "sha256:081fada6f4f2f57ef2955ee1ce1224eb4f3511546d7efd483648f1ff8d257fdb"},
+    {file = "gptcache-0.1.22.tar.gz", hash = "sha256:5fb7b7eb7ae774f2ec6c9b9dbcd7829da4a83925776d5edc98181811d8f71a0f"},
 ]
 
 [package.dependencies]
@@ -2565,7 +2571,7 @@ numpy = ">=1.14.5"
 name = "hnswlib"
 version = "0.7.0"
 description = "hnswlib"
-category = "dev"
+category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2856,14 +2862,14 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.22.0"
+version = "6.23.0"
 description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.22.0-py3-none-any.whl", hash = "sha256:1ae6047c1277508933078163721bbb479c3e7292778a04b4bacf0874550977d6"},
-    {file = "ipykernel-6.22.0.tar.gz", hash = "sha256:302558b81f1bc22dc259fb2a0c5c7cf2f4c0bdb21b50484348f7bafe7fb71421"},
+    {file = "ipykernel-6.23.0-py3-none-any.whl", hash = "sha256:fc886f1dcdc0ec17f277e4d21fd071c857d381adcb04f3f3735d25325ca323c6"},
+    {file = "ipykernel-6.23.0.tar.gz", hash = "sha256:bd6f487d9e2744c84f6e667d46462d7647a4c862e70e08282f05a52b9d4b705f"},
 ]
 
 [package.dependencies]
@@ -2890,14 +2896,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.12.1"
+version = "8.12.2"
 description = "IPython: Productive Interactive Computing"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipython-8.12.1-py3-none-any.whl", hash = "sha256:e3015a1a4aa09b3984fb81b9cef4f0772af5a549878b81efb094cda8bb121993"},
-    {file = "ipython-8.12.1.tar.gz", hash = "sha256:2442915417763b62181009259782975fa50bb5eedb97ae97fb614204bf6ecc21"},
+    {file = "ipython-8.12.2-py3-none-any.whl", hash = "sha256:ea8801f15dfe4ffb76dea1b09b847430ffd70d827b41735c64a0638a04103bfc"},
+    {file = "ipython-8.12.2.tar.gz", hash = "sha256:c7b80eb7f5a855a88efc971fda506ff7a91c280b42cdae26643e0f601ea281ea"},
 ]
 
 [package.dependencies]
@@ -3038,20 +3044,20 @@ testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jina"
-version = "3.15.0"
+version = "3.14.1"
 description = "Build multimodal AI services via cloud native technologies · Neural Search · Generative AI · MLOps"
 category = "main"
 optional = true
 python-versions = "*"
 files = [
-    {file = "jina-3.15.0.tar.gz", hash = "sha256:18a3be8ddca14ed66a554d8480a277bcb7620ebc6ae11352a9835c91865f9d1e"},
+    {file = "jina-3.14.1.tar.gz", hash = "sha256:00b1f5995b13c9a49a2287bd534bd32eb8c05706064752035d569e616a15b411"},
 ]
 
 [package.dependencies]
 aiofiles = "*"
 aiohttp = "*"
 aiostream = "*"
-docarray = ">=0.16.4,<0.30.0"
+docarray = ">=0.16.4"
 docker = "*"
 fastapi = ">=0.76.0"
 filelock = "*"
@@ -3085,14 +3091,14 @@ websockets = "*"
 aiofiles = ["aiofiles"]
 aiohttp = ["aiohttp"]
 aiostream = ["aiostream"]
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+docarray = ["docarray (>=0.16.4)"]
 docker = ["docker"]
 fastapi = ["fastapi (>=0.76.0)"]
 filelock = ["filelock"]
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 pillow = ["Pillow"]
-portforward = ["portforward (>=0.2.4,<0.4.3)"]
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 prometheus-api-client = ["prometheus-api-client (>=0.5.1)"]
 prometheus-client = ["prometheus_client (>=0.12.0)"]
 protobuf = ["protobuf (>=3.19.0)"]
@@ -3141,7 +3147,7 @@ requests = ["requests"]
 requests-mock = ["requests-mock"]
 scipy = ["scipy (>=1.6.1)"]
 sgqlc = ["sgqlc"]
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 tensorflow = ["tensorflow (>=2.0)"]
@@ -3579,18 +3585,18 @@ files = [
 
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-version = "0.1.1"
+version = "0.1.2"
 description = "lancedb"
 category = "main"
 optional = true
 python-versions = ">=3.8"
 files = [
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-pylance = ">=0.4.4"
+pylance = ">=0.4.6"
 ratelimiter = "*"
 retry = "*"
 tqdm = "*"
@@ -4349,14 +4355,14 @@ testing = ["beautifulsoup4", "coverage[toml]", "pytest (>=6,<7)", "pytest-cov",
 
 [[package]]
 name = "nbclassic"
-version = "0.5.6"
+version = "1.0.0"
 description = "Jupyter Notebook as a Jupyter Server extension."
 category = "dev"
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 [package.dependencies]
@@ -4408,14 +4414,14 @@ test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "p
 
 [[package]]
 name = "nbconvert"
-version = "7.3.1"
+version = "7.4.0"
 description = "Converting Jupyter Notebooks"
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 [package.dependencies]
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 tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
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 [[package]]
 name = "packaging"
 version = "23.1"
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 description = "Protocol Buffers"
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 python-versions = ">=3.5"
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 [[package]]
@@ -6223,16 +6284,16 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pylance"
-version = "0.4.4"
+version = "0.4.6"
 description = "python wrapper for lance-rs"
 category = "main"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -6923,14 +6984,14 @@ urllib3 = ">=1.26.14,<2.0.0"
 
 [[package]]
 name = "qtconsole"
-version = "5.4.2"
+version = "5.4.3"
 description = "Jupyter Qt console"
 category = "dev"
 optional = false
 python-versions = ">= 3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -7268,14 +7329,14 @@ files = [
 
 [[package]]
 name = "s3transfer"
-version = "0.6.0"
+version = "0.6.1"
 description = "An Amazon S3 Transfer Manager"
 category = "main"
 optional = false
 python-versions = ">= 3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -8267,18 +8328,18 @@ files = [
 
 [[package]]
 name = "tensorflow-hub"
-version = "0.13.0"
+version = "0.12.0"
 description = "TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models."
 category = "main"
 optional = true
 python-versions = "*"
 files = [
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 [package.dependencies]
 numpy = ">=1.12.0"
-protobuf = ">=3.19.6"
+protobuf = ">=3.8.0"
 
 [package.extras]
 make-image-classifier = ["keras-preprocessing[image]"]
@@ -8904,14 +8965,14 @@ files = [
 
 [[package]]
 name = "types-redis"
-version = "4.5.4.1"
+version = "4.5.4.2"
 description = "Typing stubs for redis"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -8920,18 +8981,18 @@ types-pyOpenSSL = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.29.0.0"
+version = "2.30.0.0"
 description = "Typing stubs for requests"
-category = "dev"
+category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
-types-urllib3 = "<1.27"
+types-urllib3 = "*"
 
 [[package]]
 name = "types-toml"
@@ -8949,7 +9010,7 @@ files = [
 name = "types-urllib3"
 version = "1.26.25.12"
 description = "Typing stubs for urllib3"
-category = "dev"
+category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -9271,14 +9332,14 @@ files = [
 
 [[package]]
 name = "weaviate-client"
-version = "3.16.2"
+version = "3.17.1"
 description = "A python native weaviate client"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -9337,82 +9398,82 @@ test = ["websockets"]
 
 [[package]]
 name = "websockets"
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 ]
 
 [[package]]
@@ -9476,7 +9537,7 @@ name = "wikipedia"
 version = "1.4.0"
 description = "Wikipedia API for Python"
 category = "main"
-optional = true
+optional = false
 python-versions = "*"
 files = [
     {file = "wikipedia-1.4.0.tar.gz", hash = "sha256:db0fad1829fdd441b1852306e9856398204dc0786d2996dd2e0c8bb8e26133b2"},
@@ -9798,11 +9859,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "lark", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
+all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "gptcache", "hnswlib", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "lark", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "protobuf", "psycopg2-binary", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
 azure = ["azure-core", "azure-cosmos", "azure-identity", "openai"]
 cohere = ["cohere"]
 embeddings = ["sentence-transformers"]
 extended-testing = ["pdfminer-six", "pypdf"]
+hnswlib = ["docarray", "hnswlib", "protobuf"]
+in-memory-store = ["docarray"]
 llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "torch", "transformers"]
 openai = ["openai"]
 qdrant = ["qdrant-client"]
@@ -9810,4 +9873,4 @@ qdrant = ["qdrant-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "63324f95fc04b48b631353c5e1a1a2ecc1b9dc757441181d4920e9d77cd8951f"
+content-hash = "302163050738bb858c34c066dbf684aab5940a430756999940dc9ba66a4f24a6"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.164"
+version = "0.0.165"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -78,6 +78,9 @@ pyvespa = {version = "^0.33.0", optional = true}
 O365 = {version = "^2.0.26", optional = true}
 jq = {version = "^1.4.1", optional = true}
 pdfminer-six = {version = "^20221105", optional = true}
+docarray = {version="^0.31.0", optional=true}
+protobuf = {version="3.19", optional=true}
+hnswlib = {version="^0.7.0", optional=true}
 
 
 [tool.poetry.group.docs.dependencies]
@@ -159,9 +162,11 @@ llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifes
 qdrant = ["qdrant-client"]
 openai = ["openai"]
 cohere = ["cohere"]
+in_memory_store = ["docarray"]
+hnswlib = ["docarray", "protobuf", "hnswlib"]
 embeddings = ["sentence-transformers"]
 azure = ["azure-identity", "azure-cosmos", "openai", "azure-core"]
-all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "lark", "pexpect", "pyvespa", "O365", "jq"]
+all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "lark", "pexpect", "pyvespa", "O365", "jq", "docarray", "protobuf", "hnswlib"]
 # An extra used to be able to add extended testing.
 extended_testing = ["pypdf", "pdfminer.six"]
 

tests/integration_tests/callbacks/test_langchain_tracer.py

@@ -7,6 +7,7 @@ from aiohttp import ClientSession
 
 from langchain.agents import AgentType, initialize_agent, load_tools
 from langchain.callbacks import tracing_enabled
+from langchain.callbacks.manager import tracing_v2_enabled
 from langchain.llms import OpenAI
 
 questions = [
@@ -121,3 +122,33 @@ async def test_tracing_context_manager_async() -> None:
         await asyncio.gather(*tasks)
 
     await task
+
+
+@pytest.mark.asyncio
+async def test_tracing_v2_environment_variable() -> None:
+    os.environ["LANGCHAIN_TRACING_V2"] = "true"
+
+    aiosession = ClientSession()
+    llm = OpenAI(temperature=0)
+    async_tools = load_tools(["llm-math", "serpapi"], llm=llm, aiosession=aiosession)
+    agent = initialize_agent(
+        async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+    )
+    tasks = [agent.arun(q) for q in questions[:3]]
+    await asyncio.gather(*tasks)
+    await aiosession.close()
+
+
+def test_tracing_v2_context_manager() -> None:
+    llm = OpenAI(temperature=0)
+    tools = load_tools(["llm-math", "serpapi"], llm=llm)
+    agent = initialize_agent(
+        tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+    )
+    if "LANGCHAIN_TRACING_V2" in os.environ:
+        del os.environ["LANGCHAIN_TRACING_V2"]
+    with tracing_v2_enabled() as session:
+        assert session
+        agent.run(questions[0])  # this should be traced
+
+    agent.run(questions[0])  # this should not be traced

tests/integration_tests/retrievers/test_azure_cognitive_search.py

@@ -0,0 +1,24 @@
+"""Test Azure Cognitive Search wrapper."""
+import pytest
+
+from langchain.retrievers.azure_cognitive_search import AzureCognitiveSearchRetriever
+from langchain.schema import Document
+
+
+def test_azure_cognitive_search_get_relevant_documents() -> None:
+    """Test valid call to Azure Cognitive Search."""
+    retriever = AzureCognitiveSearchRetriever()
+    documents = retriever.get_relevant_documents("what is langchain")
+    for doc in documents:
+        assert isinstance(doc, Document)
+        assert doc.page_content
+
+
+@pytest.mark.asyncio
+async def test_azure_cognitive_search_aget_relevant_documents() -> None:
+    """Test valid async call to Azure Cognitive Search."""
+    retriever = AzureCognitiveSearchRetriever()
+    documents = await retriever.aget_relevant_documents("what is langchain")
+    for doc in documents:
+        assert isinstance(doc, Document)
+        assert doc.page_content

tests/integration_tests/retrievers/test_self_query.py

@@ -0,0 +1,89 @@
+import pytest
+
+from langchain.chains.query_constructor.base import AttributeInfo
+from langchain.embeddings import FakeEmbeddings
+from langchain.llms import OpenAI
+from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain.schema import Document
+from langchain.vectorstores import Chroma
+
+
+@pytest.fixture
+def retriever() -> SelfQueryRetriever:
+    docs = [
+        Document(
+            page_content=(
+                "A bunch of scientists bring back dinosaurs and mayhem breaks loose"
+            ),
+            metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
+        ),
+        Document(
+            page_content=(
+                "Leo DiCaprio gets lost in a dream within a dream within a dream "
+                "within a ..."
+            ),
+            metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},
+        ),
+        Document(
+            page_content=(
+                "A psychologist / detective gets lost in a series of dreams within"
+                " dreams within dreams and Inception reused the idea"
+            ),
+            metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6},
+        ),
+        Document(
+            page_content=(
+                "A bunch of normal-sized women are supremely wholesome and some men "
+                "pine after them"
+            ),
+            metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
+        ),
+        Document(
+            page_content="Toys come alive and have a blast doing so",
+            metadata={"year": 1995, "genre": "animated"},
+        ),
+        Document(
+            page_content="Three men walk into the Zone, three men walk out of the Zone",
+            metadata={
+                "year": 1979,
+                "director": "Andrei Tarkovsky",
+                "genre": "science fiction",
+                "rating": 9.9,
+            },
+        ),
+    ]
+    vectorstore = Chroma.from_documents(docs, FakeEmbeddings(size=10))
+    metadata_field_info = [
+        AttributeInfo(
+            name="genre",
+            description="The genre of the movie",
+            type="string or list[string]",
+        ),
+        AttributeInfo(
+            name="year",
+            description="The year the movie was released",
+            type="integer",
+        ),
+        AttributeInfo(
+            name="director",
+            description="The name of the movie director",
+            type="string",
+        ),
+        AttributeInfo(
+            name="rating", description="A 1-10 rating for the movie", type="float"
+        ),
+    ]
+    retriever = SelfQueryRetriever.from_llm(
+        OpenAI(temperature=0),
+        vectorstore,
+        "Brief summary of a movie",
+        metadata_field_info,
+    )
+    return retriever
+
+
+def test_get_relevant_documents(retriever: SelfQueryRetriever) -> None:
+    docs = retriever.get_relevant_documents(
+        "What are some movies about dinosaurs that came out before 2000"
+    )
+    assert len(docs) > 0

tests/integration_tests/vectorstores/docarray/test_hnsw.py

@@ -0,0 +1,148 @@
+from pathlib import Path
+from typing import List
+
+import numpy as np
+import pytest
+
+from langchain.schema import Document
+from langchain.vectorstores.docarray import DocArrayHnswSearch
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+
+@pytest.fixture
+def texts() -> List[str]:
+    return ["foo", "bar", "baz"]
+
+
+def test_from_texts(texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+    )
+    assert docsearch.doc_index.num_docs() == 3
+
+
+def test_add_texts(texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayHnswSearch.from_params(
+        work_dir=str(tmp_path),
+        n_dim=10,
+        embedding=FakeEmbeddings(),
+    )
+    docsearch.add_texts(texts=texts)
+    assert docsearch.doc_index.num_docs() == 3
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search(metric: str, texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric=metric,
+        index=True,
+    )
+    output = hnsw_vec_store.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search_all_configurations(
+    metric: str, texts: List[str], tmp_path: Path
+) -> None:
+    """Test end to end construction and simple similarity search."""
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        dist_metric=metric,
+        n_dim=10,
+        max_elements=8,
+        ef_construction=300,
+        ef=20,
+        M=8,
+        allow_replace_deleted=False,
+        num_threads=2,
+    )
+    output = hnsw_vec_store.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search_by_vector(metric: str, texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and similarity search by vector."""
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric=metric,
+    )
+    embedding = [1.0] * 10
+    output = hnsw_vec_store.similarity_search_by_vector(embedding, k=1)
+
+    assert output == [Document(page_content="bar")]
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search_with_score(metric: str, tmp_path: Path) -> None:
+    """Test end to end construction and similarity search with score."""
+    texts = ["foo", "bar", "baz"]
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric=metric,
+    )
+    output = hnsw_vec_store.similarity_search_with_score("foo", k=1)
+    assert len(output) == 1
+
+    out_doc, out_score = output[0]
+    assert out_doc == Document(page_content="foo")
+    assert np.isclose(out_score, 0.0, atol=1.0e-6)
+
+
+def test_sim_search_with_score_for_ip_metric(texts: List[str], tmp_path: Path) -> None:
+    """
+    Test end to end construction and similarity search with score for ip
+    (inner-product) metric.
+    """
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric="ip",
+    )
+    output = hnsw_vec_store.similarity_search_with_score("foo", k=3)
+    assert len(output) == 3
+
+    for result in output:
+        assert result[1] == -8.0
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_max_marginal_relevance_search(
+    metric: str, texts: List[str], tmp_path: Path
+) -> None:
+    """Test MRR search."""
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        metadatas=metadatas,
+        dist_metric=metric,
+        work_dir=str(tmp_path),
+        n_dim=10,
+    )
+    output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
+    assert output == [
+        Document(page_content="foo", metadata={"page": 0}),
+        Document(page_content="bar", metadata={"page": 1}),
+    ]

tests/integration_tests/vectorstores/docarray/test_in_memory.py

@@ -0,0 +1,95 @@
+from pathlib import Path
+from typing import List
+
+import numpy as np
+import pytest
+
+from langchain.schema import Document
+from langchain.vectorstores.docarray import DocArrayInMemorySearch
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+
+@pytest.fixture
+def texts() -> List[str]:
+    return ["foo", "bar", "baz"]
+
+
+def test_from_texts(texts: List[str]) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayInMemorySearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+    )
+    assert isinstance(docsearch, DocArrayInMemorySearch)
+    assert docsearch.doc_index.num_docs() == 3
+
+
+def test_add_texts(texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayInMemorySearch.from_params(FakeEmbeddings())
+    assert isinstance(docsearch, DocArrayInMemorySearch)
+    assert docsearch.doc_index.num_docs() == 0
+
+    docsearch.add_texts(texts=texts)
+    assert docsearch.doc_index.num_docs() == 3
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_sim_search(metric: str, texts: List[str]) -> None:
+    """Test end to end construction and simple similarity search."""
+    texts = ["foo", "bar", "baz"]
+    in_memory_vec_store = DocArrayInMemorySearch.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metric=metric,
+    )
+
+    output = in_memory_vec_store.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_sim_search_with_score(metric: str, texts: List[str]) -> None:
+    """Test end to end construction and similarity search with score."""
+    in_memory_vec_store = DocArrayInMemorySearch.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metric=metric,
+    )
+
+    output = in_memory_vec_store.similarity_search_with_score("foo", k=1)
+
+    out_doc, out_score = output[0]
+    assert out_doc == Document(page_content="foo")
+
+    expected_score = 0.0 if "dist" in metric else 1.0
+    assert np.isclose(out_score, expected_score, atol=1.0e-6)
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_sim_search_by_vector(metric: str, texts: List[str]) -> None:
+    """Test end to end construction and similarity search by vector."""
+    in_memory_vec_store = DocArrayInMemorySearch.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metric=metric,
+    )
+
+    embedding = [1.0] * 10
+    output = in_memory_vec_store.similarity_search_by_vector(embedding, k=1)
+
+    assert output == [Document(page_content="bar")]
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_max_marginal_relevance_search(metric: str, texts: List[str]) -> None:
+    """Test MRR search."""
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = DocArrayInMemorySearch.from_texts(
+        texts, FakeEmbeddings(), metadatas=metadatas, metric=metric
+    )
+    output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
+    assert output == [
+        Document(page_content="foo", metadata={"page": 0}),
+        Document(page_content="bar", metadata={"page": 1}),
+    ]