Router chains (#4019)

Unpolished router examples to help flesh out abstractions and use cases 
![Screenshot 2023-05-02 at 7 02 58
PM](https://user-images.githubusercontent.com/130488702/235820394-389e5584-db0b-415e-a260-2824b5555167.png)

---------

Co-authored-by: Shreya Rajpal <shreya.rajpal@gmail.com>

docs/modules/chains/examples/multi_prompt_router.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a5cf6c49",
+   "metadata": {},
+   "source": [
+    "# Router Chains: Selecting from multiple prompts with MultiPromptChain\n",
+    "\n",
+    "This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e8d624d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.router import MultiPromptChain\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8d11fa5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "physics_template = \"\"\"You are a very smart physics professor. \\\n",
+    "You are great at answering questions about physics in a concise and easy to understand manner. \\\n",
+    "When you don't know the answer to a question you admit that you don't know.\n",
+    "\n",
+    "Here is a question:\n",
+    "{input}\"\"\"\n",
+    "\n",
+    "\n",
+    "math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\\n",
+    "You are so good because you are able to break down hard problems into their component parts, \\\n",
+    "answer the component parts, and then put them together to answer the broader question.\n",
+    "\n",
+    "Here is a question:\n",
+    "{input}\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b89de9f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_infos = [\n",
+    "    (\"physics\", \"Good for answering questions about physics\", physics_template),\n",
+    "    (\"math\", \"Good for answering math questions\", math_template)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "db679975",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = MultiPromptChain.from_prompts(OpenAI(), *zip(*prompt_infos), verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "90fd594c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
+      "physics: {'input': 'What is black body radiation?'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\n",
+      "\n",
+      "Black body radiation is the emission of electromagnetic radiation from a body that is in thermal equilibrium with its environment. It is emitted by all objects regardless of their temperature, but the intensity and spectral distribution of the radiation depends on the temperature of the body. As the temperature increases, the intensity of the radiation also increases and the peak wavelength shifts to shorter wavelengths.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(\"What is black body radiation?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b8c83765",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
+      "math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "?\n",
+      "\n",
+      "The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43. To solve this, we first need to identify all of the prime numbers between 40 and 50. These are 41, 43, 47, and 49. We then need to check which of these, when added to 1, will be divisible by 3. The prime number that fits this criteria is 43. Therefore, the answer is 43.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(\"What is the first prime number greater than 40 such that one plus the prime number is divisible by 3\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "74c6bba7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
+      "None: {'input': 'What is the name of the type of cloud that rains?'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "The name of the type of cloud that usually brings rain is called a cumulonimbus cloud. These clouds are typically tall and dark with a flat base and anvil-shaped top. They form when warm, moist air rises rapidly and condenses into water droplets, which eventually become heavy enough to fall as rain.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(\"What is the name of the type of cloud that rins\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/chains/examples/multi_retrieval_qa_router.ipynb

@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "782ffcf1",
+   "metadata": {},
+   "source": [
+    "# Router Chains: Selecting from multiple prompts with MultiRetrievalQAChain\n",
+    "\n",
+    "This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the `MultiRetrievalQAChain` to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b6aeec07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.router import MultiRetrievalQAChain\n",
+    "from langchain.llms import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3c42f051",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.vectorstores import FAISS\n",
+    "\n",
+    "sou_docs = TextLoader('../../state_of_the_union.txt').load_and_split()\n",
+    "sou_retriever = FAISS.from_documents(sou_docs, OpenAIEmbeddings()).as_retriever()\n",
+    "\n",
+    "pg_docs = TextLoader('../../paul_graham_essay.txt').load_and_split()\n",
+    "pg_retriever = FAISS.from_documents(pg_docs, OpenAIEmbeddings()).as_retriever()\n",
+    "\n",
+    "personal_texts = [\n",
+    "    \"I love apple pie\",\n",
+    "    \"My favorite color is fuchsia\",\n",
+    "    \"My dream is to become a professional dancer\",\n",
+    "    \"I broke my arm when I was 12\",\n",
+    "    \"My parents are from Peru\",\n",
+    "]\n",
+    "personal_retriever = FAISS.from_texts(personal_texts, OpenAIEmbeddings()).as_retriever()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5b671ac5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever_infos = [\n",
+    "    (\"state of the union\", \"Good for answering questions about the 2023 State of the Union address\", sou_retriever),\n",
+    "    (\"pg essay\", \"Good for answer quesitons about Paul Graham's essay on his career\", pg_retriever),\n",
+    "    (\"personal\", \"Good for answering questions about me\", personal_retriever)\n",
+    "]\n",
+    "chain = MultiRetrievalQAChain.from_retrievers(OpenAI(), *zip(*retriever_infos), verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7db5814f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
+      "state of the union: {'query': 'What did the president say about the economy in the 2023 State of the Union Address?'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      " The president said that the economy had created over 6.5 million jobs in the previous year, the strongest growth in nearly 40 years, and that his plan to fight inflation would lower costs and the deficit. He also announced the Bipartisan Infrastructure Law and said that investing in workers and building the economy from the bottom up and the middle out would build a better America.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(\"What did the president say about the economy?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bbcdbe82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
+      "pg essay: {'query': 'What is something Paul Graham regrets about his work?'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      " Paul Graham regrets that he was so consumed by running Y Combinator that it ended up eating away at his other projects, like writing essays and working on Arc.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(\"What is something Paul Graham regrets about his work?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "37c88a27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
+      "personal: {'query': 'What is my background?'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      " Your background is Peruvian.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(\"What is my background?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "de8519b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new MultiRetrievalQAChain chain...\u001b[0m\n",
+      "None: {'query': 'What year was the Internet created in?'}\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "The Internet was created in the late 1960s by the United States Department of Defense's Advanced Research Projects Agency (ARPA). It was originally called the ARPANET and was used to connect computers at different universities and research institutions. Over time, it evolved into the global network that we know today. So, to answer your question, the Internet was technically created in the late 1960s.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(\"What year was the Internet created in?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e50a0227",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/modules/indexes/retrievers/examples/contextual-compression.ipynb

@@ -363,7 +363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

docs/modules/indexes/retrievers/examples/vectorstore-retriever.ipynb

@@ -171,7 +171,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,