mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-13 13:17:52 +02:00
combine docs notebook
This commit is contained in:
Harrison Chase
committed by
Nuno Campos
Nuno Campos
parent
62e4df815a
commit
d014b13d6f
@@ -0,0 +1,309 @@
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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": "780c1001-557c-4b03-8ebd-a2a381d5f85d",
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"metadata": {},
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"source": [
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"# Combine Docs\n",
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"\n",
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"PermChain is a great choice for implementating workflows that involve operating over longer documents because of its recursive nature"
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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": "624c452c-ddd5-4390-9065-7ec55dc64b96",
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"metadata": {},
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"outputs": [],
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"source": [
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"from operator import itemgetter\n",
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"\n",
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"from langchain.chat_models.openai import ChatOpenAI\n",
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"from langchain.prompts import SystemMessagePromptTemplate, ChatPromptTemplate, PromptTemplate\n",
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"from langchain.schema.output_parser import StrOutputParser\n",
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"from langchain.runnables.openai_functions import OpenAIFunctionsRouter\n",
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"from langchain.schema.runnable import RunnableMap\n",
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"from langchain.schema.document import Document\n",
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"from langchain.schema import format_document\n",
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"\n",
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"from permchain import Pregel, channels"
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]
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},
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{
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"cell_type": "markdown",
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"id": "271728d7-b3c8-4ec6-a728-19835e282ec3",
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"metadata": {},
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"source": [
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"## Stuff Documents\n",
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"\n",
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"Stuff documents is simple - just a chain"
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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": "0462aff0-1b88-49cc-bfe2-3c169d5e1d63",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.schema.runnable import RunnableLambda"
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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": "59d6430b-c113-4498-9ffc-f4623f7a0b5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template=\"{page_content}\")\n",
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"\n",
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"_combine_documents = RunnableLambda(lambda x: format_document(x, DEFAULT_DOCUMENT_PROMPT)).map() | (lambda x: \"\\n\\n\".join(x))"
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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": "29b2668d-e4a6-4876-9b04-bdc841774c62",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = [Document(page_content=\"Harrison used to work at Kensho\"), Document(page_content=\"Ankush worked at Facebook\")]"
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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": 8,
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"id": "17da58b7-8685-4d0a-9a47-c398c085d477",
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"metadata": {},
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"outputs": [],
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"source": [
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"stuff_chain = {\n",
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" \"question\": lambda x: x[\"question\"],\n",
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" \"context\": (lambda x: x['docs']) | _combine_documents\n",
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"} |ChatPromptTemplate.from_messages([\n",
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" (\"system\", \"Answer user questions based on the following documents:\\n\\n{context}\"),\n",
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" (\"human\", \"{question}\"),\n",
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"]) | ChatOpenAI()"
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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": 9,
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"id": "87295b71-0afc-4901-b57c-a7b945aa4bd9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Harrison used to work at Kensho.')"
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]
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},
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"execution_count": 9,
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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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"stuff_chain.invoke({\"question\": \"where did harrison work\", \"docs\": docs})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fff324c1-7fbf-41e5-861f-a10ba0112dbd",
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"metadata": {},
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"source": [
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"## Reduce Documents\n",
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"\n",
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"Reduce documents tries to merge documents recursively."
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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": 10,
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"id": "b15f5abb-1cfe-4965-a021-c891506c5dd2",
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"metadata": {},
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"outputs": [],
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"source": [
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"many_docs = docs * 5"
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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": 11,
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"id": "ccad04a3-fd3f-4e73-b895-29e53535f000",
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"metadata": {},
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"outputs": [],
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"source": [
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"def _split_list_of_docs(docs, max_length=70):\n",
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" new_result_doc_list = []\n",
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" _sub_result_docs = []\n",
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" for doc in docs:\n",
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" _sub_result_docs.append(doc)\n",
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" _num_tokens = sum([len(d.page_content) for d in _sub_result_docs])\n",
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" if _num_tokens > max_length:\n",
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" if len(_sub_result_docs) == 1:\n",
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" raise ValueError(\n",
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" \"A single document was longer than the context length,\"\n",
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" \" we cannot handle this.\"\n",
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" )\n",
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" new_result_doc_list.append(_sub_result_docs[:-1])\n",
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" _sub_result_docs = _sub_result_docs[-1:]\n",
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" new_result_doc_list.append(_sub_result_docs)\n",
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" return new_result_doc_list"
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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": 12,
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"id": "11cfd337-9f3b-4b26-ba30-251e17b18994",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[[Document(page_content='Harrison used to work at Kensho'),\n",
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" Document(page_content='Ankush worked at Facebook')],\n",
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" [Document(page_content='Harrison used to work at Kensho'),\n",
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" Document(page_content='Ankush worked at Facebook')],\n",
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" [Document(page_content='Harrison used to work at Kensho'),\n",
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" Document(page_content='Ankush worked at Facebook')],\n",
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" [Document(page_content='Harrison used to work at Kensho'),\n",
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" Document(page_content='Ankush worked at Facebook')],\n",
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" [Document(page_content='Harrison used to work at Kensho'),\n",
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" Document(page_content='Ankush worked at Facebook')]]"
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]
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},
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"execution_count": 12,
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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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"# Just to show what its like split\n",
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"split_docs = _split_list_of_docs(many_docs)\n",
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"split_docs"
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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": 20,
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"id": "8d524ba6-0939-4a5d-8db0-4fa1ef06eaeb",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_inbox = channels.LastValue[str](\"input_inbox\")\n",
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"reduce_inbox = channels.LastValue[str](\"reduce_inbox\")\n",
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"collapse_inbox = channels.LastValue[str](\"collapse_inbox\")\n",
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"output_inbox = channels.LastValue[str](\"output_inbox\")"
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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": 22,
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"id": "67370694-86f4-4b64-9d4f-38b2e306abeb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Decide if should finish or should reduce one more step\n",
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"def decide_end(plan):\n",
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" if len(plan['docs']) > 1:\n",
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" return Pregel.send_to(\"reduce_inbox\")\n",
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" else:\n",
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" return {\"docs\": lambda x: x[\"docs\"][0], \"question\": lambda x: x[\"question\"]} | stuff_chain | Pregel.send_to(\"output_inbox\")\n",
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"\n",
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"# Chain that collapses documents then chooses end\n",
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"collapse_chain = Pregel.subscribe_to(input=collapse_inbox) | RunnableMap({\n",
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" \"docs\": lambda x: _split_list_of_docs(x[\"docs\"]),\n",
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" \"question\": lambda x: x[\"question\"]\n",
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"}) | decide_end\n",
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"\n",
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"\n",
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"reduce_chain = (\n",
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" Pregel.subscribe_to(input=input_inbox)\n",
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" | (lambda x: [{\"docs\": d, \"question\": x[\"question\"]} for d in x['docs']])\n",
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" | stuff_chain.map() \n",
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" | Pregel.send_to({\"collapse_inbox\": {\n",
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" \"docs\": lambda x: [Document(page_content=m.content) for m in x],\n",
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" }})\n",
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")"
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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": 23,
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"id": "3019e7d2-ab7f-4868-b43c-ad898d824a26",
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"metadata": {},
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"outputs": [
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{
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"ename": "ValidationError",
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"evalue": "6 validation errors for Pregel\nprocesses -> 0\n value is not a valid dict (type=type_error.dict)\nprocesses -> 0\n value is not a valid dict (type=type_error.dict)\nprocesses -> 1\n value is not a valid dict (type=type_error.dict)\nprocesses -> 1\n value is not a valid dict (type=type_error.dict)\nprocesses -> 2\n value is not a valid dict (type=type_error.dict)\nprocesses -> 2\n value is not a valid dict (type=type_error.dict)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pubsub \u001b[38;5;241m=\u001b[39m \u001b[43mPregel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_inbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduce_inbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollapse_inbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_inbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_inbox\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/workplace/permchain/permchain/pregel.py:244\u001b[0m, in \u001b[0;36mPregel.__init__\u001b[0;34m(self, input, output, step_timeout, *processes, **kwargs)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 238\u001b[0m \u001b[38;5;241m*\u001b[39mprocesses: PregelInvoke \u001b[38;5;241m|\u001b[39m PregelBatch,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 243\u001b[0m ):\n\u001b[0;32m--> 244\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 245\u001b[0m \u001b[43m \u001b[49m\u001b[43mprocesses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprocesses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 246\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 247\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[43m \u001b[49m\u001b[43mstep_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 249\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/.pyenv/versions/3.10.1/envs/permchain/lib/python3.10/site-packages/langchain/load/serializable.py:90\u001b[0m, in \u001b[0;36mSerializable.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lc_kwargs \u001b[38;5;241m=\u001b[39m kwargs\n",
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"File \u001b[0;32m~/.pyenv/versions/3.10.1/envs/permchain/lib/python3.10/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;31mValidationError\u001b[0m: 6 validation errors for Pregel\nprocesses -> 0\n value is not a valid dict (type=type_error.dict)\nprocesses -> 0\n value is not a valid dict (type=type_error.dict)\nprocesses -> 1\n value is not a valid dict (type=type_error.dict)\nprocesses -> 1\n value is not a valid dict (type=type_error.dict)\nprocesses -> 2\n value is not a valid dict (type=type_error.dict)\nprocesses -> 2\n value is not a valid dict (type=type_error.dict)"
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]
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}
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],
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"source": [
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"pubsub = Pregel(input_inbox, reduce_inbox, collapse_inbox, input=input_inbox, output=output_inbox)\n"
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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": 101,
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"id": "69fcb829-3dae-432a-8db3-11bbb179a7d2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content='Harrison used to work at Kensho.', additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 101,
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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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"reduce_agent.invoke({\"question\": \"where did harrison work\", \"docs\": many_docs})"
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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": "265b29cd-d4f4-4e48-8d4e-b759e909ac2e",
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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": "3.10.1"
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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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Block a user