forked from Archives/langchain
Harrison Chase
1 year ago
committed by
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parent
b83e826510
commit
705431aecc
@ -1,30 +1,52 @@
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Agents
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==========================
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.. note::
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`Conceptual Guide <https://docs.langchain.com/docs/components/agents>`_
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Some applications will require not just a predetermined chain of calls to LLMs/other tools,
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but potentially an unknown chain that depends on the user's input.
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In these types of chains, there is a “agent” which has access to a suite of tools.
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Depending on the user input, the agent can then decide which, if any, of these tools to call.
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The following sections of documentation are provided:
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In this section of documentation, we first start with a Getting Started notebook to over over how to use all things related to agents in an end-to-end manner.
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.. toctree::
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:maxdepth: 1
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:hidden:
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./agents/getting_started.ipynb
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- `Getting Started <./agents/getting_started.html>`_: A notebook to help you get started working with agents as quickly as possible.
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We then split the documentation into the following sections:
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- `Key Concepts <./agents/key_concepts.html>`_: A conceptual guide going over the various concepts related to agents.
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**Tools**
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- `How-To Guides <./agents/how_to_guides.html>`_: A collection of how-to guides. These highlight how to integrate various types of tools, how to work with different types of agents, and how to customize agents.
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An overview of the various tools LangChain supports.
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- `Reference <../reference/modules/agents.html>`_: API reference documentation for all Agent classes.
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**Agents**
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An overview of the different agent types.
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**Toolkits**
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An overview of toolkits, and examples of the different ones LangChain supports.
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**Agent Executor**
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An overview of the Agent Executor class and examples of how to use it.
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Go Deeper
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---------
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.. toctree::
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:maxdepth: 1
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:caption: Agents
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:name: Agents
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:hidden:
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./agents/getting_started.ipynb
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./agents/key_concepts.md
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./agents/how_to_guides.rst
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Reference<../reference/modules/agents.rst>
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./agents/tools.rst
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./agents/agents.rst
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./agents/agent_toolkits.rst
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./agents/agent_executors.rst
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@ -0,0 +1,17 @@
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Agent Executors
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===============
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.. note::
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`Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent-executor>`_
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Agent executors take an agent and tools and use the agent to decide which tools to call and in what order.
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In this part of the documentation we cover other related functionality to agent executors
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.. toctree::
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:maxdepth: 1
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:glob:
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./agent_executors/examples/*
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@ -0,0 +1,35 @@
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Agents
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=============
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.. note::
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`Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent>`_
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In this part of the documentation we cover the different types of agents, disregarding which specific tools they are used with.
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For a high level overview of the different types of agents, see the below documentation.
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.. toctree::
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:maxdepth: 1
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:glob:
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./agents/agent_types.md
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For documentation on how to create a custom agent, see the below.
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We also have documentation for an in-depth dive into each agent type.
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.. toctree::
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:maxdepth: 1
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:glob:
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./agents/custom_agent.ipynb
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We also have documentation for an in-depth dive into each agent type.
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.. toctree::
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:maxdepth: 1
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:glob:
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./agents/examples/*
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@ -1,12 +1,9 @@
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# Agents
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# Agent Types
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Agents use an LLM to determine which actions to take and in what order.
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An action can either be using a tool and observing its output, or returning a response to the user.
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For a list of easily loadable tools, see [here](tools.md).
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Here are the agents available in LangChain.
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For a tutorial on how to load agents, see [here](getting_started.ipynb).
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## `zero-shot-react-description`
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This agent uses the ReAct framework to determine which tool to use
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@ -1,131 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "991b1cc1",
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"metadata": {},
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"source": [
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"# Loading from LangChainHub\n",
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"\n",
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"This notebook covers how to load agents from [LangChainHub](https://github.com/hwchase17/langchain-hub)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "bd4450a2",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"No `_type` key found, defaulting to `prompt`.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
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"\u001B[32;1m\u001B[1;3m Yes.\n",
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"Follow up: Who is the reigning men's U.S. Open champion?\u001B[0m\n",
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"Intermediate answer: \u001B[36;1m\u001B[1;3m2016 · SUI · Stan Wawrinka ; 2017 · ESP · Rafael Nadal ; 2018 · SRB · Novak Djokovic ; 2019 · ESP · Rafael Nadal.\u001B[0m\n",
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"\u001B[32;1m\u001B[1;3mSo the reigning men's U.S. Open champion is Rafael Nadal.\n",
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"Follow up: What is Rafael Nadal's hometown?\u001B[0m\n",
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"Intermediate answer: \u001B[36;1m\u001B[1;3mIn 2016, he once again showed his deep ties to Mallorca and opened the Rafa Nadal Academy in his hometown of Manacor.\u001B[0m\n",
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"\u001B[32;1m\u001B[1;3mSo the final answer is: Manacor, Mallorca, Spain.\u001B[0m\n",
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"\n",
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"\u001B[1m> Finished chain.\u001B[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Manacor, Mallorca, Spain.'"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain import OpenAI, SerpAPIWrapper\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"Intermediate Answer\",\n",
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" func=search.run,\n",
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" description=\"useful for when you need to ask with search\"\n",
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" )\n",
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"]\n",
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"\n",
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"self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc://agents/self-ask-with-search/agent.json\", verbose=True)\n",
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"self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3aede965",
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"metadata": {},
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"source": [
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"# Pinning Dependencies\n",
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"\n",
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"Specific versions of LangChainHub agents can be pinned with the `lc@<ref>://` syntax."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "e679f7b6",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"No `_type` key found, defaulting to `prompt`.\n"
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]
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}
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],
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"source": [
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"self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc@2826ef9e8acdf88465e1e5fc8a7bf59e0f9d0a85://agents/self-ask-with-search/agent.json\", verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9d3d6697",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -1,154 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bfe18e28",
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"metadata": {},
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"source": [
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"# Serialization\n",
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"\n",
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"This notebook goes over how to serialize agents. For this notebook, it is important to understand the distinction we draw between `agents` and `tools`. An agent is the LLM powered decision maker that decides which actions to take and in which order. Tools are various instruments (functions) an agent has access to, through which an agent can interact with the outside world. When people generally use agents, they primarily talk about using an agent WITH tools. However, when we talk about serialization of agents, we are talking about the agent by itself. We plan to add support for serializing an agent WITH tools sometime in the future.\n",
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"\n",
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"Let's start by creating an agent with tools as we normally do:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "eb729f16",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import load_tools\n",
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"from langchain.agents import initialize_agent\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
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"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0578f566",
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"metadata": {},
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"source": [
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"Let's now serialize the agent. To be explicit that we are serializing ONLY the agent, we will call the `save_agent` method."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "dc544de6",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent.save_agent('agent.json')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "62dd45bf",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\r\n",
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" \"llm_chain\": {\r\n",
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" \"memory\": null,\r\n",
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" \"verbose\": false,\r\n",
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" \"prompt\": {\r\n",
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" \"input_variables\": [\r\n",
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" \"input\",\r\n",
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" \"agent_scratchpad\"\r\n",
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" ],\r\n",
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" \"output_parser\": null,\r\n",
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" \"template\": \"Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\r\n",
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" \"template_format\": \"f-string\",\r\n",
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" \"validate_template\": true,\r\n",
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" \"_type\": \"prompt\"\r\n",
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" },\r\n",
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" \"llm\": {\r\n",
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" \"model_name\": \"text-davinci-003\",\r\n",
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" \"temperature\": 0.0,\r\n",
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" \"max_tokens\": 256,\r\n",
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" \"top_p\": 1,\r\n",
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" \"frequency_penalty\": 0,\r\n",
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" \"presence_penalty\": 0,\r\n",
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" \"n\": 1,\r\n",
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" \"best_of\": 1,\r\n",
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" \"request_timeout\": null,\r\n",
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" \"logit_bias\": {},\r\n",
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" \"_type\": \"openai\"\r\n",
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" },\r\n",
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" \"output_key\": \"text\",\r\n",
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" \"_type\": \"llm_chain\"\r\n",
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" },\r\n",
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" \"allowed_tools\": [\r\n",
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" \"Search\",\r\n",
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" \"Calculator\"\r\n",
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" ],\r\n",
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" \"return_values\": [\r\n",
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" \"output\"\r\n",
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" ],\r\n",
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" \"_type\": \"zero-shot-react-description\"\r\n",
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"}"
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]
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}
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],
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"source": [
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"!cat agent.json"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0eb72510",
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"metadata": {},
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"source": [
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"We can now load the agent back in"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "eb660b76",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"agent = initialize_agent(tools, llm, agent_path=\"agent.json\", verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aa624ea5",
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"metadata": {},
|
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"outputs": [],
|
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"source": []
|
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}
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],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
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"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
||||
"name": "python",
|
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"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
}
|
||||
},
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||||
"nbformat": 4,
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"nbformat_minor": 5
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||||
}
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@ -1,87 +0,0 @@
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"""Run NatBot."""
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||||
import time
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from langchain.chains.natbot.base import NatBotChain
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from langchain.chains.natbot.crawler import Crawler
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def run_cmd(cmd: str, _crawler: Crawler) -> None:
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"""Run command."""
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cmd = cmd.split("\n")[0]
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if cmd.startswith("SCROLL UP"):
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_crawler.scroll("up")
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elif cmd.startswith("SCROLL DOWN"):
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_crawler.scroll("down")
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elif cmd.startswith("CLICK"):
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commasplit = cmd.split(",")
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id = commasplit[0].split(" ")[1]
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_crawler.click(id)
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elif cmd.startswith("TYPE"):
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spacesplit = cmd.split(" ")
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id = spacesplit[1]
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text_pieces = spacesplit[2:]
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text = " ".join(text_pieces)
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# Strip leading and trailing double quotes
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text = text[1:-1]
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if cmd.startswith("TYPESUBMIT"):
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text += "\n"
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_crawler.type(id, text)
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||||
time.sleep(2)
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||||
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||||
if __name__ == "__main__":
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objective = "Make a reservation for 2 at 7pm at bistro vida in menlo park"
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||||
print("\nWelcome to natbot! What is your objective?")
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||||
i = input()
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||||
if len(i) > 0:
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objective = i
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quiet = False
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||||
nat_bot_chain = NatBotChain.from_default(objective)
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||||
_crawler = Crawler()
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||||
_crawler.go_to_page("google.com")
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||||
try:
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||||
while True:
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||||
browser_content = "\n".join(_crawler.crawl())
|
||||
llm_command = nat_bot_chain.execute(_crawler.page.url, browser_content)
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||||
if not quiet:
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||||
print("URL: " + _crawler.page.url)
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||||
print("Objective: " + objective)
|
||||
print("----------------\n" + browser_content + "\n----------------\n")
|
||||
if len(llm_command) > 0:
|
||||
print("Suggested command: " + llm_command)
|
||||
|
||||
command = input()
|
||||
if command == "r" or command == "":
|
||||
run_cmd(llm_command, _crawler)
|
||||
elif command == "g":
|
||||
url = input("URL:")
|
||||
_crawler.go_to_page(url)
|
||||
elif command == "u":
|
||||
_crawler.scroll("up")
|
||||
time.sleep(1)
|
||||
elif command == "d":
|
||||
_crawler.scroll("down")
|
||||
time.sleep(1)
|
||||
elif command == "c":
|
||||
id = input("id:")
|
||||
_crawler.click(id)
|
||||
time.sleep(1)
|
||||
elif command == "t":
|
||||
id = input("id:")
|
||||
text = input("text:")
|
||||
_crawler.type(id, text)
|
||||
time.sleep(1)
|
||||
elif command == "o":
|
||||
objective = input("Objective:")
|
||||
else:
|
||||
print(
|
||||
"(g) to visit url\n(u) scroll up\n(d) scroll down\n(c) to click"
|
||||
"\n(t) to type\n(h) to view commands again"
|
||||
"\n(r/enter) to run suggested command\n(o) change objective"
|
||||
)
|
||||
except KeyboardInterrupt:
|
||||
print("\n[!] Ctrl+C detected, exiting gracefully.")
|
||||
exit(0)
|
||||
@ -1,16 +0,0 @@
|
||||
# Key Concepts
|
||||
|
||||
## Agents
|
||||
Agents use an LLM to determine which actions to take and in what order.
|
||||
For more detailed information on agents, and different types of agents in LangChain, see [this documentation](agents.md).
|
||||
|
||||
## Tools
|
||||
Tools are functions that agents can use to interact with the world.
|
||||
These tools can be generic utilities (e.g. search), other chains, or even other agents.
|
||||
For more detailed information on tools, and different types of tools in LangChain, see [this documentation](tools.md).
|
||||
|
||||
## ToolKits
|
||||
Toolkits are groups of tools that are best used together.
|
||||
They allow you to logically group and initialize a set of tools that share a particular resource (such as a database connection or json object).
|
||||
They can be used to construct an agent for a specific use-case.
|
||||
For more detailed information on toolkits and their use cases, see [this documentation](how_to_guides.rst#agent-toolkits) (the "Agent Toolkits" section).
|
||||
@ -0,0 +1,18 @@
|
||||
Toolkits
|
||||
==============
|
||||
|
||||
.. note::
|
||||
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/toolkit>`_
|
||||
|
||||
|
||||
This section of documentation covers agents with toolkits - eg an agent applied to a particular use case.
|
||||
|
||||
See below for a full list of agent toolkits
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
./toolkits/examples/*
|
||||
|
||||
@ -0,0 +1,38 @@
|
||||
Tools
|
||||
=============
|
||||
|
||||
.. note::
|
||||
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/tool>`_
|
||||
|
||||
|
||||
|
||||
Tools are ways that an agent can use to interact with the outside world.
|
||||
|
||||
For an overview of what a tool is, how to use them, and a full list of examples, please see the getting started documentation
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
./tools/getting_started.md
|
||||
|
||||
Next, we have some examples of customizing and generically working with tools
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
./tools/custom_tools.ipynb
|
||||
./tools/multi_input_tool.ipynb
|
||||
|
||||
|
||||
In this documentation we cover generic tooling functionality (eg how to create your own)
|
||||
as well as examples of tools and how to use them.
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
./tools/examples/*
|
||||
|
||||
@ -1,33 +0,0 @@
|
||||
Generic Chains
|
||||
--------------
|
||||
|
||||
A chain is made up of links, which can be either primitives or other chains.
|
||||
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
|
||||
The examples here are all generic end-to-end chains that are meant to be used to construct other chains rather than serving a specific purpose.
|
||||
|
||||
**LLMChain**
|
||||
|
||||
- **Links Used**: PromptTemplate, LLM
|
||||
- **Notes**: This chain is the simplest chain, and is widely used by almost every other chain. This chain takes arbitrary user input, creates a prompt with it from the PromptTemplate, passes that to the LLM, and then returns the output of the LLM as the final output.
|
||||
- `Example Notebook <./generic/llm_chain.html>`_
|
||||
|
||||
**Transformation Chain**
|
||||
|
||||
- **Links Used**: TransformationChain
|
||||
- **Notes**: This notebook shows how to use the Transformation Chain, which takes an arbitrary python function and applies it to inputs/outputs of other chains.
|
||||
- `Example Notebook <./generic/transformation.html>`_
|
||||
|
||||
**Sequential Chain**
|
||||
|
||||
- **Links Used**: Sequential
|
||||
- **Notes**: This notebook shows how to combine calling multiple other chains in sequence.
|
||||
- `Example Notebook <./generic/sequential_chains.html>`_
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
:caption: Generic Chains
|
||||
:name: generic
|
||||
:hidden:
|
||||
|
||||
./generic/*
|
||||
@ -1,20 +0,0 @@
|
||||
# Key Concepts
|
||||
|
||||
## Chains
|
||||
A chain is made up of links, which can be either primitives or other chains.
|
||||
They vary greatly in complexity and are combination of generic, highly configurable pipelines and more narrow (but usually more complex) pipelines.
|
||||
|
||||
## Sequential Chain
|
||||
This is a specific type of chain where multiple other chains are run in sequence, with the outputs being added as inputs
|
||||
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.
|
||||
@ -1,65 +0,0 @@
|
||||
Utility Chains
|
||||
--------------
|
||||
|
||||
A chain is made up of links, which can be either primitives or other chains.
|
||||
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
|
||||
The examples here are all end-to-end chains for specific applications, focused on interacting an LLMChain with a specific utility.
|
||||
|
||||
**LLMMath**
|
||||
|
||||
- **Links Used**: Python REPL, LLMChain
|
||||
- **Notes**: This chain takes user input (a math question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
|
||||
- `Example Notebook <./examples/llm_math.html>`_
|
||||
|
||||
**PAL**
|
||||
|
||||
- **Links Used**: Python REPL, LLMChain
|
||||
- **Notes**: This chain takes user input (a reasoning question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
|
||||
- `Paper <https://arxiv.org/abs/2211.10435>`_
|
||||
- `Example Notebook <./examples/pal.html>`_
|
||||
|
||||
**SQLDatabase Chain**
|
||||
|
||||
- **Links Used**: SQLDatabase, LLMChain
|
||||
- **Notes**: This chain takes user input (a question), uses a first LLM chain to construct a SQL query to run against the SQL database, and then uses another LLMChain to take the results of that query and use it to answer the original question.
|
||||
- `Example Notebook <./examples/sqlite.html>`_
|
||||
|
||||
**API Chain**
|
||||
|
||||
- **Links Used**: LLMChain, Requests
|
||||
- **Notes**: This chain first uses a LLM to construct the url to hit, then makes that request with the Requests wrapper, and finally runs that result through the language model again in order to product a natural language response.
|
||||
- `Example Notebook <./examples/api.html>`_
|
||||
|
||||
**LLMBash Chain**
|
||||
|
||||
- **Links Used**: BashProcess, LLMChain
|
||||
- **Notes**: This chain takes user input (a question), uses an LLM chain to convert it to a bash command to run in the terminal, and then returns that as the result.
|
||||
- `Example Notebook <./examples/llm_bash.html>`_
|
||||
|
||||
**LLMChecker Chain**
|
||||
|
||||
- **Links Used**: LLMChain
|
||||
- **Notes**: This chain takes user input (a question), uses an LLM chain to answer that question, and then uses other LLMChains to self-check that answer.
|
||||
- `Example Notebook <./examples/llm_checker.html>`_
|
||||
|
||||
**LLMRequests Chain**
|
||||
|
||||
- **Links Used**: Requests, LLMChain
|
||||
- **Notes**: This chain takes a URL and other inputs, uses Requests to get the data at that URL, and then passes that along with the other inputs into an LLMChain to generate a response. The example included shows how to ask a question to Google - it firsts constructs a Google url, then fetches the data there, then passes that data + the original question into an LLMChain to get an answer.
|
||||
- `Example Notebook <./examples/llm_requests.html>`_
|
||||
|
||||
**Moderation Chain**
|
||||
|
||||
- **Links Used**: LLMChain, ModerationChain
|
||||
- **Notes**: This chain shows how to use OpenAI's content moderation endpoint to screen output, and shows how to connect this to an LLMChain.
|
||||
- `Example Notebook <./examples/moderation.html>`_
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
:caption: Generic Chains
|
||||
:name: generic
|
||||
:hidden:
|
||||
|
||||
./examples/*
|
||||
@ -1,208 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e58f4d5a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Agent\n",
|
||||
"This notebook covers how to create a custom agent for a chat model. It will utilize chat specific prompts."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "5268c7fa",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
|
||||
"from langchain.chains import LLMChain\n",
|
||||
"from langchain.utilities import SerpAPIWrapper"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "fbaa4dbe",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"search = SerpAPIWrapper()\n",
|
||||
"tools = [\n",
|
||||
" Tool(\n",
|
||||
" name = \"Search\",\n",
|
||||
" func=search.run,\n",
|
||||
" description=\"useful for when you need to answer questions about current events\"\n",
|
||||
" )\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "f3ba6f08",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prefix = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\"\"\"\n",
|
||||
"suffix = \"\"\"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Args\"\"\"\n",
|
||||
"\n",
|
||||
"prompt = ZeroShotAgent.create_prompt(\n",
|
||||
" tools, \n",
|
||||
" prefix=prefix, \n",
|
||||
" suffix=suffix, \n",
|
||||
" input_variables=[]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "3547a37d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chat_models import ChatOpenAI\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": 8,
|
||||
"id": "a78f886f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"messages = [\n",
|
||||
" SystemMessagePromptTemplate(prompt=prompt),\n",
|
||||
" HumanMessagePromptTemplate.from_template(\"{input}\\n\\nThis was your previous work \"\n",
|
||||
" f\"(but I haven't seen any of it! I only see what \"\n",
|
||||
" \"you return as final answer):\\n{agent_scratchpad}\")\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "dadadd70",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = ChatPromptTemplate.from_messages(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "b7180182",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm_chain = LLMChain(llm=ChatOpenAI(temperature=0), prompt=prompt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "ddddb07b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tool_names = [tool.name for tool in tools]\n",
|
||||
"agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "36aef054",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "33a4d6cc",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mArrr, ye be in luck, matey! I'll find ye the answer to yer question.\n",
|
||||
"\n",
|
||||
"Thought: I need to search for the current population of Canada.\n",
|
||||
"Action: Search\n",
|
||||
"Action Input: \"current population of Canada 2023\"\n",
|
||||
"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,623,091 as of Saturday, March 4, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mAhoy, me hearties! I've found the answer to yer question.\n",
|
||||
"\n",
|
||||
"Final Answer: As of March 4, 2023, the population of Canada be 38,623,091. Arrr!\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'As of March 4, 2023, the population of Canada be 38,623,091. Arrr!'"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent_executor.run(\"How many people live in canada as of 2023?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6aefe978",
|
||||
"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
|
||||
}
|
||||
@ -1,376 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "134a0785",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Chat Vector DB\n",
|
||||
"\n",
|
||||
"This notebook goes over how to set up a chat model to chat with a vector database.\n",
|
||||
"\n",
|
||||
"This notebook is very similar to the example of using an LLM in the ConversationalRetrievalChain. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "70c4e529",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
|
||||
"from langchain.vectorstores import Chroma\n",
|
||||
"from langchain.text_splitter import CharacterTextSplitter\n",
|
||||
"from langchain.chains import ConversationalRetrievalChain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cdff94be",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Load in documents. You can replace this with a loader for whatever type of data you want"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "01c46e92",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.document_loaders import TextLoader\n",
|
||||
"loader = TextLoader('../../state_of_the_union.txt')\n",
|
||||
"documents = loader.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e9be4779",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you had multiple loaders that you wanted to combine, you do something like:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "433363a5",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# loaders = [....]\n",
|
||||
"# docs = []\n",
|
||||
"# for loader in loaders:\n",
|
||||
"# docs.extend(loader.load())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "239475d2",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "a8930cf7",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"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": [
|
||||
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
||||
"documents = text_splitter.split_documents(documents)\n",
|
||||
"\n",
|
||||
"embeddings = OpenAIEmbeddings()\n",
|
||||
"vectorstore = Chroma.from_documents(documents, embeddings)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "18415aca",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We are now going to construct a prompt specifically designed for chat models."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "c8805230",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chat_models import ChatOpenAI\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": 6,
|
||||
"id": "cc86c30e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"system_template=\"\"\"Use the following pieces of context to answer the users question. \n",
|
||||
"If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
|
||||
"----------------\n",
|
||||
"{context}\"\"\"\n",
|
||||
"messages = [\n",
|
||||
" SystemMessagePromptTemplate.from_template(system_template),\n",
|
||||
" HumanMessagePromptTemplate.from_template(\"{question}\")\n",
|
||||
"]\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3c96b118",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We now initialize the ConversationalRetrievalChain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "7b4110f3",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0), vectorstore,qa_prompt=prompt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3872432d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here's an example of asking a question with no chat history"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "7fe3e730",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"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": 9,
|
||||
"id": "bfff9cc8",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and a consensus builder. She has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result[\"answer\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9e46edf7",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here's an example of asking a question with some chat history"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "00b4cf00",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chat_history = [(query, result[\"answer\"])]\n",
|
||||
"query = \"Did he mention who came before her\"\n",
|
||||
"result = qa({\"question\": query, \"chat_history\": chat_history})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "f01828d1",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"The President mentioned Circuit Court of Appeals Judge Ketanji Brown Jackson as the nominee for the United States Supreme Court. He described her as one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. The President did not mention any specific sources of support for Judge Jackson, but he did note that advancing immigration reform is supported by everyone from labor unions to religious leaders to the U.S. Chamber of Commerce.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result['answer']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2324cdc6-98bf-4708-b8cd-02a98b1e5b67",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## ConversationalRetrievalChain with streaming to `stdout`\n",
|
||||
"\n",
|
||||
"Output from the chain will be streamed to `stdout` token by token in this example."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "2efacec3-2690-4b05-8de3-a32fd2ac3911",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chains.llm import LLMChain\n",
|
||||
"from langchain.llms import OpenAI\n",
|
||||
"from langchain.callbacks.base import CallbackManager\n",
|
||||
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
|
||||
"from langchain.chains.chat_index.prompts import CONDENSE_QUESTION_PROMPT\n",
|
||||
"from langchain.chains.question_answering import load_qa_chain\n",
|
||||
"\n",
|
||||
"# Construct a ChatVectorDBChain with a streaming llm for combine docs\n",
|
||||
"# and a separate, non-streaming llm for question generation\n",
|
||||
"llm = OpenAI(temperature=0)\n",
|
||||
"streaming_llm = ChatOpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
|
||||
"\n",
|
||||
"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
|
||||
"doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=prompt)\n",
|
||||
"\n",
|
||||
"qa = ConversationalRetrievalChain(retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "fd6d43f4-7428-44a4-81bc-26fe88a98762",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and a consensus builder. He also mentioned that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": 14,
|
||||
"id": "5ab38978-f3e8-4fa7-808c-c79dec48379a",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The context does not provide information on who Ketanji Brown Jackson succeeded on the United States Supreme Court."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chat_history = [(query, result[\"answer\"])]\n",
|
||||
"query = \"Did he mention who she suceeded\"\n",
|
||||
"result = qa({\"question\": query, \"chat_history\": chat_history})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8e8d0055",
|
||||
"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
|
||||
}
|
||||
@ -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
|
||||
}
|
||||
@ -1,169 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "07c1e3b9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Retrieval Question/Answering\n",
|
||||
"\n",
|
||||
"This example showcases using a chat model to do question answering over a vector database.\n",
|
||||
"\n",
|
||||
"This notebook is very similar to the example of using an LLM in the RetrievalQA. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "82525493",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
|
||||
"from langchain.vectorstores import Chroma\n",
|
||||
"from langchain.text_splitter import CharacterTextSplitter\n",
|
||||
"from langchain.chains import RetrievalQA"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "5c7049db",
|
||||
"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": [
|
||||
"from langchain.document_loaders import TextLoader\n",
|
||||
"loader = TextLoader('../../state_of_the_union.txt')\n",
|
||||
"documents = loader.load()\n",
|
||||
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
||||
"texts = text_splitter.split_documents(documents)\n",
|
||||
"\n",
|
||||
"embeddings = OpenAIEmbeddings()\n",
|
||||
"docsearch = Chroma.from_documents(texts, embeddings)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "35f99145",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now set up the chat model and chat model specific prompt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "32a49412",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chat_models import ChatOpenAI\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": 4,
|
||||
"id": "f231fb9b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"system_template=\"\"\"Use the following pieces of context to answer the users question. \n",
|
||||
"If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
|
||||
"----------------\n",
|
||||
"{context}\"\"\"\n",
|
||||
"messages = [\n",
|
||||
" SystemMessagePromptTemplate.from_template(system_template),\n",
|
||||
" HumanMessagePromptTemplate.from_template(\"{question}\")\n",
|
||||
"]\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "3018f865",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_type_kwargs = {\"prompt\": prompt}\n",
|
||||
"qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "032a47f8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court. He referred to her as one of our nation's top legal minds, a former federal public defender, a consensus builder, and from a family of public school educators and police officers. Since she's been nominated, she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
|
||||
"qa.run(query)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8b403637",
|
||||
"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"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,206 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "efc5be67",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Retrieval Question Answering with Sources\n",
|
||||
"\n",
|
||||
"This notebook goes over how to do question-answering with sources with a chat model over a vector database. It does this by using the `RetrievalQAWithSourcesChain`, which does the lookup of the documents from a vector database. \n",
|
||||
"\n",
|
||||
"This notebook is very similar to the example of using an LLM in the RetrievalQAWithSources. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "1c613960",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
|
||||
"from langchain.embeddings.cohere import CohereEmbeddings\n",
|
||||
"from langchain.text_splitter import CharacterTextSplitter\n",
|
||||
"from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
|
||||
"from langchain.vectorstores import Chroma"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "17d1306e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('../../state_of_the_union.txt') as f:\n",
|
||||
" state_of_the_union = f.read()\n",
|
||||
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
||||
"texts = text_splitter.split_text(state_of_the_union)\n",
|
||||
"\n",
|
||||
"embeddings = OpenAIEmbeddings()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "0e745d99",
|
||||
"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",
|
||||
"Running Chroma using direct local API.\n",
|
||||
"Using DuckDB in-memory for database. Data will be transient.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "8aa571ae",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chains import RetrievalQAWithSourcesChain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1f73b14a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now set up the chat model and chat model specific prompt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "9643c775",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chat_models import ChatOpenAI\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": 6,
|
||||
"id": "ed00e906",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"system_template=\"\"\"Use the following pieces of context to answer the users question. \n",
|
||||
"If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
|
||||
"ALWAYS return a \"SOURCES\" part in your answer.\n",
|
||||
"The \"SOURCES\" part should be a reference to the source of the document from which you got your answer.\n",
|
||||
"\n",
|
||||
"Example of your response should be:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"The answer is foo\n",
|
||||
"SOURCES: xyz\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Begin!\n",
|
||||
"----------------\n",
|
||||
"{summaries}\"\"\"\n",
|
||||
"messages = [\n",
|
||||
" SystemMessagePromptTemplate.from_template(system_template),\n",
|
||||
" HumanMessagePromptTemplate.from_template(\"{question}\")\n",
|
||||
"]\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "aa859d4c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_type_kwargs = {\"prompt\": prompt}\n",
|
||||
"chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
|
||||
" ChatOpenAI(temperature=0), \n",
|
||||
" chain_type=\"stuff\", \n",
|
||||
" retriever=docsearch.as_retriever(),\n",
|
||||
" chain_type_kwargs=chain_type_kwargs\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "8ba36fa7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'answer': 'The President honored Justice Stephen Breyer, an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, for his dedicated service to the country. \\n',\n",
|
||||
" 'sources': '31-pl'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8308fbf7",
|
||||
"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"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,29 +0,0 @@
|
||||
# Key Concepts
|
||||
|
||||
## ChatMessage
|
||||
A chat message is what we refer to as the modular unit of information.
|
||||
At the moment, this consists of "content", which refers to the content of the chat message.
|
||||
At the moment, most chat models are trained to predict sequences of Human <> AI messages.
|
||||
This is because so far the primary interaction mode has been between a human user and a singular AI system.
|
||||
|
||||
At the moment, there are four different classes of Chat Messages
|
||||
|
||||
### HumanMessage
|
||||
A HumanMessage is a ChatMessage that is sent as if from a Human's point of view.
|
||||
|
||||
### AIMessage
|
||||
An AIMessage is a ChatMessage that is sent from the point of view of the AI system to which the Human is corresponding.
|
||||
|
||||
### SystemMessage
|
||||
A SystemMessage is still a bit ambiguous, and so far seems to be a concept unique to OpenAI
|
||||
|
||||
### ChatMessage
|
||||
A chat message is a generic chat message, with not only a "content" field but also a "role" field.
|
||||
With this field, arbitrary roles may be assigned to a message.
|
||||
|
||||
## ChatGeneration
|
||||
The output of a single prediction of a chat message.
|
||||
Currently this is just a chat message itself (eg content and a role)
|
||||
|
||||
## Chat Model
|
||||
A model which takes in a list of chat messages, and predicts a chat message in response.
|
||||
@ -1,87 +0,0 @@
|
||||
How To Guides
|
||||
====================================
|
||||
|
||||
There are a lot of different document loaders that LangChain supports. Below are how-to guides for working with them
|
||||
|
||||
`File Loader <./examples/unstructured_file.html>`_: A walkthrough of how to use Unstructured to load files of arbitrary types (pdfs, txt, html, etc).
|
||||
|
||||
`Directory Loader <./examples/directory_loader.html>`_: A walkthrough of how to use Unstructured load files from a given directory.
|
||||
|
||||
`Notion <./examples/notion.html>`_: A walkthrough of how to load data for an arbitrary Notion DB.
|
||||
|
||||
`ReadTheDocs <./examples/readthedocs_documentation.html>`_: A walkthrough of how to load data for documentation generated by ReadTheDocs.
|
||||
|
||||
`HTML <./examples/html.html>`_: A walkthrough of how to load data from an html file.
|
||||
|
||||
`PDF <./examples/pdf.html>`_: A walkthrough of how to load data from a PDF file.
|
||||
|
||||
`PowerPoint <./examples/powerpoint.html>`_: A walkthrough of how to load data from a powerpoint file.
|
||||
|
||||
`Email <./examples/email.html>`_: A walkthrough of how to load data from an email (`.eml`) file.
|
||||
|
||||
`GoogleDrive <./examples/googledrive.html>`_: A walkthrough of how to load data from Google drive.
|
||||
|
||||
`Obsidian <./examples/obsidian.html>`_: A walkthrough of how to load data from an Obsidian file dump.
|
||||
|
||||
`Roam <./examples/roam.html>`_: A walkthrough of how to load data from a Roam file export.
|
||||
|
||||
`EverNote <./examples/evernote.html>`_: A walkthrough of how to load data from a EverNote (`.enex`) file.
|
||||
|
||||
`YouTube <./examples/youtube.html>`_: A walkthrough of how to load the transcript from a YouTube video.
|
||||
|
||||
`Hacker News <./examples/hn.html>`_: A walkthrough of how to load a Hacker News page.
|
||||
|
||||
`GitBook <./examples/gitbook.html>`_: A walkthrough of how to load a GitBook page.
|
||||
|
||||
`s3 File <./examples/s3_file.html>`_: A walkthrough of how to load a file from s3.
|
||||
|
||||
`s3 Directory <./examples/s3_directory.html>`_: A walkthrough of how to load all files in a directory from s3.
|
||||
|
||||
`GCS File <./examples/gcs_file.html>`_: A walkthrough of how to load a file from Google Cloud Storage (GCS).
|
||||
|
||||
`GCS Directory <./examples/gcs_directory.html>`_: A walkthrough of how to load all files in a directory from Google Cloud Storage (GCS).
|
||||
|
||||
`Web Base <./examples/web_base.html>`_: A walkthrough of how to load all text data from webpages.
|
||||
|
||||
`IMSDb <./examples/imsdb.html>`_: A walkthrough of how to load all text data from IMSDb webpage.
|
||||
|
||||
`AZLyrics <./examples/azlyrics.html>`_: A walkthrough of how to load all text data from AZLyrics webpage.
|
||||
|
||||
`College Confidential <./examples/college_confidential.html>`_: A walkthrough of how to load all text data from College Confidential webpage.
|
||||
|
||||
`Gutenberg <./examples/gutenberg.html>`_: A walkthrough of how to load data from a Gutenberg ebook text.
|
||||
|
||||
`Airbyte Json <./examples/airbyte_json.html>`_: A walkthrough of how to load data from a local Airbyte JSON file.
|
||||
|
||||
`CoNLL-U <./examples/CoNLL-U.html>`_: A walkthrough of how to load data from a ConLL-U file.
|
||||
|
||||
`iFixit <./examples/ifixit.html>`_: A walkthrough of how to search and load data like guides, technical Q&A's, and device wikis from iFixit.com
|
||||
|
||||
`Notebook <./examples/notebook.html>`_: A walkthrough of how to load data from .ipynb notebook.
|
||||
|
||||
`Copypaste <./examples/copypaste.html>`_: A walkthrough of how to load a document object from something you just want to copy and paste.
|
||||
|
||||
`CSV <./examples/csv.html>`_: A walkthrough of how to load data from a .csv file.
|
||||
|
||||
`Facebook Chat <./examples/facebook_chat.html>`_: A walkthrough of how to load data from a Facebook Chat json file.
|
||||
|
||||
`Image <./examples/image.html>`_: A walkthrough of how to load images such as JPGs PNGs into a document format that can be used downstream.
|
||||
|
||||
`Markdown <./examples/markdown.html>`_: A walkthrough of how to load data from a markdown file.
|
||||
|
||||
`SRT <./examples/srt.html>`_: A walkthrough of how to load data from a subtitle (`.srt`) file.
|
||||
|
||||
`Telegram <./examples/telegram.html>`_: A walkthrough of how to load data from a Telegram Chat json file.
|
||||
|
||||
`URL <./examples/url.html>`_: A walkthrough of how to load HTML documents from a list of URLs into a document format that we can use downstream.
|
||||
|
||||
`Word Document <./examples/word_document.html>`_: A walkthrough of how to load data from Microsoft Word files.
|
||||
|
||||
`Blackboard <./examples/blackboard.html>`_: A walkthrough of how to load data from a Blackboard course.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
:hidden:
|
||||
|
||||
examples/*
|
||||
@ -1,12 +0,0 @@
|
||||
# Key Concepts
|
||||
|
||||
## Document
|
||||
This class is a container for document information. This contains two parts:
|
||||
- `page_content`: The content of the actual page itself.
|
||||
- `metadata`: The metadata associated with the document. This can be things like the file path, the url, etc.
|
||||
|
||||
## Loader
|
||||
This base class is a way to load documents. It exposes a `load` method that returns `Document` objects.
|
||||
|
||||
## [Unstructured](https://github.com/Unstructured-IO/unstructured)
|
||||
Unstructured is a python package specifically focused on transformations from raw documents to text.
|
||||
@ -1,51 +0,0 @@
|
||||
# CombineDocuments Chains
|
||||
CombineDocuments chains are useful for when you need to run a language over multiple documents.
|
||||
Common use cases for this include question answering, question answering with sources, summarization, and more.
|
||||
For more information on specific use cases as well as different methods for **fetching** these documents, please see
|
||||
[this overview](/use_cases/combine_docs.md).
|
||||
|
||||
This documentation now picks up from after you've fetched your documents - now what?
|
||||
How do you pass them to the language model in a format it can understand?
|
||||
There are a few different methods, or chains, for doing so. LangChain supports four of the more common ones - and
|
||||
we are actively looking to include more, so if you have any ideas please reach out! Note that there is not
|
||||
one best method - the decision of which one to use is often very context specific. In order from simplest to
|
||||
most complex:
|
||||
|
||||
## Stuffing
|
||||
Stuffing is the simplest method, whereby you simply stuff all the related data into the prompt as context
|
||||
to pass to the language model. This is implemented in LangChain as the `StuffDocumentsChain`.
|
||||
|
||||
**Pros:** Only makes a single call to the LLM. When generating text, the LLM has access to all the data at once.
|
||||
|
||||
**Cons:** Most LLMs have a context length, and for large documents (or many documents) this will not work as it will result in a prompt larger than the context length.
|
||||
|
||||
The main downside of this method is that it only works on smaller pieces of data. Once you are working
|
||||
with many pieces of data, this approach is no longer feasible. The next two approaches are designed to help deal with that.
|
||||
|
||||
## Map Reduce
|
||||
This method involves running an initial prompt on each chunk of data (for summarization tasks, this
|
||||
could be a summary of that chunk; for question-answering tasks, it could be an answer based solely on that chunk).
|
||||
Then a different prompt is run to combine all the initial outputs. This is implemented in the LangChain as the `MapReduceDocumentsChain`.
|
||||
|
||||
**Pros:** Can scale to larger documents (and more documents) than `StuffDocumentsChain`. The calls to the LLM on individual documents are independent and can therefore be parallelized.
|
||||
|
||||
**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. Loses some information during the final combined call.
|
||||
|
||||
## Refine
|
||||
This method involves running an initial prompt on the first chunk of data, generating some output.
|
||||
For the remaining documents, that output is passed in, along with the next document,
|
||||
asking the LLM to refine the output based on the new document.
|
||||
|
||||
**Pros:** Can pull in more relevant context, and may be less lossy than `MapReduceDocumentsChain`.
|
||||
|
||||
**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. The calls are also NOT independent, meaning they cannot be paralleled like `MapReduceDocumentsChain`. There is also some potential dependencies on the ordering of the documents.
|
||||
|
||||
|
||||
## Map-Rerank
|
||||
This method involves running an initial prompt on each chunk of data, that not only tries to complete a
|
||||
task but also gives a score for how certain it is in its answer. The responses are then
|
||||
ranked according to this score, and the highest score is returned.
|
||||
|
||||
**Pros:** Similar pros as `MapReduceDocumentsChain`. Requires fewer calls, compared to `MapReduceDocumentsChain`.
|
||||
|
||||
**Cons:** Cannot combine information between documents. This means it is most useful when you expect there to be a single simple answer in a single document.
|
||||
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Reference in New Issue