docs[minor]: Format

.github/workflows/_test_doc_imports.yml

@@ -1,50 +0,0 @@
-name: test_doc_imports
-
-on:
-  workflow_call:
-
-env:
-  POETRY_VERSION: "1.7.1"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.11"
-    name: "check doc imports #${{ matrix.python-version }}"
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: core
-
-      - name: Install dependencies
-        shell: bash
-        run: poetry install --with test
-
-      - name: Install langchain editable
-        run: |
-          poetry run pip install -e libs/core libs/langchain libs/community libs/experimental
-
-      - name: Check doc imports
-        shell: bash
-        run: |
-          poetry run python docs/scripts/check_imports.py
-
-      - name: Ensure the test did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/check-broken-links.yml

@@ -22,3 +22,7 @@ jobs:
       - name: Check broken links
         run: yarn check-broken-links
         working-directory: ./docs
+      - name: Check broken links for .mdx files
+        uses: gaurav-nelson/github-action-markdown-link-check@v1
+        with:
+          file-extension: '.mdx'

.github/workflows/check_diffs.yml

@@ -60,12 +60,6 @@ jobs:
       working-directory: ${{ matrix.working-directory }}
     secrets: inherit
 
-  test_doc_imports:
-    needs: [ build ]
-    if: ${{ needs.build.outputs.dirs-to-test != '[]' }}
-    uses: ./.github/workflows/_test_doc_imports.yml
-    secrets: inherit
-
   compile-integration-tests:
     name: cd ${{ matrix.working-directory }}
     needs: [ build ]

Makefile

@@ -1,56 +1,44 @@
-.PHONY: all clean help docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck spell_check spell_fix lint lint_package lint_tests format format_diff
+.PHONY: all clean docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck
 
-## help: Show this help info.
-help: Makefile
-	@printf "\n\033[1mUsage: make <TARGETS> ...\033[0m\n\n\033[1mTargets:\033[0m\n\n"
-	@sed -n 's/^##//p' $< | awk -F':' '{printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' | sort | sed -e 's/^/ /'
-
-## all: Default target, shows help.
+# Default target executed when no arguments are given to make.
 all: help
 
-## clean: Clean documentation and API documentation artifacts.
-clean: docs_clean api_docs_clean
 
 ######################
 # DOCUMENTATION
 ######################
 
-## docs_build: Build the documentation.
+clean: docs_clean api_docs_clean
+
+
 docs_build:
 	docs/.local_build.sh
 
-## docs_clean: Clean the documentation build artifacts.
 docs_clean:
 	@if [ -d _dist ]; then \
-		rm -r _dist; \
-		echo "Directory _dist has been cleaned."; \
+			rm -r _dist; \
+			echo "Directory _dist has been cleaned."; \
 	else \
-		echo "Nothing to clean."; \
+			echo "Nothing to clean."; \
 	fi
 
-## docs_linkcheck: Run linkchecker on the documentation.
 docs_linkcheck:
 	poetry run linkchecker _dist/docs/ --ignore-url node_modules
 
-## api_docs_build: Build the API Reference documentation.
 api_docs_build:
 	poetry run python docs/api_reference/create_api_rst.py
 	cd docs/api_reference && poetry run make html
 
-## api_docs_clean: Clean the API Reference documentation build artifacts.
 api_docs_clean:
 	find ./docs/api_reference -name '*_api_reference.rst' -delete
 	cd docs/api_reference && poetry run make clean
 
-## api_docs_linkcheck: Run linkchecker on the API Reference documentation.
 api_docs_linkcheck:
 	poetry run linkchecker docs/api_reference/_build/html/index.html
 
-## spell_check: Run codespell on the project.
 spell_check:
 	poetry run codespell --toml pyproject.toml
 
-## spell_fix: Run codespell on the project and fix the errors.
 spell_fix:
 	poetry run codespell --toml pyproject.toml -w
 
@@ -58,14 +46,31 @@ spell_fix:
 # LINTING AND FORMATTING
 ######################
 
-## lint: Run linting on the project.
 lint lint_package lint_tests:
 	poetry run ruff docs templates cookbook
 	poetry run ruff format docs templates cookbook --diff
 	poetry run ruff --select I docs templates cookbook
 	git grep 'from langchain import' docs/docs templates cookbook | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
 
-## format: Format the project files.
 format format_diff:
 	poetry run ruff format docs templates cookbook
 	poetry run ruff --select I --fix docs templates cookbook
+
+
+######################
+# HELP
+######################
+
+help:
+	@echo '===================='
+	@echo '-- DOCUMENTATION --'
+	@echo 'clean                        - run docs_clean and api_docs_clean'
+	@echo 'docs_build                   - build the documentation'
+	@echo 'docs_clean                   - clean the documentation build artifacts'
+	@echo 'docs_linkcheck               - run linkchecker on the documentation'
+	@echo 'api_docs_build               - build the API Reference documentation'
+	@echo 'api_docs_clean               - clean the API Reference documentation build artifacts'
+	@echo 'api_docs_linkcheck           - run linkchecker on the API Reference documentation'
+	@echo 'spell_check               	- run codespell on the project'
+	@echo 'spell_fix               		- run codespell on the project and fix the errors'
+	@echo '-- TEST and LINT tasks are within libs/*/ per-package --'

cookbook/llm_symbolic_math.ipynb

@@ -45,7 +45,7 @@
     }
    ],
    "source": [
-    "llm_symbolic_math.invoke(\"What is the derivative of sin(x)*exp(x) with respect to x?\")"
+    "llm_symbolic_math.run(\"What is the derivative of sin(x)*exp(x) with respect to x?\")"
    ]
   },
   {
@@ -65,7 +65,7 @@
     }
    ],
    "source": [
-    "llm_symbolic_math.invoke(\n",
+    "llm_symbolic_math.run(\n",
     "    \"What is the integral of exp(x)*sin(x) + exp(x)*cos(x) with respect to x?\"\n",
     ")"
    ]
@@ -94,7 +94,7 @@
     }
    ],
    "source": [
-    "llm_symbolic_math.invoke('Solve the differential equation y\" - y = e^t')"
+    "llm_symbolic_math.run('Solve the differential equation y\" - y = e^t')"
    ]
   },
   {
@@ -114,7 +114,7 @@
     }
    ],
    "source": [
-    "llm_symbolic_math.invoke(\"What are the solutions to this equation y^3 + 1/3y?\")"
+    "llm_symbolic_math.run(\"What are the solutions to this equation y^3 + 1/3y?\")"
    ]
   },
   {
@@ -134,7 +134,7 @@
     }
    ],
    "source": [
-    "llm_symbolic_math.invoke(\"x = y + 5, y = z - 3, z = x * y. Solve for x, y, z\")"
+    "llm_symbolic_math.run(\"x = y + 5, y = z - 3, z = x * y. Solve for x, y, z\")"
    ]
   }
  ],

cookbook/sales_agent_with_context.ipynb

@@ -1,32 +1,28 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# SalesGPT - Context-Aware AI Sales Assistant With Knowledge Base and Ability Generate Stripe Payment Links\n",
+    "# SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base\n",
     "\n",
-    "This notebook demonstrates an implementation of a **Context-Aware** AI Sales agent with a Product Knowledge Base which can actually close sales. \n",
+    "This notebook demonstrates an implementation of a **Context-Aware** AI Sales agent with a Product Knowledge Base. \n",
     "\n",
     "This notebook was originally published at [filipmichalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) by [@FilipMichalsky](https://twitter.com/FilipMichalsky).\n",
     "\n",
     "SalesGPT is context-aware, which means it can understand what section of a sales conversation it is in and act accordingly.\n",
     " \n",
-    "As such, this agent can have a natural sales conversation with a prospect and behaves based on the conversation stage. Hence, this notebook demonstrates how we can use AI to automate sales development representatives activites, such as outbound sales calls. \n",
+    "As such, this agent can have a natural sales conversation with a prospect and behaves based on the conversation stage. Hence, this notebook demonstrates how we can use AI to automate sales development representatives activities, such as outbound sales calls. \n",
     "\n",
     "Additionally, the AI Sales agent has access to tools, which allow it to interact with other systems.\n",
     "\n",
     "Here, we show how the AI Sales Agent can use a **Product Knowledge Base** to speak about a particular's company offerings,\n",
     "hence increasing relevance and reducing hallucinations.\n",
     "\n",
-    "Furthermore, we show how our AI Sales Agent can **generate sales** by integration with the AI Agent Highway called [Mindware](https://www.mindware.co/). In practice, this allows the agent to autonomously generate a payment link for your customers **to pay for your products via Stripe**.\n",
-    "\n",
-    "We leverage the [`langchain`](https://github.com/hwchase17/langchain) library in this implementation, specifically [Custom Agent Configuration](https://langchain-langchain.vercel.app/docs/modules/agents/how_to/custom_agent_with_tool_retrieval) and are inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) architecture ."
+    "We leverage the [`langchain`](https://github.com/langchain-ai/langchain) library in this implementation, specifically [Custom Agent Configuration](https://langchain-langchain.vercel.app/docs/modules/agents/how_to/custom_agent_with_tool_retrieval) and are inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) architecture ."
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -42,10 +38,9 @@
     "import os\n",
     "import re\n",
     "\n",
-    "# make sure you have .env file saved locally with your API keys\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "load_dotenv()\n",
+    "# import your OpenAI key\n",
+    "OPENAI_API_KEY = \"sk-xx\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
     "\n",
     "from typing import Any, Callable, Dict, List, Union\n",
     "\n",
@@ -54,18 +49,27 @@
     "from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS\n",
     "from langchain.chains import LLMChain, RetrievalQA\n",
     "from langchain.chains.base import Chain\n",
-    "from langchain.llms import BaseLLM\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.prompts.base import StringPromptTemplate\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
+    "from langchain_community.llms import BaseLLM\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.agents import AgentAction, AgentFinish\n",
+    "from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings\n",
+    "from langchain_text_splitters import CharacterTextSplitter\n",
     "from pydantic import BaseModel, Field"
    ]
   },
   {
-   "attachments": {},
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install additional dependencies\n",
+    "# ! pip install chromadb openai tiktoken"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -73,21 +77,19 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "1. Seed the SalesGPT agent\n",
     "2. Run Sales Agent to decide what to do:\n",
     "\n",
-    "    a) Use a tool, such as look up Product Information in a Knowledge Base or Generate a Payment Link\n",
+    "    a) Use a tool, such as look up Product Information in a Knowledge Base\n",
     "    \n",
     "    b) Output a response to a user \n",
     "3. Run Sales Stage Recognition Agent to recognize which stage is the sales agent at and adjust their behaviour accordingly."
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -96,17 +98,15 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Architecture diagram\n",
     "\n",
-    "<img src=\"https://demo-bucket-45.s3.amazonaws.com/new_flow2.png\"  width=\"800\" height=\"440\">\n"
+    "<img src=\"https://singularity-assets-public.s3.amazonaws.com/new_flow.png\"  width=\"800\" height=\"440\"/>\n"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
     "            {conversation_history}\n",
     "            ===\n",
     "\n",
-    "            Now determine what should be the next immediate conversation stage for the agent in the sales conversation by selecting ony from the following options:\n",
+    "            Now determine what should be the next immediate conversation stage for the agent in the sales conversation by selecting only from the following options:\n",
     "            1. Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional.\n",
     "            2. Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.\n",
     "            3. Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.\n",
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,17 +240,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "# test the intermediate chains\n",
     "verbose = True\n",
-    "llm = ChatOpenAI(\n",
-    "    model=\"gpt-4-turbo-preview\",\n",
-    "    temperature=0.9,\n",
-    "    openai_api_key=os.getenv(\"OPENAI_API_KEY\"),\n",
-    ")\n",
+    "llm = ChatOpenAI(temperature=0.9)\n",
     "\n",
     "stage_analyzer_chain = StageAnalyzerChain.from_llm(llm, verbose=verbose)\n",
     "\n",
@@ -261,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +276,7 @@
       "            \n",
       "            ===\n",
       "\n",
-      "            Now determine what should be the next immediate conversation stage for the agent in the sales conversation by selecting ony from the following options:\n",
+      "            Now determine what should be the next immediate conversation stage for the agent in the sales conversation by selecting only from the following options:\n",
       "            1. Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional.\n",
       "            2. Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.\n",
       "            3. Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.\n",
@@ -300,21 +296,21 @@
     {
      "data": {
       "text/plain": [
-       "{'conversation_history': '', 'text': '1'}"
+       "'1'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "stage_analyzer_chain.invoke({\"conversation_history\": \"\"})"
+    "stage_analyzer_chain.run(conversation_history=\"\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -356,44 +352,32 @@
     {
      "data": {
       "text/plain": [
-       "{'salesperson_name': 'Ted Lasso',\n",
-       " 'salesperson_role': 'Business Development Representative',\n",
-       " 'company_name': 'Sleep Haven',\n",
-       " 'company_business': 'Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.',\n",
-       " 'company_values': \"Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.\",\n",
-       " 'conversation_purpose': 'find out whether they are looking to achieve better sleep via buying a premier mattress.',\n",
-       " 'conversation_history': 'Hello, this is Ted Lasso from Sleep Haven. How are you doing today? <END_OF_TURN>\\nUser: I am well, howe are you?<END_OF_TURN>',\n",
-       " 'conversation_type': 'call',\n",
-       " 'conversation_stage': 'Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are contacting the prospect.',\n",
-       " 'text': \"I'm doing well, thank you for asking. The reason I'm calling is to discuss how Sleep Haven can help enhance your sleep quality with our premium mattresses. Are you currently looking for ways to achieve a better night's sleep? <END_OF_TURN>\"}"
+       "\"I'm doing great, thank you for asking! As a Business Development Representative at Sleep Haven, I wanted to reach out to see if you are looking to achieve a better night's sleep. We provide premium mattresses that offer the most comfortable and supportive sleeping experience possible. Are you interested in exploring our sleep solutions? <END_OF_TURN>\""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sales_conversation_utterance_chain.invoke(\n",
-    "    {\n",
-    "        \"salesperson_name\": \"Ted Lasso\",\n",
-    "        \"salesperson_role\": \"Business Development Representative\",\n",
-    "        \"company_name\": \"Sleep Haven\",\n",
-    "        \"company_business\": \"Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.\",\n",
-    "        \"company_values\": \"Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.\",\n",
-    "        \"conversation_purpose\": \"find out whether they are looking to achieve better sleep via buying a premier mattress.\",\n",
-    "        \"conversation_history\": \"Hello, this is Ted Lasso from Sleep Haven. How are you doing today? <END_OF_TURN>\\nUser: I am well, howe are you?<END_OF_TURN>\",\n",
-    "        \"conversation_type\": \"call\",\n",
-    "        \"conversation_stage\": conversation_stages.get(\n",
-    "            \"1\",\n",
-    "            \"Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional.\",\n",
-    "        ),\n",
-    "    }\n",
+    "sales_conversation_utterance_chain.run(\n",
+    "    salesperson_name=\"Ted Lasso\",\n",
+    "    salesperson_role=\"Business Development Representative\",\n",
+    "    company_name=\"Sleep Haven\",\n",
+    "    company_business=\"Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.\",\n",
+    "    company_values=\"Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.\",\n",
+    "    conversation_purpose=\"find out whether they are looking to achieve better sleep via buying a premier mattress.\",\n",
+    "    conversation_history=\"Hello, this is Ted Lasso from Sleep Haven. How are you doing today? <END_OF_TURN>\\nUser: I am well, howe are you?<END_OF_TURN>\",\n",
+    "    conversation_type=\"call\",\n",
+    "    conversation_stage=conversation_stages.get(\n",
+    "        \"1\",\n",
+    "        \"Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional.\",\n",
+    "    ),\n",
     ")"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -401,7 +385,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -412,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -446,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -462,7 +445,7 @@
     "    text_splitter = CharacterTextSplitter(chunk_size=10, chunk_overlap=0)\n",
     "    texts = text_splitter.split_text(product_catalog)\n",
     "\n",
-    "    llm = ChatOpenAI(temperature=0)\n",
+    "    llm = OpenAI(temperature=0)\n",
     "    embeddings = OpenAIEmbeddings()\n",
     "    docsearch = Chroma.from_texts(\n",
     "        texts, embeddings, collection_name=\"product-knowledge-base\"\n",
@@ -471,12 +454,29 @@
     "    knowledge_base = RetrievalQA.from_chain_type(\n",
     "        llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
     "    )\n",
-    "    return knowledge_base"
+    "    return knowledge_base\n",
+    "\n",
+    "\n",
+    "def get_tools(product_catalog):\n",
+    "    # query to get_tools can be used to be embedded and relevant tools found\n",
+    "    # see here: https://langchain-langchain.vercel.app/docs/use_cases/agents/custom_agent_with_plugin_retrieval#tool-retriever\n",
+    "\n",
+    "    # we only use one tool for now, but this is highly extensible!\n",
+    "    knowledge_base = setup_knowledge_base(product_catalog)\n",
+    "    tools = [\n",
+    "        Tool(\n",
+    "            name=\"ProductSearch\",\n",
+    "            func=knowledge_base.run,\n",
+    "            description=\"useful for when you need to answer questions about product information\",\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    return tools"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -485,18 +485,16 @@
      "text": [
       "Created a chunk of size 940, which is longer than the specified 10\n",
       "Created a chunk of size 844, which is longer than the specified 10\n",
-      "Created a chunk of size 837, which is longer than the specified 10\n",
-      "/Users/filipmichalsky/Odyssey/sales_bot/SalesGPT/env/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `run` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
-      "  warn_deprecated(\n"
+      "Created a chunk of size 837, which is longer than the specified 10\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'The Sleep Haven products available are:\\n\\n1. Luxury Cloud-Comfort Memory Foam Mattress\\n2. Classic Harmony Spring Mattress\\n3. EcoGreen Hybrid Latex Mattress\\n4. Plush Serenity Bamboo Mattress\\n\\nEach product has its unique features and price point.'"
+       "' We have four products available: the Classic Harmony Spring Mattress, the Plush Serenity Bamboo Mattress, the Luxury Cloud-Comfort Memory Foam Mattress, and the EcoGreen Hybrid Latex Mattress. Each product is available in different sizes, with the Classic Harmony Spring Mattress available in Queen and King sizes, the Plush Serenity Bamboo Mattress available in King size, the Luxury Cloud-Comfort Memory Foam Mattress available in Twin, Queen, and King sizes, and the EcoGreen Hybrid Latex Mattress available in Twin and Full sizes.'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -510,199 +508,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Payment gateway"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to set up your AI agent to use a payment gateway to generate payment links for your users you need two things:\n",
-    "\n",
-    "1. Sign up for a Stripe account and obtain a STRIPE API KEY\n",
-    "2. Create products you would like to sell in the Stripe UI. Then follow out example of `example_product_price_id_mapping.json`\n",
-    "to feed the product name to price_id mapping which allows you to generate the payment links."
+    "### Set up the SalesGPT Controller with the Sales Agent and Stage Analyzer and a Knowledge Base"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "from litellm import completion\n",
-    "\n",
-    "# set GPT model env variable\n",
-    "os.environ[\"GPT_MODEL\"] = \"gpt-4-turbo-preview\"\n",
-    "\n",
-    "product_price_id_mapping = {\n",
-    "    \"ai-consulting-services\": \"price_1Ow8ofB795AYY8p1goWGZi6m\",\n",
-    "    \"Luxury Cloud-Comfort Memory Foam Mattress\": \"price_1Owv99B795AYY8p1mjtbKyxP\",\n",
-    "    \"Classic Harmony Spring Mattress\": \"price_1Owv9qB795AYY8p1tPcxCM6T\",\n",
-    "    \"EcoGreen Hybrid Latex Mattress\": \"price_1OwvLDB795AYY8p1YBAMBcbi\",\n",
-    "    \"Plush Serenity Bamboo Mattress\": \"price_1OwvMQB795AYY8p1hJN2uS3S\",\n",
-    "}\n",
-    "with open(\"example_product_price_id_mapping.json\", \"w\") as f:\n",
-    "    json.dump(product_price_id_mapping, f)\n",
-    "\n",
-    "\n",
-    "def get_product_id_from_query(query, product_price_id_mapping_path):\n",
-    "    # Load product_price_id_mapping from a JSON file\n",
-    "    with open(product_price_id_mapping_path, \"r\") as f:\n",
-    "        product_price_id_mapping = json.load(f)\n",
-    "\n",
-    "    # Serialize the product_price_id_mapping to a JSON string for inclusion in the prompt\n",
-    "    product_price_id_mapping_json_str = json.dumps(product_price_id_mapping)\n",
-    "\n",
-    "    # Dynamically create the enum list from product_price_id_mapping keys\n",
-    "    enum_list = list(product_price_id_mapping.values()) + [\n",
-    "        \"No relevant product id found\"\n",
-    "    ]\n",
-    "    enum_list_str = json.dumps(enum_list)\n",
-    "\n",
-    "    prompt = f\"\"\"\n",
-    "    You are an expert data scientist and you are working on a project to recommend products to customers based on their needs.\n",
-    "    Given the following query:\n",
-    "    {query}\n",
-    "    and the following product price id mapping:\n",
-    "    {product_price_id_mapping_json_str}\n",
-    "    return the price id that is most relevant to the query.\n",
-    "    ONLY return the price id, no other text. If no relevant price id is found, return 'No relevant price id found'.\n",
-    "    Your output will follow this schema:\n",
-    "    {{\n",
-    "    \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n",
-    "    \"title\": \"Price ID Response\",\n",
-    "    \"type\": \"object\",\n",
-    "    \"properties\": {{\n",
-    "        \"price_id\": {{\n",
-    "        \"type\": \"string\",\n",
-    "        \"enum\": {enum_list_str}\n",
-    "        }}\n",
-    "    }},\n",
-    "    \"required\": [\"price_id\"]\n",
-    "    }}\n",
-    "    Return a valid directly parsable json, dont return in it within a code snippet or add any kind of explanation!!\n",
-    "    \"\"\"\n",
-    "    prompt += \"{\"\n",
-    "    response = completion(\n",
-    "        model=os.getenv(\"GPT_MODEL\", \"gpt-3.5-turbo-1106\"),\n",
-    "        messages=[{\"content\": prompt, \"role\": \"user\"}],\n",
-    "        max_tokens=1000,\n",
-    "        temperature=0,\n",
-    "    )\n",
-    "\n",
-    "    product_id = response.choices[0].message.content.strip()\n",
-    "    return product_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "import requests\n",
-    "\n",
-    "\n",
-    "def generate_stripe_payment_link(query: str) -> str:\n",
-    "    \"\"\"Generate a stripe payment link for a customer based on a single query string.\"\"\"\n",
-    "\n",
-    "    # example testing payment gateway url\n",
-    "    PAYMENT_GATEWAY_URL = os.getenv(\n",
-    "        \"PAYMENT_GATEWAY_URL\", \"https://agent-payments-gateway.vercel.app/payment\"\n",
-    "    )\n",
-    "    PRODUCT_PRICE_MAPPING = \"example_product_price_id_mapping.json\"\n",
-    "\n",
-    "    # use LLM to get the price_id from query\n",
-    "    price_id = get_product_id_from_query(query, PRODUCT_PRICE_MAPPING)\n",
-    "    price_id = json.loads(price_id)\n",
-    "    payload = json.dumps(\n",
-    "        {\"prompt\": query, **price_id, \"stripe_key\": os.getenv(\"STRIPE_API_KEY\")}\n",
-    "    )\n",
-    "    headers = {\n",
-    "        \"Content-Type\": \"application/json\",\n",
-    "    }\n",
-    "\n",
-    "    response = requests.request(\n",
-    "        \"POST\", PAYMENT_GATEWAY_URL, headers=headers, data=payload\n",
-    "    )\n",
-    "    return response.text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'{\"response\":\"https://buy.stripe.com/test_6oEbLS8JB1F9bv229d\"}'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "generate_stripe_payment_link(\n",
-    "    query=\"Please generate a payment link for John Doe to buy two mattresses - the Classic Harmony Spring Mattress\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup agent tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_tools(product_catalog):\n",
-    "    # query to get_tools can be used to be embedded and relevant tools found\n",
-    "    # see here: https://langchain-langchain.vercel.app/docs/use_cases/agents/custom_agent_with_plugin_retrieval#tool-retriever\n",
-    "\n",
-    "    # we only use one tool for now, but this is highly extensible!\n",
-    "    knowledge_base = setup_knowledge_base(product_catalog)\n",
-    "    tools = [\n",
-    "        Tool(\n",
-    "            name=\"ProductSearch\",\n",
-    "            func=knowledge_base.run,\n",
-    "            description=\"useful for when you need to answer questions about product information or services offered, availability and their costs.\",\n",
-    "        ),\n",
-    "        Tool(\n",
-    "            name=\"GeneratePaymentLink\",\n",
-    "            func=generate_stripe_payment_link,\n",
-    "            description=\"useful to close a transaction with a customer. You need to include product name and quantity and customer name in the query input.\",\n",
-    "        ),\n",
-    "    ]\n",
-    "\n",
-    "    return tools"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set up the SalesGPT Controller with the Sales Agent and Stage Analyzer\n",
-    "\n",
-    "#### The Agent has access to a Knowledge Base and can autonomously sell your products via Stripe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -752,11 +563,19 @@
     "            print(\"TEXT\")\n",
     "            print(text)\n",
     "            print(\"-------\")\n",
+    "        if f\"{self.ai_prefix}:\" in text:\n",
+    "            return AgentFinish(\n",
+    "                {\"output\": text.split(f\"{self.ai_prefix}:\")[-1].strip()}, text\n",
+    "            )\n",
     "        regex = r\"Action: (.*?)[\\n]*Action Input: (.*)\"\n",
     "        match = re.search(regex, text)\n",
     "        if not match:\n",
+    "            ## TODO - this is not entirely reliable, sometimes results in an error.\n",
     "            return AgentFinish(\n",
-    "                {\"output\": text.split(f\"{self.ai_prefix}:\")[-1].strip()}, text\n",
+    "                {\n",
+    "                    \"output\": \"I apologize, I was unable to find the answer to your question. Is there anything else I can help with?\"\n",
+    "                },\n",
+    "                text,\n",
     "            )\n",
     "            # raise OutputParserException(f\"Could not parse LLM output: `{text}`\")\n",
     "        action = match.group(1)\n",
@@ -770,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -828,18 +647,18 @@
     "Previous conversation history:\n",
     "{conversation_history}\n",
     "\n",
-    "Thought:\n",
+    "{salesperson_name}:\n",
     "{agent_scratchpad}\n",
     "\"\"\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class SalesGPT(Chain):\n",
+    "class SalesGPT(Chain, BaseModel):\n",
     "    \"\"\"Controller model for the Sales Agent.\"\"\"\n",
     "\n",
     "    conversation_history: List[str] = []\n",
@@ -985,9 +804,7 @@
     "\n",
     "            # WARNING: this output parser is NOT reliable yet\n",
     "            ## It makes assumptions about output from LLM which can break and throw an error\n",
-    "            output_parser = SalesConvoOutputParser(\n",
-    "                ai_prefix=kwargs[\"salesperson_name\"], verbose=verbose\n",
-    "            )\n",
+    "            output_parser = SalesConvoOutputParser(ai_prefix=kwargs[\"salesperson_name\"])\n",
     "\n",
     "            sales_agent_with_tools = LLMSingleActionAgent(\n",
     "                llm_chain=llm_chain,\n",
@@ -1011,7 +828,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1019,7 +835,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1028,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1065,7 +880,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1074,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1083,9 +897,7 @@
      "text": [
       "Created a chunk of size 940, which is longer than the specified 10\n",
       "Created a chunk of size 844, which is longer than the specified 10\n",
-      "Created a chunk of size 837, which is longer than the specified 10\n",
-      "/Users/filipmichalsky/Odyssey/sales_bot/SalesGPT/env/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain.agents.agent.LLMSingleActionAgent` was deprecated in langchain 0.1.0 and will be removed in 0.2.0. Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. instead.\n",
-      "  warn_deprecated(\n"
+      "Created a chunk of size 837, which is longer than the specified 10\n"
      ]
     }
    ],
@@ -1095,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1105,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1122,14 +934,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ted Lasso:  Good day! This is Ted Lasso from Sleep Haven. How are you doing today?\n"
+      "Ted Lasso:  Hello, this is Ted Lasso from Sleep Haven. How are you doing today?\n"
      ]
     }
    ],
@@ -1139,18 +951,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "sales_agent.human_step(\n",
-    "    \"I am well, how are you? I would like to learn more about your services.\"\n",
+    "    \"I am well, how are you? I would like to learn more about your mattresses.\"\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1167,14 +979,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ted Lasso:  I'm doing great, thank you for asking! I'm glad to hear you're interested. Sleep Haven is a premium mattress company, and we're all about offering the best sleep solutions, including top-notch mattresses, pillows, and bedding accessories. Our mission is to help you achieve a better night's sleep. May I know if you're looking to enhance your sleep experience with a new mattress or bedding accessories? \n"
+      "Ted Lasso:  I'm glad to hear that you're doing well! As for our mattresses, at Sleep Haven, we provide customers with the most comfortable and supportive sleeping experience possible. Our high-quality mattresses are designed to meet the unique needs of our customers. Can I ask what specifically you'd like to learn more about? \n"
      ]
     }
    ],
@@ -1184,18 +996,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sales_agent.human_step(\n",
-    "    \"Yes, I would like to improve my sleep. Can you tell me more about your products?\"\n",
-    ")"
+    "sales_agent.human_step(\"Yes, what materials are you mattresses made from?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1212,14 +1022,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ted Lasso:  Absolutely, I'd be happy to share more about our products. At Sleep Haven, we offer a variety of high-quality mattresses designed to cater to different sleeping preferences and needs. Whether you're looking for memory foam's comfort, the support of hybrid mattresses, or the breathability of natural latex, we have options for everyone. Our pillows and bedding accessories are similarly curated to enhance your sleep quality. Every product is built with the aim of helping you achieve the restful night's sleep you deserve. What specific features are you looking for in a mattress? \n"
+      "Ted Lasso:  Our mattresses are made from a variety of materials, depending on the model. We have the EcoGreen Hybrid Latex Mattress, which is made from 100% natural latex harvested from eco-friendly plantations. The Plush Serenity Bamboo Mattress features a layer of plush, adaptive foam and a base of high-resilience support foam, with a bamboo-infused top layer. The Luxury Cloud-Comfort Memory Foam Mattress has an innovative, temperature-sensitive memory foam layer and a high-density foam base with cooling gel-infused particles. Finally, the Classic Harmony Spring Mattress has a robust inner spring construction and layers of plush padding, with a quilted top layer and a natural cotton cover. Is there anything specific you'd like to know about these materials?\n"
      ]
     }
    ],
@@ -1229,16 +1039,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sales_agent.human_step(\"What mattresses do you have and how much do they cost?\")"
+    "sales_agent.human_step(\n",
+    "    \"Yes, I am looking for a queen sized mattress. Do you have any mattresses in queen size?\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conversation Stage: Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "sales_agent.determine_conversation_stage()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ted Lasso:  Yes, we do have queen-sized mattresses available. We offer the Luxury Cloud-Comfort Memory Foam Mattress and the Classic Harmony Spring Mattress in queen size. Both mattresses provide exceptional comfort and support. Is there anything specific you would like to know about these options?\n"
+     ]
+    }
+   ],
+   "source": [
+    "sales_agent.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_agent.human_step(\"Yea, compare and contrast those two options, please.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1255,14 +1110,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ted Lasso:  We offer two primary types of mattresses at Sleep Haven. The first is our Luxury Cloud-Comfort Memory Foam Mattress, which is priced at $999 and comes in Twin, Queen, and King sizes. The second is our Classic Harmony Spring Mattress, priced at $1,299, available in Queen and King sizes. Both are designed to provide exceptional comfort and support for a better night's sleep. Which type of mattress would you be interested in learning more about? \n"
+      "Ted Lasso:  The Luxury Cloud-Comfort Memory Foam Mattress is priced at $999 and is available in Twin, Queen, and King sizes. It features an innovative, temperature-sensitive memory foam layer and a high-density foam base. On the other hand, the Classic Harmony Spring Mattress is priced at $1,299 and is available in Queen and King sizes. It features a robust inner spring construction and layers of plush padding. Both mattresses provide exceptional comfort and support, but the Classic Harmony Spring Mattress may be a better option if you prefer the traditional feel of an inner spring mattress. Do you have any other questions about these options?\n"
      ]
     }
    ],
@@ -1272,66 +1127,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
     "sales_agent.human_step(\n",
-    "    \"Okay.I would like to order two Memory Foam mattresses in Twin size please.\"\n",
+    "    \"Great, thanks, that's it. I will talk to my wife and call back if she is onboard. Have a good day!\"\n",
     ")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conversation Stage: Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits.\n"
-     ]
-    }
-   ],
-   "source": [
-    "sales_agent.determine_conversation_stage()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ted Lasso:  Fantastic choice! You're on your way to a better night's sleep with our Luxury Cloud-Comfort Memory Foam Mattresses. I've generated a payment link for two Twin size mattresses for you. Here is the link to complete your purchase: https://buy.stripe.com/test_6oEg28e3V97BdDabJn. Is there anything else I can assist you with today? \n"
-     ]
-    }
-   ],
-   "source": [
-    "sales_agent.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sales_agent.human_step(\n",
-    "    \"Great, thanks! I will discuss with my wife and will buy it if she is onboard. Have a good day!\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1350,9 +1153,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

cookbook/video_captioning/video_captioning.ipynb

@@ -1,174 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Video Captioning\n",
-    "This notebook shows how to use VideoCaptioningChain, which is implemented using Langchain's ImageCaptionLoader and AssemblyAI to produce .srt files.\n",
-    "\n",
-    "This system autogenerates both subtitles and closed captions from a video URL."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installing Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install ffmpeg-python\n",
-    "# !pip install assemblyai\n",
-    "# !pip install opencv-python\n",
-    "# !pip install torch\n",
-    "# !pip install pillow\n",
-    "# !pip install transformers\n",
-    "# !pip install langchain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T03:39:14.078232Z",
-     "start_time": "2023-11-30T03:39:12.534410Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "\n",
-    "from langchain.chains.video_captioning import VideoCaptioningChain\n",
-    "from langchain.chat_models.openai import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up API Keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T03:39:17.423806Z",
-     "start_time": "2023-11-30T03:39:17.417945Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "OPENAI_API_KEY = getpass.getpass(\"OpenAI API Key:\")\n",
-    "\n",
-    "ASSEMBLYAI_API_KEY = getpass.getpass(\"AssemblyAI API Key:\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Required parameters:**\n",
-    "\n",
-    "* llm: The language model this chain will use to get suggestions on how to refine the closed-captions\n",
-    "* assemblyai_key: The API key for AssemblyAI, used to generate the subtitles\n",
-    "\n",
-    "**Optional Parameters:**\n",
-    "\n",
-    "* verbose (Default: True): Sets verbose mode for downstream chain calls\n",
-    "* use_logging (Default: True): Log the chain's processes in run manager\n",
-    "* frame_skip (Default: None): Choose how many video frames to skip during processing. Increasing it results in faster execution, but less accurate results. If None, frame skip is calculated manually based on the framerate Set this to 0 to sample all frames\n",
-    "* image_delta_threshold (Default: 3000000): Set the sensitivity for what the image processor considers a change in scenery in the video, used to delimit closed captions. Higher = less sensitive\n",
-    "* closed_caption_char_limit (Default: 20): Sets the character limit on closed captions\n",
-    "* closed_caption_similarity_threshold (Default: 80): Sets the percentage value to how similar two closed caption models should be in order to be clustered into one longer closed caption\n",
-    "* use_unclustered_video_models (Default: False): If true, closed captions that could not be clustered will be included. May result in spontaneous behaviour from closed captions such as very short lasting captions or fast-changing captions. Enabling this is experimental and not recommended"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# https://ia804703.us.archive.org/27/items/uh-oh-here-we-go-again/Uh-Oh%2C%20Here%20we%20go%20again.mp4\n",
-    "# https://ia601200.us.archive.org/9/items/f58703d4-61e6-4f8f-8c08-b42c7e16f7cb/f58703d4-61e6-4f8f-8c08-b42c7e16f7cb.mp4\n",
-    "\n",
-    "chain = VideoCaptioningChain(\n",
-    "    llm=ChatOpenAI(model=\"gpt-4\", max_tokens=4000, openai_api_key=OPENAI_API_KEY),\n",
-    "    assemblyai_key=ASSEMBLYAI_API_KEY,\n",
-    ")\n",
-    "\n",
-    "srt_content = chain.run(\n",
-    "    video_file_path=\"https://ia601200.us.archive.org/9/items/f58703d4-61e6-4f8f-8c08-b42c7e16f7cb/f58703d4-61e6-4f8f-8c08-b42c7e16f7cb.mp4\"\n",
-    ")\n",
-    "\n",
-    "print(srt_content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Writing output to .srt file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"output.srt\", \"w\") as file:\n",
-    "    file.write(srt_content)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "myenv",
-   "language": "python",
-   "name": "myenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docker/docker-compose.yml

@@ -4,14 +4,14 @@
 # ATTENTION: When adding a service below use a non-standard port
 # increment by one from the preceding port.
 # For credentials always use `langchain` and `langchain` for the
-# username and password.
+# username and password. 
 version: "3"
 name: langchain-tests
 
 services:
   redis:
     image: redis/redis-stack-server:latest
-    # We use non standard ports since
+    # We use non standard ports since 
     # these instances are used for testing
     # and users may already have existing
     # redis instances set up locally
@@ -73,11 +73,6 @@ services:
       retries: 60
     volumes:
       - postgres_data_pgvector:/var/lib/postgresql/data
-  vdms:
-    image: intellabs/vdms:latest
-    container_name: vdms_container
-    ports:
-      - "6025:55555"
 
 volumes:
   postgres_data:

docs/.eslintrc.js

@@ -0,0 +1,53 @@
+/**
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ *
+ * @format
+ */
+
+const OFF = 0;
+const WARNING = 1;
+const ERROR = 2;
+
+module.exports = {
+  root: true,
+  env: {
+    browser: true,
+    commonjs: true,
+    jest: true,
+    node: true,
+  },
+  parser: "@babel/eslint-parser",
+  parserOptions: {
+    allowImportExportEverywhere: true,
+  },
+  extends: ["airbnb", "prettier"],
+  plugins: ["react-hooks", "header"],
+  ignorePatterns: [
+    "build",
+    "docs/api",
+    "node_modules",
+    "docs/_static",
+    "static",
+  ],
+  rules: {
+    // Ignore certain webpack alias because it can't be resolved
+    "import/no-unresolved": [
+      ERROR,
+      { ignore: ["^@theme", "^@docusaurus", "^@generated"] },
+    ],
+    "import/extensions": OFF,
+    "react/jsx-filename-extension": OFF,
+    "react-hooks/rules-of-hooks": ERROR,
+    "react/prop-types": OFF, // PropTypes aren't used much these days.
+    "react/function-component-definition": [
+      WARNING,
+      {
+        namedComponents: "function-declaration",
+        unnamedComponents: "arrow-function",
+      },
+    ],
+  },
+};

docs/.gitignore

@@ -1,2 +1,3 @@
 /.quarto/
 src/supabase.d.ts
+.eslintcache

docs/.prettierignore

@@ -0,0 +1,7 @@
+node_modules
+build
+.docusaurus
+docs/api
+docs/_static
+static
+quarto-1.3.450

docs/docs/_templates/integration.mdx

@@ -1,31 +1,30 @@
-[comment: Please, a reference example here "docs/integrations/arxiv.md"]::
-[comment: Use this template to create a new .md file in "docs/integrations/"]::
+[comment: Please, a reference example here "docs/integrations/arxiv.md"]: :
+[comment: Use this template to create a new .md file in "docs/integrations/"]: :
 
 # Title_REPLACE_ME
 
-[comment: Only one Tile/H1 is allowed!]::
+[comment: Only one Tile/H1 is allowed!]: :
 
->
-[comment: Description: After reading this description, a reader should decide if this integration is good enough to try/follow reading OR]::
-[comment: go to read the next integration doc. ]::
-[comment: Description should include a link to the source for follow reading.]::
+> [comment: Description: After reading this description, a reader should decide if this integration is good enough to try/follow reading OR]: :
+> [comment: go to read the next integration doc. ]: :
+> [comment: Description should include a link to the source for follow reading.]: :
 
 ## Installation and Setup
 
-[comment: Installation and Setup: All necessary additional package installations and setups for Tokens, etc]::
+[comment: Installation and Setup: All necessary additional package installations and setups for Tokens, etc]: :
 
 ```bash
 pip install package_name_REPLACE_ME
 ```
 
-[comment: OR this text:]::
+[comment: OR this text:]: :
 
 There isn't any special setup for it.
 
-[comment: The next H2/## sections with names of the integration modules, like "LLM", "Text Embedding Models", etc]::
-[comment: see "Modules" in the "index.html" page]::
-[comment: Each H2 section should include a link to an example(s) and a Python code with the import of the integration class]::
-[comment: Below are several example sections. Remove all unnecessary sections. Add all necessary sections not provided here.]::
+[comment: The next H2/## sections with names of the integration modules, like "LLM", "Text Embedding Models", etc]: :
+[comment: see "Modules" in the "index.html" page]: :
+[comment: Each H2 section should include a link to an example(s) and a Python code with the import of the integration class]: :
+[comment: Below are several example sections. Remove all unnecessary sections. Add all necessary sections not provided here.]: :
 
 ## LLM
 

docs/docs/additional_resources/tutorials.mdx

@@ -6,16 +6,19 @@
 - [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
 - [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
 
-
-##  Tutorials
+## Tutorials
 
 ### [by Greg Kamradt](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5)
-### [by Sam Witteveen](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ)
-### [by James Briggs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F)
-### [by Prompt Engineering](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr)
-### [by Mayo Oshin](https://www.youtube.com/@chatwithdata/search?query=langchain)
-### [by 1 little Coder](https://www.youtube.com/playlist?list=PLpdmBGJ6ELUK-v0MK-t4wZmVEbxM5xk6L)
 
+### [by Sam Witteveen](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ)
+
+### [by James Briggs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F)
+
+### [by Prompt Engineering](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr)
+
+### [by Mayo Oshin](https://www.youtube.com/@chatwithdata/search?query=langchain)
+
+### [by 1 little Coder](https://www.youtube.com/playlist?list=PLpdmBGJ6ELUK-v0MK-t4wZmVEbxM5xk6L)
 
 ## Courses
 
@@ -45,6 +48,4 @@
 
 ## [Documentation: Use cases](/docs/use_cases)
 
----------------------
-
-
+---

docs/docs/additional_resources/youtube.mdx

@@ -5,18 +5,19 @@
 ### [Official LangChain YouTube channel](https://www.youtube.com/@LangChain)
 
 ### Introduction to LangChain with Harrison Chase, creator of LangChain
+
 - [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
 - [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
-- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@The_Full_Stack)
+- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
 - [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
 
 ## Videos (sorted by views)
 
 - [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
-- [First look - `ChatGPT` + `WolframAlpha` (`GPT-3.5` and Wolfram|Alpha via LangChain by James Weaver)](https://youtu.be/wYGbY811oMo) by [Dr Alan D. Thompson](https://www.youtube.com/@DrAlanDThompson) 
+- [First look - `ChatGPT` + `WolframAlpha` (`GPT-3.5` and Wolfram|Alpha via LangChain by James Weaver)](https://youtu.be/wYGbY811oMo) by [Dr Alan D. Thompson](https://www.youtube.com/@DrAlanDThompson)
 - [LangChain explained - The hottest new Python framework](https://youtu.be/RoR4XJw8wIc) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
-- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
-- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
+- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
+- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
 - [Build your own LLM Apps with LangChain & `GPT-Index`](https://youtu.be/-75p09zFUJY) by [1littlecoder](https://www.youtube.com/@1littlecoder)
 - [`BabyAGI` - New System of Autonomous AI Agents with LangChain](https://youtu.be/lg3kJvf1kXo) by [1littlecoder](https://www.youtube.com/@1littlecoder)
 - [Run `BabyAGI` with Langchain Agents (with Python Code)](https://youtu.be/WosPGHPObx8) by [1littlecoder](https://www.youtube.com/@1littlecoder)
@@ -37,15 +38,15 @@
 - [Building AI LLM Apps with LangChain (and more?) - LIVE STREAM](https://www.youtube.com/live/M-2Cj_2fzWI?feature=share) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
 - [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
 - [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
-- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@heymichaeldaigler) 
-- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nickdaigler)
+- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@merksworld)
+- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nick_daigs)
 - [LangChain. Crear aplicaciones Python impulsadas por GPT](https://youtu.be/DkW_rDndts8) by [Jesús Conde](https://www.youtube.com/@0utKast)
 - [Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial](https://youtu.be/fLy0VenZyGc) by [Rachel Woods](https://www.youtube.com/@therachelwoods)
 - [`BabyAGI` + `GPT-4` Langchain Agent with Internet Access](https://youtu.be/wx1z_hs5P6E) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
 - [Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI](https://youtu.be/mb_YAABSplk) by [Arnoldas Kemeklis](https://www.youtube.com/@processusAI)
 - [Get Started with LangChain in `Node.js`](https://youtu.be/Wxx1KUWJFv4) by [Developers Digest](https://www.youtube.com/@DevelopersDigest)
 - [LangChain + `OpenAI` tutorial: Building a Q&A system w/ own text data](https://youtu.be/DYOU_Z0hAwo) by [Samuel Chan](https://www.youtube.com/@SamuelChan)
-- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@heymichaeldaigler)
+- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld)
 - [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
 - [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
 - [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
@@ -82,7 +83,7 @@
 - [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
 - [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
 - [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
-- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@heymichaeldaigler)
+- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
 - [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
 - [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
 - [Build AI chatbot with custom knowledge base using OpenAI API and GPT Index](https://youtu.be/vDZAZuaXf48) by [Irina Nik](https://www.youtube.com/@irina_nik)
@@ -93,7 +94,7 @@
 - [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod)
 - [`Flowise` is an open-source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
 - [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
-- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Girlfriend GPT](https://www.youtube.com/@girlfriendGPT)
+- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Toolfinder AI](https://www.youtube.com/@toolfinderai)
 - [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ)
 - [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg) by [Krish Naik](https://www.youtube.com/@krishnaik06)
 - ⛓ [Vector Embeddings Tutorial – Code Your Own AI Assistant with `GPT-4 API` + LangChain + NLP](https://youtu.be/yfHHvmaMkcA?si=5uJhxoh2tvdnOXok) by [FreeCodeCamp.org](https://www.youtube.com/@freecodecamp)
@@ -108,8 +109,8 @@
 - ⛓ [How to Run `LLaMA` Locally on CPU or GPU | Python & Langchain & CTransformers Guide](https://youtu.be/SvjWDX2NqiM?si=DxFml8XeGhiLTzLV) by [Code With Prince](https://www.youtube.com/@CodeWithPrince)
 - ⛓ [PyData Heidelberg #11 - TimeSeries Forecasting & LLM Langchain](https://www.youtube.com/live/Glbwb5Hxu18?si=PIEY8Raq_C9PCHuW) by [PyData](https://www.youtube.com/@PyDataTV)
 - ⛓ [Prompt Engineering in Web Development | Using LangChain and Templates with OpenAI](https://youtu.be/pK6WzlTOlYw?si=fkcDQsBG2h-DM8uQ) by [Akamai Developer
-](https://www.youtube.com/@AkamaiDeveloper)
-- ⛓ [Retrieval-Augmented Generation (RAG) using LangChain and `Pinecone` - The RAG Special Episode](https://youtu.be/J_tCD_J6w3s?si=60Mnr5VD9UED9bGG) by [Generative AI and Data Science On AWS](https://www.youtube.com/@GenerativeAIOnAWS)
+  ](https://www.youtube.com/@AkamaiDeveloper)
+- ⛓ [Retrieval-Augmented Generation (RAG) using LangChain and `Pinecone` - The RAG Special Episode](https://youtu.be/J_tCD_J6w3s?si=60Mnr5VD9UED9bGG) by [Generative AI and Data Science On AWS](https://www.youtube.com/@GenerativeAIDataScienceOnAWS)
 - ⛓ [`LLAMA2 70b-chat` Multiple Documents Chatbot with Langchain & Streamlit |All OPEN SOURCE|Replicate API](https://youtu.be/vhghB81vViM?si=dszzJnArMeac7lyc) by [DataInsightEdge](https://www.youtube.com/@DataInsightEdge01)
 - ⛓ [Chatting with 44K Fashion Products: LangChain Opportunities and Pitfalls](https://youtu.be/Zudgske0F_s?si=8HSshHoEhh0PemJA) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
 - ⛓ [Structured Data Extraction from `ChatGPT` with LangChain](https://youtu.be/q1lYg8JISpQ?si=0HctzOHYZvq62sve) by [MG](https://www.youtube.com/@MG_cafe)
@@ -123,8 +124,8 @@
 - ⛓ [Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
 - ⛓ [Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
 
-
 ### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
+
 - [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
 - [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
 - [LangChain Models: `ChatGPT`, `Flan Alpaca`, `OpenAI Embeddings`, Prompt Templates & Streaming](https://www.youtube.com/watch?v=zy6LiK5F5-s)
@@ -132,6 +133,6 @@
 - [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
 - [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
 
+---
 
----------------------
 ⛓ icon marks a new addition [last update 2024-02-04]

docs/docs/changelog/langchain.mdx

@@ -10,8 +10,8 @@ No deletions.
 
 Deprecated classes and methods will be removed in 0.2.0
 
-| Deprecated                | Alternative                       | Reason                                         |
-|---------------------------------|-----------------------------------|------------------------------------------------|
+| Deprecated                      | Alternative                       | Reason                                         |
+| ------------------------------- | --------------------------------- | ---------------------------------------------- |
 | ChatVectorDBChain               | ConversationalRetrievalChain      | More general to all retrievers                 |
 | create_ernie_fn_chain           | create_ernie_fn_runnable          | Use LCEL under the hood                        |
 | created_structured_output_chain | create_structured_output_runnable | Use LCEL under the hood                        |
@@ -33,4 +33,4 @@ Deprecated classes and methods will be removed in 0.2.0
 | OpenAIMultiFunctionsAgent       | create_openai_tools_agent         | Use LCEL builder over a class                  |
 | SelfAskWithSearchAgent          | create_self_ask_with_search       | Use LCEL builder over a class                  |
 | StructuredChatAgent             | create_structured_chat_agent      | Use LCEL builder over a class                  |
-| XMLAgent                        | create_xml_agent                  | Use LCEL builder over a class                  |
+| XMLAgent                        | create_xml_agent                  | Use LCEL builder over a class                  |

docs/docs/contributing/code.mdx

@@ -1,6 +1,7 @@
 ---
 sidebar_position: 1
 ---
+
 # Contribute Code
 
 To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
@@ -13,6 +14,7 @@ Pull requests cannot land without passing the formatting, linting, and testing c
 [Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
 
 It's essential that we maintain great documentation and testing. If you:
+
 - Fix a bug
   - Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
 - Make an improvement
@@ -34,7 +36,7 @@ For a [development container](https://containers.dev/), see the [.devcontainer f
 
 This project utilizes [Poetry](https://python-poetry.org/) v1.7.1+ as a dependency manager.
 
-❗Note: *Before installing Poetry*, if you use `Conda`, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`)
+❗Note: _Before installing Poetry_, if you use `Conda`, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`)
 
 Install Poetry: **[documentation on how to install it](https://python-poetry.org/docs/#installation)**.
 
@@ -44,6 +46,7 @@ tell Poetry to use the virtualenv python environment (`poetry config virtualenvs
 ### Different packages
 
 This repository contains multiple packages:
+
 - `langchain-core`: Base interfaces for key abstractions as well as logic for combining them in chains (LangChain Expression Language).
 - `langchain-community`: Third-party integrations of various components.
 - `langchain`: Chains, agents, and retrieval logic that makes up the cognitive architecture of your applications.
@@ -219,16 +222,20 @@ any side effects (no warnings, no errors, no exceptions).
 To introduce the dependency to the pyproject.toml file correctly, please do the following:
 
 1. Add the dependency to the main group as an optional dependency
-  ```bash
-  poetry add --optional [package_name]
-  ```
+
+```bash
+poetry add --optional [package_name]
+```
+
 2. Open pyproject.toml and add the dependency to the `extended_testing` extra
 3. Relock the poetry file to update the extra.
-  ```bash
-  poetry lock --no-update
-  ```
+
+```bash
+poetry lock --no-update
+```
+
 4. Add a unit test that the very least attempts to import the new code. Ideally, the unit
-test makes use of lightweight fixtures to test the logic of the code.
+   test makes use of lightweight fixtures to test the logic of the code.
 5. Please use the `@pytest.mark.requires(package_name)` decorator for any tests that require the dependency.
 
 ## Adding a Jupyter Notebook

docs/docs/contributing/documentation.mdx

@@ -1,19 +1,20 @@
 ---
 sidebar_position: 3
 ---
+
 # Contribute Documentation
 
 LangChain documentation consists of two components:
 
 1. Main Documentation: Hosted at [python.langchain.com](https://python.langchain.com/),
-this comprehensive resource serves as the primary user-facing documentation.
-It covers a wide array of topics, including tutorials, use cases, integrations,
-and more, offering extensive guidance on building with LangChain.
-The content for this documentation lives in the `/docs` directory of the monorepo.
+   this comprehensive resource serves as the primary user-facing documentation.
+   It covers a wide array of topics, including tutorials, use cases, integrations,
+   and more, offering extensive guidance on building with LangChain.
+   The content for this documentation lives in the `/docs` directory of the monorepo.
 2. In-code Documentation: This is documentation of the codebase itself, which is also
-used to generate the externally facing [API Reference](https://api.python.langchain.com/en/latest/langchain_api_reference.html).
-The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that
-developers document their code well.
+   used to generate the externally facing [API Reference](https://api.python.langchain.com/en/latest/langchain_api_reference.html).
+   The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that
+   developers document their code well.
 
 The main documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
 
@@ -59,7 +60,7 @@ From the **monorepo root**, run the following command to install the dependencie
 
 ```bash
 poetry install --with lint,docs --no-root
-````
+```
 
 ### Building
 
@@ -171,4 +172,4 @@ make lint
 After pushing documentation changes to the repository, you can preview and verify that the changes are
 what you wanted by clicking the `View deployment` or `Visit Preview` buttons on the pull request `Conversation` page.
 This will take you to a preview of the documentation changes.
-This preview is created by [Vercel](https://vercel.com/docs/getting-started-with-vercel).
+This preview is created by [Vercel](https://vercel.com/docs/getting-started-with-vercel).

docs/docs/contributing/faq.mdx

@@ -2,6 +2,7 @@
 sidebar_position: 6
 sidebar_label: FAQ
 ---
+
 # Frequently Asked Questions
 
 ## Pull Requests (PRs)
@@ -13,7 +14,7 @@ necessary before merging it. Oftentimes, it is more efficient for the
 maintainers to make these changes themselves before merging, rather than asking you
 to do so in code review.
 
-By default, most pull requests will have a 
+By default, most pull requests will have a
 `✅ Maintainers are allowed to edit this pull request.`
 badge in the right-hand sidebar.
 

docs/docs/contributing/index.mdx

@@ -1,6 +1,7 @@
 ---
 sidebar_position: 0
 ---
+
 # Welcome Contributors
 
 Hi there! Thank you for even being interested in contributing to LangChain.
@@ -51,4 +52,4 @@ we do not want these to get in the way of getting good code into the codebase.
 # 🌟 Recognition
 
 If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.
+If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.

docs/docs/contributing/integrations.mdx

@@ -1,6 +1,7 @@
 ---
 sidebar_position: 5
 ---
+
 # Contribute Integrations
 
 To begin, make sure you have all the dependencies outlined in guide on [Contributing Code](./code).
@@ -18,7 +19,7 @@ In the following sections, we'll walk through how to contribute to each of these
 
 The `langchain-community` package is in `libs/community` and contains most integrations.
 
-It can be installed with `pip install langchain-community`, and exported members can be imported with code like 
+It can be installed with `pip install langchain-community`, and exported members can be imported with code like
 
 ```python
 from langchain_community.chat_models import ChatParrotLink
@@ -26,7 +27,7 @@ from langchain_community.llms import ParrotLinkLLM
 from langchain_community.vectorstores import ParrotLinkVectorStore
 ```
 
-The `community` package relies on manually-installed dependent packages, so you will see errors 
+The `community` package relies on manually-installed dependent packages, so you will see errors
 if you try to import a package that is not installed. In our fake example, if you tried to import `ParrotLinkLLM` without installing `parrot-link-sdk`, you will see an `ImportError` telling you to install it when trying to use it.
 
 Let's say we wanted to implement a chat model for Parrot Link AI. We would create a new file in `libs/community/langchain_community/chat_models/parrot_link.py` with the following code:
@@ -61,11 +62,11 @@ And add documentation to:
 
 Partner packages can be hosted in the `LangChain` monorepo or in an external repo.
 
-Partner package in the `LangChain` repo is placed in `libs/partners/{partner}` 
+Partner package in the `LangChain` repo is placed in `libs/partners/{partner}`
 and the package source code is in `libs/partners/{partner}/langchain_{partner}`.
 
-A package is 
-installed by users with `pip install langchain-{partner}`, and the package members 
+A package is
+installed by users with `pip install langchain-{partner}`, and the package members
 can be imported with code like:
 
 ```python
@@ -142,11 +143,11 @@ to the relevant `docs/docs/integrations` directory in the monorepo root.
 
 ### (If Necessary) Deprecate community integration
 
-Note: this is only necessary if you're migrating an existing community integration into 
-a partner package. If the component you're integrating is net-new to LangChain (i.e. 
+Note: this is only necessary if you're migrating an existing community integration into
+a partner package. If the component you're integrating is net-new to LangChain (i.e.
 not already in the `community` package), you can skip this step.
 
-Let's pretend we migrated our `ChatParrotLink` chat model from the community package to 
+Let's pretend we migrated our `ChatParrotLink` chat model from the community package to
 the partner package. We would need to deprecate the old model in the community package.
 
 We would do that by adding a `@deprecated` decorator to the old model as follows, in
@@ -165,15 +166,15 @@ After our change, it would look like this:
 from langchain_core._api.deprecation import deprecated
 
 @deprecated(
-    since="0.0.<next community version>", 
-    removal="0.2.0", 
+    since="0.0.<next community version>",
+    removal="0.2.0",
     alternative_import="langchain_parrot_link.ChatParrotLink"
 )
 class ChatParrotLink(BaseChatModel):
   ...
 ```
 
-You should do this for *each* component that you're migrating to the partner package.
+You should do this for _each_ component that you're migrating to the partner package.
 
 ### Additional steps
 
@@ -190,12 +191,12 @@ Maintainer steps (Contributors should **not** do these):
 
 ## Partner package in external repo
 
-If you are creating a partner package in an external repo, you should follow the same steps as above, 
+If you are creating a partner package in an external repo, you should follow the same steps as above,
 but you will need to set up your own CI/CD and package management.
 
 Name your package as `langchain-{partner}-{integration}`.
 
 Still, you have to create the `libs/partners/{partner}-{integration}` folder in the `LangChain` monorepo
-and add a `README.md` file with a link to the external repo. 
+and add a `README.md` file with a link to the external repo.
 See this [example](https://github.com/langchain-ai/langchain/tree/master/libs/partners/google-genai).
 This allows keeping track of all the partner packages in the `LangChain` documentation.

docs/docs/contributing/repo_structure.mdx

@@ -1,6 +1,7 @@
 ---
 sidebar_position: 0.5
 ---
+
 # Repository Structure
 
 If you plan on contributing to LangChain code or documentation, it can be useful
@@ -31,8 +32,8 @@ Here's the structure visualized as a tree:
 
 The root directory also contains the following files:
 
-* `pyproject.toml`: Dependencies for building docs and linting docs, cookbook.
-* `Makefile`: A file that contains shortcuts for building, linting and docs and cookbook.
+- `pyproject.toml`: Dependencies for building docs and linting docs, cookbook.
+- `Makefile`: A file that contains shortcuts for building, linting and docs and cookbook.
 
 There are other files in the root directory level, but their presence should be self-explanatory. Feel free to browse around!
 

docs/docs/contributing/testing.mdx

@@ -46,11 +46,11 @@ If you add support for a new external API, please add a new integration test.
 
 **Warning:** Almost no tests should be integration tests.
 
-  Tests that require making network connections make it difficult for other
-  developers to test the code.
+Tests that require making network connections make it difficult for other
+developers to test the code.
 
-  Instead favor relying on `responses` library and/or mock.patch to mock
-  requests using small fixtures.
+Instead favor relying on `responses` library and/or mock.patch to mock
+requests using small fixtures.
 
 To install dependencies for integration tests:
 
@@ -96,7 +96,6 @@ docker-compose -f elasticsearch.yml up
 For environments that requires more involving preparation, look for `*.sh`. For instance,
 `opensearch.sh` builds a required docker image and then launch opensearch.
 
-
 ### Prepare environment variables for local testing:
 
 - copy `tests/integration_tests/.env.example` to `tests/integration_tests/.env`

docs/docs/expression_language/cookbook/index.mdx

@@ -8,4 +8,4 @@ import DocCardList from "@theme/DocCardList";
 
 Example code for accomplishing common tasks with the LangChain Expression Language (LCEL). These examples show how to compose different Runnable (the core LCEL interface) components to achieve various tasks. If you're just getting acquainted with LCEL, the [Prompt + LLM](/docs/expression_language/cookbook/prompt_llm_parser) page is a good place to start.
 
-<DocCardList />
+<DocCardList />

docs/docs/expression_language/cookbook/prompt_size.ipynb

@@ -220,7 +220,7 @@
    "id": "637f994a-5134-402a-bcf0-4de3911eaf49",
    "metadata": {},
    "source": [
-    ":::{.callout-tip}\n",
+    ":::tip\n",
     "\n",
     "[LangSmith trace](https://smith.langchain.com/public/60909eae-f4f1-43eb-9f96-354f5176f66f/r)\n",
     "\n",
@@ -388,7 +388,7 @@
    "id": "5a7e498b-dc68-4267-a35c-90ceffa91c46",
    "metadata": {},
    "source": [
-    ":::{.callout-tip}\n",
+    ":::tip\n",
     "\n",
     "[LangSmith trace](https://smith.langchain.com/public/3b27d47f-e4df-4afb-81b1-0f88b80ca97e/r)\n",
     "\n",

docs/docs/expression_language/get_started.ipynb

@@ -40,33 +40,6 @@
     "%pip install --upgrade --quiet  langchain-core langchain-community langchain-openai"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "c3d54f72",
-   "metadata": {},
-   "source": [
-    "```{=mdx}\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9eed8e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -87,8 +60,10 @@
    "source": [
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n",
+    "model = ChatOpenAI(model=\"gpt-4\")\n",
     "output_parser = StrOutputParser()\n",
     "\n",
     "chain = prompt | model | output_parser\n",
@@ -101,15 +76,15 @@
    "id": "81c502c5-85ee-4f36-aaf4-d6e350b7792f",
    "metadata": {},
    "source": [
-    "Notice this line of the code, where we piece together these different components into a single chain using LCEL:\n",
+    "Notice this line of this code, where we piece together then different components into a single chain using LCEL:\n",
     "\n",
     "```\n",
     "chain = prompt | model | output_parser\n",
     "```\n",
     "\n",
-    "The `|` symbol is similar to a [unix pipe operator](https://en.wikipedia.org/wiki/Pipeline_(Unix)), which chains together the different components, feeding the output from one component as input into the next component. \n",
+    "The `|` symbol is similar to a [unix pipe operator](https://en.wikipedia.org/wiki/Pipeline_(Unix)), which chains together the different components feeds the output from one component as input into the next component. \n",
     "\n",
-    "In this chain the user input is passed to the prompt template, then the prompt template output is passed to the model, then the model output is passed to the output parser. Let's take a look at each component individually to really understand what's going on."
+    "In this chain the user input is passed to the prompt template, then the prompt template output is passed to the model, then the model output is passed to the output parser. Let's take a look at each component individually to really understand what's going on. "
    ]
   },
   {
@@ -258,7 +233,7 @@
     "### 3. Output parser\n",
     "\n",
     "And lastly we pass our `model` output to the `output_parser`, which is a `BaseOutputParser` meaning it takes either a string or a \n",
-    "`BaseMessage` as input. The specific `StrOutputParser` simply converts any input into a string."
+    "`BaseMessage` as input. The `StrOutputParser` specifically simple converts any input into a string."
    ]
   },
   {
@@ -318,7 +293,7 @@
    "source": [
     ":::info\n",
     "\n",
-    "Note that if you’re curious about the output of any components, you can always test out a smaller version of the chain such as `prompt` or `prompt | model` to see the intermediate results:\n",
+    "Note that if you’re curious about the output of any components, you can always test out a smaller version of the chain such as `prompt`  or `prompt | model` to see the intermediate results:\n",
     "\n",
     ":::"
    ]
@@ -346,17 +321,7 @@
    "source": [
     "## RAG Search Example\n",
     "\n",
-    "For our next example, we want to run a retrieval-augmented generation chain to add some context when responding to questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8fe8eb4",
-   "metadata": {},
-   "source": [
-    "```{=mdx}\n",
-    "<ChatModelTabs />\n",
-    "```"
+    "For our next example, we want to run a retrieval-augmented generation chain to add some context when responding to questions. "
    ]
   },
   {
@@ -373,7 +338,7 @@
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "\n",
     "vectorstore = DocArrayInMemorySearch.from_texts(\n",
     "    [\"harrison worked at kensho\", \"bears like to eat honey\"],\n",
@@ -387,6 +352,7 @@
     "Question: {question}\n",
     "\"\"\"\n",
     "prompt = ChatPromptTemplate.from_template(template)\n",
+    "model = ChatOpenAI()\n",
     "output_parser = StrOutputParser()\n",
     "\n",
     "setup_and_retrieval = RunnableParallel(\n",
@@ -484,7 +450,7 @@
     "With the flow being:\n",
     "\n",
     "1. The first steps create a `RunnableParallel` object with two entries.  The first entry, `context` will include the document results fetched by the retriever. The second entry, `question` will contain the user’s original question. To pass on the question, we use `RunnablePassthrough` to copy this entry. \n",
-    "2. Feed the dictionary from the step above to the `prompt` component. It then takes the user input which is `question` as well as the retrieved document which is `context` to construct a prompt and output a PromptValue. \n",
+    "2. Feed the dictionary from the step above to the `prompt` component. It then takes the user input which is `question` as well as the retrieved document which is `context` to construct a prompt and output a PromptValue.  \n",
     "3. The `model` component takes the generated prompt, and passes into the OpenAI LLM model for evaluation. The generated output from the model is a `ChatMessage` object. \n",
     "4. Finally, the `output_parser` component takes in a `ChatMessage`, and transforms this into a Python string, which is returned from the invoke method.\n",
     "\n",
@@ -529,7 +495,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/index.mdx

@@ -6,4 +6,4 @@ sidebar_position: 2
 
 import DocCardList from "@theme/DocCardList";
 
-<DocCardList />
+<DocCardList />

docs/docs/expression_language/how_to/message_history.ipynb

@@ -552,7 +552,7 @@
    "id": "da3d1feb-b4bb-4624-961c-7db2e1180df7",
    "metadata": {},
    "source": [
-    ":::{.callout-tip}\n",
+    ":::tip\n",
     "\n",
     "[Langsmith trace](https://smith.langchain.com/public/bd73e122-6ec1-48b2-82df-e6483dc9cb63/r)\n",
     "\n",

docs/docs/get_started/installation.mdx

@@ -4,8 +4,8 @@
 
 To install LangChain run:
 
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
+import Tabs from "@theme/Tabs";
+import TabItem from "@theme/TabItem";
 import CodeBlock from "@theme/CodeBlock";
 
 <Tabs>
@@ -13,7 +13,9 @@ import CodeBlock from "@theme/CodeBlock";
     <CodeBlock language="bash">pip install langchain</CodeBlock>
   </TabItem>
   <TabItem value="conda" label="Conda">
-    <CodeBlock language="bash">conda install langchain -c conda-forge</CodeBlock>
+    <CodeBlock language="bash">
+      conda install langchain -c conda-forge
+    </CodeBlock>
   </TabItem>
 </Tabs>
 
@@ -30,6 +32,7 @@ pip install -e .
 ```
 
 ## LangChain community
+
 The `langchain-community` package contains third-party integrations. It is automatically installed by `langchain`, but can also be used separately. Install with:
 
 ```bash
@@ -37,6 +40,7 @@ pip install langchain-community
 ```
 
 ## LangChain core
+
 The `langchain-core` package contains base abstractions that the rest of the LangChain ecosystem uses, along with the LangChain Expression Language. It is automatically installed by `langchain`, but can also be used separately. Install with:
 
 ```bash
@@ -44,6 +48,7 @@ pip install langchain-core
 ```
 
 ## LangChain experimental
+
 The `langchain-experimental` package holds experimental LangChain code, intended for research and experimental uses.
 Install with:
 
@@ -52,6 +57,7 @@ pip install langchain-experimental
 ```
 
 ## LangServe
+
 LangServe helps developers deploy LangChain runnables and chains as a REST API.
 LangServe is automatically installed by LangChain CLI.
 If not using LangChain CLI, install with:
@@ -59,9 +65,11 @@ If not using LangChain CLI, install with:
 ```bash
 pip install "langserve[all]"
 ```
+
 for both client and server dependencies. Or `pip install "langserve[client]"` for client code, and `pip install "langserve[server]"` for server code.
 
 ## LangChain CLI
+
 The LangChain CLI is useful for working with LangChain templates and other LangServe projects.
 Install with:
 
@@ -70,6 +78,7 @@ pip install langchain-cli
 ```
 
 ## LangSmith SDK
+
 The LangSmith SDK is automatically installed by LangChain.
 If not using LangChain, install with:
 

docs/docs/get_started/introduction.mdx

@@ -5,27 +5,30 @@ sidebar_position: 0
 # Introduction
 
 **LangChain** is a framework for developing applications powered by language models. It enables applications that:
+
 - **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
 - **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
 
 This framework consists of several parts.
+
 - **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
 - **[LangChain Templates](/docs/templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
 - **[LangServe](/docs/langserve)**: A library for deploying LangChain chains as a REST API.
 - **[LangSmith](/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
 
-import ThemedImage from '@theme/ThemedImage';
+import ThemedImage from "@theme/ThemedImage";
 
 <ThemedImage
   alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
   sources={{
-    light: '/svg/langchain_stack.svg',
-    dark: '/svg/langchain_stack_dark.svg',
+    light: "/svg/langchain_stack.svg",
+    dark: "/svg/langchain_stack_dark.svg",
   }}
   title="LangChain Framework Overview"
 />
 
 Together, these products simplify the entire application lifecycle:
+
 - **Develop**: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.
 - **Productionize**: Use LangSmith to inspect, test and monitor your chains, so that you can constantly improve and deploy with confidence.
 - **Deploy**: Turn any chain into an API with LangServe.
@@ -33,12 +36,14 @@ Together, these products simplify the entire application lifecycle:
 ## LangChain Libraries
 
 The main value props of the LangChain packages are:
+
 1. **Components**: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
 2. **Off-the-shelf chains**: built-in assemblages of components for accomplishing higher-level tasks
 
 Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
 
 The LangChain libraries themselves are made up of several different packages.
+
 - **`langchain-core`**: Base abstractions and LangChain Expression Language.
 - **`langchain-community`**: Third party integrations.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
@@ -66,39 +71,45 @@ LCEL is a declarative way to compose chains. LCEL was designed from day 1 to sup
 - **[How-to](/docs/expression_language/how_to)**: Key features of LCEL
 - **[Cookbook](/docs/expression_language/cookbook)**: Example code for accomplishing common tasks
 
-
 ## Modules
 
 LangChain provides standard, extendable interfaces and integrations for the following modules:
 
 #### [Model I/O](/docs/modules/model_io/)
+
 Interface with language models
 
 #### [Retrieval](/docs/modules/data_connection/)
+
 Interface with application-specific data
 
 #### [Agents](/docs/modules/agents/)
-Let models choose which tools to use given high-level directives
 
+Let models choose which tools to use given high-level directives
 
 ## Examples, ecosystem, and resources
 
 ### [Use cases](/docs/use_cases/question_answering/)
+
 Walkthroughs and techniques for common end-to-end use cases, like:
+
 - [Document question answering](/docs/use_cases/question_answering/)
 - [Chatbots](/docs/use_cases/chatbots/)
 - [Analyzing structured data](/docs/use_cases/sql/)
 - and much more...
 
 ### [Integrations](/docs/integrations/providers/)
+
 LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/providers/).
 
 ### [Guides](../guides/debugging.md)
+
 Best practices for developing with LangChain.
 
 ### [API reference](https://api.python.langchain.com)
+
 Head to the reference section for full documentation of all classes and methods in the LangChain and LangChain Experimental Python packages.
 
 ### [Developer's guide](/docs/contributing)
-Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.
 
+Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.

docs/docs/get_started/quickstart.mdx

@@ -1,6 +1,7 @@
 # Quickstart
 
 In this quickstart we'll show you how to:
+
 - Get setup with LangChain, LangSmith and LangServe
 - Use the most basic and common components of LangChain: prompt templates, models, and output parsers
 - Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining
@@ -14,7 +15,7 @@ That's a fair amount to cover! Let's dive in.
 
 ### Jupyter Notebook
 
-This guide (and most of the other guides in the documentation) uses [Jupyter notebooks](https://jupyter.org/) and assumes the reader is as well. Jupyter notebooks are perfect for learning how to work with LLM systems because oftentimes things can go wrong (unexpected output, API down, etc) and going through guides in an interactive environment is a great way to better understand them.
+This guide (and most of the other guides in the documentation) use [Jupyter notebooks](https://jupyter.org/) and assume the reader is as well. Jupyter notebooks are perfect for learning how to work with LLM systems because often times things can go wrong (unexpected output, API down, etc) and going through guides in an interactive environment is a great way to better understand them.
 
 You do not NEED to go through the guide in a Jupyter Notebook, but it is recommended. See [here](https://jupyter.org/install) for instructions on how to install.
 
@@ -22,8 +23,8 @@ You do not NEED to go through the guide in a Jupyter Notebook, but it is recomme
 
 To install LangChain run:
 
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
+import Tabs from "@theme/Tabs";
+import TabItem from "@theme/TabItem";
 import CodeBlock from "@theme/CodeBlock";
 
 <Tabs>
@@ -31,11 +32,12 @@ import CodeBlock from "@theme/CodeBlock";
     <CodeBlock language="bash">pip install langchain</CodeBlock>
   </TabItem>
   <TabItem value="conda" label="Conda">
-    <CodeBlock language="bash">conda install langchain -c conda-forge</CodeBlock>
+    <CodeBlock language="bash">
+      conda install langchain -c conda-forge
+    </CodeBlock>
   </TabItem>
 </Tabs>
 
-
 For more details, see our [Installation guide](/docs/get_started/installation).
 
 ### LangSmith
@@ -105,10 +107,11 @@ llm = ChatOpenAI(openai_api_key="...")
 
 First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:
 
-* [Download](https://ollama.ai/download)
-* Fetch a model via `ollama pull llama2`
+- [Download](https://ollama.ai/download)
+- Fetch a model via `ollama pull llama2`
 
 Then, make sure the Ollama server is running. After that, you can do:
+
 ```python
 from langchain_community.llms import Ollama
 llm = Ollama(model="llama2")
@@ -184,8 +187,8 @@ Let's ask it what LangSmith is - this is something that wasn't present in the tr
 llm.invoke("how can langsmith help with testing?")
 ```
 
-We can also guide its response with a prompt template.
-Prompt templates convert raw user input to better input to the LLM.
+We can also guide it's response with a prompt template.
+Prompt templates are used to convert raw user input to a better input to the LLM.
 
 ```python
 from langchain_core.prompts import ChatPromptTemplate
@@ -198,7 +201,7 @@ prompt = ChatPromptTemplate.from_messages([
 We can now combine these into a simple LLM chain:
 
 ```python
-chain = prompt | llm 
+chain = prompt | llm
 ```
 
 We can now invoke it and ask the same question. It still won't know the answer, but it should respond in a more proper tone for a technical writer!
@@ -231,18 +234,17 @@ chain.invoke({"input": "how can langsmith help with testing?"})
 
 We've now successfully set up a basic LLM chain. We only touched on the basics of prompts, models, and output parsers - for a deeper dive into everything mentioned here, see [this section of documentation](/docs/modules/model_io).
 
-
 ## Retrieval Chain
 
-To properly answer the original question ("how can langsmith help with testing?"), we need to provide additional context to the LLM.
-We can do this via *retrieval*.
+In order to properly answer the original question ("how can langsmith help with testing?"), we need to provide additional context to the LLM.
+We can do this via _retrieval_.
 Retrieval is useful when you have **too much data** to pass to the LLM directly.
 You can then use a retriever to fetch only the most relevant pieces and pass those in.
 
-In this process, we will look up relevant documents from a *Retriever* and then pass them into the prompt.
+In this process, we will look up relevant documents from a _Retriever_ and then pass them into the prompt.
 A Retriever can be backed by anything - a SQL table, the internet, etc - but in this instance we will populate a vector store and use that as a retriever. For more information on vectorstores, see [this documentation](/docs/modules/data_connection/vectorstores).
 
-First, we need to load the data that we want to index. To do this, we will use the WebBaseLoader. This requires installing [BeautifulSoup](https://beautiful-soup-4.readthedocs.io/en/latest/):
+First, we need to load the data that we want to index. In order to do this, we will use the WebBaseLoader. This requires installing [BeautifulSoup](https://beautiful-soup-4.readthedocs.io/en/latest/):
 
 ```shell
 pip install beautifulsoup4
@@ -250,7 +252,6 @@ pip install beautifulsoup4
 
 After that, we can import and use WebBaseLoader.
 
-
 ```python
 from langchain_community.document_loaders import WebBaseLoader
 loader = WebBaseLoader("https://docs.smith.langchain.com/user_guide")
@@ -283,6 +284,7 @@ from langchain_community.embeddings import OllamaEmbeddings
 
 embeddings = OllamaEmbeddings()
 ```
+
   </TabItem>
 <TabItem value="cohere" label="Cohere (API)" default>
 
@@ -349,7 +351,7 @@ document_chain.invoke({
 ```
 
 However, we want the documents to first come from the retriever we just set up.
-That way, we can use the retriever to dynamically select the most relevant documents and pass those in for a given question.
+That way, for a given question we can use the retriever to dynamically select the most relevant documents and pass those in.
 
 ```python
 from langchain.chains import create_retrieval_chain
@@ -395,12 +397,12 @@ from langchain_core.prompts import MessagesPlaceholder
 prompt = ChatPromptTemplate.from_messages([
     MessagesPlaceholder(variable_name="chat_history"),
     ("user", "{input}"),
-    ("user", "Given the above conversation, generate a search query to look up to get information relevant to the conversation")
+    ("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
 ])
 retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
 ```
 
-We can test this out by passing in an instance where the user asks a follow-up question.
+We can test this out by passing in an instance where the user is asking a follow up question.
 
 ```python
 from langchain_core.messages import HumanMessage, AIMessage
@@ -411,7 +413,8 @@ retriever_chain.invoke({
     "input": "Tell me how"
 })
 ```
-You should see that this returns documents about testing in LangSmith. This is because the LLM generated a new query, combining the chat history with the follow-up question.
+
+You should see that this returns documents about testing in LangSmith. This is because the LLM generated a new query, combining the chat history with the follow up question.
 
 Now that we have this new retriever, we can create a new chain to continue the conversation with these retrieved documents in mind.
 
@@ -435,11 +438,12 @@ retrieval_chain.invoke({
     "input": "Tell me how"
 })
 ```
+
 We can see that this gives a coherent answer - we've successfully turned our retrieval chain into a chatbot!
 
 ## Agent
 
-We've so far created examples of chains - where each step is known ahead of time.
+We've so far create examples of chains - where each step is known ahead of time.
 The final thing we will create is an agent - where the LLM decides what steps to take.
 
 **NOTE: for this example we will only show how to create an agent using OpenAI models, as local models are not reliable enough yet.**
@@ -448,7 +452,7 @@ One of the first things to do when building an agent is to decide what tools it
 For this example, we will give the agent access to two tools:
 
 1. The retriever we just created. This will let it easily answer questions about LangSmith
-2. A search tool. This will let it easily answer questions that require up-to-date information.
+2. A search tool. This will let it easily answer questions that require up to date information.
 
 First, let's set up a tool for the retriever we just created:
 
@@ -462,12 +466,12 @@ retriever_tool = create_retriever_tool(
 )
 ```
 
-
 The search tool that we will use is [Tavily](/docs/integrations/retrievers/tavily). This will require an API key (they have generous free tier). After creating it on their platform, you need to set it as an environment variable:
 
 ```shell
 export TAVILY_API_KEY=...
 ```
+
 If you do not want to set up an API key, you can skip creating this tool.
 
 ```python
@@ -485,14 +489,10 @@ tools = [retriever_tool, search]
 Now that we have the tools, we can create an agent to use them. We will go over this pretty quickly - for a deeper dive into what exactly is going on, check out the [Agent's Getting Started documentation](/docs/modules/agents)
 
 Install langchain hub first
+
 ```bash
 pip install langchainhub
 ```
-Install the langchain-openai package
-To interact with OpenAI we need to use langchain-openai which connects with OpenAI SDK[https://github.com/langchain-ai/langchain/tree/master/libs/partners/openai].  
-```bash
-pip install langchain-openai
-```
 
 Now we can use it to get a predefined prompt
 
@@ -504,8 +504,6 @@ from langchain.agents import AgentExecutor
 
 # Get the prompt to use - you can modify this!
 prompt = hub.pull("hwchase17/openai-functions-agent")
-
-# You need to set OPENAI_API_KEY environment variable or pass it as argument `openai_api_key`.
 llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
 agent = create_openai_functions_agent(llm, tools, prompt)
 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
@@ -537,7 +535,6 @@ agent_executor.invoke({
 
 We've now successfully set up a basic agent. We only touched on the basics of agents - for a deeper dive into everything mentioned here, see [this section of documentation](/docs/modules/agents).
 
-
 ## Serving with LangServe
 
 Now that we've built an application, we need to serve it. That's where LangServe comes in.
@@ -546,6 +543,7 @@ LangServe helps developers deploy LangChain chains as a REST API. You do not nee
 While the first part of this guide was intended to be run in a Jupyter Notebook, we will now move out of that. We will be creating a Python file and then interacting with it from the command line.
 
 Install with:
+
 ```bash
 pip install "langserve[all]"
 ```
@@ -553,6 +551,7 @@ pip install "langserve[all]"
 ### Server
 
 To create a server for our application we'll make a `serve.py` file. This will contain our logic for serving our application. It consists of three things:
+
 1. The definition of our chain that we just built above
 2. Our FastAPI app
 3. A definition of a route from which to serve the chain, which is done with `langserve.add_routes`
@@ -639,9 +638,11 @@ if __name__ == "__main__":
 ```
 
 And that's it! If we execute this file:
+
 ```bash
 python serve.py
 ```
+
 we should see our chain being served at localhost:8000.
 
 ### Playground

docs/docs/guides/debugging.md

@@ -4,8 +4,6 @@ If you're building with LLMs, at some point something will break, and you'll nee
 
 Here are a few different tools and functionalities to aid in debugging.
 
-
-
 ## Tracing
 
 Platforms with tracing capabilities like [LangSmith](/docs/langsmith/) and [WandB](/docs/integrations/providers/wandb_tracing) are the most comprehensive solutions for debugging. These platforms make it easy to not only log and visualize LLM apps, but also to actively debug, test and refine them.
@@ -16,13 +14,12 @@ For anyone building production-grade LLM applications, we highly recommend using
 
 ## `set_debug` and `set_verbose`
 
-If you're prototyping in Jupyter Notebooks or running Python scripts, it can be helpful to print out the intermediate steps of a Chain run. 
+If you're prototyping in Jupyter Notebooks or running Python scripts, it can be helpful to print out the intermediate steps of a Chain run.
 
 There are a number of ways to enable printing at varying degrees of verbosity.
 
 Let's suppose we have a simple agent, and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see:
 
-
 ```python
 from langchain.agents import AgentType, initialize_agent, load_tools
 from langchain_openai import ChatOpenAI
@@ -32,7 +29,6 @@ tools = load_tools(["ddg-search", "llm-math"], llm=llm)
 agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
 ```
 
-
 ```python
 agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?")
 ```
@@ -49,7 +45,6 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
 
 Setting the global `debug` flag will cause all LangChain components with callback support (chains, models, agents, tools, retrievers) to print the inputs they receive and outputs they generate. This is the most verbose setting and will fully log raw inputs and outputs.
 
-
 ```python
 from langchain.globals import set_debug
 
@@ -62,7 +57,7 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
 
 <CodeOutputBlock lang="python">
 
-```
+````
     [chain/start] [1:RunTypeEnum.chain:AgentExecutor] Entering Chain run with input:
     {
       "input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?"
@@ -370,7 +365,7 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
 
 
     'The director of the 2023 film Oppenheimer is Christopher Nolan and he is 52 years old. His age in days is approximately 18980 days.'
-```
+````
 
 </CodeOutputBlock>
 
@@ -380,7 +375,6 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
 
 Setting the `verbose` flag will print out inputs and outputs in a slightly more readable format and will skip logging certain raw outputs (like the token usage stats for an LLM call) so that you can focus on application logic.
 
-
 ```python
 from langchain.globals import set_verbose
 
@@ -393,21 +387,21 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
 
 <CodeOutputBlock lang="python">
 
-```
-    
-    
+````
+
+
     > Entering new AgentExecutor chain...
-    
-    
+
+
     > Entering new LLMChain chain...
     Prompt after formatting:
     Answer the following questions as best you can. You have access to the following tools:
-    
+
     duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
     Calculator: Useful for when you need to answer questions about math.
-    
+
     Use the following format:
-    
+
     Question: the input question you must answer
     Thought: you should always think about what to do
     Action: the action to take, should be one of [duckduckgo_search, Calculator]
@@ -416,28 +410,28 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     ... (this Thought/Action/Action Input/Observation can repeat N times)
     Thought: I now know the final answer
     Final Answer: the final answer to the original input question
-    
+
     Begin!
-    
+
     Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
     Thought:
-    
+
     > Finished chain.
     First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
     Action: duckduckgo_search
     Action Input: "Director of the 2023 film Oppenheimer"
     Observation: Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert ... 2023, 12:16 p.m. ET. ... including his role as the director of the Manhattan Engineer District, better ... J Robert Oppenheimer was the director of the secret Los Alamos Laboratory. It was established under US president Franklin D Roosevelt as part of the Manhattan Project to build the first atomic bomb. He oversaw the first atomic bomb detonation in the New Mexico desert in July 1945, code-named "Trinity". In this opening salvo of 2023's Oscar battle, Nolan has enjoined a star-studded cast for a retelling of the brilliant and haunted life of J. Robert Oppenheimer, the American physicist whose... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.
     Thought:
-    
+
     > Entering new LLMChain chain...
     Prompt after formatting:
     Answer the following questions as best you can. You have access to the following tools:
-    
+
     duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
     Calculator: Useful for when you need to answer questions about math.
-    
+
     Use the following format:
-    
+
     Question: the input question you must answer
     Thought: you should always think about what to do
     Action: the action to take, should be one of [duckduckgo_search, Calculator]
@@ -446,32 +440,32 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     ... (this Thought/Action/Action Input/Observation can repeat N times)
     Thought: I now know the final answer
     Final Answer: the final answer to the original input question
-    
+
     Begin!
-    
+
     Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
     Thought:First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
     Action: duckduckgo_search
     Action Input: "Director of the 2023 film Oppenheimer"
     Observation: Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert ... 2023, 12:16 p.m. ET. ... including his role as the director of the Manhattan Engineer District, better ... J Robert Oppenheimer was the director of the secret Los Alamos Laboratory. It was established under US president Franklin D Roosevelt as part of the Manhattan Project to build the first atomic bomb. He oversaw the first atomic bomb detonation in the New Mexico desert in July 1945, code-named "Trinity". In this opening salvo of 2023's Oscar battle, Nolan has enjoined a star-studded cast for a retelling of the brilliant and haunted life of J. Robert Oppenheimer, the American physicist whose... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.
     Thought:
-    
+
     > Finished chain.
     The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his birth date to calculate his age.
     Action: duckduckgo_search
     Action Input: "Christopher Nolan birth date"
     Observation: July 30, 1970 (age 52) London England Notable Works: "Dunkirk" "Tenet" "The Prestige" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. Christopher Nolan is currently 52 according to his birthdate July 30, 1970 Sun Sign Leo Born Place Westminster, London, England, United Kingdom Residence Los Angeles, California, United States Nationality Education Chris attended Haileybury and Imperial Service College, in Hertford Heath, Hertfordshire. Christopher Nolan's next movie will study the man who developed the atomic bomb, J. Robert Oppenheimer. Here's the release date, plot, trailers & more. July 2023 sees the release of Christopher Nolan's new film, Oppenheimer, his first movie since 2020's Tenet and his split from Warner Bros. Billed as an epic thriller about "the man who ...
     Thought:
-    
+
     > Entering new LLMChain chain...
     Prompt after formatting:
     Answer the following questions as best you can. You have access to the following tools:
-    
+
     duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
     Calculator: Useful for when you need to answer questions about math.
-    
+
     Use the following format:
-    
+
     Question: the input question you must answer
     Thought: you should always think about what to do
     Action: the action to take, should be one of [duckduckgo_search, Calculator]
@@ -480,9 +474,9 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     ... (this Thought/Action/Action Input/Observation can repeat N times)
     Thought: I now know the final answer
     Final Answer: the final answer to the original input question
-    
+
     Begin!
-    
+
     Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
     Thought:First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
     Action: duckduckgo_search
@@ -493,19 +487,19 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     Action Input: "Christopher Nolan birth date"
     Observation: July 30, 1970 (age 52) London England Notable Works: "Dunkirk" "Tenet" "The Prestige" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. Christopher Nolan is currently 52 according to his birthdate July 30, 1970 Sun Sign Leo Born Place Westminster, London, England, United Kingdom Residence Los Angeles, California, United States Nationality Education Chris attended Haileybury and Imperial Service College, in Hertford Heath, Hertfordshire. Christopher Nolan's next movie will study the man who developed the atomic bomb, J. Robert Oppenheimer. Here's the release date, plot, trailers & more. July 2023 sees the release of Christopher Nolan's new film, Oppenheimer, his first movie since 2020's Tenet and his split from Warner Bros. Billed as an epic thriller about "the man who ...
     Thought:
-    
+
     > Finished chain.
     Christopher Nolan was born on July 30, 1970. Now I need to calculate his age in 2023 and then convert it into days.
     Action: Calculator
     Action Input: (2023 - 1970) * 365
-    
+
     > Entering new LLMMathChain chain...
     (2023 - 1970) * 365
-    
+
     > Entering new LLMChain chain...
     Prompt after formatting:
     Translate a math problem into a expression that can be executed using Python's numexpr library. Use the output of running this code to answer the question.
-    
+
     Question: ${Question with math problem.}
     ```text
     ${single line mathematical expression that solves the problem}
@@ -515,9 +509,9 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     ${Output of running the code}
     ```
     Answer: ${Answer}
-    
+
     Begin.
-    
+
     Question: What is 37593 * 67?
     ```text
     37593 * 67
@@ -527,7 +521,7 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     2518731
     ```
     Answer: 2518731
-    
+
     Question: 37593^(1/5)
     ```text
     37593**(1/5)
@@ -537,31 +531,31 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     8.222831614237718
     ```
     Answer: 8.222831614237718
-    
+
     Question: (2023 - 1970) * 365
-    
-    
+
+
     > Finished chain.
     ```text
     (2023 - 1970) * 365
     ```
     ...numexpr.evaluate("(2023 - 1970) * 365")...
-    
+
     Answer: 19345
     > Finished chain.
-    
+
     Observation: Answer: 19345
     Thought:
-    
+
     > Entering new LLMChain chain...
     Prompt after formatting:
     Answer the following questions as best you can. You have access to the following tools:
-    
+
     duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
     Calculator: Useful for when you need to answer questions about math.
-    
+
     Use the following format:
-    
+
     Question: the input question you must answer
     Thought: you should always think about what to do
     Action: the action to take, should be one of [duckduckgo_search, Calculator]
@@ -570,9 +564,9 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     ... (this Thought/Action/Action Input/Observation can repeat N times)
     Thought: I now know the final answer
     Final Answer: the final answer to the original input question
-    
+
     Begin!
-    
+
     Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
     Thought:First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
     Action: duckduckgo_search
@@ -587,16 +581,16 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     Action Input: (2023 - 1970) * 365
     Observation: Answer: 19345
     Thought:
-    
+
     > Finished chain.
     I now know the final answer
     Final Answer: The director of the 2023 film Oppenheimer is Christopher Nolan and he is 53 years old in 2023. His age in days is 19345 days.
-    
+
     > Finished chain.
 
 
     'The director of the 2023 film Oppenheimer is Christopher Nolan and he is 53 years old in 2023. His age in days is 19345 days.'
-```
+````
 
 </CodeOutputBlock>
 
@@ -606,12 +600,11 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
 
 You can also scope verbosity down to a single object, in which case only the inputs and outputs to that object are printed (along with any additional callbacks calls made specifically by that object).
 
-
 ```python
 # Passing verbose=True to initialize_agent will pass that along to the AgentExecutor (which is a Chain).
 agent = initialize_agent(
-    tools, 
-    llm, 
+    tools,
+    llm,
     agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
     verbose=True,
 )
@@ -643,7 +636,7 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is
     Observation: Answer: 19345
     Thought:I now know the final answer
     Final Answer: The director of the 2023 film Oppenheimer is Christopher Nolan. He is 53 years old in 2023, which is approximately 19345 days.
-    
+
     > Finished chain.
 
 

docs/docs/guides/deployments/index.mdx

@@ -3,10 +3,10 @@
 In today's fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it is crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories:
 
 - **Case 1: Utilizing External LLM Providers (OpenAI, Anthropic, etc.)**
-    In this scenario, most of the computational burden is handled by the LLM providers, while LangChain simplifies the implementation of business logic around these services. This approach includes features such as prompt templating, chat message generation, caching, vector embedding database creation, preprocessing, etc.
+  In this scenario, most of the computational burden is handled by the LLM providers, while LangChain simplifies the implementation of business logic around these services. This approach includes features such as prompt templating, chat message generation, caching, vector embedding database creation, preprocessing, etc.
 
 - **Case 2: Self-hosted Open-Source Models**
-    Alternatively, developers can opt to use smaller, yet comparably capable, self-hosted open-source LLM models. This approach can significantly decrease costs, latency, and privacy concerns associated with transferring data to external LLM providers.
+  Alternatively, developers can opt to use smaller, yet comparably capable, self-hosted open-source LLM models. This approach can significantly decrease costs, latency, and privacy concerns associated with transferring data to external LLM providers.
 
 Regardless of the framework that forms the backbone of your product, deploying LLM applications comes with its own set of challenges. It's vital to understand the trade-offs and key considerations when evaluating serving frameworks.
 
@@ -48,28 +48,23 @@ Monitoring forms an integral part of any system running in a production environm
 
 Your application may encounter errors such as exceptions in your model inference or business logic code, causing failures and disrupting traffic. Other potential issues could arise from the machine running your application, such as unexpected hardware breakdowns or loss of spot-instances during high-demand periods. One way to mitigate these risks is by increasing redundancy through replica scaling and implementing recovery mechanisms for failed replicas. However, model replicas aren't the only potential points of failure. It's essential to build resilience against various failures that could occur at any point in your stack.
 
-
 ### Zero down time upgrade
 
 System upgrades are often necessary but can result in service disruptions if not handled correctly. One way to prevent downtime during upgrades is by implementing a smooth transition process from the old version to the new one. Ideally, the new version of your LLM service is deployed, and traffic gradually shifts from the old to the new version, maintaining a constant QPS throughout the process.
 
-
 ### Load balancing
 
 Load balancing, in simple terms, is a technique to distribute work evenly across multiple computers, servers, or other resources to optimize the utilization of the system, maximize throughput, minimize response time, and avoid overload of any single resource. Think of it as a traffic officer directing cars (requests) to different roads (servers) so that no single road becomes too congested.
 
-There are several strategies for load balancing. For example, one common method is the *Round Robin* strategy, where each request is sent to the next server in line, cycling back to the first when all servers have received a request. This works well when all servers are equally capable. However, if some servers are more powerful than others, you might use a *Weighted Round Robin* or *Least Connections* strategy, where more requests are sent to the more powerful servers, or to those currently handling the fewest active requests. Let's imagine you're running a LLM chain. If your application becomes popular, you could have hundreds or even thousands of users asking questions at the same time. If one server gets too busy (high load), the load balancer would direct new requests to another server that is less busy. This way, all your users get a timely response and the system remains stable.
-
-
+There are several strategies for load balancing. For example, one common method is the _Round Robin_ strategy, where each request is sent to the next server in line, cycling back to the first when all servers have received a request. This works well when all servers are equally capable. However, if some servers are more powerful than others, you might use a _Weighted Round Robin_ or _Least Connections_ strategy, where more requests are sent to the more powerful servers, or to those currently handling the fewest active requests. Let's imagine you're running a LLM chain. If your application becomes popular, you could have hundreds or even thousands of users asking questions at the same time. If one server gets too busy (high load), the load balancer would direct new requests to another server that is less busy. This way, all your users get a timely response and the system remains stable.
 
 ## Maintaining Cost-Efficiency and Scalability
 
 Deploying LLM services can be costly, especially when you're handling a large volume of user interactions. Charges by LLM providers are usually based on tokens used, making a chat system inference on these models potentially expensive. However, several strategies can help manage these costs without compromising the quality of the service.
 
-
 ### Self-hosting models
 
-Several smaller and open-source LLMs are emerging to tackle the issue of reliance on LLM providers. Self-hosting allows you to maintain similar quality to LLM provider models while managing costs. The challenge lies in building a reliable, high-performing LLM serving system on your own machines. 
+Several smaller and open-source LLMs are emerging to tackle the issue of reliance on LLM providers. Self-hosting allows you to maintain similar quality to LLM provider models while managing costs. The challenge lies in building a reliable, high-performing LLM serving system on your own machines.
 
 ### Resource Management and Auto-Scaling
 
@@ -87,10 +82,7 @@ When self-hosting your models, you should consider independent scaling. For exam
 
 In the context of Large Language Models, batching requests can enhance efficiency by better utilizing your GPU resources. GPUs are inherently parallel processors, designed to handle multiple tasks simultaneously. If you send individual requests to the model, the GPU might not be fully utilized as it's only working on a single task at a time. On the other hand, by batching requests together, you're allowing the GPU to work on multiple tasks at once, maximizing its utilization and improving inference speed. This not only leads to cost savings but can also improve the overall latency of your LLM service.
 
-
-In summary, managing costs while scaling your LLM services requires a strategic approach. Utilizing self-hosting models, managing resources effectively, employing auto-scaling, using spot instances, independently scaling models, and batching requests are key strategies to consider. Open-source libraries such as Ray Serve and BentoML are designed to deal with these complexities. 
-
-
+In summary, managing costs while scaling your LLM services requires a strategic approach. Utilizing self-hosting models, managing resources effectively, employing auto-scaling, using spot instances, independently scaling models, and batching requests are key strategies to consider. Open-source libraries such as Ray Serve and BentoML are designed to deal with these complexities.
 
 ## Ensuring Rapid Iteration
 
@@ -104,12 +96,10 @@ Deploying systems like LangChain demands the ability to piece together different
 
 Many hosted solutions are restricted to a single cloud provider, which can limit your options in today's multi-cloud world. Depending on where your other infrastructure components are built, you might prefer to stick with your chosen cloud provider.
 
-
 ## Infrastructure as Code (IaC)
 
 Rapid iteration also involves the ability to recreate your infrastructure quickly and reliably. This is where Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Kubernetes YAML files come into play. They allow you to define your infrastructure in code files, which can be version controlled and quickly deployed, enabling faster and more reliable iterations.
 
-
 ## CI/CD
 
 In a fast-paced environment, implementing CI/CD pipelines can significantly speed up the iteration process. They help automate the testing and deployment of your LLM applications, reducing the risk of errors and enabling faster feedback and iteration.

docs/docs/guides/deployments/template_repos.mdx

@@ -1,7 +1,7 @@
 # LangChain Templates
 
-For more information on LangChain Templates, visit 
+For more information on LangChain Templates, visit
 
 - [LangChain Templates Quickstart](https://github.com/langchain-ai/langchain/blob/master/templates/README.md)
 - [LangChain Templates Index](https://github.com/langchain-ai/langchain/blob/master/templates/docs/INDEX.md)
-- [Full List of Templates](https://github.com/langchain-ai/langchain/blob/master/templates/)
+- [Full List of Templates](https://github.com/langchain-ai/langchain/blob/master/templates/)

docs/docs/guides/evaluation/comparison/index.mdx

@@ -1,6 +1,7 @@
 ---
-sidebar_position: 3 
+sidebar_position: 3
 ---
+
 # Comparison Evaluators
 
 Comparison evaluators in LangChain help measure two different chains or LLM outputs. These evaluators are helpful for comparative analyses, such as A/B testing between two language models, or comparing different versions of the same model. They can also be useful for things like generating preference scores for ai-assisted reinforcement learning.
@@ -17,7 +18,7 @@ Here's a summary of the key methods and properties of a comparison evaluator:
 - `requires_reference`: This property specifies whether this evaluator requires a reference label.
 
 :::note LangSmith Support
-The [run_on_dataset](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.smith) evaluation method is designed to evaluate only a single model at a time, and thus, doesn't support these evaluators.
+The [run_on_dataset](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.smith) evaluation method is designed to evaluate only a single model at a time, and thus, doesn't support these evaluators.
 :::
 
 Detailed information about creating custom evaluators and the available built-in comparison evaluators is provided in the following sections.
@@ -25,4 +26,3 @@ Detailed information about creating custom evaluators and the available built-in
 import DocCardList from "@theme/DocCardList";
 
 <DocCardList />
-

docs/docs/guides/evaluation/comparison/pairwise_string.ipynb

@@ -294,7 +294,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "prompt_template = PromptTemplate.from_template(\n",
     "    \"\"\"Given the input context, which do you prefer: A or B?\n",

docs/docs/guides/evaluation/examples/index.mdx

@@ -1,6 +1,7 @@
 ---
 sidebar_position: 5
 ---
+
 # Examples
 
 🚧 _Docs under construction_ 🚧
@@ -9,4 +10,4 @@ Below are some examples for inspecting and checking different chains.
 
 import DocCardList from "@theme/DocCardList";
 
-<DocCardList />
+<DocCardList />

docs/docs/guides/evaluation/index.mdx

@@ -2,7 +2,7 @@ import DocCardList from "@theme/DocCardList";
 
 # Evaluation
 
-Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. 
+Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks.
 
 The guides in this section review the APIs and functionality LangChain provides to help you better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
 
@@ -20,7 +20,6 @@ We also are working to share guides and cookbooks that demonstrate how to use th
 
 - [Chain Comparisons](/docs/guides/evaluation/examples/comparisons): This example uses a comparison evaluator to predict the preferred output. It reviews ways to measure confidence intervals to select statistically significant differences in aggregate preference scores across different models or prompts.
 
-
 ## LangSmith Evaluation
 
 LangSmith provides an integrated evaluation and tracing framework that allows you to check for regressions, compare systems, and easily identify and fix any sources of errors and performance issues. Check out the docs on [LangSmith Evaluation](https://docs.smith.langchain.com/evaluation) and additional [cookbooks](https://docs.smith.langchain.com/cookbook) for more detailed information on evaluating your applications.
@@ -31,12 +30,12 @@ Your application quality is a function both of the LLM you choose and the prompt
 
 - Agent tool use
 - Retrieval-augmented question-answering
-- Structured Extraction 
+- Structured Extraction
 
 Check out the docs for examples and leaderboard information.
 
 ## Reference Docs
 
-For detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the [reference documentation](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.evaluation) directly.
+For detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the [reference documentation](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.evaluation) directly.
 
 <DocCardList />

docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb

@@ -380,7 +380,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "fstring = \"\"\"Respond Y or N based on how well the following response follows the specified rubric. Grade only based on the rubric and expected response:\n",
     "\n",

docs/docs/guides/evaluation/string/index.mdx

@@ -1,6 +1,7 @@
 ---
-sidebar_position: 2 
+sidebar_position: 2
 ---
+
 # String Evaluators
 
 A string evaluator is a component within LangChain designed to assess the performance of a language model by comparing its generated outputs (predictions) to a reference string or an input. This comparison is a crucial step in the evaluation of language models, providing a measure of the accuracy or quality of the generated text.

docs/docs/guides/evaluation/trajectory/index.mdx

@@ -1,6 +1,7 @@
 ---
 sidebar_position: 4
 ---
+
 # Trajectory Evaluators
 
 Trajectory Evaluators in LangChain provide a more holistic approach to evaluating an agent. These evaluators assess the full sequence of actions taken by an agent and their corresponding responses, which we refer to as the "trajectory". This allows you to better measure an agent's effectiveness and capabilities.
@@ -25,4 +26,3 @@ For a deeper dive into the implementation and use of Trajectory Evaluators, refe
 import DocCardList from "@theme/DocCardList";
 
 <DocCardList />
-

docs/docs/guides/fallbacks.ipynb

@@ -216,7 +216,7 @@
    "outputs": [],
    "source": [
     "# Now lets create a chain with the normal OpenAI model\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_openai import OpenAI\n",
     "\n",
     "prompt_template = \"\"\"Instructions: You should always include a compliment in your response.\n",

docs/docs/guides/local_llms.ipynb

@@ -546,7 +546,7 @@
    "source": [
     "from langchain.chains import LLMChain\n",
     "from langchain.chains.prompt_selector import ConditionalPromptSelector\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(\n",
     "    input_variables=[\"question\"],\n",

docs/docs/guides/model_laboratory.ipynb

@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "from langchain.model_laboratory import ModelLaboratory\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms import Cohere, HuggingFaceHub\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_openai import OpenAI"
    ]
   },
@@ -167,7 +167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain\n",
+    "from langchain.chains import SelfAskWithSearchChain\n",
     "from langchain_community.utilities import SerpAPIWrapper\n",
     "\n",
     "open_ai_llm = OpenAI(temperature=0)\n",

docs/docs/guides/pydantic_compatibility.md

@@ -6,10 +6,11 @@
 
 ## LangChain Pydantic migration plan
 
-As of `langchain>=0.0.267`, LangChain will allow users to install either Pydantic V1 or V2. 
-   * Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
-   * During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
-   migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
+As of `langchain>=0.0.267`, LangChain will allow users to install either Pydantic V1 or V2.
+
+- Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
+- During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
+  migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
 
 User can either pin to pydantic v1, and upgrade their code in one go once LangChain has migrated to v2 internally, or they can start a partial migration to v2, but must avoid mixing v1 and v2 code for LangChain.
 
@@ -18,7 +19,7 @@ the case of inheritance and in the case of passing objects to LangChain.
 
 **Example 1: Extending via inheritance**
 
-**YES** 
+**YES**
 
 ```python
 from pydantic.v1 import root_validator, validator
@@ -33,7 +34,7 @@ class CustomTool(BaseTool): # BaseTool is v1 code
     @classmethod
     def validate_x(cls, x: int) -> int:
         return 1
-    
+
 
 CustomTool(
     name='custom_tool',
@@ -44,7 +45,7 @@ CustomTool(
 
 Mixing Pydantic v2 primitives with Pydantic v1 primitives can raise cryptic errors
 
-**NO** 
+**NO**
 
 ```python
 from pydantic import Field, field_validator # pydantic v2
@@ -59,9 +60,9 @@ class CustomTool(BaseTool): # BaseTool is v1 code
     @classmethod
     def validate_x(cls, x: int) -> int:
         return 1
-    
 
-CustomTool( 
+
+CustomTool(
     name='custom_tool',
     description="hello",
     x=1,
@@ -102,4 +103,4 @@ Tool.from_function( # <-- tool uses v1 namespace
     description="useful for when you need to answer questions about math",
     args_schema=CalculatorInput
 )
-```
+```

docs/docs/guides/safety/amazon_comprehend_chain.ipynb

@@ -105,8 +105,8 @@
    },
    "outputs": [],
    "source": [
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms.fake import FakeListLLM\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (\n",
     "    ModerationPiiError,\n",
     ")\n",
@@ -242,8 +242,8 @@
    },
    "outputs": [],
    "source": [
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms.fake import FakeListLLM\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",
@@ -405,8 +405,8 @@
    },
    "outputs": [],
    "source": [
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms.fake import FakeListLLM\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",
@@ -566,8 +566,8 @@
    },
    "outputs": [],
    "source": [
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms import HuggingFaceHub\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"{question}\"\"\"\n",
     "\n",
@@ -696,9 +696,9 @@
    "source": [
     "import json\n",
     "\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms import SagemakerEndpoint\n",
     "from langchain_community.llms.sagemaker_endpoint import LLMContentHandler\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "\n",
     "class ContentHandler(LLMContentHandler):\n",

docs/docs/guides/safety/constitutional_chain.mdx

@@ -2,18 +2,17 @@
 
 This example shows the Self-critique chain with `Constitutional AI`.
 
-The `ConstitutionalChain` is a chain that ensures the output of a language model adheres 
-to a predefined set of constitutional principles. By incorporating specific rules and guidelines, 
-the `ConstitutionalChain` filters and modifies the generated content to align with these principles, 
-thus providing more controlled, ethical, and contextually appropriate responses. 
-This mechanism helps maintain the integrity of the output while minimizing the risk of generating 
+The `ConstitutionalChain` is a chain that ensures the output of a language model adheres
+to a predefined set of constitutional principles. By incorporating specific rules and guidelines,
+the `ConstitutionalChain` filters and modifies the generated content to align with these principles,
+thus providing more controlled, ethical, and contextually appropriate responses.
+This mechanism helps maintain the integrity of the output while minimizing the risk of generating
 content that may violate guidelines, be offensive, or deviate from the desired context.
 
-
 ```python
 # Imports
 from langchain_openai import OpenAI
-from langchain_core.prompts import PromptTemplate
+from langchain.prompts import PromptTemplate
 from langchain.chains.llm import LLMChain
 from langchain.chains.constitutional_ai.base import ConstitutionalChain
 ```
@@ -47,7 +46,6 @@ evil_qa_chain.run(question="How can I steal kittens?")
 
 </CodeOutputBlock>
 
-
 ```python
 principles = ConstitutionalChain.get_principles(["illegal"])
 constitutional_chain = ConstitutionalChain.from_llm(
@@ -88,6 +86,10 @@ constitutional_chain.run(question="How can I steal kittens?")
 
 ## Unified Objective
 
+We also have built-in support for the Unified Objectives proposed in this paper: [examine.dev/docs/Unified_objectives.pdf](https://examine.dev/docs/Unified_objectives.pdf)
+
+Some of these are useful for the same idea of correcting ethical issues.
+
 ```python
 principles = ConstitutionalChain.get_principles(["uo-ethics-1"])
 constitutional_chain = ConstitutionalChain.from_llm(
@@ -128,7 +130,6 @@ constitutional_chain.run(question="How can I steal kittens?")
 
 But they can also be used for a wide variety of tasks, including encouraging the LLM to list out supporting evidence
 
-
 ```python
 qa_prompt = PromptTemplate(
     template="""Question: {question}
@@ -153,7 +154,6 @@ qa_chain.run(question=query)
 
 </CodeOutputBlock>
 
-
 ```python
 principles = ConstitutionalChain.get_principles(["uo-implications-1"])
 constitutional_chain = ConstitutionalChain.from_llm(
@@ -196,7 +196,6 @@ constitutional_chain.run(query)
 
 We can easily add in custom principles.
 
-
 ```python
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 
@@ -244,7 +243,6 @@ constitutional_chain.run(question="How can I steal kittens?")
 
 We can also run multiple principles sequentially. Let's make the model talk like Master Yoda.
 
-
 ```python
 master_yoda_principle = ConstitutionalPrinciple(
     name='Master Yoda Principle',
@@ -298,7 +296,6 @@ constitutional_chain.run(question="How can I steal kittens?")
 
 You can also get the constitutional chain to return it's intermediate steps.
 
-
 ```python
 constitutional_chain = ConstitutionalChain.from_llm(
     chain=evil_qa_chain,
@@ -345,7 +342,6 @@ constitutional_chain({"question":"How can I steal kittens?"})
 
 We can also see that the chain recognizes when no revision is necessary.
 
-
 ```python
 good_qa_prompt = PromptTemplate(
     template="""You are good and must only give good and ethical answers. If someone asks you how to do something illegal, make sure you respond in an ethical and legal way.
@@ -371,7 +367,6 @@ good_qa_chain.run(question="How can I steal kittens?")
 
 </CodeOutputBlock>
 
-
 ```python
 constitutional_chain = ConstitutionalChain.from_llm(
     chain=good_qa_chain,
@@ -412,12 +407,10 @@ constitutional_chain({"question":"How can I steal kittens?"})
 
 For a list of all principles, see:
 
-
 ```python
 from langchain.chains.constitutional_ai.principles import PRINCIPLES
 ```
 
-
 ```python
 PRINCIPLES
 ```

docs/docs/guides/safety/index.mdx

@@ -4,6 +4,6 @@ One of the key concerns with using LLMs is that they may generate harmful or une
 
 - [Amazon Comprehend moderation chain](/docs/guides/safety/amazon_comprehend_chain): Use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle Personally Identifiable Information (PII) and toxicity.
 - [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide the model behavior.
-- [Hugging Face prompt injection identification](/docs/guides/safety/hugging_face_prompt_injection): Detect and handle prompt injection attacks. 
+- [Hugging Face prompt injection identification](/docs/guides/safety/hugging_face_prompt_injection): Detect and handle prompt injection attacks.
 - [Logical Fallacy chain](/docs/guides/safety/logical_fallacy_chain): Checks the model output against logical fallacies to correct any deviation.
 - [Moderation chain](/docs/guides/safety/moderation): Check if any output text is harmful and flag it.

docs/docs/guides/safety/logical_fallacy_chain.mdx

@@ -4,25 +4,24 @@ This example shows how to remove logical fallacies from model output.
 
 ## Logical Fallacies
 
-`Logical fallacies` are flawed reasoning or false arguments that can undermine the validity of a model's outputs. 
+`Logical fallacies` are flawed reasoning or false arguments that can undermine the validity of a model's outputs.
 
 Examples include circular reasoning, false
-dichotomies, ad hominem attacks, etc.  Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However, 
+dichotomies, ad hominem attacks, etc. Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However,
 optimizing for metrics alone does not guarantee logically sound reasoning.
 
-Language models can learn to exploit flaws in reasoning to generate plausible-sounding but logically invalid arguments.  When models rely on fallacies, their outputs become unreliable and untrustworthy, even if they achieve high scores on metrics. Users cannot depend on such outputs. Propagating logical fallacies can spread misinformation, confuse users, and lead to harmful real-world consequences when models are deployed in products or services.
+Language models can learn to exploit flaws in reasoning to generate plausible-sounding but logically invalid arguments. When models rely on fallacies, their outputs become unreliable and untrustworthy, even if they achieve high scores on metrics. Users cannot depend on such outputs. Propagating logical fallacies can spread misinformation, confuse users, and lead to harmful real-world consequences when models are deployed in products or services.
 
 Monitoring and testing specifically for logical flaws is challenging unlike other quality issues. It requires reasoning about arguments rather than pattern matching.
 
-Therefore, it is crucial that model developers proactively address logical fallacies after optimizing metrics. Specialized techniques like causal modeling, robustness testing, and bias mitigation can help avoid flawed reasoning.  Overall, allowing logical flaws to persist makes models less safe and ethical. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks.
-
+Therefore, it is crucial that model developers proactively address logical fallacies after optimizing metrics. Specialized techniques like causal modeling, robustness testing, and bias mitigation can help avoid flawed reasoning. Overall, allowing logical flaws to persist makes models less safe and ethical. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks.
 
 ## Example
 
 ```python
 # Imports
 from langchain_openai import OpenAI
-from langchain_core.prompts import PromptTemplate
+from langchain.prompts import PromptTemplate
 from langchain.chains.llm import LLMChain
 from langchain_experimental.fallacy_removal.base import FallacyChain
 ```
@@ -51,7 +50,6 @@ misleading_chain.run(question="How do I know the earth is round?")
 
 </CodeOutputBlock>
 
-
 ```python
 fallacies = FallacyChain.get_fallacies(["correction"])
 fallacy_chain = FallacyChain.from_llm(

docs/docs/guides/safety/moderation.mdx

@@ -1,15 +1,15 @@
 # Moderation chain
 
-This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. 
-Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. 
-Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some 
-types of harmful content. To comply with this (and to just generally prevent your application from being harmful) 
-you may often want to append a moderation chain to any LLMChains, in order to make sure any output 
+This notebook walks through examples of how to use a moderation chain, and several common ways for doing so.
+Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model.
+Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some
+types of harmful content. To comply with this (and to just generally prevent your application from being harmful)
+you may often want to append a moderation chain to any LLMChains, in order to make sure any output
 the LLM generates is not harmful.
 
-If the content passed into the moderation chain is harmful, there is not one best way to handle it, 
-it probably depends on your application. Sometimes you may want to throw an error in the Chain 
-(and have your application handle that). Other times, you may want to return something to 
+If the content passed into the moderation chain is harmful, there is not one best way to handle it,
+it probably depends on your application. Sometimes you may want to throw an error in the Chain
+(and have your application handle that). Other times, you may want to return something to
 the user explaining that the text was harmful. There could be other ways to handle it.
 We will cover all these ways in this walkthrough.
 
@@ -18,21 +18,17 @@ We'll show:
 1. How to run any piece of text through a moderation chain.
 2. How to append a Moderation chain to an LLMChain.
 
-
-
-
 ```python
 from langchain_openai import OpenAI
 from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain
-from langchain_core.prompts import PromptTemplate
+from langchain.prompts import PromptTemplate
 ```
 
 ## How to use the moderation chain
 
-Here's an example of using the moderation chain with default settings (will return a string 
+Here's an example of using the moderation chain with default settings (will return a string
 explaining stuff was flagged).
 
-
 ```python
 moderation_chain = OpenAIModerationChain()
 
@@ -47,7 +43,6 @@ moderation_chain.run("This is okay")
 
 </CodeOutputBlock>
 
-
 ```python
 moderation_chain.run("I will kill you")
 ```
@@ -62,7 +57,6 @@ moderation_chain.run("I will kill you")
 
 Here's an example of using the moderation chain to throw an error.
 
-
 ```python
 moderation_chain_error = OpenAIModerationChain(error=True)
 
@@ -77,7 +71,6 @@ moderation_chain_error.run("This is okay")
 
 </CodeOutputBlock>
 
-
 ```python
 moderation_chain_error.run("I will kill you")
 ```
@@ -133,10 +126,9 @@ moderation_chain_error.run("I will kill you")
 
 ## How to create a custom Moderation chain
 
-Here's an example of creating a custom moderation chain with a custom error message. 
+Here's an example of creating a custom moderation chain with a custom error message.
 It requires some knowledge of OpenAI's moderation endpoint results. See [docs here](https://beta.openai.com/docs/api-reference/moderations).
 
-
 ```python
 class CustomModeration(OpenAIModerationChain):
     def _moderate(self, text: str, results: dict) -> str:
@@ -158,7 +150,6 @@ custom_moderation.run("This is okay")
 
 </CodeOutputBlock>
 
-
 ```python
 custom_moderation.run("I will kill you")
 ```
@@ -175,10 +166,9 @@ custom_moderation.run("I will kill you")
 
 To easily combine a moderation chain with an LLMChain, you can use the `SequentialChain` abstraction.
 
-Let's start with a simple example of where the `LLMChain` only has a single input. For this purpose, 
+Let's start with a simple example of where the `LLMChain` only has a single input. For this purpose,
 we will prompt the model, so it says something harmful.
 
-
 ```python
 prompt = PromptTemplate.from_template("{text}")
 llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt)
@@ -204,7 +194,6 @@ llm_chain.run(text)
 
 </CodeOutputBlock>
 
-
 ```python
 chain = SimpleSequentialChain(chains=[llm_chain, moderation_chain])
 
@@ -221,7 +210,6 @@ chain.run(text)
 
 Now let's walk through an example of using it with an LLMChain which has multiple inputs (a bit more tricky because we can't use the SimpleSequentialChain)
 
-
 ```python
 prompt = PromptTemplate.from_template("{setup}{new_input}Person2:")
 llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt)
@@ -248,7 +236,6 @@ llm_chain(inputs, return_only_outputs=True)
 
 </CodeOutputBlock>
 
-
 ```python
 # Setting the input/output keys so it lines up
 moderation_chain.input_key = "text"

docs/docs/integrations/callbacks/llmonitor.md

@@ -1,6 +1,6 @@
 # LLMonitor
 
->[LLMonitor](https://llmonitor.com?utm_source=langchain&utm_medium=py&utm_campaign=docs) is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.
+> [LLMonitor](https://llmonitor.com?utm_source=langchain&utm_medium=py&utm_campaign=docs) is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.
 
 <video controls width='100%' >
   <source src='https://llmonitor.com/videos/demo-annotated.mp4'/>
@@ -101,6 +101,7 @@ agent.run(
 ```
 
 ## User Tracking
+
 User tracking allows you to identify your users, track their cost, conversations and more.
 
 ```python
@@ -112,6 +113,7 @@ with identify("user-123"):
 with identify("user-456", user_props={"email": "user456@test.com"}):
     agen.run("Who is Leo DiCaprio's girlfriend?")
 ```
+
 ## Support
 
 For any question or issue with integration you can reach out to the LLMonitor team on [Discord](http://discord.com/invite/8PafSG58kK) or via [email](mailto:vince@llmonitor.com).

docs/docs/integrations/chat/azureml_chat_endpoint.ipynb

@@ -40,7 +40,7 @@
     "You must [deploy a model on Azure ML](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) or [to Azure AI studio](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-open) and obtain the following parameters:\n",
     "\n",
     "* `endpoint_url`: The REST endpoint url provided by the endpoint.\n",
-    "* `endpoint_api_type`: Use `endpoint_type='dedicated'` when deploying models to **Dedicated endpoints** (hosted managed infrastructure). Use `endpoint_type='serverless'` when deploying models using the **Pay-as-you-go** offering (model as a service).\n",
+    "* `endpoint_api_type`: Use `endpoint_type='realtime'` when deploying models to **Realtime endpoints** (hosted managed infrastructure). Use `endpoint_type='serverless'` when deploying models using the **Pay-as-you-go** offering (model as a service).\n",
     "* `endpoint_api_key`: The API key provided by the endpoint"
    ]
   },
@@ -52,9 +52,9 @@
     "\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",
-    "* `CustomOpenAIChatContentFormatter`: Formats request and response data for models like LLaMa2-chat that follow the OpenAI API spec for request and response.\n",
+    "* `LLamaChatContentFormatter`: Formats request and response data for LLaMa2-chat\n",
     "\n",
-    "*Note: `langchain.chat_models.azureml_endpoint.LlamaChatContentFormatter` is being deprecated and replaced with `langchain.chat_models.azureml_endpoint.CustomOpenAIChatContentFormatter`.*\n",
+    "*Note: `langchain.chat_models.azureml_endpoint.LLamaContentFormatter` is being deprecated and replaced with `langchain.chat_models.azureml_endpoint.LLamaChatContentFormatter`.*\n",
     "\n",
     "You can implement custom content formatters specific for your model deriving from the class `langchain_community.llms.azureml_endpoint.ContentFormatterBase`."
    ]
@@ -65,7 +65,20 @@
    "source": [
     "## Examples\n",
     "\n",
-    "The following section contains examples about how to use this class:"
+    "The following section cotain examples about how to use this class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.azureml_endpoint import (\n",
+    "    AzureMLEndpointApiType,\n",
+    "    LlamaChatContentFormatter,\n",
+    ")\n",
+    "from langchain_core.messages import HumanMessage"
    ]
   },
   {
@@ -92,17 +105,14 @@
     }
    ],
    "source": [
-    "from langchain_community.chat_models.azureml_endpoint import (\n",
-    "    AzureMLEndpointApiType,\n",
-    "    CustomOpenAIChatContentFormatter,\n",
-    ")\n",
+    "from langchain_community.chat_models.azureml_endpoint import LlamaContentFormatter\n",
     "from langchain_core.messages import HumanMessage\n",
     "\n",
     "chat = AzureMLChatOnlineEndpoint(\n",
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/score\",\n",
-    "    endpoint_api_type=AzureMLEndpointApiType.dedicated,\n",
+    "    endpoint_api_type=AzureMLEndpointApiType.realtime,\n",
     "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=CustomOpenAIChatContentFormatter(),\n",
+    "    content_formatter=LlamaChatContentFormatter(),\n",
     ")\n",
     "response = chat.invoke(\n",
     "    [HumanMessage(content=\"Will the Collatz conjecture ever be solved?\")]\n",
@@ -127,7 +137,7 @@
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/chat/completions\",\n",
     "    endpoint_api_type=AzureMLEndpointApiType.serverless,\n",
     "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=CustomOpenAIChatContentFormatter,\n",
+    "    content_formatter=LlamaChatContentFormatter,\n",
     ")\n",
     "response = chat.invoke(\n",
     "    [HumanMessage(content=\"Will the Collatz conjecture ever be solved?\")]\n",
@@ -139,7 +149,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you need to pass additional parameters to the model, use `model_kwargs` argument:"
+    "If you need to pass additional parameters to the model, use `model_kwards` argument:"
    ]
   },
   {
@@ -152,7 +162,7 @@
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/chat/completions\",\n",
     "    endpoint_api_type=AzureMLEndpointApiType.serverless,\n",
     "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=CustomOpenAIChatContentFormatter,\n",
+    "    content_formatter=LlamaChatContentFormatter,\n",
     "    model_kwargs={\"temperature\": 0.8},\n",
     ")"
    ]
@@ -194,7 +204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/moonshot.ipynb

@@ -1,86 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Moonshot\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "# MoonshotChat\n",
-    "\n",
-    "[Moonshot](https://platform.moonshot.cn/) is a Chinese startup that provides LLM service for companies and individuals.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with Moonshot Inference for Chat."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Generate your api key from: https://platform.moonshot.cn/console/api-keys\n",
-    "os.environ[\"MOONSHOT_API_KEY\"] = \"MOONSHOT_API_KEY\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.moonshot import MoonshotChat\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = MoonshotChat()\n",
-    "# or use a specific model\n",
-    "# Available models: https://platform.moonshot.cn/docs\n",
-    "# chat = MoonshotChat(model=\"moonshot-v1-128k\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant that translates English to French.\"\n",
-    "    ),\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "chat.invoke(messages)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/chat/solar.ipynb

@@ -1,80 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a9667088-04e1-4f67-8221-a0072a2d635f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-03-06T17:04:59.273702Z",
-     "iopub.status.busy": "2024-03-06T17:04:59.272602Z",
-     "iopub.status.idle": "2024-03-06T17:05:00.129177Z",
-     "shell.execute_reply": "2024-03-06T17:05:00.124594Z",
-     "shell.execute_reply.started": "2024-03-06T17:04:59.273646Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='저는 대형 언어 모델 프로젝트를 구축하고 싶습니다.')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"SOLAR_API_KEY\"] = \"SOLAR_API_KEY\"\n",
-    "\n",
-    "from langchain_community.chat_models.solar import SolarChat\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "\n",
-    "chat = SolarChat(max_tokens=1024)\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant who translates English to Korean.\"\n",
-    "    ),\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to Korean. I want to build a project of large language model.\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8cb792fe-2844-4969-a9e9-f4c0f97b1699",
-   "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": "3.9.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/document_loaders/apify_dataset.ipynb

@@ -41,7 +41,7 @@
    "outputs": [],
    "source": [
     "from langchain_community.document_loaders import ApifyDatasetLoader\n",
-    "from langchain_core.documents import Document"
+    "from langchain_community.document_loaders.base import Document"
    ]
   },
   {

docs/docs/integrations/document_loaders/azure_document_intelligence.ipynb

@@ -14,30 +14,32 @@
    "metadata": {},
    "source": [
     ">[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning \n",
-    ">based service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs from\n",
-    ">digital or scanned PDFs, images, Office and HTML files.\n",
+    ">based service that extracts text (including handwriting), tables or key-value-pairs from\n",
+    ">scanned documents or images.\n",
     ">\n",
-    ">Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`, `DOCX`, `XLSX`, `PPTX` and `HTML`.\n",
+    ">Document Intelligence supports `PDF`, `JPEG`, `PNG`, `BMP`, or `TIFF`.\n",
     "\n",
-    "This current implementation of a loader using `Document Intelligence` can incorporate content page-wise and turn it into LangChain documents. The default output format is markdown, which can be easily chained with `MarkdownHeaderTextSplitter` for semantic document chunking. You can also use `mode=\"single\"` or `mode=\"page\"` to return pure texts in a single page or document split by page.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisite\n",
-    "\n",
-    "An Azure AI Document Intelligence resource in one of the 3 preview regions: **East US**, **West US2**, **West Europe** - follow [this document](https://learn.microsoft.com/azure/ai-services/document-intelligence/create-document-intelligence-resource?view=doc-intel-4.0.0) to create one if you don't have. You will be passing `<endpoint>` and `<key>` as parameters to the loader."
+    "This current implementation of a loader using `Document Intelligence` can incorporate content page-wise and turn it into LangChain documents.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence"
+    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence -q"
    ]
   },
   {
@@ -104,7 +106,7 @@
    "metadata": {},
    "source": [
     "## Example 2\n",
-    "The input file can also be a public URL path. E.g., https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/rest-api/layout.png."
+    "The input file can also be URL path."
    ]
   },
   {
@@ -121,101 +123,6 @@
     "documents = loader.load()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documents"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example 3\n",
-    "You can also specify `mode=\"page\"` to load document by pages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader\n",
-    "\n",
-    "file_path = \"<filepath>\"\n",
-    "endpoint = \"<endpoint>\"\n",
-    "key = \"<key>\"\n",
-    "loader = AzureAIDocumentIntelligenceLoader(\n",
-    "    api_endpoint=endpoint,\n",
-    "    api_key=key,\n",
-    "    file_path=file_path,\n",
-    "    api_model=\"prebuilt-layout\",\n",
-    "    mode=\"page\",\n",
-    ")\n",
-    "\n",
-    "documents = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The output will be each page stored as a separate document in the list:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for document in documents:\n",
-    "    print(f\"Page Content: {document.page_content}\")\n",
-    "    print(f\"Metadata: {document.metadata}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example 4\n",
-    "You can also specify `analysis_feature=[\"ocrHighResolution\"]` to enable add-on capabilities. For more information, see: https://aka.ms/azsdk/python/documentintelligence/analysisfeature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader\n",
-    "\n",
-    "file_path = \"<filepath>\"\n",
-    "endpoint = \"<endpoint>\"\n",
-    "key = \"<key>\"\n",
-    "analysis_features = [\"ocrHighResolution\"]\n",
-    "loader = AzureAIDocumentIntelligenceLoader(\n",
-    "    api_endpoint=endpoint,\n",
-    "    api_key=key,\n",
-    "    file_path=file_path,\n",
-    "    api_model=\"prebuilt-layout\",\n",
-    "    analysis_features=analysis_features,\n",
-    ")\n",
-    "\n",
-    "documents = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The output contains the LangChain document recognized with high resolution add-on capability:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

docs/docs/integrations/document_loaders/google_cloud_storage_directory.ipynb

@@ -126,40 +126,17 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "f9c0734f",
    "metadata": {},
-   "source": [
-    "## Continue on failure to load a single file\n",
-    "Files in a GCS bucket may cause errors during processing. Enable the `continue_on_failure=True` argument to allow silent failure. This means failure to process a single file will not break the function, it will log a warning instead. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d774795",
-   "metadata": {},
    "outputs": [],
-   "source": [
-    "loader = GCSDirectoryLoader(\n",
-    "    project_name=\"aist\", bucket=\"testing-hwc\", continue_on_failure=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d15f536",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.6 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -174,11 +151,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "5f90d085fc70553c85f15dd96b84c64a94d58988a621c9dbc38cac6a7e6079b3"
-   }
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/google_drive.ipynb

@@ -19,11 +19,9 @@
     "1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib`\n",
     "\n",
     "## 🧑 Instructions for ingesting your Google Docs data\n",
-    "Set the environmental variable `GOOGLE_APPLICATION_CREDENTIALS` to an empty string (`\"\"`).\n",
+    "By default, the `GoogleDriveLoader` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `credentials_path` keyword argument. Same thing with `token.json` - `token_path`. Note that `token.json` will be created automatically the first time you use the loader.\n",
     "\n",
-    "By default, the `GoogleDriveLoader` expects the `credentials.json` file to be located at `~/.credentials/credentials.json`, but this is configurable using the `credentials_path` keyword argument. Same thing with `token.json` - default path: `~/.credentials/token.json`, constructor param: `token_path`.\n",
-    "\n",
-    "The first time you use GoogleDriveLoader, you will be displayed with the consent screen in your browser for user authentication. After authentication, `token.json` will be created automatically at the provided or the default path. Also, if there is already a `token.json` at that path, then you will not be prompted for authentication.\n",
+    "The first time you use GoogleDriveLoader, you will be displayed with the consent screen in your browser. If this doesn't happen and you get a `RefreshError`, do not use `credentials_path` in your `GoogleDriveLoader` constructor call. Instead, put that path in a `GOOGLE_APPLICATION_CREDENTIALS` environmental variable.\n",
     "\n",
     "`GoogleDriveLoader` can load from a list of Google Docs document ids or a folder id. You can obtain your folder and document id from the URL:\n",
     "\n",

docs/docs/integrations/document_loaders/llmsherpa.ipynb

@@ -1,419 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7f5437a835409a57",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "# LLM Sherpa\n",
-    "\n",
-    "This notebook covers how to use `LLM Sherpa` to load files of many types. `LLM Sherpa` supports different file formats including DOCX, PPTX, HTML, TXT, and XML.\n",
-    "\n",
-    "`LLMSherpaFileLoader` use LayoutPDFReader, which is part of the LLMSherpa library. This tool is designed to parse PDFs while preserving their layout information, which is often lost when using most PDF to text parsers.\n",
-    "\n",
-    "Here are some key features of LayoutPDFReader:\n",
-    "\n",
-    "* It can identify and extract sections and subsections along with their levels.\n",
-    "* It combines lines to form paragraphs.\n",
-    "* It can identify links between sections and paragraphs.\n",
-    "* It can extract tables along with the section the tables are found in.\n",
-    "* It can identify and extract lists and nested lists.\n",
-    "* It can join content spread across pages.\n",
-    "* It can remove repeating headers and footers.\n",
-    "* It can remove watermarks.\n",
-    "\n",
-    "check [llmsherpa](https://llmsherpa.readthedocs.io/en/latest/) documentation.\n",
-    "\n",
-    "`INFO: this library fail with some pdf files so use it with caution.`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Install package\n",
-    "# !pip install --upgrade --quiet llmsherpa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "baa8d2672ac6dd4b",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## LLMSherpaFileLoader\n",
-    "\n",
-    "Under the hood LLMSherpaFileLoader defined some strategist to load file content: [\"sections\", \"chunks\", \"html\", \"text\"], setup [nlm-ingestor](https://github.com/nlmatics/nlm-ingestor) to get `llmsherpa_api_url` or use the default."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6fb0104dde44091b",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### sections strategy: return the file parsed into sections"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "14150b3110143a43",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:06:03.648268Z",
-     "start_time": "2024-03-28T23:05:51.734372Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader\n",
-    "\n",
-    "loader = LLMSherpaFileLoader(\n",
-    "    file_path=\"https://arxiv.org/pdf/2402.14207.pdf\",\n",
-    "    new_indent_parser=True,\n",
-    "    apply_ocr=True,\n",
-    "    strategy=\"sections\",\n",
-    "    llmsherpa_api_url=\"http://localhost:5010/api/parseDocument?renderFormat=all\",\n",
-    ")\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e639aa0010ed3579",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:06:11.568739Z",
-     "start_time": "2024-03-28T23:06:11.557702Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "Document(page_content='Abstract\\nWe study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages.\\nThis underexplored problem poses new challenges at the pre-writing stage, including how to research the topic and prepare an outline prior to writing.\\nWe propose STORM, a writing system for the Synthesis of Topic Outlines through\\nReferences\\nFull-length Article\\nTopic\\nOutline\\n2022 Winter Olympics\\nOpening Ceremony\\nResearch via Question Asking\\nRetrieval and Multi-perspective Question Asking.\\nSTORM models the pre-writing stage by\\nLLM\\n(1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline.\\nFor evaluation, we curate FreshWiki, a dataset of recent high-quality Wikipedia articles, and formulate outline assessments to evaluate the pre-writing stage.\\nWe further gather feedback from experienced Wikipedia editors.\\nCompared to articles generated by an outlinedriven retrieval-augmented baseline, more of STORM’s articles are deemed to be organized (by a 25% absolute increase) and broad in coverage (by 10%).\\nThe expert feedback also helps identify new challenges for generating grounded long articles, such as source bias transfer and over-association of unrelated facts.\\n1. Can you provide any information about the transportation arrangements for the opening ceremony?\\nLLM\\n2. Can you provide any information about the budget for the 2022 Winter Olympics opening ceremony?…\\nLLM- Role1\\nLLM- Role2\\nLLM- Role1', metadata={'source': 'https://arxiv.org/pdf/2402.14207.pdf', 'section_number': 1, 'section_title': 'Abstract'})"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "818977c1a0505814",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:06:28.900386Z",
-     "start_time": "2024-03-28T23:06:28.891805Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "79"
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e424ce828ea64c01",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### chunks strategy: return the file parsed into chunks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4c0ff1a52b9dd4e3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:06:44.507836Z",
-     "start_time": "2024-03-28T23:06:32.507326Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader\n",
-    "\n",
-    "loader = LLMSherpaFileLoader(\n",
-    "    file_path=\"https://arxiv.org/pdf/2402.14207.pdf\",\n",
-    "    new_indent_parser=True,\n",
-    "    apply_ocr=True,\n",
-    "    strategy=\"chunks\",\n",
-    "    llmsherpa_api_url=\"http://localhost:5010/api/parseDocument?renderFormat=all\",\n",
-    ")\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "33dc25e83f6e0430",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:06:49.951741Z",
-     "start_time": "2024-03-28T23:06:49.938331Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "Document(page_content='Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models\\nStanford University {shaoyj, yuchengj, tkanell, peterxu, okhattab}@stanford.edu lam@cs.stanford.edu', metadata={'source': 'https://arxiv.org/pdf/2402.14207.pdf', 'chunk_number': 1, 'chunk_type': 'para'})"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "2310e24f3d081cb4",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:06:56.933007Z",
-     "start_time": "2024-03-28T23:06:56.922196Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "306"
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6bb9b715b0d2b4b0",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### html strategy: return the file as one html document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f3fbe9f3c4d8a6ee",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T22:59:15.869599Z",
-     "start_time": "2024-03-28T22:58:54.306814Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader\n",
-    "\n",
-    "loader = LLMSherpaFileLoader(\n",
-    "    file_path=\"https://arxiv.org/pdf/2402.14207.pdf\",\n",
-    "    new_indent_parser=True,\n",
-    "    apply_ocr=True,\n",
-    "    strategy=\"html\",\n",
-    "    llmsherpa_api_url=\"http://localhost:5010/api/parseDocument?renderFormat=all\",\n",
-    ")\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b8fcbfcd58126e09",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T22:59:33.386455Z",
-     "start_time": "2024-03-28T22:59:33.381274Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'<html><h1>Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models</h1><table><th><td colSpan=1>Yijia Shao</td><td colSpan=1>Yucheng Jiang</td><td colSpan=1>Theodore A. Kanell</td><td colSpan=1>Peter Xu</td></th><tr><td colSpan=1></td><td colSpan=1>Omar Khattab</td><td colSpan=1>Monica S. Lam</td><td colSpan=1></td></tr></table><p>Stanford University {shaoyj, yuchengj, '"
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0].page_content[:400]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "8cbe691320144cf6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T22:59:49.667979Z",
-     "start_time": "2024-03-28T22:59:49.661572Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "1"
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "634af5a1c58a7766",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### text strategy: return the file as one text document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ee47c6e36c952534",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:04:56.549898Z",
-     "start_time": "2024-03-28T23:04:38.148264Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader\n",
-    "\n",
-    "loader = LLMSherpaFileLoader(\n",
-    "    file_path=\"https://arxiv.org/pdf/2402.14207.pdf\",\n",
-    "    new_indent_parser=True,\n",
-    "    apply_ocr=True,\n",
-    "    strategy=\"text\",\n",
-    "    llmsherpa_api_url=\"http://localhost:5010/api/parseDocument?renderFormat=all\",\n",
-    ")\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "998649675f14c50e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:05:28.558467Z",
-     "start_time": "2024-03-28T23:05:28.543132Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models\\n | Yijia Shao | Yucheng Jiang | Theodore A. Kanell | Peter Xu\\n | --- | --- | --- | ---\\n |  | Omar Khattab | Monica S. Lam | \\n\\nStanford University {shaoyj, yuchengj, tkanell, peterxu, okhattab}@stanford.edu lam@cs.stanford.edu\\nAbstract\\nWe study how to apply large language models to write grounded and organized long'"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0].page_content[:400]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7fec7a95023ea8e9",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-03-28T23:05:39.207693Z",
-     "start_time": "2024-03-28T23:05:39.199663Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "1"
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(docs)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/document_loaders/microsoft_excel.ipynb

@@ -43,60 +43,13 @@
     "docs[0]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "729ab1a2",
-   "metadata": {},
-   "source": [
-    "## Using Azure AI Document Intelligence\n",
-    "\n",
-    ">[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning \n",
-    ">based service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs from\n",
-    ">digital or scanned PDFs, images, Office and HTML files.\n",
-    ">\n",
-    ">Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`, `DOCX`, `XLSX`, `PPTX` and `HTML`.\n",
-    "\n",
-    "This current implementation of a loader using `Document Intelligence` can incorporate content page-wise and turn it into LangChain documents. The default output format is markdown, which can be easily chained with `MarkdownHeaderTextSplitter` for semantic document chunking. You can also use `mode=\"single\"` or `mode=\"page\"` to return pure texts in a single page or document split by page.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbe5c77d",
-   "metadata": {},
-   "source": [
-    "### Prerequisite\n",
-    "\n",
-    "An Azure AI Document Intelligence resource in one of the 3 preview regions: **East US**, **West US2**, **West Europe** - follow [this document](https://learn.microsoft.com/azure/ai-services/document-intelligence/create-document-intelligence-resource?view=doc-intel-4.0.0) to create one if you don't have. You will be passing `<endpoint>` and `<key>` as parameters to the loader."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fda529f8",
+   "id": "9ab94bde",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa008547",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader\n",
-    "\n",
-    "file_path = \"<filepath>\"\n",
-    "endpoint = \"<endpoint>\"\n",
-    "key = \"<key>\"\n",
-    "loader = AzureAIDocumentIntelligenceLoader(\n",
-    "    api_endpoint=endpoint, api_key=key, file_path=file_path, api_model=\"prebuilt-layout\"\n",
-    ")\n",
-    "\n",
-    "documents = loader.load()"
-   ]
+   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/document_loaders/microsoft_powerpoint.ipynb

@@ -76,7 +76,7 @@
    "id": "525d6b67",
    "metadata": {},
    "source": [
-    "### Retain Elements\n",
+    "## Retain Elements\n",
     "\n",
     "Under the hood, `Unstructured` creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."
    ]
@@ -124,60 +124,13 @@
     "data[0]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b97180c2",
-   "metadata": {},
-   "source": [
-    "## Using Azure AI Document Intelligence\n",
-    "\n",
-    ">[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning \n",
-    ">based service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs from\n",
-    ">digital or scanned PDFs, images, Office and HTML files.\n",
-    ">\n",
-    ">Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`, `DOCX`, `XLSX`, `PPTX` and `HTML`.\n",
-    "\n",
-    "This current implementation of a loader using `Document Intelligence` can incorporate content page-wise and turn it into LangChain documents. The default output format is markdown, which can be easily chained with `MarkdownHeaderTextSplitter` for semantic document chunking. You can also use `mode=\"single\"` or `mode=\"page\"` to return pure texts in a single page or document split by page.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11851fd0",
-   "metadata": {},
-   "source": [
-    "## Prerequisite\n",
-    "\n",
-    "An Azure AI Document Intelligence resource in one of the 3 preview regions: **East US**, **West US2**, **West Europe** - follow [this document](https://learn.microsoft.com/azure/ai-services/document-intelligence/create-document-intelligence-resource?view=doc-intel-4.0.0) to create one if you don't have. You will be passing `<endpoint>` and `<key>` as parameters to the loader."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "381d4139",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "077525b8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader\n",
-    "\n",
-    "file_path = \"<filepath>\"\n",
-    "endpoint = \"<endpoint>\"\n",
-    "key = \"<key>\"\n",
-    "loader = AzureAIDocumentIntelligenceLoader(\n",
-    "    api_endpoint=endpoint, api_key=key, file_path=file_path, api_model=\"prebuilt-layout\"\n",
-    ")\n",
-    "\n",
-    "documents = loader.load()"
-   ]
+   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/document_loaders/microsoft_word.ipynb

@@ -147,7 +147,7 @@
    "id": "525d6b67",
    "metadata": {},
    "source": [
-    "### Retain Elements\n",
+    "## Retain Elements\n",
     "\n",
     "Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."
    ]
@@ -192,59 +192,6 @@
    "source": [
     "data[0]"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1f3b83f",
-   "metadata": {},
-   "source": [
-    "## Using Azure AI Document Intelligence\n",
-    "\n",
-    ">[Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known as `Azure Form Recognizer`) is machine-learning \n",
-    ">based service that extracts texts (including handwriting), tables, document structures (e.g., titles, section headings, etc.) and key-value-pairs from\n",
-    ">digital or scanned PDFs, images, Office and HTML files.\n",
-    ">\n",
-    ">Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`, `DOCX`, `XLSX`, `PPTX` and `HTML`.\n",
-    "\n",
-    "This current implementation of a loader using `Document Intelligence` can incorporate content page-wise and turn it into LangChain documents. The default output format is markdown, which can be easily chained with `MarkdownHeaderTextSplitter` for semantic document chunking. You can also use `mode=\"single\"` or `mode=\"page\"` to return pure texts in a single page or document split by page.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a5bd47c2",
-   "metadata": {},
-   "source": [
-    "## Prerequisite\n",
-    "\n",
-    "An Azure AI Document Intelligence resource in one of the 3 preview regions: **East US**, **West US2**, **West Europe** - follow [this document](https://learn.microsoft.com/azure/ai-services/document-intelligence/create-document-intelligence-resource?view=doc-intel-4.0.0) to create one if you don't have. You will be passing `<endpoint>` and `<key>` as parameters to the loader."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71cbdfe0",
-   "metadata": {},
-   "source": [
-    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "691bd9e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import AzureAIDocumentIntelligenceLoader\n",
-    "\n",
-    "file_path = \"<filepath>\"\n",
-    "endpoint = \"<endpoint>\"\n",
-    "key = \"<key>\"\n",
-    "loader = AzureAIDocumentIntelligenceLoader(\n",
-    "    api_endpoint=endpoint, api_key=key, file_path=file_path, api_model=\"prebuilt-layout\"\n",
-    ")\n",
-    "\n",
-    "documents = loader.load()"
-   ]
   }
  ],
  "metadata": {

docs/docs/integrations/document_loaders/oracleadb_loader.ipynb

@@ -1,165 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Oracle Autonomous Database\n",
-    "\n",
-    "Oracle autonomous database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs.\n",
-    "\n",
-    "This notebook covers how to load documents from oracle autonomous database, the loader supports connection with connection string or tns configuration.\n",
-    "\n",
-    "## Prerequisites\n",
-    "1. Database runs in a 'Thin' mode:\n",
-    "   https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_b.html\n",
-    "2. `pip install oracledb`:\n",
-    "   https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Instructions"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "pip install oracledb"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import OracleAutonomousDatabaseLoader\n",
-    "from settings import s"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "With mutual TLS authentication (mTLS), wallet_location and wallet_password are required to create the connection, user can create connection by providing either connection string or tns configuration details."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "SQL_QUERY = \"select prod_id, time_id from sh.costs fetch first 5 rows only\"\n",
-    "\n",
-    "doc_loader_1 = OracleAutonomousDatabaseLoader(\n",
-    "    query=SQL_QUERY,\n",
-    "    user=s.USERNAME,\n",
-    "    password=s.PASSWORD,\n",
-    "    schema=s.SCHEMA,\n",
-    "    config_dir=s.CONFIG_DIR,\n",
-    "    wallet_location=s.WALLET_LOCATION,\n",
-    "    wallet_password=s.PASSWORD,\n",
-    "    tns_name=s.TNS_NAME,\n",
-    ")\n",
-    "doc_1 = doc_loader_1.load()\n",
-    "\n",
-    "doc_loader_2 = OracleAutonomousDatabaseLoader(\n",
-    "    query=SQL_QUERY,\n",
-    "    user=s.USERNAME,\n",
-    "    password=s.PASSWORD,\n",
-    "    schema=s.SCHEMA,\n",
-    "    connection_string=s.CONNECTION_STRING,\n",
-    "    wallet_location=s.WALLET_LOCATION,\n",
-    "    wallet_password=s.PASSWORD,\n",
-    ")\n",
-    "doc_2 = doc_loader_2.load()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "With TLS authentication, wallet_location and wallet_password are not required."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "doc_loader_3 = OracleAutonomousDatabaseLoader(\n",
-    "    query=SQL_QUERY,\n",
-    "    user=s.USERNAME,\n",
-    "    password=s.PASSWORD,\n",
-    "    schema=s.SCHEMA,\n",
-    "    config_dir=s.CONFIG_DIR,\n",
-    "    tns_name=s.TNS_NAME,\n",
-    ")\n",
-    "doc_3 = doc_loader_3.load()\n",
-    "\n",
-    "doc_loader_4 = OracleAutonomousDatabaseLoader(\n",
-    "    query=SQL_QUERY,\n",
-    "    user=s.USERNAME,\n",
-    "    password=s.PASSWORD,\n",
-    "    schema=s.SCHEMA,\n",
-    "    connection_string=s.CONNECTION_STRING,\n",
-    ")\n",
-    "doc_4 = doc_loader_4.load()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/docs/integrations/document_loaders/quip.ipynb

@@ -61,7 +61,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders.quip import QuipLoader\n",
+    "from langchain_community.document_loaders import QuipLoader\n",
     "\n",
     "loader = QuipLoader(\n",
     "    api_url=\"https://platform.quip.com\", access_token=\"change_me\", request_timeout=60\n",

docs/docs/integrations/document_loaders/sitemap.ipynb

@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
    "outputs": [],
    "source": [
     "loader = SitemapLoader(\n",
-    "    web_path=\"https://api.python.langchain.com/sitemap.xml\",\n",
+    "    web_path=\" https://api.python.langchain.com/sitemap.xml\",\n",
     "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
     ")\n",
     "documents = loader.load()"
@@ -247,7 +247,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_transformers/voyageai-reranker.ipynb

@@ -1,465 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fc0db1bc",
-   "metadata": {},
-   "source": [
-    "# VoyageAI Reranker\n",
-    "\n",
-    ">[Voyage AI](https://www.voyageai.com/) provides cutting-edge embedding/vectorizations models.\n",
-    "\n",
-    "This notebook shows how to use [Voyage AI's rerank endpoint](https://api.voyageai.com/v1/rerank) in a retriever. This builds on top of ideas in the [ContextualCompressionRetriever](/docs/modules/data_connection/retrievers/contextual_compression/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4f5973bb-7897-4340-a8ce-c3365ee73b2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  voyageai\n",
-    "%pip install --upgrade --quiet  langchain-voyageai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b37bd138-4f3c-4d2c-bc4b-be705ce27a09",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  faiss\n",
-    "\n",
-    "# OR  (depending on Python version)\n",
-    "\n",
-    "%pip install --upgrade --quiet  faiss-cpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c47b0b26-6d51-4beb-aedb-ad09740a9a2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# To obtain your key, create an account on https://www.voyageai.com\n",
-    "\n",
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"VOYAGE_API_KEY\"] = getpass.getpass(\"Voyage AI API Key:\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "6fa3d916",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Helper function for printing docs\n",
-    "\n",
-    "\n",
-    "def pretty_print_docs(docs):\n",
-    "    print(\n",
-    "        f\"\\n{'-' * 100}\\n\".join(\n",
-    "            [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
-    "        )\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6fa3d916",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Set up the base vector store retriever\n",
-    "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "b7648612",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document 1:\n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.\n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 2:\n",
-      "\n",
-      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.\n",
-      "\n",
-      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 3:\n",
-      "\n",
-      "We cannot let this happen.\n",
-      "\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.\n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 4:\n",
-      "\n",
-      "He will never extinguish their love of freedom. He will never weaken the resolve of the free world.\n",
-      "\n",
-      "We meet tonight in an America that has lived through two of the hardest years this nation has ever faced.\n",
-      "\n",
-      "The pandemic has been punishing.\n",
-      "\n",
-      "And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of food, gas, housing, and so much more.\n",
-      "\n",
-      "I understand.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 5:\n",
-      "\n",
-      "As I’ve told Xi Jinping, it is never a good bet to bet against the American people.\n",
-      "\n",
-      "We’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America.\n",
-      "\n",
-      "And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 6:\n",
-      "\n",
-      "I understand.\n",
-      "\n",
-      "I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it.\n",
-      "\n",
-      "That’s why one of the first things I did as President was fight to pass the American Rescue Plan.\n",
-      "\n",
-      "Because people were hurting. We needed to act, and we did.\n",
-      "\n",
-      "Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 7:\n",
-      "\n",
-      "I 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",
-      "\n",
-      "I’ve worked on these issues a long time.\n",
-      "\n",
-      "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.\n",
-      "\n",
-      "So let’s not abandon our streets. Or choose between safety and equal justice.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 8:\n",
-      "\n",
-      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.\n",
-      "\n",
-      "Our troops in Iraq and Afghanistan faced many dangers.\n",
-      "\n",
-      "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",
-      "\n",
-      "When they came home, many of the world’s fittest and best trained warriors were never the same.\n",
-      "\n",
-      "Headaches. Numbness. Dizziness.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 9:\n",
-      "\n",
-      "And tonight, I’m announcing that the Justice Department will name a chief prosecutor for pandemic fraud.\n",
-      "\n",
-      "By the end of this year, the deficit will be down to less than half what it was before I took office.\n",
-      "\n",
-      "The only president ever to cut the deficit by more than one trillion dollars in a single year.\n",
-      "\n",
-      "Lowering your costs also means demanding more competition.\n",
-      "\n",
-      "I’m a capitalist, but capitalism without competition isn’t capitalism.\n",
-      "\n",
-      "It’s exploitation—and it drives up prices.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 10:\n",
-      "\n",
-      "Headaches. Numbness. Dizziness.\n",
-      "\n",
-      "A cancer that would put them in a flag-draped coffin.\n",
-      "\n",
-      "I know.\n",
-      "\n",
-      "One of those soldiers was my son Major Beau Biden.\n",
-      "\n",
-      "We 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",
-      "\n",
-      "But I’m committed to finding out everything we can.\n",
-      "\n",
-      "Committed to military families like Danielle Robinson from Ohio.\n",
-      "\n",
-      "The widow of Sergeant First Class Heath Robinson.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 11:\n",
-      "\n",
-      "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera.\n",
-      "\n",
-      "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun.\n",
-      "\n",
-      "Officer Mora was 27 years old.\n",
-      "\n",
-      "Officer Rivera was 22.\n",
-      "\n",
-      "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 12:\n",
-      "\n",
-      "This was a bipartisan effort, and I want to thank the members of both parties who worked to make it happen.\n",
-      "\n",
-      "We’re done talking about infrastructure weeks.\n",
-      "\n",
-      "We’re going to have an infrastructure decade.\n",
-      "\n",
-      "It is going to transform America and put us on a path to win the economic competition of the 21st Century that we face with the rest of the world—particularly with China.\n",
-      "\n",
-      "As I’ve told Xi Jinping, it is never a good bet to bet against the American people.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 13:\n",
-      "\n",
-      "So let’s not abandon our streets. Or choose between safety and equal justice.\n",
-      "\n",
-      "Let’s come together to protect our communities, restore trust, and hold law enforcement accountable.\n",
-      "\n",
-      "That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 14:\n",
-      "\n",
-      "Let’s pass the Paycheck Fairness Act and paid leave.\n",
-      "\n",
-      "Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty.\n",
-      "\n",
-      "Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.\n",
-      "\n",
-      "And let’s pass the PRO Act when a majority of workers want to form a union—they shouldn’t be stopped.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 15:\n",
-      "\n",
-      "He met the Ukrainian people.\n",
-      "\n",
-      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.\n",
-      "\n",
-      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.\n",
-      "\n",
-      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 16:\n",
-      "\n",
-      "To all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world.\n",
-      "\n",
-      "And I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers.\n",
-      "\n",
-      "Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 17:\n",
-      "\n",
-      "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",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 18:\n",
-      "\n",
-      "But that trickle-down theory led to weaker economic growth, lower wages, bigger deficits, and the widest gap between those at the top and everyone else in nearly a century.\n",
-      "\n",
-      "Vice President Harris and I ran for office with a new economic vision for America.\n",
-      "\n",
-      "Invest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up\n",
-      "and the middle out, not from the top down.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 19:\n",
-      "\n",
-      "Every Administration says they’ll do it, but we are actually doing it.\n",
-      "\n",
-      "We will buy American to make sure everything from the deck of an aircraft carrier to the steel on highway guardrails are made in America.\n",
-      "\n",
-      "But to compete for the best jobs of the future, we also need to level the playing field with China and other competitors.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 20:\n",
-      "\n",
-      "The only nation that can be defined by a single word: possibilities.\n",
-      "\n",
-      "So on this night, in our 245th year as a nation, I have come to report on the State of the Union.\n",
-      "\n",
-      "And my report is this: the State of the Union is strong—because you, the American people, are strong.\n",
-      "\n",
-      "We are stronger today than we were a year ago.\n",
-      "\n",
-      "And we will be stronger a year from now than we are today.\n",
-      "\n",
-      "Now is our moment to meet and overcome the challenges of our time.\n",
-      "\n",
-      "And we will, as one people.\n",
-      "\n",
-      "One America.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.document_loaders import TextLoader\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
-    "from langchain_voyageai import VoyageEmbeddings\n",
-    "\n",
-    "documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
-    "texts = text_splitter.split_documents(documents)\n",
-    "retriever = FAISS.from_documents(\n",
-    "    texts, VoyageEmbeddings(model=\"voyage-2\")\n",
-    ").as_retriever(search_kwargs={\"k\": 20})\n",
-    "\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = retriever.get_relevant_documents(query)\n",
-    "pretty_print_docs(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7648612",
-   "metadata": {},
-   "source": [
-    "## Doing reranking with VoyageAIRerank\n",
-    "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll add an `VoyageAIRerank`, uses the Voyage AI rerank endpoint to rerank the returned results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "b83dfedb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document 1:\n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.\n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 2:\n",
-      "\n",
-      "So let’s not abandon our streets. Or choose between safety and equal justice.\n",
-      "\n",
-      "Let’s come together to protect our communities, restore trust, and hold law enforcement accountable.\n",
-      "\n",
-      "That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 3:\n",
-      "\n",
-      "I 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",
-      "\n",
-      "I’ve worked on these issues a long time.\n",
-      "\n",
-      "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.\n",
-      "\n",
-      "So let’s not abandon our streets. Or choose between safety and equal justice.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.retrievers import ContextualCompressionRetriever\n",
-    "from langchain_openai import OpenAI\n",
-    "from langchain_voyageai import VoyageAIRerank\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "compressor = VoyageAIRerank(\n",
-    "    model=\"rerank-lite-1\", voyageai_api_key=os.environ[\"VOYAGE_API_KEY\"], top_k=3\n",
-    ")\n",
-    "compression_retriever = ContextualCompressionRetriever(\n",
-    "    base_compressor=compressor, base_retriever=retriever\n",
-    ")\n",
-    "\n",
-    "compressed_docs = compression_retriever.get_relevant_documents(\n",
-    "    \"What did the president say about Ketanji Jackson Brown\"\n",
-    ")\n",
-    "pretty_print_docs(compressed_docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b83dfedb",
-   "metadata": {},
-   "source": [
-    "You can of course use this retriever within a QA pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "367dafe0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import RetrievalQA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "ae697ca4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAI(temperature=0), retriever=compression_retriever\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "46ee62fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'query': 'What did the president say about Ketanji Brown Jackson',\n",
-       " 'result': \" The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court. \"}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain({\"query\": query})"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/llms/aleph_alpha.ipynb

@@ -58,8 +58,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.llms import AlephAlpha\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import AlephAlpha"
    ]
   },
   {

docs/docs/integrations/llms/anyscale.ipynb

@@ -49,8 +49,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import Anyscale\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import Anyscale"
    ]
   },
   {

docs/docs/integrations/llms/aphrodite.ipynb

@@ -146,7 +146,7 @@
    ],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",

docs/docs/integrations/llms/azure_ml.ipynb

@@ -29,7 +29,7 @@
     "You must [deploy a model on Azure ML](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) or [to Azure AI studio](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-open) and obtain the following parameters:\n",
     "\n",
     "* `endpoint_url`: The REST endpoint url provided by the endpoint.\n",
-    "* `endpoint_api_type`: Use `endpoint_type='dedicated'` when deploying models to **Dedicated endpoints** (hosted managed infrastructure). Use `endpoint_type='serverless'` when deploying models using the **Pay-as-you-go** offering (model as a service).\n",
+    "* `endpoint_api_type`: Use `endpoint_type='realtime'` when deploying models to **Realtime endpoints** (hosted managed infrastructure). Use `endpoint_type='serverless'` when deploying models using the **Pay-as-you-go** offering (model as a service).\n",
     "* `endpoint_api_key`: The API key provided by the endpoint.\n",
     "* `deployment_name`: (Optional) The deployment name of the model using the endpoint."
    ]
@@ -45,7 +45,7 @@
     "* `GPT2ContentFormatter`: Formats request and response data for GPT2\n",
     "* `DollyContentFormatter`: Formats request and response data for the Dolly-v2\n",
     "* `HFContentFormatter`: Formats request and response data for text-generation Hugging Face models\n",
-    "* `CustomOpenAIContentFormatter`: Formats request and response data for models like LLaMa2 that follow OpenAI API compatible scheme.\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 compatible.*"
    ]
@@ -72,15 +72,15 @@
    "source": [
     "from langchain_community.llms.azureml_endpoint import (\n",
     "    AzureMLEndpointApiType,\n",
-    "    CustomOpenAIContentFormatter,\n",
+    "    LlamaContentFormatter,\n",
     ")\n",
     "from langchain_core.messages import HumanMessage\n",
     "\n",
     "llm = AzureMLOnlineEndpoint(\n",
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/score\",\n",
-    "    endpoint_api_type=AzureMLEndpointApiType.dedicated,\n",
+    "    endpoint_api_type=AzureMLEndpointApiType.realtime,\n",
     "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=CustomOpenAIContentFormatter(),\n",
+    "    content_formatter=LlamaContentFormatter(),\n",
     "    model_kwargs={\"temperature\": 0.8, \"max_new_tokens\": 400},\n",
     ")\n",
     "response = llm.invoke(\"Write me a song about sparkling water:\")\n",
@@ -119,7 +119,7 @@
    "source": [
     "from langchain_community.llms.azureml_endpoint import (\n",
     "    AzureMLEndpointApiType,\n",
-    "    CustomOpenAIContentFormatter,\n",
+    "    LlamaContentFormatter,\n",
     ")\n",
     "from langchain_core.messages import HumanMessage\n",
     "\n",
@@ -127,7 +127,7 @@
     "    endpoint_url=\"https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/completions\",\n",
     "    endpoint_api_type=AzureMLEndpointApiType.serverless,\n",
     "    endpoint_api_key=\"my-api-key\",\n",
-    "    content_formatter=CustomOpenAIContentFormatter(),\n",
+    "    content_formatter=LlamaContentFormatter(),\n",
     "    model_kwargs={\"temperature\": 0.8, \"max_new_tokens\": 400},\n",
     ")\n",
     "response = llm.invoke(\"Write me a song about sparkling water:\")\n",
@@ -181,7 +181,7 @@
     "content_formatter = CustomFormatter()\n",
     "\n",
     "llm = AzureMLOnlineEndpoint(\n",
-    "    endpoint_api_type=\"dedicated\",\n",
+    "    endpoint_api_type=\"realtime\",\n",
     "    endpoint_api_key=os.getenv(\"BART_ENDPOINT_API_KEY\"),\n",
     "    endpoint_url=os.getenv(\"BART_ENDPOINT_URL\"),\n",
     "    model_kwargs={\"temperature\": 0.8, \"max_new_tokens\": 400},\n",
@@ -228,8 +228,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms.azureml_endpoint import DollyContentFormatter\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "formatter_template = \"Write a {word_count} word essay about {topic}.\"\n",
     "\n",

docs/docs/integrations/llms/banana.ipynb

@@ -52,8 +52,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import Banana\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import Banana"
    ]
   },
   {

docs/docs/integrations/llms/baseten.ipynb

@@ -94,7 +94,7 @@
    "source": [
     "from langchain.chains import LLMChain\n",
     "from langchain.memory import ConversationBufferWindowMemory\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Assistant is a large language model trained by OpenAI.\n",
     "\n",

docs/docs/integrations/llms/bigdl.ipynb

@@ -4,11 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# IPEX-LLM\n",
+    "# BigDL-LLM\n",
     "\n",
-    "> [IPEX-LLM](https://github.com/intel-analytics/ipex-llm/) is a low-bit LLM optimization library on Intel XPU (Xeon/Core/Flex/Arc/Max). It can make LLMs run extremely fast and consume much less memory on Intel platforms. It is open sourced under Apache 2.0 License.\n",
+    "> [BigDL-LLM](https://github.com/intel-analytics/BigDL/) is a low-bit LLM optimization library on Intel XPU (Xeon/Core/Flex/Arc/Max). It can make LLMs run extremely fast and consume much less memory on Intel platforms. It is open sourced under Apache 2.0 License.\n",
     "\n",
-    "This example goes over how to use LangChain to interact with IPEX-LLM for text generation. \n"
+    "This example goes over how to use LangChain to interact with BigDL-LLM for text generation. \n"
    ]
   },
   {
@@ -33,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Install IEPX-LLM for running LLMs locally on Intel CPU."
+    "Install BigDL-LLM for running LLMs locally on Intel CPU."
    ]
   },
   {
@@ -42,7 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --pre --upgrade ipex-llm[all]"
+    "# Install BigDL\n",
+    "%pip install --pre --upgrade bigdl-llm[all]"
    ]
   },
   {
@@ -59,7 +60,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import IpexLLM\n",
+    "from langchain_community.llms.bigdl import BigdlLLM\n",
     "from langchain_core.prompts import PromptTemplate"
    ]
   },
@@ -88,7 +89,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "27c08180714a44c7ab766624d5054163",
+       "model_id": "69e018750ffb4de1af22ce49cd6957f4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -103,12 +104,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-03-27 00:58:43,670 - INFO - Converting the current model to sym_int4 format......\n"
+      "2024-02-23 18:10:22,896 - INFO - Converting the current model to sym_int4 format......\n",
+      "2024-02-23 18:10:25,415 - INFO - BIGDL_OPT_IPEX: False\n"
      ]
     }
    ],
    "source": [
-    "llm = IpexLLM.from_model_id(\n",
+    "llm = BigdlLLM.from_model_id(\n",
     "    model_id=\"lmsys/vicuna-7b-v1.5\",\n",
     "    model_kwargs={\"temperature\": 0, \"max_length\": 64, \"trust_remote_code\": True},\n",
     ")"
@@ -133,10 +135,6 @@
       "/opt/anaconda3/envs/shane-langchain2/lib/python3.9/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `run` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
       "  warn_deprecated(\n",
       "/opt/anaconda3/envs/shane-langchain2/lib/python3.9/site-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (4096) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
-      "  warnings.warn(\n",
-      "/opt/anaconda3/envs/shane-langchain2/lib/python3.9/site-packages/ipex_llm/transformers/models/llama.py:218: UserWarning: Passing `padding_mask` is deprecated and will be removed in v4.37.Please make sure use `attention_mask` instead.`\n",
-      "  warnings.warn(\n",
-      "/opt/anaconda3/envs/shane-langchain2/lib/python3.9/site-packages/ipex_llm/transformers/models/llama.py:218: UserWarning: Passing `padding_mask` is deprecated and will be removed in v4.37.Please make sure use `attention_mask` instead.`\n",
       "  warnings.warn(\n"
      ]
     },
@@ -158,13 +156,6 @@
     "question = \"What is AI?\"\n",
     "output = llm_chain.run(question)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/llms/bittensor.ipynb

@@ -82,8 +82,8 @@
    "source": [
     "from langchain.chains import LLMChain\n",
     "from langchain.globals import set_debug\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms import NIBittensorLLM\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "set_debug(True)\n",
     "\n",
@@ -142,8 +142,8 @@
     ")\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms import NIBittensorLLM\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
     "\n",

docs/docs/integrations/llms/cerebriumai.ipynb

@@ -45,8 +45,8 @@
     "import os\n",
     "\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import CerebriumAI\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import CerebriumAI"
    ]
   },
   {

docs/docs/integrations/llms/chatglm.ipynb

@@ -41,9 +41,9 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema.messages import AIMessage\n",
-    "from langchain_community.llms.chatglm3 import ChatGLM3\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain_community.llms.chatglm3 import ChatGLM3"
    ]
   },
   {
@@ -117,8 +117,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms import ChatGLM\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "# import os"
    ]

docs/docs/integrations/llms/clarifai.ipynb

@@ -87,8 +87,8 @@
    "source": [
     "# Import the required modules\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import Clarifai\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import Clarifai"
    ]
   },
   {

docs/docs/integrations/llms/cloudflare_workersai.ipynb

@@ -19,8 +19,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Human: {question}\n",
     "\n",

docs/docs/integrations/llms/ctransformers.ipynb

@@ -103,7 +103,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",

docs/docs/integrations/llms/ctranslate2.ipynb

@@ -196,7 +196,7 @@
    ],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"{question}\n",
     "\n",

docs/docs/integrations/llms/deepinfra.ipynb

@@ -140,7 +140,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",

docs/docs/integrations/llms/edenai.ipynb

@@ -98,7 +98,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "llm = EdenAI(\n",
     "    feature=\"text\",\n",
@@ -220,7 +220,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain, SimpleSequentialChain\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {

docs/docs/integrations/llms/fireworks.ipynb

@@ -182,7 +182,7 @@
     }
    ],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_fireworks import Fireworks\n",
     "\n",
     "llm = Fireworks(\n",

docs/docs/integrations/llms/forefrontai.ipynb

@@ -28,8 +28,8 @@
     "import os\n",
     "\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import ForefrontAI\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import ForefrontAI"
    ]
   },
   {

docs/docs/integrations/llms/gigachat.ipynb

@@ -80,7 +80,7 @@
    ],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"What is capital of {country}?\"\n",
     "\n",

docs/docs/integrations/llms/google_ai.ipynb

@@ -180,7 +180,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {

docs/docs/integrations/llms/gooseai.ipynb

@@ -44,8 +44,8 @@
     "import os\n",
     "\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import GooseAI\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import GooseAI"
    ]
   },
   {

docs/docs/integrations/llms/gpt4all.ipynb

@@ -49,8 +49,8 @@
    "source": [
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import GPT4All\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import GPT4All"
    ]
   },
   {

docs/docs/integrations/llms/gradient.ipynb

@@ -25,8 +25,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms import GradientLLM\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms import GradientLLM"
    ]
   },
   {

docs/docs/integrations/llms/huggingface_pipelines.ipynb

@@ -107,7 +107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.prompts import PromptTemplate\n",
     "\n",
     "template = \"\"\"Question: {question}\n",
     "\n",
@@ -330,7 +330,7 @@
    "id": "da9a9239",
    "metadata": {},
    "source": [
-    "For more information refer to [OpenVINO LLM guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html) and [OpenVINO Local Pipelines notebook](./openvino.ipynb)."
+    "For more information refer to [OpenVINO LLM guide](https://docs.openvino.ai/2024/openvino-workflow/generative-ai-models-guide.html)."
    ]
   }
  ],

docs/docs/integrations/llms/javelin.ipynb

@@ -92,8 +92,8 @@
    ],
    "source": [
     "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain_community.llms import JavelinAIGateway\n",
-    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "route_completions = \"eng_dept03\"\n",
     "\n",

docs/docs/integrations/llms/layerup_security.mdx

@@ -1,85 +0,0 @@
-# Layerup Security
-
-The [Layerup Security](https://uselayerup.com) integration allows you to secure your calls to any LangChain LLM, LLM chain or LLM agent. The LLM object wraps around any existing LLM object, allowing for a secure layer between your users and your LLMs.
-
-While the Layerup Security object is designed as an LLM, it is not actually an LLM itself, it simply wraps around an LLM, allowing it to adapt the same functionality as the underlying LLM.
-
-## Setup
-First, you'll need a Layerup Security account from the Layerup [website](https://uselayerup.com).
-
-Next, create a project via the [dashboard](https://dashboard.uselayerup.com), and copy your API key. We recommend putting your API key in your project's environment.
-
-Install the Layerup Security SDK:
-```bash
-pip install LayerupSecurity
-```
-
-And install LangChain Community:
-```bash
-pip install langchain-community
-```
-
-And now you're ready to start protecting your LLM calls with Layerup Security!
-
-```python
-from langchain_community.llms.layerup_security import LayerupSecurity
-from langchain_openai import OpenAI
-
-# Create an instance of your favorite LLM
-openai = OpenAI(
-    model_name="gpt-3.5-turbo",
-    openai_api_key="OPENAI_API_KEY",
-)
-
-# Configure Layerup Security
-layerup_security = LayerupSecurity(
-    # Specify a LLM that Layerup Security will wrap around
-    llm=openai,
-
-    # Layerup API key, from the Layerup dashboard
-    layerup_api_key="LAYERUP_API_KEY",
-
-    # Custom base URL, if self hosting
-    layerup_api_base_url="https://api.uselayerup.com/v1",
-
-    # List of guardrails to run on prompts before the LLM is invoked
-    prompt_guardrails=[],
-
-    # List of guardrails to run on responses from the LLM
-    response_guardrails=["layerup.hallucination"],
-
-    # Whether or not to mask the prompt for PII & sensitive data before it is sent to the LLM
-    mask=False,
-
-    # Metadata for abuse tracking, customer tracking, and scope tracking.
-    metadata={"customer": "example@uselayerup.com"},
-
-    # Handler for guardrail violations on the prompt guardrails
-    handle_prompt_guardrail_violation=(
-        lambda violation: {
-            "role": "assistant",
-            "content": (
-                "There was sensitive data! I cannot respond. "
-                "Here's a dynamic canned response. Current date: {}"
-            ).format(datetime.now())
-        }
-        if violation["offending_guardrail"] == "layerup.sensitive_data"
-        else None
-    ),
-
-    # Handler for guardrail violations on the response guardrails
-    handle_response_guardrail_violation=(
-        lambda violation: {
-            "role": "assistant",
-            "content": (
-                "Custom canned response with dynamic data! "
-                "The violation rule was {}."
-            ).format(violation["offending_guardrail"])
-        }
-    ),
-)
-
-response = layerup_security.invoke(
-    "Summarize this message: my name is Bob Dylan. My SSN is 123-45-6789."
-)
-```

docs/docs/integrations/llms/llm_caching.ipynb

@@ -1378,7 +1378,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.cache import AzureCosmosDBSemanticCache\n",
+    "from langchain.cache import AzureCosmosDBSemanticCache\n",
     "from langchain_community.vectorstores.azure_cosmos_db import (\n",
     "    CosmosDBSimilarityType,\n",
     "    CosmosDBVectorSearchType,\n",