x

README.md

@@ -18,8 +18,8 @@
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
 
-**Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.
-Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.
+**Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
+Please fill out [this form](https://6w1pwbss0py.typeform.com/to/rrbrdTH2) and we'll set up a dedicated support Slack channel.
 
 ## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28
 

docs/api_reference/templates/class.rst

@@ -3,28 +3,44 @@
 
 .. currentmodule:: {{ module }}
 
+{% if '_value2member_map_' in  all_attributes %}
+    {% set classType = "enum" %}
+{% else %}
+    {% set classType = "default" %}
+{% endif %}
+
 .. autoclass:: {{ objname }}
 
-   {% block methods %}
-   {% if methods %}
-   .. rubric:: {{ _('Methods') }}
+   {% if classType == "enum" %}
+       {% if attributes %}
+       .. rubric:: {{ _('Attributes') }}
+       {% endif %}
+   {% else %}
+       {% if attributes %}
+       .. rubric:: {{ _('Attributes') }}
+       {% endif %}
 
-   .. autosummary::
-   {% for item in methods %}
-      ~{{ name }}.{{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
+       {% block methods %}
+       {% if methods %}
+       .. rubric:: {{ _('Methods') }}
 
-   {% block attributes %}
-   {% if attributes %}
-   .. rubric:: {{ _('Attributes') }}
+       .. autosummary::
+       {% for item in methods %}
+          ~{{ name }}.{{ item }}
+       {%- endfor %}
+       {% endif %}
+       {% endblock %}
 
-   .. autosummary::
-   {% for item in attributes %}
-      ~{{ name }}.{{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
+       {% block attributes %}
+       {% if attributes %}
+       .. rubric:: {{ _('Attributes') }}
 
-.. example_links:: {{ objname }}
+       .. autosummary::
+       {% for item in attributes %}
+          ~{{ name }}.{{ item }}
+       {%- endfor %}
+       {% endif %}
+       {% endblock %}
+    {% endif %}
+
+.. example_links:: {{ objname }}

docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx

@@ -28,7 +28,7 @@ navigating around a browser.
 ### [OpenAI Functions](/docs/modules/agents/agent_types/openai_functions_agent.html)
 
 Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
-function should be called and respond with the inputs that should be passed to the function.
+function should to be called and respond with the inputs that should be passed to the function.
 The OpenAI Functions Agent is designed to work with these models.
 
 ### [Conversational](/docs/modules/agents/agent_types/chat_conversation_agent.html)

docs/docs_skeleton/docs/modules/agents/agent_types/openai_functions_agent.mdx

@@ -1,6 +1,6 @@
 # OpenAI functions
 
-Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should be called and respond with the inputs that should be passed to the function.
+Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should to be called and respond with the inputs that should be passed to the function.
 In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions.
 The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.
 

docs/docs_skeleton/docs/use_cases/summarization/summarize.mdx

@@ -0,0 +1,8 @@
+# Summarization
+
+A summarization chain can be used to summarize multiple documents. One way is to input multiple smaller documents, after they have been divided into chunks, and operate over them with a MapReduceDocumentsChain. You can also choose instead for the chain that does summarization to be a StuffDocumentsChain, or a RefineDocumentsChain.
+
+import Example from "@snippets/modules/chains/popular/summarize.mdx"
+
+<Example/>
+

docs/docs_skeleton/vercel.json

@@ -3478,7 +3478,7 @@
     },
     {
       "source": "/en/latest/modules/prompts/example_selectors.html",
-      "destination": "/docs/modules/model_io/prompts/example_selectors"
+      "destination": "/docs/modules/model_io/example_selectors"
     },
     {
       "source": "/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html",
@@ -3494,7 +3494,7 @@
     },
     {
       "source": "/en/latest/modules/prompts/prompt_templates.html",
-      "destination": "/docs/modules/model_io/prompts/prompt_templates"
+      "destination": "/docs/modules/model_io/prompt_templates"
     },
     {
       "source": "/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html",
@@ -3951,10 +3951,6 @@
     {
       "source": "/docs/modules/chains/additional/tagging",
       "destination": "/docs/use_cases/tagging"
-    },
-    {
-      "source": "docs/integrations/providers/agent_with_wandb_tracing",
-      "destination": "docs/integrations/providers/wandb_tracing"
     }
   ]
 }

docs/extras/guides/expression_language/cookbook.ipynb

@@ -22,10 +22,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "id": "466b65b3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI"
@@ -33,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "id": "3c634ef0",
    "metadata": {},
    "outputs": [],
@@ -202,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "id": "f799664d",
    "metadata": {},
    "outputs": [],
@@ -347,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "id": "5d3d8ffe",
    "metadata": {},
    "outputs": [],
@@ -368,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "id": "33be32af",
    "metadata": {},
    "outputs": [],
@@ -380,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "id": "df3f3fa2",
    "metadata": {},
    "outputs": [],
@@ -392,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "id": "bfc47ec1",
    "metadata": {},
    "outputs": [],
@@ -407,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 24,
    "id": "eae31755",
    "metadata": {},
    "outputs": [],
@@ -422,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "id": "f3040b0c",
    "metadata": {
     "scrolled": false
@@ -441,7 +450,7 @@
        "'Harrison worked at Kensho.'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "id": "e1d20c7c",
    "metadata": {},
    "outputs": [],
@@ -475,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "id": "7ee8b2d4",
    "metadata": {
     "scrolled": false
@@ -494,7 +503,7 @@
        "'Harrison ha lavorato a Kensho.'"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,170 +512,6 @@
     "chain.invoke({\"question\": \"where did harrison work\", \"language\": \"italian\"})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "f007669c",
-   "metadata": {},
-   "source": [
-    "## Conversational Retrieval Chain\n",
-    "\n",
-    "We can easily add in conversation history. This primarily means adding in chat_message_history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "3f30c348",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.schema.runnable import RunnableMap\n",
-    "from langchain.schema import format_document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "64ab1dbf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
-    "\n",
-    "_template = \"\"\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\n",
-    "\n",
-    "Chat History:\n",
-    "{chat_history}\n",
-    "Follow Up Input: {question}\n",
-    "Standalone question:\"\"\"\n",
-    "CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "7d628c97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Answer the question based only on the following context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    "ANSWER_PROMPT = ChatPromptTemplate.from_template(template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "f60a5d0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template=\"{page_content}\")\n",
-    "def _combine_documents(docs, document_prompt = DEFAULT_DOCUMENT_PROMPT, document_separator=\"\\n\\n\"):\n",
-    "    doc_strings = [format_document(doc, document_prompt) for doc in docs]\n",
-    "    return document_separator.join(doc_strings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "7d007db6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Tuple, List\n",
-    "def _format_chat_history(chat_history: List[Tuple]) -> str:\n",
-    "    buffer = \"\"\n",
-    "    for dialogue_turn in chat_history:\n",
-    "        human = \"Human: \" + dialogue_turn[0]\n",
-    "        ai = \"Assistant: \" + dialogue_turn[1]\n",
-    "        buffer += \"\\n\" + \"\\n\".join([human, ai])\n",
-    "    return buffer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "5c32cc89",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "conversational_qa_chain = RunnableMap({\n",
-    "    \"standalone_question\": {\n",
-    "        \"question\": lambda x: x[\"question\"],\n",
-    "        \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
-    "    } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
-    "}) | {\n",
-    "    \"context\": itemgetter(\"standalone_question\") | retriever | _combine_documents,\n",
-    "    \"question\": lambda x: x[\"standalone_question\"]\n",
-    "} | ANSWER_PROMPT | ChatOpenAI()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "135c8205",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversational_qa_chain.invoke({\n",
-    "    \"question\": \"where did harrison work?\",\n",
-    "    \"chat_history\": [],\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "424e7e7a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Harrison worked at Kensho.', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversational_qa_chain.invoke({\n",
-    "    \"question\": \"where did he work?\",\n",
-    "    \"chat_history\": [(\"Who wrote this notebook?\", \"Harrison\")],\n",
-    "})"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0f2bf8d3",
@@ -1238,163 +1083,10 @@
     "chain.invoke({\"input\": \"whats 2 plus 2\"})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "5062941a",
-   "metadata": {},
-   "source": [
-    "## Memory\n",
-    "\n",
-    "This shows how to add memory to an arbitrary chain. Right now, you can use the memory classes but need to hook it up manually"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "7998efd8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.schema.runnable import RunnableMap\n",
-    "from langchain.prompts import MessagesPlaceholder\n",
-    "model = ChatOpenAI()\n",
-    "prompt = ChatPromptTemplate.from_messages([\n",
-    "    (\"system\", \"You are a helpful chatbot\"),\n",
-    "    MessagesPlaceholder(variable_name=\"history\"),\n",
-    "    (\"human\", \"{input}\")\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "fa0087f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationBufferMemory(return_messages=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "06b531ae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': []}"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "d9437af6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = RunnableMap({\n",
-    "    \"input\": lambda x: x[\"input\"],\n",
-    "    \"memory\": memory.load_memory_variables\n",
-    "}) | {\n",
-    "    \"input\": lambda x: x[\"input\"],\n",
-    "    \"history\": lambda x: x[\"memory\"][\"history\"]\n",
-    "} | prompt | model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "bed1e260",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = {\"input\": \"hi im bob\"}\n",
-    "response = chain.invoke(inputs)\n",
-    "response"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "890475b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory.save_context(inputs, {\"output\": response.content})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "e8fcb77f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': [HumanMessage(content='hi im bob', additional_kwargs={}, example=False),\n",
-       "  AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, example=False)]}"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "d837d5c3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Your name is Bob. You mentioned it in your previous message. Is there anything else I can help you with, Bob?', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = {\"input\": \"whats my name\"}\n",
-    "response = chain.invoke(inputs)\n",
-    "response"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "179d3c03",
+   "id": "9be88499",
    "metadata": {},
    "outputs": [],
    "source": []

docs/extras/integrations/chat/azureml_chat_endpoint.ipynb

@@ -1,95 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AzureML Chat Online Endpoint\n",
-    "\n",
-    "[AzureML](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n",
-    "\n",
-    "This notebook goes over how to use a chat model hosted on an `AzureML online endpoint`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models.azureml_endpoint import AzureMLChatOnlineEndpoint"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up\n",
-    "\n",
-    "To use the wrapper, you must [deploy a model on AzureML](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-foundation-models?view=azureml-api-2#deploying-foundation-models-to-endpoints-for-inferencing) and obtain the following parameters:\n",
-    "\n",
-    "* `endpoint_api_key`: The API key provided by the endpoint\n",
-    "* `endpoint_url`: The REST endpoint url provided by the endpoint"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Content Formatter\n",
-    "\n",
-    "The `content_formatter` parameter is a handler class for transforming the request and response of an AzureML endpoint to match with required schema. Since there are a wide range of models in the model catalog, each of which may process data differently from one another, a `ContentFormatterBase` class is provided to allow users to transform data to their liking. The following content formatters are provided:\n",
-    "\n",
-    "* `LLamaContentFormatter`: Formats request and response data for LLaMa2-chat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='  The Collatz Conjecture is one of the most famous unsolved problems in mathematics, and it has been the subject of much study and research for many years. While it is impossible to predict with certainty whether the conjecture will ever be solved, there are several reasons why it is considered a challenging and important problem:\\n\\n1. Simple yet elusive: The Collatz Conjecture is a deceptively simple statement that has proven to be extraordinarily difficult to prove or disprove. Despite its simplicity, the conjecture has eluded some of the brightest minds in mathematics, and it remains one of the most famous open problems in the field.\\n2. Wide-ranging implications: The Collatz Conjecture has far-reaching implications for many areas of mathematics, including number theory, algebra, and analysis. A solution to the conjecture could have significant impacts on these fields and potentially lead to new insights and discoveries.\\n3. Computational evidence: While the conjecture remains unproven, extensive computational evidence supports its validity. In fact, no counterexample to the conjecture has been found for any starting value up to 2^64 (a number', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chat_models.azureml_endpoint import LlamaContentFormatter\n",
-    "from langchain.schema import HumanMessage\n",
-    "\n",
-    "chat = AzureMLChatOnlineEndpoint(content_formatter=LlamaContentFormatter())\n",
-    "response = chat(messages=[\n",
-    "    HumanMessage(content=\"Will the Collatz conjecture ever be solved?\")\n",
-    "])\n",
-    "response"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/extras/integrations/chat/google_vertex_ai_palm.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -9,7 +8,11 @@
     "\n",
     "Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
     "\n",
-    "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
+    "PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). \n",
+    "\n",
+    "Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview [terms and conditions](https://cloud.google.com/trustedtester/aitos) (Preview Terms).\n",
+    "\n",
+    "For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n",
     "\n",
     "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
     "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
@@ -87,7 +90,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -140,7 +142,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "execution": {

docs/extras/integrations/document_loaders/concurrent.ipynb

@@ -1,94 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "23c6e167",
-   "metadata": {},
-   "source": [
-    "# Concurrent Loader\n",
-    "\n",
-    "Works just like the GenericLoader but concurrently for those who choose to optimize their workflow.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6ff3fb1f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import ConcurrentLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ce96fa20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = ConcurrentLoader.from_filesystem('example_data/', glob=\"**/*.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "06a6cf5d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b87d3e58",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(files)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "668f1ee5",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/integrations/document_loaders/huawei_obs_directory.ipynb

@@ -1,178 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c83b6a4c",
-   "metadata": {},
-   "source": [
-    "# Huawei OBS Directory\n",
-    "The following code demonstrates how to load objects from the Huawei OBS (Object Storage Service) as documents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2191935",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the required package\n",
-    "# pip install esdk-obs-python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "55fca3b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import OBSDirectoryLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c3ed419f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint = \"your-endpoint\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3428fd4e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure your access credentials\\n\n",
-    "config = {\n",
-    "    \"ak\": \"your-access-key\",\n",
-    "    \"sk\": \"your-secret-key\"\n",
-    "}\n",
-    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint, config=config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9beede9f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e20a839",
-   "metadata": {},
-   "source": [
-    "## Specify a Prefix for Loading\n",
-    "If you want to load objects with a specific prefix from the bucket, you can use the following code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "125f311d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint, config=config, prefix=\"test_prefix\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b3488037",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84c82c0a",
-   "metadata": {},
-   "source": [
-    "## Get Authentication Information from ECS\n",
-    "If your langchain is deployed on Huawei Cloud ECS and [Agency is set up](https://support.huaweicloud.com/intl/en-us/usermanual-ecs/ecs_03_0166.html#section7), the loader can directly get the security token from ECS without needing access key and secret key. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1db99969",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config = {\"get_token_from_ecs\": True}\n",
-    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint, config=config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57dd9f35",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30205d25",
-   "metadata": {},
-   "source": [
-    "## Use a Public Bucket\n",
-    "If your bucket's bucket policy allows anonymous access (anonymous users have `listBucket` and `GetObject` permissions), you can directly load the objects without configuring the `config` parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "4dfa2ef0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = OBSDirectoryLoader(\"your-bucket-name\", endpoint=endpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "67d4c1d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/integrations/document_loaders/huawei_obs_file.ipynb

@@ -1,180 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4394a872",
-   "metadata": {},
-   "source": [
-    "# Huawei OBS File\n",
-    "The following code demonstrates how to load an object from the Huawei OBS (Object Storage Service) as document."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c43d811b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the required package\n",
-    "# pip install esdk-obs-python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5e16bae6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.obs_file import OBSFileLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "75cc7e7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint = \"your-endpoint\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f9816984",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from obs import ObsClient\n",
-    "obs_client = ObsClient(access_key_id=\"your-access-key\", secret_access_key=\"your-secret-key\", server=endpoint)\n",
-    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\", client=obs_client)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6143b39b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "633e05ca",
-   "metadata": {},
-   "source": [
-    "## Each Loader with Separate Authentication Information\n",
-    "If you don't need to reuse OBS connections between different loaders, you can directly configure the `config`. The loader will use the config information to initialize its own OBS client."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a5dd6a5d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure your access credentials\\n\n",
-    "config = {\n",
-    "    \"ak\": \"your-access-key\",\n",
-    "    \"sk\": \"your-secret-key\"\n",
-    "}\n",
-    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\",endpoint=endpoint, config=config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9a741f1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e2e611c",
-   "metadata": {},
-   "source": [
-    "## Get Authentication Information from ECS\n",
-    "If your langchain is deployed on Huawei Cloud ECS and [Agency is set up](https://support.huaweicloud.com/intl/en-us/usermanual-ecs/ecs_03_0166.html#section7), the loader can directly get the security token from ECS without needing access key and secret key. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "338fafef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config = {\"get_token_from_ecs\": True}\n",
-    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\", endpoint=endpoint, config=config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "73976c55",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b77aa18c",
-   "metadata": {},
-   "source": [
-    "## Access a Publicly Accessible Object\n",
-    "If the object you want to access allows anonymous user access (anonymous users have `GetObject` permission), you can directly load the object without configuring the `config` parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "df83d121",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = OBSFileLoader(\"your-bucket-name\", \"your-object-key\", endpoint=endpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82a844ba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/integrations/document_loaders/news.ipynb

@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2dfc4698",
-   "metadata": {},
-   "source": [
-    "# News URL\n",
-    "\n",
-    "This covers how to load HTML news articles from a list of URLs into a document format that we can use downstream."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "16c3699e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-02T21:18:18.886031400Z",
-     "start_time": "2023-08-02T21:18:17.682345Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import NewsURLLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "836fbac1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-02T21:18:18.895539800Z",
-     "start_time": "2023-08-02T21:18:18.895539800Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "urls = [\n",
-    "    \"https://www.bbc.com/news/world-us-canada-66388172\",\n",
-    "    \"https://www.bbc.com/news/entertainment-arts-66384971\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33089aba-ff74-4d00-8f40-9449c29587cc",
-   "metadata": {},
-   "source": [
-    "Pass in urls to load them into Documents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "00f46fda",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-02T21:18:19.227074500Z",
-     "start_time": "2023-08-02T21:18:18.895539800Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First article:  page_content='In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact. Neither she nor her representatives have commented.' metadata={'title': 'Donald Trump indictment: What do we know about the six co-conspirators?', 'link': 'https://www.bbc.com/news/world-us-canada-66388172', 'authors': [], 'language': 'en', 'description': 'Six people accused of helping Mr Trump undermine the election have been described by prosecutors.', 'publish_date': None}\n",
-      "\n",
-      "Second article:  page_content='Ms Williams added: \"If there\\'s anything that I can do in my power to ensure that dancers or singers or whoever decides to work with her don\\'t have to go through that same experience, I\\'m going to do that.\"' metadata={'title': \"Lizzo dancers Arianna Davis and Crystal Williams: 'No one speaks out, they are scared'\", 'link': 'https://www.bbc.com/news/entertainment-arts-66384971', 'authors': [], 'language': 'en', 'description': 'The US pop star is being sued for sexual harassment and fat-shaming but has yet to comment.', 'publish_date': None}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = NewsURLLoader(urls=urls)\n",
-    "data = loader.load()\n",
-    "print(\"First article: \", data[0])\n",
-    "print(\"\\nSecond article: \", data[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Use nlp=True to run nlp analysis and generate keywords + summary"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "98ac26c488315bff"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b68a26b3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-02T21:18:19.585758200Z",
-     "start_time": "2023-08-02T21:18:19.227074500Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First article:  page_content='In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact. Neither she nor her representatives have commented.' metadata={'title': 'Donald Trump indictment: What do we know about the six co-conspirators?', 'link': 'https://www.bbc.com/news/world-us-canada-66388172', 'authors': [], 'language': 'en', 'description': 'Six people accused of helping Mr Trump undermine the election have been described by prosecutors.', 'publish_date': None, 'keywords': ['powell', 'know', 'donald', 'trump', 'review', 'indictment', 'telling', 'view', 'reasonable', 'person', 'testimony', 'coconspirators', 'riot', 'representatives', 'claims'], 'summary': 'In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact.\\nNeither she nor her representatives have commented.'}\n",
-      "\n",
-      "Second article:  page_content='Ms Williams added: \"If there\\'s anything that I can do in my power to ensure that dancers or singers or whoever decides to work with her don\\'t have to go through that same experience, I\\'m going to do that.\"' metadata={'title': \"Lizzo dancers Arianna Davis and Crystal Williams: 'No one speaks out, they are scared'\", 'link': 'https://www.bbc.com/news/entertainment-arts-66384971', 'authors': [], 'language': 'en', 'description': 'The US pop star is being sued for sexual harassment and fat-shaming but has yet to comment.', 'publish_date': None, 'keywords': ['davis', 'lizzo', 'singers', 'experience', 'crystal', 'ensure', 'arianna', 'theres', 'williams', 'power', 'going', 'dancers', 'im', 'speaks', 'work', 'ms', 'scared'], 'summary': 'Ms Williams added: \"If there\\'s anything that I can do in my power to ensure that dancers or singers or whoever decides to work with her don\\'t have to go through that same experience, I\\'m going to do that.\"'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = NewsURLLoader(urls=urls, nlp=True)\n",
-    "data = loader.load()\n",
-    "print(\"First article: \", data[0])\n",
-    "print(\"\\nSecond article: \", data[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['powell',\n 'know',\n 'donald',\n 'trump',\n 'review',\n 'indictment',\n 'telling',\n 'view',\n 'reasonable',\n 'person',\n 'testimony',\n 'coconspirators',\n 'riot',\n 'representatives',\n 'claims']"
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[0].metadata['keywords']"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-02T21:18:19.585758200Z",
-     "start_time": "2023-08-02T21:18:19.585758200Z"
-    }
-   },
-   "id": "ae37e004e0284b1d"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'In testimony to the congressional committee examining the 6 January riot, Mrs Powell said she did not review all of the many claims of election fraud she made, telling them that \"no reasonable person\" would view her claims as fact.\\nNeither she nor her representatives have commented.'"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[0].metadata['summary']"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-08-02T21:18:19.598966800Z",
-     "start_time": "2023-08-02T21:18:19.594950200Z"
-    }
-   },
-   "id": "7676155fb175e53e"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/integrations/document_loaders/unstructured_file.ipynb

@@ -18,7 +18,8 @@
    "outputs": [],
    "source": [
     "# # Install package\n",
-    "!pip install \"unstructured[all-docs]\"\n"
+    "!pip install \"unstructured[local-inference]\"\n",
+    "!pip install layoutparser[layoutmodels,tesseract]"
    ]
   },
   {

docs/extras/integrations/document_loaders/youtube_audio.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "e48afb8d",
    "metadata": {},
@@ -12,8 +11,7 @@
     "\n",
     "Below we show how to easily go from a YouTube url to text to chat!\n",
     "\n",
-    "We wil use the `OpenAIWhisperParser`, which will use the OpenAI Whisper API to transcribe audio to text, \n",
-    "and the  `OpenAIWhisperParserLocal` for local support and running on private clouds or on premise.\n",
+    "We wil use the `OpenAIWhisperParser`, which will use the OpenAI Whisper API to transcribe audio to text.\n",
     "\n",
     "Note: You will need to have an `OPENAI_API_KEY` supplied."
    ]
@@ -26,7 +24,7 @@
    "outputs": [],
    "source": [
     "from langchain.document_loaders.generic import GenericLoader\n",
-    "from langchain.document_loaders.parsers import OpenAIWhisperParser, OpenAIWhisperParserLocal\n",
+    "from langchain.document_loaders.parsers import OpenAIWhisperParser\n",
     "from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader"
    ]
   },
@@ -48,8 +46,7 @@
    "outputs": [],
    "source": [
     "! pip install yt_dlp\n",
-    "! pip install pydub\n",
-    "! pip install librosa"
+    "! pip install pydub"
    ]
   },
   {
@@ -66,18 +63,6 @@
     "Let's take the first lecture of Andrej Karpathy's YouTube course as an example! "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8682f256",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set a flag to switch between local and remote parsing\n",
-    "# change this to True if you want to use local parsing\n",
-    "local = False"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -117,10 +102,7 @@
     "save_dir = \"~/Downloads/YouTube\"\n",
     "\n",
     "# Transcribe the videos to text\n",
-    "if local:\n",
-    "    loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParserLocal())\n",
-    "else:\n",
-    "    loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParser())\n",
+    "loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParser())\n",
     "docs = loader.load()"
    ]
   },
@@ -293,7 +275,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -307,12 +289,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "97cc609b13305c559618ec78a438abc56230b9381f827f22d070313b9a1f3777"
-   }
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

docs/extras/integrations/llms/Fireworks.ipynb

@@ -1,231 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cc6caafa",
-   "metadata": {},
-   "source": [
-    "# Fireworks\n",
-    "\n",
-    ">[Fireworks](https://www.fireworks.ai/) is an AI startup focused on accelerating product development on generative AI by creating an innovative AI experiment and production platform. \n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `Fireworks` models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "60b6dbb2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms.fireworks import Fireworks, FireworksChat\n",
-    "from langchain import PromptTemplate, LLMChain\n",
-    "from langchain.prompts.chat import (\n",
-    "    ChatPromptTemplate,\n",
-    "    HumanMessagePromptTemplate,\n",
-    ")\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ccff689e",
-   "metadata": {},
-   "source": [
-    "# Setup\n",
-    "\n",
-    "Contact Fireworks AI for the an API Key to access our models\n",
-    "\n",
-    "Set up your model using a model id. If the model is not set, the default model is fireworks-llama-v2-13b-chat."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9ca87a2e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize a Fireworks LLM\n",
-    "os.environ['FIREWORKS_API_KEY'] = \"\" #change this to your own API KEY\n",
-    "llm = Fireworks(model_id=\"fireworks-llama-v2-13b-chat\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "43a11ba8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create LLM chain\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acc24d0c",
-   "metadata": {},
-   "source": [
-    "# Calling the Model\n",
-    "\n",
-    "You can use the LLMs to call the model for specified prompt(s). \n",
-    "\n",
-    "Current Specified Models: \n",
-    "* fireworks-falcon-7b, fireworks-falcon-40b-w8a16\n",
-    "* fireworks-guanaco-30b, fireworks-guanaco-33b-w8a16\n",
-    "* fireworks-llama-7b, fireworks-llama-13b, fireworks-llama-30b-w8a16\n",
-    "* fireworks-llama-v2-13b, fireworks-llama-v2-13b-chat, fireworks-llama-v2-13b-w8a16, fireworks-llama-v2-13b-chat-w8a16\n",
-    "* fireworks-llama-v2-7b, fireworks-llama-v2-7b-chat, fireworks-llama-v2-7b-w8a16, fireworks-llama-v2-7b-chat-w8a16"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "bf0a425c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "It's a question that has been debated for years, with different analysts and fans making their cases for various signal-callers. Here are some of the top contenders for the title of best quarterback in the NFL:\n",
-      "\n",
-      "1. Tom Brady: The New England Patriots legend has won six Super Bowls and has been named Super Bowl MVP four times. He's known for his precision passing, pocket presence, and ability to read defenses.\n",
-      "2. Aaron Rodgers: The Green Bay Packers quarterback has won two Super Bowls and has been named NFL MVP twice. He's known for his quick release, accuracy, and ability to extend plays with his feet.\n",
-      "3. Drew Brees: The New Orleans Saints quarterback has won a Super Bowl and has been named NFL MVP once. He's known for his accuracy, pocket presence, and ability to read defenses.\n",
-      "4. Patrick Mahomes: The Kansas City Chiefs quarterback has won a Super Bowl and has been named NFL MVP twice. He's known for his arm strength, athleticism, and ability to make plays outside of the pocket.\n",
-      "5. Russell Wilson: The Seattle Seahawks quarterback has won a Super Bowl and has been named NFL MVP once. He's known for his mobility, accuracy, and ability to extend plays with his feet.\n",
-      "\n",
-      "Of course, there are other talented quarterbacks in the league, such as Lamar Jackson, Deshaun Watson, and Carson Wentz, who could also be considered among the best. Ultimately, the answer to the question of who's the best quarterback in the NFL is subjective and can vary depending on individual perspectives and criteria.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#single prompt\n",
-    "output = llm(\"Who's the best quarterback in the NFL?\")\n",
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "afc7de6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "generations=[[Generation(text=\"\\nWho is the best cricket player in the world in 2016?\\nThe best cricket player in the world in 2016 is Virat Kohli. The Indian captain has had a fabulous year, scoring heavily in all formats of the game, leading India to several victories, and breaking several records. In Test cricket, Kohli has scored 1215 runs at an average of 75.33 with 6 centuries and 4 fifties, which is the highest number of runs scored by any player in a calendar year. In ODI cricket, he has scored 1143 runs at an average of 83.42 with 7 centuries and 6 fifties, which is also the highest number of runs scored by any player in a calendar year. Additionally, Kohli has led India to the number one ranking in Test cricket, and has been named the ICC Test Player of the Year and the ICC ODI Player of the Year.\\nVirat Kohli has been in incredible form in 2016, and his performances have made him the standout player of the year. Other players who have had a great year include Steve Smith, Joe Root, and Kane Williamson, but Kohli's consistency and dominance in all formats of the game make him the best cricket player in the world in 2016.\", generation_info=None)], [Generation(text=\"\\n\\nA: LeBron James.\\n\\nB: Kevin Durant.\\n\\nC: Steph Curry.\\n\\nD: James Harden.\\n\\nE: Other (please specify).\\n\\nWhat's your answer?\", generation_info=None)]] llm_output={'token_usage': {}, 'model_id': 'fireworks-llama-v2-13b-chat'} run=[RunInfo(run_id=UUID('d14b6bee-7692-46ad-8798-acb6f72fc7fb')), RunInfo(run_id=UUID('b9f5b3b5-9e62-4eaf-b269-ecf0cbbcfb82'))]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#calling multiple prompts\n",
-    "output = llm.generate([\"Who's the best cricket player in 2016?\", \"Who's the best basketball player in the league?\"])\n",
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "b801c20d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Kansas City in December can be quite chilly, with average high\n"
-     ]
-    }
-   ],
-   "source": [
-    "#setting parameters: model_id, temperature, max_tokens, top_p\n",
-    "llm = Fireworks(model_id=\"fireworks-llama-v2-13b-chat\", temperature=0.7, max_tokens=15, top_p=1.0)\n",
-    "print(llm(\"What's the weather like in Kansas City in December?\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "137662a6",
-   "metadata": {},
-   "source": [
-    "# Create and Run Chain\n",
-    "\n",
-    "Create a prompt template to be used with the LLM Chain. Once this prompt template is created, initialize the chain with the LLM and prompt template, and run the chain with the specified prompts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "fd2c6bc1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "(Note: I'm just an AI and not a branding expert, so take this as a starting point for your own research and brainstorming.)\n",
-      "A good name for a company that makes football helmets could be:\n",
-      "\n",
-      "1. Helix Pro: This name plays off the idea of a helix, or spiral, shape that is commonly associated with football helmets. \"Pro\" implies a professional-level product.\n",
-      "2. Gridiron Gear: \"Gridiron\" is a term used to describe a football field, and \"gear\" highlights the company's focus on producing high-quality football helmets.\n",
-      "3. Linebacker Lab: \"Linebacker\" is a position on the football field, and \"Lab\" suggests a focus on research and development.\n",
-      "4. Helmet Hut: This name is simple and easy to remember, and it immediately conveys the company's focus on football helmets.\n",
-      "5. Tackle Tech: \"Tackle\" is a term used in football to describe a hit or collision, and \"Tech\" implies a focus on advanced technology and innovation.\n",
-      "6. Victory Vest: \"Victory\" implies a focus on winning and success, and \"Vest\" could suggest a protective or armored design.\n",
-      "7. Pigskin Pro: \"Pigskin\" is a term used to describe a football, and \"Pro\" implies a professional-level product.\n",
-      "8. Football Fusion: This name could suggest a combination of different materials or technologies to create a high-quality football helmet.\n",
-      "9. Endzone Edge: \"Endzone\" is the area of the football field where a team scores a touchdown, and \"Edge\" implies a competitive advantage.\n",
-      "10. MVP Masks: \"MVP\" stands for \"Most Valuable Player,\" and \"Masks\" highlights the protective nature of the company's football helmets.\n",
-      "\n",
-      "Remember, the name you choose for your company should be memorable, easy to pronounce and spell, and convey a sense of quality and professionalism. It's also important to check that the name isn't already in use by another company, and to consider any potential trademark issues.\n"
-     ]
-    }
-   ],
-   "source": [
-    "human_message_prompt = HumanMessagePromptTemplate(\n",
-    "    prompt=PromptTemplate(\n",
-    "        template=\"What is a good name for a company that makes {product}?\",\n",
-    "        input_variables=[\"product\"],\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
-    "chat = Fireworks()\n",
-    "chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
-    "output = chain.run(\"football helmets\")\n",
-    "\n",
-    "print(output)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/integrations/llms/azureml_endpoint_example.ipynb

@@ -28,9 +28,9 @@
     "\n",
     "To use the wrapper, you must [deploy a model on AzureML](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-foundation-models?view=azureml-api-2#deploying-foundation-models-to-endpoints-for-inferencing) and obtain the following parameters:\n",
     "\n",
-    "* `endpoint_api_key`: Required - The API key provided by the endpoint\n",
-    "* `endpoint_url`: Required - The REST endpoint url provided by the endpoint\n",
-    "* `deployment_name`: Not required - The deployment name of the model using the endpoint"
+    "* `endpoint_api_key`: The API key provided by the endpoint\n",
+    "* `endpoint_url`: The REST endpoint url provided by the endpoint\n",
+    "* `deployment_name`: The deployment name of the endpoint"
    ]
   },
   {
@@ -39,14 +39,11 @@
    "source": [
     "## Content Formatter\n",
     "\n",
-    "The `content_formatter` parameter is a handler class for transforming the request and response of an AzureML endpoint to match with required schema. Since there are a wide range of models in the model catalog, each of which may process data differently from one another, a `ContentFormatterBase` class is provided to allow users to transform data to their liking. The following content formatters are provided:\n",
+    "The `content_formatter` parameter is a handler class for transforming the request and response of an AzureML endpoint to match with required schema. Since there are a wide range of models in the model catalog, each of which may process data differently from one another, a `ContentFormatterBase` class is provided to allow users to transform data to their liking. Additionally, there are three content formatters already provided:\n",
     "\n",
-    "* `GPT2ContentFormatter`: Formats request and response data for GPT2\n",
-    "* `DollyContentFormatter`: Formats request and response data for the Dolly-v2\n",
+    "* `OSSContentFormatter`: Formats request and response data for models from the Open Source category in the Model Catalog. Note, that not all models in the Open Source category may follow the same schema\n",
+    "* `DollyContentFormatter`: Formats request and response data for the `dolly-v2-12b` model\n",
     "* `HFContentFormatter`: Formats request and response data for text-generation Hugging Face models\n",
-    "* `LLamaContentFormatter`: Formats request and response data for LLaMa2\n",
-    "\n",
-    "*Note: `OSSContentFormatter` is being deprecated and replaced with `GPT2ContentFormatter`. The logic is the same but `GPT2ContentFormatter` is a more suitable name. You can still continue to use `OSSContentFormatter` as the changes are backwards compatibile.*\n",
     "\n",
     "Below is an example using a summarization model from Hugging Face."
    ]
@@ -103,6 +100,7 @@
     "llm = AzureMLOnlineEndpoint(\n",
     "    endpoint_api_key=os.getenv(\"BART_ENDPOINT_API_KEY\"),\n",
     "    endpoint_url=os.getenv(\"BART_ENDPOINT_URL\"),\n",
+    "    deployment_name=\"linydub-bart-large-samsum-3\",\n",
     "    model_kwargs={\"temperature\": 0.8, \"max_new_tokens\": 400},\n",
     "    content_formatter=content_formatter,\n",
     ")\n",
@@ -169,6 +167,7 @@
     "llm = AzureMLOnlineEndpoint(\n",
     "    endpoint_api_key=os.getenv(\"DOLLY_ENDPOINT_API_KEY\"),\n",
     "    endpoint_url=os.getenv(\"DOLLY_ENDPOINT_URL\"),\n",
+    "    deployment_name=\"databricks-dolly-v2-12b-4\",\n",
     "    model_kwargs={\"temperature\": 0.8, \"max_tokens\": 300},\n",
     "    content_formatter=content_formatter,\n",
     ")\n",

docs/extras/integrations/llms/google_vertex_ai_palm.ipynb

@@ -1,15 +1,18 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Google Cloud Platform Vertex AI PaLM \n",
     "\n",
-    "Note: This is seperate from the Google PaLM integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on Google Cloud. \n",
+    "Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
     "\n",
-    "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
+    "PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). \n",
+    "\n",
+    "Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview [terms and conditions](https://cloud.google.com/trustedtester/aitos) (Preview Terms).\n",
+    "\n",
+    "For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n",
     "\n",
     "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
     "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
@@ -98,7 +101,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [

docs/extras/integrations/memory/streamlit_chat_message_history.ipynb

@@ -1,61 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "91c6a7ef",
-   "metadata": {},
-   "source": [
-    "# Streamlit Chat Message History\n",
-    "\n",
-    "This notebook goes over how to use Streamlit to store chat message history. Note, StreamlitChatMessageHistory only works when run in a Streamlit app. For more on Streamlit check out their\n",
-    "[getting started documentation](https://docs.streamlit.io/library/get-started)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d15e3302",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import StreamlitChatMessageHistory\n",
-    "\n",
-    "history = StreamlitChatMessageHistory(\"foo\")\n",
-    "\n",
-    "history.add_user_message(\"hi!\")\n",
-    "history.add_ai_message(\"whats up?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64fc465e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history.messages"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv",
-   "language": "python",
-   "name": "poetry-venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/integrations/providers/agent_with_wandb_tracing.ipynb

@@ -177,7 +177,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

docs/extras/integrations/providers/fireworks.md

@@ -1,22 +0,0 @@
-# Fireworks
-
-This page covers how to use the Fireworks models within Langchain.
-
-## Installation and Setup
-
-- To use the Fireworks model, you need to have a Fireworks API key. To generate one, sign up at platform.fireworks.ai
-- Authenticate by setting the FIREWORKS_API_KEY environment variable.
-
-## LLM
-
-Fireworks integrates with Langchain through the LLM module, which allows for standardized usage of any models deployed on the Fireworks models.
-
-In this example, we'll work the llama-v2-13b. 
-```python
-from langchain.llms.fireworks import Fireworks 
-
-llm = Fireworks(model="fireworks-llama-v2-13b-chat", max_tokens=256, temperature=0.4)
-llm("Name 3 sports.")
-```
-
-For a more detailed walkthrough, see [here](/docs/extras/modules/model_io/models/llms/integrations/Fireworks.ipynb).

docs/extras/integrations/providers/mlflow_ai_gateway.mdx

@@ -1,6 +1,6 @@
 # MLflow AI Gateway
 
->`The MLflow AI Gateway` service is a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index.html) for more details.
+The MLflow AI Gateway service is a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index.html) for more details.
 
 ## Installation and Setup
 

docs/extras/integrations/providers/mlflow_tracking.ipynb

@@ -1,49 +1,19 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "5d184f91",
    "metadata": {},
    "source": [
     "# MLflow\n",
     "\n",
-    ">[MLflow](https://www.mlflow.org/docs/latest/what-is-mlflow.html) is a versatile, expandable, open-source platform for managing workflows and artifacts across the machine learning lifecycle. It has built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool. It is designed to be extensible, so you can write plugins to support new workflows, libraries, and tools.\n",
-    "\n",
-    "This notebook goes over how to track your LangChain experiments into your `MLflow Server`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea73efae-7182-4a89-a492-c865b1fcf981",
-   "metadata": {},
-   "source": [
-    "## External examples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "97361a84-4e8f-45ba-b291-814cf73cd8f2",
-   "metadata": {},
-   "source": [
-    "`MLflow` provides [several examples](https://github.com/mlflow/mlflow/tree/master/examples/langchain) for the `LangChain` integration:\n",
-    "- [simple_chain](https://github.com/mlflow/mlflow/blob/master/examples/langchain/simple_chain.py)\n",
-    "- [simple_agent](https://github.com/mlflow/mlflow/blob/master/examples/langchain/simple_agent.py)\n",
-    "- [retriever_chain](https://github.com/mlflow/mlflow/blob/master/examples/langchain/retriever_chain.py)\n",
-    "- [retrieval_qa_chain](https://github.com/mlflow/mlflow/blob/master/examples/langchain/retrieval_qa_chain.py)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0cbd74b-1542-45a4-a72b-b2eedeffd2e0",
-   "metadata": {},
-   "source": [
-    "## Example"
-   ]
+    "This notebook goes over how to track your LangChain experiments into your MLflow Server"
+   ],
+   "id": "5d184f91"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ca7bd72f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,12 +24,12 @@
     "!pip install openai\n",
     "!pip install google-search-results\n",
     "!python -m spacy download en_core_web_sm"
-   ]
+   ],
+   "id": "ca7bd72f"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bf8e1f5c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,23 +38,23 @@
     "os.environ[\"MLFLOW_TRACKING_URI\"] = \"\"\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
     "os.environ[\"SERPAPI_API_KEY\"] = \"\""
-   ]
+   ],
+   "id": "bf8e1f5c"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fd49fd45",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.callbacks import MlflowCallbackHandler\n",
     "from langchain.llms import OpenAI"
-   ]
+   ],
+   "id": "fd49fd45"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "578cac8c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,12 +70,12 @@
     "llm = OpenAI(\n",
     "    model_name=\"gpt-3.5-turbo\", temperature=0, callbacks=[mlflow_callback], verbose=True\n",
     ")"
-   ]
+   ],
+   "id": "578cac8c"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9b20acae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,23 +83,23 @@
     "llm_result = llm.generate([\"Tell me a joke\"])\n",
     "\n",
     "mlflow_callback.flush_tracker(llm)"
-   ]
+   ],
+   "id": "9b20acae"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8b872046",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.chains import LLMChain"
-   ]
+   ],
+   "id": "8b872046"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1b2627ef",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,12 +117,12 @@
     "]\n",
     "synopsis_chain.apply(test_prompts)\n",
     "mlflow_callback.flush_tracker(synopsis_chain)"
-   ]
+   ],
+   "id": "1b2627ef"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e002823a",
    "metadata": {
     "id": "_jN73xcPVEpI"
    },
@@ -160,12 +130,12 @@
    "source": [
     "from langchain.agents import initialize_agent, load_tools\n",
     "from langchain.agents import AgentType"
-   ]
+   ],
+   "id": "e002823a"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "655bd47e",
    "metadata": {
     "id": "Gpq4rk6VT9cu"
    },
@@ -184,7 +154,8 @@
     "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
     ")\n",
     "mlflow_callback.flush_tracker(agent, finish=True)"
-   ]
+   ],
+   "id": "655bd47e"
   }
  ],
  "metadata": {
@@ -206,9 +177,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/extras/integrations/providers/sagemaker_tracking.ipynb

@@ -1,916 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ef3909cf-72ca-4841-85c6-ef4e0eae3aaf",
-   "metadata": {},
-   "source": [
-    "# SageMaker Tracking\n",
-    "\n",
-    "This notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into SageMaker Experiments. Here, we use different scenarios to showcase the capability:\n",
-    "* **Scenario 1**: *Single LLM* - A case where a single LLM model is used to generate output based on a given prompt.\n",
-    "* **Scenario 2**: *Sequential Chain* - A case where a sequential chain of two LLM models is used.\n",
-    "* **Scenario 3**: *Agent with Tools (Chain of Thought)* - A case where multiple tools (search and math) are used in addition to an LLM.\n",
-    "\n",
-    "[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly and easily build, train and deploy machine learning (ML) models. \n",
-    "\n",
-    "[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability of Amazon SageMaker that lets you organize, track, compare and evaluate ML experiments and model versions.\n",
-    "\n",
-    "In this notebook, we will create a single experiment to log the prompts from each scenario."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94c22cb4-3b1c-432b-b3be-0235eec79c5c",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2353436d-17fe-4f58-a2f9-c299d56393fd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install sagemaker\n",
-    "!pip install openai\n",
-    "!pip install google-search-results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65dcf62e-7a38-4119-adb9-d9e884e82499",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "First, setup the required API keys\n",
-    "* OpenAI: https://platform.openai.com/account/api-keys (For OpenAI LLM model)\n",
-    "* Google SERP API: https://serpapi.com/manage-api-key (For Google Search Tool)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5ec2b898-0cfc-4308-8e86-569cd7b7cf41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "## Add your API keys below\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"<ADD-KEY-HERE>\"\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"<ADD-KEY-HERE>\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "80968ebf-519f-46de-8703-97532ac39e3e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain, SimpleSequentialChain\n",
-    "from langchain.agents import initialize_agent, load_tools\n",
-    "from langchain.agents import Tool\n",
-    "from langchain.callbacks import SageMakerCallbackHandler\n",
-    "\n",
-    "from sagemaker.analytics import ExperimentAnalytics\n",
-    "from sagemaker.session import Session\n",
-    "from sagemaker.experiments.run import Run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b67d031f-a01f-4009-ad29-c80ab8ad50ea",
-   "metadata": {},
-   "source": [
-    "## LLM Prompt Tracking"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "da2d70ee-173b-469d-a718-54c33d862844",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#LLM Hyperparameters\n",
-    "HPARAMS = {\n",
-    "    \"temperature\": 0.1,\n",
-    "    \"model_name\": \"text-davinci-003\",\n",
-    "}\n",
-    "\n",
-    "#Bucket used to save prompt logs (Use `None` is used to save the default bucket or otherwise change it)\n",
-    "BUCKET_NAME = None\n",
-    "\n",
-    "#Experiment name\n",
-    "EXPERIMENT_NAME = \"langchain-sagemaker-tracker\"\n",
-    "\n",
-    "#Create SageMaker Session with the given bucket\n",
-    "session = Session(default_bucket=BUCKET_NAME)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7239a39a-08d8-43cb-8922-81abdd5d9ebf",
-   "metadata": {},
-   "source": [
-    "### Scenario 1 - LLM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "abc00335-50c8-4119-adb8-4c4ab8522e23",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "RUN_NAME = \"run-scenario-1\"\n",
-    "PROMPT_TEMPLATE = \"tell me a joke about {topic}\"\n",
-    "INPUT_VARIABLES = {\"topic\": \"fish\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a3a3cbe-db85-4255-8d8b-eaafdca8c6e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with Run(experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session) as run:\n",
-    "\n",
-    "    # Create SageMaker Callback\n",
-    "    sagemaker_callback = SageMakerCallbackHandler(run)\n",
-    "\n",
-    "    # Define LLM model with callback\n",
-    "    llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)\n",
-    "\n",
-    "    # Create prompt template\n",
-    "    prompt = PromptTemplate.from_template(template=PROMPT_TEMPLATE)\n",
-    "\n",
-    "    # Create LLM Chain\n",
-    "    chain = LLMChain(llm=llm, prompt=prompt, callbacks=[sagemaker_callback])\n",
-    "\n",
-    "    # Run chain\n",
-    "    chain.run(**INPUT_VARIABLES)\n",
-    "\n",
-    "    # Reset the callback\n",
-    "    sagemaker_callback.flush_tracker()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7dc69934-9f42-40b7-9931-36a3371a38da",
-   "metadata": {},
-   "source": [
-    "### Scenario 2 - Sequential Chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "50b75ef9-9825-4ccc-8414-4cd7525a1b68",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "RUN_NAME = \"run-scenario-2\"\n",
-    "\n",
-    "PROMPT_TEMPLATE_1 = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
-    "Title: {title}\n",
-    "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "PROMPT_TEMPLATE_2 = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
-    "Play Synopsis: {synopsis}\n",
-    "Review from a New York Times play critic of the above play:\"\"\"\n",
-    "\n",
-    "INPUT_VARIABLES = {\n",
-    "    \"input\": \"documentary about good video games that push the boundary of game design\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb7fff5f-e89f-40e2-96b4-3641a0b6e9b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with Run(experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session) as run:\n",
-    "\n",
-    "    # Create SageMaker Callback\n",
-    "    sagemaker_callback = SageMakerCallbackHandler(run)\n",
-    "\n",
-    "    # Create prompt templates for the chain\n",
-    "    prompt_template1 = PromptTemplate.from_template(template=PROMPT_TEMPLATE_1)\n",
-    "    prompt_template2 = PromptTemplate.from_template(template=PROMPT_TEMPLATE_2)\n",
-    "\n",
-    "    # Define LLM model with callback\n",
-    "    llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)\n",
-    "\n",
-    "    # Create chain1\n",
-    "    chain1 = LLMChain(llm=llm, prompt=prompt_template1, callbacks=[sagemaker_callback])\n",
-    "\n",
-    "    # Create chain2\n",
-    "    chain2 = LLMChain(llm=llm, prompt=prompt_template2, callbacks=[sagemaker_callback])\n",
-    "\n",
-    "    # Create Sequential chain\n",
-    "    overall_chain = SimpleSequentialChain(chains=[chain1, chain2], callbacks=[sagemaker_callback])\n",
-    "\n",
-    "    # Run overall sequential chain\n",
-    "    overall_chain.run(**INPUT_VARIABLES)\n",
-    "\n",
-    "    # Reset the callback\n",
-    "    sagemaker_callback.flush_tracker()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b82bd0e-c626-4797-bb06-c1983f176315",
-   "metadata": {},
-   "source": [
-    "### Scenario 3 - Agent with Tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b5066f03-49dc-4868-be8e-d21ce22063fe",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "RUN_NAME = \"run-scenario-3\"\n",
-    "PROMPT_TEMPLATE = \"Who is the oldest person alive? And what is their current age raised to the power of 1.51?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "98385c42-9e44-4b03-b76d-007cb4797864",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "with Run(experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session) as run:\n",
-    "\n",
-    "    # Create SageMaker Callback\n",
-    "    sagemaker_callback = SageMakerCallbackHandler(run)\n",
-    "\n",
-    "    # Define LLM model with callback\n",
-    "    llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)\n",
-    "\n",
-    "    # Define tools\n",
-    "    tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=[sagemaker_callback])\n",
-    "\n",
-    "    # Initialize agent with all the tools\n",
-    "    agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", callbacks=[sagemaker_callback])\n",
-    "\n",
-    "    # Run agent\n",
-    "    agent.run(input=PROMPT_TEMPLATE)\n",
-    "\n",
-    "    # Reset the callback\n",
-    "    sagemaker_callback.flush_tracker()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c306a1c9-99f8-476d-96db-347746f5cfe0",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Load Log Data\n",
-    "\n",
-    "Once the prompts are logged, we can easily load and convert them to Pandas DataFrame as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec7b4af2-e01d-4f6c-9de5-70d2b4acb9e6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#Load\n",
-    "logs = ExperimentAnalytics(experiment_name=EXPERIMENT_NAME)\n",
-    "\n",
-    "#Convert as pandas dataframe\n",
-    "df = logs.dataframe(force_refresh=True)\n",
-    "\n",
-    "print(df.shape)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29991c75-f9cf-4c36-abfd-903c09fb170d",
-   "metadata": {},
-   "source": [
-    "As can be seen above, there are three runs (rows) in the experiment corresponding to each scenario. Each run logs the prompts and related LLM settings/hyperparameters as json and are saved in s3 bucket. Feel free to load and explore the log data from each json path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "61a695d6-0aef-4284-9e12-eea8bc143dbd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "availableInstances": [
-   {
-    "_defaultOrder": 0,
-    "_isFastLaunch": true,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 4,
-    "name": "ml.t3.medium",
-    "vcpuNum": 2
-   },
-   {
-    "_defaultOrder": 1,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 8,
-    "name": "ml.t3.large",
-    "vcpuNum": 2
-   },
-   {
-    "_defaultOrder": 2,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 16,
-    "name": "ml.t3.xlarge",
-    "vcpuNum": 4
-   },
-   {
-    "_defaultOrder": 3,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 32,
-    "name": "ml.t3.2xlarge",
-    "vcpuNum": 8
-   },
-   {
-    "_defaultOrder": 4,
-    "_isFastLaunch": true,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 8,
-    "name": "ml.m5.large",
-    "vcpuNum": 2
-   },
-   {
-    "_defaultOrder": 5,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 16,
-    "name": "ml.m5.xlarge",
-    "vcpuNum": 4
-   },
-   {
-    "_defaultOrder": 6,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 32,
-    "name": "ml.m5.2xlarge",
-    "vcpuNum": 8
-   },
-   {
-    "_defaultOrder": 7,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 64,
-    "name": "ml.m5.4xlarge",
-    "vcpuNum": 16
-   },
-   {
-    "_defaultOrder": 8,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 128,
-    "name": "ml.m5.8xlarge",
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-   },
-   {
-    "_defaultOrder": 9,
-    "_isFastLaunch": false,
-    "category": "General purpose",
-    "gpuNum": 0,
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-    "memoryGiB": 192,
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-   },
-   {
-    "_defaultOrder": 10,
-    "_isFastLaunch": false,
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-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 256,
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-   },
-   {
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-    "_isFastLaunch": false,
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-    "hideHardwareSpecs": false,
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-   },
-   {
-    "_defaultOrder": 12,
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-    "hideHardwareSpecs": true,
-    "memoryGiB": 0,
-    "name": "ml.geospatial.interactive",
-    "supportedImageNames": [
-     "sagemaker-geospatial-v1-0"
-    ],
-    "vcpuNum": 0
-   },
-   {
-    "_defaultOrder": 21,
-    "_isFastLaunch": true,
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-    "_isFastLaunch": false,
-    "category": "Compute optimized",
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-   },
-   {
-    "_defaultOrder": 23,
-    "_isFastLaunch": false,
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-   },
-   {
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-    "_isFastLaunch": false,
-    "category": "Compute optimized",
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-    "name": "ml.c5.12xlarge",
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-   },
-   {
-    "_defaultOrder": 27,
-    "_isFastLaunch": false,
-    "category": "Compute optimized",
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-   },
-   {
-    "_defaultOrder": 28,
-    "_isFastLaunch": false,
-    "category": "Compute optimized",
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 192,
-    "name": "ml.c5.24xlarge",
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-   },
-   {
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-    "_isFastLaunch": true,
-    "category": "Accelerated computing",
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 16,
-    "name": "ml.g4dn.xlarge",
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-   },
-   {
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-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
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-    "name": "ml.g4dn.2xlarge",
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-   },
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-   },
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-    "name": "ml.p3.2xlarge",
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-   },
-   {
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-    "hideHardwareSpecs": false,
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-    "name": "ml.p3.8xlarge",
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-    "category": "Accelerated computing",
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 488,
-    "name": "ml.p3.16xlarge",
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-   },
-   {
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-    "_isFastLaunch": false,
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-    "hideHardwareSpecs": false,
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-    "name": "ml.p3dn.24xlarge",
-    "vcpuNum": 96
-   },
-   {
-    "_defaultOrder": 39,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 16,
-    "name": "ml.r5.large",
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-   },
-   {
-    "_defaultOrder": 40,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 32,
-    "name": "ml.r5.xlarge",
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-   },
-   {
-    "_defaultOrder": 41,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 64,
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-    "_isFastLaunch": false,
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-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
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-   },
-   {
-    "_defaultOrder": 43,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 256,
-    "name": "ml.r5.8xlarge",
-    "vcpuNum": 32
-   },
-   {
-    "_defaultOrder": 44,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 384,
-    "name": "ml.r5.12xlarge",
-    "vcpuNum": 48
-   },
-   {
-    "_defaultOrder": 45,
-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 512,
-    "name": "ml.r5.16xlarge",
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-   },
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-    "_isFastLaunch": false,
-    "category": "Memory Optimized",
-    "gpuNum": 0,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 768,
-    "name": "ml.r5.24xlarge",
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-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
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-    "name": "ml.g5.2xlarge",
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-    "_isFastLaunch": false,
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 128,
-    "name": "ml.g5.8xlarge",
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-   },
-   {
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-    "_isFastLaunch": false,
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 256,
-    "name": "ml.g5.16xlarge",
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-   },
-   {
-    "_defaultOrder": 52,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
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-    "hideHardwareSpecs": false,
-    "memoryGiB": 192,
-    "name": "ml.g5.12xlarge",
-    "vcpuNum": 48
-   },
-   {
-    "_defaultOrder": 53,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 4,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 384,
-    "name": "ml.g5.24xlarge",
-    "vcpuNum": 96
-   },
-   {
-    "_defaultOrder": 54,
-    "_isFastLaunch": false,
-    "category": "Accelerated computing",
-    "gpuNum": 8,
-    "hideHardwareSpecs": false,
-    "memoryGiB": 768,
-    "name": "ml.g5.48xlarge",
-    "vcpuNum": 192
-   }
-  ],
-  "instance_type": "ml.t3.large",
-  "kernelspec": {
-   "display_name": "conda_pytorch_p310",
-   "language": "python",
-   "name": "conda_pytorch_p310"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/integrations/providers/unstructured.mdx

@@ -11,9 +11,7 @@ ecosystem within LangChain.
 If you are using a loader that runs locally, use the following steps to get `unstructured` and
 its dependencies running locally.
 
-- Install the Python SDK with `pip install unstructured`.
-    - You can install document specific dependencies with extras, i.e. `pip install "unstructured[docx]"`.
-    - To install the dependencies for all document types, use `pip install "unstructured[all-docs]"`.
+- Install the Python SDK with `pip install "unstructured[local-inference]"`
 - Install the following system dependencies if they are not already available on your system.
   Depending on what document types you're parsing, you may not need all of these.
     - `libmagic-dev` (filetype detection)

docs/extras/integrations/text_embedding/google_vertex_ai_palm.ipynb

@@ -1,15 +1,18 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Google Cloud Platform Vertex AI PaLM \n",
     "\n",
-    "Note: This is seperate from the Google PaLM integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on Google Cloud. \n",
+    "Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
     "\n",
-    "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
+    "PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). \n",
+    "\n",
+    "Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview [terms and conditions](https://cloud.google.com/trustedtester/aitos) (Preview Terms).\n",
+    "\n",
+    "For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n",
     "\n",
     "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
     "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",

docs/extras/integrations/vectorstores/chroma.ipynb

@@ -269,9 +269,8 @@
     "    )\n",
     "\n",
     "# tell LangChain to use our client and collection name\n",
-    "db4 = Chroma(client=client, collection_name=\"my_collection\", embedding_function=embedding_function)\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = db4.similarity_search(query)\n",
+    "db4 = Chroma(client=client, collection_name=\"my_collection\")\n",
+    "docs = db.similarity_search(query)\n",
     "print(docs[0].page_content)"
    ]
   },

docs/extras/integrations/vectorstores/opensearch.ipynb

@@ -1,440 +1,436 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "683953b3",
-      "metadata": {},
-      "source": [
-        "# OpenSearch\n",
-        "\n",
-        "> [OpenSearch](https://opensearch.org/) is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. `OpenSearch` is a distributed search and analytics engine based on `Apache Lucene`.\n",
-        "\n",
-        "\n",
-        "This notebook shows how to use functionality related to the `OpenSearch` database.\n",
-        "\n",
-        "To run, you should have an OpenSearch instance up and running: [see here for an easy Docker installation](https://hub.docker.com/r/opensearchproject/opensearch).\n",
-        "\n",
-        "`similarity_search` by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for\n",
-        "large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting.\n",
-        "Check [this](https://opensearch.org/docs/latest/search-plugins/knn/index/) for more details."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "94963977-9dfc-48b7-872a-53f2947f46c6",
-      "metadata": {},
-      "source": [
-        "## Installation\n",
-        "Install the Python client."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "6e606066-9386-4427-8a87-1b93f435c57e",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "!pip install opensearch-py"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "b1fa637e-4fbf-4d5a-9188-2cad826a193e",
-      "metadata": {},
-      "source": [
-        "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "28e5455e-322d-4010-9e3b-491d522ef5db",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import getpass\n",
-        "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "aac9563e",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-        "from langchain.text_splitter import CharacterTextSplitter\n",
-        "from langchain.vectorstores import OpenSearchVectorSearch\n",
-        "from langchain.document_loaders import TextLoader"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a3c3999a",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain.document_loaders import TextLoader\n",
-        "\n",
-        "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
-        "documents = loader.load()\n",
-        "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-        "docs = text_splitter.split_documents(documents)\n",
-        "\n",
-        "embeddings = OpenAIEmbeddings()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "01a9a035",
-      "metadata": {},
-      "source": [
-        "## similarity_search using Approximate k-NN\n",
-        "\n",
-        "`similarity_search` using `Approximate k-NN` Search with Custom Parameters"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "803fe12b",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "docsearch = OpenSearchVectorSearch.from_documents(\n",
-        "    docs, embeddings, opensearch_url=\"http://localhost:9200\"\n",
-        ")\n",
-        "\n",
-        "# If using the default Docker installation, use this instantiation instead:\n",
-        "# docsearch = OpenSearchVectorSearch.from_documents(\n",
-        "#     docs,\n",
-        "#     embeddings,\n",
-        "#     opensearch_url=\"https://localhost:9200\",\n",
-        "#     http_auth=(\"admin\", \"admin\"),\n",
-        "#     use_ssl = False,\n",
-        "#     verify_certs = False,\n",
-        "#     ssl_assert_hostname = False,\n",
-        "#     ssl_show_warn = False,\n",
-        "# )"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "db3fa309",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-        "docs = docsearch.similarity_search(query, k=10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "c160d5bb",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(docs[0].page_content)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "96215c90",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "docsearch = OpenSearchVectorSearch.from_documents(\n",
-        "    docs,\n",
-        "    embeddings,\n",
-        "    opensearch_url=\"http://localhost:9200\",\n",
-        "    engine=\"faiss\",\n",
-        "    space_type=\"innerproduct\",\n",
-        "    ef_construction=256,\n",
-        "    m=48,\n",
-        ")\n",
-        "\n",
-        "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-        "docs = docsearch.similarity_search(query)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "62a7cea0",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(docs[0].page_content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0d0cd877",
-      "metadata": {},
-      "source": [
-        "## similarity_search using Script Scoring\n",
-        "\n",
-        "`similarity_search` using `Script Scoring` with Custom Parameters"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "0a8e3c0e",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "docsearch = OpenSearchVectorSearch.from_documents(\n",
-        "    docs, embeddings, opensearch_url=\"http://localhost:9200\", is_appx_search=False\n",
-        ")\n",
-        "\n",
-        "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-        "docs = docsearch.similarity_search(\n",
-        "    \"What did the president say about Ketanji Brown Jackson\",\n",
-        "    k=1,\n",
-        "    search_type=\"script_scoring\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "92bc40db",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(docs[0].page_content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a4af96cc",
-      "metadata": {},
-      "source": [
-        "## similarity_search using Painless Scripting\n",
-        "\n",
-        "`similarity_search` using `Painless Scripting` with Custom Parameters"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "6d9f436e",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "docsearch = OpenSearchVectorSearch.from_documents(\n",
-        "    docs, embeddings, opensearch_url=\"http://localhost:9200\", is_appx_search=False\n",
-        ")\n",
-        "filter = {\"bool\": {\"filter\": {\"term\": {\"text\": \"smuggling\"}}}}\n",
-        "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-        "docs = docsearch.similarity_search(\n",
-        "    \"What did the president say about Ketanji Brown Jackson\",\n",
-        "    search_type=\"painless_scripting\",\n",
-        "    space_type=\"cosineSimilarity\",\n",
-        "    pre_filter=filter,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "8ca50bce",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(docs[0].page_content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4f8fb0d0",
-      "metadata": {},
-      "source": [
-        "## Maximum marginal relevance search (MMR)\n",
-        "If you\u2019d like to look up for some similar documents, but you\u2019d also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ba85e092",
-      "metadata": {
-        "collapsed": false,
-        "jupyter": {
-          "outputs_hidden": false
-        }
-      },
-      "outputs": [],
-      "source": [
-        "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-        "docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10, lambda_param=0.5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "73264864",
-      "metadata": {},
-      "source": [
-        "## Using a preexisting OpenSearch instance\n",
-        "\n",
-        "It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "82a23440",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# this is just an example, you would need to change these values to point to another opensearch instance\n",
-        "docsearch = OpenSearchVectorSearch(\n",
-        "    index_name=\"index-*\",\n",
-        "    embedding_function=embeddings,\n",
-        "    opensearch_url=\"http://localhost:9200\",\n",
-        ")\n",
-        "\n",
-        "# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata\n",
-        "docs = docsearch.similarity_search(\n",
-        "    \"Who was asking about getting lunch today?\",\n",
-        "    search_type=\"script_scoring\",\n",
-        "    space_type=\"cosinesimil\",\n",
-        "    vector_field=\"message_embedding\",\n",
-        "    text_field=\"message\",\n",
-        "    metadata_field=\"message_metadata\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Using AOSS (Amazon OpenSearch Service Serverless)"
-      ],
-      "metadata": {
-        "collapsed": false,
-        "pycharm": {
-          "name": "#%% md\n"
-        }
-      },
-      "id": "5f590d35"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "outputs": [],
-      "source": [
-        "# This is just an example to show how to use AOSS with faiss engine and efficient_filter, you need to set proper values.\n",
-        "\n",
-        "service = 'aoss' # must set the service as 'aoss'\n",
-        "region = 'us-east-2'\n",
-        "credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
-        "awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
-        "\n",
-        "docsearch = OpenSearchVectorSearch.from_documents(\n",
-        "    docs,\n",
-        "    embeddings,\n",
-        "    opensearch_url=\"host url\",\n",
-        "    http_auth=awsauth,\n",
-        "    timeout = 300,\n",
-        "    use_ssl = True,\n",
-        "    verify_certs = True,\n",
-        "    connection_class = RequestsHttpConnection,\n",
-        "    index_name=\"test-index-using-aoss\",\n",
-        "    engine=\"faiss\",\n",
-        ")\n",
-        "\n",
-        "docs = docsearch.similarity_search(\n",
-        "    \"What is feature selection\",\n",
-        "     efficient_filter=filter,\n",
-        "     k=200,\n",
-        ")"
-      ],
-      "metadata": {
-        "collapsed": false,
-        "pycharm": {
-          "name": "#%%\n"
-        }
-      },
-      "id": "de397be7"
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Using AOS (Amazon OpenSearch Service)"
-      ],
-      "metadata": {
-        "collapsed": false
-      },
-      "id": "0aa012c8"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "outputs": [],
-      "source": [
-        "# This is just an example to show how to use AOS , you need to set proper values.\n",
-        "\n",
-        "service = 'es' # must set the service as 'es'\n",
-        "region = 'us-east-2'\n",
-        "credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
-        "awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
-        "\n",
-        "docsearch = OpenSearchVectorSearch.from_documents(\n",
-        "    docs,\n",
-        "    embeddings,\n",
-        "    opensearch_url=\"host url\",\n",
-        "    http_auth=awsauth,\n",
-        "    timeout = 300,\n",
-        "    use_ssl = True,\n",
-        "    verify_certs = True,\n",
-        "    connection_class = RequestsHttpConnection,\n",
-        "    index_name=\"test-index\",\n",
-        ")\n",
-        "\n",
-        "docs = docsearch.similarity_search(\n",
-        "    \"What is feature selection\",\n",
-        "     k=200,\n",
-        ")"
-      ],
-      "metadata": {
-        "collapsed": false,
-        "pycharm": {
-          "name": "#%%\n"
-        }
-      },
-      "id": "2c47e408"
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# OpenSearch\n",
+    "\n",
+    "> [OpenSearch](https://opensearch.org/) is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. `OpenSearch` is a distributed search and analytics engine based on `Apache Lucene`.\n",
+    "\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `OpenSearch` database.\n",
+    "\n",
+    "To run, you should have an OpenSearch instance up and running: [see here for an easy Docker installation](https://hub.docker.com/r/opensearchproject/opensearch).\n",
+    "\n",
+    "`similarity_search` by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for\n",
+    "large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting.\n",
+    "Check [this](https://opensearch.org/docs/latest/search-plugins/knn/index/) for more details."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "94963977-9dfc-48b7-872a-53f2947f46c6",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "Install the Python client."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e606066-9386-4427-8a87-1b93f435c57e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install opensearch-py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1fa637e-4fbf-4d5a-9188-2cad826a193e",
+   "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28e5455e-322d-4010-9e3b-491d522ef5db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aac9563e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import OpenSearchVectorSearch\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3c3999a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "\n",
+    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01a9a035",
+   "metadata": {},
+   "source": [
+    "## similarity_search using Approximate k-NN\n",
+    "\n",
+    "`similarity_search` using `Approximate k-NN` Search with Custom Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "803fe12b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs, embeddings, opensearch_url=\"http://localhost:9200\"\n",
+    ")\n",
+    "\n",
+    "# If using the default Docker installation, use this instantiation instead:\n",
+    "# docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "#     docs,\n",
+    "#     embeddings,\n",
+    "#     opensearch_url=\"https://localhost:9200\",\n",
+    "#     http_auth=(\"admin\", \"admin\"),\n",
+    "#     use_ssl = False,\n",
+    "#     verify_certs = False,\n",
+    "#     ssl_assert_hostname = False,\n",
+    "#     ssl_show_warn = False,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db3fa309",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query, k=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c160d5bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96215c90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    opensearch_url=\"http://localhost:9200\",\n",
+    "    engine=\"faiss\",\n",
+    "    space_type=\"innerproduct\",\n",
+    "    ef_construction=256,\n",
+    "    m=48,\n",
+    ")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a7cea0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d0cd877",
+   "metadata": {},
+   "source": [
+    "## similarity_search using Script Scoring\n",
+    "\n",
+    "`similarity_search` using `Script Scoring` with Custom Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a8e3c0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs, embeddings, opensearch_url=\"http://localhost:9200\", is_appx_search=False\n",
+    ")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(\n",
+    "    \"What did the president say about Ketanji Brown Jackson\",\n",
+    "    k=1,\n",
+    "    search_type=\"script_scoring\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92bc40db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4af96cc",
+   "metadata": {},
+   "source": [
+    "## similarity_search using Painless Scripting\n",
+    "\n",
+    "`similarity_search` using `Painless Scripting` with Custom Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d9f436e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs, embeddings, opensearch_url=\"http://localhost:9200\", is_appx_search=False\n",
+    ")\n",
+    "filter = {\"bool\": {\"filter\": {\"term\": {\"text\": \"smuggling\"}}}}\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(\n",
+    "    \"What did the president say about Ketanji Brown Jackson\",\n",
+    "    search_type=\"painless_scripting\",\n",
+    "    space_type=\"cosineSimilarity\",\n",
+    "    pre_filter=filter,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ca50bce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f8fb0d0",
+   "metadata": {},
+   "source": [
+    "## Maximum marginal relevance search (MMR)\n",
+    "If you’d like to look up for some similar documents, but you’d also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba85e092",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10, lambda_param=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73264864",
+   "metadata": {},
+   "source": [
+    "## Using a preexisting OpenSearch instance\n",
+    "\n",
+    "It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82a23440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is just an example, you would need to change these values to point to another opensearch instance\n",
+    "docsearch = OpenSearchVectorSearch(\n",
+    "    index_name=\"index-*\",\n",
+    "    embedding_function=embeddings,\n",
+    "    opensearch_url=\"http://localhost:9200\",\n",
+    ")\n",
+    "\n",
+    "# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata\n",
+    "docs = docsearch.similarity_search(\n",
+    "    \"Who was asking about getting lunch today?\",\n",
+    "    search_type=\"script_scoring\",\n",
+    "    space_type=\"cosinesimil\",\n",
+    "    vector_field=\"message_embedding\",\n",
+    "    text_field=\"message\",\n",
+    "    metadata_field=\"message_metadata\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Using AOSS (Amazon OpenSearch Service Serverless)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# This is just an example to show how to use AOSS with faiss engine and efficient_filter, you need to set proper values.\n",
+    "\n",
+    "service = 'aoss' # must set the service as 'aoss'\n",
+    "region = 'us-east-2'\n",
+    "credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
+    "awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
+    "\n",
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    opensearch_url=\"host url\",\n",
+    "    http_auth=awsauth,\n",
+    "    timeout = 300,\n",
+    "    use_ssl = True,\n",
+    "    verify_certs = True,\n",
+    "    connection_class = RequestsHttpConnection,\n",
+    "    index_name=\"test-index-using-aoss\",\n",
+    "    engine=\"faiss\",\n",
+    ")\n",
+    "\n",
+    "docs = docsearch.similarity_search(\n",
+    "    \"What is feature selection\",\n",
+    "     efficient_filter=filter,\n",
+    "     k=200,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Using AOS (Amazon OpenSearch Service)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# This is just an example to show how to use AOS , you need to set proper values.\n",
+    "\n",
+    "service = 'es' # must set the service as 'es'\n",
+    "region = 'us-east-2'\n",
+    "credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
+    "awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
+    "\n",
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    opensearch_url=\"host url\",\n",
+    "    http_auth=awsauth,\n",
+    "    timeout = 300,\n",
+    "    use_ssl = True,\n",
+    "    verify_certs = True,\n",
+    "    connection_class = RequestsHttpConnection,\n",
+    "    index_name=\"test-index\",\n",
+    ")\n",
+    "\n",
+    "docs = docsearch.similarity_search(\n",
+    "    \"What is feature selection\",\n",
+    "     k=200,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

docs/extras/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store.ipynb

@@ -25,7 +25,7 @@
     "\n",
     "To start, we will use the popular open source feature store framework [Feast](https://github.com/feast-dev/feast).\n",
     "\n",
-    "This assumes you have already run the steps in the README around getting started. We will build off of that example in getting started, and create and LLMChain to write a note to a specific driver regarding their up-to-date statistics."
+    "This assumes you have already run the steps in the README around getting started. We will build of off that example in getting started, and create and LLMChain to write a note to a specific driver regarding their up-to-date statistics."
    ]
   },
   {

docs/extras/use_cases/agents/baby_agi.ipynb

@@ -343,7 +343,7 @@
     "                self.print_task_result(result)\n",
     "\n",
     "                # Step 3: Store the result in Pinecone\n",
-    "                result_id = f\"result_{task['task_id']}_{num_iters}\"\n",
+    "                result_id = f\"result_{task['task_id']}\"\n",
     "                self.vectorstore.add_texts(\n",
     "                    texts=[result],\n",
     "                    metadatas=[{\"task\": task[\"task_name\"]}],\n",

docs/extras/use_cases/agents/baby_agi_with_agent.ipynb

@@ -358,7 +358,7 @@
     "                self.print_task_result(result)\n",
     "\n",
     "                # Step 3: Store the result in Pinecone\n",
-    "                result_id = f\"result_{task['task_id']}_{num_iters}\"\n",
+    "                result_id = f\"result_{task['task_id']}\"\n",
     "                self.vectorstore.add_texts(\n",
     "                    texts=[result],\n",
     "                    metadatas=[{\"task\": task[\"task_name\"]}],\n",

docs/extras/use_cases/graph/graph_arangodb_qa.ipynb

@@ -9,7 +9,7 @@
    "source": [
     "# ArangoDB QA chain\n",
     "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/graph/graph_arangodb_qa.ipynb)\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/extras/modules/chains/additional/graph_arangodb_qa.ipynb)\n",
     "\n",
     "This notebook shows how to use LLMs to provide a natural language interface to an [ArangoDB](https://github.com/arangodb/arangodb#readme) database."
    ]

docs/extras/use_cases/question_answering/how_to/conversational_retrieval_agents.ipynb

@@ -1,583 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "839f3c76",
-   "metadata": {},
-   "source": [
-    "# Conversational Retrieval Agent\n",
-    "\n",
-    "This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation.\n",
-    "\n",
-    "To start, we will set up the retriever we want to use, and then turn it into a retriever tool. Next, we will use the high level constructor for this type of agent. Finally, we will walk through how to construct a conversational retrieval agent from components."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc66a262",
-   "metadata": {},
-   "source": [
-    "## The Retriever\n",
-    "\n",
-    "To start, we need a retriever to use! The code here is mostly just example code. Feel free to use your own retriever and skip to the section on creating a retriever tool."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "22533f46",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "loader = TextLoader('../../../../../docs/extras/modules/state_of_the_union.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c252c7e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import FAISS\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(documents)\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "db = FAISS.from_documents(texts, embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5b8a404f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = db.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9cd528f5",
-   "metadata": {},
-   "source": [
-    "## Retriever Tool\n",
-    "\n",
-    "Now we need to create a tool for our retriever. The main things we need to pass in are a name for the retriever as well as a description. These will both be used by the language model, so they should be informative."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9a82f72a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents.agent_toolkits import create_retriever_tool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "dfbd92a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool = create_retriever_tool(\n",
-    "    retriever, \n",
-    "    \"search_state_of_union\",\n",
-    "    \"Searches and returns documents regarding the state-of-the-union.\"\n",
-    ")\n",
-    "tools = [tool]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d9b7210",
-   "metadata": {},
-   "source": [
-    "## Agent Constructor\n",
-    "\n",
-    "Here, we will use the high level `create_conversational_retrieval_agent` API to construct the agent.\n",
-    "\n",
-    "Notice that beside the list of tools, the only thing we need to pass in is a language model to use.\n",
-    "Under the hood, this agent is using the OpenAIFunctionsAgent, so we need to use an ChatOpenAI model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0cd147eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents.agent_toolkits import create_conversational_retrieval_agent "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9fa4661b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "llm = ChatOpenAI(temperature = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "da2ad764",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor = create_conversational_retrieval_agent(llm, tools, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "85ee7677",
-   "metadata": {},
-   "source": [
-    "We can now try it out!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "03059322",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mHello Bob! How can I assist you today?\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = agent_executor({\"input\": \"hi, im bob\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "33073ff0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hello Bob! How can I assist you today?'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result[\"output\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f1f0b79",
-   "metadata": {},
-   "source": [
-    "Notice that it remembers your name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "4ad92bc7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mYour name is Bob.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = agent_executor({\"input\": \"whats my name?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "7ae62ecd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Your name is Bob.'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result[\"output\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e258ade",
-   "metadata": {},
-   "source": [
-    "Notice that it now does retrieval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6cd17d67",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `search_state_of_union` with `{'query': 'Kentaji Brown Jackson'}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'}), Document(page_content='One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \\n\\nWhen they came home, many of the world’s fittest and best trained warriors were never the same. \\n\\nHeadaches. Numbness. Dizziness. \\n\\nA cancer that would put them in a flag-draped coffin. \\n\\nI know. \\n\\nOne of those soldiers was my son Major Beau Biden. \\n\\nWe don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \\n\\nBut I’m committed to finding out everything we can. \\n\\nCommitted to military families like Danielle Robinson from Ohio. \\n\\nThe widow of Sergeant First Class Heath Robinson.  \\n\\nHe was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \\n\\nStationed near Baghdad, just yards from burn pits the size of football fields. \\n\\nHeath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'}), Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'}), Document(page_content='We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \\n\\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \\n\\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \\n\\nOfficer Mora was 27 years old. \\n\\nOfficer Rivera was 22. \\n\\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \\n\\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \\n\\nI’ve worked on these issues a long time. \\n\\nI know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../../../docs/extras/modules/state_of_the_union.txt'})]\u001b[0m\u001b[32;1m\u001b[1;3mIn the most recent state of the union, the President mentioned Kentaji Brown Jackson. The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. The President described Judge Ketanji Brown Jackson as one of our nation's top legal minds who will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = agent_executor({\"input\": \"what did the president say about kentaji brown jackson in the most recent state of the union?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c51de037",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"In the most recent state of the union, the President mentioned Kentaji Brown Jackson. The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. The President described Judge Ketanji Brown Jackson as one of our nation's top legal minds who will continue Justice Breyer's legacy of excellence.\""
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result[\"output\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3bad540",
-   "metadata": {},
-   "source": [
-    "Notice that the follow up question asks about information previously retrieved, so no need to do another retrieval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "527ea5fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThe President nominated Judge Ketanji Brown Jackson four days ago.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = agent_executor({\"input\": \"how long ago did he nominate her?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "a34d20fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The President nominated Judge Ketanji Brown Jackson four days ago.'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result[\"output\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e599dbd3",
-   "metadata": {},
-   "source": [
-    "## Creating from components\n",
-    "\n",
-    "What actually is going on underneath the hood? Let's take a look so we can understand how to modify going forward.\n",
-    "\n",
-    "There are a few components:\n",
-    "\n",
-    "- The memory\n",
-    "- The prompt template\n",
-    "- The agent\n",
-    "- The agent executor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "1b21be1d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is needed for both the memory and the prompt\n",
-    "memory_key = \"history\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f827f95f",
-   "metadata": {},
-   "source": [
-    "### The Memory\n",
-    "\n",
-    "In this example, we want the agent to remember not only previous conversations, but also previous intermediate steps. For that, we can use `AgentTokenBufferMemory`. Note that if you want to change whether the agent remembers intermediate steps, or how the long the buffer is, or anything like that you should change this part."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "138b0675",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents.openai_functions_agent.agent_token_buffer_memory import AgentTokenBufferMemory\n",
-    "\n",
-    "memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4827993f",
-   "metadata": {},
-   "source": [
-    "## The Prompt Template\n",
-    "\n",
-    "For the prompt template, we will use the `OpenAIFunctionsAgent` default way of creating one, but pass in a system prompt and a placeholder for memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "779272dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\n",
-    "from langchain.schema.messages import SystemMessage\n",
-    "from langchain.prompts import MessagesPlaceholder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "824bc74e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system_message = SystemMessage(\n",
-    "        content=(\n",
-    "            \"Do your best to answer the questions. \"\n",
-    "            \"Feel free to use any tools available to look up \"\n",
-    "            \"relevant information, only if neccessary\"\n",
-    "        )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "07e41722",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = OpenAIFunctionsAgent.create_prompt(\n",
-    "        system_message=system_message,\n",
-    "        extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)]\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "001e455e",
-   "metadata": {},
-   "source": [
-    "## The Agent\n",
-    "\n",
-    "We will use the OpenAIFunctionsAgent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "adf4b5b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2c5c321e",
-   "metadata": {},
-   "source": [
-    "## The Agent Executor\n",
-    "\n",
-    "Importantly, we pass in `return_intermediate_steps=True` since we are recording that with our memory object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "2e7ffe96",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import AgentExecutor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "e39a095f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True,\n",
-    "                                   return_intermediate_steps=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "96136958",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mHello Bob! How can I assist you today?\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = agent_executor({\"input\": \"hi, im bob\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "8de674cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mYour name is Bob.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = agent_executor({\"input\": \"whats my name\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf655a48",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/use_cases/summarization.ipynb

@@ -1,511 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cf13f702",
-   "metadata": {},
-   "source": [
-    "# Summarization\n",
-    "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/summarization.ipynb)\n",
-    "\n",
-    "## Use case\n",
-    "\n",
-    "Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. \n",
-    "\n",
-    "LLMs are a great tool for this given their proficiency in understanding and synthesizing text.\n",
-    "\n",
-    "In this walkthrough we'll go over how to perform document summarization using LLMs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e233997",
-   "metadata": {},
-   "source": [
-    "![Image description](/img/summarization_use_case_1.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4715b4ff",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "A central question for building a summarizer is how to pass your documents into the LLM's context window. Two common approaches for this are:\n",
-    "\n",
-    "1. `Stuff`: Simply \"stuff\" all your documents into a single prompt. This is the simplest approach (see [here](/docs/modules/chains/document/stuff) for more on the `StuffDocumentsChains`, which is used for this method).\n",
-    "\n",
-    "2. `Map-reduce`: Summarize each document on it's own in a \"map\" step and then \"reduce\" the summaries into a final summary (see [here](/docs/modules/chains/document/map_reduce) for more on the `MapReduceDocumentsChain`, which is used for this method)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08ec66bc",
-   "metadata": {},
-   "source": [
-    "![Image description](/img/summarization_use_case_2.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bea785ac",
-   "metadata": {},
-   "source": [
-    "## Quickstart\n",
-    "\n",
-    "To give you a sneak preview, either pipeline can be wrapped in a single object: `load_summarize_chain`. \n",
-    "\n",
-    "Suppose we want to summarize a blog post. We can create this in a few lines of code.\n",
-    "\n",
-    "First set environment variables and install packages:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "578d6a90",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install openai tiktoken chromadb langchain\n",
-    "\n",
-    "# Set env var OPENAI_API_KEY or load from a .env file\n",
-    "# import dotenv\n",
-    "\n",
-    "# dotenv.load_env()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36138740",
-   "metadata": {},
-   "source": [
-    "We can use `chain_type=\"stuff\"`, especially if using larger context window models such as:\n",
-    "\n",
-    "* 16k token OpenAI `gpt-3.5-turbo-16k` \n",
-    "* 100k token Anthropic [Claude-2](https://www.anthropic.com/index/claude-2)\n",
-    "\n",
-    "We can also supply `chain_type=\"map_reduce\"` or `chain_type=\"refine\"` (read more [here](/docs/modules/chains/document/refine))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fd271681",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The article discusses the concept of building autonomous agents powered by large language models (LLMs). It explores the components of such agents, including planning, memory, and tool use. The article provides case studies and proof-of-concept examples of LLM-powered agents in various domains. It also highlights the challenges and limitations of using LLMs in agent systems.'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.document_loaders import WebBaseLoader\n",
-    "from langchain.chains.summarize import load_summarize_chain\n",
-    "\n",
-    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
-    "docs = loader.load()\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-3.5-turbo-16k\")\n",
-    "chain = load_summarize_chain(llm, chain_type=\"stuff\")\n",
-    "\n",
-    "chain.run(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "615b36e1",
-   "metadata": {},
-   "source": [
-    "## Option 1. Stuff\n",
-    "\n",
-    "When we use `load_summarize_chain` with `chain_type=\"stuff\"`, we will use the [StuffDocumentsChain](/docs/modules/chains/document/stuff).\n",
-    "\n",
-    "The chain will take a list of documents, inserts them all into a prompt, and passes that prompt to an LLM:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ef45585d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The article discusses the concept of building autonomous agents powered by large language models (LLMs). It explores the components of such agents, including planning, memory, and tool use. The article provides case studies and examples of proof-of-concept demos, highlighting the challenges and limitations of LLM-powered agents. It also includes references to related research papers and provides a citation for the article.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.chains.llm import LLMChain\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
-    "\n",
-    "# Define prompt\n",
-    "prompt_template = \"\"\"Write a concise summary of the following:\n",
-    "\"{text}\"\n",
-    "CONCISE SUMMARY:\"\"\"\n",
-    "prompt = PromptTemplate.from_template(prompt_template)\n",
-    "\n",
-    "# Define LLM chain\n",
-    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-3.5-turbo-16k\")\n",
-    "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
-    "\n",
-    "# Define StuffDocumentsChain\n",
-    "stuff_chain = StuffDocumentsChain(\n",
-    "    llm_chain=llm_chain, document_variable_name=\"text\"\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(stuff_chain.run(docs))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e4e4a43",
-   "metadata": {},
-   "source": [
-    "Great! We can see that we reproduce the earlier result using the `load_summarize_chain`.\n",
-    "\n",
-    "### Go deeper\n",
-    "\n",
-    "* You can easily customize the prompt. \n",
-    "* You can easily try different LLMs, (e.g., [Claude](/docs/integrations/chat/anthropic)) via the `llm` parameter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad6cabee",
-   "metadata": {},
-   "source": [
-    "## Option 2. Map-Reduce\n",
-    "\n",
-    "Let's unpack the map reduce approach. For this, we'll first map each document to an individual summary using an `LLMChain`. Then we'll use a `ReduceDocumentsChain` to combine those summaries into a single global summary.\n",
-    " \n",
-    "First, we specfy the LLMChain to use for mapping each document to an individual summary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a1e6773c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.mapreduce import MapReduceChain\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.chains import ReduceDocumentsChain, MapReduceDocumentsChain\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0)\n",
-    "\n",
-    "# Map\n",
-    "map_template = \"\"\"The following is a set of documents\n",
-    "{docs}\n",
-    "Based on this list of docs, please identify the main themes \n",
-    "Helpful Answer:\"\"\"\n",
-    "map_prompt = PromptTemplate.from_template(map_template)\n",
-    "map_chain = LLMChain(llm=llm, prompt=map_prompt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bee3c331",
-   "metadata": {},
-   "source": [
-    "The `ReduceDocumentsChain` handles taking the document mapping results and reducing them into a single output. It wraps a generic `CombineDocumentsChain` (like `StuffDocumentsChain`) but adds the ability to collapse documents before passing it to the `CombineDocumentsChain` if their cumulative size exceeds `token_max`. In this example, we can actually re-use our chain for combining our docs to also collapse our docs.\n",
-    "\n",
-    "So if the cumulative number of tokens in our mapped documents exceeds 4000 tokens, then we'll recursively pass in the documents in batches of < 4000 tokens to our `StuffDocumentsChain` to create batched summaries. And once those batched summaries are cumulatively less than 4000 tokens, we'll pass them all one last time to the `StuffDocumentsChain` to create the final summary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1edb1b0d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reduce\n",
-    "reduce_template = \"\"\"The following is set of summaries:\n",
-    "{doc_summaries}\n",
-    "Take these and distill it into a final, consolidated summary of the main themes. \n",
-    "Helpful Answer:\"\"\"\n",
-    "reduce_prompt = PromptTemplate.from_template(reduce_template)\n",
-    "reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)\n",
-    "\n",
-    "# Takes a list of documents, combines them into a single string, and passes this to an LLMChain\n",
-    "combine_documents_chain = StuffDocumentsChain(\n",
-    "    llm_chain=reduce_chain, document_variable_name=\"doc_summaries\"\n",
-    ")\n",
-    "\n",
-    "# Combines and iteravely reduces the mapped documents\n",
-    "reduce_documents_chain = ReduceDocumentsChain(\n",
-    "    # This is final chain that is called.\n",
-    "    combine_documents_chain=combine_documents_chain,\n",
-    "    # If documents exceed context for `StuffDocumentsChain`\n",
-    "    collapse_documents_chain=combine_documents_chain,\n",
-    "    # The maximum number of tokens to group documents into.\n",
-    "    token_max=4000,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fdb5ae1a",
-   "metadata": {},
-   "source": [
-    "Combining our map and reduce chains into one:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "22f1cdc2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Created a chunk of size 1003, which is longer than the specified 1000\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Combining documents by mapping a chain over them, then combining results\n",
-    "map_reduce_chain = MapReduceDocumentsChain(\n",
-    "    # Map chain\n",
-    "    llm_chain=map_chain,\n",
-    "    # Reduce chain\n",
-    "    reduce_documents_chain=reduce_documents_chain,\n",
-    "    # The variable name in the llm_chain to put the documents in\n",
-    "    document_variable_name=\"docs\",\n",
-    "    # Return the results of the map steps in the output\n",
-    "    return_intermediate_steps=False,\n",
-    ")\n",
-    "\n",
-    "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n",
-    "    chunk_size=1000, chunk_overlap=0\n",
-    ")\n",
-    "split_docs = text_splitter.split_documents(docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c7afb8c3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The main themes identified in the provided set of documents are:\n",
-      "\n",
-      "1. LLM-powered autonomous agent systems: The documents discuss the concept of building autonomous agents with large language models (LLMs) as the core controller. They explore the potential of LLMs beyond content generation and present them as powerful problem solvers.\n",
-      "\n",
-      "2. Components of the agent system: The documents outline the key components of LLM-powered agent systems, including planning, memory, and tool use. Each component is described in detail, highlighting its role in enhancing the agent's capabilities.\n",
-      "\n",
-      "3. Planning and task decomposition: The planning component focuses on task decomposition and self-reflection. The agent breaks down complex tasks into smaller subgoals and learns from past actions to improve future results.\n",
-      "\n",
-      "4. Memory and learning: The memory component includes short-term memory for in-context learning and long-term memory for retaining and recalling information over extended periods. The use of external vector stores for fast retrieval is also mentioned.\n",
-      "\n",
-      "5. Tool use and external APIs: The agent learns to utilize external APIs for accessing additional information, code execution, and proprietary sources. This enhances the agent's knowledge and problem-solving abilities.\n",
-      "\n",
-      "6. Case studies and proof-of-concept examples: The documents provide case studies and examples to demonstrate the application of LLM-powered agents in scientific discovery, generative simulations, and other domains. These examples serve as proof-of-concept for the effectiveness of the agent system.\n",
-      "\n",
-      "7. Challenges and limitations: The documents mention challenges associated with building LLM-powered autonomous agents, such as the limitations of finite context length, difficulties in long-term planning, and reliability issues with natural language interfaces.\n",
-      "\n",
-      "8. Citation and references: The documents include a citation and reference section for acknowledging the sources and inspirations for the concepts discussed.\n",
-      "\n",
-      "Overall, the main themes revolve around the development and capabilities of LLM-powered autonomous agent systems, including their components, planning and task decomposition, memory and learning mechanisms, tool use and external APIs, case studies and proof-of-concept examples, challenges and limitations, and the importance of proper citation and references.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(map_reduce_chain.run(split_docs))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e62c21cf",
-   "metadata": {},
-   "source": [
-    "### Go deeper\n",
-    " \n",
-    "**Customization** \n",
-    "\n",
-    "* As shown above, you can customize the LLMs and prompts for map and reduce stages.\n",
-    "\n",
-    "**Real-world use-case**\n",
-    "\n",
-    "* See [this blog post](https://blog.langchain.dev/llms-to-improve-documentation/) case-study on analyzing user interactions (questions about LangChain documentation)!  \n",
-    "* The blog post and associated [repo](https://github.com/mendableai/QA_clustering) also introduce clustering as a means of summarization.\n",
-    "* This opens up a third path beyond the `stuff` or `map-reduce` approaches that is worth considering.\n",
-    "\n",
-    "![Image description](/img/summarization_use_case_3.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f08ff365",
-   "metadata": {},
-   "source": [
-    "## Option 3. Refine\n",
-    " \n",
-    "[Refine](/docs/modules/chains/document/refine) is similar to map-reduce:\n",
-    "\n",
-    "> The refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.\n",
-    "\n",
-    "This can be easily run with the `chain_type=\"refine\"` specified."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "de1dc10e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The GPT-Engineer project aims to create a repository of code for specific tasks specified in natural language. It involves breaking down tasks into smaller components and seeking clarification from the user when needed. The project emphasizes the importance of implementing every detail of the architecture as code and provides guidelines for file organization, code structure, and dependencies. However, there are challenges in long-term planning and task decomposition, as well as the reliability of the natural language interface. The system has limited communication bandwidth and struggles to adjust plans when faced with unexpected errors. The reliability of model outputs is questionable, as formatting errors and rebellious behavior can occur. The conversation also includes instructions for writing the code, including laying out the core classes, functions, and methods, and providing the code in a markdown code block format. The user is reminded to ensure that the code is fully functional and follows best practices for file naming, imports, and types. The project is powered by LLM (Large Language Models) and incorporates prompting techniques from various research papers.'"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain = load_summarize_chain(llm, chain_type=\"refine\")\n",
-    "chain.run(split_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b46f44d",
-   "metadata": {},
-   "source": [
-    "It's also possible to supply a prompt and return intermediate steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "f86c8072",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt_template = \"\"\"Write a concise summary of the following:\n",
-    "{text}\n",
-    "CONCISE SUMMARY:\"\"\"\n",
-    "prompt = PromptTemplate.from_template(prompt_template)\n",
-    "\n",
-    "refine_template = (\n",
-    "    \"Your job is to produce a final summary\\n\"\n",
-    "    \"We have provided an existing summary up to a certain point: {existing_answer}\\n\"\n",
-    "    \"We have the opportunity to refine the existing summary\"\n",
-    "    \"(only if needed) with some more context below.\\n\"\n",
-    "    \"------------\\n\"\n",
-    "    \"{text}\\n\"\n",
-    "    \"------------\\n\"\n",
-    "    \"Given the new context, refine the original summary in Italian\"\n",
-    "    \"If the context isn't useful, return the original summary.\"\n",
-    ")\n",
-    "refine_prompt = PromptTemplate.from_template(refine_template)\n",
-    "chain = load_summarize_chain(\n",
-    "    llm=llm,\n",
-    "    chain_type=\"refine\",\n",
-    "    question_prompt=prompt,\n",
-    "    refine_prompt=refine_prompt,\n",
-    "    return_intermediate_steps=True,\n",
-    "    input_key=\"input_documents\",\n",
-    "    output_key=\"output_text\",\n",
-    ")\n",
-    "result = chain({\"input_documents\": split_docs}, return_only_outputs=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "d9600b67-79d4-4f85-aba2-9fe81fa29f49",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "L'articolo discute il concetto di costruire agenti autonomi utilizzando LLM (large language model) come controller principale. Esplora i diversi componenti di un sistema di agenti alimentato da LLM, inclusa la pianificazione, la memoria e l'uso di strumenti. Dimostrazioni di concetto come AutoGPT mostrano la possibilità di creare agenti autonomi con LLM come controller principale. Approcci come Chain of Thought, Tree of Thoughts, LLM+P, ReAct e Reflexion consentono agli agenti autonomi di pianificare, riflettere su se stessi e migliorare iterativamente. Tuttavia, ci sono sfide legate alla lunghezza del contesto, alla pianificazione a lungo termine e alla decomposizione delle attività. Inoltre, l'affidabilità dell'interfaccia di linguaggio naturale tra LLM e componenti esterni come la memoria e gli strumenti è incerta. Nonostante ciò, l'uso di LLM come router per indirizzare le richieste ai moduli esperti più adatti è stato proposto come architettura neuro-simbolica per agenti autonomi nel sistema MRKL. L'articolo fa riferimento a diverse pubblicazioni che approfondiscono l'argomento, tra cui Chain of Thought, Tree of Thoughts, LLM+P, ReAct, Reflexion, e MRKL Systems.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(result[\"output_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "5f91a8eb-daa5-4191-ace4-01765801db3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This article discusses the concept of building autonomous agents using LLM (large language model) as the core controller. The article explores the different components of an LLM-powered agent system, including planning, memory, and tool use. It also provides examples of proof-of-concept demos and highlights the potential of LLM as a general problem solver.\n",
-      "\n",
-      "Questo articolo discute del concetto di costruire agenti autonomi utilizzando LLM (large language model) come controller principale. L'articolo esplora i diversi componenti di un sistema di agenti alimentato da LLM, inclusa la pianificazione, la memoria e l'uso degli strumenti. Vengono anche forniti esempi di dimostrazioni di proof-of-concept e si evidenzia il potenziale di LLM come risolutore generale di problemi. Inoltre, vengono presentati approcci come Chain of Thought, Tree of Thoughts, LLM+P, ReAct e Reflexion che consentono agli agenti autonomi di pianificare, riflettere su se stessi e migliorare iterativamente.\n",
-      "\n",
-      "Questo articolo discute del concetto di costruire agenti autonomi utilizzando LLM (large language model) come controller principale. L'articolo esplora i diversi componenti di un sistema di agenti alimentato da LLM, inclusa la pianificazione, la memoria e l'uso degli strumenti. Vengono anche forniti esempi di dimostrazioni di proof-of-concept e si evidenzia il potenziale di LLM come risolutore generale di problemi. Inoltre, vengono presentati approcci come Chain of Thought, Tree of Thoughts, LLM+P, ReAct e Reflexion che consentono agli agenti autonomi di pianificare, riflettere su se stessi e migliorare iterativamente. Il nuovo contesto riguarda l'approccio Chain of Hindsight (CoH) che permette al modello di migliorare autonomamente i propri output attraverso un processo di apprendimento supervisionato. Viene anche presentato l'approccio Algorithm Distillation (AD) che applica lo stesso concetto alle traiettorie di apprendimento per compiti di reinforcement learning.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\\n\".join(result[\"intermediate_steps\"][:3]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ddd522e-30dc-4f6a-b993-c4f97e656c4f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/extras/use_cases/summarization/index.mdx

@@ -0,0 +1,22 @@
+---
+sidebar_position: 5
+---
+
+# Summarization
+
+Summarization involves creating a smaller summary of multiple longer documents.
+This can be useful for distilling long documents into the core pieces of information.
+
+The recommended way to get started using a summarization chain is:
+
+```python
+from langchain.chains.summarize import load_summarize_chain
+chain = load_summarize_chain(llm, chain_type="map_reduce")
+chain.run(docs)
+```
+
+The following resources exist:
+- [Summarization notebook](/docs/use_cases/summarization/summarize.html): A notebook walking through how to accomplish this task.
+
+Additional related resources include:
+- [Modules for working with documents](/docs/modules/data_connection): Core components for working with documents.

docs/snippets/get_started/quickstart/llm_chain.mdx

@@ -17,7 +17,7 @@ class CommaSeparatedListOutputParser(BaseOutputParser):
         return text.strip().split(", ")
 
 template = """You are a helpful assistant who generates comma separated lists.
-A user will pass in a category, and you should generate 5 objects in that category in a comma separated list.
+A user will pass in a category, and you should generated 5 objects in that category in a comma separated list.
 ONLY return a comma separated list, and nothing more."""
 system_message_prompt = SystemMessagePromptTemplate.from_template(template)
 human_template = "{text}"

docs/snippets/modules/chains/popular/summarize.mdx

@@ -0,0 +1,384 @@
+## Prepare Data
+First we prepare the data. For this example we create multiple documents from one long one, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).
+
+```python
+from langchain import OpenAI, PromptTemplate, LLMChain
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.mapreduce import MapReduceChain
+from langchain.prompts import PromptTemplate
+
+llm = OpenAI(temperature=0)
+
+text_splitter = CharacterTextSplitter()
+```
+
+
+```python
+with open("../../state_of_the_union.txt") as f:
+    state_of_the_union = f.read()
+texts = text_splitter.split_text(state_of_the_union)
+```
+
+
+```python
+from langchain.docstore.document import Document
+
+docs = [Document(page_content=t) for t in texts[:3]]
+```
+
+## Quickstart
+If you just want to get started as quickly as possible, this is the recommended way to do it:
+
+
+```python
+from langchain.chains.summarize import load_summarize_chain
+```
+
+
+```python
+chain = load_summarize_chain(llm, chain_type="map_reduce")
+chain.run(docs)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ' In response to Russian aggression in Ukraine, the United States and its allies are taking action to hold Putin accountable, including economic sanctions, asset seizures, and military assistance. The US is also providing economic and humanitarian aid to Ukraine, and has passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and create jobs. The US remains unified and determined to protect Ukraine and the free world.'
+```
+
+</CodeOutputBlock>
+
+If you want more control and understanding over what is happening, please see the information below.
+
+## The `stuff` Chain
+
+This sections shows results of using the `stuff` Chain to do summarization.
+
+
+```python
+chain = load_summarize_chain(llm, chain_type="stuff")
+```
+
+
+```python
+chain.run(docs)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    ' In his speech, President Biden addressed the crisis in Ukraine, the American Rescue Plan, and the Bipartisan Infrastructure Law. He discussed the need to invest in America, educate Americans, and build the economy from the bottom up. He also announced the release of 60 million barrels of oil from reserves around the world, and the creation of a dedicated task force to go after the crimes of Russian oligarchs. He concluded by emphasizing the need to Buy American and use taxpayer dollars to rebuild America.'
+```
+
+</CodeOutputBlock>
+
+**Custom Prompts**
+
+You can also use your own prompts with this chain. In this example, we will respond in Italian.
+
+
+```python
+prompt_template = """Write a concise summary of the following:
+
+
+{text}
+
+
+CONCISE SUMMARY IN ITALIAN:"""
+PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+chain = load_summarize_chain(llm, chain_type="stuff", prompt=PROMPT)
+chain.run(docs)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    "\n\nIn questa serata, il Presidente degli Stati Uniti ha annunciato una serie di misure per affrontare la crisi in Ucraina, causata dall'aggressione di Putin. Ha anche annunciato l'invio di aiuti economici, militari e umanitari all'Ucraina. Ha anche annunciato che gli Stati Uniti e i loro alleati stanno imponendo sanzioni economiche a Putin e stanno rilasciando 60 milioni di barili di petrolio dalle riserve di tutto il mondo. Inoltre, ha annunciato che il Dipartimento di Giustizia degli Stati Uniti sta creando una task force dedicata ai crimini degli oligarchi russi. Il Presidente ha anche annunciato l'approvazione della legge bipartitica sull'infrastruttura, che prevede investimenti per la ricostruzione dell'America. Questo porterà a creare posti"
+```
+
+</CodeOutputBlock>
+
+## The `map_reduce` Chain
+
+This sections shows results of using the `map_reduce` Chain to do summarization.
+
+
+```python
+chain = load_summarize_chain(llm, chain_type="map_reduce")
+```
+
+
+```python
+chain.run(docs)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    " In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and releasing oil from its Strategic Petroleum Reserve. President Biden and Vice President Harris have passed legislation to help struggling families and rebuild America's infrastructure."
+```
+
+</CodeOutputBlock>
+
+**Intermediate Steps**
+
+We can also return the intermediate steps for `map_reduce` chains, should we want to inspect them. This is done with the `return_map_steps` variable.
+
+
+```python
+chain = load_summarize_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True)
+```
+
+
+```python
+chain({"input_documents": docs}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'map_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.",
+      ' The United States and its European allies are taking action to punish Russia for its invasion of Ukraine, including seizing assets, closing off airspace, and providing economic and military assistance to Ukraine. The US is also mobilizing forces to protect NATO countries and has released 30 million barrels of oil from its Strategic Petroleum Reserve to help blunt gas prices. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens.',
+      " President Biden and Vice President Harris ran for office with a new economic vision for America, and have since passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and rebuild America's infrastructure. This includes creating jobs, modernizing roads, airports, ports, and waterways, replacing lead pipes, providing affordable high-speed internet, and investing in American products to support American jobs."],
+     'output_text': " In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and passing legislation to help struggling families and rebuild America's infrastructure. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens."}
+```
+
+</CodeOutputBlock>
+
+**Custom Prompts**
+
+You can also use your own prompts with this chain. In this example, we will respond in Italian.
+
+
+```python
+prompt_template = """Write a concise summary of the following:
+
+
+{text}
+
+
+CONCISE SUMMARY IN ITALIAN:"""
+PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+chain = load_summarize_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)
+chain({"input_documents": docs}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'intermediate_steps': ["\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Gli Stati Uniti e i loro alleati stanno ora imponendo sanzioni economiche a Putin e stanno tagliando l'accesso della Russia alla tecnologia. Il Dipartimento di Giustizia degli Stati Uniti sta anche creando una task force dedicata per andare dopo i crimini degli oligarchi russi.",
+      "\n\nStiamo unendo le nostre forze con quelle dei nostri alleati europei per sequestrare yacht, appartamenti di lusso e jet privati di Putin. Abbiamo chiuso lo spazio aereo americano ai voli russi e stiamo fornendo più di un miliardo di dollari in assistenza all'Ucraina. Abbiamo anche mobilitato le nostre forze terrestri, aeree e navali per proteggere i paesi della NATO. Abbiamo anche rilasciato 60 milioni di barili di petrolio dalle riserve di tutto il mondo, di cui 30 milioni dalla nostra riserva strategica di petrolio. Stiamo affrontando una prova reale e ci vorrà del tempo, ma alla fine Putin non riuscirà a spegnere l'amore dei popoli per la libertà.",
+      "\n\nIl Presidente Biden ha lottato per passare l'American Rescue Plan per aiutare le persone che soffrivano a causa della pandemia. Il piano ha fornito sollievo economico immediato a milioni di americani, ha aiutato a mettere cibo sulla loro tavola, a mantenere un tetto sopra le loro teste e a ridurre il costo dell'assicurazione sanitaria. Il piano ha anche creato più di 6,5 milioni di nuovi posti di lavoro, il più alto numero di posti di lavoro creati in un anno nella storia degli Stati Uniti. Il Presidente Biden ha anche firmato la legge bipartitica sull'infrastruttura, la più ampia iniziativa di ricostruzione della storia degli Stati Uniti. Il piano prevede di modernizzare le strade, gli aeroporti, i porti e le vie navigabili in"],
+     'output_text': "\n\nIl Presidente Biden sta lavorando per aiutare le persone che soffrono a causa della pandemia attraverso l'American Rescue Plan e la legge bipartitica sull'infrastruttura. Gli Stati Uniti e i loro alleati stanno anche imponendo sanzioni economiche a Putin e tagliando l'accesso della Russia alla tecnologia. Stanno anche sequestrando yacht, appartamenti di lusso e jet privati di Putin e fornendo più di un miliardo di dollari in assistenza all'Ucraina. Alla fine, Putin non riuscirà a spegnere l'amore dei popoli per la libertà."}
+```
+
+</CodeOutputBlock>
+
+## The custom `MapReduceChain`
+
+**Multi input prompt**
+
+You can also use prompt with multi input. In this example, we will use a MapReduce chain to answer specific question about our code.
+
+
+```python
+from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
+from langchain.chains.combine_documents.stuff import StuffDocumentsChain
+
+map_template_string = """Give the following python code information, generate a description that explains what the code does and also mention the time complexity.
+Code:
+{code}
+
+Return the the description in the following format:
+name of the function: description of the function
+"""
+
+
+reduce_template_string = """Given the following python function names and descriptions, answer the following question
+{code_description}
+Question: {question}
+Answer:
+"""
+
+# Prompt to use in map and reduce stages 
+MAP_PROMPT = PromptTemplate(input_variables=["code"], template=map_template_string)
+REDUCE_PROMPT = PromptTemplate(input_variables=["code_description", "question"], template=reduce_template_string)
+
+# LLM to use in map and reduce stages 
+llm = OpenAI()
+map_llm_chain = LLMChain(llm=llm, prompt=MAP_PROMPT)
+reduce_llm_chain = LLMChain(llm=llm, prompt=REDUCE_PROMPT)
+
+# Takes a list of documents and combines them into a single string
+combine_documents_chain = StuffDocumentsChain(
+    llm_chain=reduce_llm_chain,
+    document_variable_name="code_description",
+)
+
+# Combines and iteravely reduces the mapped documents 
+reduce_documents_chain = ReduceDocumentsChain(
+        # This is final chain that is called.
+        combine_documents_chain=combine_documents_chain,
+        # If documents exceed context for `combine_documents_chain`
+        collapse_documents_chain=combine_documents_chain,
+        # The maximum number of tokens to group documents into
+        token_max=3000)
+
+# Combining documents by mapping a chain over them, then combining results with reduce chain
+combine_documents = MapReduceDocumentsChain(
+    # Map chain
+    llm_chain=map_llm_chain,
+     # Reduce chain
+    reduce_documents_chain=reduce_documents_chain,
+    # The variable name in the llm_chain to put the documents in
+    document_variable_name="code",
+)
+
+map_reduce = MapReduceChain(
+    combine_documents_chain=combine_documents,
+    text_splitter=CharacterTextSplitter(separator="\n##\n", chunk_size=100, chunk_overlap=0),
+)
+```
+
+
+```python
+code = """
+def bubblesort(list):
+   for iter_num in range(len(list)-1,0,-1):
+      for idx in range(iter_num):
+         if list[idx]>list[idx+1]:
+            temp = list[idx]
+            list[idx] = list[idx+1]
+            list[idx+1] = temp
+    return list
+##
+def insertion_sort(InputList):
+   for i in range(1, len(InputList)):
+      j = i-1
+      nxt_element = InputList[i]
+   while (InputList[j] > nxt_element) and (j >= 0):
+      InputList[j+1] = InputList[j]
+      j=j-1
+   InputList[j+1] = nxt_element
+   return InputList
+##
+def shellSort(input_list):
+   gap = len(input_list) // 2
+   while gap > 0:
+      for i in range(gap, len(input_list)):
+         temp = input_list[i]
+         j = i
+   while j >= gap and input_list[j - gap] > temp:
+      input_list[j] = input_list[j - gap]
+      j = j-gap
+      input_list[j] = temp
+   gap = gap//2
+   return input_list
+
+"""
+```
+
+
+```python
+map_reduce.run(input_text=code, question="Which function has a better time complexity?")
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    Created a chunk of size 247, which is longer than the specified 100
+    Created a chunk of size 267, which is longer than the specified 100
+
+
+
+
+
+    'shellSort has a better time complexity than both bubblesort and insertion_sort, as it has a time complexity of O(n^2), while the other two have a time complexity of O(n^2).'
+```
+
+</CodeOutputBlock>
+
+## The `refine` Chain
+
+This sections shows results of using the `refine` Chain to do summarization.
+
+
+```python
+chain = load_summarize_chain(llm, chain_type="refine")
+
+chain.run(docs)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This investment will"
+```
+
+</CodeOutputBlock>
+
+**Intermediate Steps**
+
+We can also return the intermediate steps for `refine` chains, should we want to inspect them. This is done with the `return_refine_steps` variable.
+
+
+```python
+chain = load_summarize_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True)
+
+chain({"input_documents": docs}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'refine_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.",
+      "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. Putin's war on Ukraine has left Russia weaker and the rest of the world stronger, with the world uniting in support of democracy and peace.",
+      "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing"],
+     'output_text': "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing"}
+```
+
+</CodeOutputBlock>
+
+**Custom Prompts**
+
+You can also use your own prompts with this chain. In this example, we will respond in Italian.
+
+
+```python
+prompt_template = """Write a concise summary of the following:
+
+
+{text}
+
+
+CONCISE SUMMARY IN ITALIAN:"""
+PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+refine_template = (
+    "Your job is to produce a final summary\n"
+    "We have provided an existing summary up to a certain point: {existing_answer}\n"
+    "We have the opportunity to refine the existing summary"
+    "(only if needed) with some more context below.\n"
+    "------------\n"
+    "{text}\n"
+    "------------\n"
+    "Given the new context, refine the original summary in Italian"
+    "If the context isn't useful, return the original summary."
+)
+refine_prompt = PromptTemplate(
+    input_variables=["existing_answer", "text"],
+    template=refine_template,
+)
+chain = load_summarize_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True, question_prompt=PROMPT, refine_prompt=refine_prompt)
+chain({"input_documents": docs}, return_only_outputs=True)
+```
+
+<CodeOutputBlock lang="python">
+
+```
+    {'intermediate_steps': ["\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia e bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi.",
+      "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare,",
+      "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare."],
+     'output_text': "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare."}
+```
+
+</CodeOutputBlock>

docs/snippets/modules/memory/types/summary.mdx

@@ -60,7 +60,7 @@ memory.predict_new_summary(messages, previous_summary)
 
 </CodeOutputBlock>
 
-## Initializing with messages/existing summary
+## Initializing with messages
 
 If you have messages outside this class, you can easily initialize the class with ChatMessageHistory. During loading, a summary will be calculated.
 
@@ -73,11 +73,7 @@ history.add_ai_message("hi there!")
 
 
 ```python
-memory = ConversationSummaryMemory.from_messages(
-    llm=OpenAI(temperature=0),
-    chat_memory=history,
-    return_messages=True
-)
+memory = ConversationSummaryMemory.from_messages(llm=OpenAI(temperature=0), chat_memory=history, return_messages=True)
 ```
 
 
@@ -93,17 +89,6 @@ memory.buffer
 
 </CodeOutputBlock>
 
-Optionally you can speed up initialization using a previously generated summary, and avoid regenerating the summary by just initializing directly.
-
-```python
-memory = ConversationSummaryMemory(
-    llm=OpenAI(temperature=0),
-    buffer="The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.",
-    chat_memory=history,
-    return_messages=True
-)
-```
-
 ## Using in a chain
 Let's walk through an example of using this in a chain, again setting `verbose=True` so we can see the prompt.
 

docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx

@@ -74,7 +74,7 @@ These chat messages differ from raw string (which you would pass into a [LLM](/d
 
 For example, in OpenAI [Chat Completion API](https://platform.openai.com/docs/guides/chat/introduction), a chat message can be associated with the AI, human or system role. The model is supposed to follow instruction from system chat message more closely.
 
-LangChain provides several prompt templates to make constructing and working with prompts easy. You are encouraged to use these chat related prompt templates instead of `PromptTemplate` when querying chat models to fully utilize the potential of the underlying chat model.
+LangChain provides several prompt templates to make constructing and working with prompts easily. You are encouraged to use these chat related prompt templates instead of `PromptTemplate` when querying chat models to fully exploit the potential of underlying chat model.
 
 
 

libs/langchain/README.md

@@ -18,8 +18,8 @@
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
 
-**Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.
-Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.
+**Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
+Please fill out [this form](https://6w1pwbss0py.typeform.com/to/rrbrdTH2) and we'll set up a dedicated support Slack channel.
 
 ## Quick Install
 

libs/langchain/langchain/agents/__init__.py

@@ -1,33 +1,4 @@
-"""
-**Agent** is a class that uses an LLM to choose a sequence of actions to take.
-
-In Chains, a sequence of actions is hardcoded. In Agents,
-a language model is used as a reasoning engine to determine which actions
-to take and in which order.
-
-Agents select and use **Tools** and **Toolkits** for actions.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseSingleActionAgent --> LLMSingleActionAgent
-                              OpenAIFunctionsAgent
-                              XMLAgent
-                              Agent --> <name>Agent  # Examples: ZeroShotAgent, ChatAgent
-                                        
-
-    BaseMultiActionAgent  --> OpenAIMultiFunctionsAgent
-    
-    
-**Main helpers:**
-
-.. code-block::
-
-    AgentType, AgentExecutor, AgentOutputParser, AgentExecutorIterator,
-    AgentAction, AgentFinish
-    
-"""  # noqa: E501
+"""Interface for agents."""
 from langchain.agents.agent import (
     Agent,
     AgentExecutor,

libs/langchain/langchain/agents/agent_toolkits/__init__.py

@@ -3,12 +3,6 @@ from langchain.agents.agent_toolkits.amadeus.toolkit import AmadeusToolkit
 from langchain.agents.agent_toolkits.azure_cognitive_services import (
     AzureCognitiveServicesToolkit,
 )
-from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (
-    create_conversational_retrieval_agent,
-)
-from langchain.agents.agent_toolkits.conversational_retrieval.tool import (
-    create_retriever_tool,
-)
 from langchain.agents.agent_toolkits.csv.base import create_csv_agent
 from langchain.agents.agent_toolkits.file_management.toolkit import (
     FileManagementToolkit,
@@ -65,18 +59,16 @@ __all__ = [
     "ZapierToolkit",
     "create_csv_agent",
     "create_json_agent",
-    "create_multion_agent",
     "create_openapi_agent",
     "create_pandas_dataframe_agent",
     "create_pbi_agent",
     "create_pbi_chat_agent",
     "create_python_agent",
-    "create_retriever_tool",
+    "create_multion_agent",
     "create_spark_dataframe_agent",
     "create_spark_sql_agent",
     "create_sql_agent",
     "create_vectorstore_agent",
     "create_vectorstore_router_agent",
     "create_xorbits_agent",
-    "create_conversational_retrieval_agent",
 ]

libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.py

@@ -1,87 +0,0 @@
-from typing import Any, List, Optional
-
-from langchain.agents.agent import AgentExecutor
-from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
-    AgentTokenBufferMemory,
-)
-from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
-from langchain.chat_models.openai import ChatOpenAI
-from langchain.memory.token_buffer import ConversationTokenBufferMemory
-from langchain.prompts.chat import MessagesPlaceholder
-from langchain.schema.language_model import BaseLanguageModel
-from langchain.schema.memory import BaseMemory
-from langchain.schema.messages import SystemMessage
-from langchain.tools.base import BaseTool
-
-
-def _get_default_system_message() -> SystemMessage:
-    return SystemMessage(
-        content=(
-            "Do your best to answer the questions. "
-            "Feel free to use any tools available to look up "
-            "relevant information, only if necessary"
-        )
-    )
-
-
-def create_conversational_retrieval_agent(
-    llm: BaseLanguageModel,
-    tools: List[BaseTool],
-    remember_intermediate_steps: bool = True,
-    memory_key: str = "chat_history",
-    system_message: Optional[SystemMessage] = None,
-    verbose: bool = False,
-    max_token_limit: int = 2000,
-    **kwargs: Any
-) -> AgentExecutor:
-    """A convenience method for creating a conversational retrieval agent.
-
-    Args:
-        llm: The language model to use, should be ChatOpenAI
-        tools: A list of tools the agent has access to
-        remember_intermediate_steps: Whether the agent should remember intermediate
-            steps or not. Intermediate steps refer to prior action/observation
-            pairs from previous questions. The benefit of remembering these is if
-            there is relevant information in there, the agent can use it to answer
-            follow up questions. The downside is it will take up more tokens.
-        memory_key: The name of the memory key in the prompt.
-        system_message: The system message to use. By default, a basic one will
-            be used.
-        verbose: Whether or not the final AgentExecutor should be verbose or not,
-            defaults to False.
-        max_token_limit: The max number of tokens to keep around in memory.
-            Defaults to 2000.
-
-    Returns:
-        An agent executor initialized appropriately
-    """
-
-    if not isinstance(llm, ChatOpenAI):
-        raise ValueError("Only supported with ChatOpenAI models.")
-    if remember_intermediate_steps:
-        memory: BaseMemory = AgentTokenBufferMemory(
-            memory_key=memory_key, llm=llm, max_token_limit=max_token_limit
-        )
-    else:
-        memory = ConversationTokenBufferMemory(
-            memory_key=memory_key,
-            return_messages=True,
-            output_key="output",
-            llm=llm,
-            max_token_limit=max_token_limit,
-        )
-
-    _system_message = system_message or _get_default_system_message()
-    prompt = OpenAIFunctionsAgent.create_prompt(
-        system_message=_system_message,
-        extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
-    )
-    agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
-    return AgentExecutor(
-        agent=agent,
-        tools=tools,
-        memory=memory,
-        verbose=verbose,
-        return_intermediate_steps=remember_intermediate_steps,
-        **kwargs
-    )

libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py

@@ -1,22 +0,0 @@
-from langchain.schema import BaseRetriever
-from langchain.tools import Tool
-
-
-def create_retriever_tool(
-    retriever: BaseRetriever, name: str, description: str
-) -> Tool:
-    """Create a tool to do retrieval of documents.
-
-    Args:
-        retriever: The retriever to use for the retrieval
-        name: The name for the tool. This will be passed to the language model,
-            so should be unique and somewhat descriptive.
-        description: The description for the tool. This will be passed to the language
-            model, so should be descriptive.
-
-    Returns:
-        Tool class to pass to an agent
-    """
-    return Tool(
-        name=name, description=description, func=retriever.get_relevant_documents
-    )

libs/langchain/langchain/agents/agent_toolkits/csv/base.py

@@ -15,7 +15,7 @@ def create_csv_agent(
     try:
         import pandas as pd
     except ImportError:
-        raise ImportError(
+        raise ValueError(
             "pandas package not found, please install with `pip install pandas`"
         )
 

libs/langchain/langchain/agents/agent_toolkits/openapi/planner_prompt.py

@@ -63,7 +63,7 @@ API_PLANNER_TOOL_DESCRIPTION = f"Can be used to generate the right API calls to
 
 # Execution.
 API_CONTROLLER_PROMPT = """You are an agent that gets a sequence of API calls and given their documentation, should execute them and return the final response.
-If you cannot complete them and run into issues, you should explain the issue. If you're unable to resolve an API call, you can retry the API call. When interacting with API objects, you should extract ids for inputs to other API calls but ids and names for outputs returned to the User.
+If you cannot complete them and run into issues, you should explain the issue. If you're able to resolve an API call, you can retry the API call. When interacting with API objects, you should extract ids for inputs to other API calls but ids and names for outputs returned to the User.
 
 
 Here is documentation on the API:

libs/langchain/langchain/agents/agent_toolkits/pandas/base.py

@@ -121,7 +121,7 @@ def _get_prompt_and_tools(
 
         pd.set_option("display.max_columns", None)
     except ImportError:
-        raise ImportError(
+        raise ValueError(
             "pandas package not found, please install with `pip install pandas`"
         )
 
@@ -232,7 +232,7 @@ def _get_functions_prompt_and_tools(
 
         pd.set_option("display.max_columns", None)
     except ImportError:
-        raise ImportError(
+        raise ValueError(
             "pandas package not found, please install with `pip install pandas`"
         )
     if input_variables is not None:

libs/langchain/langchain/agents/agent_toolkits/spark/base.py

@@ -46,7 +46,7 @@ def create_spark_dataframe_agent(
     """Construct a Spark agent from an LLM and dataframe."""
 
     if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
-        raise ImportError("Spark is not installed. run `pip install pyspark`.")
+        raise ValueError("Spark is not installed. run `pip install pyspark`.")
 
     if input_variables is None:
         input_variables = ["df", "input", "agent_scratchpad"]

libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py

@@ -1,63 +0,0 @@
-"""Memory used to save agent output AND intermediate steps."""
-from typing import Any, Dict, List
-
-from langchain.agents.openai_functions_agent.base import _format_intermediate_steps
-from langchain.memory.chat_memory import BaseChatMemory
-from langchain.schema.language_model import BaseLanguageModel
-from langchain.schema.messages import BaseMessage, get_buffer_string
-
-
-class AgentTokenBufferMemory(BaseChatMemory):
-    """Memory used to save agent output AND intermediate steps."""
-
-    human_prefix: str = "Human"
-    ai_prefix: str = "AI"
-    llm: BaseLanguageModel
-    memory_key: str = "history"
-    max_token_limit: int = 12000
-    """The max number of tokens to keep in the buffer. 
-    Once the buffer exceeds this many tokens, the oldest messages will be pruned."""
-    return_messages: bool = True
-    output_key = "output"
-    intermediate_steps_key = "intermediate_steps"
-
-    @property
-    def buffer(self) -> List[BaseMessage]:
-        """String buffer of memory."""
-        return self.chat_memory.messages
-
-    @property
-    def memory_variables(self) -> List[str]:
-        """Will always return list of memory variables.
-
-        :meta private:
-        """
-        return [self.memory_key]
-
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
-        """Return history buffer."""
-        if self.return_messages:
-            final_buffer: Any = self.buffer
-        else:
-            final_buffer = get_buffer_string(
-                self.buffer,
-                human_prefix=self.human_prefix,
-                ai_prefix=self.ai_prefix,
-            )
-        return {self.memory_key: final_buffer}
-
-    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
-        """Save context from this conversation to buffer. Pruned."""
-        input_str, output_str = self._get_input_output(inputs, outputs)
-        self.chat_memory.add_user_message(input_str)
-        steps = _format_intermediate_steps(outputs[self.intermediate_steps_key])
-        for msg in steps:
-            self.chat_memory.add_message(msg)
-        self.chat_memory.add_ai_message(output_str)
-        # Prune buffer if it exceeds max token limit
-        buffer = self.chat_memory.messages
-        curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
-        if curr_buffer_length > self.max_token_limit:
-            while curr_buffer_length > self.max_token_limit:
-                buffer.pop(0)
-                curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)

libs/langchain/langchain/cache.py

@@ -1,24 +1,4 @@
-"""
-.. warning::
-  Beta Feature!
-
-**Cache** provides an optional caching layer for LLMs.
-
-Cache is useful for two reasons:
-
-- It can save you money by reducing the number of API calls you make to the LLM
-  provider if you're often requesting the same completion multiple times.
-- It can speed up your application by reducing the number of API calls you make
-  to the LLM provider.
-
-Cache directly competes with Memory. See documentation for Pros and Cons.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseCache --> <name>Cache  # Examples: InMemoryCache, RedisCache, GPTCache
-"""
+"""Beta Feature: base interface for cache."""
 from __future__ import annotations
 
 import hashlib
@@ -477,8 +457,9 @@ class GPTCache(BaseCache):
         """
         from gptcache.adapter.api import get
 
-        _gptcache = self._get_gptcache(llm_string)
-
+        _gptcache = self.gptcache_dict.get(llm_string, None)
+        if _gptcache is None:
+            return None
         res = get(prompt, cache_obj=_gptcache)
         if res:
             return [

libs/langchain/langchain/callbacks/__init__.py

@@ -1,11 +1,4 @@
-"""**Callback handlers** allow listening to events in LangChain.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseCallbackHandler --> <name>CallbackHandler  # Example: AimCallbackHandler
-"""
+"""Callback handlers that allow listening to events in LangChain."""
 
 from langchain.callbacks.aim_callback import AimCallbackHandler
 from langchain.callbacks.argilla_callback import ArgillaCallbackHandler
@@ -27,7 +20,6 @@ from langchain.callbacks.manager import (
 from langchain.callbacks.mlflow_callback import MlflowCallbackHandler
 from langchain.callbacks.openai_info import OpenAICallbackHandler
 from langchain.callbacks.promptlayer_callback import PromptLayerCallbackHandler
-from langchain.callbacks.sagemaker_callback import SageMakerCallbackHandler
 from langchain.callbacks.stdout import StdOutCallbackHandler
 from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
@@ -67,5 +59,4 @@ __all__ = [
     "tracing_v2_enabled",
     "wandb_tracing_enabled",
     "FlyteCallbackHandler",
-    "SageMakerCallbackHandler",
 ]

libs/langchain/langchain/callbacks/sagemaker_callback.py

@@ -1,280 +0,0 @@
-import json
-import os
-import shutil
-import tempfile
-from copy import deepcopy
-from typing import Any, Dict, List, Optional, Union
-
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.callbacks.utils import (
-    flatten_dict,
-)
-from langchain.schema import AgentAction, AgentFinish, LLMResult
-
-
-def save_json(data: dict, file_path: str) -> None:
-    """Save dict to local file path.
-
-    Parameters:
-        data (dict): The dictionary to be saved.
-        file_path (str): Local file path.
-    """
-    with open(file_path, "w") as outfile:
-        json.dump(data, outfile)
-
-
-class SageMakerCallbackHandler(BaseCallbackHandler):
-    """Callback Handler that logs prompt artifacts and metrics to SageMaker Experiments.
-
-    Parameters:
-        run (sagemaker.experiments.run.Run): Run object where the experiment is logged.
-    """
-
-    def __init__(self, run: Any) -> None:
-        """Initialize callback handler."""
-        super().__init__()
-
-        self.run = run
-
-        self.metrics = {
-            "step": 0,
-            "starts": 0,
-            "ends": 0,
-            "errors": 0,
-            "text_ctr": 0,
-            "chain_starts": 0,
-            "chain_ends": 0,
-            "llm_starts": 0,
-            "llm_ends": 0,
-            "llm_streams": 0,
-            "tool_starts": 0,
-            "tool_ends": 0,
-            "agent_ends": 0,
-        }
-
-        # Create a temporary directory
-        self.temp_dir = tempfile.mkdtemp()
-
-    def _reset(self) -> None:
-        for k, v in self.metrics.items():
-            self.metrics[k] = 0
-
-    def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> None:
-        """Run when LLM starts."""
-        self.metrics["step"] += 1
-        self.metrics["llm_starts"] += 1
-        self.metrics["starts"] += 1
-
-        llm_starts = self.metrics["llm_starts"]
-
-        resp: Dict[str, Any] = {}
-        resp.update({"action": "on_llm_start"})
-        resp.update(flatten_dict(serialized))
-        resp.update(self.metrics)
-
-        for idx, prompt in enumerate(prompts):
-            prompt_resp = deepcopy(resp)
-            prompt_resp["prompt"] = prompt
-            self.jsonf(
-                prompt_resp,
-                self.temp_dir,
-                f"llm_start_{llm_starts}_prompt_{idx}",
-            )
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        """Run when LLM generates a new token."""
-        self.metrics["step"] += 1
-        self.metrics["llm_streams"] += 1
-
-        llm_streams = self.metrics["llm_streams"]
-
-        resp: Dict[str, Any] = {}
-        resp.update({"action": "on_llm_new_token", "token": token})
-        resp.update(self.metrics)
-
-        self.jsonf(resp, self.temp_dir, f"llm_new_tokens_{llm_streams}")
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        """Run when LLM ends running."""
-        self.metrics["step"] += 1
-        self.metrics["llm_ends"] += 1
-        self.metrics["ends"] += 1
-
-        llm_ends = self.metrics["llm_ends"]
-
-        resp: Dict[str, Any] = {}
-        resp.update({"action": "on_llm_end"})
-        resp.update(flatten_dict(response.llm_output or {}))
-
-        resp.update(self.metrics)
-
-        for generations in response.generations:
-            for idx, generation in enumerate(generations):
-                generation_resp = deepcopy(resp)
-                generation_resp.update(flatten_dict(generation.dict()))
-
-                self.jsonf(
-                    resp,
-                    self.temp_dir,
-                    f"llm_end_{llm_ends}_generation_{idx}",
-                )
-
-    def on_llm_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        """Run when LLM errors."""
-        self.metrics["step"] += 1
-        self.metrics["errors"] += 1
-
-    def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> None:
-        """Run when chain starts running."""
-        self.metrics["step"] += 1
-        self.metrics["chain_starts"] += 1
-        self.metrics["starts"] += 1
-
-        chain_starts = self.metrics["chain_starts"]
-
-        resp: Dict[str, Any] = {}
-        resp.update({"action": "on_chain_start"})
-        resp.update(flatten_dict(serialized))
-        resp.update(self.metrics)
-
-        chain_input = ",".join([f"{k}={v}" for k, v in inputs.items()])
-        input_resp = deepcopy(resp)
-        input_resp["inputs"] = chain_input
-
-        self.jsonf(input_resp, self.temp_dir, f"chain_start_{chain_starts}")
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        """Run when chain ends running."""
-        self.metrics["step"] += 1
-        self.metrics["chain_ends"] += 1
-        self.metrics["ends"] += 1
-
-        chain_ends = self.metrics["chain_ends"]
-
-        resp: Dict[str, Any] = {}
-        chain_output = ",".join([f"{k}={v}" for k, v in outputs.items()])
-        resp.update({"action": "on_chain_end", "outputs": chain_output})
-        resp.update(self.metrics)
-
-        self.jsonf(resp, self.temp_dir, f"chain_end_{chain_ends}")
-
-    def on_chain_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        """Run when chain errors."""
-        self.metrics["step"] += 1
-        self.metrics["errors"] += 1
-
-    def on_tool_start(
-        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
-    ) -> None:
-        """Run when tool starts running."""
-        self.metrics["step"] += 1
-        self.metrics["tool_starts"] += 1
-        self.metrics["starts"] += 1
-
-        tool_starts = self.metrics["tool_starts"]
-
-        resp: Dict[str, Any] = {}
-        resp.update({"action": "on_tool_start", "input_str": input_str})
-        resp.update(flatten_dict(serialized))
-        resp.update(self.metrics)
-
-        self.jsonf(resp, self.temp_dir, f"tool_start_{tool_starts}")
-
-    def on_tool_end(self, output: str, **kwargs: Any) -> None:
-        """Run when tool ends running."""
-        self.metrics["step"] += 1
-        self.metrics["tool_ends"] += 1
-        self.metrics["ends"] += 1
-
-        tool_ends = self.metrics["tool_ends"]
-
-        resp: Dict[str, Any] = {}
-        resp.update({"action": "on_tool_end", "output": output})
-        resp.update(self.metrics)
-
-        self.jsonf(resp, self.temp_dir, f"tool_end_{tool_ends}")
-
-    def on_tool_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        """Run when tool errors."""
-        self.metrics["step"] += 1
-        self.metrics["errors"] += 1
-
-    def on_text(self, text: str, **kwargs: Any) -> None:
-        """
-        Run when agent is ending.
-        """
-        self.metrics["step"] += 1
-        self.metrics["text_ctr"] += 1
-
-        text_ctr = self.metrics["text_ctr"]
-
-        resp: Dict[str, Any] = {}
-        resp.update({"action": "on_text", "text": text})
-        resp.update(self.metrics)
-
-        self.jsonf(resp, self.temp_dir, f"on_text_{text_ctr}")
-
-    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
-        """Run when agent ends running."""
-        self.metrics["step"] += 1
-        self.metrics["agent_ends"] += 1
-        self.metrics["ends"] += 1
-
-        agent_ends = self.metrics["agent_ends"]
-        resp: Dict[str, Any] = {}
-        resp.update(
-            {
-                "action": "on_agent_finish",
-                "output": finish.return_values["output"],
-                "log": finish.log,
-            }
-        )
-        resp.update(self.metrics)
-
-        self.jsonf(resp, self.temp_dir, f"agent_finish_{agent_ends}")
-
-    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
-        """Run on agent action."""
-        self.metrics["step"] += 1
-        self.metrics["tool_starts"] += 1
-        self.metrics["starts"] += 1
-
-        tool_starts = self.metrics["tool_starts"]
-        resp: Dict[str, Any] = {}
-        resp.update(
-            {
-                "action": "on_agent_action",
-                "tool": action.tool,
-                "tool_input": action.tool_input,
-                "log": action.log,
-            }
-        )
-        resp.update(self.metrics)
-        self.jsonf(resp, self.temp_dir, f"agent_action_{tool_starts}")
-
-    def jsonf(
-        self,
-        data: Dict[str, Any],
-        data_dir: str,
-        filename: str,
-        is_output: Optional[bool] = True,
-    ) -> None:
-        """To log the input data as json file artifact."""
-        file_path = os.path.join(data_dir, f"{filename}.json")
-        save_json(data, file_path)
-        self.run.log_file(file_path, name=filename, is_output=is_output)
-
-    def flush_tracker(self) -> None:
-        """Reset the steps and delete the temporary local directory."""
-        self._reset()
-        shutil.rmtree(self.temp_dir)

libs/langchain/langchain/callbacks/tracers/base.py

@@ -10,7 +10,7 @@ from uuid import UUID
 from tenacity import RetryCallState
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.callbacks.tracers.schemas import Run
+from langchain.callbacks.tracers.schemas import Run, RunTypeEnum
 from langchain.load.dump import dumpd
 from langchain.schema.document import Document
 from langchain.schema.output import ChatGeneration, LLMResult
@@ -110,7 +110,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
             start_time=start_time,
             execution_order=execution_order,
             child_execution_order=execution_order,
-            run_type="llm",
+            run_type=RunTypeEnum.llm,
             tags=tags or [],
         )
         self._start_trace(llm_run)
@@ -130,7 +130,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
 
         run_id_ = str(run_id)
         llm_run = self.run_map.get(run_id_)
-        if llm_run is None or llm_run.run_type != "llm":
+        if llm_run is None or llm_run.run_type != RunTypeEnum.llm:
             raise TracerException("No LLM Run found to be traced")
         llm_run.events.append(
             {
@@ -182,7 +182,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
 
         run_id_ = str(run_id)
         llm_run = self.run_map.get(run_id_)
-        if llm_run is None or llm_run.run_type != "llm":
+        if llm_run is None or llm_run.run_type != RunTypeEnum.llm:
             raise TracerException("No LLM Run found to be traced")
         llm_run.outputs = response.dict()
         for i, generations in enumerate(response.generations):
@@ -210,7 +210,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
 
         run_id_ = str(run_id)
         llm_run = self.run_map.get(run_id_)
-        if llm_run is None or llm_run.run_type != "llm":
+        if llm_run is None or llm_run.run_type != RunTypeEnum.llm:
             raise TracerException("No LLM Run found to be traced")
         llm_run.error = repr(error)
         llm_run.end_time = datetime.utcnow()
@@ -227,7 +227,6 @@ class BaseTracer(BaseCallbackHandler, ABC):
         tags: Optional[List[str]] = None,
         parent_run_id: Optional[UUID] = None,
         metadata: Optional[Dict[str, Any]] = None,
-        run_type: Optional[str] = None,
         **kwargs: Any,
     ) -> None:
         """Start a trace for a chain run."""
@@ -247,7 +246,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
             execution_order=execution_order,
             child_execution_order=execution_order,
             child_runs=[],
-            run_type=run_type or "chain",
+            run_type=RunTypeEnum.chain,
             tags=tags or [],
         )
         self._start_trace(chain_run)
@@ -260,7 +259,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
         if not run_id:
             raise TracerException("No run_id provided for on_chain_end callback.")
         chain_run = self.run_map.get(str(run_id))
-        if chain_run is None:
+        if chain_run is None or chain_run.run_type != RunTypeEnum.chain:
             raise TracerException("No chain Run found to be traced")
 
         chain_run.outputs = outputs
@@ -280,7 +279,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
         if not run_id:
             raise TracerException("No run_id provided for on_chain_error callback.")
         chain_run = self.run_map.get(str(run_id))
-        if chain_run is None:
+        if chain_run is None or chain_run.run_type != RunTypeEnum.chain:
             raise TracerException("No chain Run found to be traced")
 
         chain_run.error = repr(error)
@@ -317,7 +316,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
             execution_order=execution_order,
             child_execution_order=execution_order,
             child_runs=[],
-            run_type="tool",
+            run_type=RunTypeEnum.tool,
             tags=tags or [],
         )
         self._start_trace(tool_run)
@@ -328,7 +327,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
         if not run_id:
             raise TracerException("No run_id provided for on_tool_end callback.")
         tool_run = self.run_map.get(str(run_id))
-        if tool_run is None or tool_run.run_type != "tool":
+        if tool_run is None or tool_run.run_type != RunTypeEnum.tool:
             raise TracerException("No tool Run found to be traced")
 
         tool_run.outputs = {"output": output}
@@ -348,7 +347,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
         if not run_id:
             raise TracerException("No run_id provided for on_tool_error callback.")
         tool_run = self.run_map.get(str(run_id))
-        if tool_run is None or tool_run.run_type != "tool":
+        if tool_run is None or tool_run.run_type != RunTypeEnum.tool:
             raise TracerException("No tool Run found to be traced")
 
         tool_run.error = repr(error)
@@ -387,7 +386,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
             child_execution_order=execution_order,
             tags=tags,
             child_runs=[],
-            run_type="retriever",
+            run_type=RunTypeEnum.retriever,
         )
         self._start_trace(retrieval_run)
         self._on_retriever_start(retrieval_run)
@@ -403,7 +402,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
         if not run_id:
             raise TracerException("No run_id provided for on_retriever_error callback.")
         retrieval_run = self.run_map.get(str(run_id))
-        if retrieval_run is None or retrieval_run.run_type != "retriever":
+        if retrieval_run is None or retrieval_run.run_type != RunTypeEnum.retriever:
             raise TracerException("No retriever Run found to be traced")
 
         retrieval_run.error = repr(error)
@@ -419,7 +418,7 @@ class BaseTracer(BaseCallbackHandler, ABC):
         if not run_id:
             raise TracerException("No run_id provided for on_retriever_end callback.")
         retrieval_run = self.run_map.get(str(run_id))
-        if retrieval_run is None or retrieval_run.run_type != "retriever":
+        if retrieval_run is None or retrieval_run.run_type != RunTypeEnum.retriever:
             raise TracerException("No retriever Run found to be traced")
         retrieval_run.outputs = {"documents": documents}
         retrieval_run.end_time = datetime.utcnow()

libs/langchain/langchain/callbacks/tracers/langchain.py

@@ -11,7 +11,7 @@ from uuid import UUID
 from langsmith import Client
 
 from langchain.callbacks.tracers.base import BaseTracer
-from langchain.callbacks.tracers.schemas import Run, TracerSession
+from langchain.callbacks.tracers.schemas import Run, RunTypeEnum, TracerSession
 from langchain.env import get_runtime_environment
 from langchain.load.dump import dumpd
 from langchain.schema.messages import BaseMessage
@@ -107,7 +107,7 @@ class LangChainTracer(BaseTracer):
             start_time=start_time,
             execution_order=execution_order,
             child_execution_order=execution_order,
-            run_type="llm",
+            run_type=RunTypeEnum.llm,
             tags=tags,
         )
         self._start_trace(chat_model_run)

libs/langchain/langchain/callbacks/tracers/schemas.py

@@ -2,27 +2,16 @@
 from __future__ import annotations
 
 import datetime
-import warnings
 from typing import Any, Dict, List, Optional
 from uuid import UUID
 
 from langsmith.schemas import RunBase as BaseRunV2
-from langsmith.schemas import RunTypeEnum as RunTypeEnumDep
+from langsmith.schemas import RunTypeEnum
 from pydantic import BaseModel, Field, root_validator
 
 from langchain.schema import LLMResult
 
 
-def RunTypeEnum() -> RunTypeEnumDep:
-    """RunTypeEnum."""
-    warnings.warn(
-        "RunTypeEnum is deprecated. Please directly use a string instead"
-        " (e.g. 'llm', 'chain', 'tool').",
-        DeprecationWarning,
-    )
-    return RunTypeEnumDep
-
-
 class TracerSessionV1Base(BaseModel):
     """Base class for TracerSessionV1."""
 

libs/langchain/langchain/callbacks/tracers/wandb.py

@@ -15,7 +15,7 @@ from typing import (
 )
 
 from langchain.callbacks.tracers.base import BaseTracer
-from langchain.callbacks.tracers.schemas import Run
+from langchain.callbacks.tracers.schemas import Run, RunTypeEnum
 
 if TYPE_CHECKING:
     from wandb import Settings as WBSettings
@@ -154,11 +154,11 @@ class RunProcessor:
         :param run: The LangChain Run to convert.
         :return: The converted W&B Trace Span.
         """
-        if run.run_type == "llm":
+        if run.run_type == RunTypeEnum.llm:
             return self._convert_llm_run_to_wb_span(run)
-        elif run.run_type == "chain":
+        elif run.run_type == RunTypeEnum.chain:
             return self._convert_chain_run_to_wb_span(run)
-        elif run.run_type == "tool":
+        elif run.run_type == RunTypeEnum.tool:
             return self._convert_tool_run_to_wb_span(run)
         else:
             return self._convert_run_to_wb_span(run)

libs/langchain/langchain/chains/__init__.py

@@ -1,21 +1,16 @@
-"""**Chains** are easily reusable components linked together.
+"""Chains are easily reusable components which can be linked together.
 
-Chains encode a sequence of calls to components like models, document retrievers,
-other Chains, etc., and provide a simple interface to this sequence.
+    Chains should be used to encode a sequence of calls to components like
+    models, document retrievers, other chains, etc., and provide a simple interface
+    to this sequence.
 
-The Chain interface makes it easy to create apps that are:
-
-    - **Stateful:** add Memory to any Chain to give it state,
-    - **Observable:** pass Callbacks to a Chain to execute additional functionality,
-      like logging, outside the main sequence of component calls,
-    - **Composable:** combine Chains with other components, including other Chains.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    Chain --> <name>Chain  # Examples: LLMChain, MapReduceChain, RouterChain
-"""
+    The Chain interface makes it easy to create apps that are:
+        - Stateful: add Memory to any Chain to give it state,
+        - Observable: pass Callbacks to a Chain to execute additional functionality,
+            like logging, outside the main sequence of component calls,
+        - Composable: the Chain API is flexible enough that it is easy to combine
+            Chains with other components, including other Chains.
+    """
 
 from langchain.chains.api.base import APIChain
 from langchain.chains.api.openapi.chain import OpenAPIEndpointChain

libs/langchain/langchain/chains/combine_documents/refine.py

@@ -53,7 +53,7 @@ class RefineDocumentsChain(BaseCombineDocumentsChain):
             prompt = PromptTemplate.from_template(
                 "Summarize this content: {context}"
             )
-            initial_llm_chain = LLMChain(llm=llm, prompt=prompt)
+            llm_chain = LLMChain(llm=llm, prompt=prompt)
             initial_response_name = "prev_response"
             # The prompt here should take as an input variable the
             # `document_variable_name` as well as `initial_response_name`
@@ -61,7 +61,7 @@ class RefineDocumentsChain(BaseCombineDocumentsChain):
                 "Here's your first summary: {prev_response}. "
                 "Now add to it based on the following context: {context}"
             )
-            refine_llm_chain = LLMChain(llm=llm, prompt=prompt_refine)
+            llm_chain_refine = LLMChain(llm=llm, prompt=prompt_refine)
             chain = RefineDocumentsChain(
                 initial_llm_chain=initial_llm_chain,
                 refine_llm_chain=refine_llm_chain,

libs/langchain/langchain/chat_models/__init__.py

@@ -1,22 +1,3 @@
-"""**Chat Models** are a variation on language models.
-
-While Chat Models use language models under the hood, the interface they expose
-is a bit different. Rather than expose a "text in, text out" API, they expose
-an interface where "chat messages" are the inputs and outputs.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseLanguageModel --> BaseChatModel --> <name>  # Examples: ChatOpenAI, ChatGooglePalm
-
-**Main helpers:**
-
-.. code-block::
-
-    AIMessage, BaseMessage, HumanMessage
-"""  # noqa: E501
-
 from langchain.chat_models.anthropic import ChatAnthropic
 from langchain.chat_models.azure_openai import AzureChatOpenAI
 from langchain.chat_models.fake import FakeListChatModel

libs/langchain/langchain/chat_models/azureml_endpoint.py

@@ -1,151 +0,0 @@
-import json
-from typing import Any, Dict, List, Optional
-
-from pydantic import validator
-
-from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.chat_models.base import SimpleChatModel
-from langchain.llms.azureml_endpoint import AzureMLEndpointClient, ContentFormatterBase
-from langchain.schema.messages import (
-    AIMessage,
-    BaseMessage,
-    ChatMessage,
-    HumanMessage,
-    SystemMessage,
-)
-from langchain.utils import get_from_dict_or_env
-
-
-class LlamaContentFormatter(ContentFormatterBase):
-    """Content formatter for LLaMa"""
-
-    SUPPORTED_ROLES = ["user", "assistant", "system"]
-
-    @staticmethod
-    def _convert_message_to_dict(message: BaseMessage) -> Dict:
-        """Converts message to a dict according to role"""
-        if isinstance(message, HumanMessage):
-            return {"role": "user", "content": message.content}
-        elif isinstance(message, AIMessage):
-            return {"role": "assistant", "content": message.content}
-        elif isinstance(message, SystemMessage):
-            return {"role": "system", "content": message.content}
-        elif (
-            isinstance(message, ChatMessage)
-            and message.role in LlamaContentFormatter.SUPPORTED_ROLES
-        ):
-            return {"role": message.role, "content": message.content}
-        else:
-            supported = ",".join(
-                [role for role in LlamaContentFormatter.SUPPORTED_ROLES]
-            )
-            raise ValueError(
-                f"""Received unsupported role. 
-                Supported roles for the LLaMa Foundation Model: {supported}"""
-            )
-
-    def _format_request_payload(
-        self, messages: List[BaseMessage], model_kwargs: Dict
-    ) -> bytes:
-        chat_messages = [
-            LlamaContentFormatter._convert_message_to_dict(message)
-            for message in messages
-        ]
-        prompt = json.dumps(
-            {"input_data": {"input_string": chat_messages, "parameters": model_kwargs}}
-        )
-        return self.format_request_payload(prompt=prompt, model_kwargs=model_kwargs)
-
-    def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
-        """Formats the request according the the chosen api"""
-        return str.encode(prompt)
-
-    def format_response_payload(self, output: bytes) -> str:
-        """Formats response"""
-        return json.loads(output)["output"]
-
-
-class AzureMLChatOnlineEndpoint(SimpleChatModel):
-    """Azure ML Chat Online Endpoint models.
-
-    Example:
-        .. code-block:: python
-
-            azure_chat = AzureMLChatOnlineEndpoint(
-                endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
-                endpoint_api_key="my-api-key",
-                content_formatter=content_formatter,
-            )
-    """
-
-    endpoint_url: str = ""
-    """URL of pre-existing Endpoint. Should be passed to constructor or specified as 
-        env var `AZUREML_ENDPOINT_URL`."""
-
-    endpoint_api_key: str = ""
-    """Authentication Key for Endpoint. Should be passed to constructor or specified as
-        env var `AZUREML_ENDPOINT_API_KEY`."""
-
-    http_client: Any = None  #: :meta private:
-
-    content_formatter: Any = None
-    """The content formatter that provides an input and output
-    transform function to handle formats between the LLM and
-    the endpoint"""
-
-    model_kwargs: Optional[dict] = None
-    """Key word arguments to pass to the model."""
-
-    @validator("http_client", always=True, allow_reuse=True)
-    @classmethod
-    def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
-        """Validate that api key and python package exists in environment."""
-        endpoint_key = get_from_dict_or_env(
-            values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY"
-        )
-        endpoint_url = get_from_dict_or_env(
-            values, "endpoint_url", "AZUREML_ENDPOINT_URL"
-        )
-        http_client = AzureMLEndpointClient(endpoint_url, endpoint_key)
-        return http_client
-
-    @property
-    def _identifying_params(self) -> Dict[str, Any]:
-        """Get the identifying parameters."""
-        _model_kwargs = self.model_kwargs or {}
-        return {
-            **{"model_kwargs": _model_kwargs},
-        }
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "azureml_chat_endpoint"
-
-    def _call(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> str:
-        """Call out to an AzureML Managed Online endpoint.
-        Args:
-            messages: The messages in the conversation with the chat model.
-            stop: Optional list of stop words to use when generating.
-        Returns:
-            The string generated by the model.
-        Example:
-            .. code-block:: python
-                response = azureml_model("Tell me a joke.")
-        """
-        _model_kwargs = self.model_kwargs or {}
-
-        request_payload = self.content_formatter._format_request_payload(
-            messages, _model_kwargs
-        )
-        response_payload = self.http_client.call(request_payload, **kwargs)
-        generated_text = self.content_formatter.format_response_payload(
-            response_payload
-        )
-        return generated_text

libs/langchain/langchain/chat_models/openai.py

@@ -570,9 +570,7 @@ class ChatOpenAI(BaseChatModel):
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
-                # Cast str(value) in case the message value is not a string
-                # This occurs with function messages
-                num_tokens += len(encoding.encode(str(value)))
+                num_tokens += len(encoding.encode(value))
                 if key == "name":
                     num_tokens += tokens_per_name
         # every reply is primed with <im_start>assistant

libs/langchain/langchain/docstore/__init__.py

@@ -1,19 +1,4 @@
-"""**Docstores** are classes to store and load Documents.
-
-The **Docstore** is a simplified version of the Document Loader.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    Docstore --> <name> # Examples: InMemoryDocstore, Wikipedia
-
-**Main helpers:**
-
-.. code-block::
-
-    Document, AddableMixin
-"""
+"""Wrappers on top of docstores."""
 from langchain.docstore.arbitrary_fn import DocstoreFn
 from langchain.docstore.in_memory import InMemoryDocstore
 from langchain.docstore.wikipedia import Wikipedia

libs/langchain/langchain/document_loaders/__init__.py

@@ -1,19 +1,4 @@
-"""**Document Loaders**  are classes to load Documents.
-
-**Document Loaders** are usually used to load a lot of Documents in a single run.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseLoader --> <name>Loader  # Examples: TextLoader, UnstructuredFileLoader
-
-**Main helpers:**
-
-.. code-block::
-
-    Document, <name>TextSplitter
-"""
+"""All different types of document loaders."""
 
 from langchain.document_loaders.acreom import AcreomLoader
 from langchain.document_loaders.airbyte_json import AirbyteJSONLoader
@@ -43,7 +28,6 @@ from langchain.document_loaders.brave_search import BraveSearchLoader
 from langchain.document_loaders.browserless import BrowserlessLoader
 from langchain.document_loaders.chatgpt import ChatGPTLoader
 from langchain.document_loaders.college_confidential import CollegeConfidentialLoader
-from langchain.document_loaders.concurrent import ConcurrentLoader
 from langchain.document_loaders.confluence import ConfluenceLoader
 from langchain.document_loaders.conllu import CoNLLULoader
 from langchain.document_loaders.csv_loader import CSVLoader, UnstructuredCSVLoader
@@ -95,12 +79,9 @@ from langchain.document_loaders.mediawikidump import MWDumpLoader
 from langchain.document_loaders.merge import MergedDataLoader
 from langchain.document_loaders.mhtml import MHTMLLoader
 from langchain.document_loaders.modern_treasury import ModernTreasuryLoader
-from langchain.document_loaders.news import NewsURLLoader
 from langchain.document_loaders.notebook import NotebookLoader
 from langchain.document_loaders.notion import NotionDirectoryLoader
 from langchain.document_loaders.notiondb import NotionDBLoader
-from langchain.document_loaders.obs_directory import OBSDirectoryLoader
-from langchain.document_loaders.obs_file import OBSFileLoader
 from langchain.document_loaders.obsidian import ObsidianLoader
 from langchain.document_loaders.odt import UnstructuredODTLoader
 from langchain.document_loaders.onedrive import OneDriveLoader
@@ -251,12 +232,9 @@ __all__ = [
     "MergedDataLoader",
     "MHTMLLoader",
     "ModernTreasuryLoader",
-    "NewsURLLoader",
     "NotebookLoader",
     "NotionDBLoader",
     "NotionDirectoryLoader",
-    "OBSDirectoryLoader",
-    "OBSFileLoader",
     "ObsidianLoader",
     "OneDriveFileLoader",
     "OneDriveLoader",
@@ -327,5 +305,4 @@ __all__ = [
     "XorbitsLoader",
     "YoutubeAudioLoader",
     "YoutubeLoader",
-    "ConcurrentLoader",
 ]

libs/langchain/langchain/document_loaders/blob_loaders/youtube_audio.py

@@ -21,7 +21,7 @@ class YoutubeAudioLoader(BlobLoader):
         try:
             import yt_dlp
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "yt_dlp package not found, please install it with "
                 "`pip install yt_dlp`"
             )

libs/langchain/langchain/document_loaders/concurrent.py

@@ -1,65 +0,0 @@
-from __future__ import annotations
-
-import concurrent.futures
-from pathlib import Path
-from typing import Iterator, Literal, Optional, Sequence, Union
-
-from langchain.document_loaders.base import BaseBlobParser
-from langchain.document_loaders.blob_loaders import BlobLoader, FileSystemBlobLoader
-from langchain.document_loaders.generic import GenericLoader
-from langchain.document_loaders.parsers.registry import get_parser
-from langchain.schema import Document
-
-_PathLike = Union[str, Path]
-
-DEFAULT = Literal["default"]
-
-
-class ConcurrentLoader(GenericLoader):
-    """
-    A generic document loader that loads and parses documents concurrently.
-    """
-
-    def __init__(
-        self, blob_loader: BlobLoader, blob_parser: BaseBlobParser, num_workers: int = 4
-    ) -> None:
-        super().__init__(blob_loader, blob_parser)
-        self.num_workers = num_workers
-
-    def lazy_load(
-        self,
-    ) -> Iterator[Document]:
-        """Load documents lazily with concurrent parsing."""
-        with concurrent.futures.ThreadPoolExecutor(
-            max_workers=self.num_workers
-        ) as executor:
-            futures = {
-                executor.submit(self.blob_parser.lazy_parse, blob)
-                for blob in self.blob_loader.yield_blobs()
-            }
-            for future in concurrent.futures.as_completed(futures):
-                yield from future.result()
-
-    @classmethod
-    def from_filesystem(
-        cls,
-        path: _PathLike,
-        *,
-        glob: str = "**/[!.]*",
-        suffixes: Optional[Sequence[str]] = None,
-        show_progress: bool = False,
-        parser: Union[DEFAULT, BaseBlobParser] = "default",
-        num_workers: int = 4,
-    ) -> ConcurrentLoader:
-        """
-        Create a concurrent generic document loader using a
-        filesystem blob loader.
-        """
-        blob_loader = FileSystemBlobLoader(
-            path, glob=glob, suffixes=suffixes, show_progress=show_progress
-        )
-        if isinstance(parser, str):
-            blob_parser = get_parser(parser)
-        else:
-            blob_parser = parser
-        return cls(blob_loader, blob_parser, num_workers)

libs/langchain/langchain/document_loaders/directory.py

@@ -121,11 +121,7 @@ class DirectoryLoader(BaseLoader):
                 if self.silent_errors:
                     logger.warning(e)
                 else:
-                    raise ImportError(
-                        "To log the progress of DirectoryLoader "
-                        "you need to install tqdm, "
-                        "`pip install tqdm`"
-                    )
+                    raise e
 
         if self.use_multithreading:
             with concurrent.futures.ThreadPoolExecutor(

libs/langchain/langchain/document_loaders/evernote.py

@@ -74,11 +74,12 @@ class EverNoteLoader(BaseLoader):
 
             return html2text.html2text(content).strip()
         except ImportError as e:
-            raise ImportError(
+            logger.error(
                 "Could not import `html2text`. Although it is not a required package "
                 "to use Langchain, using the EverNote loader requires `html2text`. "
                 "Please install `html2text` via `pip install html2text` and try again."
-            ) from e
+            )
+            raise e
 
     @staticmethod
     def _parse_resource(resource: list) -> dict:

libs/langchain/langchain/document_loaders/geodataframe.py

@@ -20,7 +20,7 @@ class GeoDataFrameLoader(BaseLoader):
         try:
             import geopandas as gpd
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "geopandas package not found, please install it with "
                 "`pip install geopandas`"
             )

libs/langchain/langchain/document_loaders/news.py

@@ -1,124 +0,0 @@
-"""Loader that uses unstructured to load HTML files."""
-import logging
-from typing import Any, Iterator, List
-
-from langchain.docstore.document import Document
-from langchain.document_loaders.base import BaseLoader
-
-logger = logging.getLogger(__name__)
-
-
-class NewsURLLoader(BaseLoader):
-    """Loader that uses newspaper to load news articles from URLs.
-
-    Args:
-        urls: URLs to load. Each is loaded into its own document.
-        text_mode: If True, extract text from URL and use that for page content.
-            Otherwise, extract raw HTML.
-        nlp: If True, perform NLP on the extracted contents, like providing a summary
-            and extracting keywords.
-        continue_on_failure: If True, continue loading documents even if
-            loading fails for a particular URL.
-        show_progress_bar: If True, use tqdm to show a loading progress bar. Requires
-            tqdm to be installed, ``pip install tqdm``.
-        **newspaper_kwargs: Any additional named arguments to pass to
-            newspaper.Article().
-
-    Example:
-        .. code-block:: python
-
-            from langchain.document_loaders import NewsURLLoader
-
-            loader = NewsURLLoader(
-                urls=["<url-1>", "<url-2>"],
-            )
-            docs = loader.load()
-
-    Newspaper reference:
-        https://newspaper.readthedocs.io/en/latest/
-    """
-
-    def __init__(
-        self,
-        urls: List[str],
-        text_mode: bool = True,
-        nlp: bool = False,
-        continue_on_failure: bool = True,
-        show_progress_bar: bool = False,
-        **newspaper_kwargs: Any,
-    ) -> None:
-        """Initialize with file path."""
-        try:
-            import newspaper  # noqa:F401
-
-            self.__version = newspaper.__version__
-        except ImportError:
-            raise ImportError(
-                "newspaper package not found, please install it with "
-                "`pip install newspaper3k`"
-            )
-
-        self.urls = urls
-        self.text_mode = text_mode
-        self.nlp = nlp
-        self.continue_on_failure = continue_on_failure
-        self.newspaper_kwargs = newspaper_kwargs
-        self.show_progress_bar = show_progress_bar
-
-    def load(self) -> List[Document]:
-        iter = self.lazy_load()
-        if self.show_progress_bar:
-            try:
-                from tqdm import tqdm
-            except ImportError as e:
-                raise ImportError(
-                    "Package tqdm must be installed if show_progress_bar=True. "
-                    "Please install with 'pip install tqdm' or set "
-                    "show_progress_bar=False."
-                ) from e
-            iter = tqdm(iter)
-        return list(iter)
-
-    def lazy_load(self) -> Iterator[Document]:
-        try:
-            from newspaper import Article
-        except ImportError as e:
-            raise ImportError(
-                "Cannot import newspaper, please install with `pip install newspaper3k`"
-            ) from e
-
-        for url in self.urls:
-            try:
-                article = Article(url, **self.newspaper_kwargs)
-                article.download()
-                article.parse()
-
-                if self.nlp:
-                    article.nlp()
-
-            except Exception as e:
-                if self.continue_on_failure:
-                    logger.error(f"Error fetching or processing {url}, exception: {e}")
-                    continue
-                else:
-                    raise e
-
-            metadata = {
-                "title": getattr(article, "title", ""),
-                "link": getattr(article, "url", getattr(article, "canonical_link", "")),
-                "authors": getattr(article, "authors", []),
-                "language": getattr(article, "meta_lang", ""),
-                "description": getattr(article, "meta_description", ""),
-                "publish_date": getattr(article, "publish_date", ""),
-            }
-
-            if self.text_mode:
-                content = article.text
-            else:
-                content = article.html
-
-            if self.nlp:
-                metadata["keywords"] = getattr(article, "keywords", [])
-                metadata["summary"] = getattr(article, "summary", "")
-
-            yield Document(page_content=content, metadata=metadata)

libs/langchain/langchain/document_loaders/obs_directory.py

@@ -1,82 +0,0 @@
-# coding:utf-8
-from typing import List, Optional
-
-from langchain.docstore.document import Document
-from langchain.document_loaders.base import BaseLoader
-from langchain.document_loaders.obs_file import OBSFileLoader
-
-
-class OBSDirectoryLoader(BaseLoader):
-    """Loading logic for loading documents from Huawei OBS."""
-
-    def __init__(
-        self,
-        bucket: str,
-        endpoint: str,
-        config: Optional[dict] = None,
-        prefix: str = "",
-    ):
-        """Initialize the OBSDirectoryLoader with the specified settings.
-
-        Args:
-            bucket (str): The name of the OBS bucket to be used.
-            endpoint (str): The endpoint URL of your OBS bucket.
-            config (dict): The parameters for connecting to OBS, provided as a dictionary. The dictionary could have the following keys:
-                - "ak" (str, optional): Your OBS access key (required if `get_token_from_ecs` is False and bucket policy is not public read).
-                - "sk" (str, optional): Your OBS secret key (required if `get_token_from_ecs` is False and bucket policy is not public read).
-                - "token" (str, optional): Your security token (required if using temporary credentials).
-                - "get_token_from_ecs" (bool, optional): Whether to retrieve the security token from ECS. Defaults to False if not provided. If set to True, `ak`, `sk`, and `token` will be ignored.
-            prefix (str, optional): The prefix to be added to the OBS key. Defaults to "".
-
-        Note:
-            Before using this class, make sure you have registered with OBS and have the necessary credentials. The `ak`, `sk`, and `endpoint` values are mandatory unless `get_token_from_ecs` is True or the bucket policy is public read. `token` is required when using temporary credentials.
-        Example:
-            To create a new OBSDirectoryLoader:
-            ```
-            config = {
-                "ak": "your-access-key",
-                "sk": "your-secret-key"
-            }
-            ```
-            directory_loader = OBSDirectoryLoader("your-bucket-name", "your-end-endpoint", config, "your-prefix")
-        """  # noqa: E501
-        try:
-            from obs import ObsClient
-        except ImportError:
-            raise ValueError(
-                "Could not import esdk-obs-python python package. "
-                "Please install it with `pip install esdk-obs-python`."
-            )
-        if not config:
-            config = dict()
-        if config.get("get_token_from_ecs"):
-            self.client = ObsClient(server=endpoint, security_provider_policy="ECS")
-        else:
-            self.client = ObsClient(
-                access_key_id=config.get("ak"),
-                secret_access_key=config.get("sk"),
-                security_token=config.get("token"),
-                server=endpoint,
-            )
-
-        self.bucket = bucket
-        self.prefix = prefix
-
-    def load(self) -> List[Document]:
-        """Load documents."""
-        max_num = 1000
-        mark = None
-        docs = []
-        while True:
-            resp = self.client.listObjects(
-                self.bucket, prefix=self.prefix, marker=mark, max_keys=max_num
-            )
-            if resp.status < 300:
-                for content in resp.body.contents:
-                    loader = OBSFileLoader(self.bucket, content.key, client=self.client)
-                    docs.extend(loader.load())
-                if resp.body.is_truncated is True:
-                    mark = resp.body.next_marker
-                else:
-                    break
-        return docs

libs/langchain/langchain/document_loaders/obs_file.py

@@ -1,104 +0,0 @@
-# coding:utf-8
-
-import os
-import tempfile
-from typing import Any, List, Optional
-
-from langchain.docstore.document import Document
-from langchain.document_loaders.base import BaseLoader
-from langchain.document_loaders.unstructured import UnstructuredFileLoader
-
-
-class OBSFileLoader(BaseLoader):
-    """Loader for Huawei OBS file."""
-
-    def __init__(
-        self,
-        bucket: str,
-        key: str,
-        client: Any = None,
-        endpoint: str = "",
-        config: Optional[dict] = None,
-    ) -> None:
-        """Initialize the OBSFileLoader with the specified settings.
-
-        Args:
-            bucket (str): The name of the OBS bucket to be used.
-            key (str): The name of the object in the OBS bucket.
-            client (ObsClient, optional): An instance of the ObsClient to connect to OBS.
-            endpoint (str, optional): The endpoint URL of your OBS bucket. This parameter is mandatory if `client` is not provided.
-            config (dict, optional): The parameters for connecting to OBS, provided as a dictionary. This parameter is ignored if `client` is provided. The dictionary could have the following keys:
-                - "ak" (str, optional): Your OBS access key (required if `get_token_from_ecs` is False and bucket policy is not public read).
-                - "sk" (str, optional): Your OBS secret key (required if `get_token_from_ecs` is False and bucket policy is not public read).
-                - "token" (str, optional): Your security token (required if using temporary credentials).
-                - "get_token_from_ecs" (bool, optional): Whether to retrieve the security token from ECS. Defaults to False if not provided. If set to True, `ak`, `sk`, and `token` will be ignored.
-
-        Raises:
-            ValueError: If the `esdk-obs-python` package is not installed.
-            TypeError: If the provided `client` is not an instance of ObsClient.
-            ValueError: If `client` is not provided, but `endpoint` is missing.
-
-        Note:
-            Before using this class, make sure you have registered with OBS and have the necessary credentials. The `ak`, `sk`, and `endpoint` values are mandatory unless `get_token_from_ecs` is True or the bucket policy is public read. `token` is required when using temporary credentials.
-
-        Example:
-            To create a new OBSFileLoader with a new client:
-            ```
-            config = {
-                "ak": "your-access-key",
-                "sk": "your-secret-key"
-            }
-            obs_loader = OBSFileLoader("your-bucket-name", "your-object-key", config=config)
-            ```
-
-            To create a new OBSFileLoader with an existing client:
-            ```
-            from obs import ObsClient
-
-            # Assuming you have an existing ObsClient object 'obs_client'
-            obs_loader = OBSFileLoader("your-bucket-name", "your-object-key", client=obs_client)
-            ```
-
-            To create a new OBSFileLoader without an existing client:
-            ```
-            obs_loader = OBSFileLoader("your-bucket-name", "your-object-key", endpoint="your-endpoint-url")
-            ```
-        """  # noqa: E501
-        try:
-            from obs import ObsClient
-        except ImportError:
-            raise ValueError(
-                "Could not import esdk-obs-python python package. "
-                "Please install it with `pip install esdk-obs-python`."
-            )
-        if not client:
-            if not endpoint:
-                raise ValueError("Either OBSClient or endpoint must be provided.")
-            if not config:
-                config = dict()
-            if config.get("get_token_from_ecs"):
-                client = ObsClient(server=endpoint, security_provider_policy="ECS")
-            else:
-                client = ObsClient(
-                    access_key_id=config.get("ak"),
-                    secret_access_key=config.get("sk"),
-                    security_token=config.get("token"),
-                    server=endpoint,
-                )
-        if not isinstance(client, ObsClient):
-            raise TypeError("Client must be ObsClient type")
-        self.client = client
-        self.bucket = bucket
-        self.key = key
-
-    def load(self) -> List[Document]:
-        """Load documents."""
-        with tempfile.TemporaryDirectory() as temp_dir:
-            file_path = f"{temp_dir}/{self.bucket}/{self.key}"
-            os.makedirs(os.path.dirname(file_path), exist_ok=True)
-            # Download the file to a destination
-            self.client.downloadFile(
-                bucketName=self.bucket, objectKey=self.key, downloadFile=file_path
-            )
-            loader = UnstructuredFileLoader(file_path)
-            return loader.load()

libs/langchain/langchain/document_loaders/parsers/audio.py

@@ -21,14 +21,14 @@ class OpenAIWhisperParser(BaseBlobParser):
         try:
             import openai
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "openai package not found, please install it with "
                 "`pip install openai`"
             )
         try:
             from pydub import AudioSegment
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "pydub package not found, please install it with " "`pip install pydub`"
             )
 
@@ -73,107 +73,3 @@ class OpenAIWhisperParser(BaseBlobParser):
                 page_content=transcript.text,
                 metadata={"source": blob.source, "chunk": split_number},
             )
-
-
-class OpenAIWhisperParserLocal(BaseBlobParser):
-    """Transcribe and parse audio files.
-    Audio transcription is with OpenAI Whisper model locally from transformers
-    NOTE: By default uses the gpu if available, if you want to use cpu,
-    please set device = "cpu"
-    """
-
-    def __init__(self, device: str = "0", lang_model: Optional[str] = None):
-        try:
-            from transformers import pipeline
-        except ImportError:
-            raise ImportError(
-                "transformers package not found, please install it with "
-                "`pip install transformers`"
-            )
-        try:
-            import torch
-        except ImportError:
-            raise ImportError(
-                "torch package not found, please install it with " "`pip install torch`"
-            )
-
-        # set device, cpu by default check if there is a GPU available
-        if device == "cpu":
-            self.device = "cpu"
-            if lang_model is not None:
-                self.lang_model = lang_model
-                print("WARNING! Model override. Using model: ", self.lang_model)
-            else:
-                # unless overridden, use the small base model on cpu
-                self.lang_model = "openai/whisper-base"
-        else:
-            if torch.cuda.is_available():
-                self.device = "cuda:0"
-                # check GPU memory and select automatically the model
-                mem = torch.cuda.get_device_properties(self.device).total_memory / (
-                    1024**2
-                )
-                if mem < 5000:
-                    rec_model = "openai/whisper-base"
-                elif mem < 7000:
-                    rec_model = "openai/whisper-small"
-                elif mem < 12000:
-                    rec_model = "openai/whisper-medium"
-                else:
-                    rec_model = "openai/whisper-large"
-
-                # check if model is overridden
-                if lang_model is not None:
-                    self.lang_model = lang_model
-                    print("WARNING! Model override. Might not fit in your GPU")
-                else:
-                    self.lang_model = rec_model
-            else:
-                "cpu"
-
-        print("Using the following model: ", self.lang_model)
-
-        # load model for inference
-        self.pipe = pipeline(
-            "automatic-speech-recognition",
-            model="openai/whisper-medium",
-            chunk_length_s=30,
-            device=self.device,
-        )
-
-    def lazy_parse(self, blob: Blob) -> Iterator[Document]:
-        """Lazily parse the blob."""
-
-        import io
-
-        try:
-            from pydub import AudioSegment
-        except ImportError:
-            raise ValueError(
-                "pydub package not found, please install it with " "`pip install pydub`"
-            )
-
-        try:
-            import librosa
-        except ImportError:
-            raise ValueError(
-                "librosa package not found, please install it with "
-                "`pip install librosa`"
-            )
-
-        # Audio file from disk
-        audio = AudioSegment.from_file(blob.path)
-
-        file_obj = io.BytesIO(audio.export(format="mp3").read())
-
-        # Transcribe
-        print(f"Transcribing part {blob.path}!")
-
-        y, sr = librosa.load(file_obj, sr=16000)
-
-        prediction = self.pipe(y.copy(), batch_size=8)["text"]
-
-        yield Document(
-            page_content=prediction,
-            metadata={"source": blob.source},
-        )

libs/langchain/langchain/document_loaders/parsers/grobid.py

@@ -1,4 +1,3 @@
-import logging
 from typing import Dict, Iterator, List, Union
 
 import requests
@@ -7,8 +6,6 @@ from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseBlobParser
 from langchain.document_loaders.blob_loaders import Blob
 
-logger = logging.getLogger(__name__)
-
 
 class ServerUnavailableException(Exception):
     """Exception raised when the GROBID server is unavailable."""
@@ -29,7 +26,7 @@ class GrobidParser(BaseBlobParser):
         try:
             requests.get(grobid_server)
         except requests.exceptions.RequestException:
-            logger.error(
+            print(
                 "GROBID server does not appear up and running, \
                 please ensure Grobid is installed and the server is running"
             )
@@ -139,7 +136,6 @@ class GrobidParser(BaseBlobParser):
             )
             xml_data = r.text
         except requests.exceptions.ReadTimeout:
-            logger.error("GROBID server timed out. Return None.")
             xml_data = None
 
         if xml_data is None:

libs/langchain/langchain/document_loaders/parsers/html/bs4.py

@@ -24,7 +24,7 @@ class BS4HTMLParser(BaseBlobParser):
         try:
             import bs4  # noqa:F401
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "beautifulsoup4 package not found, please install it with "
                 "`pip install beautifulsoup4`"
             )

libs/langchain/langchain/document_loaders/parsers/language/code_segmenter.py

@@ -13,8 +13,8 @@ class CodeSegmenter(ABC):
 
     @abstractmethod
     def simplify_code(self) -> str:
-        raise NotImplementedError()  # pragma: no cover
+        raise NotImplementedError  # pragma: no cover
 
     @abstractmethod
     def extract_functions_classes(self) -> List[str]:
-        raise NotImplementedError()  # pragma: no cover
+        raise NotImplementedError  # pragma: no cover

libs/langchain/langchain/document_loaders/parsers/pdf.py

@@ -87,7 +87,7 @@ class PyPDFium2Parser(BaseBlobParser):
         try:
             import pypdfium2  # noqa:F401
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "pypdfium2 package not found, please install it with"
                 " `pip install pypdfium2`"
             )

libs/langchain/langchain/document_loaders/reddit.py

@@ -14,7 +14,7 @@ def _dependable_praw_import() -> praw:
     try:
         import praw
     except ImportError:
-        raise ImportError(
+        raise ValueError(
             "praw package not found, please install it with `pip install praw`"
         )
     return praw

libs/langchain/langchain/document_loaders/tencent_cos_directory.py

@@ -27,7 +27,7 @@ class TencentCOSDirectoryLoader(BaseLoader):
         try:
             from qcloud_cos import CosS3Client
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "Could not import cos-python-sdk-v5 python package. "
                 "Please install it with `pip install cos-python-sdk-v5`."
             )

libs/langchain/langchain/document_loaders/tencent_cos_file.py

@@ -29,7 +29,7 @@ class TencentCOSFileLoader(BaseLoader):
         try:
             from qcloud_cos import CosS3Client
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "Could not import cos-python-sdk-v5 python package. "
                 "Please install it with `pip install cos-python-sdk-v5`."
             )

libs/langchain/langchain/document_loaders/text.py

@@ -43,7 +43,7 @@ class TextLoader(BaseLoader):
             if self.autodetect_encoding:
                 detected_encodings = detect_file_encodings(self.file_path)
                 for encoding in detected_encodings:
-                    logger.debug(f"Trying encoding: {encoding.encoding}")
+                    logger.debug("Trying encoding: ", encoding.encoding)
                     try:
                         with open(self.file_path, encoding=encoding.encoding) as f:
                             text = f.read()

libs/langchain/langchain/document_loaders/url.py

@@ -49,7 +49,7 @@ class UnstructuredURLLoader(BaseLoader):
 
             self.__version = __unstructured_version__
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "unstructured package not found, please install it with "
                 "`pip install unstructured`"
             )

libs/langchain/langchain/document_loaders/url_playwright.py

@@ -38,7 +38,7 @@ class PlaywrightURLLoader(BaseLoader):
         try:
             import unstructured  # noqa:F401
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "unstructured package not found, please install it with "
                 "`pip install unstructured`"
             )

libs/langchain/langchain/document_loaders/web_base.py

@@ -76,7 +76,7 @@ class WebBaseLoader(BaseLoader):
         try:
             import bs4  # noqa:F401
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 "bs4 package not found, please install it with " "`pip install bs4`"
             )
 

libs/langchain/langchain/document_transformers/__init__.py

@@ -1,20 +1,3 @@
-"""**Document Transformers** are classes to transform Documents.
-
-**Document Transformers** usually used to transform a lot of Documents in a single run.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseDocumentTransformer --> <name>  # Examples: DoctranQATransformer, DoctranTextTranslator
-
-**Main helpers:**
-
-.. code-block::
-
-    Document
-"""  # noqa: E501
-
 from langchain.document_transformers.doctran_text_extract import (
     DoctranPropertyExtractor,
 )
@@ -27,7 +10,6 @@ from langchain.document_transformers.embeddings_redundant_filter import (
 )
 from langchain.document_transformers.html2text import Html2TextTransformer
 from langchain.document_transformers.long_context_reorder import LongContextReorder
-from langchain.document_transformers.openai_functions import OpenAIMetadataTagger
 
 __all__ = [
     "DoctranQATransformer",
@@ -40,3 +22,5 @@ __all__ = [
     "OpenAIMetadataTagger",
     "Html2TextTransformer",
 ]
+
+from langchain.document_transformers.openai_functions import OpenAIMetadataTagger

libs/langchain/langchain/document_transformers/embeddings_redundant_filter.py

@@ -83,7 +83,7 @@ def _filter_cluster_embeddings(
     try:
         from sklearn.cluster import KMeans
     except ImportError:
-        raise ImportError(
+        raise ValueError(
             "sklearn package not found, please install it with "
             "`pip install scikit-learn`"
         )

libs/langchain/langchain/document_transformers/html2text.py

@@ -20,7 +20,7 @@ class Html2TextTransformer(BaseDocumentTransformer):
         try:
             import html2text
         except ImportError:
-            raise ImportError(
+            raise ValueError(
                 """html2text package not found, please 
                 install it with `pip install html2text`"""
             )

libs/langchain/langchain/embeddings/__init__.py

@@ -1,16 +1,4 @@
-"""**Embedding models**  are wrappers around embedding models
-from different APIs and services.
-
-**Embedding models** can be LLMs or not.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    Embeddings --> <name>Embeddings  # Examples: OpenAIEmbeddings, HuggingFaceEmbeddings
-"""
-
-
+"""Wrappers around embedding modules."""
 import logging
 from typing import Any
 

libs/langchain/langchain/embeddings/openai.py

@@ -454,6 +454,42 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
         return embeddings
 
+    def _embedding_func(self, text: str, *, engine: str) -> List[float]:
+        """Call out to OpenAI's embedding endpoint."""
+        # handle large input text
+        if len(text) > self.embedding_ctx_length:
+            return self._get_len_safe_embeddings([text], engine=engine)[0]
+        else:
+            if self.model.endswith("001"):
+                # See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
+                # replace newlines, which can negatively affect performance.
+                text = text.replace("\n", " ")
+            return embed_with_retry(
+                self,
+                input=[text],
+                **self._invocation_params,
+            )[
+                "data"
+            ][0]["embedding"]
+
+    async def _aembedding_func(self, text: str, *, engine: str) -> List[float]:
+        """Call out to OpenAI's embedding endpoint."""
+        # handle large input text
+        if len(text) > self.embedding_ctx_length:
+            return (await self._aget_len_safe_embeddings([text], engine=engine))[0]
+        else:
+            if self.model.endswith("001"):
+                # See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
+                # replace newlines, which can negatively affect performance.
+                text = text.replace("\n", " ")
+            return (
+                await async_embed_with_retry(
+                    self,
+                    input=[text],
+                    **self._invocation_params,
+                )
+            )["data"][0]["embedding"]
+
     def embed_documents(
         self, texts: List[str], chunk_size: Optional[int] = 0
     ) -> List[List[float]]:
@@ -497,7 +533,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         Returns:
             Embedding for the text.
         """
-        return self.embed_documents([text])[0]
+        embedding = self._embedding_func(text, engine=self.deployment)
+        return embedding
 
     async def aembed_query(self, text: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint async for embedding query text.
@@ -508,5 +545,5 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         Returns:
             Embedding for the text.
         """
-        embeddings = await self.aembed_documents([text])
-        return embeddings[0]
+        embedding = await self._aembedding_func(text, engine=self.deployment)
+        return embedding

libs/langchain/langchain/env.py

@@ -4,7 +4,7 @@ from functools import lru_cache
 
 @lru_cache(maxsize=1)
 def get_runtime_environment() -> dict:
-    """Get information about the LangChain runtime environment."""
+    """Get information about the environment."""
     # Lazy import to avoid circular imports
     from langchain import __version__
 

libs/langchain/langchain/evaluation/__init__.py

@@ -1,4 +1,4 @@
-"""**Evaluation** chains for grading LLM and Chain outputs.
+"""Evaluation chains for grading LLM and Chain outputs.
 
 This module contains off-the-shelf evaluation chains for grading the output of
 LangChain primitives such as language models and chains.

libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py

@@ -40,7 +40,7 @@ class TrajectoryEval(TypedDict):
     """A named tuple containing the score and reasoning for a trajectory."""
 
     score: float
-    """The score for the trajectory, normalized from 0 to 1."""
+    """The score for the trajectory, normalized from 0 to 1.s"""
     reasoning: str
     """The reasoning for the score."""
 

libs/langchain/langchain/graphs/__init__.py

@@ -1,9 +1,7 @@
-"""**Graphs** provide a natural language interface to graph databases."""
-
+"""Graph implementations."""
 from langchain.graphs.arangodb_graph import ArangoGraph
 from langchain.graphs.hugegraph import HugeGraph
 from langchain.graphs.kuzu_graph import KuzuGraph
-from langchain.graphs.memgraph_graph import MemgraphGraph
 from langchain.graphs.nebula_graph import NebulaGraph
 from langchain.graphs.neo4j_graph import Neo4jGraph
 from langchain.graphs.neptune_graph import NeptuneGraph
@@ -11,7 +9,6 @@ from langchain.graphs.networkx_graph import NetworkxEntityGraph
 from langchain.graphs.rdf_graph import RdfGraph
 
 __all__ = [
-    "MemgraphGraph",
     "NetworkxEntityGraph",
     "Neo4jGraph",
     "NebulaGraph",

libs/langchain/langchain/graphs/memgraph_graph.py

@@ -1,26 +0,0 @@
-from langchain.graphs.neo4j_graph import Neo4jGraph
-
-SCHEMA_QUERY = """
-CALL llm_util.schema("prompt_ready")
-YIELD *
-RETURN *
-"""
-
-
-class MemgraphGraph(Neo4jGraph):
-    """Memgraph wrapper for graph operations."""
-
-    def __init__(
-        self, url: str, username: str, password: str, *, database: str = "memgraph"
-    ) -> None:
-        """Create a new Memgraph graph wrapper instance."""
-        super().__init__(url, username, password, database=database)
-
-    def refresh_schema(self) -> None:
-        """
-        Refreshes the Memgraph graph schema information.
-        """
-
-        db_schema = self.query(SCHEMA_QUERY)[0].get("schema")
-        assert db_schema is not None
-        self.schema = db_schema

libs/langchain/langchain/graphs/neo4j_graph.py

@@ -81,7 +81,7 @@ class Neo4jGraph:
                 data = session.run(query, params)
                 return [r.data() for r in data]
             except CypherSyntaxError as e:
-                raise ValueError(f"Generated Cypher Statement is not valid\n{e}")
+                raise ValueError("Generated Cypher Statement is not valid\n" f"{e}")
 
     def refresh_schema(self) -> None:
         """

libs/langchain/langchain/indexes/__init__.py

@@ -1,4 +1,4 @@
-"""**Index** utilities."""
+"""All index utils."""
 from langchain.indexes.graph import GraphIndexCreator
 from langchain.indexes.vectorstore import VectorstoreIndexCreator
 

libs/langchain/langchain/llms/__init__.py

@@ -1,22 +1,4 @@
-"""
-**LLM** classes provide
-access to the large language model (**LLM**) APIs and services.
-
-**Class hierarchy:**
-
-.. code-block::
-
-    BaseLanguageModel --> BaseLLM --> LLM --> <name>  # Examples: AI21, HuggingFaceHub, OpenAI
-
-**Main helpers:**
-
-.. code-block::
-
-    LLMResult, PromptValue,
-    CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun,
-    CallbackManager, AsyncCallbackManager,
-    AIMessage, BaseMessage
-"""  # noqa: E501
+"""Access to the large language model APIs and services."""
 from typing import Dict, Type
 
 from langchain.llms.ai21 import AI21
@@ -39,7 +21,6 @@ from langchain.llms.ctransformers import CTransformers
 from langchain.llms.databricks import Databricks
 from langchain.llms.deepinfra import DeepInfra
 from langchain.llms.fake import FakeListLLM
-from langchain.llms.fireworks import Fireworks, FireworksChat
 from langchain.llms.forefrontai import ForefrontAI
 from langchain.llms.google_palm import GooglePalm
 from langchain.llms.gooseai import GooseAI
@@ -99,8 +80,6 @@ __all__ = [
     "Databricks",
     "DeepInfra",
     "FakeListLLM",
-    "Fireworks",
-    "FireworksChat",
     "ForefrontAI",
     "GPT4All",
     "GooglePalm",