documentation (#191)

docs/examples/agents.rst

@@ -8,6 +8,4 @@ The examples here are all end-to-end agents for specific applications.
    :glob:
    :caption: Agents
 
-   agents/mrkl.ipynb
+   agents/*
-   agents/react.ipynb
-   agents/self_ask_with_search.ipynb

docs/examples/chains.rst

@@ -8,8 +8,4 @@ The examples here are all end-to-end chains for specific applications.
    :glob:
    :caption: Chains
 
-   chains/llm_chain.ipynb
+   chains/*
-   chains/llm_math.ipynb
-   chains/map_reduce.ipynb
-   chains/sqlite.ipynb
-   chains/vector_db_qa.ipynb

docs/getting_started/memory.ipynb

@@ -1,5 +1,25 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d31df93e",
+   "metadata": {},
+   "source": [
+    "# Memory\n",
+    "So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of \"memory\" so that it may remember information about its previous interactions. The most clear and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of \"short-term memory\". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of \"long-term memory\".\n",
+    "\n",
+    "LangChain provides several specially created chains just for this purpose. This notebook walk throughs using one of those chains (the `ConversationChain`) with two different types of memory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d051c1da",
+   "metadata": {},
+   "source": [
+    "### ConversationChain with default memory\n",
+    "By default, the `ConversationChain` has a simple type of memory which remebers all previes inputs/outputs and adds them to the context that is passed. Let's take a look at using this chain (setting `verbose=True` so we can see the prompt)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -37,7 +57,6 @@
    ],
    "source": [
     "from langchain import OpenAI, ConversationChain\n",
-    "from langchain.chains.conversation.memory import ConversationSummaryMemory\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "conversation = ConversationChain(llm=llm, verbose=True)\n",
@@ -129,9 +148,30 @@
     "conversation.predict(input=\"Tell me about yourself.\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4fad9448",
+   "metadata": {},
+   "source": [
+    "### ConversationChain with ConversationSummaryMemory\n",
+    "Now lets 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 walk through an example, again setting `verbose=True` so we can see the prompt."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "f60a2fe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.conversation.memory import ConversationSummaryMemory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "b7274f2c",
    "metadata": {},
    "outputs": [
@@ -159,7 +199,7 @@
        "\"\\n\\nI'm doing well, thank you for asking. I'm currently working on a project that I'm really excited about.\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "a6b6b88f",
    "metadata": {},
    "outputs": [
@@ -187,7 +227,7 @@
       "\n",
       "Current conversation:\n",
       "\n",
-      "The human greets the AI and asks how it is doing. The AI responds that it is doing well and is currently working on a project that it is excited about.\n",
+      "The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about.\n",
       "Human: Tell me more about it!\n",
       "AI:\u001b[0m\n",
       "\n",
@@ -197,10 +237,10 @@
     {
      "data": {
       "text/plain": [
-       "\"\\n\\nI'm working on a project that involves helping people to better understand and use artificial intelligence. I'm really excited about it because I think it has the potential to make a big difference in people's lives.\""
+       "\"\\n\\nI'm working on a project that I'm really excited about. It's a lot of work, but I think it's going to be really great when it's finished. I can't wait to show it to you!\""
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "dad869fe",
    "metadata": {},
    "outputs": [
@@ -228,7 +268,7 @@
       "Current conversation:\n",
       "\n",
       "\n",
-      "The human greets the AI and asks how it is doing. The AI responds that it is doing well and is currently working on a project that it is excited about - a project that involves helping people to better understand and use artificial intelligence.\n",
+      "The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about. The AI said that the project is a lot of work, but it is going to be great when it is finished.\n",
       "Human: Very cool -- what is the scope of the project?\n",
       "AI:\u001b[0m\n",
       "\n",
@@ -238,10 +278,10 @@
     {
      "data": {
       "text/plain": [
-       "'\\n\\nThe project is still in the early stages, but the goal is to create a resource that will help people to understand artificial intelligence and how to use it effectively.'"
+       "'\\n\\nThe project is quite large in scope. It involves a lot of data analysis and work with artificial intelligence algorithms.'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/index.rst

@@ -91,6 +91,7 @@ The documentation is structured into the following sections:
    getting_started/llm_chain.md
    getting_started/sequential_chains.md
    getting_started/agents.ipynb
+   getting_started/memory.ipynb
 
 Goes over a simple walk through and tutorial for getting started setting up a simple chain that generates a company name based on what the company makes.
 Covers installation, environment set up, calling LLMs, and using prompts.