diff --git a/docs/deployments.md b/docs/deployments.md
index fd661717..b77652d0 100644
--- a/docs/deployments.md
+++ b/docs/deployments.md
@@ -22,3 +22,9 @@ This repo serves as a template for how deploy a LangChain with Gradio.
 It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills).
 It also contains instructions for how to deploy this app on the Hugging Face platform.
 This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
+
+## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
+
+This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
+
+It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API.
\ No newline at end of file
diff --git a/docs/gallery.rst b/docs/gallery.rst
index d5f5a50d..6e7fdafa 100644
--- a/docs/gallery.rst
+++ b/docs/gallery.rst
@@ -77,6 +77,17 @@ Open Source
 
     +++
 
+    A jupyter notebook demonstrating how you could create a semantic search engine on documents in one of your Google Folders
+
+    ---
+
+    .. link-button:: https://github.com/venuv/langchain_semantic_search
+        :type: url
+        :text: Google Folder Semantic Search
+        :classes: stretched-link btn-lg
+
+    +++
+
     Build a GitHub support bot with GPT3, LangChain, and Python.
 
     ---
diff --git a/docs/modules/chains/examples/pal.ipynb b/docs/modules/chains/examples/pal.ipynb
index 3e0de46b..f13dc36f 100644
--- a/docs/modules/chains/examples/pal.ipynb
+++ b/docs/modules/chains/examples/pal.ipynb
@@ -21,6 +21,24 @@
     "from langchain import OpenAI"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a58e15e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "095adc76",
+   "metadata": {},
+   "source": [
+    "## Math Prompt"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -28,7 +46,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
     "pal_chain = PALChain.from_math_prompt(llm, verbose=True)"
    ]
   },
@@ -64,7 +81,7 @@
       "    result = total_pets\n",
       "    return result\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished PALChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -82,6 +99,14 @@
     "pal_chain.run(question)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0269d20a",
+   "metadata": {},
+   "source": [
+    "## Colored Objects"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -89,7 +114,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
     "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)"
    ]
   },
@@ -147,10 +171,94 @@
     "pal_chain.run(question)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "fc3d7f10",
+   "metadata": {},
+   "source": [
+    "## Intermediate Steps\n",
+    "You can also use the intermediate steps flag to return the code executed that generates the answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9d2d9c61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True, return_intermediate_steps=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b29b971b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2c40c28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "objects = []\n",
+      "objects += [('booklet', 'blue')] * 2\n",
+      "objects += [('booklet', 'purple')] * 2\n",
+      "objects += [('sunglasses', 'yellow')] * 2\n",
+      "\n",
+      "# Remove all pairs of sunglasses\n",
+      "objects = [object for object in objects if object[0] != 'sunglasses']\n",
+      "\n",
+      "# Count number of purple objects\n",
+      "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
+      "answer = num_purple\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = pal_chain({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "efddd033",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"# Put objects into a list to record ordering\\nobjects = []\\nobjects += [('booklet', 'blue')] * 2\\nobjects += [('booklet', 'purple')] * 2\\nobjects += [('sunglasses', 'yellow')] * 2\\n\\n# Remove all pairs of sunglasses\\nobjects = [object for object in objects if object[0] != 'sunglasses']\\n\\n# Count number of purple objects\\nnum_purple = len([object for object in objects if object[1] == 'purple'])\\nanswer = num_purple\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['intermediate_steps']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4ab20fec",
+   "id": "dfd88594",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/docs/modules/prompts/examples/example_selectors.ipynb b/docs/modules/prompts/examples/example_selectors.ipynb
index f755920d..a0ffe4a4 100644
--- a/docs/modules/prompts/examples/example_selectors.ipynb
+++ b/docs/modules/prompts/examples/example_selectors.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "8244ff60",
    "metadata": {},
    "outputs": [],
@@ -81,7 +81,7 @@
     "    template=\"Input: {input}\\nOutput: {output}\",\n",
     ")\n",
     "example_selector = LengthBasedExampleSelector(\n",
-    "    # These are the examples is has available to choose from.\n",
+    "    # These are the examples it has available to choose from.\n",
     "    examples=examples, \n",
     "    # This is the PromptTemplate being used to format the examples.\n",
     "    example_prompt=example_prompt, \n",
@@ -439,10 +439,242 @@
     "print(similar_prompt.format(adjective=\"worried\"))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4aaeed2f",
+   "metadata": {},
+   "source": [
+    "## NGram Overlap ExampleSelector\n",
+    "\n",
+    "The NGramOverlapExampleSelector selects and orders examples based on which examples are most similar to the input, according to an ngram overlap score. The ngram overlap score is a float between 0.0 and 1.0, inclusive. \n",
+    "\n",
+    "The selector allows for a threshold score to be set. Examples with an ngram overlap score less than or equal to the threshold are excluded. The threshold is set to -1.0, by default, so will not exclude any examples, only reorder them. Setting the threshold to 0.0 will exclude examples that have no ngram overlaps with the input.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9cbc0acc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.prompts.example_selector.ngram_overlap import NGramOverlapExampleSelector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4f318f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These are examples of a fictional translation task.\n",
+    "examples = [\n",
+    "    {\"input\": \"See Spot run.\", \"output\": \"Ver correr a Spot.\"},\n",
+    "    {\"input\": \"My dog barks.\", \"output\": \"Mi perro ladra.\"},\n",
+    "    {\"input\": \"Spot can run.\", \"output\": \"Spot puede correr.\"},\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bf75e0fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"output\"],\n",
+    "    template=\"Input: {input}\\nOutput: {output}\",\n",
+    ")\n",
+    "example_selector = NGramOverlapExampleSelector(\n",
+    "    # These are the examples it has available to choose from.\n",
+    "    examples=examples, \n",
+    "    # This is the PromptTemplate being used to format the examples.\n",
+    "    example_prompt=example_prompt, \n",
+    "    # This is the threshold, at which selector stops.\n",
+    "    # It is set to -1.0 by default.\n",
+    "    threshold=-1.0,\n",
+    "    # For negative threshold:\n",
+    "    # Selector sorts examples by ngram overlap score, and excludes none.\n",
+    "    # For threshold greater than 1.0:\n",
+    "    # Selector excludes all examples, and returns an empty list.\n",
+    "    # For threshold equal to 0.0:\n",
+    "    # Selector sorts examples by ngram overlap score,\n",
+    "    # and excludes those with no ngram overlap with input.\n",
+    ")\n",
+    "dynamic_prompt = FewShotPromptTemplate(\n",
+    "    # We provide an ExampleSelector instead of examples.\n",
+    "    example_selector=example_selector,\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=\"Give the Spanish translation of every input\",\n",
+    "    suffix=\"Input: {sentence}\\nOutput:\", \n",
+    "    input_variables=[\"sentence\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "83fb218a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: See Spot run.\n",
+      "Output: Ver correr a Spot.\n",
+      "\n",
+      "Input: My dog barks.\n",
+      "Output: Mi perro ladra.\n",
+      "\n",
+      "Input: Spot can run fast.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# An example input with large ngram overlap with \"Spot can run.\"\n",
+    "# and no overlap with \"My dog barks.\"\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "485f5307",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: See Spot run.\n",
+      "Output: Ver correr a Spot.\n",
+      "\n",
+      "Input: Spot plays fetch.\n",
+      "Output: Spot juega a buscar.\n",
+      "\n",
+      "Input: My dog barks.\n",
+      "Output: Mi perro ladra.\n",
+      "\n",
+      "Input: Spot can run fast.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can add examples to NGramOverlapExampleSelector as well.\n",
+    "new_example = {\"input\": \"Spot plays fetch.\", \"output\": \"Spot juega a buscar.\"}\n",
+    "\n",
+    "example_selector.add_example(new_example)\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "606ce697",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: See Spot run.\n",
+      "Output: Ver correr a Spot.\n",
+      "\n",
+      "Input: Spot plays fetch.\n",
+      "Output: Spot juega a buscar.\n",
+      "\n",
+      "Input: Spot can run fast.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can set a threshold at which examples are excluded.\n",
+    "# For example, setting threshold equal to 0.0\n",
+    "# excludes examples with no ngram overlaps with input.\n",
+    "# Since \"My dog barks.\" has no ngram overlaps with \"Spot can run fast.\"\n",
+    "# it is excluded.\n",
+    "example_selector.threshold=0.0\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "7f8d72f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: Spot plays fetch.\n",
+      "Output: Spot juega a buscar.\n",
+      "\n",
+      "Input: Spot can play fetch.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setting small nonzero threshold\n",
+    "example_selector.threshold=0.09\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can play fetch.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "09633aa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can play fetch.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setting threshold greater than 1.0\n",
+    "example_selector.threshold=1.0+1e-9\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can play fetch.\"))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c746d6f4",
+   "id": "39f30097",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/docs/modules/utils/combine_docs_examples/embeddings.ipynb b/docs/modules/utils/combine_docs_examples/embeddings.ipynb
index 686afe6d..9ddae5ae 100644
--- a/docs/modules/utils/combine_docs_examples/embeddings.ipynb
+++ b/docs/modules/utils/combine_docs_examples/embeddings.ipynb
@@ -77,7 +77,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "42f76e43",
    "metadata": {},
@@ -138,7 +137,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ed47bb62",
    "metadata": {},
@@ -196,11 +194,137 @@
    "source": [
     "doc_result = embeddings.embed_documents([text])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fff4734f",
+   "metadata": {},
+   "source": [
+    "## TensorflowHub\n",
+    "Let's load the TensorflowHub Embedding class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f822104b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import TensorflowHubEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bac84e46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings = TensorflowHubEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4790d770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f556dcdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59428e05",
+   "metadata": {},
+   "source": [
+    "## InstructEmbeddings\n",
+    "Let's load the HuggingFace instruct Embeddings class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "92c5b61e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "062547b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load INSTRUCTOR_Transformer\n",
+      "max_seq_length  512\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings = HuggingFaceInstructEmbeddings(query_instruction=\"Represent the query for retrieval: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e1dcc4bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "90f0db94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a961cdb5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cohere",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -214,7 +338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/modules/utils/combine_docs_examples/textsplitter.ipynb b/docs/modules/utils/combine_docs_examples/textsplitter.ipynb
index 2837f459..6f1110bd 100644
--- a/docs/modules/utils/combine_docs_examples/textsplitter.ipynb
+++ b/docs/modules/utils/combine_docs_examples/textsplitter.ipynb
@@ -152,7 +152,7 @@
    "metadata": {},
    "source": [
     "## Document creation\n",
-    "We can also use the text splitter to create \"Documents\" directly. Documents a way of bundling pieces of text with associated metadata so that chains can interact with them. We can also create documents with empty metadata though!\n",
+    "We can also use the text splitter to create \"Documents\" directly. Documents are a way of bundling pieces of text with associated metadata so that chains can interact with them. We can also create documents with empty metadata though!\n",
     "\n",
     "In the below example, we pass two pieces of text to get split up (we pass two just to show off the interface of splitting multiple pieces of text)."
    ]
diff --git a/langchain/agents/conversational/base.py b/langchain/agents/conversational/base.py
index 3cbccf86..d97fc417 100644
--- a/langchain/agents/conversational/base.py
+++ b/langchain/agents/conversational/base.py
@@ -75,8 +75,8 @@ class ConversationalAgent(Agent):
         return self.ai_prefix
 
     def _extract_tool_and_input(self, llm_output: str) -> Optional[Tuple[str, str]]:
-        if f"{self.ai_prefix}: " in llm_output:
-            return self.ai_prefix, llm_output.split(f"{self.ai_prefix}: ")[-1]
+        if f"{self.ai_prefix}:" in llm_output:
+            return self.ai_prefix, llm_output.split(f"{self.ai_prefix}:")[-1].strip()
         regex = r"Action: (.*?)\nAction Input: (.*)"
         match = re.search(regex, llm_output)
         if not match:
diff --git a/langchain/chains/__init__.py b/langchain/chains/__init__.py
index 21bb09d9..718e53cf 100644
--- a/langchain/chains/__init__.py
+++ b/langchain/chains/__init__.py
@@ -22,7 +22,6 @@ from langchain.chains.transform import TransformChain
 from langchain.chains.vector_db_qa.base import VectorDBQA
 
 __all__ = [
-    "APIChain",
     "ConversationChain",
     "LLMChain",
     "LLMBashChain",
diff --git a/langchain/chains/llm.py b/langchain/chains/llm.py
index ce576318..33174bf4 100644
--- a/langchain/chains/llm.py
+++ b/langchain/chains/llm.py
@@ -1,4 +1,5 @@
 """Chain that just formats a prompt and calls an LLM."""
+from string import Formatter
 from typing import Any, Dict, List, Sequence, Union
 
 from pydantic import BaseModel, Extra
@@ -7,6 +8,7 @@ from langchain.chains.base import Chain
 from langchain.input import get_colored_text
 from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
+from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import LLMResult
 
 
@@ -126,3 +128,14 @@ class LLMChain(Chain, BaseModel):
     @property
     def _chain_type(self) -> str:
         return "llm_chain"
+
+    @classmethod
+    def from_string(cls, llm: BaseLLM, template: str) -> Chain:
+        """Create LLMChain from LLM and template."""
+        input_variables = {
+            v for _, v, _, _ in Formatter().parse(template) if v is not None
+        }
+        prompt_template = PromptTemplate(
+            input_variables=list(input_variables), template=template
+        )
+        return cls(llm=llm, prompt=prompt_template)
diff --git a/langchain/chains/natbot/crawler.py b/langchain/chains/natbot/crawler.py
index 6fcf9b4b..7275ce7a 100644
--- a/langchain/chains/natbot/crawler.py
+++ b/langchain/chains/natbot/crawler.py
@@ -336,7 +336,7 @@ class Crawler:
                 element_node_value = strings[node_value[index]]
                 if (
                     element_node_value == "|"
-                ):  # commonly used as a seperator, does not add much context - lets save ourselves some token space
+                ):  # commonly used as a separator, does not add much context - lets save ourselves some token space
                     continue
             elif (
                 node_name == "input"
diff --git a/langchain/chains/pal/base.py b/langchain/chains/pal/base.py
index ccecd9ec..5f574aaa 100644
--- a/langchain/chains/pal/base.py
+++ b/langchain/chains/pal/base.py
@@ -4,7 +4,7 @@ As in https://arxiv.org/pdf/2211.10435.pdf.
 """
 from __future__ import annotations
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra
 
@@ -24,7 +24,10 @@ class PALChain(Chain, BaseModel):
     prompt: BasePromptTemplate
     stop: str = "\n\n"
     get_answer_expr: str = "print(solution())"
+    python_globals: Optional[Dict[str, Any]] = None
+    python_locals: Optional[Dict[str, Any]] = None
     output_key: str = "result"  #: :meta private:
+    return_intermediate_steps: bool = False
 
     class Config:
         """Configuration for this pydantic object."""
@@ -46,7 +49,10 @@ class PALChain(Chain, BaseModel):
 
         :meta private:
         """
-        return [self.output_key]
+        if not self.return_intermediate_steps:
+            return [self.output_key]
+        else:
+            return [self.output_key, "intermediate_steps"]
 
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
@@ -54,9 +60,12 @@ class PALChain(Chain, BaseModel):
         self.callback_manager.on_text(
             code, color="green", end="\n", verbose=self.verbose
         )
-        repl = PythonREPL()
+        repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
         res = repl.run(code + f"\n{self.get_answer_expr}")
-        return {self.output_key: res.strip()}
+        output = {self.output_key: res.strip()}
+        if self.return_intermediate_steps:
+            output["intermediate_steps"] = code
+        return output
 
     @classmethod
     def from_math_prompt(cls, llm: BaseLLM, **kwargs: Any) -> PALChain:
diff --git a/langchain/chains/sql_database/base.py b/langchain/chains/sql_database/base.py
index 0c35acbe..78eec071 100644
--- a/langchain/chains/sql_database/base.py
+++ b/langchain/chains/sql_database/base.py
@@ -21,7 +21,7 @@ class SQLDatabaseChain(Chain, BaseModel):
 
             from langchain import SQLDatabaseChain, OpenAI, SQLDatabase
             db = SQLDatabase(...)
-            db_chain = SelfAskWithSearchChain(llm=OpenAI(), database=db)
+            db_chain = SQLDatabaseChain(llm=OpenAI(), database=db)
     """
 
     llm: BaseLLM
diff --git a/langchain/embeddings/__init__.py b/langchain/embeddings/__init__.py
index 972b7712..ee981a64 100644
--- a/langchain/embeddings/__init__.py
+++ b/langchain/embeddings/__init__.py
@@ -3,9 +3,13 @@ import logging
 from typing import Any
 
 from langchain.embeddings.cohere import CohereEmbeddings
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.huggingface import (
+    HuggingFaceEmbeddings,
+    HuggingFaceInstructEmbeddings,
+)
 from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
 
 logger = logging.getLogger(__name__)
 
@@ -14,6 +18,8 @@ __all__ = [
     "HuggingFaceEmbeddings",
     "CohereEmbeddings",
     "HuggingFaceHubEmbeddings",
+    "TensorflowHubEmbeddings",
+    "HuggingFaceInstructEmbeddings",
 ]
 
 
diff --git a/langchain/embeddings/cohere.py b/langchain/embeddings/cohere.py
index b55e4aaa..de3648a9 100644
--- a/langchain/embeddings/cohere.py
+++ b/langchain/embeddings/cohere.py
@@ -25,6 +25,9 @@ class CohereEmbeddings(BaseModel, Embeddings):
     model: str = "large"
     """Model name to use."""
 
+    truncate: str = "NONE"
+    """Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")"""
+
     cohere_api_key: Optional[str] = None
 
     class Config:
@@ -58,7 +61,9 @@ class CohereEmbeddings(BaseModel, Embeddings):
         Returns:
             List of embeddings, one for each text.
         """
-        embeddings = self.client.embed(model=self.model, texts=texts).embeddings
+        embeddings = self.client.embed(
+            model=self.model, texts=texts, truncate=self.truncate
+        ).embeddings
         return embeddings
 
     def embed_query(self, text: str) -> List[float]:
@@ -70,5 +75,7 @@ class CohereEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        embedding = self.client.embed(model=self.model, texts=[text]).embeddings[0]
+        embedding = self.client.embed(
+            model=self.model, texts=[text], truncate=self.truncate
+        ).embeddings[0]
         return embedding
diff --git a/langchain/embeddings/huggingface.py b/langchain/embeddings/huggingface.py
index 98f9986a..095a2c91 100644
--- a/langchain/embeddings/huggingface.py
+++ b/langchain/embeddings/huggingface.py
@@ -6,6 +6,11 @@ from pydantic import BaseModel, Extra
 from langchain.embeddings.base import Embeddings
 
 DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
+DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
+DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
+DEFAULT_QUERY_INSTRUCTION = (
+    "Represent the question for retrieving supporting documents: "
+)
 
 
 class HuggingFaceEmbeddings(BaseModel, Embeddings):
@@ -68,3 +73,68 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         text = text.replace("\n", " ")
         embedding = self.client.encode(text)
         return embedding.tolist()
+
+
+class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
+    """Wrapper around sentence_transformers embedding models.
+
+    To use, you should have the ``sentence_transformers`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import HuggingFaceInstructEmbeddings
+            model_name = "hkunlp/instructor-large"
+            hf = HuggingFaceInstructEmbeddings(model_name=model_name)
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = DEFAULT_INSTRUCT_MODEL
+    """Model name to use."""
+    embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
+    """Instruction to use for embedding documents."""
+    query_instruction: str = DEFAULT_QUERY_INSTRUCTION
+    """Instruction to use for embedding query."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the sentence_transformer."""
+        super().__init__(**kwargs)
+        try:
+            from InstructorEmbedding import INSTRUCTOR
+
+            self.client = INSTRUCTOR(self.model_name)
+        except ImportError as e:
+            raise ValueError("Dependencies for InstructorEmbedding not found.") from e
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a HuggingFace instruct model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        instruction_pairs = []
+        for text in texts:
+            instruction_pairs.append([self.embed_instruction, text])
+        embeddings = self.client.encode(instruction_pairs)
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a HuggingFace instruct model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        instruction_pair = [self.query_instruction, text]
+        embedding = self.client.encode([instruction_pair])[0]
+        return embedding.tolist()
diff --git a/langchain/embeddings/tensorflow_hub.py b/langchain/embeddings/tensorflow_hub.py
new file mode 100644
index 00000000..25e63949
--- /dev/null
+++ b/langchain/embeddings/tensorflow_hub.py
@@ -0,0 +1,70 @@
+"""Wrapper around TensorflowHub embedding models."""
+from typing import Any, List
+
+from pydantic import BaseModel, Extra
+
+from langchain.embeddings.base import Embeddings
+
+DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
+
+
+class TensorflowHubEmbeddings(BaseModel, Embeddings):
+    """Wrapper around tensorflow_hub embedding models.
+
+    To use, you should have the ``tensorflow_text`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import TensorflowHubEmbeddings
+            url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
+            tf = TensorflowHubEmbeddings(model_url=url)
+    """
+
+    embed: Any  #: :meta private:
+    model_url: str = DEFAULT_MODEL_URL
+    """Model name to use."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the tensorflow_hub and tensorflow_text."""
+        super().__init__(**kwargs)
+        try:
+            import tensorflow_hub
+            import tensorflow_text  # noqa
+
+            self.embed = tensorflow_hub.load(self.model_url)
+        except ImportError as e:
+            raise ValueError(
+                "Could not import some python packages." "Please install them."
+            ) from e
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a TensorflowHub embedding model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        texts = list(map(lambda x: x.replace("\n", " "), texts))
+        embeddings = self.embed(texts).numpy()
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a TensorflowHub embedding model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        text = text.replace("\n", " ")
+        embedding = self.embed(text).numpy()[0]
+        return embedding.tolist()
diff --git a/langchain/llms/base.py b/langchain/llms/base.py
index 2ec51ccd..f6a31fb4 100644
--- a/langchain/llms/base.py
+++ b/langchain/llms/base.py
@@ -2,7 +2,7 @@
 import json
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Mapping, Optional, Union, Tuple
 
 import yaml
 from pydantic import BaseModel, Extra, Field, validator
@@ -17,7 +17,8 @@ def _get_verbosity() -> bool:
     return langchain.verbose
 
 
-def get_prompts(params, prompts):
+def get_prompts(params: Dict[str, Any], prompts: List[str]) -> tuple[Dict[int, list], str, list[int], list[str]]:
+    """Get prompts that are already cached."""
     llm_string = str(sorted([(k, v) for k, v in params.items()]))
     missing_prompts = []
     missing_prompt_idxs = []
@@ -32,7 +33,10 @@ def get_prompts(params, prompts):
     return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
 
 
-def get_llm_output(existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts):
+def get_llm_output(
+    existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
+):
+    """Get the LLM output."""
     for i, result in enumerate(new_results.generations):
         existing_prompts[missing_prompt_idxs[i]] = result
         prompt = prompts[missing_prompt_idxs[i]]
@@ -83,7 +87,7 @@ class BaseLLM(BaseModel, ABC):
         """Run the LLM on the given prompts."""
 
     @abstractmethod
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         """Run the LLM on the given prompts."""
@@ -111,7 +115,12 @@ class BaseLLM(BaseModel, ABC):
             return output
         params = self.dict()
         params["stop"] = stop
-        existing_prompts, llm_string, missing_prompt_idxs, missing_prompts = get_prompts(params, prompts)
+        (
+            existing_prompts,
+            llm_string,
+            missing_prompt_idxs,
+            missing_prompts,
+        ) = get_prompts(params, prompts)
         if len(missing_prompts) > 0:
             self.callback_manager.on_llm_start(
                 {"name": self.__class__.__name__}, missing_prompts, verbose=self.verbose
@@ -122,13 +131,15 @@ class BaseLLM(BaseModel, ABC):
                 self.callback_manager.on_llm_error(e, verbose=self.verbose)
                 raise e
             self.callback_manager.on_llm_end(new_results, verbose=self.verbose)
-            llm_output = get_llm_output(existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts)
+            llm_output = get_llm_output(
+                existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
+            )
         else:
             llm_output = {}
         generations = [existing_prompts[i] for i in range(len(prompts))]
         return LLMResult(generations=generations, llm_output=llm_output)
 
-    async def async_generate(
+    async def agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         disregard_cache = self.cache is not None and not self.cache
@@ -142,7 +153,7 @@ class BaseLLM(BaseModel, ABC):
                 {"name": self.__class__.__name__}, prompts, verbose=self.verbose
             )
             try:
-                output = await self._async_generate(prompts, stop=stop)
+                output = await self._agenerate(prompts, stop=stop)
             except (KeyboardInterrupt, Exception) as e:
                 self.callback_manager.on_llm_error(e, verbose=self.verbose)
                 raise e
@@ -150,18 +161,25 @@ class BaseLLM(BaseModel, ABC):
             return output
         params = self.dict()
         params["stop"] = stop
-        existing_prompts, llm_string, missing_prompt_idxs, missing_prompts = get_prompts(params, prompts)
+        (
+            existing_prompts,
+            llm_string,
+            missing_prompt_idxs,
+            missing_prompts,
+        ) = get_prompts(params, prompts)
         if len(missing_prompts) > 0:
             self.callback_manager.on_llm_start(
                 {"name": self.__class__.__name__}, missing_prompts, verbose=self.verbose
             )
             try:
-                new_results = await self._async_generate(missing_prompts, stop=stop)
+                new_results = await self._agenerate(missing_prompts, stop=stop)
             except (KeyboardInterrupt, Exception) as e:
                 self.callback_manager.on_llm_error(e, verbose=self.verbose)
                 raise e
             self.callback_manager.on_llm_end(new_results, verbose=self.verbose)
-            llm_output = get_llm_output(existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts)
+            llm_output = get_llm_output(
+                existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
+            )
         else:
             llm_output = {}
         generations = [existing_prompts[i] for i in range(len(prompts))]
@@ -268,7 +286,7 @@ class LLM(BaseLLM):
             generations.append([Generation(text=text)])
         return LLMResult(generations=generations)
 
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         """Run the LLM on the given prompt and input."""
diff --git a/langchain/llms/openai.py b/langchain/llms/openai.py
index 5a8924c6..ca640edc 100644
--- a/langchain/llms/openai.py
+++ b/langchain/llms/openai.py
@@ -1,9 +1,16 @@
 """Wrapper around OpenAI APIs."""
 import logging
 import sys
-from typing import Any, Dict, Generator, List, Mapping, Optional, Tuple, Union
+from typing import Any, Dict, Generator, List, Mapping, Optional, Tuple, Union, Set
 
 from pydantic import BaseModel, Extra, Field, root_validator
+from tenacity import (
+    after_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
 
 from langchain.llms.base import BaseLLM
 from langchain.schema import Generation, LLMResult
@@ -12,6 +19,16 @@ from langchain.utils import get_from_dict_or_env
 logger = logging.getLogger(__name__)
 
 
+def update_token_usage(keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]) -> None:
+    """Update token usage."""
+    _keys_to_use = keys.intersection(response["usage"])
+    for _key in _keys_to_use:
+        if _key not in token_usage:
+            token_usage[_key] = response["usage"][_key]
+        else:
+            token_usage[_key] += response["usage"][_key]
+
+
 class BaseOpenAI(BaseLLM, BaseModel):
     """Wrapper around OpenAI large language models.
 
@@ -56,6 +73,8 @@ class BaseOpenAI(BaseLLM, BaseModel):
     """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
     logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
     """Adjust the probability of specific tokens being generated."""
+    max_retries: int = 6
+    """Maximum number of retries to make when generating."""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -115,6 +134,32 @@ class BaseOpenAI(BaseLLM, BaseModel):
         }
         return {**normal_params, **self.model_kwargs}
 
+    def completion_with_retry(self, **kwargs: Any) -> Any:
+        """Use tenacity to retry the completion call."""
+        import openai
+
+        min_seconds = 4
+        max_seconds = 10
+        # Wait 2^x * 1 second between each retry starting with
+        # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
+
+        @retry(
+            reraise=True,
+            stop=stop_after_attempt(self.max_retries),
+            wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+            retry=(
+                retry_if_exception_type(openai.error.Timeout)
+                | retry_if_exception_type(openai.error.APIError)
+                | retry_if_exception_type(openai.error.APIConnectionError)
+                | retry_if_exception_type(openai.error.RateLimitError)
+            ),
+            after=after_log(logger, logging.DEBUG),
+        )
+        def _completion_with_retry(**kwargs: Any) -> Any:
+            return self.client.create(**kwargs)
+
+        return _completion_with_retry(**kwargs)
+
     def _generate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
@@ -141,19 +186,15 @@ class BaseOpenAI(BaseLLM, BaseModel):
         # Includes prompt, completion, and total tokens used.
         _keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
         for _prompts in sub_prompts:
-            response = self.client.create(prompt=_prompts, **params)
+            response = self.completion_with_retry(prompt=_prompts, **params)
             choices.extend(response["choices"])
-            _keys_to_use = _keys.intersection(response["usage"])
-            for _key in _keys_to_use:
-                if _key not in token_usage:
-                    token_usage[_key] = response["usage"][_key]
-                else:
-                    token_usage[_key] += response["usage"][_key]
+            update_token_usage(_keys, response, token_usage)
         return self.create_llm_result(choices, prompts, token_usage)
 
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
+        """Call out to OpenAI's endpoint async with k unique prompts."""
         params = self._invocation_params
         sub_prompts = self.get_sub_prompts(params, prompts, stop)
         choices = []
@@ -164,15 +205,11 @@ class BaseOpenAI(BaseLLM, BaseModel):
         for _prompts in sub_prompts:
             response = await self.client.acreate(prompt=_prompts, **params)
             choices.extend(response["choices"])
-            _keys_to_use = _keys.intersection(response["usage"])
-            for _key in _keys_to_use:
-                if _key not in token_usage:
-                    token_usage[_key] = response["usage"][_key]
-                else:
-                    token_usage[_key] += response["usage"][_key]
+            update_token_usage(_keys, response, token_usage)
         return self.create_llm_result(choices, prompts, token_usage)
 
     def get_sub_prompts(self, params, prompts, stop):
+        """Get the sub prompts for llm call."""
         if stop is not None:
             if "stop" in params:
                 raise ValueError("`stop` found in both the input and default params.")
diff --git a/langchain/prompts/example_selector/ngram_overlap.py b/langchain/prompts/example_selector/ngram_overlap.py
new file mode 100644
index 00000000..335331ec
--- /dev/null
+++ b/langchain/prompts/example_selector/ngram_overlap.py
@@ -0,0 +1,112 @@
+"""Select and order examples based on ngram overlap score (sentence_bleu score).
+
+https://www.nltk.org/_modules/nltk/translate/bleu_score.html
+https://aclanthology.org/P02-1040.pdf
+"""
+from typing import Dict, List
+
+import numpy as np
+from pydantic import BaseModel, root_validator
+
+from langchain.prompts.example_selector.base import BaseExampleSelector
+from langchain.prompts.prompt import PromptTemplate
+
+
+def ngram_overlap_score(source: List[str], example: List[str]) -> float:
+    """Compute ngram overlap score of source and example as sentence_bleu score.
+
+    Use sentence_bleu with method1 smoothing function and auto reweighting.
+    Return float value between 0.0 and 1.0 inclusive.
+    https://www.nltk.org/_modules/nltk/translate/bleu_score.html
+    https://aclanthology.org/P02-1040.pdf
+    """
+    from nltk.translate.bleu_score import (  # type: ignore
+        SmoothingFunction,
+        sentence_bleu,
+    )
+
+    hypotheses = source[0].split()
+    references = [s.split() for s in example]
+
+    return float(
+        sentence_bleu(
+            references,
+            hypotheses,
+            smoothing_function=SmoothingFunction().method1,
+            auto_reweigh=True,
+        )
+    )
+
+
+class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel):
+    """Select and order examples based on ngram overlap score (sentence_bleu score).
+
+    https://www.nltk.org/_modules/nltk/translate/bleu_score.html
+    https://aclanthology.org/P02-1040.pdf
+    """
+
+    examples: List[dict]
+    """A list of the examples that the prompt template expects."""
+
+    example_prompt: PromptTemplate
+    """Prompt template used to format the examples."""
+
+    threshold: float = -1.0
+    """Threshold at which algorithm stops. Set to -1.0 by default.
+
+    For negative threshold:
+    select_examples sorts examples by ngram_overlap_score, but excludes none.
+    For threshold greater than 1.0:
+    select_examples excludes all examples, and returns an empty list.
+    For threshold equal to 0.0:
+    select_examples sorts examples by ngram_overlap_score,
+    and excludes examples with no ngram overlap with input.
+    """
+
+    @root_validator(pre=True)
+    def check_dependencies(cls, values: Dict) -> Dict:
+        """Check that valid dependencies exist."""
+        try:
+            from nltk.translate.bleu_score import (  # noqa: disable=F401
+                SmoothingFunction,
+                sentence_bleu,
+            )
+        except ImportError as e:
+            raise ValueError(
+                "Not all the correct dependencies for this ExampleSelect exist"
+            ) from e
+
+        return values
+
+    def add_example(self, example: Dict[str, str]) -> None:
+        """Add new example to list."""
+        self.examples.append(example)
+
+    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+        """Return list of examples sorted by ngram_overlap_score with input.
+
+        Descending order.
+        Excludes any examples with ngram_overlap_score less than or equal to threshold.
+        """
+        inputs = list(input_variables.values())
+        examples = []
+        k = len(self.examples)
+        score = [0.0] * k
+        first_prompt_template_key = self.example_prompt.input_variables[0]
+
+        for i in range(k):
+            score[i] = ngram_overlap_score(
+                inputs, [self.examples[i][first_prompt_template_key]]
+            )
+
+        while True:
+            arg_max = np.argmax(score)
+            if (score[arg_max] < self.threshold) or abs(
+                score[arg_max] - self.threshold
+            ) < 1e-9:
+                break
+
+            examples.append(self.examples[arg_max])
+            score[arg_max] = self.threshold - 1.0
+
+        return examples
diff --git a/langchain/utilities/google_search.py b/langchain/utilities/google_search.py
index e1b7fb0a..19b8d464 100644
--- a/langchain/utilities/google_search.py
+++ b/langchain/utilities/google_search.py
@@ -96,7 +96,8 @@ class GoogleSearchAPIWrapper(BaseModel):
         if len(results) == 0:
             return "No good Google Search Result was found"
         for result in results:
-            snippets.append(result["snippet"])
+            if "snippet" in result:
+                snippets.append(result["snippet"])
 
         return " ".join(snippets)
 
@@ -119,10 +120,11 @@ class GoogleSearchAPIWrapper(BaseModel):
             return [{"Result": "No good Google Search Result was found"}]
         for result in results:
             metadata_result = {
-                "snippet": result["snippet"],
                 "title": result["title"],
                 "link": result["link"],
             }
+            if "snippet" in result:
+                metadata_result["snippet"] = result["snippet"]
             metadata_results.append(metadata_result)
 
         return metadata_results
diff --git a/poetry.lock b/poetry.lock
index dc2adb7c..6e3d8eaa 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,17 @@
 # This file is automatically @generated by Poetry and should not be changed by hand.
 
+[[package]]
+name = "absl-py"
+version = "1.4.0"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+category = "main"
+optional = true
+python-versions = ">=3.6"
+files = [
+    {file = "absl-py-1.4.0.tar.gz", hash = "sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d"},
+    {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"},
+]
+
 [[package]]
 name = "alabaster"
 version = "0.7.13"
@@ -136,6 +148,22 @@ six = "*"
 [package.extras]
 test = ["astroid", "pytest"]
 
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+description = "An AST unparser for Python"
+category = "main"
+optional = true
+python-versions = "*"
+files = [
+    {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
+    {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
+]
+
+[package.dependencies]
+six = ">=1.6.1,<2.0"
+wheel = ">=0.23.0,<1.0"
+
 [[package]]
 name = "async-timeout"
 version = "4.0.2"
@@ -287,14 +315,14 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "5.0.1"
+version = "6.0.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "bleach-5.0.1-py3-none-any.whl", hash = "sha256:085f7f33c15bd408dd9b17a4ad77c577db66d76203e5984b1bd59baeee948b2a"},
-    {file = "bleach-5.0.1.tar.gz", hash = "sha256:0d03255c47eb9bd2f26aa9bb7f2107732e7e8fe195ca2f64709fcf3b0a4a085c"},
+    {file = "bleach-6.0.0-py3-none-any.whl", hash = "sha256:33c16e3353dbd13028ab4799a0f89a83f113405c766e9c122df8a06f5b85b3f4"},
+    {file = "bleach-6.0.0.tar.gz", hash = "sha256:1a1a85c1595e07d8db14c5f09f09e6433502c51c595970edc090551f0db99414"},
 ]
 
 [package.dependencies]
@@ -303,7 +331,6 @@ webencodings = "*"
 
 [package.extras]
 css = ["tinycss2 (>=1.1.0,<1.2)"]
-dev = ["Sphinx (==4.3.2)", "black (==22.3.0)", "build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "mypy (==0.961)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)"]
 
 [[package]]
 name = "blis"
@@ -365,14 +392,14 @@ urllib3 = ">=1.25.3,<3"
 
 [[package]]
 name = "cachetools"
-version = "5.2.1"
+version = "5.3.0"
 description = "Extensible memoizing collections and decorators"
 category = "main"
 optional = true
 python-versions = "~=3.7"
 files = [
-    {file = "cachetools-5.2.1-py3-none-any.whl", hash = "sha256:8462eebf3a6c15d25430a8c27c56ac61340b2ecf60c9ce57afc2b97e450e47da"},
-    {file = "cachetools-5.2.1.tar.gz", hash = "sha256:5991bc0e08a1319bb618d3195ca5b6bc76646a49c21d55962977197b301cc1fe"},
+    {file = "cachetools-5.3.0-py3-none-any.whl", hash = "sha256:429e1a1e845c008ea6c85aa35d4b98b65d6a9763eeef3e37e92728a12d1de9d4"},
+    {file = "cachetools-5.3.0.tar.gz", hash = "sha256:13dfddc7b8df938c21a940dfa6557ce6e94a2f1cdfa58eb90c805721d58f2c14"},
 ]
 
 [[package]]
@@ -637,63 +664,63 @@ srsly = ">=2.4.0,<3.0.0"
 
 [[package]]
 name = "coverage"
-version = "7.0.5"
+version = "7.1.0"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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@@ -806,30 +833,29 @@ dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (
 
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 [[package]]
@@ -1002,14 +1028,14 @@ develop = ["aiohttp", "mock", "pytest", "pytest-asyncio", "pytest-cov", "pytest-
 
 [[package]]
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+version = "8.6.1"
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 category = "main"
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 python-versions = ">=3.6, <4"
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 ]
 
 [package.dependencies]
@@ -1019,18 +1045,6 @@ elastic-transport = ">=8,<9"
 async = ["aiohttp (>=3,<4)"]
 requests = ["requests (>=2.4.0,<3.0.0)"]
 
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 [[package]]
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@@ -1146,20 +1160,32 @@ pyflakes = ">=3.0.0,<3.1.0"
 
 [[package]]
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+version = "1.7.0"
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 ]
 
 [package.dependencies]
 flake8 = ">=3"
 pydocstyle = ">=2.1"
 
+[[package]]
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+version = "23.1.21"
+description = "The FlatBuffers serialization format for Python"
+category = "main"
+optional = true
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+[[package]]
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 six = "*"
 
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 ]
 
 [package.dependencies]
-grpcio = ">=1.51.1"
-protobuf = ">=4.21.6,<5.0dev"
+grpcio = ">=1.48.2"
+protobuf = ">=3.12.0,<4.0dev"
 setuptools = "*"
 
 [[package]]
@@ -1509,6 +1583,44 @@ files = [
 hpack = ">=4.0,<5"
 hyperframe = ">=6.0,<7"
 
+[[package]]
+name = "h5py"
+version = "3.8.0"
+description = "Read and write HDF5 files from Python"
+category = "main"
+optional = true
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+numpy = ">=1.14.5"
+
 [[package]]
 name = "hpack"
 version = "4.0.0"
@@ -1585,14 +1697,14 @@ socks = ["socksio (>=1.0.0,<2.0.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.11.1"
+version = "0.12.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 category = "main"
 optional = true
 python-versions = ">=3.7.0"
 files = [
-    {file = "huggingface_hub-0.11.1-py3-none-any.whl", hash = "sha256:11eed7aab4fa4d1fb532f2aea3379ef4998d9f6bc24a330834dfedd3dac7f441"},
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+    {file = "huggingface_hub-0.12.0.tar.gz", hash = "sha256:da82c9ec8f9d8f976ffd3fd8249d20bb35c2dd3145a9f7ca1106f0ebefd9afa0"},
 ]
 
 [package.dependencies]
@@ -1600,17 +1712,17 @@ filelock = "*"
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
-tqdm = "*"
+tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 quality = ["black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "mypy (==0.982)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "isort (>=5.5.4)", "jedi", "pytest", "pytest-cov", "pytest-env", "soundfile"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "isort (>=5.5.4)", "jedi", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile"]
 torch = ["torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 
@@ -1736,14 +1848,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.8.0"
+version = "8.9.0"
 description = "IPython: Productive Interactive Computing"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipython-8.8.0-py3-none-any.whl", hash = "sha256:da01e6df1501e6e7c32b5084212ddadd4ee2471602e2cf3e0190f4de6b0ea481"},
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+    {file = "ipython-8.9.0-py3-none-any.whl", hash = "sha256:9c207b0ef2d276d1bfcfeb9a62804336abbe4b170574ea061500952319b1d78c"},
+    {file = "ipython-8.9.0.tar.gz", hash = "sha256:71618e82e6d59487bea059626e7c79fb4a5b760d1510d02fab1160db6fdfa1f7"},
 ]
 
 [package.dependencies]
@@ -1755,7 +1867,7 @@ jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
 pickleshare = "*"
-prompt-toolkit = ">=3.0.11,<3.1.0"
+prompt-toolkit = ">=3.0.30,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
@@ -1824,19 +1936,19 @@ arrow = ">=0.15.0"
 
 [[package]]
 name = "isort"
-version = "5.11.4"
+version = "5.12.0"
 description = "A Python utility / library to sort Python imports."
 category = "dev"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 files = [
-    {file = "isort-5.11.4-py3-none-any.whl", hash = "sha256:c033fd0edb91000a7f09527fe5c75321878f98322a77ddcc81adbd83724afb7b"},
-    {file = "isort-5.11.4.tar.gz", hash = "sha256:6db30c5ded9815d813932c04c2f85a360bcdd35fed496f4d8f35495ef0a261b6"},
+    {file = "isort-5.12.0-py3-none-any.whl", hash = "sha256:f84c2818376e66cf843d497486ea8fed8700b340f308f076c6fb1229dff318b6"},
+    {file = "isort-5.12.0.tar.gz", hash = "sha256:8bef7dde241278824a6d83f44a544709b065191b95b6e50894bdc722fcba0504"},
 ]
 
 [package.extras]
-colors = ["colorama (>=0.4.3,<0.5.0)"]
-pipfile-deprecated-finder = ["pipreqs", "requirementslib"]
+colors = ["colorama (>=0.4.3)"]
+pipfile-deprecated-finder = ["pip-shims (>=0.5.2)", "pipreqs", "requirementslib"]
 plugins = ["setuptools"]
 requirements-deprecated-finder = ["pip-api", "pipreqs"]
 
@@ -1999,28 +2111,27 @@ testing = ["coverage", "ipykernel", "jupytext", "matplotlib", "nbdime", "nbforma
 
 [[package]]
 name = "jupyter-client"
-version = "7.4.9"
+version = "8.0.1"
 description = "Jupyter protocol implementation and client libraries"
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-7.4.9-py3-none-any.whl", hash = "sha256:214668aaea208195f4c13d28eb272ba79f945fc0cf3f11c7092c20b2ca1980e7"},
-    {file = "jupyter_client-7.4.9.tar.gz", hash = "sha256:52be28e04171f07aed8f20e1616a5a552ab9fee9cbbe6c1896ae170c3880d392"},
+    {file = "jupyter_client-8.0.1-py3-none-any.whl", hash = "sha256:6016b874fd1111d721bc5bee30624399e876e79e6f395d1a559e6dce9fb2e1ba"},
+    {file = "jupyter_client-8.0.1.tar.gz", hash = "sha256:3f67b1c8b7687e6db09bef10ff97669932b5e6ef6f5a8ee56d444b89022c5007"},
 ]
 
 [package.dependencies]
-entrypoints = "*"
-jupyter-core = ">=4.9.2"
-nest-asyncio = ">=1.5.4"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
 tornado = ">=6.2"
-traitlets = "*"
+traitlets = ">=5.3"
 
 [package.extras]
-doc = ["ipykernel", "myst-parser", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
-test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
+docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["codecov", "coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-console"
@@ -2046,14 +2157,14 @@ test = ["pexpect"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.1.3"
+version = "5.1.5"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.1.3-py3-none-any.whl", hash = "sha256:d23ab7db81ca1759f13780cd6b65f37f59bf8e0186ac422d5ca4982cc7d56716"},
-    {file = "jupyter_core-5.1.3.tar.gz", hash = "sha256:82e1cff0ef804c38677eff7070d5ff1d45037fef01a2d9ba9e6b7b8201831e9f"},
+    {file = "jupyter_core-5.1.5-py3-none-any.whl", hash = "sha256:83064d61bb2a9bc874e8184331c117b3778c2a7e1851f60cb00d273ceb3285ae"},
+    {file = "jupyter_core-5.1.5.tar.gz", hash = "sha256:8e54c48cde1e0c8345f64bcf9658b78044ddf02b273726cea9d9f59be4b02130"},
 ]
 
 [package.dependencies]
@@ -2170,6 +2281,17 @@ files = [
     {file = "jupyterlab_widgets-3.0.5.tar.gz", hash = "sha256:eeaecdeaf6c03afc960ddae201ced88d5979b4ca9c3891bcb8f6631af705f5ef"},
 ]
 
+[[package]]
+name = "keras"
+version = "2.11.0"
+description = "Deep learning for humans."
+category = "main"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "keras-2.11.0-py2.py3-none-any.whl", hash = "sha256:38c6fff0ea9a8b06a2717736565c92a73c8cd9b1c239e7125ccb188b7848f65e"},
+]
+
 [[package]]
 name = "langcodes"
 version = "3.3.0"
@@ -2185,6 +2307,24 @@ files = [
 [package.extras]
 data = ["language-data (>=1.1,<2.0)"]
 
+[[package]]
+name = "libclang"
+version = "15.0.6.1"
+description = "Clang Python Bindings, mirrored from the official LLVM repo: https://github.com/llvm/llvm-project/tree/main/clang/bindings/python, to make the installation process easier."
+category = "main"
+optional = true
+python-versions = "*"
+files = [
+    {file = "libclang-15.0.6.1-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:8621795e07b87e17fc7aac9f071bc7fe6b52ed6110c0a96a9975d8113c8c2527"},
+    {file = "libclang-15.0.6.1-py2.py3-none-manylinux2010_x86_64.whl", hash = "sha256:69b01a23ab543908a661532595daa23cf88bd96d80e41f58ba0eaa6a378fe0d8"},
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+]
+
 [[package]]
 name = "linkchecker"
 version = "10.2.1"
@@ -2354,6 +2494,21 @@ all = ["Flask (>=2.1.2)", "accelerate (>=0.10.0)", "autopep8 (>=1.6.0)", "black
 api = ["Flask (>=2.1.2)", "accelerate (>=0.10.0)", "torch (>=1.8.0)", "transformers (>=4.20.0)"]
 dev = ["autopep8 (>=1.6.0)", "black (>=22.3.0)", "docformatter (>=1.4)", "flake8 (>=4.0.0)", "flake8-docstrings (>=1.6.0)", "isort (>=5.9.3)", "mypy (>=0.950)", "nbsphinx (>=0.8.0)", "pep8-naming (>=0.12.1)", "pre-commit (>=2.14.0)", "pytest (>=7.0.0)", "pytest-cov (>=3.0.0)", "python-dotenv (>=0.20.0)", "recommonmark (>=0.7.1)", "sphinx-autobuild", "sphinx-rtd-theme (>=0.5.1)", "twine", "types-PyYAML (>=6.0.7)", "types-protobuf (>=3.19.21)", "types-python-dateutil (>=2.8.16)", "types-redis (>=4.2.6)", "types-requests (>=2.27.29)", "types-setuptools (>=57.4.17)"]
 
+[[package]]
+name = "markdown"
+version = "3.4.1"
+description = "Python implementation of Markdown."
+category = "main"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "Markdown-3.4.1-py3-none-any.whl", hash = "sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186"},
+    {file = "Markdown-3.4.1.tar.gz", hash = "sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff"},
+]
+
+[package.extras]
+testing = ["coverage", "pyyaml"]
+
 [[package]]
 name = "markdown-it-py"
 version = "2.1.0"
@@ -2717,14 +2872,14 @@ testing = ["beautifulsoup4", "coverage[toml]", "pytest (>=6,<7)", "pytest-cov",
 
 [[package]]
 name = "nbclassic"
-version = "0.4.8"
-description = "A web-based notebook environment for interactive computing"
+version = "0.5.1"
+description = "Jupyter Notebook as a Jupyter Server extension."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2734,7 +2889,7 @@ ipython-genutils = "*"
 jinja2 = "*"
 jupyter-client = ">=6.1.1"
 jupyter-core = ">=4.6.1"
-jupyter-server = ">=1.8"
+jupyter-server = ">=1.17.0"
 nbconvert = ">=5"
 nbformat = "*"
 nest-asyncio = ">=1.5"
@@ -2749,7 +2904,7 @@ traitlets = ">=4.2.1"
 [package.extras]
 docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
 json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"]
+test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"]
 
 [[package]]
 name = "nbclient"
@@ -2775,14 +2930,14 @@ test = ["black", "check-manifest", "flake8", "ipykernel", "ipython (<8.0.0)", "i
 
 [[package]]
 name = "nbconvert"
-version = "7.2.8"
+version = "7.2.9"
 description = "Converting Jupyter Notebooks"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3048,6 +3203,42 @@ files = [
 setuptools = "*"
 wheel = "*"
 
+[[package]]
+name = "oauthlib"
+version = "3.2.2"
+description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
+category = "main"
+optional = true
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.extras]
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+signals = ["blinker (>=1.4.0)"]
+signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
+
+[[package]]
+name = "opt-einsum"
+version = "3.3.0"
+description = "Optimizing numpys einsum function"
+category = "main"
+optional = true
+python-versions = ">=3.5"
+files = [
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+]
+
+[package.dependencies]
+numpy = ">=1.7"
+
+[package.extras]
+docs = ["numpydoc", "sphinx (==1.2.3)", "sphinx-rtd-theme", "sphinxcontrib-napoleon"]
+tests = ["pytest", "pytest-cov", "pytest-pep8"]
+
 [[package]]
 name = "packaging"
 version = "23.0"
@@ -3090,14 +3281,14 @@ testing = ["docopt", "pytest (<6.0.0)"]
 
 [[package]]
 name = "pathspec"
-version = "0.10.3"
+version = "0.11.0"
 description = "Utility library for gitignore style pattern matching of file paths."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -3205,19 +3396,19 @@ test = ["appdirs (==1.4.4)", "covdefaults (>=2.2.2)", "pytest (>=7.2)", "pytest-
 
 [[package]]
 name = "playwright"
-version = "1.29.1"
+version = "1.30.0"
 description = "A high-level API to automate web browsers"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3285,14 +3476,14 @@ murmurhash = ">=0.28.0,<1.1.0"
 
 [[package]]
 name = "prometheus-client"
-version = "0.15.0"
+version = "0.16.0"
 description = "Python client for the Prometheus monitoring system."
 category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.extras]
@@ -3315,26 +3506,37 @@ wcwidth = "*"
 
 [[package]]
 name = "protobuf"
-version = "4.21.12"
-description = ""
+version = "3.19.6"
+description = "Protocol Buffers"
 category = "main"
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 [[package]]
@@ -3691,6 +3900,25 @@ tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 [package.extras]
 testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
 
+[[package]]
+name = "pytest-asyncio"
+version = "0.20.3"
+description = "Pytest support for asyncio"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+pytest = ">=6.1.0"
+
+[package.extras]
+docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
+testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
+
 [[package]]
 name = "pytest-cov"
 version = "4.0.0"
@@ -3758,14 +3986,14 @@ six = ">=1.5"
 
 [[package]]
 name = "python-dotenv"
-version = "0.21.0"
+version = "0.21.1"
 description = "Read key-value pairs from a .env file and set them as environment variables"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -3977,14 +4205,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "qdrant-client"
-version = "0.11.7"
+version = "0.11.9"
 description = "Client library for the Qdrant vector search engine"
 category = "main"
 optional = true
 python-versions = ">=3.7,<4.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -4178,6 +4406,25 @@ urllib3 = ">=1.21.1,<1.27"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-oauthlib"
+version = "1.3.1"
+description = "OAuthlib authentication support for Requests."
+category = "main"
+optional = true
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
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+]
+
+[package.dependencies]
+oauthlib = ">=3.0.0"
+requests = ">=2.0.0"
+
+[package.extras]
+rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
+
 [[package]]
 name = "responses"
 version = "0.22.0"
@@ -4278,14 +4525,14 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "66.0.0"
+version = "67.0.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
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+    {file = "setuptools-67.0.0.tar.gz", hash = "sha256:883131c5b6efa70b9101c7ef30b2b7b780a4283d5fc1616383cdf22c83cbefe6"},
 ]
 
 [package.extras]
@@ -4452,14 +4699,14 @@ transformers = ["spacy-transformers (>=1.1.2,<1.3.0)"]
 
 [[package]]
 name = "spacy-legacy"
-version = "3.0.11"
+version = "3.0.12"
 description = "Legacy registered functions for spaCy backwards compatibility"
 category = "main"
 optional = true
 python-versions = ">=3.6"
 files = [
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+    {file = "termcolor-2.2.0-py3-none-any.whl", hash = "sha256:91ddd848e7251200eac969846cbae2dacd7d71c2871e92733289e7e3666f48e7"},
+    {file = "termcolor-2.2.0.tar.gz", hash = "sha256:dfc8ac3f350788f23b2947b3e6cfa5a53b630b612e6cd8965a015a776020b99a"},
+]
+
+[package.extras]
+tests = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "terminado"
 version = "0.17.1"
@@ -5197,19 +5704,19 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
 
 [[package]]
 name = "transformers"
-version = "4.25.1"
+version = "4.26.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 category = "main"
 optional = true
 python-versions = ">=3.7.0"
 files = [
-    {file = "transformers-4.25.1-py3-none-any.whl", hash = "sha256:60f1be15e17e4a54373c787c713ec149dabcc63464131ac45611618fe7c2016e"},
-    {file = "transformers-4.25.1.tar.gz", hash = "sha256:6dad398b792d45dc04e9ee7e9e06bf758ab19dca2efc119065e661bb0f8f843b"},
+    {file = "transformers-4.26.0-py3-none-any.whl", hash = "sha256:6a902eee6098d9a737faadf185b8df5a169acc695ebbde5a81b90528f43e665f"},
+    {file = "transformers-4.26.0.tar.gz", hash = "sha256:d7859bd83829a3682ca632197ee5c72556e1063d199ab84eec35c4f23b3d73a3"},
 ]
 
 [package.dependencies]
 filelock = "*"
-huggingface-hub = ">=0.10.0,<1.0"
+huggingface-hub = ">=0.11.0,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
@@ -5220,22 +5727,22 @@ tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.10.0)"]
-all = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
+all = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.12)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.10.0)", "deepspeed (>=0.6.5)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.6.5)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "flax (>=0.4.1)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pyknp (>=0.6.1)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pyknp (>=0.6.1)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
-docs = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.6.5)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "sentencepiece (>=0.1.91,!=0.1.92)", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow", "accelerate (>=0.10.0)", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "flax (>=0.4.1)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.4,<2.12)", "tensorflow-text", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.4,<2.12)", "tensorflow-text", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (==22.3)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flake8 (>=3.8.3)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+docs = ["Pillow", "accelerate (>=0.10.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.12)", "tensorflow-text", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "torchaudio"]
 docs-specific = ["hf-doc-builder"]
 fairscale = ["fairscale (>0.3)"]
 flax = ["flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "optax (>=0.0.8)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
 integrations = ["optuna", "ray[tune]", "sigopt"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "pyknp (>=0.6.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
 modelcreation = ["cookiecutter (==1.7.3)"]
 natten = ["natten (>=0.14.4)"]
 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
@@ -5251,14 +5758,15 @@ sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (==22.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.11)", "tensorflow-text", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.11)", "tensorflow-text", "tf2onnx"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.12)", "tensorflow-text", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.12)", "tensorflow-text", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
 tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
 torch = ["torch (>=1.7,!=1.12.0)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torchhub = ["filelock", "huggingface-hub (>=0.10.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.2)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.11.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.2)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "tqdm (>=4.27)"]
+video = ["decord (==0.6.0)"]
 vision = ["Pillow"]
 
 [[package]]
@@ -5311,14 +5819,14 @@ files = [
 
 [[package]]
 name = "types-redis"
-version = "4.4.0.3"
+version = "4.4.0.4"
 description = "Typing stubs for redis"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-redis-4.4.0.3.tar.gz", hash = "sha256:99fc86307fb19b775a0ad5de91d2fc0ccdb9a2be7ac790f4553911d2f2abdf61"},
-    {file = "types_redis-4.4.0.3-py3-none-any.whl", hash = "sha256:fc25550bc108908a32bb47cfdecde8d2155b6b7e40688af99a4bacbd7e3e857e"},
+    {file = "types-redis-4.4.0.4.tar.gz", hash = "sha256:b70829ca3401d3153d628e28d860070eff1b36b2fa3e5af3e583c1d167383cab"},
+    {file = "types_redis-4.4.0.4-py3-none-any.whl", hash = "sha256:802e893ad3f88e03d3a2feb0d23a715d60b0bb330bc598a52f1de237fc2547a5"},
 ]
 
 [package.dependencies]
@@ -5523,14 +6031,14 @@ files = [
 
 [[package]]
 name = "weaviate-client"
-version = "3.10.0"
+version = "3.11.0"
 description = "A python native weaviate client"
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "weaviate-client-3.10.0.tar.gz", hash = "sha256:e771f0aea47fb70d6cc85e101aafbe672ca24255c5e7c70659f8922d00081630"},
-    {file = "weaviate_client-3.10.0-py3-none-any.whl", hash = "sha256:b2fe3e4e79bb0a66c406cb68fea9d1c2069199f0516a99ff8fc4bc62b0a55829"},
+    {file = "weaviate-client-3.11.0.tar.gz", hash = "sha256:08b4b45d7e4198f86a6fe084f7d2ac94c621ee08b7fbb3a500522a984ca82514"},
+    {file = "weaviate_client-3.11.0-py3-none-any.whl", hash = "sha256:e992154cbe9378073a879bc438e0eb21683d0062958530e98b670b450a7432b3"},
 ]
 
 [package.dependencies]
@@ -5565,14 +6073,14 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.4.2"
+version = "1.5.0"
 description = "WebSocket client for Python with low level API options"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "websocket-client-1.4.2.tar.gz", hash = "sha256:d6e8f90ca8e2dd4e8027c4561adeb9456b54044312dba655e7cae652ceb9ae59"},
-    {file = "websocket_client-1.4.2-py3-none-any.whl", hash = "sha256:d6b06432f184438d99ac1f456eaf22fe1ade524c3dd16e661142dc54e9cba574"},
+    {file = "websocket-client-1.5.0.tar.gz", hash = "sha256:561ca949e5bbb5d33409a37235db55c279235c78ee407802f1d2314fff8a8536"},
+    {file = "websocket_client-1.5.0-py3-none-any.whl", hash = "sha256:fb5d81b95d350f3a54838ebcb4c68a5353bbd1412ae8f068b1e5280faeb13074"},
 ]
 
 [package.extras]
@@ -5580,6 +6088,24 @@ docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
+[[package]]
+name = "werkzeug"
+version = "2.2.2"
+description = "The comprehensive WSGI web application library."
+category = "main"
+optional = true
+python-versions = ">=3.7"
+files = [
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+
+[package.dependencies]
+MarkupSafe = ">=2.1.1"
+
+[package.extras]
+watchdog = ["watchdog"]
+
 [[package]]
 name = "wheel"
 version = "0.38.4"
@@ -5658,6 +6184,80 @@ xmltodict = "*"
 docs = ["jaraco.packaging (>=8.2)", "rst.linker (>=1.9)", "sphinx"]
 testing = ["keyring", "pmxbot", "pytest (>=3.5,!=3.7.3)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=1.2.3)", "pytest-cov", "pytest-enabler", "pytest-flake8", "pytest-mypy"]
 
+[[package]]
+name = "wrapt"
+version = "1.14.1"
+description = "Module for decorators, wrappers and monkey patching."
+category = "main"
+optional = true
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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+    {file = "wrapt-1.14.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:34aa51c45f28ba7f12accd624225e2b1e5a3a45206aa191f6f9aac931d9d56fe"},
+    {file = "wrapt-1.14.1-cp39-cp39-win32.whl", hash = "sha256:dee0ce50c6a2dd9056c20db781e9c1cfd33e77d2d569f5d1d9321c641bb903d5"},
+    {file = "wrapt-1.14.1-cp39-cp39-win_amd64.whl", hash = "sha256:dee60e1de1898bde3b238f18340eec6148986da0455d8ba7848d50470a7a32fb"},
+    {file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"},
+]
+
 [[package]]
 name = "xmltodict"
 version = "0.13.0"
@@ -5672,25 +6272,25 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.11.0"
+version = "3.12.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "zipp-3.11.0-py3-none-any.whl", hash = "sha256:83a28fcb75844b5c0cdaf5aa4003c2d728c77e05f5aeabe8e95e56727005fbaa"},
-    {file = "zipp-3.11.0.tar.gz", hash = "sha256:a7a22e05929290a67401440b39690ae6563279bced5f314609d9d03798f56766"},
+    {file = "zipp-3.12.0-py3-none-any.whl", hash = "sha256:9eb0a4c5feab9b08871db0d672745b53450d7f26992fd1e4653aa43345e97b86"},
+    {file = "zipp-3.12.0.tar.gz", hash = "sha256:73efd63936398aac78fd92b6f4865190119d6c91b531532e798977ea8dd402eb"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
 [extras]
-all = ["manifest-ml", "elasticsearch", "faiss-cpu", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client"]
+all = ["manifest-ml", "elasticsearch", "faiss-cpu", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
 llms = ["manifest-ml", "torch", "transformers"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "bed5e0cb4cfa8b6173dd9574982bd9154ebc61721704002b85474af3c2b675ca"
+content-hash = "b4470de82ffcc2fab1aa0bdb6bdadd1b647cebe9194f7f47429ff257616e607f"
diff --git a/pyproject.toml b/pyproject.toml
index d093d5b3..51dc4c22 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.74"
+version = "0.0.76"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -35,6 +35,8 @@ google-api-python-client = {version = "2.70.0", optional = true}
 wolframalpha = {version = "5.0.0", optional = true}
 qdrant-client = {version = "^0.11.7", optional = true}
 dataclasses-json = "^0.5.7"
+tensorflow-text = {version = "^2.11.0", optional = true, python = "^3.10, <3.12"}
+tenacity = "^8.1.0"
 
 [tool.poetry.group.docs.dependencies]
 autodoc_pydantic = "^1.8.0"
@@ -58,6 +60,7 @@ duckdb-engine = "^0.6.6"
 pytest-watcher = "^0.2.6"
 freezegun = "^1.2.2"
 responses = "^0.22.0"
+pytest-asyncio = "^0.20.3"
 
 [tool.poetry.group.lint.dependencies]
 flake8-docstrings = "^1.6.0"
@@ -81,7 +84,7 @@ playwright = "^1.28.0"
 
 [tool.poetry.extras]
 llms = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
-all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client"]
+all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
 
 [tool.isort]
 profile = "black"
diff --git a/tests/integration_tests/embeddings/test_huggingface.py b/tests/integration_tests/embeddings/test_huggingface.py
index e71fbb00..4c941580 100644
--- a/tests/integration_tests/embeddings/test_huggingface.py
+++ b/tests/integration_tests/embeddings/test_huggingface.py
@@ -1,7 +1,10 @@
 """Test huggingface embeddings."""
 import unittest
 
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.huggingface import (
+    HuggingFaceEmbeddings,
+    HuggingFaceInstructEmbeddings,
+)
 
 
 @unittest.skip("This test causes a segfault.")
@@ -21,3 +24,20 @@ def test_huggingface_embedding_query() -> None:
     embedding = HuggingFaceEmbeddings()
     output = embedding.embed_query(document)
     assert len(output) == 768
+
+
+def test_huggingface_instructor_embedding_documents() -> None:
+    """Test huggingface embeddings."""
+    documents = ["foo bar"]
+    embedding = HuggingFaceInstructEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 768
+
+
+def test_huggingface_instructor_embedding_query() -> None:
+    """Test huggingface embeddings."""
+    query = "foo bar"
+    embedding = HuggingFaceInstructEmbeddings()
+    output = embedding.embed_query(query)
+    assert len(output) == 768
diff --git a/tests/integration_tests/embeddings/test_tensorflow_hub.py b/tests/integration_tests/embeddings/test_tensorflow_hub.py
new file mode 100644
index 00000000..96bb0073
--- /dev/null
+++ b/tests/integration_tests/embeddings/test_tensorflow_hub.py
@@ -0,0 +1,19 @@
+"""Test TensorflowHub embeddings."""
+from langchain.embeddings import TensorflowHubEmbeddings
+
+
+def test_tensorflowhub_embedding_documents() -> None:
+    """Test tensorflowhub embeddings."""
+    documents = ["foo bar"]
+    embedding = TensorflowHubEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 512
+
+
+def test_tensorflowhub_embedding_query() -> None:
+    """Test tensorflowhub embeddings."""
+    document = "foo bar"
+    embedding = TensorflowHubEmbeddings()
+    output = embedding.embed_query(document)
+    assert len(output) == 512
diff --git a/tests/integration_tests/llms/test_openai.py b/tests/integration_tests/llms/test_openai.py
index 9c03b92f..232e1dd9 100644
--- a/tests/integration_tests/llms/test_openai.py
+++ b/tests/integration_tests/llms/test_openai.py
@@ -7,6 +7,7 @@ import pytest
 
 from langchain.llms.loading import load_llm
 from langchain.llms.openai import OpenAI
+from langchain.schema import LLMResult
 
 
 def test_openai_call() -> None:
@@ -74,3 +75,11 @@ def test_openai_streaming_error() -> None:
     llm = OpenAI(best_of=2)
     with pytest.raises(ValueError):
         llm.stream("I'm Pickle Rick")
+
+
+@pytest.mark.asyncio
+async def test_openai_async_generate() -> None:
+    """Test async generation."""
+    llm = OpenAI(max_tokens=10)
+    output = await llm.agenerate(["Hello, how are you?"])
+    assert isinstance(output, LLMResult)
diff --git a/tests/integration_tests/test_ngram_overlap_example_selector.py b/tests/integration_tests/test_ngram_overlap_example_selector.py
new file mode 100644
index 00000000..5c7bd4b1
--- /dev/null
+++ b/tests/integration_tests/test_ngram_overlap_example_selector.py
@@ -0,0 +1,73 @@
+"""Test functionality related to ngram overlap based selector."""
+
+import pytest
+
+from langchain.prompts.example_selector.ngram_overlap import (
+    NGramOverlapExampleSelector,
+    ngram_overlap_score,
+)
+from langchain.prompts.prompt import PromptTemplate
+
+EXAMPLES = [
+    {"input": "See Spot run.", "output": "foo1"},
+    {"input": "My dog barks.", "output": "foo2"},
+    {"input": "Spot can run.", "output": "foo3"},
+]
+
+
+@pytest.fixture
+def selector() -> NGramOverlapExampleSelector:
+    """Get ngram overlap based selector to use in tests."""
+    prompts = PromptTemplate(
+        input_variables=["input", "output"], template="Input: {input}\nOutput: {output}"
+    )
+    selector = NGramOverlapExampleSelector(
+        examples=EXAMPLES,
+        example_prompt=prompts,
+    )
+    return selector
+
+
+def test_selector_valid(selector: NGramOverlapExampleSelector) -> None:
+    """Test NGramOverlapExampleSelector can select examples."""
+    sentence = "Spot can run."
+    output = selector.select_examples({"input": sentence})
+    assert output == [EXAMPLES[2], EXAMPLES[0], EXAMPLES[1]]
+
+
+def test_selector_add_example(selector: NGramOverlapExampleSelector) -> None:
+    """Test NGramOverlapExampleSelector can add an example."""
+    new_example = {"input": "Spot plays fetch.", "output": "foo4"}
+    selector.add_example(new_example)
+    sentence = "Spot can run."
+    output = selector.select_examples({"input": sentence})
+    assert output == [EXAMPLES[2], EXAMPLES[0]] + [new_example] + [EXAMPLES[1]]
+
+
+def test_selector_threshold_zero(selector: NGramOverlapExampleSelector) -> None:
+    """Tests NGramOverlapExampleSelector threshold set to 0.0."""
+    selector.threshold = 0.0
+    sentence = "Spot can run."
+    output = selector.select_examples({"input": sentence})
+    assert output == [EXAMPLES[2], EXAMPLES[0]]
+
+
+def test_selector_threshold_more_than_one(
+    selector: NGramOverlapExampleSelector,
+) -> None:
+    """Tests NGramOverlapExampleSelector threshold greater than 1.0."""
+    selector.threshold = 1.0 + 1e-9
+    sentence = "Spot can run."
+    output = selector.select_examples({"input": sentence})
+    assert output == []
+
+
+def test_ngram_overlap_score(selector: NGramOverlapExampleSelector) -> None:
+    """Tests that ngram_overlap_score returns correct values."""
+    selector.threshold = 1.0 + 1e-9
+    none = ngram_overlap_score(["Spot can run."], ["My dog barks."])
+    some = ngram_overlap_score(["Spot can run."], ["See Spot run."])
+    complete = ngram_overlap_score(["Spot can run."], ["Spot can run."])
+
+    check = [abs(none - 0.0) < 1e-9, 0.0 < some < 1.0, abs(complete - 1.0) < 1e-9]
+    assert check == [True, True, True]
diff --git a/tests/unit_tests/chains/test_hyde.py b/tests/unit_tests/chains/test_hyde.py
index 9441b382..fd7f3d61 100644
--- a/tests/unit_tests/chains/test_hyde.py
+++ b/tests/unit_tests/chains/test_hyde.py
@@ -33,7 +33,7 @@ class FakeLLM(BaseLLM, BaseModel):
     ) -> LLMResult:
         return LLMResult(generations=[[Generation(text="foo") for _ in range(self.n)]])
 
-    async def _async_generate(
+    async def _agenerate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
         return LLMResult(generations=[[Generation(text="foo") for _ in range(self.n)]])
diff --git a/tests/unit_tests/llms/llm_test.py b/tests/unit_tests/llms/llm_test.py
index a9e40ed8..c610f828 100644
--- a/tests/unit_tests/llms/llm_test.py
+++ b/tests/unit_tests/llms/llm_test.py
@@ -1,6 +1,7 @@
-from langchain.llms import OpenAI
 import asyncio
 
+from langchain.llms import OpenAI
+
 
 def generate_serially():
     llm = OpenAI(temperature=0)
@@ -10,7 +11,7 @@ def generate_serially():
 
 
 async def async_generate(llm):
-    resp = await llm.async_generate(["Hello, how are you?"])
+    resp = await llm.agenerate(["Hello, how are you?"])
     # print(resp)
 
 
@@ -22,6 +23,7 @@ async def generate_concurrently():
 
 if __name__ == "__main__":
     import time
+
     s = time.perf_counter()
     asyncio.run(generate_concurrently())
     elapsed = time.perf_counter() - s