bump version to 0076 (#847)

Co-authored-by: Sean Spriggens <ssprigge@syr.edu>

.coveragerc

@@ -1,2 +0,0 @@
-[run]
-omit = tests/*

CITATION.cff

@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Chase"
+  given-names: "Harrison"
+title: "LangChain"
+date-released: 2022-10-17
+url: "https://github.com/hwchase17/langchain"

CONTRIBUTING.md

@@ -47,7 +47,7 @@ good code into the codebase.
 ### 🏭Release process
 
 As of now, LangChain has an ad hoc release process: releases are cut with high frequency via by
-a developer and published to [PyPI](https://pypi.org/project/ruff/).
+a developer and published to [PyPI](https://pypi.org/project/langchain/).
 
 LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
 even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).

README.md

@@ -4,6 +4,9 @@
 
 [![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
 
+**Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
+Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set up a dedicated support Slack channel.
+
 ## Quick Install
 
 `pip install langchain`
@@ -15,7 +18,22 @@ developers to build applications that they previously could not.
 But using these LLMs in isolation is often not enough to
 create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
 
-This library is aimed at assisting in the development of those types of applications.
+This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
+
+**❓ Question Answering over specific documents**
+
+- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html)
+- End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa)
+
+**💬 Chatbots**
+
+- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/chatbots.html)
+- End-to-end Example: [Chat-LangChain](https://github.com/hwchase17/chat-langchain)
+
+**🤖 Agents**
+
+- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/agents.html)
+- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
 
 ## 📖 Documentation
 

docs/deployments.md

@@ -22,3 +22,9 @@ This repo serves as a template for how deploy a LangChain with Gradio.
 It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills).
 It also contains instructions for how to deploy this app on the Hugging Face platform.
 This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
+
+## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
+
+This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
+
+It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API.

docs/ecosystem/google_search.md

@@ -1,7 +1,7 @@
 # Google Search Wrapper
 
 This page covers how to use the Google Search API within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
+It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper.
 
 ## Installation and Setup
 - Install requirements with `pip install google-api-python-client`

docs/ecosystem/serpapi.md

@@ -1,7 +1,7 @@
 # SerpAPI
 
 This page covers how to use the SerpAPI search APIs within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
+It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.
 
 ## Installation and Setup
 - Install requirements with `pip install google-search-results`

docs/gallery.rst

@@ -77,6 +77,17 @@ Open Source
 
     +++
 
+    A jupyter notebook demonstrating how you could create a semantic search engine on documents in one of your Google Folders
+
+    ---
+
+    .. link-button:: https://github.com/venuv/langchain_semantic_search
+        :type: url
+        :text: Google Folder Semantic Search
+        :classes: stretched-link btn-lg
+
+    +++
+
     Build a GitHub support bot with GPT3, LangChain, and Python.
 
     ---

docs/index.rst

@@ -7,7 +7,22 @@ But using these LLMs in isolation is often not enough to
 create a truly powerful app - the real power comes when you are able to
 combine them with other sources of computation or knowledge.
 
-This library is aimed at assisting in the development of those types of applications.
+This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
+
+**❓ Question Answering over specific documents**
+
+- `Documentation <./use_cases/question_answering.html>`_
+- End-to-end Example: `Question Answering over Notion Database <https://github.com/hwchase17/notion-qa>`_
+
+**💬 Chatbots**
+
+- `Documentation <./use_cases/chatbots.html>`_
+- End-to-end Example: `Chat-LangChain <https://github.com/hwchase17/chat-langchain>`_
+
+**🤖 Agents**
+
+- `Documentation <./use_cases/agents.html>`_
+- End-to-end Example: `GPT+WolframAlpha <https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain>`_
 
 Getting Started
 ----------------
@@ -137,6 +152,8 @@ Additional Resources
 
 Additional collection of resources we think may be useful as you develop your application!
 
+- `LangChainHub <https://github.com/hwchase17/langchain-hub>`_: The LangChainHub is a place to share and explore other prompts, chains, and agents.
+
 - `Glossary <./glossary.html>`_: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!
 
 - `Gallery <./gallery.html>`_: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.
@@ -145,6 +162,10 @@ Additional collection of resources we think may be useful as you develop your ap
 
 - `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
 
+- `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
+
+- `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
+
 
 .. toctree::
    :maxdepth: 1
@@ -152,6 +173,10 @@ Additional collection of resources we think may be useful as you develop your ap
    :name: resources
    :hidden:
 
+   LangChainHub <https://github.com/hwchase17/langchain-hub>
    ./glossary.md
    ./gallery.rst
    ./deployments.md
+   ./tracing.md
+   Discord <https://discord.gg/6adMQxSpJS>
+   Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

docs/modules/agents/examples/custom_agent.ipynb

@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "becda2a1",
    "metadata": {},
    "outputs": [],
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "339b1bb8",
    "metadata": {},
    "outputs": [],
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "e21d2098",
    "metadata": {},
    "outputs": [
@@ -134,7 +134,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "5e028e6d",
    "metadata": {},
@@ -146,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "id": "9b1cc2a2",
    "metadata": {},
    "outputs": [],
@@ -156,17 +155,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "id": "e4f5092f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools)"
+    "tool_names = [tool.name for tool in tools]\n",
+    "agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "id": "490604e9",
    "metadata": {},
    "outputs": [],
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -191,22 +191,23 @@
       "Action: Search\n",
       "Action Input: Population of Canada\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out the exact population of Canada\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out the population of Canada\n",
       "Action: Search\n",
-      "Action Input: Population of Canada 2020\u001b[0m\n",
+      "Action Input: Population of Canada\u001b[0m\n",
       "Observation: \u001b[36;1m\u001b[1;3mCanada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, covering over 9.98 million square kilometres, making it the world's second-largest country by total area.\u001b[0m\n",
       "Thought:\u001b[32;1m\u001b[1;3m I now know the population of Canada\n",
-      "Final Answer: Arrr, Canada be home to 37.59 million people!\u001b[0m\n",
-      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
+      "Final Answer: Arrr, Canada be home to over 37 million people!\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'Arrr, Canada be home to 37.59 million people!'"
+       "'Arrr, Canada be home to over 37 million people!'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -361,7 +362,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -375,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12 (default, Feb 15 2022, 17:41:09) \n[Clang 12.0.5 (clang-1205.0.22.11)]"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/examples/custom_tools.ipynb

@@ -10,15 +10,17 @@
     "When constructing your own agent, you will need to provide it with a list of Tools that it can use. A Tool is defined as below.\n",
     "\n",
     "```python\n",
-    "class Tool(NamedTuple):\n",
+    "@dataclass \n",
+    "class Tool:\n",
     "    \"\"\"Interface for tools.\"\"\"\n",
     "\n",
     "    name: str\n",
     "    func: Callable[[str], str]\n",
     "    description: Optional[str] = None\n",
+    "    return_direct: bool = True\n",
     "```\n",
     "\n",
-    "The two required components of a Tool are the name and then the tool itself. A tool description is optional, as it is needed for some agents but not all."
+    "The two required components of a Tool are the name and then the tool itself. A tool description is optional, as it is needed for some agents but not all. You can create these tools directly, but we also provide a decorator to easily convert any function into a tool."
    ]
   },
   {
@@ -151,6 +153,94 @@
     "agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "824eaf74",
+   "metadata": {},
+   "source": [
+    "## Using the `tool` decorator\n",
+    "\n",
+    "To make it easier to define custom tools, a `@tool` decorator is provided. This decorator can be used to quickly create a `Tool` from a simple function. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function's docstring as the tool's description."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8f15307d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import tool\n",
+    "\n",
+    "@tool\n",
+    "def search_api(query: str) -> str:\n",
+    "    \"\"\"Searches the API for the query.\"\"\"\n",
+    "    return \"Results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0a23b91b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tool(name='search_api', func=<function search_api at 0x10dad7d90>, description='search_api(query: str) -> str - Searches the API for the query.', return_direct=False)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_api"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc6ee8c1",
+   "metadata": {},
+   "source": [
+    "You can also provide arguments like the tool name and whether to return directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "28cdf04d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool(\"search\", return_direct=True)\n",
+    "def search_api(query: str) -> str:\n",
+    "    \"\"\"Searches the API for the query.\"\"\"\n",
+    "    return \"Results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1085a4bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tool(name='search', func=<function search_api at 0x112301bd0>, description='search(query: str) -> str - Searches the API for the query.', return_direct=True)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_api"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1d0430d6",
@@ -432,7 +522,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "cb23c3a7a387ab03496baa08507270f8e0861b23170e79d5edc545893cdca840"
+    "hash": "e90c8aa204a57276aa905271aff2d11799d0acb3547adabc5892e639a5e45e34"
    }
   }
  },

docs/modules/agents/examples/load_from_hub.ipynb

@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "991b1cc1",
+   "metadata": {},
+   "source": [
+    "# Loading from LangChainHub\n",
+    "\n",
+    "This notebook covers how to load agents from [LangChainHub](https://github.com/hwchase17/langchain-hub)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bd4450a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3m2016 · SUI · Stan Wawrinka ; 2017 · ESP · Rafael Nadal ; 2018 · SRB · Novak Djokovic ; 2019 · ESP · Rafael Nadal.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mSo the reigning men's U.S. Open champion is Rafael Nadal.\n",
+      "Follow up: What is Rafael Nadal's hometown?\u001b[0m\n",
+      "Intermediate answer: \u001b[36;1m\u001b[1;3mIn 2016, he once again showed his deep ties to Mallorca and opened the Rafa Nadal Academy in his hometown of Manacor.\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mSo the final answer is: Manacor, Mallorca, Spain.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Manacor, Mallorca, Spain.'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import OpenAI, SerpAPIWrapper\n",
+    "from langchain.agents import initialize_agent, Tool\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "search = SerpAPIWrapper()\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name=\"Intermediate Answer\",\n",
+    "        func=search.run\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc://agents/self-ask-with-search/agent.json\", verbose=True)\n",
+    "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "3aede965",
+   "metadata": {},
+   "source": [
+    "# Pinning Dependencies\n",
+    "\n",
+    "Specific versions of LangChainHub agents can be pinned with the `lc@<ref>://` syntax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e679f7b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc@2826ef9e8acdf88465e1e5fc8a7bf59e0f9d0a85://agents/self-ask-with-search/agent.json\", verbose=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/agents/examples/serialization.ipynb

@@ -0,0 +1,148 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bfe18e28",
+   "metadata": {},
+   "source": [
+    "# Serialization\n",
+    "\n",
+    "This notebook goes over how to serialize agents. For this notebook, it is important to understand the distinction we draw between `agents` and `tools`. An agent is the LLM powered decision maker that decides which actions to take and in which order. Tools are various instruments (functions) an agent has access to, through which an agent can interact with the outside world. When people generally use agents, they primarily talk about using an agent WITH tools. However, when we talk about serialization of agents, we are talking about the agent by itself. We plan to add support for serializing an agent WITH tools sometime in the future.\n",
+    "\n",
+    "Let's start by creating an agent with tools as we normally do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eb729f16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0578f566",
+   "metadata": {},
+   "source": [
+    "Let's now serialize the agent. To be explicit that we are serializing ONLY the agent, we will call the `save_agent` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dc544de6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent.save_agent('agent.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "62dd45bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"llm_chain\": {\r\n",
+      "        \"memory\": null,\r\n",
+      "        \"verbose\": false,\r\n",
+      "        \"prompt\": {\r\n",
+      "            \"input_variables\": [\r\n",
+      "                \"input\",\r\n",
+      "                \"agent_scratchpad\"\r\n",
+      "            ],\r\n",
+      "            \"output_parser\": null,\r\n",
+      "            \"template\": \"Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\r\n",
+      "            \"template_format\": \"f-string\"\r\n",
+      "        },\r\n",
+      "        \"llm\": {\r\n",
+      "            \"model_name\": \"text-davinci-003\",\r\n",
+      "            \"temperature\": 0.0,\r\n",
+      "            \"max_tokens\": 256,\r\n",
+      "            \"top_p\": 1,\r\n",
+      "            \"frequency_penalty\": 0,\r\n",
+      "            \"presence_penalty\": 0,\r\n",
+      "            \"n\": 1,\r\n",
+      "            \"best_of\": 1,\r\n",
+      "            \"request_timeout\": null,\r\n",
+      "            \"logit_bias\": {},\r\n",
+      "            \"_type\": \"openai\"\r\n",
+      "        },\r\n",
+      "        \"output_key\": \"text\",\r\n",
+      "        \"_type\": \"llm_chain\"\r\n",
+      "    },\r\n",
+      "    \"return_values\": [\r\n",
+      "        \"output\"\r\n",
+      "    ],\r\n",
+      "    \"_type\": \"zero-shot-react-description\"\r\n",
+      "}"
+     ]
+    }
+   ],
+   "source": [
+    "!cat agent.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eb72510",
+   "metadata": {},
+   "source": [
+    "We can now load the agent back in"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "eb660b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent(tools, llm, agent_path=\"agent.json\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa624ea5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/agents/getting_started.ipynb

@@ -152,7 +152,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.0 64-bit ('llm-env')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

docs/modules/agents/how_to_guides.rst

@@ -3,6 +3,8 @@ How-To Guides
 
 The first category of how-to guides here cover specific parts of working with agents.
 
+`Load From Hub <./examples/load_from_hub.html>`_: This notebook covers how to load agents from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.
+
 `Custom Tools <./examples/custom_tools.html>`_: How to create custom tools that an agent can use.
 
 `Intermediate Steps <./examples/intermediate_steps.html>`_: How to access and use intermediate steps to get more visibility into the internals of an agent.

docs/modules/agents/implementations/natbot.py

@@ -2,7 +2,7 @@
 import time
 
 from langchain.chains.natbot.base import NatBotChain
-from langchain.chains.natbot.crawler import Crawler  # type: ignore
+from langchain.chains.natbot.crawler import Crawler
 
 
 def run_cmd(cmd: str, _crawler: Crawler) -> None:

docs/modules/agents/tools.md

@@ -22,6 +22,7 @@ tools = load_tools(tool_names, llm=llm)
 ```
 
 Below is a list of all supported tools and relevant information:
+
 - Tool Name: The name the LLM refers to the tool by.
 - Tool Description: The description of the tool that is passed to the LLM.
 - Notes: Notes about the tool that are NOT passed to the LLM.
@@ -31,61 +32,71 @@ Below is a list of all supported tools and relevant information:
 ## List of Tools
 
 **python_repl**
+
 - Tool Name: Python REPL
 - Tool Description: A Python shell. Use this to execute python commands. Input should be a valid python command. If you expect output it should be printed out.
 - Notes: Maintains state.
 - Requires LLM: No
 
-
 **serpapi**
+
 - Tool Name: Search
 - Tool Description: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.
 - Notes: Calls the Serp API and then parses results.
 - Requires LLM: No
 
 **wolfram-alpha**
+
 - Tool Name: Wolfram Alpha
 - Tool Description: A wolfram alpha search engine. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.
 - Notes: Calls the Wolfram Alpha API and then parses results.
 - Requires LLM: No
+- Extra Parameters: `wolfram_alpha_appid`: The Wolfram Alpha app id.
 
 **requests**
+
 - Tool Name: Requests
 - Tool Description: A portal to the internet. Use this when you need to get specific content from a site. Input should be a specific url, and the output will be all the text on that page.
 - Notes: Uses the Python requests module.
 - Requires LLM: No
 
 **terminal**
+
 - Tool Name: Terminal
 - Tool Description: Executes commands in a terminal. Input should be valid commands, and the output will be any output from running that command.
 - Notes: Executes commands with subprocess.
 - Requires LLM: No
 
 **pal-math**
+
 - Tool Name: PAL-MATH
 - Tool Description: A language model that is excellent at solving complex word math problems. Input should be a fully worded hard word math problem.
 - Notes: Based on [this paper](https://arxiv.org/pdf/2211.10435.pdf).
 - Requires LLM: Yes
 
 **pal-colored-objects**
+
 - Tool Name: PAL-COLOR-OBJ
 - Tool Description: A language model that is wonderful at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning problem. Make sure to include all information about the objects AND the final question you want to answer.
 - Notes: Based on [this paper](https://arxiv.org/pdf/2211.10435.pdf).
 - Requires LLM: Yes
 
 **llm-math**
+
 - Tool Name: Calculator
 - Tool Description: Useful for when you need to answer questions about math.
 - Notes: An instance of the `LLMMath` chain.
 - Requires LLM: Yes
 
 **open-meteo-api**
+
 - Tool Name: Open Meteo API
 - Tool Description: Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.
 - Notes: A natural language connection to the Open Meteo API (`https://api.open-meteo.com/`), specifically the `/v1/forecast` endpoint.
 - Requires LLM: Yes
 
 **news-api**
+
 - Tool Name: News API
 - Tool Description: Use this when you want to get information about the top headlines of current news stories. The input should be a question in natural language that this API can answer.
 - Notes: A natural language connection to the News API (`https://newsapi.org`), specifically the `/v2/top-headlines` endpoint.
@@ -93,8 +104,18 @@ Below is a list of all supported tools and relevant information:
 - Extra Parameters: `news_api_key` (your API key to access this endpoint)
 
 **tmdb-api**
+
 - Tool Name: TMDB API
 - Tool Description: Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.
 - Notes: A natural language connection to the TMDB API (`https://api.themoviedb.org/3`), specifically the `/search/movie` endpoint.
 - Requires LLM: Yes
 - Extra Parameters: `tmdb_bearer_token` (your Bearer Token to access this endpoint - note that this is different from the API key)
+
+**google-search**
+
+- Tool Name: Search
+- Tool Description: A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query.
+- Notes: Uses the Google Custom Search API
+- Requires LLM: No
+- Extra Parameters: `google_api_key`, `google_cse_id`
+- For more information on this, see [this page](../../ecosystem/google_search.md)

docs/modules/chains/combine_docs_examples/vector_db_qa_with_sources.ipynb

@@ -187,7 +187,7 @@
     }
    ],
    "source": [
-    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+    "qa({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
    ]
   }
  ],

docs/modules/chains/combine_docs_examples/vector_db_text_generation.ipynb

@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vector DB Text Generation\n",
+    "\n",
+    "This notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a large body of custom text, for example, generating blog posts that have an understanding of previous blog posts written, or product tutorials that can refer to product documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare Data\n",
+    "\n",
+    "First, we prepare the data. For this example, we fetch a documentation site that consists of markdown files hosted on Github and split them into small enough Documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.docstore.document import Document\n",
+    "import requests\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "import pathlib\n",
+    "import subprocess\n",
+    "import tempfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Cloning into '.'...\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_github_docs(repo_owner, repo_name):\n",
+    "    with tempfile.TemporaryDirectory() as d:\n",
+    "        subprocess.check_call(\n",
+    "            f\"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .\",\n",
+    "            cwd=d,\n",
+    "            shell=True,\n",
+    "        )\n",
+    "        git_sha = (\n",
+    "            subprocess.check_output(\"git rev-parse HEAD\", shell=True, cwd=d)\n",
+    "            .decode(\"utf-8\")\n",
+    "            .strip()\n",
+    "        )\n",
+    "        repo_path = pathlib.Path(d)\n",
+    "        markdown_files = list(repo_path.glob(\"*/*.md\")) + list(\n",
+    "            repo_path.glob(\"*/*.mdx\")\n",
+    "        )\n",
+    "        for markdown_file in markdown_files:\n",
+    "            with open(markdown_file, \"r\") as f:\n",
+    "                relative_path = markdown_file.relative_to(repo_path)\n",
+    "                github_url = f\"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}\"\n",
+    "                yield Document(page_content=f.read(), metadata={\"source\": github_url})\n",
+    "\n",
+    "sources = get_github_docs(\"yirenlu92\", \"deno-manual-forked\")\n",
+    "\n",
+    "source_chunks = []\n",
+    "splitter = CharacterTextSplitter(separator=\" \", chunk_size=1024, chunk_overlap=0)\n",
+    "for source in sources:\n",
+    "    for chunk in splitter.split_text(source.page_content):\n",
+    "        source_chunks.append(Document(page_content=chunk, metadata=source.metadata))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set Up Vector DB\n",
+    "\n",
+    "Now that we have the documentation content in chunks, let's put all this information in a vector index for easy retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search_index = FAISS.from_documents(source_chunks, OpenAIEmbeddings())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set Up LLM Chain with Custom Prompt\n",
+    "\n",
+    "Next, let's set up a simple LLM chain but give it a custom prompt for blog post generation. Note that the custom prompt is parameterized and takes two inputs: `context`, which will be the documents fetched from the vector search, and `topic`, which is given by the user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "prompt_template = \"\"\"Use the context below to write a 400 word blog post about the topic below:\n",
+    "    Context: {context}\n",
+    "    Topic: {topic}\n",
+    "    Blog post:\"\"\"\n",
+    "\n",
+    "PROMPT = PromptTemplate(\n",
+    "    template=prompt_template, input_variables=[\"context\", \"topic\"]\n",
+    ")\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "\n",
+    "chain = LLMChain(llm=llm, prompt=PROMPT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Text\n",
+    "\n",
+    "Finally, we write a function to apply our inputs to the chain. The function takes an input parameter `topic`. We find the documents in the vector index that correspond to that `topic`, and use them as additional context in our simple LLM chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_blog_post(topic):\n",
+    "    docs = search_index.similarity_search(topic, k=4)\n",
+    "    inputs = [{\"context\": doc.page_content, \"topic\": topic} for doc in docs]\n",
+    "    print(chain.apply(inputs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'text': '\\n\\nEnvironment variables are a great way to store and access sensitive information in your Deno applications. Deno offers built-in support for environment variables with `Deno.env`, and you can also use a `.env` file to store and access environment variables.\\n\\nUsing `Deno.env` is simple. It has getter and setter methods, so you can easily set and retrieve environment variables. For example, you can set the `FIREBASE_API_KEY` and `FIREBASE_AUTH_DOMAIN` environment variables like this:\\n\\n```ts\\nDeno.env.set(\"FIREBASE_API_KEY\", \"examplekey123\");\\nDeno.env.set(\"FIREBASE_AUTH_DOMAIN\", \"firebasedomain.com\");\\n\\nconsole.log(Deno.env.get(\"FIREBASE_API_KEY\")); // examplekey123\\nconsole.log(Deno.env.get(\"FIREBASE_AUTH_DOMAIN\")); // firebasedomain.com\\n```\\n\\nYou can also store environment variables in a `.env` file. This is a great'}, {'text': '\\n\\nEnvironment variables are a powerful tool for managing configuration settings in a program. They allow us to set values that can be used by the program, without having to hard-code them into the code. This makes it easier to change settings without having to modify the code.\\n\\nIn Deno, environment variables can be set in a few different ways. The most common way is to use the `VAR=value` syntax. This will set the environment variable `VAR` to the value `value`. This can be used to set any number of environment variables before running a command. For example, if we wanted to set the environment variable `VAR` to `hello` before running a Deno command, we could do so like this:\\n\\n```\\nVAR=hello deno run main.ts\\n```\\n\\nThis will set the environment variable `VAR` to `hello` before running the command. We can then access this variable in our code using the `Deno.env.get()` function. For example, if we ran the following command:\\n\\n```\\nVAR=hello && deno eval \"console.log(\\'Deno: \\' + Deno.env.get(\\'VAR'}, {'text': '\\n\\nEnvironment variables are a powerful tool for developers, allowing them to store and access data without having to hard-code it into their applications. In Deno, you can access environment variables using the `Deno.env.get()` function.\\n\\nFor example, if you wanted to access the `HOME` environment variable, you could do so like this:\\n\\n```js\\n// env.js\\nDeno.env.get(\"HOME\");\\n```\\n\\nWhen running this code, you\\'ll need to grant the Deno process access to environment variables. This can be done by passing the `--allow-env` flag to the `deno run` command. You can also specify which environment variables you want to grant access to, like this:\\n\\n```shell\\n# Allow access to only the HOME env var\\ndeno run --allow-env=HOME env.js\\n```\\n\\nIt\\'s important to note that environment variables are case insensitive on Windows, so Deno also matches them case insensitively (on Windows only).\\n\\nAnother thing to be aware of when using environment variables is subprocess permissions. Subprocesses are powerful and can access system resources regardless of the permissions you granted to the Den'}, {'text': '\\n\\nEnvironment variables are an important part of any programming language, and Deno is no exception. Deno is a secure JavaScript and TypeScript runtime built on the V8 JavaScript engine, and it recently added support for environment variables. This feature was added in Deno version 1.6.0, and it is now available for use in Deno applications.\\n\\nEnvironment variables are used to store information that can be used by programs. They are typically used to store configuration information, such as the location of a database or the name of a user. In Deno, environment variables are stored in the `Deno.env` object. This object is similar to the `process.env` object in Node.js, and it allows you to access and set environment variables.\\n\\nThe `Deno.env` object is a read-only object, meaning that you cannot directly modify the environment variables. Instead, you must use the `Deno.env.set()` function to set environment variables. This function takes two arguments: the name of the environment variable and the value to set it to. For example, if you wanted to set the `FOO` environment variable to `bar`, you would use the following code:\\n\\n```'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_blog_post(\"environment variables\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/chains/combine_docs_how_to.rst

@@ -11,6 +11,8 @@ The examples here are all end-to-end chains for working with documents.
 
 `Summarization <./combine_docs_examples/summarize.html>`_: A walkthrough of how to use LangChain for summarization over specific documents.
 
+`Vector DB Text Generation <./combine_docs_examples/vector_db_text_generation.html>`_: A walkthrough of how to use LangChain for text generation over a vector database.
+
 `Vector DB Question Answering <./combine_docs_examples/vector_db_qa.html>`_: A walkthrough of how to use LangChain for question answering over a vector database.
 
 `Vector DB Question Answering with Sources <./combine_docs_examples/vector_db_qa_with_sources.html>`_: A walkthrough of how to use LangChain for question answering (with sources) over a vector database.

docs/modules/chains/examples/pal.ipynb

@@ -21,6 +21,24 @@
     "from langchain import OpenAI"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a58e15e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "095adc76",
+   "metadata": {},
+   "source": [
+    "## Math Prompt"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -28,7 +46,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
     "pal_chain = PALChain.from_math_prompt(llm, verbose=True)"
    ]
   },
@@ -64,7 +81,7 @@
       "    result = total_pets\n",
       "    return result\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished PALChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -82,6 +99,14 @@
     "pal_chain.run(question)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0269d20a",
+   "metadata": {},
+   "source": [
+    "## Colored Objects"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -89,7 +114,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
     "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)"
    ]
   },
@@ -147,10 +171,94 @@
     "pal_chain.run(question)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "fc3d7f10",
+   "metadata": {},
+   "source": [
+    "## Intermediate Steps\n",
+    "You can also use the intermediate steps flag to return the code executed that generates the answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9d2d9c61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True, return_intermediate_steps=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b29b971b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2c40c28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
+      "objects = []\n",
+      "objects += [('booklet', 'blue')] * 2\n",
+      "objects += [('booklet', 'purple')] * 2\n",
+      "objects += [('sunglasses', 'yellow')] * 2\n",
+      "\n",
+      "# Remove all pairs of sunglasses\n",
+      "objects = [object for object in objects if object[0] != 'sunglasses']\n",
+      "\n",
+      "# Count number of purple objects\n",
+      "num_purple = len([object for object in objects if object[1] == 'purple'])\n",
+      "answer = num_purple\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = pal_chain({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "efddd033",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"# Put objects into a list to record ordering\\nobjects = []\\nobjects += [('booklet', 'blue')] * 2\\nobjects += [('booklet', 'purple')] * 2\\nobjects += [('sunglasses', 'yellow')] * 2\\n\\n# Remove all pairs of sunglasses\\nobjects = [object for object in objects if object[0] != 'sunglasses']\\n\\n# Count number of purple objects\\nnum_purple = len([object for object in objects if object[1] == 'purple'])\\nanswer = num_purple\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['intermediate_steps']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4ab20fec",
+   "id": "dfd88594",
    "metadata": {},
    "outputs": [],
    "source": []

docs/modules/chains/examples/sqlite.ipynb

@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a8fc8f23",
    "metadata": {},
    "outputs": [],
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "15ff81df",
    "metadata": {
     "pycharm": {
@@ -96,7 +96,7 @@
        "' There are 9 employees.'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -188,6 +188,62 @@
     "db_chain.run(\"How many employees are there in the foobar table?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "88d8b969",
+   "metadata": {},
+   "source": [
+    "## Return Intermediate Steps\n",
+    "\n",
+    "You can also return the intermediate steps of the SQLDatabaseChain. This allows you to access the SQL statement that was generated, as well as the result of running that against the SQL Database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "38559487",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "78b6af4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
+      "How many employees are there in the foobar table? \n",
+      "SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[(9,)]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m There are 9 employees in the foobar table.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[' SELECT COUNT(*) FROM Employee;', '[(9,)]']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = db_chain(\"How many employees are there in the foobar table?\")\n",
+    "result[\"intermediate_steps\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b408f800",
@@ -242,6 +298,74 @@
     "db_chain.run(\"What are some example tracks by composer Johann Sebastian Bach?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "bcc5e936",
+   "metadata": {},
+   "source": [
+    "## Adding first row of each table\n",
+    "Sometimes, the format of the data is not obvious and it is optimal to include the first row of the table in the prompt to allow the LLM to understand the data before providing a final query. Here we will use this feature to let the LLM know that artists are saved with their full names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9a22ee47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = SQLDatabase.from_uri(\n",
+    "    \"sqlite:///../../../../notebooks/Chinook.db\", \n",
+    "    include_tables=['Track'], # we include only one table to save tokens in the prompt :)\n",
+    "    sample_row_in_table_info=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bcb7a489",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "81e05d82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
+      "What are some example tracks by Bach? \n",
+      "SQLQuery:Table 'Track' has columns: TrackId (INTEGER), Name (NVARCHAR(200)), AlbumId (INTEGER), MediaTypeId (INTEGER), GenreId (INTEGER), Composer (NVARCHAR(220)), Milliseconds (INTEGER), Bytes (INTEGER), UnitPrice (NUMERIC(10, 2)). Here is an example row for this table (long strings are truncated): ['1', 'For Those About To Rock (We Salute You)', '1', '1', '1', 'Angus Young, Malcolm Young, Brian Johnson', '343719', '11170334', '0.99'].\n",
+      "\u001b[32;1m\u001b[1;3m SELECT TrackId, Name, Composer FROM Track WHERE Composer LIKE '%Bach%' ORDER BY Name LIMIT 5;\u001b[0m\n",
+      "SQLResult: \u001b[33;1m\u001b[1;3m[(1709, 'American Woman', 'B. Cummings/G. Peterson/M.J. Kale/R. Bachman'), (3408, 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Johann Sebastian Bach'), (3433, 'Concerto No.2 in F Major, BWV1047, I. Allegro', 'Johann Sebastian Bach'), (3407, 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), (3490, 'Partita in E Major, BWV 1006A: I. Prelude', 'Johann Sebastian Bach')]\u001b[0m\n",
+      "Answer:\u001b[32;1m\u001b[1;3m Some example tracks by Bach are 'American Woman', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Concerto No.2 in F Major, BWV1047, I. Allegro', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', and 'Partita in E Major, BWV 1006A: I. Prelude'.\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Some example tracks by Bach are \\'American Woman\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', \\'Concerto No.2 in F Major, BWV1047, I. Allegro\\', \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', and \\'Partita in E Major, BWV 1006A: I. Prelude\\'.'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db_chain.run(\"What are some example tracks by Bach?\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c12ae15a",
@@ -319,14 +443,6 @@
    "source": [
     "chain.run(\"How many employees are also customers?\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b2998b03",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/modules/chains/generic/from_hub.ipynb

@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "25c90e9e",
+   "metadata": {},
+   "source": [
+    "# Loading from LangChainHub\n",
+    "\n",
+    "This notebook covers how to load chains from [LangChainHub](https://github.com/hwchase17/langchain-hub)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8b54479e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import load_chain\n",
+    "\n",
+    "chain = load_chain(\"lc://chains/llm-math/chain.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4828f31f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
+      "whats 2 raised to .12\u001b[32;1m\u001b[1;3m\n",
+      "Answer: 1.0791812460476249\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Answer: 1.0791812460476249'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"whats 2 raised to .12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8db72cda",
+   "metadata": {},
+   "source": [
+    "Sometimes chains will require extra arguments that were not serialized with the chain. For example, a chain that does question answering over a vector database will require a vector database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aab39528",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores.faiss import FAISS\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain import OpenAI, VectorDBQA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "16a85d5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../../state_of_the_union.txt') as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "vectorstore = FAISS.from_texts(texts, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6a82e91e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_chain(\"lc://chains/vector-db-qa/stuff/chain.json\", vectorstore=vectorstore)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "efe9b25b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" The president said that Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "chain.run(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f910a32f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/chains/generic/llm.json

@@ -0,0 +1,13 @@
+{
+    "model_name": "text-davinci-003",
+    "temperature": 0.0,
+    "max_tokens": 256,
+    "top_p": 1,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+    "n": 1,
+    "best_of": 1,
+    "request_timeout": null,
+    "logit_bias": {},
+    "_type": "openai"
+}

docs/modules/chains/generic/llm_chain.json

@@ -0,0 +1,27 @@
+{
+    "memory": null,
+    "verbose": true,
+    "prompt": {
+        "input_variables": [
+            "question"
+        ],
+        "output_parser": null,
+        "template": "Question: {question}\n\nAnswer: Let's think step by step.",
+        "template_format": "f-string"
+    },
+    "llm": {
+        "model_name": "text-davinci-003",
+        "temperature": 0.0,
+        "max_tokens": 256,
+        "top_p": 1,
+        "frequency_penalty": 0,
+        "presence_penalty": 0,
+        "n": 1,
+        "best_of": 1,
+        "request_timeout": null,
+        "logit_bias": {},
+        "_type": "openai"
+    },
+    "output_key": "text",
+    "_type": "llm_chain"
+}

docs/modules/chains/generic/llm_chain_separate.json

@@ -0,0 +1,8 @@
+{
+  "memory": null,
+  "verbose": true,
+  "prompt_path": "prompt.json",
+  "llm_path": "llm.json",
+  "output_key": "text",
+  "_type": "llm_chain"
+}

docs/modules/chains/generic/prompt.json

@@ -0,0 +1,8 @@
+{
+    "input_variables": [
+        "question"
+    ],
+    "output_parser": null,
+    "template": "Question: {question}\n\nAnswer: Let's think step by step.",
+    "template_format": "f-string"
+}

docs/modules/chains/generic/serialization.ipynb

@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cbe47c3a",
+   "metadata": {},
+   "source": [
+    "# Serialization\n",
+    "This notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow the number of supported chains over time.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4a8a447",
+   "metadata": {},
+   "source": [
+    "## Saving a chain to disk\n",
+    "First, let's go over how to save a chain to disk. This can be done with the `.save` method, and specifying a file path with a json or yaml extension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "26e28451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate, OpenAI, LLMChain\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bfa18e1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain.save(\"llm_chain.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea82665d",
+   "metadata": {},
+   "source": [
+    "Let's now take a look at what's inside this saved file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0fd33328",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"memory\": null,\r\n",
+      "    \"verbose\": true,\r\n",
+      "    \"prompt\": {\r\n",
+      "        \"input_variables\": [\r\n",
+      "            \"question\"\r\n",
+      "        ],\r\n",
+      "        \"output_parser\": null,\r\n",
+      "        \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",\r\n",
+      "        \"template_format\": \"f-string\"\r\n",
+      "    },\r\n",
+      "    \"llm\": {\r\n",
+      "        \"model_name\": \"text-davinci-003\",\r\n",
+      "        \"temperature\": 0.0,\r\n",
+      "        \"max_tokens\": 256,\r\n",
+      "        \"top_p\": 1,\r\n",
+      "        \"frequency_penalty\": 0,\r\n",
+      "        \"presence_penalty\": 0,\r\n",
+      "        \"n\": 1,\r\n",
+      "        \"best_of\": 1,\r\n",
+      "        \"request_timeout\": null,\r\n",
+      "        \"logit_bias\": {},\r\n",
+      "        \"_type\": \"openai\"\r\n",
+      "    },\r\n",
+      "    \"output_key\": \"text\",\r\n",
+      "    \"_type\": \"llm_chain\"\r\n",
+      "}"
+     ]
+    }
+   ],
+   "source": [
+    "!cat llm_chain.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2012c724",
+   "metadata": {},
+   "source": [
+    "## Loading a chain from disk\n",
+    "We can load a chain from disk by using the `load_chain` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "342a1974",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import load_chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "394b7da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_chain(\"llm_chain.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "20d99787",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
+      "\n",
+      "Answer: Let's think step by step.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' 2 + 2 = 4'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"whats 2 + 2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14449679",
+   "metadata": {},
+   "source": [
+    "## Saving components separately\n",
+    "In the above example, we can see that the prompt and llm configuration information is saved in the same json as the overall chain. Alternatively, we can split them up and save them separately. This is often useful to make the saved components more modular. In order to do this, we just need to specify `llm_path` instead of the `llm` component, and `prompt_path` instead of the `prompt` component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "50ec35ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain.prompt.save(\"prompt.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c48b39aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"input_variables\": [\r\n",
+      "        \"question\"\r\n",
+      "    ],\r\n",
+      "    \"output_parser\": null,\r\n",
+      "    \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",\r\n",
+      "    \"template_format\": \"f-string\"\r\n",
+      "}"
+     ]
+    }
+   ],
+   "source": [
+    "!cat prompt.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "13c92944",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain.llm.save(\"llm.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1b815f89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "    \"model_name\": \"text-davinci-003\",\r\n",
+      "    \"temperature\": 0.0,\r\n",
+      "    \"max_tokens\": 256,\r\n",
+      "    \"top_p\": 1,\r\n",
+      "    \"frequency_penalty\": 0,\r\n",
+      "    \"presence_penalty\": 0,\r\n",
+      "    \"n\": 1,\r\n",
+      "    \"best_of\": 1,\r\n",
+      "    \"request_timeout\": null,\r\n",
+      "    \"logit_bias\": {},\r\n",
+      "    \"_type\": \"openai\"\r\n",
+      "}"
+     ]
+    }
+   ],
+   "source": [
+    "!cat llm.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7e6aa9ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {\n",
+    "    \"memory\": None,\n",
+    "    \"verbose\": True,\n",
+    "    \"prompt_path\": \"prompt.json\",\n",
+    "    \"llm_path\": \"llm.json\",\n",
+    "    \"output_key\": \"text\",\n",
+    "    \"_type\": \"llm_chain\"\n",
+    "}\n",
+    "import json\n",
+    "with open(\"llm_chain_separate.json\", \"w\") as f:\n",
+    "    json.dump(config, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8e959ca6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"memory\": null,\r\n",
+      "  \"verbose\": true,\r\n",
+      "  \"prompt_path\": \"prompt.json\",\r\n",
+      "  \"llm_path\": \"llm.json\",\r\n",
+      "  \"output_key\": \"text\",\r\n",
+      "  \"_type\": \"llm_chain\"\r\n",
+      "}"
+     ]
+    }
+   ],
+   "source": [
+    "!cat llm_chain_separate.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "662731c0",
+   "metadata": {},
+   "source": [
+    "We can then load it in the same way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d69ceb93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = load_chain(\"llm_chain_separate.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a99d61b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
+      "\n",
+      "Answer: Let's think step by step.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' 2 + 2 = 4'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"whats 2 + 2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "822b7c12",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/chains/how_to_guides.rst

@@ -18,3 +18,7 @@ They are broken up into three categories:
    ./generic_how_to.rst
    ./combine_docs_how_to.rst
    ./utility_how_to.rst
+
+In addition to different types of chains, we also have the following how-to guides for working with chains in general:
+
+`Load From Hub <./generic/from_hub.html>`_: This notebook covers how to load chains from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.

docs/modules/llms/examples/token_usage_tracking.ipynb

@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e5715368",
+   "metadata": {},
+   "source": [
+    "# Token Usage Tracking\n",
+    "\n",
+    "This notebook goes over how to track your token usage for specific calls. It is currently only implemented for the OpenAI API.\n",
+    "\n",
+    "Let's first look at an extremely simple example of tracking token usage for a single LLM call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9455db35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks import get_openai_callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d1c55cc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(model_name=\"text-davinci-002\", n=2, best_of=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "31667d54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "42\n"
+     ]
+    }
+   ],
+   "source": [
+    "with get_openai_callback() as cb:\n",
+    "    result = llm(\"Tell me a joke\")\n",
+    "    print(cb.total_tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ab6d27",
+   "metadata": {},
+   "source": [
+    "Anything inside the context manager will get tracked. Here's an example of using it to track multiple calls in sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e09420f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "83\n"
+     ]
+    }
+   ],
+   "source": [
+    "with get_openai_callback() as cb:\n",
+    "    result = llm(\"Tell me a joke\")\n",
+    "    result2 = llm(\"Tell me a joke\")\n",
+    "    print(cb.total_tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8186e7b",
+   "metadata": {},
+   "source": [
+    "If a chain or agent with multiple steps in it is used, it will track all those steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5d1125c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2f98c536",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
+      "Action: Search\n",
+      "Action Input: \"Olivia Wilde boyfriend\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mJason Sudeikis\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Jason Sudeikis' age\n",
+      "Action: Search\n",
+      "Action Input: \"Jason Sudeikis age\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m47 years\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 47 raised to the 0.23 power\n",
+      "Action: Calculator\n",
+      "Action Input: 47^0.23\u001b[0m\n",
+      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.4242784855673896\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "1465\n"
+     ]
+    }
+   ],
+   "source": [
+    "with get_openai_callback() as cb:\n",
+    "    response = agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")\n",
+    "    print(cb.total_tokens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80ca77a3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/llms/generic_how_to.rst

@@ -9,6 +9,8 @@ The examples here all address certain "how-to" guides for working with LLMs.
 
 `Custom LLM <./examples/custom_llm.html>`_: How to create and use a custom LLM class, in case you have an LLM not from one of the standard providers (including one that you host yourself).
 
+`Token Usage Tracking <./examples/token_usage_tracking.html>`_: How to track the token usage of various chains/agents/LLM calls.
+
 
 .. toctree::
    :maxdepth: 1

docs/modules/memory/examples/chatgpt_clone.ipynb

@@ -35,7 +35,7 @@
       "Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n",
       "\n",
       "\n",
-      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
+      "Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\n",
       "Assistant:\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",

docs/modules/memory/examples/entity_summary_memory.ipynb

@@ -0,0 +1,459 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ff31084d",
+   "metadata": {},
+   "source": [
+    "# Entity Memory\n",
+    "This notebook shows how to work with a memory module that remembers things about specific entities. It extracts information on entities (using LLMs) and builds up its knowledge about that entity over time (also using LLMs)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "13471fbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, ConversationChain\n",
+    "from langchain.chains.conversation.memory import ConversationEntityMemory\n",
+    "from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE\n",
+    "from pydantic import BaseModel\n",
+    "from typing import List, Dict, Any"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "183346e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "conversation = ConversationChain(\n",
+    "    llm=llm, \n",
+    "    verbose=True,\n",
+    "    prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,\n",
+    "    memory=ConversationEntityMemory(llm=llm)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7eb1460a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are an assistant to a human, powered by a large language model trained by OpenAI.\n",
+      "\n",
+      "You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
+      "\n",
+      "You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.\n",
+      "\n",
+      "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
+      "\n",
+      "Context:\n",
+      "{'Deven': '', 'Sam': ''}\n",
+      "\n",
+      "Current conversation:\n",
+      "\n",
+      "Last line:\n",
+      "Human: Deven & Sam are working on a hackathon project\n",
+      "You:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' That sounds like a great project! What kind of project are they working on?'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"Deven & Sam are working on a hackathon project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "46324ca8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are an assistant to a human, powered by a large language model trained by OpenAI.\n",
+      "\n",
+      "You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
+      "\n",
+      "You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.\n",
+      "\n",
+      "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
+      "\n",
+      "Context:\n",
+      "{'Deven': 'Deven is working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.', 'Langchain': ''}\n",
+      "\n",
+      "Current conversation:\n",
+      "Human: Deven & Sam are working on a hackathon project\n",
+      "AI:  That sounds like a great project! What kind of project are they working on?\n",
+      "Last line:\n",
+      "Human: They are trying to add more complex memory structures to Langchain\n",
+      "You:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' That sounds like an interesting project! What kind of memory structures are they trying to add?'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"They are trying to add more complex memory structures to Langchain\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ff2ebf6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are an assistant to a human, powered by a large language model trained by OpenAI.\n",
+      "\n",
+      "You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
+      "\n",
+      "You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.\n",
+      "\n",
+      "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
+      "\n",
+      "Context:\n",
+      "{'Deven': 'Deven is working on a hackathon project with Sam to add more complex memory structures to Langchain.', 'Sam': 'Sam is working on a hackathon project with Deven to add more complex memory structures to Langchain.', 'Langchain': 'Langchain is a project that seeks to add more complex memory structures.', 'Key-Value Store': ''}\n",
+      "\n",
+      "Current conversation:\n",
+      "Human: Deven & Sam are working on a hackathon project\n",
+      "AI:  That sounds like a great project! What kind of project are they working on?\n",
+      "Human: They are trying to add more complex memory structures to Langchain\n",
+      "AI:  That sounds like an interesting project! What kind of memory structures are they trying to add?\n",
+      "Last line:\n",
+      "Human: They are adding in a key-value store for entities mentioned so far in the conversation.\n",
+      "You:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' That sounds like a great idea! How will the key-value store work?'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"They are adding in a key-value store for entities mentioned so far in the conversation.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "56cfd4ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are an assistant to a human, powered by a large language model trained by OpenAI.\n",
+      "\n",
+      "You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
+      "\n",
+      "You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.\n",
+      "\n",
+      "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
+      "\n",
+      "Context:\n",
+      "{'Deven': 'Deven is working on a hackathon project with Sam to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.', 'Sam': 'Sam is working on a hackathon project with Deven to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}\n",
+      "\n",
+      "Current conversation:\n",
+      "Human: Deven & Sam are working on a hackathon project\n",
+      "AI:  That sounds like a great project! What kind of project are they working on?\n",
+      "Human: They are trying to add more complex memory structures to Langchain\n",
+      "AI:  That sounds like an interesting project! What kind of memory structures are they trying to add?\n",
+      "Human: They are adding in a key-value store for entities mentioned so far in the conversation.\n",
+      "AI:  That sounds like a great idea! How will the key-value store work?\n",
+      "Last line:\n",
+      "Human: What do you know about Deven & Sam?\n",
+      "You:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Deven and Sam are working on a hackathon project to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be very motivated and passionate about their project, and are working hard to make it a success.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"What do you know about Deven & Sam?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e6df549",
+   "metadata": {},
+   "source": [
+    "## Inspecting the memory store\n",
+    "We can also inspect the memory store directly. In the following examaples, we look at it directly, and then go through some examples of adding information and watch how it changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "038b4d3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Deven': 'Deven is working on a hackathon project with Sam to add more '\n",
+      "          'complex memory structures to Langchain, including a key-value store '\n",
+      "          'for entities mentioned so far in the conversation.',\n",
+      " 'Key-Value Store': 'Key-Value Store: A data structure that stores values '\n",
+      "                    'associated with a unique key, allowing for efficient '\n",
+      "                    'retrieval of values. Deven and Sam are adding a key-value '\n",
+      "                    'store for entities mentioned so far in the conversation.',\n",
+      " 'Langchain': 'Langchain is a project that seeks to add more complex memory '\n",
+      "              'structures, including a key-value store for entities mentioned '\n",
+      "              'so far in the conversation.',\n",
+      " 'Sam': 'Sam is working on a hackathon project with Deven to add more complex '\n",
+      "        'memory structures to Langchain, including a key-value store for '\n",
+      "        'entities mentioned so far in the conversation.'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "pprint(conversation.memory.store)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2df4800e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are an assistant to a human, powered by a large language model trained by OpenAI.\n",
+      "\n",
+      "You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
+      "\n",
+      "You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.\n",
+      "\n",
+      "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
+      "\n",
+      "Context:\n",
+      "{'Daimon': '', 'Sam': 'Sam is working on a hackathon project with Deven to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}\n",
+      "\n",
+      "Current conversation:\n",
+      "Human: They are trying to add more complex memory structures to Langchain\n",
+      "AI:  That sounds like an interesting project! What kind of memory structures are they trying to add?\n",
+      "Human: They are adding in a key-value store for entities mentioned so far in the conversation.\n",
+      "AI:  That sounds like a great idea! How will the key-value store work?\n",
+      "Human: What do you know about Deven & Sam?\n",
+      "AI:  Deven and Sam are working on a hackathon project to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be very motivated and passionate about their project, and are working hard to make it a success.\n",
+      "Last line:\n",
+      "Human: Sam is the founder of a company called Daimon.\n",
+      "You:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nThat's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"Sam is the founder of a company called Daimon.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ebe9e36f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Daimon': 'Daimon is a company founded by Sam.',\n",
+      " 'Deven': 'Deven is working on a hackathon project with Sam to add more '\n",
+      "          'complex memory structures to Langchain, including a key-value store '\n",
+      "          'for entities mentioned so far in the conversation.',\n",
+      " 'Key-Value Store': 'Key-Value Store: A data structure that stores values '\n",
+      "                    'associated with a unique key, allowing for efficient '\n",
+      "                    'retrieval of values. Deven and Sam are adding a key-value '\n",
+      "                    'store for entities mentioned so far in the conversation.',\n",
+      " 'Langchain': 'Langchain is a project that seeks to add more complex memory '\n",
+      "              'structures, including a key-value store for entities mentioned '\n",
+      "              'so far in the conversation.',\n",
+      " 'Sam': 'Sam is working on a hackathon project with Deven to add more complex '\n",
+      "        'memory structures to Langchain, including a key-value store for '\n",
+      "        'entities mentioned so far in the conversation. He is also the founder '\n",
+      "        'of a company called Daimon.'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "pprint(conversation.memory.store)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "dd547144",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mYou are an assistant to a human, powered by a large language model trained by OpenAI.\n",
+      "\n",
+      "You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
+      "\n",
+      "You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.\n",
+      "\n",
+      "Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.\n",
+      "\n",
+      "Context:\n",
+      "{'Sam': 'Sam is working on a hackathon project with Deven to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. He is also the founder of a company called Daimon.', 'Daimon': 'Daimon is a company founded by Sam.'}\n",
+      "\n",
+      "Current conversation:\n",
+      "Human: They are adding in a key-value store for entities mentioned so far in the conversation.\n",
+      "AI:  That sounds like a great idea! How will the key-value store work?\n",
+      "Human: What do you know about Deven & Sam?\n",
+      "AI:  Deven and Sam are working on a hackathon project to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be very motivated and passionate about their project, and are working hard to make it a success.\n",
+      "Human: Sam is the founder of a company called Daimon.\n",
+      "AI: \n",
+      "That's impressive! It sounds like Sam is a very successful entrepreneur. What kind of company is Daimon?\n",
+      "Last line:\n",
+      "Human: What do you know about Sam?\n",
+      "You:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' Sam is the founder of a company called Daimon. He is also working on a hackathon project with Deven to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. He seems to be very motivated and passionate about his project, and is working hard to make it a success.'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conversation.predict(input=\"What do you know about Sam?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e00463b5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/memory/how_to_guides.rst

@@ -7,6 +7,9 @@ The examples here all highlight how to use memory in different ways.
 
 `ChatGPT Clone <./examples/chatgpt_clone.html>`_: How to recreate ChatGPT with LangChain prompting + memory components.
 
+`Entity Memory <./examples/entity_summary_memory.html>`_: How to use a type of memory that organizes information by entity.
+
+
 `Adding Memory to Multi-Input Chain <./examples/adding_memory_chain_multiple_inputs.html>`_: How to add a memory component to any multiple input chain.
 
 `Conversational Memory Customization <./examples/conversational_customization.html>`_: How to customize existing conversation memory components.

docs/modules/memory/key_concepts.md

@@ -12,3 +12,8 @@ There are a few different ways to accomplish this:
 - Summary: This involves summarizing previous conversations and passing that summary in, instead of the raw dialouge itself. Compared to `Buffer`, this compresses information: meaning it is more lossy, but also less likely to run into context length limits.
 - Combination: A combination of the above two approaches, where you compute a summary but also pass in some previous interfactions directly!
 
+## Entity Memory
+A more complex form of memory is remembering information about specific entities in the conversation.
+This is a more direct and organized way of remembering information over time.
+Putting it a more structured form also has the benefit of allowing easy inspection of what is known about specific entities.
+For a guide on how to use this type of memory, see [this notebook](./examples/entity_summary_memory.ipynb).

docs/modules/prompts/examples/custom_prompt_template.md

@@ -7,7 +7,7 @@ Let's suppose we want the LLM to generate English language explanations of a fun
 LangChain provides a set of default prompt templates that can be used to generate prompts for a variety of tasks. However, there may be cases where the default prompt templates do not meet your needs. For example, you may want to create a prompt template with specific dynamic instructions for your language model. In such cases, you can create a custom prompt template.
 
 :::{note}
-Take a look at the current set of default prompt templates [here](../prompt_templates.md).
+Take a look at the current set of default prompt templates [here](../getting_started.md).
 :::
 <!-- TODO(shreya): Add correct link here. -->
 
@@ -34,7 +34,7 @@ Next, we'll create a custom prompt template that takes in the function name as i
 
 ```python
 from langchain.prompts import BasePromptTemplate
-from pydantic import BaseModel
+from pydantic import BaseModel, validator
 
 
 class FunctionExplainerPromptTemplate(BasePromptTemplate, BaseModel):
@@ -54,7 +54,7 @@ class FunctionExplainerPromptTemplate(BasePromptTemplate, BaseModel):
         # Generate the prompt to be sent to the language model
         prompt = f"""
         Given the function name and source code, generate an English language explanation of the function.
-        Function Name: {kwargs["function_name"]}
+        Function Name: {kwargs["function_name"].__name__}
         Source Code:
         {source_code}
         Explanation:

docs/modules/prompts/examples/example_selectors.ipynb

@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "8244ff60",
    "metadata": {},
    "outputs": [],
@@ -48,6 +48,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.prompts import PromptTemplate\n",
     "from langchain.prompts.example_selector import LengthBasedExampleSelector"
    ]
   },
@@ -75,8 +76,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "example_prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"output\"],\n",
+    "    template=\"Input: {input}\\nOutput: {output}\",\n",
+    ")\n",
     "example_selector = LengthBasedExampleSelector(\n",
-    "    # These are the examples is has available to choose from.\n",
+    "    # These are the examples it has available to choose from.\n",
     "    examples=examples, \n",
     "    # This is the PromptTemplate being used to format the examples.\n",
     "    example_prompt=example_prompt, \n",
@@ -434,10 +439,242 @@
     "print(similar_prompt.format(adjective=\"worried\"))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4aaeed2f",
+   "metadata": {},
+   "source": [
+    "## NGram Overlap ExampleSelector\n",
+    "\n",
+    "The NGramOverlapExampleSelector selects and orders examples based on which examples are most similar to the input, according to an ngram overlap score. The ngram overlap score is a float between 0.0 and 1.0, inclusive. \n",
+    "\n",
+    "The selector allows for a threshold score to be set. Examples with an ngram overlap score less than or equal to the threshold are excluded. The threshold is set to -1.0, by default, so will not exclude any examples, only reorder them. Setting the threshold to 0.0 will exclude examples that have no ngram overlaps with the input.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9cbc0acc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.prompts.example_selector.ngram_overlap import NGramOverlapExampleSelector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4f318f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# These are examples of a fictional translation task.\n",
+    "examples = [\n",
+    "    {\"input\": \"See Spot run.\", \"output\": \"Ver correr a Spot.\"},\n",
+    "    {\"input\": \"My dog barks.\", \"output\": \"Mi perro ladra.\"},\n",
+    "    {\"input\": \"Spot can run.\", \"output\": \"Spot puede correr.\"},\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bf75e0fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"output\"],\n",
+    "    template=\"Input: {input}\\nOutput: {output}\",\n",
+    ")\n",
+    "example_selector = NGramOverlapExampleSelector(\n",
+    "    # These are the examples it has available to choose from.\n",
+    "    examples=examples, \n",
+    "    # This is the PromptTemplate being used to format the examples.\n",
+    "    example_prompt=example_prompt, \n",
+    "    # This is the threshold, at which selector stops.\n",
+    "    # It is set to -1.0 by default.\n",
+    "    threshold=-1.0,\n",
+    "    # For negative threshold:\n",
+    "    # Selector sorts examples by ngram overlap score, and excludes none.\n",
+    "    # For threshold greater than 1.0:\n",
+    "    # Selector excludes all examples, and returns an empty list.\n",
+    "    # For threshold equal to 0.0:\n",
+    "    # Selector sorts examples by ngram overlap score,\n",
+    "    # and excludes those with no ngram overlap with input.\n",
+    ")\n",
+    "dynamic_prompt = FewShotPromptTemplate(\n",
+    "    # We provide an ExampleSelector instead of examples.\n",
+    "    example_selector=example_selector,\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=\"Give the Spanish translation of every input\",\n",
+    "    suffix=\"Input: {sentence}\\nOutput:\", \n",
+    "    input_variables=[\"sentence\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "83fb218a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: See Spot run.\n",
+      "Output: Ver correr a Spot.\n",
+      "\n",
+      "Input: My dog barks.\n",
+      "Output: Mi perro ladra.\n",
+      "\n",
+      "Input: Spot can run fast.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# An example input with large ngram overlap with \"Spot can run.\"\n",
+    "# and no overlap with \"My dog barks.\"\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "485f5307",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: See Spot run.\n",
+      "Output: Ver correr a Spot.\n",
+      "\n",
+      "Input: Spot plays fetch.\n",
+      "Output: Spot juega a buscar.\n",
+      "\n",
+      "Input: My dog barks.\n",
+      "Output: Mi perro ladra.\n",
+      "\n",
+      "Input: Spot can run fast.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can add examples to NGramOverlapExampleSelector as well.\n",
+    "new_example = {\"input\": \"Spot plays fetch.\", \"output\": \"Spot juega a buscar.\"}\n",
+    "\n",
+    "example_selector.add_example(new_example)\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "606ce697",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: See Spot run.\n",
+      "Output: Ver correr a Spot.\n",
+      "\n",
+      "Input: Spot plays fetch.\n",
+      "Output: Spot juega a buscar.\n",
+      "\n",
+      "Input: Spot can run fast.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can set a threshold at which examples are excluded.\n",
+    "# For example, setting threshold equal to 0.0\n",
+    "# excludes examples with no ngram overlaps with input.\n",
+    "# Since \"My dog barks.\" has no ngram overlaps with \"Spot can run fast.\"\n",
+    "# it is excluded.\n",
+    "example_selector.threshold=0.0\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can run fast.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "7f8d72f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can run.\n",
+      "Output: Spot puede correr.\n",
+      "\n",
+      "Input: Spot plays fetch.\n",
+      "Output: Spot juega a buscar.\n",
+      "\n",
+      "Input: Spot can play fetch.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setting small nonzero threshold\n",
+    "example_selector.threshold=0.09\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can play fetch.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "09633aa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the Spanish translation of every input\n",
+      "\n",
+      "Input: Spot can play fetch.\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setting threshold greater than 1.0\n",
+    "example_selector.threshold=1.0+1e-9\n",
+    "print(dynamic_prompt.format(sentence=\"Spot can play fetch.\"))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c746d6f4",
+   "id": "39f30097",
    "metadata": {},
    "outputs": [],
    "source": []

docs/modules/prompts/examples/examples.yaml

@@ -0,0 +1,4 @@
+- input: happy
+  output: sad
+- input: tall
+  output: short

docs/modules/prompts/examples/few_shot_prompt_yaml_examples.yaml

@@ -0,0 +1,14 @@
+_type: few_shot
+input_variables:
+    ["adjective"]
+prefix: 
+    Write antonyms for the following words.
+example_prompt:
+    input_variables:
+        ["input", "output"]
+    template:
+        "Input: {input}\nOutput: {output}"
+examples:
+    examples.yaml
+suffix:
+    "Input: {adjective}\nOutput:"

docs/modules/prompts/examples/prompt_serialization.ipynb

@@ -225,6 +225,35 @@
     "!cat examples.json"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d3052850",
+   "metadata": {},
+   "source": [
+    "And here is what the same examples stored as yaml might look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "901385d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- input: happy\r\n",
+      "  output: sad\r\n",
+      "- input: tall\r\n",
+      "  output: short\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples.yaml"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8e300335",
@@ -236,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "e2bec0fc",
    "metadata": {},
    "outputs": [
@@ -267,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "98c8f356",
    "metadata": {},
    "outputs": [
@@ -293,6 +322,73 @@
     "print(prompt.format(adjective=\"funny\"))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "13620324",
+   "metadata": {},
+   "source": [
+    "The same would work if you loaded examples from the yaml file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "831e5e4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_type: few_shot\r\n",
+      "input_variables:\r\n",
+      "    [\"adjective\"]\r\n",
+      "prefix: \r\n",
+      "    Write antonyms for the following words.\r\n",
+      "example_prompt:\r\n",
+      "    input_variables:\r\n",
+      "        [\"input\", \"output\"]\r\n",
+      "    template:\r\n",
+      "        \"Input: {input}\\nOutput: {output}\"\r\n",
+      "examples:\r\n",
+      "    examples.yaml\r\n",
+      "suffix:\r\n",
+      "    \"Input: {adjective}\\nOutput:\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat few_shot_prompt_yaml_examples.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6f0a7eaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Write antonyms for the following words.\n",
+      "\n",
+      "Input: happy\n",
+      "Output: sad\n",
+      "\n",
+      "Input: tall\n",
+      "Output: short\n",
+      "\n",
+      "Input: funny\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = load_prompt(\"few_shot_prompt_yaml_examples.yaml\")\n",
+    "print(prompt.format(adjective=\"funny\"))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4870aa9d",
@@ -304,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "9d996a86",
    "metadata": {},
    "outputs": [
@@ -332,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "dd2c10bb",
    "metadata": {},
    "outputs": [
@@ -369,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "6cd781ef",
    "metadata": {},
    "outputs": [
@@ -400,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "533ab8a7",
    "metadata": {},
    "outputs": [
@@ -437,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "0b6dd7b8",
    "metadata": {},
    "outputs": [
@@ -458,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "76a1065d",
    "metadata": {},
    "outputs": [
@@ -483,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "744d275d",
    "metadata": {},
    "outputs": [
@@ -530,7 +626,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
+    "hash": "8eb71adebe840dca1185e9603533462bc47eb1b1a73bf7dab2d0a8a4c932882e"
    }
   }
  },

docs/modules/prompts/getting_started.md

@@ -80,6 +80,20 @@ Currently, the template should be formatted as a Python f-string. We also suppor
 :::
 
 
+## Load a prompt template from LangChainHub
+
+LangChainHub contains a collection of prompts which can be loaded directly via LangChain.
+
+
+```python
+from langchain.prompts import load_prompt
+
+prompt = load_prompt("lc://prompts/conversation/prompt.json")
+prompt.format(history="", input="What is 1 + 1?")
+```
+
+You can read more about LangChainHub and the prompts available with it [here](https://github.com/hwchase17/langchain-hub).
+
 ## Pass few shot examples to a prompt template
 
 Few shot examples are a set of examples that can be used to help the language model generate a better response.
@@ -155,11 +169,11 @@ from langchain.prompts.example_selector import LengthBasedExampleSelector
 
 # These are a lot of examples of a pretend task of creating antonyms.
 examples = [
-    {"input": "happy", "output": "sad"},
-    {"input": "tall", "output": "short"},
-    {"input": "energetic", "output": "lethargic"},
-    {"input": "sunny", "output": "gloomy"},
-    {"input": "windy", "output": "calm"},
+    {"word": "happy", "antonym": "sad"},
+    {"word": "tall", "antonym": "short"},
+    {"word": "energetic", "antonym": "lethargic"},
+    {"word": "sunny", "antonym": "gloomy"},
+    {"word": "windy", "antonym": "calm"},
 ]
 
 # We'll use the `LengthBasedExampleSelector` to select the examples.
@@ -174,7 +188,7 @@ example_selector = LengthBasedExampleSelector(
 )
 
 # We can now use the `example_selector` to create a `FewShotPromptTemplate`.
-few_shot_prompt = FewShotPromptTemplate(
+dynamic_prompt = FewShotPromptTemplate(
     # We provide an ExampleSelector instead of examples.
     example_selector=example_selector,
     example_prompt=example_prompt,
@@ -185,7 +199,7 @@ few_shot_prompt = FewShotPromptTemplate(
 )
 
 # We can now generate a prompt using the `format` method.
-print(few_shot_prompt.format(input="big"))
+print(dynamic_prompt.format(input="big"))
 # -> Give the antonym of every input
 # ->
 # -> Word: happy
@@ -211,7 +225,7 @@ In contrast, if we provide a very long input, the `LengthBasedExampleSelector` w
 
 ```python
 long_string = "big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else"
-print(dynamic_prompt.format(adjective=long_string))
+print(dynamic_prompt.format(input=long_string))
 # -> Give the antonym of every input
 
 # -> Word: happy
@@ -224,4 +238,4 @@ print(dynamic_prompt.format(adjective=long_string))
 <!-- TODO(shreya): Add correct link here. -->
 LangChain comes with a few example selectors that you can use. For more details on how to use them, see [Example Selectors](./examples/example_selectors.ipynb).
 
-You can create custom example selectors that select examples based on any criteria you want. For more details on how to do this, see [Creating a custom example selector](examples/custom_example_selector.ipynb).
+You can create custom example selectors that select examples based on any criteria you want. For more details on how to do this, see [Creating a custom example selector](examples/custom_example_selector.ipynb).

docs/modules/utils/combine_docs_examples/embeddings.ipynb

@@ -77,7 +77,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "42f76e43",
    "metadata": {},
@@ -138,7 +137,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ed47bb62",
    "metadata": {},
@@ -196,11 +194,137 @@
    "source": [
     "doc_result = embeddings.embed_documents([text])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fff4734f",
+   "metadata": {},
+   "source": [
+    "## TensorflowHub\n",
+    "Let's load the TensorflowHub Embedding class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f822104b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import TensorflowHubEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bac84e46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings = TensorflowHubEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4790d770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f556dcdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59428e05",
+   "metadata": {},
+   "source": [
+    "## InstructEmbeddings\n",
+    "Let's load the HuggingFace instruct Embeddings class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "92c5b61e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "062547b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load INSTRUCTOR_Transformer\n",
+      "max_seq_length  512\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings = HuggingFaceInstructEmbeddings(query_instruction=\"Represent the query for retrieval: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e1dcc4bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "90f0db94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a961cdb5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cohere",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -214,7 +338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/utils/combine_docs_examples/hyde.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical answer, and then embeds that generated document and uses that as the final example. \n",
     "\n",
-    "In order to use HyDE, we therefor need to provide a base embedding model, as well as an LLMChain that can be used to generate those documents. By default, the HyDE class comes with some default prompts to use (see the paper for more details on them), but we can also create our own."
+    "In order to use HyDE, we therefore need to provide a base embedding model, as well as an LLMChain that can be used to generate those documents. By default, the HyDE class comes with some default prompts to use (see the paper for more details on them), but we can also create our own."
    ]
   },
   {
@@ -21,8 +21,8 @@
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
-    "from langchain.embeddings import OpenAIEmbeddings, HypotheticalDocumentEmbedder\n",
-    "from langchain.chains import LLMChain\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.chains import LLMChain, HypotheticalDocumentEmbedder\n",
     "from langchain.prompts import PromptTemplate"
    ]
   },
@@ -220,7 +220,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "llm-env",
    "language": "python",
    "name": "python3"
   },
@@ -234,7 +234,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.0 (default, Nov 15 2020, 06:25:35) \n[Clang 10.0.0 ]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9dd01537e9ab68cf47cb0398488d182358f774f73101197b3bd1b5502c6ec7f9"
+   }
   }
  },
  "nbformat": 4,

docs/modules/utils/combine_docs_examples/textsplitter.ipynb

@@ -1,13 +1,14 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b118c9dc",
    "metadata": {},
    "source": [
     "# Text Splitter\n",
     "\n",
-    "When you want to deal wit long pieces of text, it is necessary to split up that text into chunks.\n",
+    "When you want to deal with long pieces of text, it is necessary to split up that text into chunks.\n",
     "This notebook showcases several ways to do that.\n",
     "\n",
     "At a high level, text splitters work as following:\n",
@@ -151,7 +152,7 @@
    "metadata": {},
    "source": [
     "## Document creation\n",
-    "We can also use the text splitter to create \"Documents\" directly. Documents a way of bundling pieces of text with associated metadata so that chains can interact with them. We can also create documents with empty metadata though!\n",
+    "We can also use the text splitter to create \"Documents\" directly. Documents are a way of bundling pieces of text with associated metadata so that chains can interact with them. We can also create documents with empty metadata though!\n",
     "\n",
     "In the below example, we pass two pieces of text to get split up (we pass two just to show off the interface of splitting multiple pieces of text)."
    ]
@@ -486,7 +487,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -500,7 +501,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.12 (main, Mar 26 2022, 15:51:15) \n[Clang 13.1.6 (clang-1316.0.21.2)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
   }
  },
  "nbformat": 4,

docs/modules/utils/combine_docs_examples/vectorstores.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "965eecee",
    "metadata": {
     "pycharm": {
@@ -27,12 +27,12 @@
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS"
+    "from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS, Qdrant"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "68481687",
    "metadata": {
     "pycharm": {
@@ -514,10 +514,62 @@
     "docs[0]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9b852079",
+   "metadata": {},
+   "source": [
+    "## Qdrant"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7d74bd2",
+   "id": "e5ec70ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "host = \"<---host name here --->\"\n",
+    "api_key = \"<---api key here--->\"\n",
+    "qdrant = Qdrant.from_texts(texts, embeddings, host=host, prefer_grpc=True, api_key=api_key)\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "9805ad1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = qdrant.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bd097a0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(page_content='In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \\n\\nWe cannot let this happen. \\n\\nTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', lookup_str='', metadata={}, lookup_index=0)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ffd66e2",
    "metadata": {},
    "outputs": [],
    "source": []

docs/modules/utils/examples/bing_search.ipynb

@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bing Search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook goes over how to use the bing search component.\n",
+    "\n",
+    "First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found [here](https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e).\n",
+    "\n",
+    "Then we will need to set some environment variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"BING_SUBSCRIPTION_KEY\"] = \"\"\n",
+    "os.environ[\"BING_SEARCH_URL\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import BingSearchAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = BingSearchAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor. <b>Python</b> releases by version number: Release version Release date Click for more. <b>Python</b> 3.11.1 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.10.9 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.9.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.8.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in <b>Python</b> and see how it works with several simple examples.. The operator ‘+=’ is a shorthand for the addition assignment operator.It adds two values and assigns the sum to a variable (left operand). W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, <b>Python</b>, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the <b>Python</b> language and system. It helps to have a <b>Python</b> interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The <b>Python</b> Standard ... <b>Python</b> is a general-purpose, versatile, and powerful programming language. It&#39;s a great first language because <b>Python</b> code is concise and easy to read. Whatever you want to do, <b>python</b> can do it. From web development to machine learning to data science, <b>Python</b> is the language for you. To install <b>Python</b> using the Microsoft Store: Go to your Start menu (lower left Windows icon), type &quot;Microsoft Store&quot;, select the link to open the store. Once the store is open, select Search from the upper-right menu and enter &quot;<b>Python</b>&quot;. Select which version of <b>Python</b> you would like to use from the results under Apps. Under the “<b>Python</b> Releases for Mac OS X” heading, click the link for the Latest <b>Python</b> 3 Release - <b>Python</b> 3.x.x. As of this writing, the latest version was <b>Python</b> 3.8.4. Scroll to the bottom and click macOS 64-bit installer to start the download. When the installer is finished downloading, move on to the next step. Step 2: Run the Installer'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search.run(\"python\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Number of results\n",
+    "You can use the `k` parameter to set the number of results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = BingSearchAPIWrapper(k=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor.'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search.run(\"python\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metadata Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run query through BingSearch and return snippet, title, and link metadata.\n",
+    "\n",
+    "- Snippet: The description of the result.\n",
+    "- Title: The title of the result.\n",
+    "- Link: The link to the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = BingSearchAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'snippet': 'Lady Alice. Pink Lady <b>apples</b> aren’t the only lady in the apple family. Lady Alice <b>apples</b> were discovered growing, thanks to bees pollinating, in Washington. They are smaller and slightly more stout in appearance than other varieties. Their skin color appears to have red and yellow stripes running from stem to butt.',\n",
+       "  'title': '25 Types of Apples - Jessica Gavin',\n",
+       "  'link': 'https://www.jessicagavin.com/types-of-apples/'},\n",
+       " {'snippet': '<b>Apples</b> can do a lot for you, thanks to plant chemicals called flavonoids. And they have pectin, a fiber that breaks down in your gut. If you take off the apple’s skin before eating it, you won ...',\n",
+       "  'title': 'Apples: Nutrition &amp; Health Benefits - WebMD',\n",
+       "  'link': 'https://www.webmd.com/food-recipes/benefits-apples'},\n",
+       " {'snippet': '<b>Apples</b> boast many vitamins and minerals, though not in high amounts. However, <b>apples</b> are usually a good source of vitamin C. Vitamin C. Also called ascorbic acid, this vitamin is a common ...',\n",
+       "  'title': 'Apples 101: Nutrition Facts and Health Benefits',\n",
+       "  'link': 'https://www.healthline.com/nutrition/foods/apples'},\n",
+       " {'snippet': 'Weight management. The fibers in <b>apples</b> can slow digestion, helping one to feel greater satisfaction after eating. After following three large prospective cohorts of 133,468 men and women for 24 years, researchers found that higher intakes of fiber-rich fruits with a low glycemic load, particularly <b>apples</b> and pears, were associated with the least amount of weight gain over time.',\n",
+       "  'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health',\n",
+       "  'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search.results(\"apples\", 5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/modules/utils/examples/google_search.ipynb

@@ -16,19 +16,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "34bb5968",
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ[\"GOOGLE_CSE_ID\"] = \n",
-    "os.environ[\"GOOGLE_API_KEY\"] = "
+    "os.environ[\"GOOGLE_CSE_ID\"] = \"\"\n",
+    "os.environ[\"GOOGLE_API_KEY\"] = \"\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "ac4910f8",
    "metadata": {},
    "outputs": [],
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "84b8f773",
    "metadata": {},
    "outputs": [],
@@ -48,17 +48,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "068991a6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'STATE OF HAWAII. 1 Child\\'s First Name. (Type or print). 2. Sex. BARACK. 3. This Birth. CERTIFICATE OF LIVE BIRTH. FILE. NUMBER 151 le. lb. Middle Name. Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party,\\xa0... First Lady Michelle LaVaughn Robinson Obama is a lawyer, writer, and the wife of the 44th President, Barack Obama. She is the first African-American First\\xa0... Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and the first\\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama\\'s first name. Miller knew that every answer had to\\xa0... Feb 9, 2015 ... Michael Jordan misspelled Barack Obama\\'s first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\\xa0... His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Jan 16, 2007 ... 4, 1961, in Honolulu. His first name means \"one who is blessed\" in Swahili. While Obama\\'s father, Barack Hussein Obama Sr., was from Kenya, his\\xa0... Jan 19, 2017 ... Hopeful parents named their sons for the first Black president, whose name is a variation of the Hebrew name Baruch, which means “blessed”\\xa0... Feb 27, 2020 ... President Barack Obama was born Barack Hussein Obama, II, as shown here on his birth certificate here . As reported by Reuters here , his\\xa0...'"
+       "'1 Child\\'s First Name. 2. 6. 7d. Street Address. 71. (Type or print). BARACK. Sex. 3. This Birth. 4. If Twin or Triplet,. Was Child Born. Barack Hussein Obama II is an American retired politician who served as the 44th president of the United States from 2009 to 2017. His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Feb 9, 2015 ... Michael Jordan misspelled Barack Obama\\'s first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama\\'s first name. Miller knew that every answer had to end\\xa0... First Lady Michelle LaVaughn Robinson Obama is a lawyer, writer, and the wife of the 44th President, Barack Obama. She is the first African-American First\\xa0... Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and the first\\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Feb 27, 2020 ... President Barack Obama was born Barack Hussein Obama, II, as shown here on his birth certificate here . As reported by Reuters here , his\\xa0... Jan 16, 2007 ... 4, 1961, in Honolulu. His first name means \"one who is blessed\" in Swahili. While Obama\\'s father, Barack Hussein Obama Sr., was from Kenya, his\\xa0...'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -67,13 +67,118 @@
     "search.run(\"Obama's first name?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "074b7f07",
+   "metadata": {},
+   "source": [
+    "## Number of Results\n",
+    "You can use the `k` parameter to set the number of results"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
+   "id": "5083fbdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = GoogleSearchAPIWrapper(k=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "77aaa857",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The official home of the Python Programming Language.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search.run(\"python\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11c8d94f",
+   "metadata": {},
+   "source": [
+    "'The official home of the Python Programming Language.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73473110",
+   "metadata": {},
+   "source": [
+    "## Metadata Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "109fe796",
+   "metadata": {},
+   "source": [
+    "Run query through GoogleSearch and return snippet, title, and link metadata.\n",
+    "\n",
+    "- Snippet: The description of the result.\n",
+    "- Title: The title of the result.\n",
+    "- Link: The link to the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "id": "028f4cba",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "search = GoogleSearchAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4d8f734f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'snippet': 'Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment,\\xa0...',\n",
+       "  'title': 'Apple',\n",
+       "  'link': 'https://www.apple.com/'},\n",
+       " {'snippet': \"Jul 10, 2022 ... Whether or not you're up on your apple trivia, no doubt you know how delicious this popular fruit is, and how nutritious. Apples are rich in\\xa0...\",\n",
+       "  'title': '25 Types of Apples and What to Make With Them - Parade ...',\n",
+       "  'link': 'https://parade.com/1330308/bethlipton/types-of-apples/'},\n",
+       " {'snippet': 'An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the\\xa0...',\n",
+       "  'title': 'Apple - Wikipedia',\n",
+       "  'link': 'https://en.wikipedia.org/wiki/Apple'},\n",
+       " {'snippet': 'Apples are a popular fruit. They contain antioxidants, vitamins, dietary fiber, and a range of other nutrients. Due to their varied nutrient content,\\xa0...',\n",
+       "  'title': 'Apples: Benefits, nutrition, and tips',\n",
+       "  'link': 'https://www.medicalnewstoday.com/articles/267290'},\n",
+       " {'snippet': \"An apple is a crunchy, bright-colored fruit, one of the most popular in the United States. You've probably heard the age-old saying, “An apple a day keeps\\xa0...\",\n",
+       "  'title': 'Apples: Nutrition & Health Benefits',\n",
+       "  'link': 'https://www.webmd.com/food-recipes/benefits-apples'}]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search.results(\"apples\", 5)"
+   ]
   }
  ],
  "metadata": {
@@ -93,6 +198,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
+   }
   }
  },
  "nbformat": 4,

docs/tracing.md

@@ -0,0 +1,57 @@
+# Tracing
+
+By enabling tracing in your LangChain runs, you’ll be able to more effectively visualize, step through, and debug your chains and agents.
+
+First, you should install tracing and set up your environment properly.
+You can use either a locally hosted version of this (uses Docker) or a cloud hosted version (in closed alpha).
+If you're interested in using the hosted platform, please fill out the form [here](https://forms.gle/tRCEMSeopZf6TE3b6).
+
+
+- [Locally Hosted Setup](./tracing/local_installation.md)
+- [Cloud Hosted Setup](./tracing/hosted_installation.md)
+
+## Tracing Walkthrough
+
+When you first access the UI, you should see a page with your tracing sessions. 
+An initial one "default" should already be created for you. 
+A session is just a way to group traces together. 
+If you click on a session, it will take you to a page with no recorded traces that says "No Runs." 
+You can create a new session with the new session form.
+
+![](tracing/homepage.png)
+
+If we click on the `default` session, we can see that to start we have no traces stored.
+
+![](tracing/default_empty.png)
+
+If we now start running chains and agents with tracing enabled, we will see data show up here.
+To do so, we can run [this notebook](tracing/agent_with_tracing.ipynb) as an example.
+After running it, we will see an initial trace show up.
+
+![](tracing/first_trace.png)
+
+From here we can explore the trace at a high level by clicking on the arrow to show nested runs.
+We can keep on clicking further and further down to explore deeper and deeper.
+
+![](tracing/explore.png)
+
+We can also click on the "Explore" button of the top level run to dive even deeper. 
+Here, we can see the inputs and outputs in full, as well as all the nested traces.
+
+![](tracing/explore_trace.png)
+
+We can keep on exploring each of these nested traces in more detail.
+For example, here is the lowest level trace with the exact inputs/outputs to the LLM.
+
+![](tracing/explore_llm.png)
+
+## Changing Sessions
+1. To initially record traces to a session other than `"default"`, you can set the `LANGCHAIN_SESSION` environment variable to the name of the session you want to record to:
+
+```python
+import os
+os.environ["LANGCHAIN_HANDLER"] = "langchain"
+os.environ["LANGCHAIN_SESSION"] = "my_session" # Make sure this session actually exists. You can create a new session in the UI.
+```
+
+2. To switch sessions mid-script or mid-notebook, do NOT set the `LANGCHAIN_SESSION` environment variable. Instead: `langchain.set_tracing_callback_manager(session_name="my_session")`

docs/tracing/agent_with_tracing.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5371a9bb",
+   "metadata": {},
+   "source": [
+    "# Tracing Walkthrough"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "17c04cc6-c93d-4b6c-a033-e897577f4ed1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\"\n",
+    "\n",
+    "## Uncomment this if using hosted setup.\n",
+    "\n",
+    "# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://langchain-api-gateway-57eoxz8z.uc.gateway.dev\" \n",
+    "\n",
+    "## Uncomment this if you want traces to be recorded to \"my_session\" instead of default.\n",
+    "\n",
+    "# os.environ[\"LANGCHAIN_SESSION\"] = \"my_session\"  \n",
+    "\n",
+    "## Better to set this environment variable in the terminal\n",
+    "## Uncomment this if using hosted version. Replace \"my_api_key\" with your actual API Key.\n",
+    "\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = \"my_api_key\"  \n",
+    "\n",
+    "import langchain\n",
+    "from langchain.agents import Tool, initialize_agent, load_tools\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bfa16b79-aa4b-4d41-a067-70d1f593f667",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to use a calculator to solve this.\n",
+      "Action: Calculator\n",
+      "Action Input: 2^.123243\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: 1.0891804557407723\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'1.0891804557407723'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.\n",
+    "\n",
+    "llm = OpenAI(temperature=0)\n",
+    "tools = load_tools([\"llm-math\"], llm=llm)\n",
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=\"zero-shot-react-description\", verbose=True\n",
+    ")\n",
+    "\n",
+    "agent.run(\"What is 2 raised to .123243 power?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25addd7f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/tracing/hosted_installation.md

@@ -0,0 +1,36 @@
+# Cloud Hosted Setup
+
+We offer a hosted version of tracing at [langchainplus.vercel.app](https://langchainplus.vercel.app/). You can use this to view traces from your run without having to run the server locally.
+
+Note: we are currently only offering this to a limited number of users. The hosted platform is VERY alpha, in active development, and data might be dropped at any time. Don't depend on data being persisted in the system long term and don't log traces that may contain sensitive information. If you're interested in using the hosted platform, please fill out the form [here](https://forms.gle/tRCEMSeopZf6TE3b6).
+
+## Installation
+
+1. Login to the system and click "API Key" in the top right corner. Generate a new key and keep it safe. You will need it to authenticate with the system.
+
+## Environment Setup
+
+After installation, you must now set up your environment to use tracing.
+
+This can be done by setting an environment variable in your terminal by running `export LANGCHAIN_HANDLER=langchain`.
+
+You can also do this by adding the below snippet to the top of every script. **IMPORTANT:** this must go at the VERY TOP of your script, before you import anything from `langchain`. 
+
+```python
+import os
+os.environ["LANGCHAIN_HANDLER"] = "langchain"
+```
+
+You will also need to set an environment variable to specify the endpoint and your API key. This can be done with the following environment variables:
+
+1. `LANGCHAIN_ENDPOINT` = "https://langchain-api-gateway-57eoxz8z.uc.gateway.dev"
+2. `LANGCHAIN_API_KEY` - set this to the API key you generated during installation.
+
+An example of adding all relevant environment variables is below:
+
+```python
+import os
+os.environ["LANGCHAIN_HANDLER"] = "langchain"
+os.environ["LANGCHAIN_ENDPOINT"] = "https://langchain-api-gateway-57eoxz8z.uc.gateway.dev"
+os.environ["LANGCHAIN_API_KEY"] = "my_api_key"  # Don't commit this to your repo! Better to set it in your terminal.
+```

docs/tracing/local_installation.md

@@ -0,0 +1,35 @@
+# Locally Hosted Setup
+
+This page contains instructions for installing and then setting up the environment to use the locally hosted version of tracing.
+
+## Installation
+
+1. Ensure you have Docker installed (see [Get Docker](https://docs.docker.com/get-docker/)) and that it’s running.
+2. Install the latest version of `langchain`: `pip install langchain` or `pip install langchain -U` to upgrade your
+   existing version.
+3. Run `langchain-server`
+    1. This will spin up the server in the terminal.
+    2. Once you see the terminal
+       output `langchain-langchain-frontend-1 | ➜ Local: [http://localhost:4173/](http://localhost:4173/)`, navigate
+       to [http://localhost:4173/](http://localhost:4173/)
+
+4. You should see a page with your tracing sessions. See the overview page for a walkthrough of the UI.
+
+5. Currently, trace data is not guaranteed to be persisted between runs of `langchain-server`. If you want to
+       persist your data, you can mount a volume to the Docker container. See the [Docker docs](https://docs.docker.com/storage/volumes/) for more info.
+6. To stop the server, press `Ctrl+C` in the terminal where you ran `langchain-server`.
+
+
+## Environment Setup
+
+After installation, you must now set up your environment to use tracing.
+
+This can be done by setting an environment variable in your terminal by running `export LANGCHAIN_HANDLER=langchain`.
+
+You can also do this by adding the below snippet to the top of every script. **IMPORTANT:** this must go at the VERY TOP of your script, before you import anything from `langchain`. 
+
+```python
+import os
+os.environ["LANGCHAIN_HANDLER"] = "langchain"
+```
+

langchain/__init__.py

@@ -4,7 +4,11 @@ from typing import Optional
 
 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
 from langchain.cache import BaseCache
-from langchain.callbacks import set_default_callback_manager, set_handler
+from langchain.callbacks import (
+    set_default_callback_manager,
+    set_handler,
+    set_tracing_callback_manager,
+)
 from langchain.chains import (
     ConversationChain,
     LLMBashChain,
@@ -68,4 +72,5 @@ __all__ = [
     "QAWithSourcesChain",
     "PALChain",
     "set_handler",
+    "set_tracing_callback_manager",
 ]

langchain/agents/__init__.py

@@ -1,12 +1,13 @@
 """Interface for agents."""
 from langchain.agents.agent import Agent, AgentExecutor
 from langchain.agents.conversational.base import ConversationalAgent
+from langchain.agents.initialize import initialize_agent
 from langchain.agents.load_tools import get_all_tool_names, load_tools
-from langchain.agents.loading import initialize_agent
+from langchain.agents.loading import load_agent
 from langchain.agents.mrkl.base import MRKLChain, ZeroShotAgent
 from langchain.agents.react.base import ReActChain, ReActTextWorldAgent
 from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain
-from langchain.agents.tools import Tool
+from langchain.agents.tools import Tool, tool
 
 __all__ = [
     "MRKLChain",
@@ -15,10 +16,12 @@ __all__ = [
     "AgentExecutor",
     "Agent",
     "Tool",
+    "tool",
     "initialize_agent",
     "ZeroShotAgent",
     "ReActTextWorldAgent",
     "load_tools",
     "get_all_tool_names",
     "ConversationalAgent",
+    "load_agent",
 ]

langchain/agents/agent.py

@@ -1,10 +1,13 @@
 """Chain that takes in an input and produces an action and action input."""
 from __future__ import annotations
 
+import json
 import logging
 from abc import abstractmethod
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+import yaml
 from pydantic import BaseModel, root_validator
 
 from langchain.agents.tools import Tool
@@ -30,6 +33,7 @@ class Agent(BaseModel):
     """
 
     llm_chain: LLMChain
+    allowed_tools: Optional[List[str]] = None
     return_values: List[str] = ["output"]
 
     @abstractmethod
@@ -44,6 +48,29 @@ class Agent(BaseModel):
     def _stop(self) -> List[str]:
         return [f"\n{self.observation_prefix}"]
 
+    def _construct_scratchpad(
+        self, intermediate_steps: List[Tuple[AgentAction, str]]
+    ) -> str:
+        """Construct the scratchpad that lets the agent continue its thought process."""
+        thoughts = ""
+        for action, observation in intermediate_steps:
+            thoughts += action.log
+            thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
+        return thoughts
+
+    def _get_next_action(self, full_inputs: Dict[str, str]) -> AgentAction:
+        full_output = self.llm_chain.predict(**full_inputs)
+        parsed_output = self._extract_tool_and_input(full_output)
+        while parsed_output is None:
+            full_output = self._fix_text(full_output)
+            full_inputs["agent_scratchpad"] += full_output
+            output = self.llm_chain.predict(**full_inputs)
+            full_output += output
+            parsed_output = self._extract_tool_and_input(full_output)
+        return AgentAction(
+            tool=parsed_output[0], tool_input=parsed_output[1], log=full_output
+        )
+
     def plan(
         self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
     ) -> Union[AgentAction, AgentFinish]:
@@ -57,24 +84,14 @@ class Agent(BaseModel):
         Returns:
             Action specifying what tool to use.
         """
-        thoughts = ""
-        for action, observation in intermediate_steps:
-            thoughts += action.log
-            thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
+        thoughts = self._construct_scratchpad(intermediate_steps)
         new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
         full_inputs = {**kwargs, **new_inputs}
-        full_output = self.llm_chain.predict(**full_inputs)
-        parsed_output = self._extract_tool_and_input(full_output)
-        while parsed_output is None:
-            full_output = self._fix_text(full_output)
-            full_inputs["agent_scratchpad"] += full_output
-            output = self.llm_chain.predict(**full_inputs)
-            full_output += output
-            parsed_output = self._extract_tool_and_input(full_output)
-        tool, tool_input = parsed_output
-        if tool == self.finish_tool_name:
-            return AgentFinish({"output": tool_input}, full_output)
-        return AgentAction(tool, tool_input, full_output)
+
+        action = self._get_next_action(full_inputs)
+        if action.tool == self.finish_tool_name:
+            return AgentFinish({"output": action.tool_input}, action.log)
+        return action
 
     def prepare_for_new_call(self) -> None:
         """Prepare the agent for new call, if needed."""
@@ -146,7 +163,8 @@ class Agent(BaseModel):
             prompt=cls.create_prompt(tools),
             callback_manager=callback_manager,
         )
-        return cls(llm_chain=llm_chain, **kwargs)
+        tool_names = [tool.name for tool in tools]
+        return cls(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
 
     def return_stopped_response(
         self,
@@ -192,6 +210,50 @@ class Agent(BaseModel):
                 f"got {early_stopping_method}"
             )
 
+    @property
+    @abstractmethod
+    def _agent_type(self) -> str:
+        """Return Identifier of agent type."""
+
+    def dict(self, **kwargs: Any) -> Dict:
+        """Return dictionary representation of agent."""
+        _dict = super().dict()
+        _dict["_type"] = self._agent_type
+        return _dict
+
+    def save(self, file_path: Union[Path, str]) -> None:
+        """Save the agent.
+
+        Args:
+            file_path: Path to file to save the agent to.
+
+        Example:
+        .. code-block:: python
+
+            # If working with agent executor
+            agent.agent.save(file_path="path/agent.yaml")
+        """
+        # Convert file to Path object.
+        if isinstance(file_path, str):
+            save_path = Path(file_path)
+        else:
+            save_path = file_path
+
+        directory_path = save_path.parent
+        directory_path.mkdir(parents=True, exist_ok=True)
+
+        # Fetch dictionary to save
+        agent_dict = self.dict()
+
+        if save_path.suffix == ".json":
+            with open(file_path, "w") as f:
+                json.dump(agent_dict, f, indent=4)
+        elif save_path.suffix == ".yaml":
+            with open(file_path, "w") as f:
+                yaml.dump(agent_dict, f, default_flow_style=False)
+        else:
+            raise ValueError(f"{save_path} must be json or yaml")
+
 
 class AgentExecutor(Chain, BaseModel):
     """Consists of an agent using tools."""
@@ -199,7 +261,7 @@ class AgentExecutor(Chain, BaseModel):
     agent: Agent
     tools: List[Tool]
     return_intermediate_steps: bool = False
-    max_iterations: Optional[int] = None
+    max_iterations: Optional[int] = 15
     early_stopping_method: str = "force"
 
     @classmethod
@@ -215,6 +277,31 @@ class AgentExecutor(Chain, BaseModel):
             agent=agent, tools=tools, callback_manager=callback_manager, **kwargs
         )
 
+    @root_validator()
+    def validate_tools(cls, values: Dict) -> Dict:
+        """Validate that tools are compatible with agent."""
+        agent = values["agent"]
+        tools = values["tools"]
+        if agent.allowed_tools is not None:
+            if set(agent.allowed_tools) != set([tool.name for tool in tools]):
+                raise ValueError(
+                    f"Allowed tools ({agent.allowed_tools}) different than "
+                    f"provided tools ({[tool.name for tool in tools]})"
+                )
+        return values
+
+    def save(self, file_path: Union[Path, str]) -> None:
+        """Raise error - saving not supported for Agent Executors."""
+        raise ValueError(
+            "Saving not supported for agent executors. "
+            "If you are trying to save the agent, please use the "
+            "`.save_agent(...)`"
+        )
+
+    def save_agent(self, file_path: Union[Path, str]) -> None:
+        """Save the underlying agent."""
+        return self.agent.save(file_path)
+
     @property
     def input_keys(self) -> List[str]:
         """Return the input keys.
@@ -241,8 +328,9 @@ class AgentExecutor(Chain, BaseModel):
             return iterations < self.max_iterations
 
     def _return(self, output: AgentFinish, intermediate_steps: list) -> Dict[str, Any]:
-        if self.verbose:
-            self.callback_manager.on_agent_finish(output, color="green")
+        self.callback_manager.on_agent_finish(
+            output, color="green", verbose=self.verbose
+        )
         final_output = output.return_values
         if self.return_intermediate_steps:
             final_output["intermediate_steps"] = intermediate_steps
@@ -272,35 +360,35 @@ class AgentExecutor(Chain, BaseModel):
             # Otherwise we lookup the tool
             if output.tool in name_to_tool_map:
                 tool = name_to_tool_map[output.tool]
-                if self.verbose:
-                    self.callback_manager.on_tool_start(
-                        {"name": str(tool.func)[:60] + "..."}, output, color="green"
-                    )
+                self.callback_manager.on_tool_start(
+                    {"name": str(tool.func)[:60] + "..."},
+                    output,
+                    color="green",
+                    verbose=self.verbose,
+                )
                 try:
                     # We then call the tool on the tool input to get an observation
                     observation = tool.func(output.tool_input)
                     color = color_mapping[output.tool]
                     return_direct = tool.return_direct
-                except Exception as e:
-                    if self.verbose:
-                        self.callback_manager.on_tool_error(e)
+                except (KeyboardInterrupt, Exception) as e:
+                    self.callback_manager.on_tool_error(e, verbose=self.verbose)
                     raise e
             else:
-                if self.verbose:
-                    self.callback_manager.on_tool_start(
-                        {"name": "N/A"}, output, color="green"
-                    )
+                self.callback_manager.on_tool_start(
+                    {"name": "N/A"}, output, color="green", verbose=self.verbose
+                )
                 observation = f"{output.tool} is not a valid tool, try another one."
                 color = None
                 return_direct = False
-            if self.verbose:
-                llm_prefix = "" if return_direct else self.agent.llm_prefix
-                self.callback_manager.on_tool_end(
-                    observation,
-                    color=color,
-                    observation_prefix=self.agent.observation_prefix,
-                    llm_prefix=llm_prefix,
-                )
+            llm_prefix = "" if return_direct else self.agent.llm_prefix
+            self.callback_manager.on_tool_end(
+                observation,
+                color=color,
+                observation_prefix=self.agent.observation_prefix,
+                llm_prefix=llm_prefix,
+                verbose=self.verbose,
+            )
             intermediate_steps.append((output, observation))
             if return_direct:
                 # Set the log to "" because we do not want to log it.

langchain/agents/conversational/base.py

@@ -18,6 +18,11 @@ class ConversationalAgent(Agent):
 
     ai_prefix: str = "AI"
 
+    @property
+    def _agent_type(self) -> str:
+        """Return Identifier of agent type."""
+        return "conversational-react-description"
+
     @property
     def observation_prefix(self) -> str:
         """Prefix to append the observation with."""
@@ -70,15 +75,15 @@ class ConversationalAgent(Agent):
         return self.ai_prefix
 
     def _extract_tool_and_input(self, llm_output: str) -> Optional[Tuple[str, str]]:
-        if f"{self.ai_prefix}: " in llm_output:
-            return self.ai_prefix, llm_output.split(f"{self.ai_prefix}: ")[-1]
+        if f"{self.ai_prefix}:" in llm_output:
+            return self.ai_prefix, llm_output.split(f"{self.ai_prefix}:")[-1].strip()
         regex = r"Action: (.*?)\nAction Input: (.*)"
         match = re.search(regex, llm_output)
         if not match:
             raise ValueError(f"Could not parse LLM output: `{llm_output}`")
         action = match.group(1)
         action_input = match.group(2)
-        return action, action_input.strip(" ").strip('"')
+        return action.strip(), action_input.strip(" ").strip('"')
 
     @classmethod
     def from_llm_and_tools(
@@ -86,18 +91,29 @@ class ConversationalAgent(Agent):
         llm: BaseLLM,
         tools: List[Tool],
         callback_manager: Optional[BaseCallbackManager] = None,
+        prefix: str = PREFIX,
+        suffix: str = SUFFIX,
         ai_prefix: str = "AI",
         human_prefix: str = "Human",
+        input_variables: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Agent:
         """Construct an agent from an LLM and tools."""
         cls._validate_tools(tools)
         prompt = cls.create_prompt(
-            tools, ai_prefix=ai_prefix, human_prefix=human_prefix
+            tools,
+            ai_prefix=ai_prefix,
+            human_prefix=human_prefix,
+            prefix=prefix,
+            suffix=suffix,
+            input_variables=input_variables,
         )
         llm_chain = LLMChain(
             llm=llm,
             prompt=prompt,
             callback_manager=callback_manager,
         )
-        return cls(llm_chain=llm_chain, ai_prefix=ai_prefix, **kwargs)
+        tool_names = [tool.name for tool in tools]
+        return cls(
+            llm_chain=llm_chain, allowed_tools=tool_names, ai_prefix=ai_prefix, **kwargs
+        )

langchain/agents/initialize.py

@@ -0,0 +1,72 @@
+"""Load agent."""
+from typing import Any, List, Optional
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.loading import AGENT_TO_CLASS, load_agent
+from langchain.agents.tools import Tool
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.llms.base import BaseLLM
+
+
+def initialize_agent(
+    tools: List[Tool],
+    llm: BaseLLM,
+    agent: Optional[str] = None,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    agent_path: Optional[str] = None,
+    agent_kwargs: Optional[dict] = None,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """Load agent given tools and LLM.
+
+    Args:
+        tools: List of tools this agent has access to.
+        llm: Language model to use as the agent.
+        agent: The agent to use. Valid options are:
+            `zero-shot-react-description`
+            `react-docstore`
+            `self-ask-with-search`
+            `conversational-react-description`
+            If None and agent_path is also None, will default to
+            `zero-shot-react-description`.
+        callback_manager: CallbackManager to use. Global callback manager is used if
+            not provided. Defaults to None.
+        agent_path: Path to serialized agent to use.
+        **kwargs: Additional key word arguments to pass to the agent.
+
+    Returns:
+        An agent.
+    """
+    if agent is None and agent_path is None:
+        agent = "zero-shot-react-description"
+    if agent is not None and agent_path is not None:
+        raise ValueError(
+            "Both `agent` and `agent_path` are specified, "
+            "but at most only one should be."
+        )
+    if agent is not None:
+        if agent not in AGENT_TO_CLASS:
+            raise ValueError(
+                f"Got unknown agent type: {agent}. "
+                f"Valid types are: {AGENT_TO_CLASS.keys()}."
+            )
+        agent_cls = AGENT_TO_CLASS[agent]
+        agent_kwargs = agent_kwargs or {}
+        agent_obj = agent_cls.from_llm_and_tools(
+            llm, tools, callback_manager=callback_manager, **agent_kwargs
+        )
+    elif agent_path is not None:
+        agent_obj = load_agent(
+            agent_path, llm=llm, tools=tools, callback_manager=callback_manager
+        )
+    else:
+        raise ValueError(
+            "Somehow both `agent` and `agent_path` are None, "
+            "this should never happen."
+        )
+    return AgentExecutor.from_agent_and_tools(
+        agent=agent_obj,
+        tools=tools,
+        callback_manager=callback_manager,
+        **kwargs,
+    )

langchain/agents/load_tools.py

@@ -24,30 +24,6 @@ def _get_python_repl() -> Tool:
     )
 
 
-def _get_serpapi() -> Tool:
-    return Tool(
-        "Search",
-        SerpAPIWrapper().run,
-        "A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
-    )
-
-
-def _get_google_search() -> Tool:
-    return Tool(
-        "Google Search",
-        GoogleSearchAPIWrapper().run,
-        "A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query.",
-    )
-
-
-def _get_wolfram_alpha() -> Tool:
-    return Tool(
-        "Wolfram Alpha",
-        WolframAlphaAPIWrapper().run,
-        "A wrapper around Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.",
-    )
-
-
 def _get_requests() -> Tool:
     return Tool(
         "Requests",
@@ -66,11 +42,8 @@ def _get_terminal() -> Tool:
 
 _BASE_TOOLS = {
     "python_repl": _get_python_repl,
-    "serpapi": _get_serpapi,
     "requests": _get_requests,
     "terminal": _get_terminal,
-    "google-search": _get_google_search,
-    "wolfram-alpha": _get_wolfram_alpha,
 }
 
 
@@ -141,10 +114,39 @@ def _get_tmdb_api(llm: BaseLLM, **kwargs: Any) -> Tool:
     )
 
 
-_EXTRA_TOOLS = {
+def _get_wolfram_alpha(**kwargs: Any) -> Tool:
+    return Tool(
+        "Wolfram Alpha",
+        WolframAlphaAPIWrapper(**kwargs).run,
+        "A wrapper around Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.",
+    )
+
+
+def _get_google_search(**kwargs: Any) -> Tool:
+    return Tool(
+        "Google Search",
+        GoogleSearchAPIWrapper(**kwargs).run,
+        "A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query.",
+    )
+
+
+def _get_serpapi(**kwargs: Any) -> Tool:
+    return Tool(
+        "Search",
+        SerpAPIWrapper(**kwargs).run,
+        "A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
+    )
+
+
+_EXTRA_LLM_TOOLS = {
     "news-api": (_get_news_api, ["news_api_key"]),
     "tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
 }
+_EXTRA_OPTIONAL_TOOLS = {
+    "wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha_appid"]),
+    "google-search": (_get_google_search, ["google_api_key", "google_cse_id"]),
+    "serpapi": (_get_serpapi, ["serpapi_api_key"]),
+}
 
 
 def load_tools(
@@ -167,10 +169,10 @@ def load_tools(
             if llm is None:
                 raise ValueError(f"Tool {name} requires an LLM to be provided")
             tools.append(_LLM_TOOLS[name](llm))
-        elif name in _EXTRA_TOOLS:
+        elif name in _EXTRA_LLM_TOOLS:
             if llm is None:
                 raise ValueError(f"Tool {name} requires an LLM to be provided")
-            _get_tool_func, extra_keys = _EXTRA_TOOLS[name]
+            _get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[name]
             missing_keys = set(extra_keys).difference(kwargs)
             if missing_keys:
                 raise ValueError(
@@ -178,7 +180,12 @@ def load_tools(
                     f"provided: {missing_keys}"
                 )
             sub_kwargs = {k: kwargs[k] for k in extra_keys}
-            tools.append(_get_tool_func(llm=llm, **sub_kwargs))
+            tools.append(_get_llm_tool_func(llm=llm, **sub_kwargs))
+        elif name in _EXTRA_OPTIONAL_TOOLS:
+            _get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
+            sub_kwargs = {k: kwargs[k] for k in extra_keys if k in kwargs}
+            tools.append(_get_tool_func(**sub_kwargs))
+
         else:
             raise ValueError(f"Got unknown tool {name}")
     return tools
@@ -186,4 +193,9 @@ def load_tools(
 
 def get_all_tool_names() -> List[str]:
     """Get a list of all possible tool names."""
-    return list(_BASE_TOOLS) + list(_EXTRA_TOOLS) + list(_LLM_TOOLS)
+    return (
+        list(_BASE_TOOLS)
+        + list(_EXTRA_OPTIONAL_TOOLS)
+        + list(_EXTRA_LLM_TOOLS)
+        + list(_LLM_TOOLS)
+    )

langchain/agents/loading.py

@@ -1,14 +1,19 @@
-"""Load agent."""
-from typing import Any, List, Optional
+"""Functionality for loading agents."""
+import json
+from pathlib import Path
+from typing import Any, List, Optional, Union
 
-from langchain.agents.agent import AgentExecutor
+import yaml
+
+from langchain.agents.agent import Agent
 from langchain.agents.conversational.base import ConversationalAgent
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.react.base import ReActDocstoreAgent
 from langchain.agents.self_ask_with_search.base import SelfAskWithSearchAgent
 from langchain.agents.tools import Tool
-from langchain.callbacks.base import BaseCallbackManager
+from langchain.chains.loading import load_chain, load_chain_from_config
 from langchain.llms.base import BaseLLM
+from langchain.utilities.loading import try_load_from_hub
 
 AGENT_TO_CLASS = {
     "zero-shot-react-description": ZeroShotAgent,
@@ -17,43 +22,86 @@ AGENT_TO_CLASS = {
     "conversational-react-description": ConversationalAgent,
 }
 
+URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"
 
-def initialize_agent(
-    tools: List[Tool],
-    llm: BaseLLM,
-    agent: str = "zero-shot-react-description",
-    callback_manager: Optional[BaseCallbackManager] = None,
+
+def _load_agent_from_tools(
+    config: dict, llm: BaseLLM, tools: List[Tool], **kwargs: Any
+) -> Agent:
+    config_type = config.pop("_type")
+    if config_type not in AGENT_TO_CLASS:
+        raise ValueError(f"Loading {config_type} agent not supported")
+
+    if config_type not in AGENT_TO_CLASS:
+        raise ValueError(f"Loading {config_type} agent not supported")
+    agent_cls = AGENT_TO_CLASS[config_type]
+    combined_config = {**config, **kwargs}
+    return agent_cls.from_llm_and_tools(llm, tools, **combined_config)
+
+
+def load_agent_from_config(
+    config: dict,
+    llm: Optional[BaseLLM] = None,
+    tools: Optional[List[Tool]] = None,
     **kwargs: Any,
-) -> AgentExecutor:
-    """Load agent given tools and LLM.
+) -> Agent:
+    """Load agent from Config Dict."""
+    if "_type" not in config:
+        raise ValueError("Must specify an agent Type in config")
+    load_from_tools = config.pop("load_from_llm_and_tools", False)
+    if load_from_tools:
+        if llm is None:
+            raise ValueError(
+                "If `load_from_llm_and_tools` is set to True, "
+                "then LLM must be provided"
+            )
+        if tools is None:
+            raise ValueError(
+                "If `load_from_llm_and_tools` is set to True, "
+                "then tools must be provided"
+            )
+        return _load_agent_from_tools(config, llm, tools, **kwargs)
+    config_type = config.pop("_type")
 
-    Args:
-        tools: List of tools this agent has access to.
-        llm: Language model to use as the agent.
-        agent: The agent to use. Valid options are:
-            `zero-shot-react-description`
-            `react-docstore`
-            `self-ask-with-search`
-            `conversational-react-description`.
-        callback_manager: CallbackManager to use. Global callback manager is used if
-            not provided. Defaults to None.
-        **kwargs: Additional key word arguments to pass to the agent.
+    if config_type not in AGENT_TO_CLASS:
+        raise ValueError(f"Loading {config_type} agent not supported")
 
-    Returns:
-        An agent.
-    """
-    if agent not in AGENT_TO_CLASS:
-        raise ValueError(
-            f"Got unknown agent type: {agent}. "
-            f"Valid types are: {AGENT_TO_CLASS.keys()}."
-        )
-    agent_cls = AGENT_TO_CLASS[agent]
-    agent_obj = agent_cls.from_llm_and_tools(
-        llm, tools, callback_manager=callback_manager
-    )
-    return AgentExecutor.from_agent_and_tools(
-        agent=agent_obj,
-        tools=tools,
-        callback_manager=callback_manager,
-        **kwargs,
-    )
+    agent_cls = AGENT_TO_CLASS[config_type]
+    if "llm_chain" in config:
+        config["llm_chain"] = load_chain_from_config(config.pop("llm_chain"))
+    elif "llm_chain_path" in config:
+        config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
+    else:
+        raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
+    combined_config = {**config, **kwargs}
+    return agent_cls(**combined_config)  # type: ignore
+
+
+def load_agent(path: Union[str, Path], **kwargs: Any) -> Agent:
+    """Unified method for loading a agent from LangChainHub or local fs."""
+    if hub_result := try_load_from_hub(
+        path, _load_agent_from_file, "agents", {"json", "yaml"}
+    ):
+        return hub_result
+    else:
+        return _load_agent_from_file(path, **kwargs)
+
+
+def _load_agent_from_file(file: Union[str, Path], **kwargs: Any) -> Agent:
+    """Load agent from file."""
+    # Convert file to Path object.
+    if isinstance(file, str):
+        file_path = Path(file)
+    else:
+        file_path = file
+    # Load from either json or yaml.
+    if file_path.suffix == ".json":
+        with open(file_path) as f:
+            config = json.load(f)
+    elif file_path.suffix == ".yaml":
+        with open(file_path, "r") as f:
+            config = yaml.safe_load(f)
+    else:
+        raise ValueError("File type must be json or yaml")
+    # Load the agent from the config now.
+    return load_agent_from_config(config, **kwargs)

langchain/agents/mrkl/base.py

@@ -7,6 +7,8 @@ from typing import Any, Callable, List, NamedTuple, Optional, Tuple
 from langchain.agents.agent import Agent, AgentExecutor
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
 from langchain.agents.tools import Tool
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.chains import LLMChain
 from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
 
@@ -41,7 +43,7 @@ def get_action_and_input(llm_output: str) -> Tuple[str, str]:
     match = re.search(regex, llm_output)
     if not match:
         raise ValueError(f"Could not parse LLM output: `{llm_output}`")
-    action = match.group(1)
+    action = match.group(1).strip()
     action_input = match.group(2)
     return action, action_input.strip(" ").strip('"')
 
@@ -49,6 +51,11 @@ def get_action_and_input(llm_output: str) -> Tuple[str, str]:
 class ZeroShotAgent(Agent):
     """Agent for the MRKL chain."""
 
+    @property
+    def _agent_type(self) -> str:
+        """Return Identifier of agent type."""
+        return "zero-shot-react-description"
+
     @property
     def observation_prefix(self) -> str:
         """Prefix to append the observation with."""
@@ -87,6 +94,30 @@ class ZeroShotAgent(Agent):
             input_variables = ["input", "agent_scratchpad"]
         return PromptTemplate(template=template, input_variables=input_variables)
 
+    @classmethod
+    def from_llm_and_tools(
+        cls,
+        llm: BaseLLM,
+        tools: List[Tool],
+        callback_manager: Optional[BaseCallbackManager] = None,
+        prefix: str = PREFIX,
+        suffix: str = SUFFIX,
+        input_variables: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> Agent:
+        """Construct an agent from an LLM and tools."""
+        cls._validate_tools(tools)
+        prompt = cls.create_prompt(
+            tools, prefix=prefix, suffix=suffix, input_variables=input_variables
+        )
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=prompt,
+            callback_manager=callback_manager,
+        )
+        tool_names = [tool.name for tool in tools]
+        return cls(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+
     @classmethod
     def _validate_tools(cls, tools: List[Tool]) -> None:
         for tool in tools:

langchain/agents/react/base.py

@@ -15,7 +15,12 @@ from langchain.prompts.base import BasePromptTemplate
 
 
 class ReActDocstoreAgent(Agent, BaseModel):
-    """Agent for the ReAct chin."""
+    """Agent for the ReAct chain."""
+
+    @property
+    def _agent_type(self) -> str:
+        """Return Identifier of agent type."""
+        return "react-docstore"
 
     @classmethod
     def create_prompt(cls, tools: List[Tool]) -> BasePromptTemplate:

langchain/agents/self_ask_with_search/base.py

@@ -12,6 +12,11 @@ from langchain.serpapi import SerpAPIWrapper
 class SelfAskWithSearchAgent(Agent):
     """Agent for the self-ask-with-search paper."""
 
+    @property
+    def _agent_type(self) -> str:
+        """Return Identifier of agent type."""
+        return "self-ask-with-search"
+
     @classmethod
     def create_prompt(cls, tools: List[Tool]) -> BasePromptTemplate:
         """Prompt does not depend on tools."""

langchain/agents/tools.py

@@ -1,6 +1,7 @@
 """Interface for tools."""
 from dataclasses import dataclass
-from typing import Callable, Optional
+from inspect import signature
+from typing import Any, Callable, Optional, Union
 
 
 @dataclass
@@ -11,3 +12,65 @@ class Tool:
     func: Callable[[str], str]
     description: Optional[str] = None
     return_direct: bool = False
+
+    def __call__(self, *args: Any, **kwargs: Any) -> str:
+        """Make tools callable by piping through to `func`."""
+        return self.func(*args, **kwargs)
+
+
+def tool(
+    *args: Union[str, Callable], return_direct: bool = False
+) -> Union[Callable, Tool]:
+    """Make tools out of functions, can be used with or without arguments.
+
+    Requires:
+        - Function must be of type (str) -> str
+        - Function must have a docstring
+
+    Examples:
+        .. code-block:: python
+
+            @tool
+            def search_api(query: str) -> str:
+                # Searches the API for the query.
+                return
+
+            @tool("search", return_direct=True)
+            def search_api(query: str) -> str:
+                # Searches the API for the query.
+                return
+    """
+
+    def _make_with_name(tool_name: str) -> Callable:
+        def _make_tool(func: Callable[[str], str]) -> Tool:
+            assert func.__doc__, "Function must have a docstring"
+            # Description example:
+            #   search_api(query: str) - Searches the API for the query.
+            description = f"{tool_name}{signature(func)} - {func.__doc__.strip()}"
+            tool = Tool(
+                name=tool_name,
+                func=func,
+                description=description,
+                return_direct=return_direct,
+            )
+            return tool
+
+        return _make_tool
+
+    if len(args) == 1 and isinstance(args[0], str):
+        # if the argument is a string, then we use the string as the tool name
+        # Example usage: @tool("search", return_direct=True)
+        return _make_with_name(args[0])
+    elif len(args) == 1 and callable(args[0]):
+        # if the argument is a function, then we use the function name as the tool name
+        # Example usage: @tool
+        return _make_with_name(args[0].__name__)(args[0])
+    elif len(args) == 0:
+        # if there are no arguments, then we use the function name as the tool name
+        # Example usage: @tool(return_direct=True)
+        def _partial(func: Callable[[str], str]) -> Tool:
+            return _make_with_name(func.__name__)(func)
+
+        return _partial
+    else:
+        raise ValueError("Too many arguments for tool decorator")

langchain/cache.py

@@ -4,8 +4,7 @@ from typing import Any, Dict, List, Optional, Tuple
 
 from sqlalchemy import Column, Integer, String, create_engine, select
 from sqlalchemy.engine.base import Engine
-from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy.orm import Session
+from sqlalchemy.orm import Session, declarative_base
 
 from langchain.schema import Generation
 
@@ -60,7 +59,7 @@ class SQLAlchemyCache(BaseCache):
         """Initialize by creating all tables."""
         self.engine = engine
         self.cache_schema = cache_schema
-        Base.metadata.create_all(self.engine)
+        self.cache_schema.metadata.create_all(self.engine)
 
     def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
         """Look up based on prompt and llm_string."""

langchain/callbacks/__init__.py

@@ -1,7 +1,13 @@
 """Callback handlers that allow listening to events in LangChain."""
+import os
+from contextlib import contextmanager
+from typing import Generator, Optional
+
 from langchain.callbacks.base import BaseCallbackHandler, BaseCallbackManager
+from langchain.callbacks.openai_info import OpenAICallbackHandler
 from langchain.callbacks.shared import SharedCallbackManager
 from langchain.callbacks.stdout import StdOutCallbackHandler
+from langchain.callbacks.tracers import SharedLangChainTracer
 
 
 def get_callback_manager() -> BaseCallbackManager:
@@ -17,4 +23,38 @@ def set_handler(handler: BaseCallbackHandler) -> None:
 
 def set_default_callback_manager() -> None:
     """Set default callback manager."""
-    set_handler(StdOutCallbackHandler())
+    default_handler = os.environ.get("LANGCHAIN_HANDLER", "stdout")
+    if default_handler == "stdout":
+        set_handler(StdOutCallbackHandler())
+    elif default_handler == "langchain":
+        session = os.environ.get("LANGCHAIN_SESSION")
+        set_tracing_callback_manager(session)
+    else:
+        raise ValueError(
+            f"LANGCHAIN_HANDLER should be one of `stdout` "
+            f"or `langchain`, got {default_handler}"
+        )
+
+
+def set_tracing_callback_manager(session_name: Optional[str] = None) -> None:
+    """Set tracing callback manager."""
+    handler = SharedLangChainTracer()
+    callback = get_callback_manager()
+    callback.set_handlers([handler, StdOutCallbackHandler()])
+    if session_name is None:
+        handler.load_default_session()
+    else:
+        try:
+            handler.load_session(session_name)
+        except Exception:
+            raise ValueError(f"session {session_name} not found")
+
+
+@contextmanager
+def get_openai_callback() -> Generator[OpenAICallbackHandler, None, None]:
+    """Get OpenAI callback handler in a context manager."""
+    handler = OpenAICallbackHandler()
+    manager = get_callback_manager()
+    manager.add_handler(handler)
+    yield handler
+    manager.remove_handler(handler)

langchain/callbacks/base.py

@@ -1,19 +1,33 @@
 """Base callback handler that can be used to handle callbacks from langchain."""
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List
-
-from pydantic import BaseModel
+from typing import Any, Dict, List, Union
 
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 
-class BaseCallbackHandler(BaseModel, ABC):
+class BaseCallbackHandler(ABC):
     """Base callback handler that can be used to handle callbacks from langchain."""
 
-    ignore_llm: bool = False
-    ignore_chain: bool = False
-    ignore_agent: bool = False
+    @property
+    def always_verbose(self) -> bool:
+        """Whether to call verbose callbacks even if verbose is False."""
+        return False
+
+    @property
+    def ignore_llm(self) -> bool:
+        """Whether to ignore LLM callbacks."""
+        return False
+
+    @property
+    def ignore_chain(self) -> bool:
+        """Whether to ignore chain callbacks."""
+        return False
+
+    @property
+    def ignore_agent(self) -> bool:
+        """Whether to ignore agent callbacks."""
+        return False
 
     @abstractmethod
     def on_llm_start(
@@ -22,14 +36,13 @@ class BaseCallbackHandler(BaseModel, ABC):
         """Run when LLM starts running."""
 
     @abstractmethod
-    def on_llm_end(
-        self,
-        response: LLMResult,
-    ) -> None:
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Run when LLM ends running."""
 
     @abstractmethod
-    def on_llm_error(self, error: Exception) -> None:
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Run when LLM errors."""
 
     @abstractmethod
@@ -39,11 +52,13 @@ class BaseCallbackHandler(BaseModel, ABC):
         """Run when chain starts running."""
 
     @abstractmethod
-    def on_chain_end(self, outputs: Dict[str, Any]) -> None:
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Run when chain ends running."""
 
     @abstractmethod
-    def on_chain_error(self, error: Exception) -> None:
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Run when chain errors."""
 
     @abstractmethod
@@ -57,7 +72,9 @@ class BaseCallbackHandler(BaseModel, ABC):
         """Run when tool ends running."""
 
     @abstractmethod
-    def on_tool_error(self, error: Exception) -> None:
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Run when tool errors."""
 
     @abstractmethod
@@ -80,89 +97,136 @@ class BaseCallbackManager(BaseCallbackHandler, ABC):
     def remove_handler(self, handler: BaseCallbackHandler) -> None:
         """Remove a handler from the callback manager."""
 
-    @abstractmethod
     def set_handler(self, handler: BaseCallbackHandler) -> None:
         """Set handler as the only handler on the callback manager."""
+        self.set_handlers([handler])
+
+    @abstractmethod
+    def set_handlers(self, handlers: List[BaseCallbackHandler]) -> None:
+        """Set handlers as the only handlers on the callback manager."""
 
 
 class CallbackManager(BaseCallbackManager):
     """Callback manager that can be used to handle callbacks from langchain."""
 
-    handlers: List[BaseCallbackHandler]
+    def __init__(self, handlers: List[BaseCallbackHandler]) -> None:
+        """Initialize callback manager."""
+        self.handlers: List[BaseCallbackHandler] = handlers
 
     def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+        self,
+        serialized: Dict[str, Any],
+        prompts: List[str],
+        verbose: bool = False,
+        **kwargs: Any
     ) -> None:
         """Run when LLM starts running."""
         for handler in self.handlers:
             if not handler.ignore_llm:
-                handler.on_llm_start(serialized, prompts, **kwargs)
+                if verbose or handler.always_verbose:
+                    handler.on_llm_start(serialized, prompts, **kwargs)
 
     def on_llm_end(
-        self,
-        response: LLMResult,
+        self, response: LLMResult, verbose: bool = False, **kwargs: Any
     ) -> None:
         """Run when LLM ends running."""
         for handler in self.handlers:
             if not handler.ignore_llm:
-                handler.on_llm_end(response)
+                if verbose or handler.always_verbose:
+                    handler.on_llm_end(response)
 
-    def on_llm_error(self, error: Exception) -> None:
+    def on_llm_error(
+        self,
+        error: Union[Exception, KeyboardInterrupt],
+        verbose: bool = False,
+        **kwargs: Any
+    ) -> None:
         """Run when LLM errors."""
         for handler in self.handlers:
             if not handler.ignore_llm:
-                handler.on_llm_error(error)
+                if verbose or handler.always_verbose:
+                    handler.on_llm_error(error)
 
     def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+        self,
+        serialized: Dict[str, Any],
+        inputs: Dict[str, Any],
+        verbose: bool = False,
+        **kwargs: Any
     ) -> None:
         """Run when chain starts running."""
         for handler in self.handlers:
             if not handler.ignore_chain:
-                handler.on_chain_start(serialized, inputs, **kwargs)
+                if verbose or handler.always_verbose:
+                    handler.on_chain_start(serialized, inputs, **kwargs)
 
-    def on_chain_end(self, outputs: Dict[str, Any]) -> None:
+    def on_chain_end(
+        self, outputs: Dict[str, Any], verbose: bool = False, **kwargs: Any
+    ) -> None:
         """Run when chain ends running."""
         for handler in self.handlers:
             if not handler.ignore_chain:
-                handler.on_chain_end(outputs)
+                if verbose or handler.always_verbose:
+                    handler.on_chain_end(outputs)
 
-    def on_chain_error(self, error: Exception) -> None:
+    def on_chain_error(
+        self,
+        error: Union[Exception, KeyboardInterrupt],
+        verbose: bool = False,
+        **kwargs: Any
+    ) -> None:
         """Run when chain errors."""
         for handler in self.handlers:
             if not handler.ignore_chain:
-                handler.on_chain_error(error)
+                if verbose or handler.always_verbose:
+                    handler.on_chain_error(error)
 
     def on_tool_start(
-        self, serialized: Dict[str, Any], action: AgentAction, **kwargs: Any
+        self,
+        serialized: Dict[str, Any],
+        action: AgentAction,
+        verbose: bool = False,
+        **kwargs: Any
     ) -> None:
         """Run when tool starts running."""
         for handler in self.handlers:
             if not handler.ignore_agent:
-                handler.on_tool_start(serialized, action, **kwargs)
+                if verbose or handler.always_verbose:
+                    handler.on_tool_start(serialized, action, **kwargs)
 
-    def on_tool_end(self, output: str, **kwargs: Any) -> None:
+    def on_tool_end(self, output: str, verbose: bool = False, **kwargs: Any) -> None:
         """Run when tool ends running."""
         for handler in self.handlers:
             if not handler.ignore_agent:
-                handler.on_tool_end(output, **kwargs)
+                if verbose or handler.always_verbose:
+                    handler.on_tool_end(output, **kwargs)
 
-    def on_tool_error(self, error: Exception) -> None:
+    def on_tool_error(
+        self,
+        error: Union[Exception, KeyboardInterrupt],
+        verbose: bool = False,
+        **kwargs: Any
+    ) -> None:
         """Run when tool errors."""
         for handler in self.handlers:
             if not handler.ignore_agent:
-                handler.on_tool_error(error)
+                if verbose or handler.always_verbose:
+                    handler.on_tool_error(error)
 
-    def on_text(self, text: str, **kwargs: Any) -> None:
+    def on_text(self, text: str, verbose: bool = False, **kwargs: Any) -> None:
         """Run on additional input from chains and agents."""
         for handler in self.handlers:
-            handler.on_text(text, **kwargs)
+            if verbose or handler.always_verbose:
+                handler.on_text(text, **kwargs)
 
-    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
+    def on_agent_finish(
+        self, finish: AgentFinish, verbose: bool = False, **kwargs: Any
+    ) -> None:
         """Run on agent end."""
         for handler in self.handlers:
             if not handler.ignore_agent:
-                handler.on_agent_finish(finish, **kwargs)
+                if verbose or handler.always_verbose:
+                    handler.on_agent_finish(finish, **kwargs)
 
     def add_handler(self, handler: BaseCallbackHandler) -> None:
         """Add a handler to the callback manager."""
@@ -172,6 +236,6 @@ class CallbackManager(BaseCallbackManager):
         """Remove a handler from the callback manager."""
         self.handlers.remove(handler)
 
-    def set_handler(self, handler: BaseCallbackHandler) -> None:
-        """Set handler as the only handler on the callback manager."""
-        self.handlers = [handler]
+    def set_handlers(self, handlers: List[BaseCallbackHandler]) -> None:
+        """Set handlers as the only handlers on the callback manager."""
+        self.handlers = handlers

langchain/callbacks/openai_info.py

@@ -0,0 +1,95 @@
+"""Callback Handler that prints to std out."""
+from typing import Any, Dict, List, Optional, Union
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.schema import AgentAction, AgentFinish, LLMResult
+
+
+class OpenAICallbackHandler(BaseCallbackHandler):
+    """Callback Handler that tracks OpenAI info."""
+
+    total_tokens: int = 0
+
+    @property
+    def always_verbose(self) -> bool:
+        """Whether to call verbose callbacks even if verbose is False."""
+        return True
+
+    def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> None:
+        """Print out the prompts."""
+        pass
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Do nothing."""
+        if response.llm_output is not None:
+            if "token_usage" in response.llm_output:
+                token_usage = response.llm_output["token_usage"]
+                if "total_tokens" in token_usage:
+                    self.total_tokens += token_usage["total_tokens"]
+
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+        pass
+
+    def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> None:
+        """Print out that we are entering a chain."""
+        pass
+
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+        """Print out that we finished a chain."""
+        pass
+
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+        pass
+
+    def on_tool_start(
+        self,
+        serialized: Dict[str, Any],
+        action: AgentAction,
+        color: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Print out the log in specified color."""
+        pass
+
+    def on_tool_end(
+        self,
+        output: str,
+        color: Optional[str] = None,
+        observation_prefix: Optional[str] = None,
+        llm_prefix: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        """If not the final action, print out observation."""
+        pass
+
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing."""
+        pass
+
+    def on_text(
+        self,
+        text: str,
+        color: Optional[str] = None,
+        end: str = "",
+        **kwargs: Optional[str],
+    ) -> None:
+        """Run when agent ends."""
+        pass
+
+    def on_agent_finish(
+        self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
+    ) -> None:
+        """Run on agent end."""
+        pass

langchain/callbacks/shared.py

@@ -1,7 +1,7 @@
 """A shared CallbackManager."""
 
 import threading
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Union
 
 from langchain.callbacks.base import (
     BaseCallbackHandler,
@@ -41,18 +41,17 @@ class SharedCallbackManager(Singleton, BaseCallbackManager):
         with self._lock:
             self._callback_manager.on_llm_start(serialized, prompts, **kwargs)
 
-    def on_llm_end(
-        self,
-        response: LLMResult,
-    ) -> None:
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Run when LLM ends running."""
         with self._lock:
-            self._callback_manager.on_llm_end(response)
+            self._callback_manager.on_llm_end(response, **kwargs)
 
-    def on_llm_error(self, error: Exception) -> None:
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Run when LLM errors."""
         with self._lock:
-            self._callback_manager.on_llm_error(error)
+            self._callback_manager.on_llm_error(error, **kwargs)
 
     def on_chain_start(
         self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
@@ -61,15 +60,17 @@ class SharedCallbackManager(Singleton, BaseCallbackManager):
         with self._lock:
             self._callback_manager.on_chain_start(serialized, inputs, **kwargs)
 
-    def on_chain_end(self, outputs: Dict[str, Any]) -> None:
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Run when chain ends running."""
         with self._lock:
-            self._callback_manager.on_chain_end(outputs)
+            self._callback_manager.on_chain_end(outputs, **kwargs)
 
-    def on_chain_error(self, error: Exception) -> None:
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Run when chain errors."""
         with self._lock:
-            self._callback_manager.on_chain_error(error)
+            self._callback_manager.on_chain_error(error, **kwargs)
 
     def on_tool_start(
         self, serialized: Dict[str, Any], action: AgentAction, **kwargs: Any
@@ -83,10 +84,12 @@ class SharedCallbackManager(Singleton, BaseCallbackManager):
         with self._lock:
             self._callback_manager.on_tool_end(output, **kwargs)
 
-    def on_tool_error(self, error: Exception) -> None:
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Run when tool errors."""
         with self._lock:
-            self._callback_manager.on_tool_error(error)
+            self._callback_manager.on_tool_error(error, **kwargs)
 
     def on_text(self, text: str, **kwargs: Any) -> None:
         """Run on arbitrary text."""
@@ -108,7 +111,7 @@ class SharedCallbackManager(Singleton, BaseCallbackManager):
         with self._lock:
             self._callback_manager.remove_handler(callback)
 
-    def set_handler(self, handler: BaseCallbackHandler) -> None:
-        """Set handler as the only handler on the callback manager."""
+    def set_handlers(self, handlers: List[BaseCallbackHandler]) -> None:
+        """Set handlers as the only handlers on the callback manager."""
         with self._lock:
-            self._callback_manager.handlers = [handler]
+            self._callback_manager.handlers = handlers

langchain/callbacks/stdout.py

@@ -1,5 +1,5 @@
 """Callback Handler that prints to std out."""
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.input import print_text
@@ -15,11 +15,13 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         """Print out the prompts."""
         pass
 
-    def on_llm_end(self, response: LLMResult) -> None:
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Do nothing."""
         pass
 
-    def on_llm_error(self, error: Exception) -> None:
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Do nothing."""
         pass
 
@@ -30,11 +32,13 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         class_name = serialized["name"]
         print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m")
 
-    def on_chain_end(self, outputs: Dict[str, Any]) -> None:
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""
         print("\n\033[1m> Finished chain.\033[0m")
 
-    def on_chain_error(self, error: Exception) -> None:
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Do nothing."""
         pass
 
@@ -61,7 +65,9 @@ class StdOutCallbackHandler(BaseCallbackHandler):
         print_text(output, color=color)
         print_text(f"\n{llm_prefix}")
 
-    def on_tool_error(self, error: Exception) -> None:
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Do nothing."""
         pass
 

langchain/callbacks/streamlit.py

@@ -1,5 +1,5 @@
 """Callback Handler that logs to streamlit."""
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import streamlit as st
 
@@ -18,11 +18,13 @@ class StreamlitCallbackHandler(BaseCallbackHandler):
         for prompt in prompts:
             st.write(prompt)
 
-    def on_llm_end(self, response: LLMResult) -> None:
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Do nothing."""
         pass
 
-    def on_llm_error(self, error: Exception) -> None:
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Do nothing."""
         pass
 
@@ -33,11 +35,13 @@ class StreamlitCallbackHandler(BaseCallbackHandler):
         class_name = serialized["name"]
         st.write(f"Entering new {class_name} chain...")
 
-    def on_chain_end(self, outputs: Dict[str, Any]) -> None:
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""
         st.write("Finished chain.")
 
-    def on_chain_error(self, error: Exception) -> None:
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Do nothing."""
         pass
 
@@ -62,7 +66,9 @@ class StreamlitCallbackHandler(BaseCallbackHandler):
         st.write(f"{observation_prefix}{output}")
         st.write(llm_prefix)
 
-    def on_tool_error(self, error: Exception) -> None:
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
         """Do nothing."""
         pass
 

langchain/callbacks/tracers/__init__.py

@@ -0,0 +1,12 @@
+"""Tracers that record execution of LangChain runs."""
+
+from langchain.callbacks.tracers.base import SharedTracer, Tracer
+from langchain.callbacks.tracers.langchain import BaseLangChainTracer
+
+
+class SharedLangChainTracer(SharedTracer, BaseLangChainTracer):
+    """Shared tracer that records LangChain execution to LangChain endpoint."""
+
+
+class LangChainTracer(Tracer, BaseLangChainTracer):
+    """Tracer that records LangChain execution to LangChain endpoint."""

langchain/callbacks/tracers/base.py

@@ -0,0 +1,334 @@
+"""Base interfaces for tracing runs."""
+from __future__ import annotations
+
+import threading
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Union
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.callbacks.shared import Singleton
+from langchain.callbacks.tracers.schemas import (
+    ChainRun,
+    LLMRun,
+    ToolRun,
+    TracerSession,
+    TracerSessionCreate,
+)
+from langchain.schema import AgentAction, AgentFinish, LLMResult
+
+
+class TracerException(Exception):
+    """Base class for exceptions in tracers module."""
+
+
+class BaseTracer(BaseCallbackHandler, ABC):
+    """Base interface for tracers."""
+
+    @abstractmethod
+    def _add_child_run(
+        self,
+        parent_run: Union[ChainRun, ToolRun],
+        child_run: Union[LLMRun, ChainRun, ToolRun],
+    ) -> None:
+        """Add child run to a chain run or tool run."""
+
+    @abstractmethod
+    def _persist_run(self, run: Union[LLMRun, ChainRun, ToolRun]) -> None:
+        """Persist a run."""
+
+    @abstractmethod
+    def _persist_session(self, session: TracerSessionCreate) -> TracerSession:
+        """Persist a tracing session."""
+
+    @abstractmethod
+    def _generate_id(self) -> Optional[Union[int, str]]:
+        """Generate an id for a run."""
+
+    def new_session(self, name: Optional[str] = None, **kwargs: Any) -> TracerSession:
+        """NOT thread safe, do not call this method from multiple threads."""
+        session_create = TracerSessionCreate(name=name, extra=kwargs)
+        session = self._persist_session(session_create)
+        self._session = session
+        return session
+
+    @abstractmethod
+    def load_session(self, session_name: str) -> TracerSession:
+        """Load a tracing session and set it as the Tracer's session."""
+
+    @abstractmethod
+    def load_default_session(self) -> TracerSession:
+        """Load the default tracing session and set it as the Tracer's session."""
+
+    @property
+    @abstractmethod
+    def _stack(self) -> List[Union[LLMRun, ChainRun, ToolRun]]:
+        """Get the tracer stack."""
+
+    @property
+    @abstractmethod
+    def _execution_order(self) -> int:
+        """Get the execution order for a run."""
+
+    @_execution_order.setter
+    @abstractmethod
+    def _execution_order(self, value: int) -> None:
+        """Set the execution order for a run."""
+
+    @property
+    @abstractmethod
+    def _session(self) -> Optional[TracerSession]:
+        """Get the tracing session."""
+
+    @_session.setter
+    @abstractmethod
+    def _session(self, value: TracerSession) -> None:
+        """Set the tracing session."""
+
+    def _start_trace(self, run: Union[LLMRun, ChainRun, ToolRun]) -> None:
+        """Start a trace for a run."""
+        self._execution_order += 1
+
+        if self._stack:
+            if not (
+                isinstance(self._stack[-1], ChainRun)
+                or isinstance(self._stack[-1], ToolRun)
+            ):
+                raise TracerException(
+                    f"Nested {run.__class__.__name__} can only be"
+                    f" logged inside a ChainRun or ToolRun"
+                )
+            self._add_child_run(self._stack[-1], run)
+        self._stack.append(run)
+
+    def _end_trace(self) -> None:
+        """End a trace for a run."""
+        run = self._stack.pop()
+        if not self._stack:
+            self._execution_order = 1
+            self._persist_run(run)
+
+    def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> None:
+        """Start a trace for an LLM run."""
+        if self._session is None:
+            raise TracerException(
+                "Initialize a session with `new_session()` before starting a trace."
+            )
+
+        llm_run = LLMRun(
+            serialized=serialized,
+            prompts=prompts,
+            extra=kwargs,
+            start_time=datetime.utcnow(),
+            execution_order=self._execution_order,
+            session_id=self._session.id,
+            id=self._generate_id(),
+        )
+        self._start_trace(llm_run)
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """End a trace for an LLM run."""
+        if not self._stack or not isinstance(self._stack[-1], LLMRun):
+            raise TracerException("No LLMRun found to be traced")
+
+        self._stack[-1].end_time = datetime.utcnow()
+        self._stack[-1].response = response
+
+        self._end_trace()
+
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Handle an error for an LLM run."""
+        if not self._stack or not isinstance(self._stack[-1], LLMRun):
+            raise TracerException("No LLMRun found to be traced")
+
+        self._stack[-1].error = repr(error)
+        self._stack[-1].end_time = datetime.utcnow()
+
+        self._end_trace()
+
+    def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> None:
+        """Start a trace for a chain run."""
+        if self._session is None:
+            raise TracerException(
+                "Initialize a session with `new_session()` before starting a trace."
+            )
+
+        chain_run = ChainRun(
+            serialized=serialized,
+            inputs=inputs,
+            extra=kwargs,
+            start_time=datetime.utcnow(),
+            execution_order=self._execution_order,
+            child_runs=[],
+            session_id=self._session.id,
+            id=self._generate_id(),
+        )
+        self._start_trace(chain_run)
+
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+        """End a trace for a chain run."""
+        if not self._stack or not isinstance(self._stack[-1], ChainRun):
+            raise TracerException("No ChainRun found to be traced")
+
+        self._stack[-1].end_time = datetime.utcnow()
+        self._stack[-1].outputs = outputs
+
+        self._end_trace()
+
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Handle an error for a chain run."""
+        if not self._stack or not isinstance(self._stack[-1], ChainRun):
+            raise TracerException("No ChainRun found to be traced")
+
+        self._stack[-1].end_time = datetime.utcnow()
+        self._stack[-1].error = repr(error)
+
+        self._end_trace()
+
+    def on_tool_start(
+        self, serialized: Dict[str, Any], action: AgentAction, **kwargs: Any
+    ) -> None:
+        """Start a trace for a tool run."""
+        if self._session is None:
+            raise TracerException(
+                "Initialize a session with `new_session()` before starting a trace."
+            )
+
+        tool_run = ToolRun(
+            serialized=serialized,
+            action=action.tool,
+            tool_input=action.tool_input,
+            extra=kwargs,
+            start_time=datetime.utcnow(),
+            execution_order=self._execution_order,
+            child_runs=[],
+            session_id=self._session.id,
+            id=self._generate_id(),
+        )
+        self._start_trace(tool_run)
+
+    def on_tool_end(self, output: str, **kwargs: Any) -> None:
+        """End a trace for a tool run."""
+        if not self._stack or not isinstance(self._stack[-1], ToolRun):
+            raise TracerException("No ToolRun found to be traced")
+
+        self._stack[-1].end_time = datetime.utcnow()
+        self._stack[-1].output = output
+
+        self._end_trace()
+
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Handle an error for a tool run."""
+        if not self._stack or not isinstance(self._stack[-1], ToolRun):
+            raise TracerException("No ToolRun found to be traced")
+
+        self._stack[-1].end_time = datetime.utcnow()
+        self._stack[-1].error = repr(error)
+
+        self._end_trace()
+
+    def on_text(self, text: str, **kwargs: Any) -> None:
+        """Handle a text message."""
+        pass
+
+    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
+        """Handle an agent finish message."""
+        pass
+
+
+class Tracer(BaseTracer, ABC):
+    """A non-thread safe implementation of the BaseTracer interface."""
+
+    def __init__(self) -> None:
+        """Initialize a tracer."""
+        self._tracer_stack: List[Union[LLMRun, ChainRun, ToolRun]] = []
+        self._tracer_execution_order = 1
+        self._tracer_session: Optional[TracerSession] = None
+
+    @property
+    def _stack(self) -> List[Union[LLMRun, ChainRun, ToolRun]]:
+        """Get the tracer stack."""
+        return self._tracer_stack
+
+    @property
+    def _execution_order(self) -> int:
+        """Get the execution order for a run."""
+        return self._tracer_execution_order
+
+    @_execution_order.setter
+    def _execution_order(self, value: int) -> None:
+        """Set the execution order for a run."""
+        self._tracer_execution_order = value
+
+    @property
+    def _session(self) -> Optional[TracerSession]:
+        """Get the tracing session."""
+        return self._tracer_session
+
+    @_session.setter
+    def _session(self, value: TracerSession) -> None:
+        """Set the tracing session."""
+        if self._stack:
+            raise TracerException(
+                "Cannot set a session while a trace is being recorded"
+            )
+        self._tracer_session = value
+
+
+@dataclass
+class TracerStack(threading.local):
+    """A stack of runs used for logging."""
+
+    stack: List[Union[LLMRun, ChainRun, ToolRun]] = field(default_factory=list)
+    execution_order: int = 1
+
+
+class SharedTracer(Singleton, BaseTracer, ABC):
+    """A thread-safe Singleton implementation of BaseTracer."""
+
+    _tracer_stack = TracerStack()
+    _tracer_session = None
+
+    @property
+    def _stack(self) -> List[Union[LLMRun, ChainRun, ToolRun]]:
+        """Get the tracer stack."""
+        return self._tracer_stack.stack
+
+    @property
+    def _execution_order(self) -> int:
+        """Get the execution order for a run."""
+        return self._tracer_stack.execution_order
+
+    @_execution_order.setter
+    def _execution_order(self, value: int) -> None:
+        """Set the execution order for a run."""
+        self._tracer_stack.execution_order = value
+
+    @property
+    def _session(self) -> Optional[TracerSession]:
+        """Get the tracing session."""
+        return self._tracer_session
+
+    @_session.setter
+    def _session(self, value: TracerSession) -> None:
+        """Set the tracing session."""
+        with self._lock:
+            # TODO: currently, we are only checking current thread's stack.
+            #  Need to make sure that we are not in the middle of a trace
+            #  in any thread.
+            if self._stack:
+                raise TracerException(
+                    "Cannot set a session while a trace is being recorded"
+                )
+            self._tracer_session = value

langchain/callbacks/tracers/langchain.py

@@ -0,0 +1,112 @@
+"""A Tracer implementation that records to LangChain endpoint."""
+from __future__ import annotations
+
+import logging
+import os
+from abc import ABC
+from typing import Any, Dict, Optional, Union
+
+import requests
+
+from langchain.callbacks.tracers.base import BaseTracer
+from langchain.callbacks.tracers.schemas import (
+    ChainRun,
+    LLMRun,
+    ToolRun,
+    TracerSession,
+    TracerSessionCreate,
+)
+
+
+class BaseLangChainTracer(BaseTracer, ABC):
+    """An implementation of the SharedTracer that POSTS to the langchain endpoint."""
+
+    always_verbose: bool = True
+    _endpoint: str = os.getenv("LANGCHAIN_ENDPOINT", "http://localhost:8000")
+    _headers: Dict[str, Any] = {"Content-Type": "application/json"}
+    if os.getenv("LANGCHAIN_API_KEY"):
+        _headers["x-api-key"] = os.getenv("LANGCHAIN_API_KEY")
+
+    def _persist_run(self, run: Union[LLMRun, ChainRun, ToolRun]) -> None:
+        """Persist a run."""
+        if isinstance(run, LLMRun):
+            endpoint = f"{self._endpoint}/llm-runs"
+        elif isinstance(run, ChainRun):
+            endpoint = f"{self._endpoint}/chain-runs"
+        else:
+            endpoint = f"{self._endpoint}/tool-runs"
+
+        try:
+            requests.post(
+                endpoint,
+                data=run.json(),
+                headers=self._headers,
+            )
+        except Exception as e:
+            logging.warning(f"Failed to persist run: {e}")
+
+    def _persist_session(self, session_create: TracerSessionCreate) -> TracerSession:
+        """Persist a session."""
+        try:
+            r = requests.post(
+                f"{self._endpoint}/sessions",
+                data=session_create.json(),
+                headers=self._headers,
+            )
+            session = TracerSession(id=r.json()["id"], **session_create.dict())
+        except Exception as e:
+            logging.warning(f"Failed to create session, using default session: {e}")
+            session = TracerSession(id=1, **session_create.dict())
+        return session
+
+    def load_session(self, session_name: str) -> TracerSession:
+        """Load a session from the tracer."""
+        try:
+            r = requests.get(
+                f"{self._endpoint}/sessions?name={session_name}",
+                headers=self._headers,
+            )
+            tracer_session = TracerSession(**r.json()[0])
+            self._session = tracer_session
+            return tracer_session
+        except Exception as e:
+            logging.warning(
+                f"Failed to load session {session_name}, using empty session: {e}"
+            )
+            tracer_session = TracerSession(id=1)
+            self._session = tracer_session
+            return tracer_session
+
+    def load_default_session(self) -> TracerSession:
+        """Load the default tracing session and set it as the Tracer's session."""
+        try:
+            r = requests.get(
+                f"{self._endpoint}/sessions",
+                headers=self._headers,
+            )
+            # Use the first session result
+            tracer_session = TracerSession(**r.json()[0])
+            self._session = tracer_session
+            return tracer_session
+        except Exception as e:
+            logging.warning(f"Failed to default session, using empty session: {e}")
+            tracer_session = TracerSession(id=1)
+            self._session = tracer_session
+            return tracer_session
+
+    def _add_child_run(
+        self,
+        parent_run: Union[ChainRun, ToolRun],
+        child_run: Union[LLMRun, ChainRun, ToolRun],
+    ) -> None:
+        """Add child run to a chain run or tool run."""
+        if isinstance(child_run, LLMRun):
+            parent_run.child_llm_runs.append(child_run)
+        elif isinstance(child_run, ChainRun):
+            parent_run.child_chain_runs.append(child_run)
+        else:
+            parent_run.child_tool_runs.append(child_run)
+
+    def _generate_id(self) -> Optional[Union[int, str]]:
+        """Generate an id for a run."""
+        return None

langchain/callbacks/tracers/schemas.py

@@ -0,0 +1,76 @@
+"""Schemas for tracers."""
+from __future__ import annotations
+
+import datetime
+from typing import Any, Dict, List, Optional, Union
+
+from pydantic import BaseModel, Field
+
+from langchain.schema import LLMResult
+
+
+class TracerSessionBase(BaseModel):
+    """Base class for TracerSession."""
+
+    start_time: datetime.datetime = Field(default_factory=datetime.datetime.utcnow)
+    name: Optional[str] = None
+    extra: Optional[Dict[str, Any]] = None
+
+
+class TracerSessionCreate(TracerSessionBase):
+    """Create class for TracerSession."""
+
+    pass
+
+
+class TracerSession(TracerSessionBase):
+    """TracerSession schema."""
+
+    id: int
+
+
+class BaseRun(BaseModel):
+    """Base class for Run."""
+
+    id: Optional[Union[int, str]] = None
+    start_time: datetime.datetime = Field(default_factory=datetime.datetime.utcnow)
+    end_time: datetime.datetime = Field(default_factory=datetime.datetime.utcnow)
+    extra: Optional[Dict[str, Any]] = None
+    execution_order: int
+    serialized: Dict[str, Any]
+    session_id: int
+    error: Optional[str] = None
+
+
+class LLMRun(BaseRun):
+    """Class for LLMRun."""
+
+    prompts: List[str]
+    response: Optional[LLMResult] = None
+
+
+class ChainRun(BaseRun):
+    """Class for ChainRun."""
+
+    inputs: Dict[str, Any]
+    outputs: Optional[Dict[str, Any]] = None
+    child_llm_runs: List[LLMRun] = Field(default_factory=list)
+    child_chain_runs: List[ChainRun] = Field(default_factory=list)
+    child_tool_runs: List[ToolRun] = Field(default_factory=list)
+    child_runs: List[Union[LLMRun, ChainRun, ToolRun]] = Field(default_factory=list)
+
+
+class ToolRun(BaseRun):
+    """Class for ToolRun."""
+
+    tool_input: str
+    output: Optional[str] = None
+    action: str
+    child_llm_runs: List[LLMRun] = Field(default_factory=list)
+    child_chain_runs: List[ChainRun] = Field(default_factory=list)
+    child_tool_runs: List[ToolRun] = Field(default_factory=list)
+    child_runs: List[Union[LLMRun, ChainRun, ToolRun]] = Field(default_factory=list)
+
+
+ChainRun.update_forward_refs()
+ToolRun.update_forward_refs()

langchain/chains/__init__.py

@@ -1,11 +1,13 @@
 """Chains are easily reusable components which can be linked together."""
 from langchain.chains.api.base import APIChain
 from langchain.chains.conversation.base import ConversationChain
+from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.base import LLMBashChain
 from langchain.chains.llm_checker.base import LLMCheckerChain
 from langchain.chains.llm_math.base import LLMMathChain
 from langchain.chains.llm_requests import LLMRequestsChain
+from langchain.chains.loading import load_chain
 from langchain.chains.mapreduce import MapReduceChain
 from langchain.chains.moderation import OpenAIModerationChain
 from langchain.chains.pal.base import PALChain
@@ -20,7 +22,6 @@ from langchain.chains.transform import TransformChain
 from langchain.chains.vector_db_qa.base import VectorDBQA
 
 __all__ = [
-    "APIChain",
     "ConversationChain",
     "LLMChain",
     "LLMBashChain",
@@ -39,4 +40,6 @@ __all__ = [
     "MapReduceChain",
     "OpenAIModerationChain",
     "SQLDatabaseSequentialChain",
+    "load_chain",
+    "HypotheticalDocumentEmbedder",
 ]

langchain/chains/api/base.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
+from pydantic import BaseModel, Field, root_validator
 
 from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
 from langchain.chains.base import Chain
@@ -18,7 +18,7 @@ class APIChain(Chain, BaseModel):
 
     api_request_chain: LLMChain
     api_answer_chain: LLMChain
-    requests_wrapper: RequestsWrapper
+    requests_wrapper: RequestsWrapper = Field(exclude=True)
     api_docs: str
     question_key: str = "question"  #: :meta private:
     output_key: str = "output"  #: :meta private:
@@ -66,11 +66,13 @@ class APIChain(Chain, BaseModel):
         api_url = self.api_request_chain.predict(
             question=question, api_docs=self.api_docs
         )
-        if self.verbose:
-            self.callback_manager.on_text(api_url, color="green", end="\n")
+        self.callback_manager.on_text(
+            api_url, color="green", end="\n", verbose=self.verbose
+        )
         api_response = self.requests_wrapper.run(api_url)
-        if self.verbose:
-            self.callback_manager.on_text(api_response, color="yellow", end="\n")
+        self.callback_manager.on_text(
+            api_response, color="yellow", end="\n", verbose=self.verbose
+        )
         answer = self.api_answer_chain.predict(
             question=question,
             api_docs=self.api_docs,
@@ -100,3 +102,7 @@ class APIChain(Chain, BaseModel):
             api_docs=api_docs,
             **kwargs,
         )
+
+    @property
+    def _chain_type(self) -> str:
+        return "api_chain"

langchain/chains/base.py

@@ -1,7 +1,10 @@
 """Base interface that all chains should implement."""
+import json
 from abc import ABC, abstractmethod
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
+import yaml
 from pydantic import BaseModel, Extra, Field, validator
 
 import langchain
@@ -44,7 +47,9 @@ class Chain(BaseModel, ABC):
     """Base interface that all chains should implement."""
 
     memory: Optional[Memory] = None
-    callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager)
+    callback_manager: BaseCallbackManager = Field(
+        default_factory=get_callback_manager, exclude=True
+    )
     verbose: bool = Field(
         default_factory=_get_verbosity
     )  # Whether to print the response text
@@ -54,6 +59,10 @@ class Chain(BaseModel, ABC):
 
         arbitrary_types_allowed = True
 
+    @property
+    def _chain_type(self) -> str:
+        raise NotImplementedError("Saving not supported for this chain type.")
+
     @validator("callback_manager", pre=True, always=True)
     def set_callback_manager(
         cls, callback_manager: Optional[BaseCallbackManager]
@@ -134,18 +143,17 @@ class Chain(BaseModel, ABC):
             external_context = self.memory.load_memory_variables(inputs)
             inputs = dict(inputs, **external_context)
         self._validate_inputs(inputs)
-        if self.verbose:
-            self.callback_manager.on_chain_start(
-                {"name": self.__class__.__name__}, inputs
-            )
+        self.callback_manager.on_chain_start(
+            {"name": self.__class__.__name__},
+            inputs,
+            verbose=self.verbose,
+        )
         try:
             outputs = self._call(inputs)
-        except Exception as e:
-            if self.verbose:
-                self.callback_manager.on_chain_error(e)
+        except (KeyboardInterrupt, Exception) as e:
+            self.callback_manager.on_chain_error(e, verbose=self.verbose)
             raise e
-        if self.verbose:
-            self.callback_manager.on_chain_end(outputs)
+        self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
         self._validate_outputs(outputs)
         if self.memory is not None:
             self.memory.save_context(inputs, outputs)
@@ -178,3 +186,43 @@ class Chain(BaseModel, ABC):
             f"`run` supported with either positional arguments or keyword arguments"
             f" but not both. Got args: {args} and kwargs: {kwargs}."
         )
+
+    def dict(self, **kwargs: Any) -> Dict:
+        """Return dictionary representation of chain."""
+        if self.memory is not None:
+            raise ValueError("Saving of memory is not yet supported.")
+        _dict = super().dict()
+        _dict["_type"] = self._chain_type
+        return _dict
+
+    def save(self, file_path: Union[Path, str]) -> None:
+        """Save the chain.
+
+        Args:
+            file_path: Path to file to save the chain to.
+
+        Example:
+        .. code-block:: python
+
+            chain.save(file_path="path/chain.yaml")
+        """
+        # Convert file to Path object.
+        if isinstance(file_path, str):
+            save_path = Path(file_path)
+        else:
+            save_path = file_path
+
+        directory_path = save_path.parent
+        directory_path.mkdir(parents=True, exist_ok=True)
+
+        # Fetch dictionary to save
+        chain_dict = self.dict()
+
+        if save_path.suffix == ".json":
+            with open(file_path, "w") as f:
+                json.dump(chain_dict, f, indent=4)
+        elif save_path.suffix == ".yaml":
+            with open(file_path, "w") as f:
+                yaml.dump(chain_dict, f, default_flow_style=False)
+        else:
+            raise ValueError(f"{save_path} must be json or yaml")

langchain/chains/combine_documents/map_reduce.py

@@ -168,3 +168,7 @@ class MapReduceDocumentsChain(BaseCombineDocumentsChain, BaseModel):
             extra_return_dict = {}
         output, _ = self.combine_document_chain.combine_docs(result_docs, **kwargs)
         return output, extra_return_dict
+
+    @property
+    def _chain_type(self) -> str:
+        return "map_reduce_documents_chain"

langchain/chains/combine_documents/map_rerank.py

@@ -111,3 +111,7 @@ class MapRerankDocumentsChain(BaseCombineDocumentsChain, BaseModel):
         if self.return_intermediate_steps:
             extra_info["intermediate_steps"] = results
         return output[self.answer_key], extra_info
+
+    @property
+    def _chain_type(self) -> str:
+        return "map_rerank_documents_chain"

langchain/chains/combine_documents/refine.py

@@ -113,3 +113,7 @@ class RefineDocumentsChain(BaseCombineDocumentsChain, BaseModel):
         else:
             extra_return_dict = {}
         return res, extra_return_dict
+
+    @property
+    def _chain_type(self) -> str:
+        return "refine_documents_chain"

langchain/chains/combine_documents/stuff.py

@@ -83,3 +83,7 @@ class StuffDocumentsChain(BaseCombineDocumentsChain, BaseModel):
         inputs = self._get_inputs(docs, **kwargs)
         # Call predict on the LLM.
         return self.llm_chain.predict(**inputs), {}
+
+    @property
+    def _chain_type(self) -> str:
+        return "stuff_documents_chain"

langchain/chains/conversation/memory.py

@@ -4,7 +4,11 @@ from typing import Any, Dict, List, Optional
 from pydantic import BaseModel, Field, root_validator
 
 from langchain.chains.base import Memory
-from langchain.chains.conversation.prompt import SUMMARY_PROMPT
+from langchain.chains.conversation.prompt import (
+    ENTITY_EXTRACTION_PROMPT,
+    ENTITY_SUMMARIZATION_PROMPT,
+    SUMMARY_PROMPT,
+)
 from langchain.chains.llm import LLMChain
 from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
@@ -216,6 +220,89 @@ class ConversationSummaryMemory(Memory, BaseModel):
         self.buffer = ""
 
 
+class ConversationEntityMemory(Memory, BaseModel):
+    """Entity extractor & summarizer to memory."""
+
+    buffer: List[str] = []
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    """Prefix to use for AI generated responses."""
+    llm: BaseLLM
+    entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
+    entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+    store: Dict[str, Optional[str]] = {}
+    entity_cache: List[str] = []
+    k: int = 3
+    chat_history_key: str = "history"
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+
+        :meta private:
+        """
+        return ["entities", self.chat_history_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        """Return history buffer."""
+        chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
+        if self.input_key is None:
+            prompt_input_key = _get_prompt_input_key(inputs, self.memory_variables)
+        else:
+            prompt_input_key = self.input_key
+        output = chain.predict(
+            history="\n".join(self.buffer[-self.k :]),
+            input=inputs[prompt_input_key],
+        )
+        if output.strip() == "NONE":
+            entities = []
+        else:
+            entities = [w.strip() for w in output.split(",")]
+        entity_summaries = {}
+        for entity in entities:
+            entity_summaries[entity] = self.store.get(entity, "")
+        self.entity_cache = entities
+        return {
+            self.chat_history_key: "\n".join(self.buffer[-self.k :]),
+            "entities": entity_summaries,
+        }
+
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """Save context from this conversation to buffer."""
+        if self.input_key is None:
+            prompt_input_key = _get_prompt_input_key(inputs, self.memory_variables)
+        else:
+            prompt_input_key = self.input_key
+        if self.output_key is None:
+            if len(outputs) != 1:
+                raise ValueError(f"One output key expected, got {outputs.keys()}")
+            output_key = list(outputs.keys())[0]
+        else:
+            output_key = self.output_key
+        human = f"{self.human_prefix}: " + inputs[prompt_input_key]
+        ai = f"{self.ai_prefix}: " + outputs[output_key]
+        for entity in self.entity_cache:
+            chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt)
+            # key value store for entity
+            existing_summary = self.store.get(entity, "")
+            output = chain.predict(
+                summary=existing_summary,
+                history="\n".join(self.buffer[-self.k :]),
+                input=inputs[prompt_input_key],
+                entity=entity,
+            )
+            self.store[entity] = output.strip()
+        new_lines = "\n".join([human, ai])
+        self.buffer.append(new_lines)
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        self.buffer = []
+        self.store = {}
+
+
 class ConversationSummaryBufferMemory(Memory, BaseModel):
     """Buffer with summarizer for storing conversation memory."""
 

langchain/chains/conversation/prompt.py

@@ -11,6 +11,28 @@ PROMPT = PromptTemplate(
     input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
 )
 
+_DEFAULT_ENTITY_MEMORY_CONVERSATION_TEMPLATE = """You are an assistant to a human, powered by a large language model trained by OpenAI.
+
+You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
+
+You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.
+
+Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.
+
+Context:
+{entities}
+
+Current conversation:
+{history}
+Last line:
+Human: {input}
+You:"""
+
+ENTITY_MEMORY_CONVERSATION_TEMPLATE = PromptTemplate(
+    input_variables=["entities", "history", "input"],
+    template=_DEFAULT_ENTITY_MEMORY_CONVERSATION_TEMPLATE,
+)
+
 _DEFAULT_SUMMARIZER_TEMPLATE = """Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.
 
 EXAMPLE
@@ -35,3 +57,64 @@ New summary:"""
 SUMMARY_PROMPT = PromptTemplate(
     input_variables=["summary", "new_lines"], template=_DEFAULT_SUMMARIZER_TEMPLATE
 )
+
+_DEFAULT_ENTITY_EXTRACTION_TEMPLATE = """You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.
+
+The conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.
+
+Return the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).
+
+EXAMPLE
+Conversation history:
+Person #1: how's it going today?
+AI: "It's going great! How about you?"
+Person #1: good! busy working on Langchain. lots to do.
+AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
+Last line:
+Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.
+Output: Langchain
+END OF EXAMPLE
+
+EXAMPLE
+Conversation history:
+Person #1: how's it going today?
+AI: "It's going great! How about you?"
+Person #1: good! busy working on Langchain. lots to do.
+AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
+Last line:
+Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Person #2.
+Output: Langchain, Person #2
+END OF EXAMPLE
+
+Conversation history (for reference only):
+{history}
+Last line of conversation (for extraction):
+Human: {input}
+
+Output:"""
+ENTITY_EXTRACTION_PROMPT = PromptTemplate(
+    input_variables=["history", "input"], template=_DEFAULT_ENTITY_EXTRACTION_TEMPLATE
+)
+
+_DEFAULT_ENTITY_SUMMARIZATION_TEMPLATE = """You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.
+The update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.
+
+If there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.
+
+Full conversation history (for context):
+{history}
+
+Entity to summarize:
+{entity}
+
+Existing summary of {entity}:
+{summary}
+
+Last line of conversation:
+Human: {input}
+Updated summary:"""
+
+ENTITY_SUMMARIZATION_PROMPT = PromptTemplate(
+    input_variables=["entity", "summary", "history", "input"],
+    template=_DEFAULT_ENTITY_SUMMARIZATION_TEMPLATE,
+)

langchain/chains/hyde/base.py

@@ -4,18 +4,19 @@ https://arxiv.org/abs/2212.10496
 """
 from __future__ import annotations
 
-from typing import List
+from typing import Dict, List
 
 import numpy as np
 from pydantic import BaseModel, Extra
 
+from langchain.chains.base import Chain
+from langchain.chains.hyde.prompts import PROMPT_MAP
 from langchain.chains.llm import LLMChain
 from langchain.embeddings.base import Embeddings
-from langchain.embeddings.hyde.prompts import PROMPT_MAP
 from langchain.llms.base import BaseLLM
 
 
-class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
+class HypotheticalDocumentEmbedder(Chain, Embeddings, BaseModel):
     """Generate hypothetical document for query, and then embed that.
 
     Based on https://arxiv.org/abs/2212.10496
@@ -30,10 +31,24 @@ class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
+    @property
+    def input_keys(self) -> List[str]:
+        """Input keys for Hyde's LLM chain."""
+        return self.llm_chain.input_keys
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Output keys for Hyde's LLM chain."""
+        return self.llm_chain.output_keys
+
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Call the base embeddings."""
         return self.base_embeddings.embed_documents(texts)
 
+    def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
+        """Combine embeddings into final embeddings."""
+        return list(np.array(embeddings).mean(axis=0))
+
     def embed_query(self, text: str) -> List[float]:
         """Generate a hypothetical document and embedded it."""
         var_name = self.llm_chain.input_keys[0]
@@ -42,9 +57,9 @@ class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
         embeddings = self.embed_documents(documents)
         return self.combine_embeddings(embeddings)
 
-    def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
-        """Combine embeddings into final embeddings."""
-        return list(np.array(embeddings).mean(axis=0))
+    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+        """Call the internal llm chain."""
+        return self.llm_chain._call(inputs)
 
     @classmethod
     def from_llm(
@@ -54,3 +69,7 @@ class HypotheticalDocumentEmbedder(Embeddings, BaseModel):
         prompt = PROMPT_MAP[prompt_key]
         llm_chain = LLMChain(llm=llm, prompt=prompt)
         return cls(base_embeddings=base_embeddings, llm_chain=llm_chain)
+
+    @property
+    def _chain_type(self) -> str:
+        return "hyde_chain"

langchain/chains/llm.py

@@ -1,4 +1,5 @@
 """Chain that just formats a prompt and calls an LLM."""
+from string import Formatter
 from typing import Any, Dict, List, Sequence, Union
 
 from pydantic import BaseModel, Extra
@@ -7,6 +8,7 @@ from langchain.chains.base import Chain
 from langchain.input import get_colored_text
 from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
+from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import LLMResult
 
 
@@ -61,10 +63,9 @@ class LLMChain(Chain, BaseModel):
         for inputs in input_list:
             selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
             prompt = self.prompt.format(**selected_inputs)
-            if self.verbose:
-                _colored_text = get_colored_text(prompt, "green")
-                _text = "Prompt after formatting:\n" + _colored_text
-                self.callback_manager.on_text(_text, end="\n")
+            _colored_text = get_colored_text(prompt, "green")
+            _text = "Prompt after formatting:\n" + _colored_text
+            self.callback_manager.on_text(_text, end="\n", verbose=self.verbose)
             if "stop" in inputs and inputs["stop"] != stop:
                 raise ValueError(
                     "If `stop` is present in any inputs, should be present in all."
@@ -123,3 +124,18 @@ class LLMChain(Chain, BaseModel):
             return new_result
         else:
             return result
+
+    @property
+    def _chain_type(self) -> str:
+        return "llm_chain"
+
+    @classmethod
+    def from_string(cls, llm: BaseLLM, template: str) -> Chain:
+        """Create LLMChain from LLM and template."""
+        input_variables = {
+            v for _, v, _, _ in Formatter().parse(template) if v is not None
+        }
+        prompt_template = PromptTemplate(
+            input_variables=list(input_variables), template=template
+        )
+        return cls(llm=llm, prompt=prompt_template)

langchain/chains/llm_bash/__init__.py

@@ -0,0 +1 @@
+"""Chain that interprets a prompt and executes bash code to perform bash operations."""

langchain/chains/llm_bash/base.py

@@ -52,12 +52,10 @@ class LLMBashChain(Chain, BaseModel):
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         llm_executor = LLMChain(prompt=self.prompt, llm=self.llm)
         bash_executor = BashProcess()
-        if self.verbose:
-            self.callback_manager.on_text(inputs[self.input_key])
+        self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
 
         t = llm_executor.predict(question=inputs[self.input_key])
-        if self.verbose:
-            self.callback_manager.on_text(t, color="green")
+        self.callback_manager.on_text(t, color="green", verbose=self.verbose)
 
         t = t.strip()
         if t.startswith("```bash"):
@@ -69,10 +67,13 @@ class LLMBashChain(Chain, BaseModel):
             command_list = [s for s in command_list[1:-1]]
             output = bash_executor.run(command_list)
 
-            if self.verbose:
-                self.callback_manager.on_text("\nAnswer: ")
-                self.callback_manager.on_text(output, color="yellow")
+            self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
+            self.callback_manager.on_text(output, color="yellow", verbose=self.verbose)
 
         else:
             raise ValueError(f"unknown format from LLM: {t}")
         return {self.output_key: output}
+
+    @property
+    def _chain_type(self) -> str:
+        return "llm_bash_chain"

langchain/chains/llm_checker/base.py

@@ -97,3 +97,7 @@ class LLMCheckerChain(Chain, BaseModel):
         )
         output = question_to_checked_assertions_chain({"question": question})
         return {self.output_key: output["revised_statement"]}
+
+    @property
+    def _chain_type(self) -> str:
+        return "llm_checker_chain"

langchain/chains/llm_math/base.py

@@ -53,21 +53,22 @@ class LLMMathChain(Chain, BaseModel):
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         llm_executor = LLMChain(prompt=self.prompt, llm=self.llm)
         python_executor = PythonREPL()
-        if self.verbose:
-            self.callback_manager.on_text(inputs[self.input_key])
+        self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
         t = llm_executor.predict(question=inputs[self.input_key], stop=["```output"])
-        if self.verbose:
-            self.callback_manager.on_text(t, color="green")
+        self.callback_manager.on_text(t, color="green", verbose=self.verbose)
         t = t.strip()
         if t.startswith("```python"):
             code = t[9:-4]
             output = python_executor.run(code)
-            if self.verbose:
-                self.callback_manager.on_text("\nAnswer: ")
-                self.callback_manager.on_text(output, color="yellow")
+            self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
+            self.callback_manager.on_text(output, color="yellow", verbose=self.verbose)
             answer = "Answer: " + output
         elif t.startswith("Answer:"):
             answer = t
         else:
             raise ValueError(f"unknown format from LLM: {t}")
         return {self.output_key: answer}
+
+    @property
+    def _chain_type(self) -> str:
+        return "llm_math_chain"

langchain/chains/llm_requests.py

@@ -18,7 +18,9 @@ class LLMRequestsChain(Chain, BaseModel):
     """Chain that hits a URL and then uses an LLM to parse results."""
 
     llm_chain: LLMChain
-    requests_wrapper: RequestsWrapper = Field(default_factory=RequestsWrapper)
+    requests_wrapper: RequestsWrapper = Field(
+        default_factory=RequestsWrapper, exclude=True
+    )
     text_length: int = 8000
     requests_key: str = "requests_result"  #: :meta private:
     input_key: str = "url"  #: :meta private:
@@ -71,3 +73,7 @@ class LLMRequestsChain(Chain, BaseModel):
         other_keys[self.requests_key] = soup.get_text()[: self.text_length]
         result = self.llm_chain.predict(**other_keys)
         return {self.output_key: result}
+
+    @property
+    def _chain_type(self) -> str:
+        return "llm_requests_chain"

langchain/chains/loading.py

@@ -0,0 +1,467 @@
+"""Functionality for loading chains."""
+import json
+from pathlib import Path
+from typing import Any, Union
+
+import yaml
+
+from langchain.chains.api.base import APIChain
+from langchain.chains.base import Chain
+from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
+from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
+from langchain.chains.combine_documents.refine import RefineDocumentsChain
+from langchain.chains.combine_documents.stuff import StuffDocumentsChain
+from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
+from langchain.chains.llm import LLMChain
+from langchain.chains.llm_bash.base import LLMBashChain
+from langchain.chains.llm_checker.base import LLMCheckerChain
+from langchain.chains.llm_math.base import LLMMathChain
+from langchain.chains.llm_requests import LLMRequestsChain
+from langchain.chains.pal.base import PALChain
+from langchain.chains.qa_with_sources.base import QAWithSourcesChain
+from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain
+from langchain.chains.sql_database.base import SQLDatabaseChain
+from langchain.chains.vector_db_qa.base import VectorDBQA
+from langchain.llms.loading import load_llm, load_llm_from_config
+from langchain.prompts.loading import load_prompt, load_prompt_from_config
+from langchain.utilities.loading import try_load_from_hub
+
+URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/chains/"
+
+
+def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain:
+    """Load LLM chain from config dict."""
+    if "llm" in config:
+        llm_config = config.pop("llm")
+        llm = load_llm_from_config(llm_config)
+    elif "llm_path" in config:
+        llm = load_llm(config.pop("llm_path"))
+    else:
+        raise ValueError("One of `llm` or `llm_path` must be present.")
+
+    if "prompt" in config:
+        prompt_config = config.pop("prompt")
+        prompt = load_prompt_from_config(prompt_config)
+    elif "prompt_path" in config:
+        prompt = load_prompt(config.pop("prompt_path"))
+    else:
+        raise ValueError("One of `prompt` or `prompt_path` must be present.")
+
+    return LLMChain(llm=llm, prompt=prompt, **config)
+
+
+def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedder:
+    """Load hypothetical document embedder chain from config dict."""
+    if "llm_chain" in config:
+        llm_chain_config = config.pop("llm_chain")
+        llm_chain = load_chain_from_config(llm_chain_config)
+    elif "llm_chain_path" in config:
+        llm_chain = load_chain(config.pop("llm_chain_path"))
+    else:
+        raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
+    if "embeddings" in kwargs:
+        embeddings = kwargs.pop("embeddings")
+    else:
+        raise ValueError("`embeddings` must be present.")
+    return HypotheticalDocumentEmbedder(
+        llm_chain=llm_chain, base_embeddings=embeddings, **config
+    )
+
+
+def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain:
+    if "llm_chain" in config:
+        llm_chain_config = config.pop("llm_chain")
+        llm_chain = load_chain_from_config(llm_chain_config)
+    elif "llm_chain_path" in config:
+        llm_chain = load_chain(config.pop("llm_chain_path"))
+    else:
+        raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
+
+    if not isinstance(llm_chain, LLMChain):
+        raise ValueError(f"Expected LLMChain, got {llm_chain}")
+
+    if "document_prompt" in config:
+        prompt_config = config.pop("document_prompt")
+        document_prompt = load_prompt_from_config(prompt_config)
+    elif "document_prompt_path" in config:
+        document_prompt = load_prompt(config.pop("document_prompt_path"))
+    else:
+        raise ValueError(
+            "One of `document_prompt` or `document_prompt_path` must be present."
+        )
+
+    return StuffDocumentsChain(
+        llm_chain=llm_chain, document_prompt=document_prompt, **config
+    )
+
+
+def _load_map_reduce_documents_chain(
+    config: dict, **kwargs: Any
+) -> MapReduceDocumentsChain:
+    if "llm_chain" in config:
+        llm_chain_config = config.pop("llm_chain")
+        llm_chain = load_chain_from_config(llm_chain_config)
+    elif "llm_chain_path" in config:
+        llm_chain = load_chain(config.pop("llm_chain_path"))
+    else:
+        raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
+
+    if not isinstance(llm_chain, LLMChain):
+        raise ValueError(f"Expected LLMChain, got {llm_chain}")
+
+    if "combine_document_chain" in config:
+        combine_document_chain_config = config.pop("combine_document_chain")
+        combine_document_chain = load_chain_from_config(combine_document_chain_config)
+    elif "combine_document_chain_path" in config:
+        combine_document_chain = load_chain(config.pop("combine_document_chain_path"))
+    else:
+        raise ValueError(
+            "One of `combine_document_chain` or "
+            "`combine_document_chain_path` must be present."
+        )
+    if "collapse_document_chain" in config:
+        collapse_document_chain_config = config.pop("collapse_document_chain")
+        if collapse_document_chain_config is None:
+            collapse_document_chain = None
+        else:
+            collapse_document_chain = load_chain_from_config(
+                collapse_document_chain_config
+            )
+    elif "collapse_document_chain_path" in config:
+        collapse_document_chain = load_chain(config.pop("collapse_document_chain_path"))
+    return MapReduceDocumentsChain(
+        llm_chain=llm_chain,
+        combine_document_chain=combine_document_chain,
+        collapse_document_chain=collapse_document_chain,
+        **config,
+    )
+
+
+def _load_llm_bash_chain(config: dict, **kwargs: Any) -> LLMBashChain:
+    if "llm" in config:
+        llm_config = config.pop("llm")
+        llm = load_llm_from_config(llm_config)
+    elif "llm_path" in config:
+        llm = load_llm(config.pop("llm_path"))
+    else:
+        raise ValueError("One of `llm` or `llm_path` must be present.")
+    if "prompt" in config:
+        prompt_config = config.pop("prompt")
+        prompt = load_prompt_from_config(prompt_config)
+    elif "prompt_path" in config:
+        prompt = load_prompt(config.pop("prompt_path"))
+    return LLMBashChain(llm=llm, prompt=prompt, **config)
+
+
+def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain:
+    if "llm" in config:
+        llm_config = config.pop("llm")
+        llm = load_llm_from_config(llm_config)
+    elif "llm_path" in config:
+        llm = load_llm(config.pop("llm_path"))
+    else:
+        raise ValueError("One of `llm` or `llm_path` must be present.")
+    if "create_draft_answer_prompt" in config:
+        create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt")
+        create_draft_answer_prompt = load_prompt_from_config(
+            create_draft_answer_prompt_config
+        )
+    elif "create_draft_answer_prompt_path" in config:
+        create_draft_answer_prompt = load_prompt(
+            config.pop("create_draft_answer_prompt_path")
+        )
+    if "list_assertions_prompt" in config:
+        list_assertions_prompt_config = config.pop("list_assertions_prompt")
+        list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config)
+    elif "list_assertions_prompt_path" in config:
+        list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path"))
+    if "check_assertions_prompt" in config:
+        check_assertions_prompt_config = config.pop("check_assertions_prompt")
+        check_assertions_prompt = load_prompt_from_config(
+            check_assertions_prompt_config
+        )
+    elif "check_assertions_prompt_path" in config:
+        check_assertions_prompt = load_prompt(
+            config.pop("check_assertions_prompt_path")
+        )
+    if "revised_answer_prompt" in config:
+        revised_answer_prompt_config = config.pop("revised_answer_prompt")
+        revised_answer_prompt = load_prompt_from_config(revised_answer_prompt_config)
+    elif "revised_answer_prompt_path" in config:
+        revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path"))
+    return LLMCheckerChain(
+        llm=llm,
+        create_draft_answer_prompt=create_draft_answer_prompt,
+        list_assertions_prompt=list_assertions_prompt,
+        check_assertions_prompt=check_assertions_prompt,
+        revised_answer_prompt=revised_answer_prompt,
+        **config,
+    )
+
+
+def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain:
+    if "llm" in config:
+        llm_config = config.pop("llm")
+        llm = load_llm_from_config(llm_config)
+    elif "llm_path" in config:
+        llm = load_llm(config.pop("llm_path"))
+    else:
+        raise ValueError("One of `llm` or `llm_path` must be present.")
+    if "prompt" in config:
+        prompt_config = config.pop("prompt")
+        prompt = load_prompt_from_config(prompt_config)
+    elif "prompt_path" in config:
+        prompt = load_prompt(config.pop("prompt_path"))
+    return LLMMathChain(llm=llm, prompt=prompt, **config)
+
+
+def _load_map_rerank_documents_chain(
+    config: dict, **kwargs: Any
+) -> MapRerankDocumentsChain:
+    if "llm_chain" in config:
+        llm_chain_config = config.pop("llm_chain")
+        llm_chain = load_chain_from_config(llm_chain_config)
+    elif "llm_chain_path" in config:
+        llm_chain = load_chain(config.pop("llm_chain_path"))
+    else:
+        raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
+    return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
+
+
+def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain:
+    if "llm" in config:
+        llm_config = config.pop("llm")
+        llm = load_llm_from_config(llm_config)
+    elif "llm_path" in config:
+        llm = load_llm(config.pop("llm_path"))
+    else:
+        raise ValueError("One of `llm` or `llm_path` must be present.")
+    if "prompt" in config:
+        prompt_config = config.pop("prompt")
+        prompt = load_prompt_from_config(prompt_config)
+    elif "prompt_path" in config:
+        prompt = load_prompt(config.pop("prompt_path"))
+    else:
+        raise ValueError("One of `prompt` or `prompt_path` must be present.")
+    return PALChain(llm=llm, prompt=prompt, **config)
+
+
+def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
+    if "initial_llm_chain" in config:
+        initial_llm_chain_config = config.pop("initial_llm_chain")
+        initial_llm_chain = load_chain_from_config(initial_llm_chain_config)
+    elif "initial_llm_chain_path" in config:
+        initial_llm_chain = load_chain(config.pop("initial_llm_chain_path"))
+    else:
+        raise ValueError(
+            "One of `initial_llm_chain` or `initial_llm_chain_config` must be present."
+        )
+    if "refine_llm_chain" in config:
+        refine_llm_chain_config = config.pop("refine_llm_chain")
+        refine_llm_chain = load_chain_from_config(refine_llm_chain_config)
+    elif "refine_llm_chain_path" in config:
+        refine_llm_chain = load_chain(config.pop("refine_llm_chain_path"))
+    else:
+        raise ValueError(
+            "One of `refine_llm_chain` or `refine_llm_chain_config` must be present."
+        )
+    if "document_prompt" in config:
+        prompt_config = config.pop("document_prompt")
+        document_prompt = load_prompt_from_config(prompt_config)
+    elif "document_prompt_path" in config:
+        document_prompt = load_prompt(config.pop("document_prompt_path"))
+    return RefineDocumentsChain(
+        initial_llm_chain=initial_llm_chain,
+        refine_llm_chain=refine_llm_chain,
+        document_prompt=document_prompt,
+        **config,
+    )
+
+
+def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain:
+    if "combine_documents_chain" in config:
+        combine_documents_chain_config = config.pop("combine_documents_chain")
+        combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
+    elif "combine_documents_chain_path" in config:
+        combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
+    else:
+        raise ValueError(
+            "One of `combine_documents_chain` or "
+            "`combine_documents_chain_path` must be present."
+        )
+    return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config)
+
+
+def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain:
+    if "database" in kwargs:
+        database = kwargs.pop("database")
+    else:
+        raise ValueError("`database` must be present.")
+    if "llm" in config:
+        llm_config = config.pop("llm")
+        llm = load_llm_from_config(llm_config)
+    elif "llm_path" in config:
+        llm = load_llm(config.pop("llm_path"))
+    else:
+        raise ValueError("One of `llm` or `llm_path` must be present.")
+    if "prompt" in config:
+        prompt_config = config.pop("prompt")
+        prompt = load_prompt_from_config(prompt_config)
+    return SQLDatabaseChain(database=database, llm=llm, prompt=prompt, **config)
+
+
+def _load_vector_db_qa_with_sources_chain(
+    config: dict, **kwargs: Any
+) -> VectorDBQAWithSourcesChain:
+    if "vectorstore" in kwargs:
+        vectorstore = kwargs.pop("vectorstore")
+    else:
+        raise ValueError("`vectorstore` must be present.")
+    if "combine_documents_chain" in config:
+        combine_documents_chain_config = config.pop("combine_documents_chain")
+        combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
+    elif "combine_documents_chain_path" in config:
+        combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
+    else:
+        raise ValueError(
+            "One of `combine_documents_chain` or "
+            "`combine_documents_chain_path` must be present."
+        )
+    return VectorDBQAWithSourcesChain(
+        combine_documents_chain=combine_documents_chain,
+        vectorstore=vectorstore,
+        **config,
+    )
+
+
+def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
+    if "vectorstore" in kwargs:
+        vectorstore = kwargs.pop("vectorstore")
+    else:
+        raise ValueError("`vectorstore` must be present.")
+    if "combine_documents_chain" in config:
+        combine_documents_chain_config = config.pop("combine_documents_chain")
+        combine_documents_chain = load_chain_from_config(combine_documents_chain_config)
+    elif "combine_documents_chain_path" in config:
+        combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
+    else:
+        raise ValueError(
+            "One of `combine_documents_chain` or "
+            "`combine_documents_chain_path` must be present."
+        )
+    return VectorDBQA(
+        combine_documents_chain=combine_documents_chain,
+        vectorstore=vectorstore,
+        **config,
+    )
+
+
+def _load_api_chain(config: dict, **kwargs: Any) -> APIChain:
+    if "api_request_chain" in config:
+        api_request_chain_config = config.pop("api_request_chain")
+        api_request_chain = load_chain_from_config(api_request_chain_config)
+    elif "api_request_chain_path" in config:
+        api_request_chain = load_chain(config.pop("api_request_chain_path"))
+    else:
+        raise ValueError(
+            "One of `api_request_chain` or `api_request_chain_path` must be present."
+        )
+    if "api_answer_chain" in config:
+        api_answer_chain_config = config.pop("api_answer_chain")
+        api_answer_chain = load_chain_from_config(api_answer_chain_config)
+    elif "api_answer_chain_path" in config:
+        api_answer_chain = load_chain(config.pop("api_answer_chain_path"))
+    else:
+        raise ValueError(
+            "One of `api_answer_chain` or `api_answer_chain_path` must be present."
+        )
+    if "requests_wrapper" in kwargs:
+        requests_wrapper = kwargs.pop("requests_wrapper")
+    else:
+        raise ValueError("`requests_wrapper` must be present.")
+    return APIChain(
+        api_request_chain=api_request_chain,
+        api_answer_chain=api_answer_chain,
+        requests_wrapper=requests_wrapper,
+        **config,
+    )
+
+
+def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
+    if "llm_chain" in config:
+        llm_chain_config = config.pop("llm_chain")
+        llm_chain = load_chain_from_config(llm_chain_config)
+    elif "llm_chain_path" in config:
+        llm_chain = load_chain(config.pop("llm_chain_path"))
+    else:
+        raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
+    if "requests_wrapper" in kwargs:
+        requests_wrapper = kwargs.pop("requests_wrapper")
+        return LLMRequestsChain(
+            llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config
+        )
+    else:
+        return LLMRequestsChain(llm_chain=llm_chain, **config)
+
+
+type_to_loader_dict = {
+    "api_chain": _load_api_chain,
+    "hyde_chain": _load_hyde_chain,
+    "llm_chain": _load_llm_chain,
+    "llm_bash_chain": _load_llm_bash_chain,
+    "llm_checker_chain": _load_llm_checker_chain,
+    "llm_math_chain": _load_llm_math_chain,
+    "llm_requests_chain": _load_llm_requests_chain,
+    "pal_chain": _load_pal_chain,
+    "qa_with_sources_chain": _load_qa_with_sources_chain,
+    "stuff_documents_chain": _load_stuff_documents_chain,
+    "map_reduce_documents_chain": _load_map_reduce_documents_chain,
+    "map_rerank_documents_chain": _load_map_rerank_documents_chain,
+    "refine_documents_chain": _load_refine_documents_chain,
+    "sql_database_chain": _load_sql_database_chain,
+    "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
+    "vector_db_qa": _load_vector_db_qa,
+}
+
+
+def load_chain_from_config(config: dict, **kwargs: Any) -> Chain:
+    """Load chain from Config Dict."""
+    if "_type" not in config:
+        raise ValueError("Must specify a chain Type in config")
+    config_type = config.pop("_type")
+
+    if config_type not in type_to_loader_dict:
+        raise ValueError(f"Loading {config_type} chain not supported")
+
+    chain_loader = type_to_loader_dict[config_type]
+    return chain_loader(config, **kwargs)
+
+
+def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain:
+    """Unified method for loading a chain from LangChainHub or local fs."""
+    if hub_result := try_load_from_hub(
+        path, _load_chain_from_file, "chains", {"json", "yaml"}
+    ):
+        return hub_result
+    else:
+        return _load_chain_from_file(path, **kwargs)
+
+
+def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain:
+    """Load chain from file."""
+    # Convert file to Path object.
+    if isinstance(file, str):
+        file_path = Path(file)
+    else:
+        file_path = file
+    # Load from either json or yaml.
+    if file_path.suffix == ".json":
+        with open(file_path) as f:
+            config = json.load(f)
+    elif file_path.suffix == ".yaml":
+        with open(file_path, "r") as f:
+            config = yaml.safe_load(f)
+    else:
+        raise ValueError("File type must be json or yaml")
+    # Load the chain from the config now.
+    return load_chain_from_config(config, **kwargs)

langchain/chains/natbot/base.py

@@ -94,3 +94,7 @@ class NatBotChain(Chain, BaseModel):
             self.input_browser_content_key: browser_content,
         }
         return self(_inputs)[self.output_key]
+
+    @property
+    def _chain_type(self) -> str:
+        return "nat_bot_chain"

langchain/chains/natbot/crawler.py

@@ -1,9 +1,23 @@
 # flake8: noqa
-# type: ignore
 import time
 from sys import platform
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    TypedDict,
+    Union,
+)
 
-black_listed_elements = {
+if TYPE_CHECKING:
+    from playwright.sync_api import Browser, CDPSession, Page, sync_playwright
+
+black_listed_elements: Set[str] = {
     "html",
     "head",
     "title",
@@ -19,8 +33,21 @@ black_listed_elements = {
 }
 
 
+class ElementInViewPort(TypedDict):
+    node_index: str
+    backend_node_id: int
+    node_name: Optional[str]
+    node_value: Optional[str]
+    node_meta: List[str]
+    is_clickable: bool
+    origin_x: int
+    origin_y: int
+    center_x: int
+    center_y: int
+
+
 class Crawler:
-    def __init__(self):
+    def __init__(self) -> None:
         try:
             from playwright.sync_api import sync_playwright
         except ImportError:
@@ -28,16 +55,20 @@ class Crawler:
                 "Could not import playwright python package. "
                 "Please it install it with `pip install playwright`."
             )
-        self.browser = sync_playwright().start().chromium.launch(headless=False)
-        self.page = self.browser.new_page()
+        self.browser: Browser = (
+            sync_playwright().start().chromium.launch(headless=False)
+        )
+        self.page: Page = self.browser.new_page()
         self.page.set_viewport_size({"width": 1280, "height": 1080})
+        self.page_element_buffer: Dict[int, ElementInViewPort]
+        self.client: CDPSession
 
-    def go_to_page(self, url):
+    def go_to_page(self, url: str) -> None:
         self.page.goto(url=url if "://" in url else "http://" + url)
         self.client = self.page.context.new_cdp_session(self.page)
         self.page_element_buffer = {}
 
-    def scroll(self, direction):
+    def scroll(self, direction: str) -> None:
         if direction == "up":
             self.page.evaluate(
                 "(document.scrollingElement || document.body).scrollTop = (document.scrollingElement || document.body).scrollTop - window.innerHeight;"
@@ -47,7 +78,7 @@ class Crawler:
                 "(document.scrollingElement || document.body).scrollTop = (document.scrollingElement || document.body).scrollTop + window.innerHeight;"
             )
 
-    def click(self, id):
+    def click(self, id: Union[str, int]) -> None:
         # Inject javascript into the page which removes the target= attribute from all links
         js = """
 		links = document.getElementsByTagName("a");
@@ -59,41 +90,37 @@ class Crawler:
 
         element = self.page_element_buffer.get(int(id))
         if element:
-            x = element.get("center_x")
-            y = element.get("center_y")
+            x: float = element["center_x"]
+            y: float = element["center_y"]
 
             self.page.mouse.click(x, y)
         else:
             print("Could not find element")
 
-    def type(self, id, text):
+    def type(self, id: Union[str, int], text: str) -> None:
         self.click(id)
         self.page.keyboard.type(text)
 
-    def enter(self):
+    def enter(self) -> None:
         self.page.keyboard.press("Enter")
 
-    def crawl(self):
+    def crawl(self) -> List[str]:
         page = self.page
         page_element_buffer = self.page_element_buffer
         start = time.time()
 
         page_state_as_text = []
 
-        device_pixel_ratio = page.evaluate("window.devicePixelRatio")
+        device_pixel_ratio: float = page.evaluate("window.devicePixelRatio")
         if platform == "darwin" and device_pixel_ratio == 1:  # lies
             device_pixel_ratio = 2
 
-        win_scroll_x = page.evaluate("window.scrollX")
-        win_scroll_y = page.evaluate("window.scrollY")
-        win_upper_bound = page.evaluate("window.pageYOffset")
-        win_left_bound = page.evaluate("window.pageXOffset")
-        win_width = page.evaluate("window.screen.width")
-        win_height = page.evaluate("window.screen.height")
-        win_right_bound = win_left_bound + win_width
-        win_lower_bound = win_upper_bound + win_height
-        document_offset_height = page.evaluate("document.body.offsetHeight")
-        document_scroll_height = page.evaluate("document.body.scrollHeight")
+        win_upper_bound: float = page.evaluate("window.pageYOffset")
+        win_left_bound: float = page.evaluate("window.pageXOffset")
+        win_width: float = page.evaluate("window.screen.width")
+        win_height: float = page.evaluate("window.screen.height")
+        win_right_bound: float = win_left_bound + win_width
+        win_lower_bound: float = win_upper_bound + win_height
 
         # 		percentage_progress_start = (win_upper_bound / document_scroll_height) * 100
         # 		percentage_progress_end = (
@@ -116,40 +143,35 @@ class Crawler:
             "DOMSnapshot.captureSnapshot",
             {"computedStyles": [], "includeDOMRects": True, "includePaintOrder": True},
         )
-        strings = tree["strings"]
-        document = tree["documents"][0]
-        nodes = document["nodes"]
-        backend_node_id = nodes["backendNodeId"]
-        attributes = nodes["attributes"]
-        node_value = nodes["nodeValue"]
-        parent = nodes["parentIndex"]
-        node_types = nodes["nodeType"]
-        node_names = nodes["nodeName"]
-        is_clickable = set(nodes["isClickable"]["index"])
+        strings: Dict[int, str] = tree["strings"]
+        document: Dict[str, Any] = tree["documents"][0]
+        nodes: Dict[str, Any] = document["nodes"]
+        backend_node_id: Dict[int, int] = nodes["backendNodeId"]
+        attributes: Dict[int, Dict[int, Any]] = nodes["attributes"]
+        node_value: Dict[int, int] = nodes["nodeValue"]
+        parent: Dict[int, int] = nodes["parentIndex"]
+        node_names: Dict[int, int] = nodes["nodeName"]
+        is_clickable: Set[int] = set(nodes["isClickable"]["index"])
 
-        text_value = nodes["textValue"]
-        text_value_index = text_value["index"]
-        text_value_values = text_value["value"]
+        input_value: Dict[str, Any] = nodes["inputValue"]
+        input_value_index: List[int] = input_value["index"]
+        input_value_values: List[int] = input_value["value"]
 
-        input_value = nodes["inputValue"]
-        input_value_index = input_value["index"]
-        input_value_values = input_value["value"]
+        layout: Dict[str, Any] = document["layout"]
+        layout_node_index: List[int] = layout["nodeIndex"]
+        bounds: Dict[int, List[float]] = layout["bounds"]
 
-        input_checked = nodes["inputChecked"]
-        layout = document["layout"]
-        layout_node_index = layout["nodeIndex"]
-        bounds = layout["bounds"]
+        cursor: int = 0
 
-        cursor = 0
-        html_elements_text = []
+        child_nodes: Dict[str, List[Dict[str, Any]]] = {}
+        elements_in_view_port: List[ElementInViewPort] = []
 
-        child_nodes = {}
-        elements_in_view_port = []
+        anchor_ancestry: Dict[str, Tuple[bool, Optional[int]]] = {"-1": (False, None)}
+        button_ancestry: Dict[str, Tuple[bool, Optional[int]]] = {"-1": (False, None)}
 
-        anchor_ancestry = {"-1": (False, None)}
-        button_ancestry = {"-1": (False, None)}
-
-        def convert_name(node_name, has_click_handler):
+        def convert_name(
+            node_name: Optional[str], has_click_handler: Optional[bool]
+        ) -> str:
             if node_name == "a":
                 return "link"
             if node_name == "input":
@@ -163,7 +185,9 @@ class Crawler:
             else:
                 return "text"
 
-        def find_attributes(attributes, keys):
+        def find_attributes(
+            attributes: Dict[int, Any], keys: List[str]
+        ) -> Dict[str, str]:
             values = {}
 
             for [key_index, value_index] in zip(*(iter(attributes),) * 2):
@@ -181,7 +205,13 @@ class Crawler:
 
             return values
 
-        def add_to_hash_tree(hash_tree, tag, node_id, node_name, parent_id):
+        def add_to_hash_tree(
+            hash_tree: Dict[str, Tuple[bool, Optional[int]]],
+            tag: str,
+            node_id: int,
+            node_name: Optional[str],
+            parent_id: int,
+        ) -> Tuple[bool, Optional[int]]:
             parent_id_str = str(parent_id)
             if not parent_id_str in hash_tree:
                 parent_name = strings[node_names[parent_id]].lower()
@@ -195,7 +225,7 @@ class Crawler:
 
             # even if the anchor is nested in another anchor, we set the "root" for all descendants to be ::Self
             if node_name == tag:
-                value = (True, node_id)
+                value: Tuple[bool, Optional[int]] = (True, node_id)
             elif (
                 is_parent_desc_anchor
             ):  # reuse the parent's anchor_id (which could be much higher in the tree)
@@ -212,7 +242,7 @@ class Crawler:
 
         for index, node_name_index in enumerate(node_names):
             node_parent = parent[index]
-            node_name = strings[node_name_index].lower()
+            node_name: Optional[str] = strings[node_name_index].lower()
 
             is_ancestor_of_anchor, anchor_id = add_to_hash_tree(
                 anchor_ancestry, "a", index, node_name, node_parent
@@ -253,7 +283,7 @@ class Crawler:
             if not partially_is_in_viewport:
                 continue
 
-            meta_data = []
+            meta_data: List[str] = []
 
             # inefficient to grab the same set of keys for kinds of objects, but it's fine for now
             element_attributes = find_attributes(
@@ -274,7 +304,7 @@ class Crawler:
                 else child_nodes.setdefault(str(ancestor_node_key), [])
             )
 
-            if node_name == "#text" and ancestor_exception:
+            if node_name == "#text" and ancestor_exception and ancestor_node:
                 text = strings[node_value[index]]
                 if text == "|" or text == "•":
                     continue
@@ -289,7 +319,7 @@ class Crawler:
                     )  # prevent [button ... (button)..]
 
                 for key in element_attributes:
-                    if ancestor_exception:
+                    if ancestor_exception and ancestor_node:
                         ancestor_node.append(
                             {
                                 "type": "attribute",
@@ -306,7 +336,7 @@ class Crawler:
                 element_node_value = strings[node_value[index]]
                 if (
                     element_node_value == "|"
-                ):  # commonly used as a seperator, does not add much context - lets save ourselves some token space
+                ):  # commonly used as a separator, does not add much context - lets save ourselves some token space
                     continue
             elif (
                 node_name == "input"
@@ -344,36 +374,32 @@ class Crawler:
         for element in elements_in_view_port:
             node_index = element.get("node_index")
             node_name = element.get("node_name")
-            node_value = element.get("node_value")
-            is_clickable = element.get("is_clickable")
-            origin_x = element.get("origin_x")
-            origin_y = element.get("origin_y")
-            center_x = element.get("center_x")
-            center_y = element.get("center_y")
-            meta_data = element.get("node_meta")
+            element_node_value = element.get("node_value")
+            node_is_clickable = element.get("is_clickable")
+            node_meta_data: Optional[List[str]] = element.get("node_meta")
 
-            inner_text = f"{node_value} " if node_value else ""
+            inner_text = f"{element_node_value} " if element_node_value else ""
             meta = ""
 
             if node_index in child_nodes:
-                for child in child_nodes.get(node_index):
+                for child in child_nodes[node_index]:
                     entry_type = child.get("type")
                     entry_value = child.get("value")
 
-                    if entry_type == "attribute":
+                    if entry_type == "attribute" and node_meta_data:
                         entry_key = child.get("key")
-                        meta_data.append(f'{entry_key}="{entry_value}"')
+                        node_meta_data.append(f'{entry_key}="{entry_value}"')
                     else:
                         inner_text += f"{entry_value} "
 
-            if meta_data:
-                meta_string = " ".join(meta_data)
+            if node_meta_data:
+                meta_string = " ".join(node_meta_data)
                 meta = f" {meta_string}"
 
             if inner_text != "":
                 inner_text = f"{inner_text.strip()}"
 
-            converted_node_name = convert_name(node_name, is_clickable)
+            converted_node_name = convert_name(node_name, node_is_clickable)
 
             # not very elegant, more like a placeholder
             if (