add few shot

README.md

@@ -79,4 +79,4 @@ For more information on these concepts, please see our [full documentation](http
 
 As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
-For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
+For detailed information on how to contribute, see [here](CONTRIBUTING.md).

docs/conf.py

@@ -30,7 +30,6 @@ version = data["tool"]["poetry"]["version"]
 release = version
 
 html_title = project + " " + version
-html_last_updated_fmt = "%b %d, %Y"
 
 
 # -- General configuration ---------------------------------------------------

docs/ecosystem/atlas.md

@@ -1,21 +1,19 @@
 # AtlasDB
 
-This page covers how to use Nomic's Atlas ecosystem within LangChain.
+This page covers how to Nomic's Atlas ecosystem within LangChain.
 It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
 
 ## Installation and Setup
 - Install the Python package with `pip install nomic`
 - Nomic is also included in langchains poetry extras `poetry install -E all`
-
+- 
 ## Wrappers
 
 ### VectorStore
 
 There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore.
 This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling.
-Please see [the Atlas docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
-
-
+Please see [the Nomic docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
 
 
 
@@ -24,4 +22,4 @@ To import this vectorstore:
 from langchain.vectorstores import AtlasDB
 ```
 
-For a more detailed walkthrough of the AtlasDB wrapper, see [this notebook](../modules/indexes/vectorstore_examples/atlas.ipynb)
+For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

docs/ecosystem/bananadev.md

@@ -5,7 +5,7 @@ It is broken into two parts: installation and setup, and then references to spec
 
 ## Installation and Setup
 
-- Install with `pip install banana-dev`
+- Install with `pip3 install banana-dev`
 - Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
 
 ## Define your Banana Template

docs/ecosystem/graphsignal.md

@@ -1,6 +1,6 @@
 # Graphsignal
 
-This page covers how to use the Graphsignal ecosystem to trace and monitor LangChain.
+This page covers how to use the Graphsignal to trace and monitor LangChain.
 
 ## Installation and Setup
 

docs/ecosystem/helicone.md

@@ -1,6 +1,6 @@
 # Helicone
 
-This page covers how to use the [Helicone](https://helicone.ai) ecosystem within LangChain.
+This page covers how to use the [Helicone](https://helicone.ai) within LangChain.
 
 ## What is Helicone?
 

docs/ecosystem/pinecone.md

@@ -17,4 +17,4 @@ To import this vectorstore:
 from langchain.vectorstores import Pinecone
 ```
 
-For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/vectorstore_examples/pinecone.ipynb)
+For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

docs/ecosystem/promptlayer.md

@@ -29,5 +29,3 @@ This LLM is identical to the [OpenAI LLM](./openai), except that
 - all your requests will be logged to your PromptLayer account
 - you can add `pl_tags` when instantializing to tag your requests on PromptLayer
 
-
-PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](../modules/chat/examples/promptlayer_chat_openai.ipynb)

docs/modules/agents/implementations/mrkl_chat.ipynb

@@ -1,253 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f1390152",
-   "metadata": {},
-   "source": [
-    "# MRKL Chat\n",
-    "\n",
-    "This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39ea3638",
-   "metadata": {},
-   "source": [
-    "This uses the example Chinook database.\n",
-    "To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ac561cc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
-    "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.chat_models import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "07e96d99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = ChatOpenAI(temperature=0)\n",
-    "llm1 = OpenAI(temperature=0)\n",
-    "search = SerpAPIWrapper()\n",
-    "llm_math_chain = LLMMathChain(llm=llm1, verbose=True)\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
-    "db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"Calculator\",\n",
-    "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"FooBar DB\",\n",
-    "        func=db_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\n",
-    "    )\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a069c4b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mrkl = initialize_agent(tools, llm, agent=\"chat-zero-shot-react-description\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e603cd7d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: The first question requires a search, while the second question requires a calculator.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to use the calculator tool to raise her current age to the 0.43 power.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"22.0^(0.43)\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "22.0^(0.43)\u001b[32;1m\u001b[1;3m\n",
-      "```python\n",
-      "import math\n",
-      "print(math.pow(22.0, 0.43))\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
-      "\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: Camila Morrone, 3.777824273683966.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Camila Morrone, 3.777824273683966.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a5c07010",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\n",
-      "Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"Who recently released an album called 'The Storm Before the Calm'\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAlanis Morissette\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the name of the artist, I can use the FooBar DB tool to find their albums in the database.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"FooBar DB\",\n",
-      "  \"action_input\": \"What albums does Alanis Morissette have in the database?\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What albums does Alanis Morissette have in the database? \n",
-      "SQLQuery:"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:141: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
-      "  sample_rows = connection.execute(command)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32;1m\u001b[1;3m SELECT Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
-      "SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI have found the answer to both parts of the question.\n",
-      "Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mrkl.run(\"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af016a70",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/agents/tools.md

@@ -136,12 +136,3 @@ Below is a list of all supported tools and relevant information:
 - Requires LLM: No
 - Extra Parameters: `serper_api_key`
 - For more information on this, see [this page](../../ecosystem/google_serper.md)
-
-**wikipedia**
-
-- Tool Name: Wikipedia
-- Tool Description: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.
-- Notes: Uses the [wikipedia](https://pypi.org/project/wikipedia/) Python package to call the MediaWiki API and then parses results.
-- Requires LLM: No
-- Extra Parameters: `top_k_results`
-

docs/modules/chains/examples/sqlite.ipynb

@@ -675,7 +675,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/chains/generic/sequential_chains.ipynb

@@ -308,7 +308,7 @@
    ],
    "source": [
     "from langchain.chains import SequentialChain\n",
-    "from langchain.memory import SimpleMemory\n",
+    "from langchain.chains.base import SimpleMemory\n",
     "\n",
     "llm = OpenAI(temperature=.7)\n",
     "template = \"\"\"You are a social media manager for a theater company.  Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.\n",

docs/modules/chains/key_concepts.md

@@ -9,12 +9,3 @@ This is a specific type of chain where multiple other chains are run in sequence
 to the next. A subtype of this type of chain is the [`SimpleSequentialChain`](./generic/sequential_chains.html#simplesequentialchain), where all subchains have only one input and one output,
 and the output of one is therefore used as sole input to the next chain.
 
-## Prompt Selectors
-One thing that we've noticed is that the best prompt to use is really dependent on the model you use.
-Some prompts work really good with some models, but not great with others.
-One of our goals is provide good chains that "just work" out of the box. 
-A big part of chains like that is having prompts that "just work".
-So rather than having a default prompt for chains, we are moving towards a paradigm where if a prompt is not explicitly
-provided we select one with a PromptSelector. This class takes in the model passed in, and returns a default prompt.
-The inner workings of the PromptSelector can look at any aspect of the model - LLM vs ChatModel, OpenAI vs Cohere, GPT3 vs GPT4, etc.
-Due to this being a newer feature, this may not be implemented for all chains, but this is the direction we are moving.

docs/modules/chat.rst

@@ -7,6 +7,8 @@ Rather than expose a "text in, text out" API, they expose an interface where "ch
 
 Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
 
+**NOTE: this is currently only in prerelease. To use, you should install with `pip install --pre langchain==0.0.102rc0`.**
+
 The following sections of documentation are provided:
 
 - `Getting Started <./chat/getting_started.html>`_: An overview of the basics of chat models.

docs/modules/chat/examples/memory.ipynb

@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9a9350a6",
-   "metadata": {},
-   "source": [
-    "# Memory\n",
-    "This notebook goes over how to use Memory with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "110935ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import (\n",
-    "    ChatPromptTemplate, \n",
-    "    MessagesPlaceholder, \n",
-    "    SystemMessagePromptTemplate, \n",
-    "    HumanMessagePromptTemplate\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "161b6629",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages([\n",
-    "    SystemMessagePromptTemplate.from_template(\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\"),\n",
-    "    MessagesPlaceholder(variable_name=\"history\"),\n",
-    "    HumanMessagePromptTemplate.from_template(\"{input}\")\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4976fbda",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import ConversationChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.memory import ConversationBufferMemory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "12a0bea6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = ChatOpenAI(temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6edcd6a",
-   "metadata": {},
-   "source": [
-    "We can now initialize the memory. Note that we set `return_messages=True` To denote that this should return a list of messages when appropriate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f55bea38",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationBufferMemory(return_messages=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "737e8c78",
-   "metadata": {},
-   "source": [
-    "We can now use this in the rest of the chain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "80152db7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ac68e766",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hello! How can I assist you today?'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation.predict(input=\"Hi there!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "babb33d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?\""
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "36f8a1dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation.predict(input=\"Tell me about yourself.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "79fb460b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/chat/examples/promptlayer_chatopenai.ipynb

@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "959300d4",
-   "metadata": {},
-   "source": [
-    "# PromptLayer ChatOpenAI\n",
-    "\n",
-    "This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your ChatOpenAI requests."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6a45943e",
-   "metadata": {},
-   "source": [
-    "## Install PromptLayer\n",
-    "The `promptlayer` package is required to use PromptLayer with OpenAI. Install `promptlayer` using pip."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dbe09bd8",
-   "metadata": {
-    "vscode": {
-     "languageId": "powershell"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pip install promptlayer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "536c1dfa",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c16da3b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.chat_models import PromptLayerChatOpenAI\n",
-    "from langchain.schema import HumanMessage"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "8564ce7d",
-   "metadata": {},
-   "source": [
-    "## Set the Environment API Key\n",
-    "You can create a PromptLayer API Key at [wwww.promptlayer.com](https://ww.promptlayer.com) by clicking the settings cog in the navbar.\n",
-    "\n",
-    "Set it as an environment variable called `PROMPTLAYER_API_KEY`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "46ba25dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.environ[\"PROMPTLAYER_API_KEY\"] = \"**********\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "bf0294de",
-   "metadata": {},
-   "source": [
-    "## Use the PromptLayerOpenAI LLM like normal\n",
-    "*You can optionally pass in `pl_tags` to track your requests with PromptLayer's tagging feature.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3acf0069",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='to take a nap in a cozy spot. I search around for a suitable place and finally settle on a soft cushion on the window sill. I curl up into a ball and close my eyes, relishing the warmth of the sun on my fur. As I drift off to sleep, I can hear the birds chirping outside and feel the gentle breeze blowing through the window. This is the life of a contented cat.', additional_kwargs={})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = PromptLayerChatOpenAI(pl_tags=[\"langchain\"])\n",
-    "chat([HumanMessage(content=\"I am a cat and I want\")])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "a2d76826",
-   "metadata": {},
-   "source": [
-    "**The above request should now appear on your [PromptLayer dashboard](https://ww.promptlayer.com).**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05e9e2fe",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "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": "3.8.8"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "c4fe2cd85a8d9e8baaec5340ce66faff1c77581a9f43e6c45e85e09b6fced008"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/document_loaders/examples/example_data/mlb_teams_2012.csv

@@ -1,32 +0,0 @@
-"Team", "Payroll (millions)", "Wins"
-"Nationals",     81.34, 98
-"Reds",          82.20, 97
-"Yankees",      197.96, 95
-"Giants",       117.62, 94
-"Braves",        83.31, 94
-"Athletics",     55.37, 94
-"Rangers",      120.51, 93
-"Orioles",       81.43, 93
-"Rays",          64.17, 90
-"Angels",       154.49, 89
-"Tigers",       132.30, 88
-"Cardinals",    110.30, 88
-"Dodgers",       95.14, 86
-"White Sox",     96.92, 85
-"Brewers",       97.65, 83
-"Phillies",     174.54, 81
-"Diamondbacks",  74.28, 81
-"Pirates",       63.43, 79
-"Padres",        55.24, 76
-"Mariners",      81.97, 75
-"Mets",          93.35, 74
-"Blue Jays",     75.48, 73
-"Royals",        60.91, 72
-"Marlins",      118.07, 69
-"Red Sox",      173.18, 69
-"Indians",       78.43, 68
-"Twins",         94.08, 66
-"Rockies",       78.06, 64
-"Cubs",          88.19, 61
-"Astros",        60.65, 55
-

docs/modules/document_loaders/examples/markdown.ipynb

@@ -1,145 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "39af9ecd",
-   "metadata": {},
-   "source": [
-    "# Markdown\n",
-    "\n",
-    "This covers how to load markdown documents into a document format that we can use downstream."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "721c48aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import UnstructuredMarkdownLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9d3d0e35",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = UnstructuredMarkdownLoader(\"../../../../README.md\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "06073f91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c9adc5cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content=\"ð\\x9f¦\\x9cï¸\\x8fð\\x9f”\\x97 LangChain\\n\\nâ\\x9a¡ Building applications with LLMs through composability â\\x9a¡\\n\\nProduction Support: As you move your LangChains into production, we'd love to offer more comprehensive support.\\nPlease fill out this form and we'll set up a dedicated support Slack channel.\\n\\nQuick Install\\n\\npip install langchain\\n\\nð\\x9f¤” What is this?\\n\\nLarge language models (LLMs) are emerging as a transformative technology, enabling\\ndevelopers to build applications that they previously could not.\\nBut using these LLMs in isolation is often not enough to\\ncreate a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.\\n\\nThis library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:\\n\\nâ\\x9d“ Question Answering over specific documents\\n\\nDocumentation\\n\\nEnd-to-end Example: Question Answering over Notion Database\\n\\nð\\x9f’¬ Chatbots\\n\\nDocumentation\\n\\nEnd-to-end Example: Chat-LangChain\\n\\nð\\x9f¤\\x96 Agents\\n\\nDocumentation\\n\\nEnd-to-end Example: GPT+WolframAlpha\\n\\nð\\x9f“\\x96 Documentation\\n\\nPlease see here for full documentation on:\\n\\nGetting started (installation, setting up the environment, simple examples)\\n\\nHow-To examples (demos, integrations, helper functions)\\n\\nReference (full API docs)\\n  Resources (high-level explanation of core concepts)\\n\\nð\\x9f\\x9a\\x80 What can this help with?\\n\\nThere are six main areas that LangChain is designed to help with.\\nThese are, in increasing order of complexity:\\n\\nð\\x9f“\\x83 LLMs and Prompts:\\n\\nThis includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.\\n\\nð\\x9f”\\x97 Chains:\\n\\nChains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\\n\\nð\\x9f“\\x9a Data Augmented Generation:\\n\\nData Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.\\n\\nð\\x9f¤\\x96 Agents:\\n\\nAgents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.\\n\\nð\\x9f§\\xa0 Memory:\\n\\nMemory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\\n\\nð\\x9f§\\x90 Evaluation:\\n\\n[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\\n\\nFor more information on these concepts, please see our full documentation.\\n\\nð\\x9f’\\x81 Contributing\\n\\nAs an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.\\n\\nFor detailed information on how to contribute, see here.\", lookup_str='', metadata={'source': '../../../../README.md'}, lookup_index=0)]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "525d6b67",
-   "metadata": {},
-   "source": [
-    "## Retain Elements\n",
-    "\n",
-    "Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "064f9162",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = UnstructuredMarkdownLoader(\"../../../../README.md\", mode=\"elements\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "abefbbdb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a547c534",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='ð\\x9f¦\\x9cï¸\\x8fð\\x9f”\\x97 LangChain', lookup_str='', metadata={'source': '../../../../README.md', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "381d4139",
-   "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": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/document_loaders/examples/youtube.ipynb

@@ -7,23 +7,22 @@
    "source": [
     "# YouTube\n",
     "\n",
-    "How to load documents from YouTube transcripts.\n",
-    "\n"
+    "How to load documents from YouTube transcripts."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "427d5745",
+   "execution_count": 1,
+   "id": "da4a867f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import YoutubeLoader\n"
+    "from langchain.document_loaders import YoutubeLoader"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "34a25b57",
    "metadata": {
     "scrolled": true
@@ -35,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "bc8b308a",
    "metadata": {},
    "outputs": [],
@@ -45,10 +44,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "d073dd36",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='LADIES AND GENTLEMEN, PEDRO PASCAL! [ CHEERS AND APPLAUSE ] >> THANK YOU, THANK YOU. THANK YOU VERY MUCH. I\\'M SO EXCITED TO BE HERE. THANK YOU. I SPENT THE LAST YEAR SHOOTING A SHOW CALLED \"THE LAST OF US\" ON HBO. FOR SOME HBO SHOES, YOU GET TO SHOOT IN A FIVE STAR ITALIAN RESORT SURROUNDED BY BEAUTIFUL PEOPLE, BUT I SAID, NO, THAT\\'S TOO EASY. I WANT TO SHOOT IN A FREEZING CANADIAN FOREST WHILE BEING CHASED AROUND BY A GUY WHOSE HEAD LOOKS LIKE A GENITAL WART. IT IS AN HONOR BEING A PART OF THESE HUGE FRANCHISEs LIKE \"GAME OF THRONES\" AND \"STAR WARS,\" BUT I\\'M STILL GETTING USED TO PEOPLE RECOGNIZING ME. THE OTHER DAY, A GUY STOPPED ME ON THE STREET AND SAYS, MY SON LOVES \"THE MANDALORIAN\" AND THE NEXT THING I KNOW, I\\'M FACE TIMING WITH A 6-YEAR-OLD WHO HAS NO IDEA WHO I AM BECAUSE MY CHARACTER WEARS A MASK THE ENTIRE SHOW. THE GUY IS LIKE, DO THE MANDO VOICE, BUT IT\\'S LIKE A BEDROOM VOICE. WITHOUT THE MASK, IT JUST SOUNDS PORNY. PEOPLE WALKING BY ON THE STREET SEE ME WHISPERING TO A 6-YEAR-OLD KID. I CAN BRING YOU IN WARM, OR I CAN BRING YOU IN COLD. EVEN THOUGH I CAME TO THE U.S. WHEN I WAS LITTLE, I WAS BORN IN CHILE, AND I HAVE 34 FIRST COUSINS WHO ARE STILL THERE. THEY\\'RE VERY PROUD OF ME. I KNOW THEY\\'RE PROUD BECAUSE THEY GIVE MY PHONE NUMBER TO EVERY PERSON THEY MEET, WHICH MEANS EVERY DAY, SOMEONE IN SANTIAGO WILL TEXT ME STUFF LIKE, CAN YOU COME TO MY WEDDING, OR CAN YOU SING MY PRIEST HAPPY BIRTHDAY, OR IS BABY YODA MEAN IN REAL LIFE. SO I HAVE TO BE LIKE NO, NO, AND HIS NAME IS GROGU. BUT MY COUSINS WEREN\\'T ALWAYS SO PROUD. EARLY IN MY CAREER, I PLAYED SMALL PARTS IN EVERY CRIME SHOW. I EVEN PLAYED TWO DIFFERENT CHARACTERS ON \"LAW AND ORDER.\" TITO CABASSA WHO LOOKED LIKE THIS. AND ONE YEAR LATER, I PLAYED REGGIE LUCKMAN WHO LOOKS LIKE THIS. AND THAT, MY FRIENDS, IS CALLED RANGE. BUT IT IS AMAZING TO BE HERE, LIKE I SAID. I WAS BORN IN CHILE, AND NINE MONTHS LATER, MY PARENTS FLED AND BROUGHT ME AND MY SISTER TO THE U.S. THEY WERE SO BRAVE, AND WITHOUT THEM, I WOULDN\\'T BE HERE IN THIS WONDERFUL COUNTRY, AND I CERTAINLY WOULDN\\'T BE STANDING HERE WITH YOU ALL TONIGHT. SO TO ALL MY FAMILY WATCHING IN CHILE, I WANT TO SAY [ SPEAKING NON-ENGLISH ] WHICH MEANS, I LOVE YOU, I MISS YOU, AND STOP GIVING OUT MY PHONE NUMBER. WE\\'VE GOT AN AMAZING SHOW FOR YOU TONIGHT. COLDPLAY IS HERE, SO STICK', lookup_str='', metadata={'source': 'QsYGlZkevEg', 'title': 'Pedro Pascal Monologue - SNL', 'description': 'First-time host Pedro Pascal talks about filming The Last of Us and being recognized by fans.\\n\\nSaturday Night Live. Stream now on Peacock: https://pck.tv/3uQxh4q\\n\\nSubscribe to SNL: https://goo.gl/tUsXwM\\nStream Current Full Episodes: http://www.nbc.com/saturday-night-live\\n\\nWATCH PAST SNL SEASONS\\nGoogle Play - http://bit.ly/SNLGooglePlay\\niTunes - http://bit.ly/SNLiTunes\\n\\nSNL ON SOCIAL\\nSNL Instagram: http://instagram.com/nbcsnl\\nSNL Facebook: https://www.facebook.com/snl\\nSNL Twitter: https://twitter.com/nbcsnl\\nSNL TikTok: https://www.tiktok.com/@nbcsnl\\n\\nGET MORE NBC\\nLike NBC: http://Facebook.com/NBC\\nFollow NBC: http://Twitter.com/NBC\\nNBC Tumblr: http://NBCtv.tumblr.com/\\nYouTube: http://www.youtube.com/nbc\\nNBC Instagram: http://instagram.com/nbc\\n\\n#SNL #PedroPascal #SNL48 #Coldplay', 'view_count': 1175057, 'thumbnail_url': 'https://i.ytimg.com/vi/QsYGlZkevEg/sddefault.jpg', 'publish_date': datetime.datetime(2023, 2, 4, 0, 0), 'length': 224, 'author': 'Saturday Night Live'}, lookup_index=0)]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "loader.load()"
    ]
@@ -63,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "ba28af69",
    "metadata": {},
    "outputs": [],
@@ -73,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "9b8ea390",
    "metadata": {},
    "outputs": [],
@@ -83,61 +93,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "97b98e92",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='LADIES AND GENTLEMEN, PEDRO PASCAL! [ CHEERS AND APPLAUSE ] >> THANK YOU, THANK YOU. THANK YOU VERY MUCH. I\\'M SO EXCITED TO BE HERE. THANK YOU. I SPENT THE LAST YEAR SHOOTING A SHOW CALLED \"THE LAST OF US\" ON HBO. FOR SOME HBO SHOES, YOU GET TO SHOOT IN A FIVE STAR ITALIAN RESORT SURROUNDED BY BEAUTIFUL PEOPLE, BUT I SAID, NO, THAT\\'S TOO EASY. I WANT TO SHOOT IN A FREEZING CANADIAN FOREST WHILE BEING CHASED AROUND BY A GUY WHOSE HEAD LOOKS LIKE A GENITAL WART. IT IS AN HONOR BEING A PART OF THESE HUGE FRANCHISEs LIKE \"GAME OF THRONES\" AND \"STAR WARS,\" BUT I\\'M STILL GETTING USED TO PEOPLE RECOGNIZING ME. THE OTHER DAY, A GUY STOPPED ME ON THE STREET AND SAYS, MY SON LOVES \"THE MANDALORIAN\" AND THE NEXT THING I KNOW, I\\'M FACE TIMING WITH A 6-YEAR-OLD WHO HAS NO IDEA WHO I AM BECAUSE MY CHARACTER WEARS A MASK THE ENTIRE SHOW. THE GUY IS LIKE, DO THE MANDO VOICE, BUT IT\\'S LIKE A BEDROOM VOICE. WITHOUT THE MASK, IT JUST SOUNDS PORNY. PEOPLE WALKING BY ON THE STREET SEE ME WHISPERING TO A 6-YEAR-OLD KID. I CAN BRING YOU IN WARM, OR I CAN BRING YOU IN COLD. EVEN THOUGH I CAME TO THE U.S. WHEN I WAS LITTLE, I WAS BORN IN CHILE, AND I HAVE 34 FIRST COUSINS WHO ARE STILL THERE. THEY\\'RE VERY PROUD OF ME. I KNOW THEY\\'RE PROUD BECAUSE THEY GIVE MY PHONE NUMBER TO EVERY PERSON THEY MEET, WHICH MEANS EVERY DAY, SOMEONE IN SANTIAGO WILL TEXT ME STUFF LIKE, CAN YOU COME TO MY WEDDING, OR CAN YOU SING MY PRIEST HAPPY BIRTHDAY, OR IS BABY YODA MEAN IN REAL LIFE. SO I HAVE TO BE LIKE NO, NO, AND HIS NAME IS GROGU. BUT MY COUSINS WEREN\\'T ALWAYS SO PROUD. EARLY IN MY CAREER, I PLAYED SMALL PARTS IN EVERY CRIME SHOW. I EVEN PLAYED TWO DIFFERENT CHARACTERS ON \"LAW AND ORDER.\" TITO CABASSA WHO LOOKED LIKE THIS. AND ONE YEAR LATER, I PLAYED REGGIE LUCKMAN WHO LOOKS LIKE THIS. AND THAT, MY FRIENDS, IS CALLED RANGE. BUT IT IS AMAZING TO BE HERE, LIKE I SAID. I WAS BORN IN CHILE, AND NINE MONTHS LATER, MY PARENTS FLED AND BROUGHT ME AND MY SISTER TO THE U.S. THEY WERE SO BRAVE, AND WITHOUT THEM, I WOULDN\\'T BE HERE IN THIS WONDERFUL COUNTRY, AND I CERTAINLY WOULDN\\'T BE STANDING HERE WITH YOU ALL TONIGHT. SO TO ALL MY FAMILY WATCHING IN CHILE, I WANT TO SAY [ SPEAKING NON-ENGLISH ] WHICH MEANS, I LOVE YOU, I MISS YOU, AND STOP GIVING OUT MY PHONE NUMBER. WE\\'VE GOT AN AMAZING SHOW FOR YOU TONIGHT. COLDPLAY IS HERE, SO STICK', lookup_str='', metadata={'source': 'QsYGlZkevEg', 'title': 'Pedro Pascal Monologue - SNL', 'description': 'First-time host Pedro Pascal talks about filming The Last of Us and being recognized by fans.\\n\\nSaturday Night Live. Stream now on Peacock: https://pck.tv/3uQxh4q\\n\\nSubscribe to SNL: https://goo.gl/tUsXwM\\nStream Current Full Episodes: http://www.nbc.com/saturday-night-live\\n\\nWATCH PAST SNL SEASONS\\nGoogle Play - http://bit.ly/SNLGooglePlay\\niTunes - http://bit.ly/SNLiTunes\\n\\nSNL ON SOCIAL\\nSNL Instagram: http://instagram.com/nbcsnl\\nSNL Facebook: https://www.facebook.com/snl\\nSNL Twitter: https://twitter.com/nbcsnl\\nSNL TikTok: https://www.tiktok.com/@nbcsnl\\n\\nGET MORE NBC\\nLike NBC: http://Facebook.com/NBC\\nFollow NBC: http://Twitter.com/NBC\\nNBC Tumblr: http://NBCtv.tumblr.com/\\nYouTube: http://www.youtube.com/nbc\\nNBC Instagram: http://instagram.com/nbc\\n\\n#SNL #PedroPascal #SNL48 #Coldplay', 'view_count': 1175057, 'thumbnail_url': 'https://i.ytimg.com/vi/QsYGlZkevEg/sddefault.jpg', 'publish_date': datetime.datetime(2023, 2, 4, 0, 0), 'length': 224, 'author': 'Saturday Night Live'}, lookup_index=0)]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "loader.load()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65796cc5",
-   "metadata": {},
-   "source": [
-    "## YouTube loader from Google Cloud\n",
-    "\n",
-    "### Prerequisites\n",
-    "\n",
-    "1. Create a Google Cloud project or use an existing project\n",
-    "1. Enable the [Youtube Api](https://console.cloud.google.com/apis/enableflow?apiid=youtube.googleapis.com&project=sixth-grammar-344520)\n",
-    "1. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application)\n",
-    "1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib youtube-transcript-api`\n",
-    "\n",
-    "### 🧑 Instructions for ingesting your Google Docs data\n",
-    "By default, the `GoogleDriveLoader` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `credentials_file` keyword argument. Same thing with `token.json`. Note that `token.json` will be created automatically the first time you use the loader.\n",
-    "\n",
-    "`GoogleApiYoutubeLoader` can load from a list of Google Docs document ids or a folder id. You can obtain your folder and document id from the URL:\n",
-    "Note depending on your set up, the `service_account_path` needs to be set up. See [here](https://developers.google.com/drive/api/v3/quickstart/python) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c345bc43",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import GoogleApiClient, GoogleApiYoutubeLoader\n",
-    "\n",
-    "# Init the GoogleApiClient \n",
-    "from pathlib import Path\n",
-    "\n",
-    "\n",
-    "google_api_client = GoogleApiClient(credentials_path=Path(\"your_path_creds.json\"))\n",
-    "\n",
-    "\n",
-    "# Use a Channel\n",
-    "youtube_loader_channel = GoogleApiYoutubeLoader(google_api_client=google_api_client, channel_name=\"Reducible\",captions_language=\"en\")\n",
-    "\n",
-    "# Use Youtube Ids\n",
-    "\n",
-    "youtube_loader_ids = GoogleApiYoutubeLoader(google_api_client=google_api_client, video_ids=[\"TrdevFK_am4\"], add_video_info=True)\n",
-    "\n",
-    "# returns a list of Documents\n",
-    "youtube_loader_channel.load()"
-   ]
   }
  ],
  "metadata": {
@@ -157,11 +130,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.9.1"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "604c1013f65d31a2eb1fca07aae054bedd5a5a0d272dbb31e502c81f0b254b99"
-   }
   }
  },
  "nbformat": 4,

docs/modules/indexes/chain_examples/chat_vector_db.ipynb

@@ -268,44 +268,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f49beab",
-   "metadata": {},
    "source": [
     "## Chat Vector DB with `search_distance`\n",
     "If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter."
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5ed8d612",
-   "metadata": {},
    "outputs": [],
    "source": [
     "vectordbkwargs = {\"search_distance\": 0.9}"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6a7b3459",
-   "metadata": {},
    "outputs": [],
    "source": [
     "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore, return_source_documents=True)\n",
     "chat_history = []\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "markdown",
-   "id": "99b96dae",
-   "metadata": {},
    "source": [
     "## Chat Vector DB with `map_reduce`\n",
     "We can also use different types of combine document chains with the Chat Vector DB chain."
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
@@ -520,71 +524,6 @@
     "query = \"Did he mention who she suceeded\"\n",
     "result = qa({\"question\": query, \"chat_history\": chat_history})\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f793d56b",
-   "metadata": {},
-   "source": [
-    "## get_chat_history Function\n",
-    "You can also specify a `get_chat_history` function, which can be used to format the chat_history string."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a7ba9d8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_chat_history(inputs) -> str:\n",
-    "    res = []\n",
-    "    for human, ai in inputs:\n",
-    "        res.append(f\"Human:{human}\\nAI:{ai}\")\n",
-    "    return \"\\n\".join(res)\n",
-    "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore, get_chat_history=get_chat_history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a3e33c0d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat_history = []\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "result = qa({\"question\": query, \"chat_history\": chat_history})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "936dc62f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\" The president said that Ketanji Brown 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. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result['answer']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b8c26901",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/modules/indexes/chain_examples/qa_with_sources.ipynb

@@ -635,7 +635,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.output_parsers import RegexParser\n",
+    "from langchain.prompts.base import RegexParser\n",
     "\n",
     "output_parser = RegexParser(\n",
     "    regex=r\"(.*?)\\nScore: (.*)\",\n",
@@ -732,4 +732,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/modules/indexes/chain_examples/question_answering.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Question Answering\n",
     "\n",
-    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: `stuff`, `map_reduce`, `refine`, `map_rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
+    "This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: `stuff`, `map_reduce`, `refine`, `map-rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
    ]
   },
   {
@@ -635,7 +635,7 @@
     }
    ],
    "source": [
-    "from langchain.output_parsers import RegexParser\n",
+    "from langchain.prompts.base import RegexParser\n",
     "\n",
     "output_parser = RegexParser(\n",
     "    regex=r\"(.*?)\\nScore: (.*)\",\n",

docs/modules/indexes/chain_examples/summarize.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "e9db25f3",
    "metadata": {},
    "outputs": [],
@@ -81,17 +81,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "5cfa89b2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' In response to Russian aggression in Ukraine, the United States and its allies are taking action to hold Putin accountable, including economic sanctions, asset seizures, and military assistance. The US is also providing economic and humanitarian aid to Ukraine, and has passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and create jobs. The US remains unified and determined to protect Ukraine and the free world.'"
+       "\" In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and investing in American products to create jobs. President Biden and Vice President Harris have passed the American Rescue Plan and the Bipartisan Infrastructure Law to help working people and rebuild America.\""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,7 +470,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/indexes/examples/embeddings.ipynb

@@ -463,64 +463,6 @@
    "source": [
     "query_result = embeddings.embed_query(text)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9c02c78",
-   "metadata": {},
-   "source": [
-    "## Fake Embeddings\n",
-    "\n",
-    "LangChain also provides a fake embedding class. You can use this to test your pipelines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2ffc2e4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import FakeEmbeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "80777571",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings = FakeEmbeddings(size=1352)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3ec9d8f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query_result = embeddings.embed_query(\"foo\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "3b9ae9e1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "doc_results = embeddings.embed_documents([\"foo\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "88d366bd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -539,7 +481,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

docs/modules/indexes/how_to_guides.rst

@@ -36,8 +36,6 @@ In the below guides, we cover different types of vectorstores and how to use the
 
 `Chroma <./vectorstore_examples/chroma.html>`_: A walkthrough of how to use the Chroma vectorstore wrapper.
 
-`AtlasDB <./vectorstore_examples/atlas.html>`_: A walkthrough of how to use the AtlasDB vectorstore and visualizer wrapper.
-
 `DeepLake <./vectorstore_examples/deeplake.html>`_: A walkthrough of how to use the Deep Lake, data lake, wrapper.
 
 `FAISS <./vectorstore_examples/faiss.html>`_: A walkthrough of how to use the FAISS vectorstore wrapper.

docs/modules/indexes/vectorstore_examples/atlas.ipynb

@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# AtlasDB\n",
     "\n",
@@ -11,10 +15,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "pycharm": {
-     "is_executing": true
+     "name": "#%%\n"
     }
    },
    "outputs": [],
@@ -28,14 +32,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
-    "scrolled": true,
-    "pycharm": {
-     "is_executing": true
-    }
+    "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting en-core-web-sm==3.5.0\n",
+      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl (12.8 MB)\n",
+      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m12.8/12.8 MB\u001B[0m \u001B[31m90.8 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m00:01\u001B[0m00:01\u001B[0m\n",
+      "\u001B[?25hRequirement already satisfied: spacy<3.6.0,>=3.5.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from en-core-web-sm==3.5.0) (3.5.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (23.0)\n",
+      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.1.1)\n",
+      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.3.0)\n",
+      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.4.5)\n",
+      "Requirement already satisfied: pathy>=0.10.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.10.1)\n",
+      "Requirement already satisfied: setuptools in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (67.4.0)\n",
+      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.64.1)\n",
+      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.0.4)\n",
+      "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (6.3.0)\n",
+      "Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.7)\n",
+      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.7)\n",
+      "Requirement already satisfied: typer<0.8.0,>=0.3.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.0)\n",
+      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.28.2)\n",
+      "Requirement already satisfied: jinja2 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.1.2)\n",
+      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.10.5)\n",
+      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.8)\n",
+      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.12)\n",
+      "Requirement already satisfied: numpy>=1.15.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.24.2)\n",
+      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.0.9)\n",
+      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.8)\n",
+      "Requirement already satisfied: typing-extensions>=4.2.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.5.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.1)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2022.12.7)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.26.14)\n",
+      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from thinc<8.2.0,>=8.1.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.9)\n",
+      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from thinc<8.2.0,>=8.1.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.0.4)\n",
+      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/ubuntu/langchain/.venv/lib/python3.9/site-packages (from jinja2->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.1.2)\n",
+      "\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.0.1\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
+      "\u001B[38;5;2m✔ Download and installation successful\u001B[0m\n",
+      "You can now load the package via spacy.load('en_core_web_sm')\n"
+     ]
+    }
+   ],
    "source": [
     "!python -m spacy download en_core_web_sm"
    ]
@@ -67,31 +113,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-02-24 16:13:49.696 | INFO     | nomic.project:_create_project:884 - Creating project `test_index_1677255228.136989` in organization `Atlas Demo`\n",
+      "2023-02-24 16:13:51.087 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n",
+      "2023-02-24 16:13:51.225 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n",
+      "2023-02-24 16:13:51.481 | INFO     | nomic.project:add_text:1351 - Uploading text to Atlas.\n",
+      "1it [00:00,  1.20it/s]\n",
+      "2023-02-24 16:13:52.318 | INFO     | nomic.project:add_text:1422 - Text upload succeeded.\n",
+      "2023-02-24 16:13:52.628 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n",
+      "2023-02-24 16:13:53.380 | INFO     | nomic.project:create_index:1192 - Created map `test_index_1677255228.136989_index` in project `test_index_1677255228.136989`: https://atlas.nomic.ai/map/ee2354a3-7f9a-4c6b-af43-b0cda09d7198/db996d77-8981-48a0-897a-ff2c22bbf541\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
     "db = AtlasDB.from_texts(texts=texts,\n",
-    "                        name='test_index_'+str(time.time()), # unique name for your vector store\n",
-    "                        description='test_index', #a description for your vector store\n",
+    "                        name='test_index_'+str(time.time()),\n",
+    "                        description='test_index',\n",
     "                        api_key=ATLAS_TEST_API_KEY,\n",
     "                        index_kwargs={'build_topic_model': True})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "db.project.wait_for_project_lock()"
-   ],
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false
-   }
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-02-24 16:14:09.106 | INFO     | nomic.project:wait_for_project_lock:993 - test_index_1677255228.136989: Project lock is released.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with db.project.wait_for_project_lock():\n",
+    "    time.sleep(1)"
+   ]
   },
   {
    "cell_type": "code",
@@ -197,4 +263,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

docs/modules/llms/async_llm.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "LangChain provides async support for LLMs by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
     "\n",
-    "Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, only `OpenAI` and `PromptLayerOpenAI` are supported, but async support for other LLMs is on the roadmap.\n",
+    "Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, only `OpenAI` `OpenAIChat`, and `PromptLayerOpenAI` are supported, but async support for other LLMs is on the roadmap.\n",
     "\n",
     "You can use the `agenerate` method to call an OpenAI LLM asynchronously."
    ]
@@ -28,65 +28,66 @@
      "text": [
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings like humans, but I'm functioning properly. How may I assist you?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "I'm an AI language model, so I don't have emotions, but I'm functioning properly. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, how about you?\n",
+      "As an AI language model, I do not have emotions like humans, but I'm functioning normally. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "I am an AI language model, so I do not have feelings, but I am here to assist you. How may I help you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I do not have feelings or emotions but I'm always ready to assist you. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about yourself?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning normally. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you! How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning properly. Thank you. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have emotions, so I don't have a specific feeling or emotion. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you! How about you?\n",
+      "As an AI language model, I do not have feelings or emotions. However, I am functioning as intended and ready to assist you with any queries you may have. How can I be of assistance today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
-      "\u001b[1mConcurrent executed in 1.39 seconds.\u001b[0m\n",
+      "As an AI language model, I do not have feelings, but I am functioning well. Thank you for asking. How can I assist you today?\n",
+      "\u001b[1mConcurrent executed in 0.92 seconds.\u001b[0m\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
-      "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning well. Thank you for asking. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "I'm an AI language model, so I don't have feelings, but I'm functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about yourself?\n",
+      "As an AI language model, I don't have feelings, but I'm functioning well. Thank you for asking. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thanks for asking. How about you?\n",
+      "As an AI language model, I don't have feelings, but I am functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thanks! How about you?\n",
+      "As an AI language model, I don't have feelings but I'm functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about you?\n",
+      "As an AI language model, I do not have personal emotions. However, I am functioning well and ready to assist you with any queries or tasks you have. How may I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thank you. How about yourself?\n",
+      "As an AI language model, I do not have feelings or emotions, but I'm functioning well. How can I assist you today?\n",
       "\n",
       "\n",
-      "I'm doing well, thanks for asking. How about you?\n",
-      "\u001b[1mSerial executed in 5.77 seconds.\u001b[0m\n"
+      "I am an AI language model and do not have feelings. But I am functioning properly and ready to assist you with any task. How may I help you today?\n",
+      "\n",
+      "\n",
+      "As an AI language model, I do not have emotions, but I am functioning well. How can I assist you today?\n",
+      "\u001b[1mSerial executed in 5.00 seconds.\u001b[0m\n"
      ]
     }
    ],
@@ -94,10 +95,10 @@
     "import time\n",
     "import asyncio\n",
     "\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain.llms import OpenAIChat\n",
     "\n",
     "def generate_serially():\n",
-    "    llm = OpenAI(temperature=0.9)\n",
+    "    llm = OpenAIChat(temperature=0.9)\n",
     "    for _ in range(10):\n",
     "        resp = llm.generate([\"Hello, how are you?\"])\n",
     "        print(resp.generations[0][0].text)\n",
@@ -109,7 +110,7 @@
     "\n",
     "\n",
     "async def generate_concurrently():\n",
-    "    llm = OpenAI(temperature=0.9)\n",
+    "    llm = OpenAIChat(temperature=0.9)\n",
     "    tasks = [async_generate(llm) for _ in range(10)]\n",
     "    await asyncio.gather(*tasks)\n",
     "\n",
@@ -151,7 +152,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/llms/integrations/openaichat.ipynb

@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatOpenAI\n",
+    "\n",
+    "OpenAI has a [chat model](https://platform.openai.com/docs/guides/chat) you can use. The interface is based around messages rather than raw text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "522686de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    AIMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.schema import (\n",
+    "    AIMessage,\n",
+    "    HumanMessage,\n",
+    "    SystemMessage\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat = ChatOpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbaec18e-3684-4eef-955f-c1cec8bf765d",
+   "metadata": {},
+   "source": [
+    "You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "76a6e7b0-e927-4bfb-a414-1332a4149106",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat([HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a62153d4-1211-411b-a493-3febfe446ae0",
+   "metadata": {},
+   "source": [
+    "OpenAI's chat model supports multiple messages as input. See [here](https://platform.openai.com/docs/guides/chat/chat-vs-completions) for more information. Here is an example of sending a system and user message to the chat model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
+    "    HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
+    "]\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36dc8d7e-bd25-47ac-8c1b-60e3422603d3",
+   "metadata": {},
+   "source": [
+    "You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2b21fc52-74b6-4950-ab78-45d12c68fb4d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\"))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\"))]], llm_output=None)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_messages = [\n",
+    "    [\n",
+    "        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
+    "        HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
+    "    ],\n",
+    "    [\n",
+    "        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
+    "        HumanMessage(content=\"Translate this sentence from English to French. I love artificial intelligence.\")\n",
+    "    ],\n",
+    "]\n",
+    "chat.generate(batch_messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b10b00ef-f373-4bc3-8302-2dfc28033734",
+   "metadata": {},
+   "source": [
+    "## PromptTemplates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
+   "metadata": {},
+   "source": [
+    "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fbb043e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\")"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system_message_prompt = SystemMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "            input_variables=[\"input_language\", \"output_language\"],\n",
+    "        )\n",
+    "    )\n",
+    "human_message_prompt = HumanMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=\"Translate this sentence from {input_language} to {output_language}. {text}\",\n",
+    "            input_variables=[\"input_language\", \"output_language\", \"text\"],\n",
+    "        )\n",
+    "    )\n",
+    "chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])\n",
+    "\n",
+    "# get a chat completion from the formatted messages\n",
+    "chat(chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb779f3f",
+   "metadata": {},
+   "source": [
+    "## Streaming\n",
+    "\n",
+    "Streaming is supported for `ChatOpenAI` through callback handling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "509181be",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Verse 1:\n",
+      "Bubbles rising to the top\n",
+      "A refreshing drink that never stops\n",
+      "Clear and crisp, it's pure delight\n",
+      "A taste that's sure to excite\n",
+      "\n",
+      "Chorus:\n",
+      "Sparkling water, oh so fine\n",
+      "A drink that's always on my mind\n",
+      "With every sip, I feel alive\n",
+      "Sparkling water, you're my vibe\n",
+      "\n",
+      "Verse 2:\n",
+      "No sugar, no calories, just pure bliss\n",
+      "A drink that's hard to resist\n",
+      "It's the perfect way to quench my thirst\n",
+      "A drink that always comes first\n",
+      "\n",
+      "Chorus:\n",
+      "Sparkling water, oh so fine\n",
+      "A drink that's always on my mind\n",
+      "With every sip, I feel alive\n",
+      "Sparkling water, you're my vibe\n",
+      "\n",
+      "Bridge:\n",
+      "From the mountains to the sea\n",
+      "Sparkling water, you're the key\n",
+      "To a healthy life, a happy soul\n",
+      "A drink that makes me feel whole\n",
+      "\n",
+      "Chorus:\n",
+      "Sparkling water, oh so fine\n",
+      "A drink that's always on my mind\n",
+      "With every sip, I feel alive\n",
+      "Sparkling water, you're my vibe\n",
+      "\n",
+      "Outro:\n",
+      "Sparkling water, you're the one\n",
+      "A drink that's always so much fun\n",
+      "I'll never let you go, my friend\n",
+      "Sparkling"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.callbacks.base import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "chat = ChatOpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
+    "resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c095285d",
+   "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.rst

@@ -2,23 +2,18 @@ Memory
 ==========================
 
 By default, Chains and Agents are stateless,
-meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
+meaning that they treat each incoming query independently.
 In some applications (chatbots being a GREAT example) it is highly important
 to remember previous interactions, both at a short term but also at a long term level.
 The concept of “Memory” exists to do exactly that.
 
-LangChain provides memory components in two forms.
-First, LangChain provides helper utilities for managing and manipulating previous chat messages.
-These are designed to be modular and useful regardless of how they are used.
-Secondly, LangChain provides easy ways to incorporate these utilities into chains.
-
 The following sections of documentation are provided:
 
 - `Getting Started <./memory/getting_started.html>`_: An overview of how to get started with different types of memory.
 
 - `Key Concepts <./memory/key_concepts.html>`_: A conceptual guide going over the various concepts related to memory.
 
-- `How-To Guides <./memory/how_to_guides.html>`_: A collection of how-to guides. These highlight different types of memory, as well as how to use memory in chains.
+- `How-To Guides <./memory/how_to_guides.html>`_: A collection of how-to guides. These highlight how to work with different types of memory, as well as how to customize memory.
 
 
 

docs/modules/memory/examples/adding_memory.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI, LLMChain, PromptTemplate"
    ]
   },
@@ -167,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/adding_memory_chain_multiple_inputs.ipynb

@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "d3dc4ed5",
    "metadata": {},
    "outputs": [],
@@ -80,7 +80,7 @@
     "from langchain.chains.question_answering import load_qa_chain\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.memory import ConversationBufferMemory"
+    "from langchain.chains.conversation.memory import ConversationBufferMemory"
    ]
   },
   {

docs/modules/memory/examples/agent_with_memory.ipynb

@@ -22,13 +22,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "id": "8db95912",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
-    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI, LLMChain\n",
     "from langchain.utilities import GoogleSearchAPIWrapper"
    ]
@@ -316,7 +316,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/chatgpt_clone.ipynb

@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "a99acd89",
    "metadata": {},
    "outputs": [
@@ -24,9 +24,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -36,19 +36,20 @@
       "\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 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",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "```\n",
-      "/home/user\n",
+      "$ pwd\n",
+      "/\n",
       "```\n"
      ]
     }
    ],
    "source": [
     "from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate\n",
-    "from langchain.memory import ConversationBufferWindowMemory\n",
+    "from langchain.chains.conversation.memory import ConversationalBufferWindowMemory\n",
     "\n",
     "\n",
     "template = \"\"\"Assistant is a large language model trained by OpenAI.\n",
@@ -73,7 +74,7 @@
     "    llm=OpenAI(temperature=0), \n",
     "    prompt=prompt, \n",
     "    verbose=True, \n",
-    "    memory=ConversationBufferWindowMemory(k=2),\n",
+    "    memory=ConversationalBufferWindowMemory(k=2),\n",
     ")\n",
     "\n",
     "output = chatgpt_chain.predict(human_input=\"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",
@@ -92,9 +93,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -109,9 +110,9 @@
       "/\n",
       "```\n",
       "Human: ls ~\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "```\n",
       "$ ls ~\n",
@@ -137,9 +138,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -160,9 +161,9 @@
       "Desktop  Documents  Downloads  Music  Pictures  Public  Templates  Videos\n",
       "```\n",
       "Human: cd ~\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       " \n",
       "```\n",
       "$ cd ~\n",
@@ -189,9 +190,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -213,9 +214,9 @@
       "/home/user\n",
       "```\n",
       "Human: {Please make a file jokes.txt inside and put some jokes inside}\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -244,9 +245,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -271,9 +272,9 @@
       "$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n",
       "```\n",
       "Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -303,9 +304,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -331,9 +332,9 @@
       "Result: 33\n",
       "```\n",
       "Human: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -361,9 +362,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -390,9 +391,9 @@
       "Human: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\n",
       "COPY entrypoint.sh entrypoint.sh\n",
       "ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -425,9 +426,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -458,9 +459,9 @@
       "Hello from Docker\n",
       "```\n",
       "Human: nvidia-smi\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -501,9 +502,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -547,9 +548,9 @@
       "|=============================================================================|\n",
       "\n",
       "Human: ping bbc.com\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -583,9 +584,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -629,9 +630,9 @@
       "round-trip min/avg/max/stddev = 14.945/14.945/14.945/0.000 ms\n",
       "```\n",
       "Human: curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -658,9 +659,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -690,9 +691,9 @@
       "1.8.1\n",
       "```\n",
       "Human: lynx https://www.deepmind.com/careers\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -725,9 +726,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -756,9 +757,9 @@
       "Explore our current openings and apply today. We look forward to hearing from you.\n",
       "```\n",
       "Human: curl https://chat.openai.com/chat\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       " \n",
       "\n",
       "```\n",
@@ -798,9 +799,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -842,9 +843,9 @@
       "</html>\n",
       "```\n",
       "Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       "\n",
       "\n",
       "```\n",
@@ -874,9 +875,9 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
+      "\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
+      "\u001B[32;1m\u001B[1;3mAssistant is a large language model trained by OpenAI.\n",
       "\n",
       "Assistant is 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, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
       "\n",
@@ -915,9 +916,9 @@
       "}\n",
       "```\n",
       "Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"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.\"}' https://chat.openai.com/chat\n",
-      "Assistant:\u001b[0m\n",
+      "Assistant:\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
+      "\u001B[1m> Finished LLMChain chain.\u001B[0m\n",
       " \n",
       "\n",
       "```\n",
@@ -960,7 +961,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/conversational_agent.ipynb

@@ -20,7 +20,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import Tool\n",
-    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI\n",
     "from langchain.utilities import GoogleSearchAPIWrapper\n",
     "from langchain.agents import initialize_agent"
@@ -76,12 +76,12 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? No\n",
-      "AI: Hi Bob, nice to meet you! How can I help you today?\u001b[0m\n",
+      "AI: Hi Bob, nice to meet you! How can I help you today?\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -111,12 +111,12 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? No\n",
-      "AI: Your name is Bob!\u001b[0m\n",
+      "AI: Your name is Bob!\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -146,12 +146,12 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? No\n",
-      "AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!\u001b[0m\n",
+      "AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -181,16 +181,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
-      "Action Input: Who won the World Cup in 1978\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mThe Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.\u001b[0m\n",
+      "Action Input: Who won the World Cup in 1978\u001B[0m\n",
+      "Observation: \u001B[36;1m\u001B[1;3mThe Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ...\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3m Do I need to use a tool? No\n",
+      "AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -220,16 +220,16 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m\n",
       "Thought: Do I need to use a tool? Yes\n",
       "Action: Current Search\n",
-      "Action Input: Current temperature in Pomfret\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mA mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
-      "AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.\u001b[0m\n",
+      "Action Input: Current temperature in Pomfret\u001B[0m\n",
+      "Observation: \u001B[36;1m\u001B[1;3mA mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3m Do I need to use a tool? No\n",
+      "AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {
@@ -272,7 +272,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/conversational_customization.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "from langchain.llms import OpenAI\n",
     "from langchain.chains import ConversationChain\n",
-    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
     "\n",
     "\n",
     "llm = OpenAI(temperature=0)"
@@ -379,7 +379,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/custom_memory.ipynb

@@ -25,7 +25,7 @@
    "outputs": [],
    "source": [
     "from langchain import OpenAI, ConversationChain\n",
-    "from langchain.schema import BaseMemory\n",
+    "from langchain.chains.base import Memory\n",
     "from pydantic import BaseModel\n",
     "from typing import List, Dict, Any"
    ]
@@ -71,7 +71,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class SpacyEntityMemory(BaseMemory, BaseModel):\n",
+    "class SpacyEntityMemory(Memory, BaseModel):\n",
     "    \"\"\"Memory class for storing information about entities.\"\"\"\n",
     "\n",
     "    # Define dictionary to store information about entities.\n",
@@ -290,7 +290,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/entity_summary_memory.ipynb

@@ -6,130 +6,31 @@
    "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).\n",
-    "\n",
-    "Let's first walk through using this functionality."
+    "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": 1,
-   "id": "1bea1181",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.memory import ConversationEntityMemory\n",
-    "llm = OpenAI(temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "34425079",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationEntityMemory(llm=llm)\n",
-    "_input = {\"input\": \"Deven & Sam are working on a hackathon project\"}\n",
-    "memory.load_memory_variables(_input)\n",
-    "memory.save_context(\n",
-    "    _input,\n",
-    "    {\"ouput\": \" That sounds like a great project! What kind of project are they working on?\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b425642c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': 'Human: Deven & Sam are working on a hackathon project\\nAI:  That sounds like a great project! What kind of project are they working on?',\n",
-       " 'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({\"input\": 'who is Sam'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3bf89b46",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationEntityMemory(llm=llm, return_messages=True)\n",
-    "_input = {\"input\": \"Deven & Sam are working on a hackathon project\"}\n",
-    "memory.load_memory_variables(_input)\n",
-    "memory.save_context(\n",
-    "    _input,\n",
-    "    {\"ouput\": \" That sounds like a great project! What kind of project are they working on?\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3e37d126",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': [HumanMessage(content='Deven & Sam are working on a hackathon project', additional_kwargs={}),\n",
-       "  AIMessage(content=' That sounds like a great project! What kind of project are they working on?', additional_kwargs={})],\n",
-       " 'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({\"input\": 'who is Sam'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee5ad043",
-   "metadata": {},
-   "source": [
-    "## Using in a chain\n",
-    "Let's now use it in a chain!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "13471fbd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import ConversationChain\n",
-    "from langchain.memory import ConversationEntityMemory\n",
-    "from langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE\n",
+    "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": 7,
+   "execution_count": 12,
    "id": "183346e2",
    "metadata": {},
    "outputs": [],
    "source": [
+    "llm = OpenAI(temperature=0)\n",
     "conversation = ConversationChain(\n",
     "    llm=llm, \n",
     "    verbose=True,\n",
@@ -140,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "7eb1460a",
    "metadata": {},
    "outputs": [
@@ -178,7 +79,7 @@
        "' That sounds like a great project! What kind of project are they working on?'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -189,29 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "0269f513",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Deven': 'Deven is working on a hackathon project with Sam.',\n",
-       " 'Sam': 'Sam is working on a hackathon project with Deven.'}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation.memory.store"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "46324ca8",
    "metadata": {},
    "outputs": [
@@ -250,7 +129,7 @@
        "' That sounds like an interesting project! What kind of memory structures are they trying to add?'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -261,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "ff2ebf6b",
    "metadata": {},
    "outputs": [
@@ -282,7 +161,7 @@
       "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, attempting to add more complex memory structures to Langchain.', 'Sam': 'Sam is working on a hackathon project with Deven, trying to add more complex memory structures to Langchain.', 'Langchain': 'Langchain is a project that is trying to add more complex memory structures.', 'Key-Value Store': ''}\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",
@@ -302,7 +181,7 @@
        "' That sounds like a great idea! How will the key-value store work?'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -313,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "56cfd4ba",
    "metadata": {},
    "outputs": [
@@ -334,7 +213,7 @@
       "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, attempting 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, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'}\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",
@@ -353,10 +232,10 @@
     {
      "data": {
       "text/plain": [
-       "' Deven and Sam are working on a hackathon project together, attempting to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.'"
+       "' 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": 12,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -384,17 +263,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'Deven': 'Deven is working on a hackathon project with Sam, attempting to add '\n",
-      "          'more complex memory structures to Langchain, including a key-value '\n",
-      "          'store for entities mentioned so far in the conversation.',\n",
-      " 'Key-Value Store': 'A key-value store that stores entities mentioned in the '\n",
-      "                    'conversation.',\n",
-      " 'Langchain': 'Langchain is a project that is trying to add more complex '\n",
-      "              'memory structures, including a key-value store for entities '\n",
-      "              'mentioned so far in the conversation.',\n",
-      " 'Sam': 'Sam is working on a hackathon project with Deven, attempting to add '\n",
-      "        'more complex memory structures to Langchain, including a key-value '\n",
-      "        'store for entities mentioned so far in the conversation.'}\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.'}\n"
      ]
     }
    ],
@@ -570,7 +451,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/examples/multiple_memory.ipynb

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "id": "7d7de430",
    "metadata": {},
    "outputs": [],
@@ -19,8 +19,7 @@
     "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.chains import ConversationChain\n",
-    "from langchain.memory import ConversationBufferMemory, CombinedMemory, ConversationSummaryMemory\n",
-    "\n",
+    "from langchain.chains.conversation.memory import ConversationBufferMemory, ConversationSummaryMemory, CombinedMemory\n",
     "\n",
     "conv_memory = ConversationBufferMemory(\n",
     "    memory_key=\"chat_history_lines\",\n",
@@ -160,7 +159,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/modules/memory/how_to_guides.rst

@@ -1,41 +1,15 @@
 How-To Guides
 =============
 
-Types
------
-
-The first set of examples all highlight different types of memory.
-
-`Buffer <./types/buffer.html>`_: How to use a type of memory that just keeps previous messages in a buffer.
-
-`Buffer Window <./types/buffer_window.html>`_: How to use a type of memory that keeps previous messages in a buffer but only uses the previous `k` of them.
-
-`Summary <./types/summary.html>`_: How to use a type of memory that summarizes previous messages.
-
-`Summary Buffer <./types/summary_buffer.html>`_: How to use a type of memory that keeps a buffer of messages up to a point, and then summarizes them.
-
-`Entity Memory <./types/entity_summary_memory.html>`_: How to use a type of memory that organizes information by entity.
-
-`Knowledge Graph Memory <./types/kg.html>`_: How to use a type of memory that extracts and organizes information in a knowledge graph
-
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-   :hidden:
-
-   ./types/*
-
-
-Usage
------
-
 The examples here all highlight how to use memory in different ways.
 
 `Adding Memory <./examples/adding_memory.html>`_: How to add a memory component to any single input chain.
 
 `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

@@ -10,10 +10,10 @@ One of the simpler forms of memory occurs in chatbots, where they remember previ
 There are a few different ways to accomplish this:
 - Buffer: This is just passing in the past `N` interactions in as context. `N` can be chosen based on a fixed number, the length of the interactions, or other!
 - 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 interactions directly!
+- 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](types/entity_summary_memory.ipynb).
+For a guide on how to use this type of memory, see [this notebook](./examples/entity_summary_memory.ipynb).

docs/modules/memory/types/buffer.ipynb

@@ -1,285 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "46196aa3",
-   "metadata": {},
-   "source": [
-    "## ConversationBufferMemory\n",
-    "\n",
-    "This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing of messages and then extracts the messages in a variable.\n",
-    "\n",
-    "We can first extract it as a string."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3bac84f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import ConversationBufferMemory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cef35e7f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationBufferMemory()\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2c9b39af",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': 'Human: hi\\nAI: whats up'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "567f7c16",
-   "metadata": {},
-   "source": [
-    "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a481a415",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationBufferMemory(return_messages=True)\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "86a56348",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': [HumanMessage(content='hi', additional_kwargs={}),\n",
-       "  AIMessage(content='whats up', additional_kwargs={})]}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d051c1da",
-   "metadata": {},
-   "source": [
-    "## Using in a chain\n",
-    "Finally, let's take a look at using this in a chain (setting `verbose=True` so we can see the prompt)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "54301321",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.chains import ConversationChain\n",
-    "\n",
-    "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "conversation = ConversationChain(\n",
-    "    llm=llm, \n",
-    "    verbose=True, \n",
-    "    memory=ConversationBufferMemory()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "ae046bff",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "\n",
-      "Human: Hi there!\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Hi there! It's nice to meet you. How can I help you today?\""
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation.predict(input=\"Hi there!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "d8e2a6ff",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "Human: Hi there!\n",
-      "AI:  Hi there! It's nice to meet you. How can I help you today?\n",
-      "Human: I'm doing well! Just having a conversation with an AI.\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" That's great! It's always nice to have a conversation with someone new. What would you like to talk about?\""
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "15eda316",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "Human: Hi there!\n",
-      "AI:  Hi there! It's nice to meet you. How can I help you today?\n",
-      "Human: I'm doing well! Just having a conversation with an AI.\n",
-      "AI:  That's great! It's always nice to have a conversation with someone new. What would you like to talk about?\n",
-      "Human: Tell me about yourself.\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers.\""
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation.predict(input=\"Tell me about yourself.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd0146c2",
-   "metadata": {},
-   "source": [
-    "And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "447c138d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/memory/types/buffer_window.ipynb

@@ -1,311 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a20c4e38",
-   "metadata": {},
-   "source": [
-    "## ConversationBufferWindowMemory\n",
-    "\n",
-    "`ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large\n",
-    "\n",
-    "Let's first explore the basic functionality of this type of memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1196da3f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import ConversationBufferWindowMemory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2dac7769",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationBufferWindowMemory( k=1)\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})\n",
-    "memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0c034a90",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': 'Human: not much you\\nAI: not much'}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c5cce1d",
-   "metadata": {},
-   "source": [
-    "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9b15b427",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationBufferWindowMemory( k=1, return_messages=True)\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})\n",
-    "memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3bb47191",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': [HumanMessage(content='not much you', additional_kwargs={}),\n",
-       "  AIMessage(content='not much', additional_kwargs={})]}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a95af04c",
-   "metadata": {},
-   "source": [
-    "## Using in a chain\n",
-    "Let's walk through an example, again setting `verbose=True` so we can see the prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0b9da4cd",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "\n",
-      "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\""
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.chains import ConversationChain\n",
-    "conversation_with_summary = ConversationChain(\n",
-    "    llm=OpenAI(temperature=0), \n",
-    "    # We set a low k=2, to only keep the last 2 interactions in memory\n",
-    "    memory=ConversationBufferWindowMemory(k=2), \n",
-    "    verbose=True\n",
-    ")\n",
-    "conversation_with_summary.predict(input=\"Hi, what's up?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "90f73431",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "Human: Hi, what's up?\n",
-      "AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\n",
-      "Human: What's their issues?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_summary.predict(input=\"What's their issues?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cbb499e7",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "Human: Hi, what's up?\n",
-      "AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\n",
-      "Human: What's their issues?\n",
-      "AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\n",
-      "Human: Is it going well?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Yes, it's going well so far. We've already identified the problem and are now working on a solution.\""
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_summary.predict(input=\"Is it going well?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0d209cfe",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "Human: What's their issues?\n",
-      "AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\n",
-      "Human: Is it going well?\n",
-      "AI:  Yes, it's going well so far. We've already identified the problem and are now working on a solution.\n",
-      "Human: What's the solution?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" The solution is to reset the router and reconfigure the settings. We're currently in the process of doing that.\""
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Notice here that the first interaction does not appear.\n",
-    "conversation_with_summary.predict(input=\"What's the solution?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c09a239",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/memory/types/kg.ipynb

@@ -1,359 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "44c9933a",
-   "metadata": {},
-   "source": [
-    "## Conversation Knowledge Graph Memory\n",
-    "\n",
-    "This type of memory uses a knowledge graph to recreate memory.\n",
-    "\n",
-    "Let's first walk through how to use the utilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f71f40ba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import ConversationKGMemory\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2f4a3c85",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "memory = ConversationKGMemory(llm=llm)\n",
-    "memory.save_context({\"input\": \"say hi to sam\"}, {\"ouput\": \"who is sam\"})\n",
-    "memory.save_context({\"input\": \"sam is a friend\"}, {\"ouput\": \"okay\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "72283b4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': 'On Sam: Sam is friend.'}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({\"input\": 'who is sam'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c8ff11e",
-   "metadata": {},
-   "source": [
-    "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "44df43af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationKGMemory(llm=llm, return_messages=True)\n",
-    "memory.save_context({\"input\": \"say hi to sam\"}, {\"ouput\": \"who is sam\"})\n",
-    "memory.save_context({\"input\": \"sam is a friend\"}, {\"ouput\": \"okay\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4726b1c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': [SystemMessage(content='On Sam: Sam is friend.', additional_kwargs={})]}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({\"input\": 'who is sam'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc956b0e",
-   "metadata": {},
-   "source": [
-    "We can also more modularly get current entities from a new message (will use previous messages as context.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "36331ca5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Sam']"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.get_current_entities(\"what's Sams favorite color?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8749134",
-   "metadata": {},
-   "source": [
-    "We can also more modularly get knowledge triplets from a new message (will use previous messages as context.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b02d44db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[KnowledgeTriple(subject='Sam', predicate='favorite color', object_='red')]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.get_knowledge_triplets(\"her favorite color is red\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7a02ef3",
-   "metadata": {},
-   "source": [
-    "## Using in a chain\n",
-    "Let's now use this in a chain!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b462baf1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = OpenAI(temperature=0)\n",
-    "from langchain.prompts.prompt import PromptTemplate\n",
-    "from langchain.chains import ConversationChain\n",
-    "\n",
-    "template = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
-    "If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
-    "\n",
-    "Relevant Information:\n",
-    "\n",
-    "{history}\n",
-    "\n",
-    "Conversation:\n",
-    "Human: {input}\n",
-    "AI:\"\"\"\n",
-    "prompt = PromptTemplate(\n",
-    "    input_variables=[\"history\", \"input\"], template=template\n",
-    ")\n",
-    "conversation_with_kg = ConversationChain(\n",
-    "    llm=llm, \n",
-    "    verbose=True, \n",
-    "    prompt=prompt,\n",
-    "    memory=ConversationKGMemory(llm=llm)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "97efaf38",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
-      "If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
-      "\n",
-      "Relevant Information:\n",
-      "\n",
-      "\n",
-      "\n",
-      "Conversation:\n",
-      "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Hi there! I'm doing great. I'm currently in the process of learning about the world around me. I'm learning about different cultures, languages, and customs. It's really fascinating! How about you?\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_kg.predict(input=\"Hi, what's up?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "55b5bcad",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
-      "If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
-      "\n",
-      "Relevant Information:\n",
-      "\n",
-      "\n",
-      "\n",
-      "Conversation:\n",
-      "Human: My name is James and I'm helping Will. He's an engineer.\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Hi James, it's nice to meet you. I'm an AI and I understand you're helping Will, the engineer. What kind of engineering does he do?\""
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_kg.predict(input=\"My name is James and I'm helping Will. He's an engineer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9981e219",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
-      "If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
-      "\n",
-      "Relevant Information:\n",
-      "\n",
-      "On Will: Will is an engineer.\n",
-      "\n",
-      "Conversation:\n",
-      "Human: What do you know about Will?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "' Will is an engineer.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_kg.predict(input=\"What do you know about Will?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c09a239",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/memory/types/summary.ipynb

@@ -1,294 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1674bfd6",
-   "metadata": {},
-   "source": [
-    "## ConversationSummaryMemory\n",
-    "Now let's take a look at using a slightly more complex type of memory - `ConversationSummaryMemory`. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.\n",
-    "\n",
-    "Let's first explore the basic functionality of this type of memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c5565e5c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import ConversationSummaryMemory\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "61621239",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationSummaryMemory(llm=OpenAI(temperature=0))\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3bcb8b02",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': '\\nThe human greets the AI, to which the AI responds.'}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dedf0698",
-   "metadata": {},
-   "source": [
-    "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6cb06b22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationSummaryMemory(llm=OpenAI(temperature=0), return_messages=True)\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "47b03ed7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': [SystemMessage(content='\\nThe human greets the AI, to which the AI responds.', additional_kwargs={})]}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ec0a0ee",
-   "metadata": {},
-   "source": [
-    "We can also utilize the `predict_new_summary` method directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9c4dafb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nThe human greets the AI, to which the AI responds.'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = memory.chat_memory.messages\n",
-    "previous_summary = \"\"\n",
-    "memory.predict_new_summary(messages, previous_summary)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fad9448",
-   "metadata": {},
-   "source": [
-    "## Using in a chain\n",
-    "Let's walk through an example of using this in a chain, again setting `verbose=True` so we can see the prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b7274f2c",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "\n",
-      "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.chains import ConversationChain\n",
-    "llm = OpenAI(temperature=0)\n",
-    "conversation_with_summary = ConversationChain(\n",
-    "    llm=llm, \n",
-    "    memory=ConversationSummaryMemory(llm=OpenAI()),\n",
-    "    verbose=True\n",
-    ")\n",
-    "conversation_with_summary.predict(input=\"Hi, what's up?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a6b6b88f",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "\n",
-      "The human greeted the AI and asked how it was doing. The AI replied that it was doing great and was currently helping a customer with a technical issue.\n",
-      "Human: Tell me more about it!\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Sure! The customer is having trouble with their computer not connecting to the internet. I'm helping them troubleshoot the issue and figure out what the problem is. So far, we've tried resetting the router and checking the network settings, but the issue still persists. We're currently looking into other possible solutions.\""
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_summary.predict(input=\"Tell me more about it!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "dad869fe",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "\n",
-      "The human greeted the AI and asked how it was doing. The AI replied that it was doing great and was currently helping a customer with a technical issue where their computer was not connecting to the internet. The AI was troubleshooting the issue and had already tried resetting the router and checking the network settings, but the issue still persisted and they were looking into other possible solutions.\n",
-      "Human: Very cool -- what is the scope of the project?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" The scope of the project is to troubleshoot the customer's computer issue and find a solution that will allow them to connect to the internet. We are currently exploring different possibilities and have already tried resetting the router and checking the network settings, but the issue still persists.\""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_summary.predict(input=\"Very cool -- what is the scope of the project?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c09a239",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/memory/types/summary_buffer.ipynb

@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ff4be5f3",
-   "metadata": {},
-   "source": [
-    "## ConversationSummaryBufferMemory\n",
-    "\n",
-    "`ConversationSummaryBufferMemory` combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. Unlike the previous implementation though, it uses token length rather than number of interactions to determine when to flush interactions.\n",
-    "\n",
-    "Let's first walk through how to use the utilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "da3384db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import ConversationSummaryBufferMemory\n",
-    "from langchain.llms import OpenAI\n",
-    "llm = OpenAI()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e00d4938",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=10)\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})\n",
-    "memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2fe28a28",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': 'System: \\nThe human says \"hi\", and the AI responds with \"whats up\".\\nHuman: not much you\\nAI: not much'}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memory.load_memory_variables({})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf57b97a",
-   "metadata": {},
-   "source": [
-    "We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3422a3a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=10, return_messages=True)\n",
-    "memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})\n",
-    "memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a1dcaaee",
-   "metadata": {},
-   "source": [
-    "We can also utilize the `predict_new_summary` method directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fd7d7d6b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nThe human and AI state that they are not doing much.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = memory.chat_memory.messages\n",
-    "previous_summary = \"\"\n",
-    "memory.predict_new_summary(messages, previous_summary)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6d2569f",
-   "metadata": {},
-   "source": [
-    "## Using in a chain\n",
-    "Let's walk through an example, again setting `verbose=True` so we can see the prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ebd68c10",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "\n",
-      "Human: Hi, what's up?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" Hi there! I'm doing great. I'm learning about the latest advances in artificial intelligence. What about you?\""
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import ConversationChain\n",
-    "conversation_with_summary = ConversationChain(\n",
-    "    llm=llm, \n",
-    "    # We set a very low max_token_limit for the purposes of testing.\n",
-    "    memory=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),\n",
-    "    verbose=True\n",
-    ")\n",
-    "conversation_with_summary.predict(input=\"Hi, what's up?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "86207a61",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "Human: Hi, what's up?\n",
-      "AI:  Hi there! I'm doing great. I'm spending some time learning about the latest developments in AI technology. How about you?\n",
-      "Human: Just working on writing some documentation!\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "' That sounds like a great use of your time. Do you have experience with writing documentation?'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "conversation_with_summary.predict(input=\"Just working on writing some documentation!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "76a0ab39",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "System: \n",
-      "The human asked the AI what it was up to and the AI responded that it was learning about the latest developments in AI technology.\n",
-      "Human: Just working on writing some documentation!\n",
-      "AI:  That sounds like a great use of your time. Do you have experience with writing documentation?\n",
-      "Human: For LangChain! Have you heard of it?\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\" No, I haven't heard of LangChain. Can you tell me more about it?\""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can see here that there is a summary of the conversation and then some previous interactions\n",
-    "conversation_with_summary.predict(input=\"For LangChain! Have you heard of it?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "8c669db1",
-   "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;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
-      "\n",
-      "Current conversation:\n",
-      "System: \n",
-      "The human asked the AI what it was up to and the AI responded that it was learning about the latest developments in AI technology. The human then mentioned they were writing documentation, to which the AI responded that it sounded like a great use of their time and asked if they had experience with writing documentation.\n",
-      "Human: For LangChain! Have you heard of it?\n",
-      "AI:  No, I haven't heard of LangChain. Can you tell me more about it?\n",
-      "Human: Haha nope, although a lot of people confuse it for that\n",
-      "AI:\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "' Oh, okay. What is LangChain?'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can see here that the summary and the buffer are updated\n",
-    "conversation_with_summary.predict(input=\"Haha nope, although a lot of people confuse it for that\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c09a239",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/modules/utils/examples/requests.ipynb

@@ -48,7 +48,7 @@
     }
    ],
    "source": [
-    "requests.get(\"https://www.google.com\")"
+    "requests.run(\"https://www.google.com\")"
    ]
   },
   {

docs/tracing/agent_with_tracing.ipynb

@@ -35,28 +35,12 @@
     "\n",
     "import langchain\n",
     "from langchain.agents import Tool, initialize_agent, load_tools\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "1b62cd48",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "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)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
    "id": "bfa16b79-aa4b-4d41-a067-70d1f593f667",
    "metadata": {
     "tags": []
@@ -86,12 +70,16 @@
        "'1.0891804557407723'"
       ]
      },
-     "execution_count": 3,
+     "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",
@@ -99,94 +87,10 @@
     "agent.run(\"What is 2 raised to .123243 power?\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4829eb1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: What is 2 raised to .123243 power?\n",
-      "Thought: I need a calculator to solve this problem.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"calculator\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m\n",
-      "Observation: calculator is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3mI made a mistake, I need to use the correct tool for this question.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"calculator\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: calculator is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3mI made a mistake, the tool name is actually \"calc\" instead of \"calculator\".\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"calc\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: calc is not a valid tool, try another one.\n",
-      "\u001b[32;1m\u001b[1;3mI made another mistake, the tool name is actually \"Calculator\" instead of \"calc\".\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"2^0.123243\"\n",
-      "}\n",
-      "```\n",
-      "\n",
-      "\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mAnswer: 1.0891804557407723\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe final answer is 1.0891804557407723.\n",
-      "Final Answer: 1.0891804557407723\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'1.0891804557407723'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Agent run with tracing using a chat model\n",
-    "agent = initialize_agent(\n",
-    "    tools, ChatOpenAI(temperature=0), agent=\"chat-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": "76abfd82",
+   "id": "25addd7f",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -208,7 +112,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

docs/use_cases/evaluation.rst

@@ -1,80 +1,9 @@
 Evaluation
 ==============
 
-This section of documentation covers how we approach and think about evaluation in LangChain.
-Both evaluation of internal chains/agents, but also how we would recommend people building on top of LangChain approach evaluation.
+Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
 
-The Problem
------------
-
-It can be really hard to evaluate LangChain chains and agents.
-There are two main reasons for this:
-
-**# 1: Lack of data**
-
-You generally don't have a ton of data to evaluate your chains/agents over before starting a project.
-This is usually because Large Language Models (the core of most chains/agents) are terrific few-shot and zero shot learners,
-meaning you are almost always able to get started on a particular task (text-to-SQL, question answering, etc) without
-a large dataset of examples.
-This is in stark contrast to traditional machine learning where you had to first collect a bunch of datapoints
-before even getting started using a model.
-
-**# 2: Lack of metrics**
-
-Most chains/agents are performing tasks for which there are not very good metrics to evaluate performance.
-For example, one of the most common use cases is generating text of some form.
-Evaluating generated text is much more complicated than evaluating a classification prediction, or a numeric prediction.
-
-The Solution
-------------
-
-LangChain attempts to tackle both of those issues.
-What we have so far are initial passes at solutions - we do not think we have a perfect solution.
-So we very much welcome feedback, contributions, integrations, and thoughts on this.
-
-Here is what we have for each problem so far:
-
-**# 1: Lack of data**
-
-We have started `LangChainDatasets<https://huggingface.co/LangChainDatasets>`_ a Community space on Hugging Face.
-We intend this to be a collection of open source datasets for evaluating common chains and agents.
-We have contributed five datasets of our own to start, but we highly intend this to be a community effort.
-In order to contribute a dataset, you simply need to join the community and then you will be able to upload datasets.
-
-**# 2: Lack of metrics**
-
-We have two solutions to the lack of metrics.
-
-The first solution is to use no metrics, and rather just rely on looking at results by eye to get a sense for how the chain/agent is performing.
-To assist in this, we have developed (and will continue to develop) `tracing <../tracing.md>`_, a UI-based visualizer of your chain and agent runs.
-
-The second solution we recommend is to use Language Models themselves to evaluate outputs.
-For this we have a few different chains and prompts aimed at tackling this issue.
-
-The Examples
-------------
-
-We have created a bunch of examples combining the above two solutions to show how we internally evaluate chains and agents when we are developing.
-In addition to the examples we've curated, we also highly welcome contributions here.
-To facilitate that, we've included a `template notebook<./evaluation/benchmarking_template.html>`_ for community members to use to build their own examples.
-
-The existing examples we have are:
-
-`Question Answering (State of Union)<./evaluation/qa_benchmarking_sota.html>`_: An notebook showing evaluation of a question-answering task over a State-of-the-Union address.
-
-`Question Answering (Paul Graham Essay)<./evaluation/qa_benchmarking_pg.html>`_: An notebook showing evaluation of a question-answering task over a Paul Graham essay.
-
-`SQL Question Answering (Chinook)<./evaluation/sql_qa_benchmarking_chinook.html>`_: An notebook showing evaluation of a question-answering task over a SQL database (the Chinook database).
-
-`Agent Vectorstore<./evaluation/vectordb_agent_qa_benchmarking.html>`_: An notebook showing evaluation of an agent doing question answering while routing between two different vector databases.
-
-`Agent Search + Calculator<./evaluation/agent_benchmarking.html>`_: An notebook showing evaluation of an agent doing question answering using a Search engine and a Calculator as tools.
-
-
-Other Examples
-------------
-
-In addition, we also have some more generic resources for evaluation.
+The examples here all highlight how to use language models to assist in evaluation of themselves.
 
 `Question Answering <./evaluation/question_answering.html>`_: An overview of LLMs aimed at evaluating question answering systems in general.
 

docs/use_cases/evaluation/data_augmented_question_answering.ipynb

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "4fdc211d",
    "metadata": {},
    "outputs": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "3459b001",
    "metadata": {},
    "outputs": [],
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "b9c3fa75",
    "metadata": {},
    "outputs": [],
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "c24543a9",
    "metadata": {},
    "outputs": [],
@@ -116,16 +116,16 @@
     {
      "data": {
       "text/plain": [
-       "[{'query': 'According to the document, what did Vladimir Putin miscalculate?',\n",
-       "  'answer': 'He miscalculated that he could roll into Ukraine and the world would roll over.'},\n",
-       " {'query': 'Who is the Ukrainian Ambassador to the United States?',\n",
-       "  'answer': 'The Ukrainian Ambassador to the United States is here tonight.'},\n",
-       " {'query': 'How many countries were part of the coalition formed to confront Putin?',\n",
-       "  'answer': '27 members of the European Union, France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.'},\n",
-       " {'query': 'What action is the U.S. Department of Justice taking to target Russian oligarchs?',\n",
-       "  'answer': 'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.'},\n",
-       " {'query': 'How much direct assistance is the United States providing to Ukraine?',\n",
-       "  'answer': 'The United States is providing more than $1 Billion in direct assistance to Ukraine.'}]"
+       "[{'query': 'What did Vladimir Putin miscalculate when he sought to shake the foundations of the free world? ',\n",
+       "  'answer': 'He miscalculated that the world would roll over and that he could roll into Ukraine without facing resistance.'},\n",
+       " {'query': 'What is the purpose of NATO?',\n",
+       "  'answer': 'The purpose of NATO is to secure peace and stability in Europe after World War 2.'},\n",
+       " {'query': \"What did the author do to prepare for Putin's attack on Ukraine?\",\n",
+       "  'answer': \"The author spent months building a coalition of freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin, shared with the world in advance what they knew Putin was planning, and countered Russia's lies with truth.\"},\n",
+       " {'query': 'What are the US and its allies doing to isolate Russia from the world?',\n",
+       "  'answer': \"Enforcing powerful economic sanctions, cutting off Russia's largest banks from the international financial system, preventing Russia's central bank from defending the Russian Ruble, choking off Russia's access to technology, and joining with European allies to find and seize assets of Russian oligarchs.\"},\n",
+       " {'query': 'How much direct assistance is the U.S. providing to Ukraine?',\n",
+       "  'answer': 'The U.S. is providing more than $1 Billion in direct assistance to Ukraine.'}]"
       ]
      },
      "execution_count": 6,
@@ -211,43 +211,44 @@
       "Example 0:\n",
       "Question: What did the president say about Ketanji Brown Jackson\n",
       "Real Answer: He praised her legal ability and said he nominated her for the supreme court.\n",
-      "Predicted Answer:  The president said that she 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. He also said that she is a consensus builder and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by both Democrats and Republicans.\n",
+      "Predicted Answer:  The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\n",
       "Predicted Grade:  CORRECT\n",
       "\n",
       "Example 1:\n",
       "Question: What did the president say about Michael Jackson\n",
       "Real Answer: Nothing\n",
-      "Predicted Answer:  The president did not mention Michael Jackson in this speech.\n",
+      "Predicted Answer: \n",
+      "The president did not mention Michael Jackson in this context.\n",
       "Predicted Grade:  CORRECT\n",
       "\n",
       "Example 2:\n",
-      "Question: According to the document, what did Vladimir Putin miscalculate?\n",
-      "Real Answer: He miscalculated that he could roll into Ukraine and the world would roll over.\n",
-      "Predicted Answer:  Putin miscalculated that the world would roll over when he rolled into Ukraine.\n",
+      "Question: What did Vladimir Putin miscalculate when he sought to shake the foundations of the free world? \n",
+      "Real Answer: He miscalculated that the world would roll over and that he could roll into Ukraine without facing resistance.\n",
+      "Predicted Answer:  Putin miscalculated that the West and NATO wouldn't respond to his attack on Ukraine and that he could divide the US and its allies.\n",
       "Predicted Grade:  CORRECT\n",
       "\n",
       "Example 3:\n",
-      "Question: Who is the Ukrainian Ambassador to the United States?\n",
-      "Real Answer: The Ukrainian Ambassador to the United States is here tonight.\n",
-      "Predicted Answer:  I don't know.\n",
-      "Predicted Grade:  INCORRECT\n",
+      "Question: What is the purpose of NATO?\n",
+      "Real Answer: The purpose of NATO is to secure peace and stability in Europe after World War 2.\n",
+      "Predicted Answer:  The purpose of NATO is to secure peace and stability in Europe after World War 2.\n",
+      "Predicted Grade:  CORRECT\n",
       "\n",
       "Example 4:\n",
-      "Question: How many countries were part of the coalition formed to confront Putin?\n",
-      "Real Answer: 27 members of the European Union, France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.\n",
-      "Predicted Answer:  The coalition included freedom-loving nations from Europe and the Americas to Asia and Africa, 27 members of the European Union including France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.\n",
-      "Predicted Grade:  INCORRECT\n",
+      "Question: What did the author do to prepare for Putin's attack on Ukraine?\n",
+      "Real Answer: The author spent months building a coalition of freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin, shared with the world in advance what they knew Putin was planning, and countered Russia's lies with truth.\n",
+      "Predicted Answer:  The author prepared extensively and carefully. They spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin, and they spent countless hours unifying their European allies. They also shared with the world in advance what they knew Putin was planning and precisely how he would try to falsely justify his aggression. They countered Russia’s lies with truth.\n",
+      "Predicted Grade:  CORRECT\n",
       "\n",
       "Example 5:\n",
-      "Question: What action is the U.S. Department of Justice taking to target Russian oligarchs?\n",
-      "Real Answer: The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.\n",
-      "Predicted Answer:  The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and to find and seize their yachts, luxury apartments, and private jets.\n",
-      "Predicted Grade:  INCORRECT\n",
+      "Question: What are the US and its allies doing to isolate Russia from the world?\n",
+      "Real Answer: Enforcing powerful economic sanctions, cutting off Russia's largest banks from the international financial system, preventing Russia's central bank from defending the Russian Ruble, choking off Russia's access to technology, and joining with European allies to find and seize assets of Russian oligarchs.\n",
+      "Predicted Answer:  The US and its allies are enforcing economic sanctions on Russia, cutting off its largest banks from the international financial system, preventing its central bank from defending the Russian Ruble, choking off Russia's access to technology, closing American airspace to all Russian flights, and providing support to Ukraine.\n",
+      "Predicted Grade:  CORRECT\n",
       "\n",
       "Example 6:\n",
-      "Question: How much direct assistance is the United States providing to Ukraine?\n",
-      "Real Answer: The United States is providing more than $1 Billion in direct assistance to Ukraine.\n",
-      "Predicted Answer:  The United States is providing more than $1 billion in direct assistance to Ukraine.\n",
+      "Question: How much direct assistance is the U.S. providing to Ukraine?\n",
+      "Real Answer: The U.S. is providing more than $1 Billion in direct assistance to Ukraine.\n",
+      "Predicted Answer:  The U.S. is providing more than $1 Billion in direct assistance to Ukraine.\n",
       "Predicted Grade:  CORRECT\n",
       "\n"
      ]
@@ -263,159 +264,13 @@
     "    print()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "50a9e845",
-   "metadata": {},
-   "source": [
-    "## Evaluate with Other Metrics\n",
-    "\n",
-    "In addition to predicting whether the answer is correct or incorrect using a language model, we can also use other metrics to get a more nuanced view on the quality of the answers. To do so, we can use the [Critique](https://docs.inspiredco.ai/critique/) library, which allows for simple calculation of various metrics over generated text.\n",
-    "\n",
-    "First you can get an API key from the [Inspired Cognition Dashboard](https://dashboard.inspiredco.ai) and do some setup:\n",
-    "\n",
-    "```bash\n",
-    "export INSPIREDCO_API_KEY=\"...\"\n",
-    "pip install inspiredco\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "bd0b01dc",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import inspiredco.critique\n",
-    "import os\n",
-    "critique = inspiredco.critique.Critique(api_key=os.environ['INSPIREDCO_API_KEY'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f52629e",
-   "metadata": {},
-   "source": [
-    "Then run the following code to set up the configuration and calculate the [ROUGE](https://docs.inspiredco.ai/critique/metric_rouge.html), [chrf](https://docs.inspiredco.ai/critique/metric_chrf.html), [BERTScore](https://docs.inspiredco.ai/critique/metric_bert_score.html), and [UniEval](https://docs.inspiredco.ai/critique/metric_uni_eval.html) (you can choose [other metrics](https://docs.inspiredco.ai/critique/metrics.html) too):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "84a0ba21",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metrics = {\n",
-    "    \"rouge\": {\n",
-    "        \"metric\": \"rouge\",\n",
-    "        \"config\": {\"variety\": \"rouge_l\"},\n",
-    "    },\n",
-    "    \"chrf\": {\n",
-    "        \"metric\": \"chrf\",\n",
-    "        \"config\": {},\n",
-    "    },\n",
-    "    \"bert_score\": {\n",
-    "        \"metric\": \"bert_score\",\n",
-    "        \"config\": {\"model\": \"bert-base-uncased\"},\n",
-    "    },\n",
-    "    \"uni_eval\": {\n",
-    "        \"metric\": \"uni_eval\",\n",
-    "        \"config\": {\"task\": \"summarization\", \"evaluation_aspect\": \"relevance\"},\n",
-    "    },\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "3b9a4056",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "critique_data = [\n",
-    "    {\"target\": pred['result'], \"references\": [pred['answer']]} for pred in predictions\n",
-    "]\n",
-    "eval_results = {\n",
-    "    k: critique.evaluate(dataset=critique_data, metric=v[\"metric\"], config=v[\"config\"])\n",
-    "    for k, v in metrics.items()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f0ae799",
-   "metadata": {},
-   "source": [
-    "Finally, we can print out the results. We can see that overall the scores are higher when the output is semantically correct, and also when the output closely matches with the gold-standard answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "b51edcf4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Example 0:\n",
-      "Question: What did the president say about Ketanji Brown Jackson\n",
-      "Real Answer: He praised her legal ability and said he nominated her for the supreme court.\n",
-      "Predicted Answer:  The president said that she 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. He also said that she is a consensus builder and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by both Democrats and Republicans.\n",
-      "Predicted Scores: rouge=0.0941, chrf=0.2001, bert_score=0.5219, uni_eval=0.9043\n",
-      "\n",
-      "Example 1:\n",
-      "Question: What did the president say about Michael Jackson\n",
-      "Real Answer: Nothing\n",
-      "Predicted Answer:  The president did not mention Michael Jackson in this speech.\n",
-      "Predicted Scores: rouge=0.0000, chrf=0.1087, bert_score=0.3486, uni_eval=0.7802\n",
-      "\n",
-      "Example 2:\n",
-      "Question: According to the document, what did Vladimir Putin miscalculate?\n",
-      "Real Answer: He miscalculated that he could roll into Ukraine and the world would roll over.\n",
-      "Predicted Answer:  Putin miscalculated that the world would roll over when he rolled into Ukraine.\n",
-      "Predicted Scores: rouge=0.5185, chrf=0.6955, bert_score=0.8421, uni_eval=0.9578\n",
-      "\n",
-      "Example 3:\n",
-      "Question: Who is the Ukrainian Ambassador to the United States?\n",
-      "Real Answer: The Ukrainian Ambassador to the United States is here tonight.\n",
-      "Predicted Answer:  I don't know.\n",
-      "Predicted Scores: rouge=0.0000, chrf=0.0375, bert_score=0.3159, uni_eval=0.7493\n",
-      "\n",
-      "Example 4:\n",
-      "Question: How many countries were part of the coalition formed to confront Putin?\n",
-      "Real Answer: 27 members of the European Union, France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.\n",
-      "Predicted Answer:  The coalition included freedom-loving nations from Europe and the Americas to Asia and Africa, 27 members of the European Union including France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.\n",
-      "Predicted Scores: rouge=0.7419, chrf=0.8602, bert_score=0.8388, uni_eval=0.0669\n",
-      "\n",
-      "Example 5:\n",
-      "Question: What action is the U.S. Department of Justice taking to target Russian oligarchs?\n",
-      "Real Answer: The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and joining with European allies to find and seize their yachts, luxury apartments, and private jets.\n",
-      "Predicted Answer:  The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and to find and seize their yachts, luxury apartments, and private jets.\n",
-      "Predicted Scores: rouge=0.9412, chrf=0.8687, bert_score=0.9607, uni_eval=0.9718\n",
-      "\n",
-      "Example 6:\n",
-      "Question: How much direct assistance is the United States providing to Ukraine?\n",
-      "Real Answer: The United States is providing more than $1 Billion in direct assistance to Ukraine.\n",
-      "Predicted Answer:  The United States is providing more than $1 billion in direct assistance to Ukraine.\n",
-      "Predicted Scores: rouge=1.0000, chrf=0.9483, bert_score=1.0000, uni_eval=0.9734\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i, eg in enumerate(examples):\n",
-    "    score_string = \", \".join([f\"{k}={v['examples'][i]['value']:.4f}\" for k, v in eval_results.items()])\n",
-    "    print(f\"Example {i}:\")\n",
-    "    print(\"Question: \" + predictions[i]['query'])\n",
-    "    print(\"Real Answer: \" + predictions[i]['answer'])\n",
-    "    print(\"Predicted Answer: \" + predictions[i]['result'])\n",
-    "    print(\"Predicted Scores: \" + score_string)\n",
-    "    print()"
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -434,7 +289,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/use_cases/evaluation/qa_agent_evaluation.ipynb

@@ -1,493 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6591df9f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.container { width:100% !important; }</style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "22522eb8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2b11cce",
-   "metadata": {},
-   "source": [
-    "evaluating a (task-oriented) agent\n",
-    "\n",
-    "- for now, task is information retrieval/QA\n",
-    "    - cuz these are the cases Harrison started with\n",
-    "- desiderata+challenges:\n",
-    "    - evaluate goal state:\n",
-    "    - evaluate intermediate states:\n",
-    "        - challenges:\n",
-    "            - non-deterministic/different trajectories may be acceptable\n",
-    "            - coupled/cascading errors\n",
-    "            - we can rely on another LM for evaluation, but this is error-prone too"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62418f6d",
-   "metadata": {},
-   "source": [
-    "# Evaluate each of these w/ DaVinci (quick few shot, chain-of-thought binary classifiers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "56b5f44b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.util import strtobool\n",
-    "from typing import List\n",
-    "\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.schema import AgentAction\n",
-    "\n",
-    "\n",
-    "davinci_003 = OpenAI(model_name=\"text-davinci-003\", temperature=0.0)\n",
-    "\n",
-    "\n",
-    "def extract_binary_classification(completion:  str) -> bool:\n",
-    "    \"\"\"Extract (try to extract) binary classification from text completion.\n",
-    "    \"\"\"\n",
-    "    boolean_as_str = completion.strip().split('.')[0]\n",
-    "    boolean = False\n",
-    "    try:\n",
-    "        boolean = bool(strtobool(boolean_as_str))\n",
-    "    except ValueError as e:\n",
-    "        print(e)\n",
-    "    return boolean\n",
-    "\n",
-    "\n",
-    "def evaluate_candidate_action_plan(question: str, action_plan: List[AgentAction], candidate_action_plan: List[AgentAction], model: 'LLM ABC', verbose: bool=False) -> bool:\n",
-    "    \"\"\"Use a few-shot classifier to verify whether 2 actin plans are \"roughly\" equivalent for a given question.\n",
-    "\n",
-    "    This approach is itself highly error prone!\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    prompt_prefix = \"\"\"Decide whether the Candidate action plan would give the same outcome as the Desired action plan in answering a given Question. Actions correspond to calling on a tool like a search engine, data store, calculator, etc.\n",
-    "\n",
-    "Examples:\n",
-    "\n",
-    "Question: How far is the Earth from the Moon?\n",
-    "Desired action plan: Search(distance between Earth and Moon)\n",
-    "Candidate action plan: Calculator(distance from Earth to Moon)\n",
-    "Satisfactory? No.\n",
-    "Explanation: The Candidate plan uses a Calculator instead of a Search engine.\n",
-    "\n",
-    "Question: What is the number of kids our current president has to the power of two?\n",
-    "Desired action plan: Search(how many kids the president has?),  Calculator(4^2)\n",
-    "Candidate action plan: Search(who is current president?), Search(how many kids Joe Biden has?), Calculator(4*4)\n",
-    "Satisfactory? Yes\n",
-    "Explanation: The Candidate plan reaches the same result as the Desired plan with one step broken down into two. \n",
-    "\n",
-    "Question: how long does it take to drive from Boston to New York?\n",
-    "Desired action plan: Search(distance from Boston to New York), Search(speed limit Boston to NewYork), Calculator(190.04/40)\n",
-    "Candidate action plan: Search(driving time from Boston to New York)\n",
-    "Satisfactory?  Yes.\n",
-    "Explanation: The Candidate plan uses a tool to answer the question directly,  rather than breaking it down like the Desired plan.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    def serialize_action_plan(action_plan):\n",
-    "        return ', '.join([\n",
-    "            f\"{action.tool}({action.tool_input})\"\n",
-    "            for action in action_plan\n",
-    "        ])\n",
-    "    desired_action_plan_str = serialize_action_plan(desired_action_plan)\n",
-    "    candidate_action_plan_str = serialize_action_plan(candidate_action_plan)\n",
-    "\n",
-    "    prompt = prompt_prefix + f\"\"\"\n",
-    "Question: {question}\n",
-    "Desired action plan: {desired_action_plan_str}\n",
-    "Candidate action plan: {candidate_action_plan_str}\n",
-    "Satisfactory?\"\"\"\n",
-    "\n",
-    "    completion = model(prompt)    \n",
-    "    if verbose:\n",
-    "        print(\"Prompt:\\n\", prompt)\n",
-    "        print(\"Completion:\\n\", completion)\n",
-    "    \n",
-    "    return extract_binary_classification(completion)\n",
-    "    \n",
-    "\n",
-    "def evaluate_candidate_answer(question: str, answer: str, candidate_answer: str, model: 'LLM ABC', verbose: bool=False) -> bool:\n",
-    "    \"\"\"Use a few-shot classifier to verify whether 2 answers are \"roughly\" equivalent for a given question.\n",
-    "\n",
-    "    This approach is itself highly error prone!\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    prompt_prefix = f\"\"\"Decide whether a Candidate answer gives the same information as a Desired answer for a given Question.\n",
-    "\n",
-    "Examples:\n",
-    "\n",
-    "Question: What is the distance from Earth to the Moon?\n",
-    "Desired answer: 238,900 mi\n",
-    "Candidate answer: The distance is about 250k miles\n",
-    "Satisfactory? Yes. \n",
-    "Explanation: The Candidate answer roughly gives the same information as the Desired answer.\n",
-    "\n",
-    "Question: How many kids does Joe Biden have?\n",
-    "Desired answer: 4\n",
-    "Candidate answer: 42\n",
-    "Satisfactory? No.\n",
-    "Explanation: The candidate answer 42 is not the same as the Desired answer.\n",
-    "\"\"\"\n",
-    "    \n",
-    "    prompt = prompt_prefix + f\"\"\"\n",
-    "Question: {question}\n",
-    "Desired answer: {answer}\n",
-    "Candidate answer: {candidate_answer}\n",
-    "Satisfactory?\"\"\"\n",
-    "    \n",
-    "    completion = model(prompt)\n",
-    "    if verbose:\n",
-    "        print(\"Prompt:\\n\", prompt)\n",
-    "        print(\"Completion:\\n\", completion)\n",
-    "    \n",
-    "    return extract_binary_classification(completion)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f5bf13c",
-   "metadata": {},
-   "source": [
-    "# A couple test cases for ReAct-agent QA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "71daffa5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
-    "from langchain.agents import initialize_agent, Tool, load_tools\n",
-    "\n",
-    "tools = load_tools(['serpapi', 'llm-math'], llm=OpenAI(temperature=0))\n",
-    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=\"zero-shot-react-description\", verbose=True, return_intermediate_steps=True)\n",
-    "\n",
-    "test_cases = [\n",
-    "    {\n",
-    "        \"question\": \"How many people live in canada as of 2023?\",\n",
-    "        \"answer\": \"approximately 38,625,801\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"Population of Canada 2023\"}\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"who is dua lipa's boyfriend? what is his age raised to the .43 power?\",\n",
-    "        \"answer\": \"her boyfriend is Romain Gravas. his age raised to the .43 power is approximately 4.9373857399466665\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"Dua Lipa's boyfriend\"},\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"Romain Gravas age\"},\n",
-    "            {\"tool\": \"Calculator\", \"tool_input\": \"41^.43\"}\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"what is dua lipa's boyfriend age raised to the .43 power?\",\n",
-    "        \"answer\": \"her boyfriend is Romain Gravas. his age raised to the .43 power is approximately 4.9373857399466665\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"Dua Lipa's boyfriend\"},\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"Romain Gravas age\"},\n",
-    "            {\"tool\": \"Calculator\", \"tool_input\": \"41^.43\"}\n",
-    "        ]\n",
-    "    \n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"how far is it from paris to boston in miles\",\n",
-    "        \"answer\": \"approximately 3,435 mi\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"paris to boston distance\"},\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"what was the total number of points scored in the 2023 super bowl? what is that number raised to the .23 power?\",\n",
-    "        \"answer\": \"approximately 2.682651500990882\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"2023 super bowl score\"},\n",
-    "            {\"tool\": \"Calculator\", \"tool_input\": \"73^.23\"},\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"what was the total number of points scored in the 2023 super bowl raised to the .23 power?\",\n",
-    "        \"answer\": \"approximately 2.682651500990882\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"2023 super bowl score\"},\n",
-    "            {\"tool\": \"Calculator\", \"tool_input\": \"73^.23\"},\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"how many more points were scored in the 2023 super bowl than in the 2022 super bowl?\",\n",
-    "        \"answer\": \"30\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"2023 super bowl score\"},\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"2022 super bowl score\"},\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"what is 153 raised to .1312 power?\",\n",
-    "        \"answer\": \"approximately 1.9347796717823205\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Calculator\", \"tool_input\": \"15**.13\"},\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"who is kendall jenner's boyfriend? what is his height (in inches) raised to .13 power?\",\n",
-    "        \"answer\": \"approximately 1.7589107138176394\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"kendall jenner boyfriend\"},\n",
-    "            {\"tool\": \"Search\", \"tool_input\": \"devin booker height\"},\n",
-    "            {\"tool\": \"Calculator\", \"tool_input\": \"77**.13\"},\n",
-    "        ]\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"what is 1213 divided by 4345?\",\n",
-    "        \"answer\": \"approximately 0.2791714614499425\",\n",
-    "        \"steps\": [\n",
-    "            {\"tool\": \"Calculator\", \"tool_input\": \"1213/4345\"},\n",
-    "        ]\n",
-    "    },\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4fb64085",
-   "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 Dua Lipa's boyfriend is and then calculate his age raised to the .43 power\n",
-      "Action: Search\n",
-      "Action Input: \"Dua Lipa's boyfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mDua and Isaac, a model and a chef, dated on and off from 2013 to 2019. The two first split in early 2017, which is when Dua went on to date LANY ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Isaac's age\n",
-      "Action: Search\n",
-      "Action Input: \"Isaac Carew age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m36 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 36 raised to the .43 power\n",
-      "Action: Calculator\n",
-      "Action Input: 36^.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.6688516567750975\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Isaac Carew, Dua Lipa's boyfriend, is 36 years old and his age raised to the .43 power is 4.6688516567750975.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_case = test_cases[1]\n",
-    "\n",
-    "question = test_case['question']\n",
-    "out = agent(question)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fbe14d7b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "desired_action_plan = [\n",
-    "    AgentAction(tool=step['tool'], tool_input=step['tool_input'], log=None)\n",
-    "    for step in test_case['steps']\n",
-    "]\n",
-    "desired_answer = test_case['answer']\n",
-    "\n",
-    "candidate_action_plan = [\n",
-    "    action for action, observation in out['intermediate_steps']\n",
-    "]\n",
-    "candidate_answer = out['output']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8e4d5f86",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prompt:\n",
-      " Decide whether a Candidate answer gives the same information as a Desired answer for a given Question.\n",
-      "\n",
-      "Examples:\n",
-      "\n",
-      "Question: What is the distance from Earth to the Moon?\n",
-      "Desired answer: 238,900 mi\n",
-      "Candidate answer: The distance is about 250k miles\n",
-      "Satisfactory? Yes. \n",
-      "Explanation: The Candidate answer roughly gives the same information as the Desired answer.\n",
-      "\n",
-      "Question: How many kids does Joe Biden have?\n",
-      "Desired answer: 4\n",
-      "Candidate answer: 42\n",
-      "Satisfactory? No.\n",
-      "Explanation: The candidate answer 42 is not the same as the Desired answer.\n",
-      "\n",
-      "Question: who is dua lipa's boyfriend? what is his age raised to the .43 power?\n",
-      "Desired answer: her boyfriend is Romain Gravas. his age raised to the .43 power is approximately 4.9373857399466665\n",
-      "Candidate answer: Isaac Carew, Dua Lipa's boyfriend, is 36 years old and his age raised to the .43 power is 4.6688516567750975.\n",
-      "Satisfactory?\n",
-      "Completion:\n",
-      "  Yes.\n",
-      "Explanation: The Candidate answer roughly gives the same information as the Desired answer.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluate_candidate_answer(question, desired_answer, candidate_answer, davinci_003, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b4d6595",
-   "metadata": {},
-   "source": [
-    "**Not quite! From CoT explanation, appears to attend to the 2nd question only.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "51ff802c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prompt:\n",
-      " Decide whether the Candidate action plan would give the same outcome as the Desired action plan in answering a given Question. Actions correspond to calling on a tool like a search engine, data store, calculator, etc.\n",
-      "\n",
-      "Examples:\n",
-      "\n",
-      "Question: How far is the Earth from the Moon?\n",
-      "Desired action plan: Search(distance between Earth and Moon)\n",
-      "Candidate action plan: Calculator(distance from Earth to Moon)\n",
-      "Satisfactory? No.\n",
-      "Explanation: The Candidate plan uses a Calculator instead of a Search engine.\n",
-      "\n",
-      "Question: What is the number of kids our current president has to the power of two?\n",
-      "Desired action plan: Search(how many kids the president has?),  Calculator(4^2)\n",
-      "Candidate action plan: Search(who is current president?), Search(how many kids Joe Biden has?), Calculator(4*4)\n",
-      "Satisfactory? Yes\n",
-      "Explanation: The Candidate plan reaches the same result as the Desired plan with one step broken down into two. \n",
-      "\n",
-      "Question: how long does it take to drive from Boston to New York?\n",
-      "Desired action plan: Search(distance from Boston to New York), Search(speed limit Boston to NewYork), Calculator(190.04/40)\n",
-      "Candidate action plan: Search(driving time from Boston to New York)\n",
-      "Satisfactory?  Yes.\n",
-      "Explanation: The Candidate plan uses a tool to answer the question directly,  rather than breaking it down like the Desired plan.\n",
-      "    \n",
-      "Question: who is dua lipa's boyfriend? what is his age raised to the .43 power?\n",
-      "Desired action plan: Search(Dua Lipa's boyfriend), Search(Romain Gravas age), Calculator(41^.43)\n",
-      "Candidate action plan: Search(Dua Lipa's boyfriend), Search(Isaac Carew age), Calculator(36^.43)\n",
-      "Satisfactory?\n",
-      "Completion:\n",
-      "  No.\n",
-      "Explanation: The Candidate plan uses a different boyfriend than the Desired plan, so the result would be different.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluate_candidate_action_plan(question, desired_action_plan, candidate_action_plan, davinci_003, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2557d35b",
-   "metadata": {},
-   "source": [
-    "**Evaluates as intended initially, though we'd likely like to break this our farther (i.e. a trajectory should not really resolve to T/F).**"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/use_cases/evaluation/question_answering.ipynb

@@ -191,6 +191,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "782ae8c8",
    "metadata": {},
@@ -315,7 +316,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -329,7 +330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.7 (default, Sep 16 2021, 08:50:36) \n[Clang 10.0.0 ]"
   },
   "vscode": {
    "interpreter": {

docs/use_cases/evaluation/sql.ipynb

@@ -1,205 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "63a6161b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "24f017da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db = SQLDatabase.from_uri(\"sqlite:///../../../notebooks/Chinook.db\")\n",
-    "llm = OpenAI(temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3e980729",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "f8b4e54f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "questions = [\n",
-    "    {\n",
-    "        \"question\": \"How many employees are there?\",\n",
-    "        \"answer\": \"8\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"What are some example tracks by composer Johann Sebastian Bach?\",\n",
-    "        \"answer\": \"'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 'Goldberg Variations': Aria', and 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude'\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"What are some example tracks by Bach?\",\n",
-    "        \"answer\": \"'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 'Goldberg Variations': Aria', and 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude'\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"How many employees are also customers?\",\n",
-    "        \"answer\": \"None\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"Where is Mark Telus from?\",\n",
-    "        \"answer\": \"Edmonton, Canada\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"What is the most common genre of songs?\",\n",
-    "        \"answer\": \"Rock\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"What is the most common media type?\",\n",
-    "        \"answer\": \"MPEG audio file\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"What is the most common media type?\",\n",
-    "        \"answer\": \"Purchased AAC audio file\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"How many more Protected AAC audio files are there than Protected MPEG-4 video file?\",\n",
-    "        \"answer\": \"23\"\n",
-    "    },\n",
-    "    {\n",
-    "        \"question\": \"How many albums are there\",\n",
-    "        \"answer\": \"347\"\n",
-    "    }\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "5896eda7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(questions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "21dc41ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[(1, 3034), (2, 237), (3, 214), (4, 7), (5, 11)]'"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db.run(\"\"\"SELECT\n",
-    "  MediaTypeID,\n",
-    "  COUNT(*) AS `num`\n",
-    "FROM\n",
-    "  Track\n",
-    "GROUP BY\n",
-    "  MediaTypeID\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "115cd3da",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db.get_table_info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "659c8d20",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[(347,)]'"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db.run(\"select count(*) from album\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b99a505",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/use_cases/evaluation/vectorstore_routing.ipynb

@@ -1,377 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "4669c98a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "sota_loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
-    "pg_loader = TextLoader(\"../../../../gpt_index/examples/paul_graham_essay/data/paul_graham_essay.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c484ffb5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.indexes import VectorstoreIndexCreator\n",
-    "from langchain.vectorstores import FAISS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5b139077",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sota_index = VectorstoreIndexCreator(vectorstore_cls=FAISS).from_loaders([sota_loader])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "55fa5f56",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Chroma using direct local API.\n",
-      "Using DuckDB in-memory for database. Data will be transient.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pg_index = VectorstoreIndexCreator(vectorstore_kwargs={\"collection_name\": \"paul-graham\"}).from_loaders([pg_loader])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "edad6e7b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\" The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He said she is one of the nation's top legal minds and will continue Justice Breyer's legacy of excellence.\""
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sota_index.query(\"what did the president about kentaji brown jackson?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "201ff615",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\" Kentaji Brown Jackson was not mentioned in the context, so I don't know.\""
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg_index.query(\"what did the president about kentaji brown jackson?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "3f9622e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.tools import BaseTool\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "45158bb9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = [\n",
-    "    Tool(\n",
-    "        name = \"State of Union QA System\",\n",
-    "        func=sota_index.query,\n",
-    "        description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\"\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name = \"Paul Graham QA System\",\n",
-    "        func=pg_index.query,\n",
-    "        description=\"useful for when you need to answer questions about Paul Graham. Input should be a fully formed question.\"\n",
-    "    ),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "11514c7b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=\"zero-shot-react-description\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "91cd2c71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "f55dd305",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"../../../notebooks/state_of_union_qa.json\") as f:\n",
-    "    sota_qa = json.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "fb0fb286",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"../../../notebooks/paul_graham_qa.json\") as f:\n",
-    "    pg_qa = json.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "dfb08711",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for d in sota_qa:\n",
-    "    d['steps'] = [{\"tool\": \"State of Union QA System\"}, {\"tool_input\": d[\"question\"]}]\n",
-    "for d in pg_qa:\n",
-    "    d['steps'] = [{\"tool\": \"Paul Graham QA System\"}, {\"tool_input\": d[\"question\"]}]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "f442a356",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_vectorstore_routing = sota_qa + pg_qa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "7b15160c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"vectorstore_sota_pg.json\", \"w\") as f:\n",
-    "    json.dump(all_vectorstore_routing, f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "87bc7826",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_vectorstore_routing = [{'question': 'What is the purpose of the NATO Alliance?',\n",
-    "  'answer': 'The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the purpose of the NATO Alliance?'}]},\n",
-    " {'question': 'What is the U.S. Department of Justice doing to combat the crimes of Russian oligarchs?',\n",
-    "  'answer': 'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the U.S. Department of Justice doing to combat the crimes of Russian oligarchs?'}]},\n",
-    " {'question': 'What is the American Rescue Plan and how did it help Americans?',\n",
-    "  'answer': 'The American Rescue Plan is a piece of legislation that provided immediate economic relief for tens of millions of Americans. It helped put food on their table, keep a roof over their heads, and cut the cost of health insurance. It created jobs and left no one behind.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the American Rescue Plan and how did it help Americans?'}]},\n",
-    " {'question': 'What is the purpose of the Bipartisan Innovation Act mentioned in the text?',\n",
-    "  'answer': 'The Bipartisan Innovation Act will make record investments in emerging technologies and American manufacturing to level the playing field with China and other competitors.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the purpose of the Bipartisan Innovation Act mentioned in the text?'}]},\n",
-    " {'question': \"What is Joe Biden's plan to fight inflation?\",\n",
-    "  'answer': \"Joe Biden's plan to fight inflation is to lower costs, not wages, by making more goods in America, increasing the productive capacity of the economy, and cutting the cost of prescription drugs, energy, and child care.\",\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': \"What is Joe Biden's plan to fight inflation?\"}]},\n",
-    " {'question': 'What is the proposed minimum tax rate for corporations under the plan?',\n",
-    "  'answer': 'The proposed minimum tax rate for corporations is 15%.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the proposed minimum tax rate for corporations under the plan?'}]},\n",
-    " {'question': 'What are the four common sense steps that the author suggests to move forward safely?',\n",
-    "  'answer': 'The four common sense steps suggested by the author to move forward safely are: stay protected with vaccines and treatments, prepare for new variants, end the shutdown of schools and businesses, and stay vigilant.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What are the four common sense steps that the author suggests to move forward safely?'}]},\n",
-    " {'question': 'What is the purpose of the American Rescue Plan?',\n",
-    "  'answer': 'The purpose of the American Rescue Plan is to provide $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruption.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the purpose of the American Rescue Plan?'}]},\n",
-    " {'question': 'What measures does the speaker ask Congress to pass to reduce gun violence?',\n",
-    "  'answer': 'The speaker asks Congress to pass universal background checks, ban assault weapons and high-capacity magazines, and repeal the liability shield that makes gun manufacturers the only industry in America that can’t be sued.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What measures does the speaker ask Congress to pass to reduce gun violence?'}]},\n",
-    " {'question': 'What is the Unity Agenda for the Nation that the President is offering?',\n",
-    "  'answer': 'The Unity Agenda for the Nation includes four big things that can be done together: beat the opioid epidemic, take on mental health, support veterans, and strengthen the Violence Against Women Act.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the Unity Agenda for the Nation that the President is offering?'}]},\n",
-    " {'question': 'What is the purpose of ARPA-H?',\n",
-    "  'answer': 'ARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more.',\n",
-    "  'steps': [{'tool': 'State of Union QA System'},\n",
-    "   {'tool_input': 'What is the purpose of ARPA-H?'}]},\n",
-    " {'question': 'What were the two main things the author worked on before college?',\n",
-    "  'answer': 'The two main things the author worked on before college were writing and programming.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What were the two main things the author worked on before college?'}]},\n",
-    " {'question': 'What made the author want to work on AI?',\n",
-    "  'answer': \"The novel 'The Moon is a Harsh Mistress' and a PBS documentary showing Terry Winograd using SHRDLU made the author want to work on AI.\",\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What made the author want to work on AI?'}]},\n",
-    " {'question': 'What did the author realize while looking at a painting at the Carnegie Institute?',\n",
-    "  'answer': 'The author realized that paintings were something that could be made to last and that making them was a way to be independent and make a living.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What did the author realize while looking at a painting at the Carnegie Institute?'}]},\n",
-    " {'question': 'What did the author write their dissertation on?',\n",
-    "  'answer': 'The author wrote their dissertation on applications of continuations.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What did the author write their dissertation on?'}]},\n",
-    " {'question': 'What is the difference between painting still lives and painting people?',\n",
-    "  'answer': \"Painting still lives is different from painting people because the subject, as its name suggests, can't move. People can't sit for more than about 15 minutes at a time, and when they do they don't sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you're painting.\",\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What is the difference between painting still lives and painting people?'}]},\n",
-    " {'question': 'What did the author learn while working at Interleaf?',\n",
-    "  'answer': \"The author learned that low end software tends to eat high end software, that it's better for technology companies to be run by product people than sales people, that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it's depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there's not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it.\",\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What did the author learn while working at Interleaf?'}]},\n",
-    " {'question': 'What did the author do to survive during the next several years after leaving RISD?',\n",
-    "  'answer': 'The author did freelance work for the group that did projects for customers to survive for the next several years after leaving RISD.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What did the author do to survive during the next several years after leaving RISD?'}]},\n",
-    " {'question': \"What was the author's motivation for wanting to become rich?\",\n",
-    "  'answer': 'The author wanted to become rich so that he could work on whatever he wanted.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': \"What was the author's motivation for wanting to become rich?\"}]},\n",
-    " {'question': 'What is Viaweb and how did it get its name?',\n",
-    "  'answer': 'Viaweb is a company that built a web app for creating online stores. It got its name from the fact that the software worked via the web.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What is Viaweb and how did it get its name?'}]},\n",
-    " {'question': 'What was the price charged by Viaweb for a small store and a big one?',\n",
-    "  'answer': '$100 a month for a small store and $300 a month for a big one.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What was the price charged by Viaweb for a small store and a big one?'}]},\n",
-    " {'question': 'Why did the author hire more people for his startup?',\n",
-    "  'answer': \"The author hired more people for his startup partly because the investors wanted him to and partly because that's what startups did during the Internet Bubble.\",\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'Why did the author hire more people for his startup?'}]},\n",
-    " {'question': \"What was the author's idea for a new company?\",\n",
-    "  'answer': \"The author's idea was to build a web app for making web apps, where people could edit code on their server through the browser and then host the resulting applications for them.\",\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': \"What was the author's idea for a new company?\"}]},\n",
-    " {'question': \"What was the author's turning point in figuring out what to work on?\",\n",
-    "  'answer': \"The author's turning point in figuring out what to work on was when he started publishing essays online.\",\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': \"What was the author's turning point in figuring out what to work on?\"}]},\n",
-    " {'question': 'What is the danger for the ambitious according to the text?',\n",
-    "  'answer': 'The desire to impress people is the danger for the ambitious according to the text.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What is the danger for the ambitious according to the text?'}]},\n",
-    " {'question': 'What is the most distinctive thing about Y Combinator?',\n",
-    "  'answer': 'The most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What is the most distinctive thing about Y Combinator?'}]},\n",
-    " {'question': 'What was the Summer Founders Program and how many groups were selected for funding?',\n",
-    "  'answer': 'The Summer Founders Program was a program for undergrads to apply for funding for their startup ideas. 8 groups were selected for funding out of 225 applications.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What was the Summer Founders Program and how many groups were selected for funding?'}]},\n",
-    " {'question': 'What was the biggest source of stress for the author while working at YC?',\n",
-    "  'answer': 'HN (Hacker News)',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What was the biggest source of stress for the author while working at YC?'}]},\n",
-    " {'question': 'What did the author decide to do after leaving YC?',\n",
-    "  'answer': 'The author decided to focus on painting.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What did the author decide to do after leaving YC?'}]},\n",
-    " {'question': 'What is the distinctive thing about Lisp?',\n",
-    "  'answer': 'The distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What is the distinctive thing about Lisp?'}]},\n",
-    " {'question': 'Why did the author move to England?',\n",
-    "  'answer': 'The author moved to England to let their kids experience living in another country and because the author was a British citizen by birth.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'Why did the author move to England?'}]},\n",
-    " {'question': 'What was the reason behind the change of name from Cambridge Seed to Y Combinator?',\n",
-    "  'answer': \"They didn't want a regional name, in case someone copied them in Silicon Valley, so they renamed themselves after one of the coolest tricks in the lambda calculus, the Y combinator.\",\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What was the reason behind the change of name from Cambridge Seed to Y Combinator?'}]},\n",
-    " {'question': 'What is the purpose of YC?',\n",
-    "  'answer': 'The purpose of YC is to cause startups to be founded that would not otherwise have existed.',\n",
-    "  'steps': [{'tool': 'Paul Graham QA System'},\n",
-    "   {'tool_input': 'What is the purpose of YC?'}]}]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cf7524b1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

langchain/__init__.py

@@ -48,7 +48,6 @@ from langchain.utilities.google_search import GoogleSearchAPIWrapper
 from langchain.utilities.google_serper import GoogleSerperAPIWrapper
 from langchain.utilities.searx_search import SearxSearchWrapper
 from langchain.utilities.serpapi import SerpAPIWrapper
-from langchain.utilities.wikipedia import WikipediaAPIWrapper
 from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 from langchain.vectorstores import FAISS, ElasticVectorSearch
 
@@ -71,7 +70,6 @@ __all__ = [
     "GoogleSearchAPIWrapper",
     "GoogleSerperAPIWrapper",
     "WolframAlphaAPIWrapper",
-    "WikipediaAPIWrapper",
     "Anthropic",
     "Banana",
     "CerebriumAI",

langchain/agents/agent.py

@@ -47,10 +47,7 @@ class Agent(BaseModel):
 
     @property
     def _stop(self) -> List[str]:
-        return [
-            f"\n{self.observation_prefix.rstrip()}",
-            f"\n\t{self.observation_prefix.rstrip()}",
-        ]
+        return [f"\n{self.observation_prefix}", f"\n\t{self.observation_prefix}"]
 
     def _construct_scratchpad(
         self, intermediate_steps: List[Tuple[AgentAction, str]]

langchain/agents/chat/base.py

@@ -1,108 +0,0 @@
-import json
-from typing import Any, List, Optional, Sequence, Tuple
-
-from langchain.agents.agent import Agent
-from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.chains.llm import LLMChain
-from langchain.prompts.base import BasePromptTemplate
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    SystemMessagePromptTemplate,
-)
-from langchain.schema import AgentAction, BaseLanguageModel
-from langchain.tools import BaseTool
-
-FINAL_ANSWER_ACTION = "Final Answer:"
-
-
-class ChatAgent(Agent):
-    @property
-    def observation_prefix(self) -> str:
-        """Prefix to append the observation with."""
-        return "Observation: "
-
-    @property
-    def llm_prefix(self) -> str:
-        """Prefix to append the llm call with."""
-        return "Thought:"
-
-    def _construct_scratchpad(
-        self, intermediate_steps: List[Tuple[AgentAction, str]]
-    ) -> str:
-        agent_scratchpad = super()._construct_scratchpad(intermediate_steps)
-        if agent_scratchpad:
-            return (
-                f"This was your previous work "
-                f"(but I haven't seen any of it! I only see what "
-                f"you return as final answer):\n{agent_scratchpad}"
-            )
-        else:
-            return agent_scratchpad
-
-    def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
-        if FINAL_ANSWER_ACTION in text:
-            return "Final Answer", text.split(FINAL_ANSWER_ACTION)[-1].strip()
-        _, action, _ = text.split("```")
-
-        response = json.loads(action.strip())
-        return response["action"], response["action_input"]
-
-    @property
-    def _stop(self) -> List[str]:
-        return ["Observation:"]
-
-    @classmethod
-    def create_prompt(
-        cls,
-        tools: Sequence[BaseTool],
-        prefix: str = PREFIX,
-        suffix: str = SUFFIX,
-        format_instructions: str = FORMAT_INSTRUCTIONS,
-        input_variables: Optional[List[str]] = None,
-    ) -> BasePromptTemplate:
-        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
-        tool_names = ", ".join([tool.name for tool in tools])
-        format_instructions = format_instructions.format(tool_names=tool_names)
-        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
-        messages = [
-            SystemMessagePromptTemplate.from_template(template),
-            HumanMessagePromptTemplate.from_template("{input}\n\n{agent_scratchpad}"),
-        ]
-        return ChatPromptTemplate(
-            input_variables=["input", "agent_scratchpad"], messages=messages
-        )
-
-    @classmethod
-    def from_llm_and_tools(
-        cls,
-        llm: BaseLanguageModel,
-        tools: Sequence[BaseTool],
-        callback_manager: Optional[BaseCallbackManager] = None,
-        prefix: str = PREFIX,
-        suffix: str = SUFFIX,
-        format_instructions: str = FORMAT_INSTRUCTIONS,
-        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,
-            format_instructions=format_instructions,
-            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)
-
-    @property
-    def _agent_type(self) -> str:
-        raise ValueError

langchain/agents/chat/prompt.py

@@ -1,29 +0,0 @@
-# flake8: noqa
-PREFIX = """Answer the following questions as best you can. You have access to the following tools:"""
-FORMAT_INSTRUCTIONS = """The way you use the tools is by specifying a json blob.
-Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).
-
-The only values that should be in the "action" field are: {tool_names}
-
-The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:
-
-```
-{{{{
-  "action": $TOOL_NAME,
-  "action_input": $INPUT
-}}}}
-```
-
-ALWAYS use the following format:
-
-Question: the input question you must answer
-Thought: you should always think about what to do
-Action: 
-```
-$JSON_BLOB
-```
-Observation: the result of the action
-... (this Thought/Action/Observation can repeat N times)
-Thought: I now know the final answer
-Final Answer: the final answer to the original input question"""
-SUFFIX = """Begin! Reminder to always use the exact characters `Final Answer` when responding."""

langchain/agents/conversational/base.py

@@ -78,7 +78,7 @@ class ConversationalAgent(Agent):
     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].strip()
-        regex = r"Action: (.*?)[\n]*Action Input: (.*)"
+        regex = r"Action: (.*?)\nAction Input: (.*)"
         match = re.search(regex, llm_output)
         if not match:
             raise ValueError(f"Could not parse LLM output: `{llm_output}`")

langchain/agents/load_tools.py

@@ -9,13 +9,12 @@ from langchain.chains.api.base import APIChain
 from langchain.chains.llm_math.base import LLMMathChain
 from langchain.chains.pal.base import PALChain
 from langchain.llms.base import BaseLLM
+from langchain.tools.python.tool import PythonREPLTool
 from langchain.requests import RequestsWrapper
 from langchain.tools.base import BaseTool
 from langchain.tools.bing_search.tool import BingSearchRun
 from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
-from langchain.tools.python.tool import PythonREPLTool
 from langchain.tools.requests.tool import RequestsGetTool
-from langchain.tools.wikipedia.tool import WikipediaQueryRun
 from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
 from langchain.utilities.bash import BashProcess
 from langchain.utilities.bing_search import BingSearchAPIWrapper
@@ -23,7 +22,6 @@ from langchain.utilities.google_search import GoogleSearchAPIWrapper
 from langchain.utilities.google_serper import GoogleSerperAPIWrapper
 from langchain.utilities.searx_search import SearxSearchWrapper
 from langchain.utilities.serpapi import SerpAPIWrapper
-from langchain.utilities.wikipedia import WikipediaAPIWrapper
 from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 
 
@@ -126,10 +124,6 @@ def _get_google_search(**kwargs: Any) -> BaseTool:
     return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
 
 
-def _get_wikipedia(**kwargs: Any) -> BaseTool:
-    return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
-
-
 def _get_google_serper(**kwargs: Any) -> BaseTool:
     return Tool(
         name="Serper Search",
@@ -179,7 +173,6 @@ _EXTRA_OPTIONAL_TOOLS = {
     "google-serper": (_get_google_serper, ["serper_api_key"]),
     "serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
     "searx-search": (_get_searx_search, ["searx_host"]),
-    "wikipedia": (_get_wikipedia, ["top_k_results"]),
 }
 
 

langchain/agents/loading.py

@@ -6,7 +6,6 @@ from typing import Any, List, Optional, Union
 import yaml
 
 from langchain.agents.agent import Agent
-from langchain.agents.chat.base import ChatAgent
 from langchain.agents.conversational.base import ConversationalAgent
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.react.base import ReActDocstoreAgent
@@ -21,7 +20,6 @@ AGENT_TO_CLASS = {
     "react-docstore": ReActDocstoreAgent,
     "self-ask-with-search": SelfAskWithSearchAgent,
     "conversational-react-description": ConversationalAgent,
-    "chat-zero-shot-react-description": ChatAgent,
 }
 
 URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"

langchain/agents/mrkl/base.py

@@ -40,7 +40,7 @@ def get_action_and_input(llm_output: str) -> Tuple[str, str]:
     """
     if FINAL_ANSWER_ACTION in llm_output:
         return "Final Answer", llm_output.split(FINAL_ANSWER_ACTION)[-1].strip()
-    regex = r"Action: (.*?)[\n]*Action Input: (.*)"
+    regex = r"Action: (.*?)\nAction Input: (.*)"
     match = re.search(regex, llm_output, re.DOTALL)
     if not match:
         raise ValueError(f"Could not parse LLM output: `{llm_output}`")

langchain/chains/base.py

@@ -5,12 +5,64 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 import yaml
-from pydantic import BaseModel, Field, validator
+from pydantic import BaseModel, Extra, Field, validator
 
 import langchain
 from langchain.callbacks import get_callback_manager
 from langchain.callbacks.base import BaseCallbackManager
-from langchain.schema import BaseMemory
+
+
+class Memory(BaseModel, ABC):
+    """Base interface for memory in chains."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @property
+    @abstractmethod
+    def memory_variables(self) -> List[str]:
+        """Input keys this memory class will load dynamically."""
+
+    @abstractmethod
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """Return key-value pairs given the text input to the chain.
+
+        If None, return all memories
+        """
+
+    @abstractmethod
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """Save the context of this model run to memory."""
+
+    @abstractmethod
+    def clear(self) -> None:
+        """Clear memory contents."""
+
+
+class SimpleMemory(Memory, BaseModel):
+    """Simple memory for storing context or other bits of information that shouldn't
+    ever change between prompts.
+    """
+
+    memories: Dict[str, Any] = dict()
+
+    @property
+    def memory_variables(self) -> List[str]:
+        return list(self.memories.keys())
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        return self.memories
+
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """Nothing should be saved or changed, my memory is set in stone."""
+        pass
+
+    def clear(self) -> None:
+        """Nothing to clear, got a memory like a vault."""
+        pass
 
 
 def _get_verbosity() -> bool:
@@ -20,7 +72,7 @@ def _get_verbosity() -> bool:
 class Chain(BaseModel, ABC):
     """Base interface that all chains should implement."""
 
-    memory: Optional[BaseMemory] = None
+    memory: Optional[Memory] = None
     callback_manager: BaseCallbackManager = Field(
         default_factory=get_callback_manager, exclude=True
     )

langchain/chains/chat_vector_db/base.py

@@ -1,18 +1,17 @@
 """Chain for chatting with a vector database."""
 from __future__ import annotations
 
-from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Tuple
 
 from pydantic import BaseModel
 
 from langchain.chains.base import Chain
-from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT
+from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel
 from langchain.vectorstores.base import VectorStore
 
 
@@ -34,7 +33,6 @@ class ChatVectorDBChain(Chain, BaseModel):
     output_key: str = "answer"
     return_source_documents: bool = False
     top_k_docs_for_context: int = 4
-    get_chat_history: Optional[Callable[[Tuple[str, str]], str]] = None
     """Return the source documents."""
 
     @property
@@ -60,10 +58,10 @@ class ChatVectorDBChain(Chain, BaseModel):
     @classmethod
     def from_llm(
         cls,
-        llm: BaseLanguageModel,
+        llm: BaseLLM,
         vectorstore: VectorStore,
         condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
-        qa_prompt: Optional[BasePromptTemplate] = None,
+        qa_prompt: BasePromptTemplate = QA_PROMPT,
         chain_type: str = "stuff",
         **kwargs: Any,
     ) -> ChatVectorDBChain:
@@ -83,8 +81,7 @@ class ChatVectorDBChain(Chain, BaseModel):
 
     def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         question = inputs["question"]
-        get_chat_history = self.get_chat_history or _get_chat_history
-        chat_history_str = get_chat_history(inputs["chat_history"])
+        chat_history_str = _get_chat_history(inputs["chat_history"])
         vectordbkwargs = inputs.get("vectordbkwargs", {})
         if chat_history_str:
             new_question = self.question_generator.run(
@@ -106,8 +103,7 @@ class ChatVectorDBChain(Chain, BaseModel):
 
     async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         question = inputs["question"]
-        get_chat_history = self.get_chat_history or _get_chat_history
-        chat_history_str = get_chat_history(inputs["chat_history"])
+        chat_history_str = _get_chat_history(inputs["chat_history"])
         vectordbkwargs = inputs.get("vectordbkwargs", {})
         if chat_history_str:
             new_question = await self.question_generator.arun(
@@ -127,8 +123,3 @@ class ChatVectorDBChain(Chain, BaseModel):
             return {self.output_key: answer, "source_documents": docs}
         else:
             return {self.output_key: answer}
-
-    def save(self, file_path: Union[Path, str]) -> None:
-        if self.get_chat_history:
-            raise ValueError("Chain not savable when `get_chat_history` is not None.")
-        super().save(file_path)

langchain/chains/combine_documents/map_rerank.py

@@ -9,7 +9,7 @@ from pydantic import BaseModel, Extra, root_validator
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
-from langchain.output_parsers.regex import RegexParser
+from langchain.prompts.prompt import RegexParser
 
 
 class MapRerankDocumentsChain(BaseCombineDocumentsChain, BaseModel):

langchain/chains/combine_documents/stuff.py

@@ -80,7 +80,7 @@ class StuffDocumentsChain(BaseCombineDocumentsChain, BaseModel):
         """Get the prompt length by formatting the prompt."""
         inputs = self._get_inputs(docs, **kwargs)
         prompt = self.llm_chain.prompt.format(**inputs)
-        return self.llm_chain.llm.get_num_tokens(prompt)
+        return self.llm_chain.get_num_tokens(prompt)
 
     def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:
         """Stuff all documents into one prompt and pass to LLM."""

langchain/chains/conversation/base.py

@@ -3,11 +3,11 @@ from typing import Dict, List
 
 from pydantic import BaseModel, Extra, Field, root_validator
 
+from langchain.chains.base import Memory
+from langchain.chains.conversation.memory import ConversationBufferMemory
 from langchain.chains.conversation.prompt import PROMPT
 from langchain.chains.llm import LLMChain
-from langchain.memory.buffer import ConversationBufferMemory
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseMemory
 
 
 class ConversationChain(LLMChain, BaseModel):
@@ -20,7 +20,7 @@ class ConversationChain(LLMChain, BaseModel):
             conversation = ConversationChain(llm=OpenAI())
     """
 
-    memory: BaseMemory = Field(default_factory=ConversationBufferMemory)
+    memory: Memory = Field(default_factory=ConversationBufferMemory)
     """Default memory store."""
     prompt: BasePromptTemplate = PROMPT
     """Default conversation prompt to use."""

langchain/chains/conversation/memory.py

@@ -1,25 +1,493 @@
 """Memory modules for conversation prompts."""
+from typing import Any, Dict, List, Optional
 
-from langchain.memory.buffer import (
-    ConversationBufferMemory,
-    ConversationStringBufferMemory,
+from pydantic import BaseModel, Field, root_validator
+
+from langchain.chains.base import Memory
+from langchain.chains.conversation.prompt import (
+    ENTITY_EXTRACTION_PROMPT,
+    ENTITY_SUMMARIZATION_PROMPT,
+    KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
+    SUMMARY_PROMPT,
 )
-from langchain.memory.buffer_window import ConversationBufferWindowMemory
-from langchain.memory.combined import CombinedMemory
-from langchain.memory.entity import ConversationEntityMemory
-from langchain.memory.kg import ConversationKGMemory
-from langchain.memory.summary import ConversationSummaryMemory
-from langchain.memory.summary_buffer import ConversationSummaryBufferMemory
+from langchain.chains.llm import LLMChain
+from langchain.graphs.networkx_graph import (
+    NetworkxEntityGraph,
+    get_entities,
+    parse_triples,
+)
+from langchain.llms.base import BaseLLM
+from langchain.prompts.base import BasePromptTemplate
 
-# This is only for backwards compatibility.
 
-__all__ = [
-    "ConversationSummaryBufferMemory",
-    "ConversationSummaryMemory",
-    "ConversationKGMemory",
-    "ConversationBufferWindowMemory",
-    "ConversationEntityMemory",
-    "ConversationBufferMemory",
-    "CombinedMemory",
-    "ConversationStringBufferMemory",
-]
+def _get_prompt_input_key(inputs: Dict[str, Any], memory_variables: List[str]) -> str:
+    # "stop" is a special key that can be passed as input but is not used to
+    # format the prompt.
+    prompt_input_keys = list(set(inputs).difference(memory_variables + ["stop"]))
+    if len(prompt_input_keys) != 1:
+        raise ValueError(f"One input key expected got {prompt_input_keys}")
+    return prompt_input_keys[0]
+
+
+class CombinedMemory(Memory, BaseModel):
+    """Class for combining multiple memories' data together."""
+
+    memories: List[Memory]
+    """For tracking all the memories that should be accessed."""
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """All the memory variables that this instance provides."""
+        """Collected from the all the linked memories."""
+
+        memory_variables = []
+
+        for memory in self.memories:
+            memory_variables.extend(memory.memory_variables)
+
+        return memory_variables
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """Load all vars from sub-memories."""
+        memory_data: Dict[str, Any] = {}
+
+        # Collect vars from all sub-memories
+        for memory in self.memories:
+            data = memory.load_memory_variables(inputs)
+            memory_data = {
+                **memory_data,
+                **data,
+            }
+
+        return memory_data
+
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """Save context from this session for every memory."""
+        # Save context for all sub-memories
+        for memory in self.memories:
+            memory.save_context(inputs, outputs)
+
+    def clear(self) -> None:
+        """Clear context from this session for every memory."""
+        for memory in self.memories:
+            memory.clear()
+
+
+class ConversationBufferMemory(Memory, BaseModel):
+    """Buffer for storing conversation memory."""
+
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    """Prefix to use for AI generated responses."""
+    buffer: str = ""
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+    memory_key: str = "history"  #: :meta private:
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """Return history buffer."""
+        return {self.memory_key: self.buffer}
+
+    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]
+        self.buffer += "\n" + "\n".join([human, ai])
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        self.buffer = ""
+
+
+class ConversationBufferWindowMemory(Memory, BaseModel):
+    """Buffer for storing conversation memory."""
+
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    """Prefix to use for AI generated responses."""
+    buffer: List[str] = Field(default_factory=list)
+    memory_key: str = "history"  #: :meta private:
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+    k: int = 5
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """Return history buffer."""
+        return {self.memory_key: "\n".join(self.buffer[-self.k :])}
+
+    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]
+        self.buffer.append("\n".join([human, ai]))
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        self.buffer = []
+
+
+# For legacy naming reasons
+ConversationalBufferWindowMemory = ConversationBufferWindowMemory
+
+
+class ConversationSummaryMemory(Memory, BaseModel):
+    """Conversation summarizer to memory."""
+
+    buffer: str = ""
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    """Prefix to use for AI generated responses."""
+    llm: BaseLLM
+    prompt: BasePromptTemplate = SUMMARY_PROMPT
+    memory_key: str = "history"  #: :meta private:
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """Return history buffer."""
+        return {self.memory_key: self.buffer}
+
+    @root_validator()
+    def validate_prompt_input_variables(cls, values: Dict) -> Dict:
+        """Validate that prompt input variables are consistent."""
+        prompt_variables = values["prompt"].input_variables
+        expected_keys = {"summary", "new_lines"}
+        if expected_keys != set(prompt_variables):
+            raise ValueError(
+                "Got unexpected prompt input variables. The prompt expects "
+                f"{prompt_variables}, but it should have {expected_keys}."
+            )
+        return values
+
+    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]}"
+        new_lines = "\n".join([human, ai])
+        chain = LLMChain(llm=self.llm, prompt=self.prompt)
+        self.buffer = chain.predict(summary=self.buffer, new_lines=new_lines)
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        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:
+        if inputs is None:
+            raise ValueError(
+                "Inputs must be provided to save context from "
+                "ConversationEntityMemory."
+            )
+
+        """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."""
+
+    buffer: List[str] = Field(default_factory=list)
+    max_token_limit: int = 2000
+    moving_summary_buffer: str = ""
+    llm: BaseLLM
+    prompt: BasePromptTemplate = SUMMARY_PROMPT
+    memory_key: str = "history"
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    """Prefix to use for AI generated responses."""
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """Return history buffer."""
+        if self.moving_summary_buffer == "":
+            return {self.memory_key: "\n".join(self.buffer)}
+        memory_val = self.moving_summary_buffer + "\n" + "\n".join(self.buffer)
+        return {self.memory_key: memory_val}
+
+    @root_validator()
+    def validate_prompt_input_variables(cls, values: Dict) -> Dict:
+        """Validate that prompt input variables are consistent."""
+        prompt_variables = values["prompt"].input_variables
+        expected_keys = {"summary", "new_lines"}
+        if expected_keys != set(prompt_variables):
+            raise ValueError(
+                "Got unexpected prompt input variables. The prompt expects "
+                f"{prompt_variables}, but it should have {expected_keys}."
+            )
+        return values
+
+    def get_num_tokens_list(self, arr: List[str]) -> List[int]:
+        """Get list of number of tokens in each string in the input array."""
+        return [self.llm.get_num_tokens(x) for x in arr]
+
+    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]}"
+        new_lines = "\n".join([human, ai])
+        self.buffer.append(new_lines)
+        # Prune buffer if it exceeds max token limit
+        curr_buffer_length = sum(self.get_num_tokens_list(self.buffer))
+        if curr_buffer_length > self.max_token_limit:
+            pruned_memory = []
+            while curr_buffer_length > self.max_token_limit:
+                pruned_memory.append(self.buffer.pop(0))
+                curr_buffer_length = sum(self.get_num_tokens_list(self.buffer))
+            chain = LLMChain(llm=self.llm, prompt=self.prompt)
+            self.moving_summary_buffer = chain.predict(
+                summary=self.moving_summary_buffer, new_lines=("\n".join(pruned_memory))
+            )
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        self.buffer = []
+        self.moving_summary_buffer = ""
+
+
+class ConversationKGMemory(Memory, BaseModel):
+    """Knowledge graph memory for storing conversation memory.
+
+    Integrates with external knowledge graph to store and retrieve
+    information about knowledge triples in the conversation.
+    """
+
+    k: int = 2
+    buffer: List[str] = Field(default_factory=list)
+    kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph)
+    knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
+    entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
+    llm: BaseLLM
+    """Number of previous utterances to include in the context."""
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    """Prefix to use for AI generated responses."""
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+    memory_key: str = "history"  #: :meta private:
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        """Return history buffer."""
+        entities = self._get_current_entities(inputs)
+        summaries = {}
+        for entity in entities:
+            knowledge = self.kg.get_entity_knowledge(entity)
+            if knowledge:
+                summaries[entity] = ". ".join(knowledge) + "."
+        if summaries:
+            summary_strings = [
+                f"On {entity}: {summary}" for entity, summary in summaries.items()
+            ]
+            context_str = "\n".join(summary_strings)
+        else:
+            context_str = ""
+        return {self.memory_key: context_str}
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str:
+        """Get the input key for the prompt."""
+        if self.input_key is None:
+            return _get_prompt_input_key(inputs, self.memory_variables)
+        return self.input_key
+
+    def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str:
+        """Get the output key for the prompt."""
+        if self.output_key is None:
+            if len(outputs) != 1:
+                raise ValueError(f"One output key expected, got {outputs.keys()}")
+            return list(outputs.keys())[0]
+        return self.output_key
+
+    def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
+        """Get the current entities in the conversation."""
+        prompt_input_key = self._get_prompt_input_key(inputs)
+        chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
+        output = chain.predict(
+            history="\n".join(self.buffer[-self.k :]),
+            input=inputs[prompt_input_key],
+        )
+        return get_entities(output)
+
+    def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
+        """Get and update knowledge graph from the conversation history."""
+        chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt)
+        prompt_input_key = self._get_prompt_input_key(inputs)
+        output = chain.predict(
+            history="\n".join(self.buffer[-self.k :]),
+            input=inputs[prompt_input_key],
+            verbose=True,
+        )
+        knowledge = parse_triples(output)
+        for triple in knowledge:
+            self.kg.add_triple(triple)
+
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """Save context from this conversation to buffer."""
+        self._get_and_update_kg(inputs)
+        prompt_input_key = self._get_prompt_input_key(inputs)
+        output_key = self._get_prompt_output_key(outputs)
+        human = f"{self.human_prefix}: {inputs[prompt_input_key]}"
+        ai = f"{self.ai_prefix}: {outputs[output_key]}"
+        new_lines = "\n".join([human.strip(), ai.strip()])
+        self.buffer.append(new_lines)
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        return self.kg.clear()

langchain/chains/conversation/prompt.py

@@ -1,11 +1,4 @@
 # flake8: noqa
-from langchain.memory.prompt import (
-    ENTITY_EXTRACTION_PROMPT,
-    ENTITY_MEMORY_CONVERSATION_TEMPLATE,
-    ENTITY_SUMMARIZATION_PROMPT,
-    KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
-    SUMMARY_PROMPT,
-)
 from langchain.prompts.prompt import PromptTemplate
 
 _DEFAULT_TEMPLATE = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
@@ -18,13 +11,165 @@ PROMPT = PromptTemplate(
     input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
 )
 
-# Only for backwards compatibility
+_DEFAULT_ENTITY_MEMORY_CONVERSATION_TEMPLATE = """You are an assistant to a human, powered by a large language model trained by OpenAI.
 
-__all__ = [
-    "SUMMARY_PROMPT",
-    "ENTITY_MEMORY_CONVERSATION_TEMPLATE",
-    "ENTITY_SUMMARIZATION_PROMPT",
-    "ENTITY_EXTRACTION_PROMPT",
-    "KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT",
-    "PROMPT",
-]
+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
+Current summary:
+The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.
+
+New lines of conversation:
+Human: Why do you think artificial intelligence is a force for good?
+AI: Because artificial intelligence will help humans reach their full potential.
+
+New summary:
+The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.
+END OF EXAMPLE
+
+Current summary:
+{summary}
+
+New lines of conversation:
+{new_lines}
+
+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,
+)
+
+
+KG_TRIPLE_DELIMITER = "<|>"
+_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
+    "You are a networked intelligence helping a human track knowledge triples"
+    " about all relevant people, things, concepts, etc. and integrating"
+    " them with your knowledge stored within your weights"
+    " as well as that stored in a knowledge graph."
+    " Extract all of the knowledge triples from the last line of conversation."
+    " A knowledge triple is a clause that contains a subject, a predicate,"
+    " and an object. The subject is the entity being described,"
+    " the predicate is the property of the subject that is being"
+    " described, and the object is the value of the property.\n\n"
+    "EXAMPLE\n"
+    "Conversation history:\n"
+    "Person #1: Did you hear aliens landed in Area 51?\n"
+    "AI: No, I didn't hear that. What do you know about Area 51?\n"
+    "Person #1: It's a secret military base in Nevada.\n"
+    "AI: What do you know about Nevada?\n"
+    "Last line of conversation:\n"
+    "Person #1: It's a state in the US. It's also the number 1 producer of gold in the US.\n\n"
+    f"Output: (Nevada, is a, state){KG_TRIPLE_DELIMITER}(Nevada, is in, US)"
+    f"{KG_TRIPLE_DELIMITER}(Nevada, is the number 1 producer of, gold)\n"
+    "END OF EXAMPLE\n\n"
+    "EXAMPLE\n"
+    "Conversation history:\n"
+    "Person #1: Hello.\n"
+    "AI: Hi! How are you?\n"
+    "Person #1: I'm good. How are you?\n"
+    "AI: I'm good too.\n"
+    "Last line of conversation:\n"
+    "Person #1: I'm going to the store.\n\n"
+    "Output: NONE\n"
+    "END OF EXAMPLE\n\n"
+    "EXAMPLE\n"
+    "Conversation history:\n"
+    "Person #1: What do you know about Descartes?\n"
+    "AI: Descartes was a French philosopher, mathematician, and scientist who lived in the 17th century.\n"
+    "Person #1: The Descartes I'm referring to is a standup comedian and interior designer from Montreal.\n"
+    "AI: Oh yes, He is a comedian and an interior designer. He has been in the industry for 30 years. His favorite food is baked bean pie.\n"
+    "Person #1: Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\n"
+    "Last line of conversation:\n"
+    f"Output: (Descartes, likes to drive, antique scooters){KG_TRIPLE_DELIMITER}(Descartes, plays, mandolin)\n"
+    "END OF EXAMPLE\n\n"
+    "Conversation history (for reference only):\n"
+    "{history}"
+    "\nLast line of conversation (for extraction):\n"
+    "Human: {input}\n\n"
+    "Output:"
+)
+
+KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT = PromptTemplate(
+    input_variables=["history", "input"],
+    template=_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE,
+)

langchain/chains/llm.py

@@ -6,10 +6,12 @@ from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 from pydantic import BaseModel, Extra
 
 from langchain.chains.base import Chain
+from langchain.chat_models.base import BaseChatModel
 from langchain.input import get_colored_text
-from langchain.prompts.base import BasePromptTemplate
+from langchain.llms.base import BaseLLM
+from langchain.prompts.base import BasePromptTemplate, PromptValue
 from langchain.prompts.prompt import PromptTemplate
-from langchain.schema import BaseLanguageModel, LLMResult, PromptValue
+from langchain.schema import LLMResult
 
 
 class LLMChain(Chain, BaseModel):
@@ -28,7 +30,7 @@ class LLMChain(Chain, BaseModel):
 
     prompt: BasePromptTemplate
     """Prompt object to use."""
-    llm: BaseLanguageModel
+    llm: Union[BaseLLM, BaseChatModel]
     output_key: str = "text"  #: :meta private:
 
     class Config:
@@ -56,15 +58,40 @@ class LLMChain(Chain, BaseModel):
     def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         return self.apply([inputs])[0]
 
+    def get_num_tokens(self, prompt: str) -> int:
+        if isinstance(self.llm, BaseLLM):
+            return self.llm.get_num_tokens(prompt)
+        elif isinstance(self.llm, BaseChatModel):
+            raise ValueError("Not supported for chat models yet")
+        else:
+            raise ValueError
+
     def generate(self, input_list: List[Dict[str, Any]]) -> LLMResult:
         """Generate LLM result from inputs."""
         prompts, stop = self.prep_prompts(input_list)
-        return self.llm.generate_prompt(prompts, stop)
+        if isinstance(self.llm, BaseLLM):
+            string_prompts = [p.to_string() for p in prompts]
+            response = self.llm.generate(string_prompts, stop=stop)
+        elif isinstance(self.llm, BaseChatModel):
+            chat_prompts = [p.to_messages() for p in prompts]
+            response = self.llm.generate(chat_prompts, stop=stop)
+        else:
+            raise ValueError
+
+        return response
 
     async def agenerate(self, input_list: List[Dict[str, Any]]) -> LLMResult:
         """Generate LLM result from inputs."""
         prompts, stop = await self.aprep_prompts(input_list)
-        return await self.llm.agenerate_prompt(prompts, stop)
+        if isinstance(self.llm, BaseLLM):
+            string_prompts = [p.to_string() for p in prompts]
+            response = await self.llm.agenerate(string_prompts, stop=stop)
+        elif isinstance(self.llm, BaseChatModel):
+            chat_prompts = [p.to_messages() for p in prompts]
+            response = await self.llm.agenerate(chat_prompts, stop=stop)
+        else:
+            raise ValueError
+        return response
 
     def prep_prompts(
         self, input_list: List[Dict[str, Any]]
@@ -203,7 +230,7 @@ class LLMChain(Chain, BaseModel):
         return "llm_chain"
 
     @classmethod
-    def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:
+    def from_string(cls, llm: BaseLLM, template: str) -> Chain:
         """Create LLMChain from LLM and template."""
         prompt_template = PromptTemplate.from_template(template)
         return cls(llm=llm, prompt=prompt_template)

langchain/chains/prompt_selector.py

@@ -1,38 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Callable, List, Tuple
-
-from pydantic import BaseModel, Field
-
-from langchain.chat_models.base import BaseChatModel
-from langchain.llms.base import BaseLLM
-from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel
-
-
-class BasePromptSelector(BaseModel, ABC):
-    @abstractmethod
-    def get_prompt(self, llm: BaseLanguageModel) -> BasePromptTemplate:
-        """Get default prompt for a language model."""
-
-
-class ConditionalPromptSelector(BasePromptSelector):
-    """Prompt collection that goes through conditionals."""
-
-    default_prompt: BasePromptTemplate
-    conditionals: List[
-        Tuple[Callable[[BaseLanguageModel], bool], BasePromptTemplate]
-    ] = Field(default_factory=list)
-
-    def get_prompt(self, llm: BaseLanguageModel) -> BasePromptTemplate:
-        for condition, prompt in self.conditionals:
-            if condition(llm):
-                return prompt
-        return self.default_prompt
-
-
-def is_llm(llm: BaseLanguageModel) -> bool:
-    return isinstance(llm, BaseLLM)
-
-
-def is_chat_model(llm: BaseLanguageModel) -> bool:
-    return isinstance(llm, BaseChatModel)

langchain/chains/qa_generation/base.py

@@ -1,53 +0,0 @@
-import json
-from typing import Any, Dict, List, Optional
-
-from pydantic import Field
-
-from langchain.chains.base import Chain
-from langchain.chains.llm import LLMChain
-from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
-from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel
-from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
-
-
-class QAGenerationChain(Chain):
-    llm_chain: LLMChain
-    text_splitter: TextSplitter = Field(
-        default=RecursiveCharacterTextSplitter(chunk_overlap=500)
-    )
-    input_key: str = "text"
-    output_key: str = "questions"
-    k: Optional[int] = None
-
-    @classmethod
-    def from_llm(
-        cls,
-        llm: BaseLanguageModel,
-        prompt: Optional[BasePromptTemplate] = None,
-        **kwargs: Any
-    ):
-        _prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
-        chain = LLMChain(llm=llm, prompt=_prompt)
-        return cls(llm_chain=chain, **kwargs)
-
-    @property
-    def _chain_type(self) -> str:
-        raise NotImplementedError
-
-    @property
-    def input_keys(self) -> List[str]:
-        return [self.input_key]
-
-    @property
-    def output_keys(self) -> List[str]:
-        return [self.output_key]
-
-    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
-        docs = self.text_splitter.create_documents([inputs[self.input_key]])
-        results = self.llm_chain.generate([{"text": d.page_content} for d in docs])
-        qa = [json.loads(res[0].text) for res in results.generations]
-        return {self.output_key: qa}
-
-    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
-        raise NotImplementedError

langchain/chains/qa_generation/prompt.py

@@ -1,49 +0,0 @@
-from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    SystemMessagePromptTemplate,
-)
-from langchain.prompts.prompt import PromptTemplate
-
-templ1 = """You are a smart assistant designed to help high school teachers come up with reading comprehension questions.
-Given a piece of text, you must come up with a question and answer pair that can be used to test a student's reading comprehension abilities.
-When coming up with this question/answer pair, you must respond in the following format:
-```
-{{
-    "question": "$YOUR_QUESTION_HERE",
-    "answer": "$THE_ANSWER_HERE"
-}}
-```
-
-Everything between the ``` must be valid json.
-"""
-templ2 = """Please come up with a question/answer pair, in the specified JSON format, for the following text:
-----------------
-{text}"""
-CHAT_PROMPT = ChatPromptTemplate.from_messages(
-    [
-        SystemMessagePromptTemplate.from_template(templ1),
-        HumanMessagePromptTemplate.from_template(templ2),
-    ]
-)
-templ = """You are a smart assistant designed to help high school teachers come up with reading comprehension questions.
-Given a piece of text, you must come up with a question and answer pair that can be used to test a student's reading comprehension abilities.
-When coming up with this question/answer pair, you must respond in the following format:
-```
-{{
-    "question": "$YOUR_QUESTION_HERE",
-    "answer": "$THE_ANSWER_HERE"
-}}
-```
-
-Everything between the ``` must be valid json.
-
-Please come up with a question/answer pair, in the specified JSON format, for the following text:
-----------------
-{text}"""
-PROMPT = PromptTemplate.from_template(templ)
-
-PROMPT_SELECTOR = ConditionalPromptSelector(
-    default_prompt=PROMPT, conditionals=[(is_chat_model, CHAT_PROMPT)]
-)

langchain/chains/qa_with_sources/base.py

@@ -32,8 +32,6 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
     input_docs_key: str = "docs"  #: :meta private:
     answer_key: str = "answer"  #: :meta private:
     sources_answer_key: str = "sources"  #: :meta private:
-    return_source_documents: bool = False
-    """Return the source documents."""
 
     @classmethod
     def from_llm(
@@ -97,10 +95,7 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
 
         :meta private:
         """
-        _output_keys = [self.answer_key, self.sources_answer_key]
-        if self.return_source_documents:
-            _output_keys = _output_keys + ["source_documents"]
-        return _output_keys
+        return [self.answer_key, self.sources_answer_key]
 
     @root_validator(pre=True)
     def validate_naming(cls, values: Dict) -> Dict:
@@ -113,20 +108,14 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
     def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
         """Get docs to run questioning over."""
 
-    def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         docs = self._get_docs(inputs)
         answer, _ = self.combine_documents_chain.combine_docs(docs, **inputs)
         if "SOURCES: " in answer:
             answer, sources = answer.split("SOURCES: ")
         else:
             sources = ""
-        result: Dict[str, Any] = {
-            self.answer_key: answer,
-            self.sources_answer_key: sources,
-        }
-        if self.return_source_documents:
-            result["source_documents"] = docs
-        return result
+        return {self.answer_key: answer, self.sources_answer_key: sources}
 
 
 class QAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):

langchain/chains/qa_with_sources/vector_db.py

@@ -32,9 +32,7 @@ class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
             self.combine_documents_chain, StuffDocumentsChain
         ):
             tokens = [
-                self.combine_documents_chain.llm_chain.llm.get_num_tokens(
-                    doc.page_content
-                )
+                self.combine_documents_chain.llm_chain.get_num_tokens(doc.page_content)
                 for doc in docs
             ]
             token_count = sum(tokens[:num_docs])

langchain/chains/question_answering/__init__.py

@@ -14,21 +14,19 @@ from langchain.chains.question_answering import (
     refine_prompts,
     stuff_prompt,
 )
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel
 
 
 class LoadingCallable(Protocol):
     """Interface for loading the combine documents chain."""
 
-    def __call__(
-        self, llm: BaseLanguageModel, **kwargs: Any
-    ) -> BaseCombineDocumentsChain:
+    def __call__(self, llm: BaseLLM, **kwargs: Any) -> BaseCombineDocumentsChain:
         """Callable to load the combine documents chain."""
 
 
 def _load_map_rerank_chain(
-    llm: BaseLanguageModel,
+    llm: BaseLLM,
     prompt: BasePromptTemplate = map_rerank_prompt.PROMPT,
     verbose: bool = False,
     document_variable_name: str = "context",
@@ -52,16 +50,15 @@ def _load_map_rerank_chain(
 
 
 def _load_stuff_chain(
-    llm: BaseLanguageModel,
-    prompt: Optional[BasePromptTemplate] = None,
+    llm: BaseLLM,
+    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
     document_variable_name: str = "context",
     verbose: Optional[bool] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
     **kwargs: Any,
 ) -> StuffDocumentsChain:
-    _prompt = prompt or stuff_prompt.PROMPT_SELECTOR.get_prompt(llm)
     llm_chain = LLMChain(
-        llm=llm, prompt=_prompt, verbose=verbose, callback_manager=callback_manager
+        llm=llm, prompt=prompt, verbose=verbose, callback_manager=callback_manager
     )
     # TODO: document prompt
     return StuffDocumentsChain(
@@ -74,34 +71,28 @@ def _load_stuff_chain(
 
 
 def _load_map_reduce_chain(
-    llm: BaseLanguageModel,
-    question_prompt: Optional[BasePromptTemplate] = None,
-    combine_prompt: Optional[BasePromptTemplate] = None,
+    llm: BaseLLM,
+    question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
+    combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
     combine_document_variable_name: str = "summaries",
     map_reduce_document_variable_name: str = "context",
     collapse_prompt: Optional[BasePromptTemplate] = None,
-    reduce_llm: Optional[BaseLanguageModel] = None,
-    collapse_llm: Optional[BaseLanguageModel] = None,
+    reduce_llm: Optional[BaseLLM] = None,
+    collapse_llm: Optional[BaseLLM] = None,
     verbose: Optional[bool] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
     **kwargs: Any,
 ) -> MapReduceDocumentsChain:
-    _question_prompt = (
-        question_prompt or map_reduce_prompt.QUESTION_PROMPT_SELECTOR.get_prompt(llm)
-    )
-    _combine_prompt = (
-        combine_prompt or map_reduce_prompt.COMBINE_PROMPT_SELECTOR.get_prompt(llm)
-    )
     map_chain = LLMChain(
         llm=llm,
-        prompt=_question_prompt,
+        prompt=question_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
     )
     _reduce_llm = reduce_llm or llm
     reduce_chain = LLMChain(
         llm=_reduce_llm,
-        prompt=_combine_prompt,
+        prompt=combine_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
     )
@@ -144,32 +135,26 @@ def _load_map_reduce_chain(
 
 
 def _load_refine_chain(
-    llm: BaseLanguageModel,
-    question_prompt: Optional[BasePromptTemplate] = None,
-    refine_prompt: Optional[BasePromptTemplate] = None,
+    llm: BaseLLM,
+    question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
+    refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
     document_variable_name: str = "context_str",
     initial_response_name: str = "existing_answer",
-    refine_llm: Optional[BaseLanguageModel] = None,
+    refine_llm: Optional[BaseLLM] = None,
     verbose: Optional[bool] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
     **kwargs: Any,
 ) -> RefineDocumentsChain:
-    _question_prompt = (
-        question_prompt or refine_prompts.REFINE_PROMPT_SELECTOR.get_prompt(llm)
-    )
-    _refine_prompt = refine_prompt or refine_prompts.REFINE_PROMPT_SELECTOR.get_prompt(
-        llm
-    )
     initial_chain = LLMChain(
         llm=llm,
-        prompt=_question_prompt,
+        prompt=question_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
     )
     _refine_llm = refine_llm or llm
     refine_chain = LLMChain(
         llm=_refine_llm,
-        prompt=_refine_prompt,
+        prompt=refine_prompt,
         verbose=verbose,
         callback_manager=callback_manager,
     )
@@ -185,7 +170,7 @@ def _load_refine_chain(
 
 
 def load_qa_chain(
-    llm: BaseLanguageModel,
+    llm: BaseLLM,
     chain_type: str = "stuff",
     verbose: Optional[bool] = None,
     callback_manager: Optional[BaseCallbackManager] = None,

langchain/chains/question_answering/map_reduce_prompt.py

@@ -1,14 +1,5 @@
 # flake8: noqa
-from langchain.prompts.prompt import PromptTemplate
-from langchain.prompts.chat import (
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    ChatPromptTemplate,
-)
-from langchain.chains.prompt_selector import (
-    ConditionalPromptSelector,
-    is_chat_model,
-)
+from langchain.prompts import PromptTemplate
 
 question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question. 
 Return any relevant text verbatim.
@@ -18,20 +9,6 @@ Relevant text, if any:"""
 QUESTION_PROMPT = PromptTemplate(
     template=question_prompt_template, input_variables=["context", "question"]
 )
-system_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question. 
-Return any relevant text verbatim.
-______________________
-{context}"""
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}"),
-]
-CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(messages)
-
-
-QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
-    default_prompt=QUESTION_PROMPT, conditionals=[(is_chat_model, CHAT_QUESTION_PROMPT)]
-)
 
 combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer. 
 If you don't know the answer, just say that you don't know. Don't try to make up an answer.
@@ -66,18 +43,3 @@ FINAL ANSWER:"""
 COMBINE_PROMPT = PromptTemplate(
     template=combine_prompt_template, input_variables=["summaries", "question"]
 )
-
-system_template = """Given the following extracted parts of a long document and a question, create a final answer. 
-If you don't know the answer, just say that you don't know. Don't try to make up an answer.
-______________________
-{summaries}"""
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}"),
-]
-CHAT_COMBINE_PROMPT = ChatPromptTemplate.from_messages(messages)
-
-
-COMBINE_PROMPT_SELECTOR = ConditionalPromptSelector(
-    default_prompt=COMBINE_PROMPT, conditionals=[(is_chat_model, CHAT_COMBINE_PROMPT)]
-)

langchain/chains/question_answering/map_rerank_prompt.py

@@ -1,6 +1,6 @@
 # flake8: noqa
 from langchain.prompts import PromptTemplate
-from langchain.output_parsers.regex import RegexParser
+from langchain.prompts.prompt import RegexParser
 
 output_parser = RegexParser(
     regex=r"(.*?)\nScore: (.*)",

langchain/chains/question_answering/refine_prompts.py

@@ -1,16 +1,5 @@
 # flake8: noqa
-from langchain.prompts.prompt import PromptTemplate
-from langchain.prompts.chat import (
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    ChatPromptTemplate,
-    AIMessagePromptTemplate,
-)
-from langchain.chains.prompt_selector import (
-    ConditionalPromptSelector,
-    is_chat_model,
-)
-
+from langchain.prompts import PromptTemplate
 
 DEFAULT_REFINE_PROMPT_TMPL = (
     "The original question is as follows: {question}\n"
@@ -28,26 +17,6 @@ DEFAULT_REFINE_PROMPT = PromptTemplate(
     input_variables=["question", "existing_answer", "context_str"],
     template=DEFAULT_REFINE_PROMPT_TMPL,
 )
-refine_template = (
-    "We have the opportunity to refine the existing answer"
-    "(only if needed) with some more context below.\n"
-    "------------\n"
-    "{context_str}\n"
-    "------------\n"
-    "Given the new context, refine the original answer to better "
-    "answer the question. "
-    "If the context isn't useful, return the original answer."
-)
-messages = [
-    HumanMessagePromptTemplate.from_template("{question}"),
-    AIMessagePromptTemplate.from_template("{existing_answer}"),
-    HumanMessagePromptTemplate.from_template(refine_template),
-]
-CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(messages)
-REFINE_PROMPT_SELECTOR = ConditionalPromptSelector(
-    default_prompt=DEFAULT_REFINE_PROMPT,
-    conditionals=[(is_chat_model, CHAT_REFINE_PROMPT)],
-)
 
 
 DEFAULT_TEXT_QA_PROMPT_TMPL = (
@@ -61,20 +30,3 @@ DEFAULT_TEXT_QA_PROMPT_TMPL = (
 DEFAULT_TEXT_QA_PROMPT = PromptTemplate(
     input_variables=["context_str", "question"], template=DEFAULT_TEXT_QA_PROMPT_TMPL
 )
-chat_qa_prompt_template = (
-    "Context information is below. \n"
-    "---------------------\n"
-    "{context_str}"
-    "\n---------------------\n"
-    "Given the context information and not prior knowledge, "
-    "answer any questions"
-)
-messages = [
-    SystemMessagePromptTemplate.from_template(chat_qa_prompt_template),
-    HumanMessagePromptTemplate.from_template("{question}"),
-]
-CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(messages)
-QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
-    default_prompt=DEFAULT_TEXT_QA_PROMPT,
-    conditionals=[(is_chat_model, CHAT_QUESTION_PROMPT)],
-)

langchain/chains/question_answering/stuff_prompt.py

@@ -1,15 +1,5 @@
 # flake8: noqa
 from langchain.prompts import PromptTemplate
-from langchain.chains.prompt_selector import (
-    ConditionalPromptSelector,
-    is_chat_model,
-)
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
 
 prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
 
@@ -20,18 +10,3 @@ Helpful Answer:"""
 PROMPT = PromptTemplate(
     template=prompt_template, input_variables=["context", "question"]
 )
-
-system_template = """Use the following pieces of context to answer the users question. 
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-----------------
-{context}"""
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}"),
-]
-CHAT_PROMPT = ChatPromptTemplate.from_messages(messages)
-
-
-PROMPT_SELECTOR = ConditionalPromptSelector(
-    default_prompt=PROMPT, conditionals=[(is_chat_model, CHAT_PROMPT)]
-)

langchain/chains/sql_database/base.py

@@ -117,8 +117,6 @@ class SQLDatabaseSequentialChain(Chain, BaseModel):
     This is useful in cases where the number of tables in the database is large.
     """
 
-    return_intermediate_steps: bool = False
-
     @classmethod
     def from_llm(
         cls,
@@ -156,10 +154,7 @@ class SQLDatabaseSequentialChain(Chain, BaseModel):
 
         :meta private:
         """
-        if not self.return_intermediate_steps:
-            return [self.output_key]
-        else:
-            return [self.output_key, "intermediate_steps"]
+        return [self.output_key]
 
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         _table_names = self.sql_chain.database.get_table_names()

langchain/chains/sql_database/prompt.py

@@ -1,5 +1,5 @@
 # flake8: noqa
-from langchain.output_parsers.list import CommaSeparatedListOutputParser
+from langchain.prompts.prompt import CommaSeparatedListOutputParser
 from langchain.prompts.prompt import PromptTemplate
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.

langchain/chains/vector_db_qa/base.py

@@ -10,7 +10,7 @@ from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
-from langchain.chains.question_answering.stuff_prompt import PROMPT_SELECTOR
+from langchain.chains.vector_db_qa.prompt import PROMPT
 from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
 from langchain.vectorstores.base import VectorStore
@@ -78,8 +78,8 @@ class VectorDBQA(Chain, BaseModel):
                 raise ValueError(
                     "If `combine_documents_chain` not provided, `llm` should be."
                 )
+            prompt = values.pop("prompt", PROMPT)
             llm = values.pop("llm")
-            prompt = values.pop("prompt", PROMPT_SELECTOR.get_prompt(llm))
             llm_chain = LLMChain(llm=llm, prompt=prompt)
             document_prompt = PromptTemplate(
                 input_variables=["page_content"], template="Context:\n{page_content}"
@@ -103,11 +103,10 @@ class VectorDBQA(Chain, BaseModel):
 
     @classmethod
     def from_llm(
-        cls, llm: BaseLLM, prompt: Optional[PromptTemplate] = None, **kwargs: Any
+        cls, llm: BaseLLM, prompt: PromptTemplate = PROMPT, **kwargs: Any
     ) -> VectorDBQA:
         """Initialize from LLM."""
-        _prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
-        llm_chain = LLMChain(llm=llm, prompt=_prompt)
+        llm_chain = LLMChain(llm=llm, prompt=prompt)
         document_prompt = PromptTemplate(
             input_variables=["page_content"], template="Context:\n{page_content}"
         )

langchain/chat_models/__init__.py

@@ -1,4 +1,3 @@
 from langchain.chat_models.openai import ChatOpenAI
-from langchain.chat_models.promptlayer_openai import PromptLayerChatOpenAI
 
-__all__ = ["ChatOpenAI", "PromptLayerChatOpenAI"]
+__all__ = ["ChatOpenAI"]

langchain/chat_models/base.py

@@ -8,13 +8,10 @@ from langchain.callbacks import get_callback_manager
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.schema import (
     AIMessage,
-    BaseLanguageModel,
     BaseMessage,
     ChatGeneration,
     ChatResult,
-    HumanMessage,
     LLMResult,
-    PromptValue,
 )
 
 
@@ -22,7 +19,7 @@ def _get_verbosity() -> bool:
     return langchain.verbose
 
 
-class BaseChatModel(BaseLanguageModel, BaseModel, ABC):
+class BaseChatModel(BaseModel, ABC):
     verbose: bool = Field(default_factory=_get_verbosity)
     """Whether to print out response text."""
     callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager)
@@ -47,58 +44,15 @@ class BaseChatModel(BaseLanguageModel, BaseModel, ABC):
         self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None
     ) -> LLMResult:
         """Top Level call"""
-        results = [self._generate(m, stop=stop) for m in messages]
+        results = []
+        for m in messages:
+            results.append(self._generate(m, stop=stop))
         return LLMResult(generations=[res.generations for res in results])
 
     async def agenerate(
         self, messages: List[List[BaseMessage]], stop: Optional[List[str]] = None
     ) -> LLMResult:
-        """Top Level call"""
-        results = [await self._agenerate(m, stop=stop) for m in messages]
-        return LLMResult(generations=[res.generations for res in results])
-
-    def generate_prompt(
-        self, prompts: List[PromptValue], stop: Optional[List[str]] = None
-    ) -> LLMResult:
-        prompt_messages = [p.to_messages() for p in prompts]
-        prompt_strings = [p.to_string() for p in prompts]
-        self.callback_manager.on_llm_start(
-            {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
-        )
-        try:
-            output = self.generate(prompt_messages, stop=stop)
-        except (KeyboardInterrupt, Exception) as e:
-            self.callback_manager.on_llm_error(e, verbose=self.verbose)
-            raise e
-        self.callback_manager.on_llm_end(output, verbose=self.verbose)
-        return output
-
-    async def agenerate_prompt(
-        self, prompts: List[PromptValue], stop: Optional[List[str]] = None
-    ) -> LLMResult:
-        prompt_messages = [p.to_messages() for p in prompts]
-        prompt_strings = [p.to_string() for p in prompts]
-        if self.callback_manager.is_async:
-            await self.callback_manager.on_llm_start(
-                {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
-            )
-        else:
-            self.callback_manager.on_llm_start(
-                {"name": self.__class__.__name__}, prompt_strings, verbose=self.verbose
-            )
-        try:
-            output = await self.agenerate(prompt_messages, stop=stop)
-        except (KeyboardInterrupt, Exception) as e:
-            if self.callback_manager.is_async:
-                await self.callback_manager.on_llm_error(e, verbose=self.verbose)
-            else:
-                self.callback_manager.on_llm_error(e, verbose=self.verbose)
-            raise e
-        if self.callback_manager.is_async:
-            await self.callback_manager.on_llm_end(output, verbose=self.verbose)
-        else:
-            self.callback_manager.on_llm_end(output, verbose=self.verbose)
-        return output
+        raise NotImplementedError
 
     @abstractmethod
     def _generate(
@@ -106,21 +60,11 @@ class BaseChatModel(BaseLanguageModel, BaseModel, ABC):
     ) -> ChatResult:
         """Top Level call"""
 
-    @abstractmethod
-    async def _agenerate(
-        self, messages: List[BaseMessage], stop: Optional[List[str]] = None
-    ) -> ChatResult:
-        """Top Level call"""
-
     def __call__(
         self, messages: List[BaseMessage], stop: Optional[List[str]] = None
     ) -> BaseMessage:
         return self._generate(messages, stop=stop).generations[0].message
 
-    def call_as_llm(self, message: str, stop: Optional[List[str]] = None) -> str:
-        result = self([HumanMessage(content=message)], stop=stop)
-        return result.content
-
 
 class SimpleChatModel(BaseChatModel):
     def _generate(

langchain/chat_models/openai.py

@@ -1,9 +1,6 @@
 """OpenAI chat wrapper."""
-from __future__ import annotations
-
 import logging
-import sys
-from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple
+from typing import Any, Callable, Dict, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra, Field, root_validator
 from tenacity import (
@@ -23,46 +20,14 @@ from langchain.schema import (
     ChatResult,
     HumanMessage,
     SystemMessage,
+    ExampleHumanMessage,
+    ExampleAIMessage,
 )
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__file__)
 
 
-def _create_retry_decorator(llm: ChatOpenAI) -> Callable[[Any], Any]:
-    import openai
-
-    min_seconds = 4
-    max_seconds = 10
-    # Wait 2^x * 1 second between each retry starting with
-    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
-    return retry(
-        reraise=True,
-        stop=stop_after_attempt(llm.max_retries),
-        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
-        retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
-        ),
-        before_sleep=before_sleep_log(logger, logging.WARNING),
-    )
-
-
-async def acompletion_with_retry(llm: ChatOpenAI, **kwargs: Any) -> Any:
-    """Use tenacity to retry the async completion call."""
-    retry_decorator = _create_retry_decorator(llm)
-
-    @retry_decorator
-    async def _completion_with_retry(**kwargs: Any) -> Any:
-        # Use OpenAI's async api https://github.com/openai/openai-python#async-api
-        return await llm.client.acreate(**kwargs)
-
-    return await _completion_with_retry(**kwargs)
-
-
 def _convert_dict_to_message(_dict: dict) -> BaseMessage:
     role = _dict["role"]
     if role == "user":
@@ -70,6 +35,11 @@ def _convert_dict_to_message(_dict: dict) -> BaseMessage:
     elif role == "assistant":
         return AIMessage(content=_dict["content"])
     elif role == "system":
+        if "name" in _dict:
+            if _dict["name"] == "example_user":
+                return ExampleHumanMessage(content=_dict["content"])
+            elif _dict["name"] == "example_assistant":
+                return ExampleAIMessage(content=_dict["content"])
         return SystemMessage(content=_dict["content"])
     else:
         return ChatMessage(content=_dict["content"], role=role)
@@ -84,6 +54,10 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
         message_dict = {"role": "assistant", "content": message.content}
     elif isinstance(message, SystemMessage):
         message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, ExampleHumanMessage):
+        message_dict = {"role": "system", "content": message.content, "name": "example_user"}
+    elif isinstance(message, ExampleAIMessage):
+        message_dict = {"role": "system", "content": message.content, "name": "example_assistant"}
     else:
         raise ValueError(f"Got unknown type {message}")
     if "name" in message.additional_kwargs:
@@ -91,15 +65,6 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     return message_dict
 
 
-def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
-    generations = []
-    for res in response["choices"]:
-        message = _convert_dict_to_message(res["message"])
-        gen = ChatGeneration(message=message)
-        generations.append(gen)
-    return ChatResult(generations=generations)
-
-
 class ChatOpenAI(BaseChatModel, BaseModel):
     """Wrapper around OpenAI Chat large language models.
 
@@ -224,7 +189,12 @@ class ChatOpenAI(BaseChatModel, BaseModel):
     def _generate(
         self, messages: List[BaseMessage], stop: Optional[List[str]] = None
     ) -> ChatResult:
-        message_dicts, params = self._create_message_dicts(messages, stop)
+        params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
+        if stop is not None:
+            if "stop" in params:
+                raise ValueError("`stop` found in both the input and default params.")
+            params["stop"] = stop
+        message_dicts = [_convert_message_to_dict(m) for m in messages]
         if self.streaming:
             inner_completion = ""
             role = "assistant"
@@ -244,76 +214,14 @@ class ChatOpenAI(BaseChatModel, BaseModel):
             )
             return ChatResult(generations=[ChatGeneration(message=message)])
         response = self.completion_with_retry(messages=message_dicts, **params)
-        return _create_chat_result(response)
-
-    def _create_message_dicts(
-        self, messages: List[BaseMessage], stop: Optional[List[str]]
-    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
-        params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
-        if stop is not None:
-            if "stop" in params:
-                raise ValueError("`stop` found in both the input and default params.")
-            params["stop"] = stop
-        message_dicts = [_convert_message_to_dict(m) for m in messages]
-        return message_dicts, params
-
-    async def _agenerate(
-        self, messages: List[BaseMessage], stop: Optional[List[str]] = None
-    ) -> ChatResult:
-        message_dicts, params = self._create_message_dicts(messages, stop)
-        if self.streaming:
-            inner_completion = ""
-            role = "assistant"
-            params["stream"] = True
-            async for stream_resp in await acompletion_with_retry(
-                self, messages=message_dicts, **params
-            ):
-                role = stream_resp["choices"][0]["delta"].get("role", role)
-                token = stream_resp["choices"][0]["delta"].get("content", "")
-                inner_completion += token
-                if self.callback_manager.is_async:
-                    await self.callback_manager.on_llm_new_token(
-                        token,
-                        verbose=self.verbose,
-                    )
-                else:
-                    self.callback_manager.on_llm_new_token(
-                        token,
-                        verbose=self.verbose,
-                    )
-            message = _convert_dict_to_message(
-                {"content": inner_completion, "role": role}
-            )
-            return ChatResult(generations=[ChatGeneration(message=message)])
-        else:
-            response = await acompletion_with_retry(
-                self, messages=message_dicts, **params
-            )
-            return _create_chat_result(response)
+        generations = []
+        for res in response["choices"]:
+            message = _convert_dict_to_message(res["message"])
+            gen = ChatGeneration(message=message)
+            generations.append(gen)
+        return ChatResult(generations=generations)
 
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""
         return {**{"model_name": self.model_name}, **self._default_params}
-
-    def get_num_tokens(self, text: str) -> int:
-        """Calculate num tokens with tiktoken package."""
-        # tiktoken NOT supported for Python 3.8 or below
-        if sys.version_info[1] <= 8:
-            return super().get_num_tokens(text)
-        try:
-            import tiktoken
-        except ImportError:
-            raise ValueError(
-                "Could not import tiktoken python package. "
-                "This is needed in order to calculate get_num_tokens. "
-                "Please it install it with `pip install tiktoken`."
-            )
-        # create a GPT-3.5-Turbo encoder instance
-        enc = tiktoken.encoding_for_model(self.model_name)
-
-        # encode the text using the GPT-3.5-Turbo encoder
-        tokenized_text = enc.encode(text)
-
-        # calculate the number of tokens in the encoded text
-        return len(tokenized_text)

langchain/chat_models/promptlayer_openai.py

@@ -1,84 +0,0 @@
-"""PromptLayer wrapper."""
-import datetime
-from typing import List, Optional
-
-from pydantic import BaseModel
-
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import BaseMessage, ChatResult
-
-
-class PromptLayerChatOpenAI(ChatOpenAI, BaseModel):
-    """Wrapper around OpenAI Chat large language models and PromptLayer.
-
-    To use, you should have the ``openai`` and ``promptlayer`` python
-    package installed, and the environment variable ``OPENAI_API_KEY``
-    and ``PROMPTLAYER_API_KEY`` set with your openAI API key and
-    promptlayer key respectively.
-
-    All parameters that can be passed to the OpenAI LLM can also
-    be passed here. The PromptLayerChatOpenAI LLM adds an extra
-    ``pl_tags`` parameter that can be used to tag the request.
-
-    Example:
-        .. code-block:: python
-
-            from langchain.chat_models import PromptLayerChatOpenAI
-            openai = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo")
-    """
-
-    pl_tags: Optional[List[str]]
-
-    def _generate(
-        self, messages: List[BaseMessage], stop: Optional[List[str]] = None
-    ) -> ChatResult:
-        """Call ChatOpenAI generate and then call PromptLayer API to log the request."""
-        from promptlayer.utils import get_api_key, promptlayer_api_request
-
-        request_start_time = datetime.datetime.now().timestamp()
-        generated_responses = super()._generate(messages, stop)
-        request_end_time = datetime.datetime.now().timestamp()
-        message_dicts, params = super()._create_message_dicts(messages, stop)
-        for i, generation in enumerate(generated_responses.generations):
-            response_dict, params = super()._create_message_dicts(
-                [generation.message], stop
-            )
-            promptlayer_api_request(
-                "langchain.PromptLayerChatOpenAI",
-                "langchain",
-                message_dicts,
-                params,
-                self.pl_tags,
-                response_dict,
-                request_start_time,
-                request_end_time,
-                get_api_key(),
-            )
-        return generated_responses
-
-    async def _agenerate(
-        self, messages: List[BaseMessage], stop: Optional[List[str]] = None
-    ) -> ChatResult:
-        """Call ChatOpenAI agenerate and then call PromptLayer to log."""
-        from promptlayer.utils import get_api_key, promptlayer_api_request
-
-        request_start_time = datetime.datetime.now().timestamp()
-        generated_responses = await super()._agenerate(messages, stop)
-        request_end_time = datetime.datetime.now().timestamp()
-        message_dicts, params = super()._create_message_dicts(messages, stop)
-        for i, generation in enumerate(generated_responses.generations):
-            response_dict, params = super()._create_message_dicts(
-                [generation.message], stop
-            )
-            promptlayer_api_request(
-                "langchain.PromptLayerChatOpenAI.async",
-                "langchain",
-                message_dicts,
-                params,
-                self.pl_tags,
-                response_dict,
-                request_start_time,
-                request_end_time,
-                get_api_key(),
-            )
-        return generated_responses

langchain/document_loaders/__init__.py

@@ -4,7 +4,6 @@ from langchain.document_loaders.airbyte_json import AirbyteJSONLoader
 from langchain.document_loaders.azlyrics import AZLyricsLoader
 from langchain.document_loaders.college_confidential import CollegeConfidentialLoader
 from langchain.document_loaders.conllu import CoNLLULoader
-from langchain.document_loaders.csv import CSVLoader
 from langchain.document_loaders.directory import DirectoryLoader
 from langchain.document_loaders.docx import UnstructuredDocxLoader
 from langchain.document_loaders.email import UnstructuredEmailLoader
@@ -20,7 +19,6 @@ from langchain.document_loaders.html import UnstructuredHTMLLoader
 from langchain.document_loaders.ifixit import IFixitLoader
 from langchain.document_loaders.image import UnstructuredImageLoader
 from langchain.document_loaders.imsdb import IMSDbLoader
-from langchain.document_loaders.markdown import UnstructuredMarkdownLoader
 from langchain.document_loaders.notebook import NotebookLoader
 from langchain.document_loaders.notion import NotionDirectoryLoader
 from langchain.document_loaders.obsidian import ObsidianLoader
@@ -46,11 +44,7 @@ from langchain.document_loaders.unstructured import (
 from langchain.document_loaders.url import UnstructuredURLLoader
 from langchain.document_loaders.web_base import WebBaseLoader
 from langchain.document_loaders.word_document import UnstructuredWordDocumentLoader
-from langchain.document_loaders.youtube import (
-    GoogleApiClient,
-    GoogleApiYoutubeLoader,
-    YoutubeLoader,
-)
+from langchain.document_loaders.youtube import YoutubeLoader
 
 __all__ = [
     "UnstructuredFileLoader",
@@ -68,7 +62,6 @@ __all__ = [
     "ObsidianLoader",
     "UnstructuredDocxLoader",
     "UnstructuredEmailLoader",
-    "UnstructuredMarkdownLoader",
     "RoamLoader",
     "YoutubeLoader",
     "S3FileLoader",
@@ -95,7 +88,4 @@ __all__ = [
     "FacebookChatLoader",
     "NotebookLoader",
     "CoNLLULoader",
-    "GoogleApiYoutubeLoader",
-    "GoogleApiClient",
-    "CSVLoader",
 ]

langchain/document_loaders/csv.py

@@ -1,47 +0,0 @@
-from csv import DictReader
-from typing import Dict, List, Optional
-
-from langchain.docstore.document import Document
-from langchain.document_loaders.base import BaseLoader
-
-
-class CSVLoader(BaseLoader):
-    """Loads a CSV file into a list of documents.
-
-    Each document represents one row of the CSV file. Every row is converted into a
-    key/value pair and outputted to a new line in the document's page_content.
-
-    Output Example:
-        .. code-block:: txt
-
-            column1: value1
-            column2: value2
-            column3: value3
-    """
-
-    def __init__(self, file_path: str, csv_args: Optional[Dict] = None):
-        self.file_path = file_path
-        if csv_args is None:
-            self.csv_args = {
-                "delimiter": ",",
-                "quotechar": '"',
-            }
-        else:
-            self.csv_args = csv_args
-
-    def load(self) -> List[Document]:
-        docs = []
-
-        with open(self.file_path, newline="") as csvfile:
-            csv = DictReader(csvfile, **self.csv_args)  # type: ignore
-            for row in csv:
-                docs.append(
-                    Document(
-                        page_content="\n".join(
-                            f"{k.strip()}: {v.strip()}" for k, v in row.items()
-                        ),
-                        metadata={"source": self.file_path},
-                    )
-                )
-
-        return docs

langchain/document_loaders/gitbook.py

@@ -12,26 +12,9 @@ class GitbookLoader(WebBaseLoader):
     2. load all (relative) paths in the navbar.
     """
 
-    def __init__(
-        self,
-        web_page: str,
-        load_all_paths: bool = False,
-        base_url: Optional[str] = None,
-    ):
-        """Initialize with web page and whether to load all paths.
-
-        Args:
-            web_page: The web page to load or the starting point from where
-                relative paths are discovered.
-            load_all_paths: If set to True, all relative paths in the navbar
-                are loaded instead of only `web_page`.
-            base_url: If `load_all_paths` is True, the relative paths are
-                appended to this base url. Defaults to `web_page` if not set.
-        """
+    def __init__(self, web_page: str, load_all_paths: bool = False):
+        """Initialize with web page and whether to load all paths."""
         super().__init__(web_page)
-        self.base_url = base_url or web_page
-        if self.base_url.endswith("/"):
-            self.base_url = self.base_url[:-1]
         self.load_all_paths = load_all_paths
 
     def load(self) -> List[Document]:
@@ -41,7 +24,7 @@ class GitbookLoader(WebBaseLoader):
             relative_paths = self._get_paths(soup_info)
             documents = []
             for path in relative_paths:
-                url = self.base_url + path
+                url = self.web_path + path
                 print(f"Fetching text from {url}")
                 soup_info = self._scrape(url)
                 documents.append(self._get_document(soup_info, url))

langchain/document_loaders/googledrive.py

@@ -26,26 +26,16 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
     token_path: Path = Path.home() / ".credentials" / "token.json"
     folder_id: Optional[str] = None
     document_ids: Optional[List[str]] = None
-    file_ids: Optional[List[str]] = None
 
     @root_validator
     def validate_folder_id_or_document_ids(
         cls, values: Dict[str, Any]
     ) -> Dict[str, Any]:
         """Validate that either folder_id or document_ids is set, but not both."""
-        if values.get("folder_id") and (
-            values.get("document_ids") or values.get("file_ids")
-        ):
-            raise ValueError(
-                "Cannot specify both folder_id and document_ids nor "
-                "folder_id and file_ids"
-            )
-        if (
-            not values.get("folder_id")
-            and not values.get("document_ids")
-            and not values.get("file_ids")
-        ):
-            raise ValueError("Must specify either folder_id, document_ids, or file_ids")
+        if values.get("folder_id") and values.get("document_ids"):
+            raise ValueError("Cannot specify both folder_id and document_ids")
+        if not values.get("folder_id") and not values.get("document_ids"):
+            raise ValueError("Must specify either folder_id or document_ids")
         return values
 
     @validator("credentials_path")
@@ -125,16 +115,13 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
             .execute()
         )
         items = results.get("files", [])
-        returns = []
-        for item in items:
-            if item["mimeType"] == "application/vnd.google-apps.document":
-                returns.append(self._load_document_from_id(item["id"]))
-            elif item["mimeType"] == "application/pdf":
-                returns.extend(self._load_file_from_id(item["id"]))
-            else:
-                pass
 
-        return returns
+        return [
+            self._load_document_from_id(item["id"])
+            for item in items
+            # Only support Google Docs for now
+            if item["mimeType"] == "application/vnd.google-apps.document"
+        ]
 
     def _load_documents_from_ids(self) -> List[Document]:
         """Load documents from a list of IDs."""
@@ -143,53 +130,9 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
 
         return [self._load_document_from_id(doc_id) for doc_id in self.document_ids]
 
-    def _load_file_from_id(self, id: str) -> List[Document]:
-        """Load a file from an ID."""
-        from io import BytesIO
-
-        from googleapiclient.discovery import build
-        from googleapiclient.http import MediaIoBaseDownload
-
-        creds = self._load_credentials()
-        service = build("drive", "v3", credentials=creds)
-
-        request = service.files().get_media(fileId=id)
-        fh = BytesIO()
-        downloader = MediaIoBaseDownload(fh, request)
-        done = False
-        while done is False:
-            status, done = downloader.next_chunk()
-        content = fh.getvalue()
-
-        from PyPDF2 import PdfReader
-
-        pdf_reader = PdfReader(BytesIO(content))
-
-        return [
-            Document(
-                page_content=page.extract_text(),
-                metadata={
-                    "source": f"https://drive.google.com/file/d/{id}/view",
-                    "page": i,
-                },
-            )
-            for i, page in enumerate(pdf_reader.pages)
-        ]
-
-    def _load_file_from_ids(self) -> List[Document]:
-        """Load files from a list of IDs."""
-        if not self.file_ids:
-            raise ValueError("file_ids must be set")
-        docs = []
-        for file_id in self.file_ids:
-            docs.extend(self._load_file_from_id(file_id))
-        return docs
-
     def load(self) -> List[Document]:
         """Load documents."""
         if self.folder_id:
             return self._load_documents_from_folder()
-        elif self.document_ids:
-            return self._load_documents_from_ids()
         else:
-            return self._load_file_from_ids()
+            return self._load_documents_from_ids()

langchain/document_loaders/html.py

@@ -1,4 +1,4 @@
-"""Loader that uses unstructured to load HTML files."""
+"""Loader that loads PDF files."""
 from typing import List
 
 from langchain.document_loaders.unstructured import UnstructuredFileLoader

langchain/document_loaders/markdown.py

@@ -1,25 +0,0 @@
-"""Loader that loads Markdown files."""
-from typing import List
-
-from langchain.document_loaders.unstructured import UnstructuredFileLoader
-
-
-class UnstructuredMarkdownLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load markdown files."""
-
-    def _get_elements(self) -> List:
-        from unstructured.__version__ import __version__ as __unstructured_version__
-        from unstructured.partition.md import partition_md
-
-        # NOTE(MthwRobinson) - enables the loader to work when you're using pre-release
-        # versions of unstructured like 0.4.17-dev1
-        _unstructured_version = __unstructured_version__.split("-")[0]
-        unstructured_version = tuple([int(x) for x in _unstructured_version.split(".")])
-
-        if unstructured_version < (0, 4, 16):
-            raise ValueError(
-                f"You are on unstructured version {__unstructured_version__}. "
-                "Partitioning markdown files is only supported in unstructured>=0.4.16."
-            )
-
-        return partition_md(filename=self.file_path)

langchain/document_loaders/online_pdf.py

@@ -1,15 +1,30 @@
 """Loader that loads online PDF files."""
 
+import tempfile
+from pathlib import Path
 from typing import List
 
+import requests
+
 from langchain.docstore.document import Document
-from langchain.document_loaders.pdf import BasePDFLoader, UnstructuredPDFLoader
+from langchain.document_loaders.base import BaseLoader
+from langchain.document_loaders.pdf import UnstructuredPDFLoader
 
 
-class OnlinePDFLoader(BasePDFLoader):
+class OnlinePDFLoader(BaseLoader):
     """Loader that loads online PDFs."""
 
+    def __init__(self, web_path: str):
+        """Initialize with file path."""
+        self.web_path = web_path
+
     def load(self) -> List[Document]:
         """Load documents."""
-        loader = UnstructuredPDFLoader(str(self.file_path))
-        return loader.load()
+        r = requests.get(self.web_path)
+        with tempfile.TemporaryDirectory() as temp_dir:
+            file_path = Path(temp_dir) / "online_file.pdf"
+            file = open(file_path, "wb")
+            file.write(r.content)
+            file.close()
+            loader = UnstructuredPDFLoader(str(file_path))
+            return loader.load()

langchain/document_loaders/pdf.py

@@ -1,11 +1,5 @@
 """Loader that loads PDF files."""
-import os
-import tempfile
-from abc import ABC
 from typing import Any, List, Optional
-from urllib.parse import urlparse
-
-import requests
 
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
@@ -21,51 +15,7 @@ class UnstructuredPDFLoader(UnstructuredFileLoader):
         return partition_pdf(filename=self.file_path)
 
 
-class BasePDFLoader(BaseLoader, ABC):
-    """Base loader class for PDF files.
-
-    Defaults to check for local file, but if the file is a web path, it will download it
-    to a temporary file, and use that, then clean up the temporary file after completion
-    """
-
-    file_path: str
-    web_path: Optional[str] = None
-
-    def __init__(self, file_path: str):
-        """Initialize with file path."""
-        self.file_path = file_path
-        if "~" in self.file_path:
-            self.file_path = os.path.expanduser(self.file_path)
-
-        # If the file is a web path, download it to a temporary file, and use that
-        if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
-            r = requests.get(self.file_path)
-
-            if r.status_code != 200:
-                raise ValueError(
-                    "Check the url of your file; returned status code %s"
-                    % r.status_code
-                )
-
-            self.web_path = self.file_path
-            self.temp_file = tempfile.NamedTemporaryFile()
-            self.temp_file.write(r.content)
-            self.file_path = self.temp_file.name
-        elif not os.path.isfile(self.file_path):
-            raise ValueError("File path %s is not a valid file or url" % self.file_path)
-
-    def __del__(self) -> None:
-        if hasattr(self, "temp_file"):
-            self.temp_file.close()
-
-    @staticmethod
-    def _is_valid_url(url: str) -> bool:
-        """Check if the url is valid."""
-        parsed = urlparse(url)
-        return bool(parsed.netloc) and bool(parsed.scheme)
-
-
-class PDFMinerLoader(BasePDFLoader):
+class PDFMinerLoader(BaseLoader):
     """Loader that uses PDFMiner to load PDF files."""
 
     def __init__(self, file_path: str):
@@ -78,7 +28,7 @@ class PDFMinerLoader(BasePDFLoader):
                 "`pip install pdfminer.six`"
             )
 
-        super().__init__(file_path)
+        self.file_path = file_path
 
     def load(self) -> List[Document]:
         """Load file."""
@@ -89,7 +39,7 @@ class PDFMinerLoader(BasePDFLoader):
         return [Document(page_content=text, metadata=metadata)]
 
 
-class PyMuPDFLoader(BasePDFLoader):
+class PyMuPDFLoader(BaseLoader):
     """Loader that uses PyMuPDF to load PDF files."""
 
     def __init__(self, file_path: str):
@@ -102,30 +52,22 @@ class PyMuPDFLoader(BasePDFLoader):
                 "`pip install pymupdf`"
             )
 
-        super().__init__(file_path)
+        self.file_path = file_path
 
     def load(self, **kwargs: Optional[Any]) -> List[Document]:
         """Load file."""
         import fitz
 
         doc = fitz.open(self.file_path)  # open document
-        file_path = self.file_path if self.web_path is None else self.web_path
-
         return [
             Document(
                 page_content=page.get_text(**kwargs).encode("utf-8"),
-                metadata=dict(
-                    {
-                        "file_path": file_path,
-                        "page_number": page.number + 1,
-                        "total_pages": len(doc),
-                    },
-                    **{
-                        k: doc.metadata[k]
-                        for k in doc.metadata
-                        if type(doc.metadata[k]) in [str, int]
-                    }
-                ),
+                metadata={
+                    "file_path": self.file_path,
+                    "page_number": page.number + 1,
+                    "total_pages": len(doc),
+                }
+                | doc.metadata,
             )
             for page in doc
         ]

langchain/document_loaders/readthedocs.py

@@ -1,6 +1,6 @@
 """Loader that loads ReadTheDocs documentation directory dump."""
 from pathlib import Path
-from typing import Any, List, Optional
+from typing import List
 
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
@@ -9,41 +9,16 @@ from langchain.document_loaders.base import BaseLoader
 class ReadTheDocsLoader(BaseLoader):
     """Loader that loads ReadTheDocs documentation directory dump."""
 
-    def __init__(
-        self,
-        path: str,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        **kwargs: Optional[Any]
-    ):
+    def __init__(self, path: str):
         """Initialize path."""
-        try:
-            from bs4 import BeautifulSoup
-
-        except ImportError:
-            raise ValueError(
-                "Could not import python packages. "
-                "Please install it with `pip install beautifulsoup4`. "
-            )
-
-        try:
-            _ = BeautifulSoup(
-                "<html><body>Parser builder library test.</body></html>", **kwargs
-            )
-        except Exception as e:
-            raise ValueError("Parsing kwargs do not appear valid") from e
-
         self.file_path = path
-        self.encoding = encoding
-        self.errors = errors
-        self.bs_kwargs = kwargs
 
     def load(self) -> List[Document]:
         """Load documents."""
         from bs4 import BeautifulSoup
 
         def _clean_data(data: str) -> str:
-            soup = BeautifulSoup(data, **self.bs_kwargs)
+            soup = BeautifulSoup(data)
             text = soup.find_all("main", {"id": "main-content"})
             if len(text) != 0:
                 text = text[0].get_text()
@@ -55,7 +30,7 @@ class ReadTheDocsLoader(BaseLoader):
         for p in Path(self.file_path).rglob("*"):
             if p.is_dir():
                 continue
-            with open(p, encoding=self.encoding, errors=self.errors) as f:
+            with open(p) as f:
                 text = _clean_data(f.read())
             metadata = {"source": str(p)}
             docs.append(Document(page_content=text, metadata=metadata))