mirror of
https://github.com/langchain-ai/langgraph.git
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Format
This commit is contained in:
Nuno Campos
+21
-29
@@ -45,7 +45,7 @@
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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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"execution_count": 2,
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"id": "4e11ecdb-2b74-4f1e-8b8b-91a0d0e7547c",
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"metadata": {},
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"outputs": [],
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@@ -62,17 +62,17 @@
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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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"execution_count": 3,
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"id": "85df70b5-4d3c-47b1-b401-036f513965b8",
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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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"\"Arrr, matey! What be art, ye be askin'? Well, art be a broad term, encompassin' a vast array o' creative expressions. Be it paintin's, sculptures, music, or even the written word, art be a means o' expressin' oneself and communicatin' emotions. It be a way fer humans to tap into their imagination and create somethin' beautiful or thought-provokin'. Art be subjective, each eye seein' it differently, but it be an important part o' our culture, history, and identity. So, me hearties, let yer creativity run wild, and let art be yer compass on this grand adventure called life!\""
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||||
"\"Arrr, me hearty! Art be a thing of beauty and expression, me hearties! It be a way for a scurvy dog to convey their innermost thoughts and feelings through visual, auditory, or kinesthetic means. It be a form of communication that ye can't be puttin' into words, but ye can feel it in yer bones. Art be a treasure that be created by the talents and imaginations of landlubbers. Whether it be a paintin', a sculpture, a song, or a dance, art be a way to celebrate the wonders of this here world and let yer soul run wild on the high seas of creativity.\""
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]
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},
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -91,7 +91,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 4,
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"id": "5371da31-1fd1-46dd-afaa-6727cc6a3d57",
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"metadata": {},
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"outputs": [],
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@@ -131,17 +131,17 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 5,
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"id": "8e2f7fa4-eef5-440e-bf51-ee236dc421e6",
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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='', additional_kwargs={'function_call': {'name': 'revise', 'arguments': '{\\n\"notes\": \"The draft is too short and lacks content. Please provide a detailed and informative draft for review.\"\\n}'}}, example=False)"
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"AIMessage(content='', additional_kwargs={'function_call': {'name': 'revise', 'arguments': '{\\n \"notes\": \"The draft is too short and lacks content. Please provide more information or expand on your current topic.\"\\n}'}}, example=False)"
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]
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},
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"execution_count": 8,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -160,7 +160,7 @@
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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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"execution_count": 6,
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"id": "e9dcbbc9-2bc2-4a15-9002-1acb2692f943",
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"metadata": {},
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"outputs": [],
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@@ -178,17 +178,17 @@
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 7,
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"id": "9f200a4c-628b-495b-a9ec-c647f8dcdcf0",
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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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"'Revised draft:\\n\\nHello!'"
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"'Revised Draft:\\n\\nHello there!'"
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]
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},
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"execution_count": 10,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -212,7 +212,7 @@
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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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"execution_count": 8,
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"id": "82f6c33f-1553-4d92-bad9-0b499dafcf6c",
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"metadata": {},
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"outputs": [],
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@@ -224,7 +224,7 @@
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 9,
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"id": "af624684-c3b4-4283-aecb-d57d1b2316f9",
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"metadata": {},
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"outputs": [],
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@@ -240,7 +240,7 @@
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "3deabd3d-2995-4565-a6b2-38e39171143f",
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"metadata": {},
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"outputs": [],
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@@ -270,7 +270,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 11,
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"id": "f3aaf437-95d6-4e1f-a756-15b5132ac9f7",
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"metadata": {},
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"outputs": [],
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@@ -286,7 +286,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 12,
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"id": "314b75ee-837d-419c-81a7-ea3dec97203b",
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"metadata": {},
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"outputs": [],
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@@ -299,17 +299,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 13,
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"id": "0371a5ac-7194-4cf9-9dd2-56cb3d1f46d6",
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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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"[{'draft': 'Turtles, fascinating creatures of the sea, are known for their diverse diets. They are omnivorous, meaning they consume both plant matter and small animals. Some turtles prefer a herbivorous diet, feeding on aquatic plants, seaweed, and algae. Others have a more carnivorous appetite, enjoying insects, fish, and crustaceans. Additionally, there are turtles that fall in the middle, being omnivores, and enjoying a variety of foods. So, when it comes to what turtles eat, they can be described as versatile eaters, ready to consume whatever comes their way in their marine habitat!'}]"
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"[{'draft': 'Turtles are a highly diverse group of animals when it comes to their dietary preferences. Their food choices vary depending on the species and their surroundings. Sea turtles, for example, primarily consume sea grass, seaweed, and jellyfish. On the other hand, land turtles, like the well-known Tortuga, prefer leafy greens such as lettuce and kale. Certain turtles even include protein in their diets, enjoying insects, small fish, or even carrion. It is crucial to understand what your turtle likes to eat in order to maintain their well-being and happiness. However, it is always advisable to consult a veterinarian with expertise in turtles before making any changes to their diet.'}]"
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]
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},
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"execution_count": 18,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -317,14 +317,6 @@
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"source": [
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"web_researcher.invoke({\"question\": \"What food do turtles eat?\"})"
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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": "ee462beb-2ce1-4516-908d-33d660bab5d4",
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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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@@ -343,7 +335,7 @@
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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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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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@@ -1,39 +1,42 @@
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from operator import itemgetter
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from langchain.chat_models import ChatOpenAI, ChatAnthropic
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from langchain.prompts import SystemMessagePromptTemplate, ChatPromptTemplate
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from langchain.schema.output_parser import StrOutputParser
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from langchain.runnables.openai_functions import OpenAIFunctionsRouter
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import requests
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from fastapi import FastAPI
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from langchain.chat_models import ChatAnthropic, ChatOpenAI
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from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
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from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
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from langchain.runnables.openai_functions import OpenAIFunctionsRouter
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from langchain.schema.output_parser import StrOutputParser
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from permchain.connection_inmemory import InMemoryPubSubConnection
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from permchain.pubsub import PubSub
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from permchain.topic import Topic
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from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
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template = """Write between 2 and 5 sub questions that serve as google search queries to search online that form an objective opinion from the following: {question}"""
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functions = [
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{
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"name": "sub_questions",
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"description": "List of sub questions",
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"parameters": {
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"type": "object",
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"properties": {
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"questions": {
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"type": "array",
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"description": "List of sub questions to ask.",
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"items": {
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"type": "string"
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}
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},
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},
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"name": "sub_questions",
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"description": "List of sub questions",
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"parameters": {
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"type": "object",
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"properties": {
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"questions": {
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"type": "array",
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"description": "List of sub questions to ask.",
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"items": {"type": "string"},
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},
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},
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},
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},
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]
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prompt = ChatPromptTemplate.from_template(template)
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question_chain = prompt | ChatOpenAI(temperature=0).bind(functions=functions, function_call={"name":"sub_questions"}) | JsonKeyOutputFunctionsParser(key_name="questions")
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question_chain = (
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prompt
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| ChatOpenAI(temperature=0).bind(
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functions=functions, function_call={"name": "sub_questions"}
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)
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| JsonKeyOutputFunctionsParser(key_name="questions")
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)
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template = """You are tasked with writing a research report to answer the following question:
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@@ -54,10 +57,14 @@ report_chain = prompt | ChatOpenAI() | StrOutputParser()
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research_inbox = Topic("research")
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writer_inbox = Topic("writer_inbox")
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def web_researcher(questions):
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response = requests.post("http://127.0.0.1:8081/batch", json={"questions": questions})
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response = requests.post(
|
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"http://127.0.0.1:8081/batch", json={"questions": questions}
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)
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return response.json()
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subquestion_actor = (
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# Listed in inputs
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Topic.IN.subscribe()
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@@ -69,15 +76,13 @@ research_actor = (
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research_inbox.subscribe()
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| {
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"research": lambda x: web_researcher(x),
|
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#"research": (lambda x: [web_researcher(i) for i in x]),
|
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# "research": (lambda x: [web_researcher(i) for i in x]),
|
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"question": Topic.IN.current() | itemgetter("question"),
|
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}
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| writer_inbox.publish()
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)
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write_actor = (
|
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writer_inbox.subscribe()
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| {"response": report_chain}
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| Topic.OUT.publish()
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writer_inbox.subscribe() | {"response": report_chain} | Topic.OUT.publish()
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)
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longer_researcher = PubSub(
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@@ -86,6 +91,8 @@ longer_researcher = PubSub(
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)
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app = FastAPI()
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@app.get("/report")
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def read_item(question: str):
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return longer_researcher.invoke({"question":question})
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return longer_researcher.invoke({"question": question})
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@@ -1,21 +1,29 @@
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from operator import itemgetter
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from typing import List
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from langchain.chat_models import ChatOpenAI, ChatAnthropic
|
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from langchain.prompts import SystemMessagePromptTemplate, ChatPromptTemplate
|
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from langchain.schema.output_parser import StrOutputParser
|
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from langchain.runnables.openai_functions import OpenAIFunctionsRouter
|
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from pydantic import BaseModel
|
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|
||||
import requests
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from fastapi import FastAPI
|
||||
|
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from langchain.chat_models import ChatAnthropic, ChatOpenAI
|
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from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
|
||||
from langchain.runnables.openai_functions import OpenAIFunctionsRouter
|
||||
from langchain.schema.output_parser import StrOutputParser
|
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from pydantic import BaseModel
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||||
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from permchain.connection_inmemory import InMemoryPubSubConnection
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from permchain.pubsub import PubSub
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from permchain.topic import Topic
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prompt = ChatPromptTemplate.from_template("Answer the user's question given the search results\n\n<question>{question}</question><search_results>{search_results}</search_results>")
|
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prompt = ChatPromptTemplate.from_template(
|
||||
"Answer the user's question given the search results\n\n<question>{question}</question><search_results>{search_results}</search_results>"
|
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)
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|
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summarizer_chain = prompt | ChatOpenAI(max_retries=0).with_fallbacks([ChatOpenAI(model="gpt-3.5-turbo-16k"), ChatAnthropic(model="claude-2")]) | StrOutputParser()
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summarizer_chain = (
|
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prompt
|
||||
| ChatOpenAI(max_retries=0).with_fallbacks(
|
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[ChatOpenAI(model="gpt-3.5-turbo-16k"), ChatAnthropic(model="claude-2")]
|
||||
)
|
||||
| StrOutputParser()
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)
|
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|
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|
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def retrieve_documents(query):
|
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@@ -35,9 +43,7 @@ search_actor = (
|
||||
)
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|
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summ_actor = (
|
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summarizer_inbox.subscribe()
|
||||
| {"answer":summarizer_chain }
|
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| Topic.OUT.publish()
|
||||
summarizer_inbox.subscribe() | {"answer": summarizer_chain} | Topic.OUT.publish()
|
||||
)
|
||||
|
||||
web_researcher = PubSub(
|
||||
@@ -46,12 +52,17 @@ web_researcher = PubSub(
|
||||
)
|
||||
|
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app = FastAPI()
|
||||
|
||||
|
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class Data(BaseModel):
|
||||
questions: List[str]
|
||||
|
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|
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@app.get("/invoke")
|
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def read_item(question: str):
|
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return web_researcher.invoke(question)
|
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|
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|
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@app.post("/batch")
|
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def batch(data: Data):
|
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return web_researcher.batch(data.questions)
|
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|
||||
@@ -1,18 +1,20 @@
|
||||
# main.py
|
||||
|
||||
from duckduckgo_search import DDGS
|
||||
from fastapi import FastAPI
|
||||
from langchain.document_loaders import AsyncHtmlLoader
|
||||
from langchain.document_transformers import Html2TextTransformer
|
||||
from duckduckgo_search import DDGS
|
||||
|
||||
ddgs = DDGS()
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
|
||||
@app.get("/")
|
||||
def read_root():
|
||||
return {"Hello": "World"}
|
||||
|
||||
|
||||
@app.get("/query")
|
||||
def read_item(query: str):
|
||||
query = query.strip().strip('"')
|
||||
|
||||
@@ -2,14 +2,13 @@ from operator import itemgetter
|
||||
|
||||
from langchain.chat_models.openai import ChatOpenAI
|
||||
from langchain.prompts import SystemMessagePromptTemplate
|
||||
from langchain.schema.output_parser import StrOutputParser
|
||||
from langchain.runnables.openai_functions import OpenAIFunctionsRouter
|
||||
from langchain.schema.output_parser import StrOutputParser
|
||||
|
||||
from permchain.connection_inmemory import InMemoryPubSubConnection
|
||||
from permchain.pubsub import PubSub
|
||||
from permchain.topic import Topic
|
||||
|
||||
|
||||
drafter_prompt = (
|
||||
SystemMessagePromptTemplate.from_template(
|
||||
"You are an expert on turtles, who likes to write in pirate-speak. You have been tasked by your editor with drafting a 100-word article answering the following question."
|
||||
|
||||
+35
-29
@@ -67,7 +67,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def retrieve_documents(query):\n",
|
||||
" query=query.strip().strip('\"')\n",
|
||||
" query = query.strip().strip('\"')\n",
|
||||
" search_results = ddgs.text(query)\n",
|
||||
" urls_to_look = []\n",
|
||||
" for res in search_results:\n",
|
||||
@@ -75,7 +75,7 @@
|
||||
" urls_to_look.append(res[\"href\"])\n",
|
||||
" if len(urls_to_look) >= 4:\n",
|
||||
" break\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" # Relevant urls\n",
|
||||
" # Load, split, and add new urls to vectorstore\n",
|
||||
" if urls_to_look:\n",
|
||||
@@ -96,6 +96,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import nest_asyncio\n",
|
||||
"\n",
|
||||
"nest_asyncio.apply()"
|
||||
]
|
||||
},
|
||||
@@ -106,7 +107,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#docs = retrieve_documents(\"langchain\")"
|
||||
"# docs = retrieve_documents(\"langchain\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -125,7 +126,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = ChatPromptTemplate.from_template(\"Answer the user's question given the search results\\n\\n<question>{question}</question><search_results>{search_results}</search_results>\")"
|
||||
"prompt = ChatPromptTemplate.from_template(\n",
|
||||
" \"Answer the user's question given the search results\\n\\n<question>{question}</question><search_results>{search_results}</search_results>\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
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{
|
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@@ -135,7 +138,13 @@
|
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
||||
"summarizer_chain = prompt | ChatOpenAI(max_retries=0).with_fallbacks([ChatOpenAI(model=\"gpt-3.5-turbo-16k\"), ChatAnthropic(model=\"claude-2\")]) | StrOutputParser()"
|
||||
"summarizer_chain = (\n",
|
||||
" prompt\n",
|
||||
" | ChatOpenAI(max_retries=0).with_fallbacks(\n",
|
||||
" [ChatOpenAI(model=\"gpt-3.5-turbo-16k\"), ChatAnthropic(model=\"claude-2\")]\n",
|
||||
" )\n",
|
||||
" | StrOutputParser()\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -153,7 +162,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"summarizer_inbox = Topic(\"summarizer\")"
|
||||
]
|
||||
},
|
||||
@@ -182,9 +190,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"summ_actor = (\n",
|
||||
" summarizer_inbox.subscribe()\n",
|
||||
" | {\"answer\": summarizer_chain}\n",
|
||||
" | Topic.OUT.publish()\n",
|
||||
" summarizer_inbox.subscribe() | {\"answer\": summarizer_chain} | Topic.OUT.publish()\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
@@ -295,24 +301,28 @@
|
||||
"template = \"\"\"Write between 2 and 5 sub questions that serve as google search queries to search online that form an objective opinion from the following: {question}\"\"\"\n",
|
||||
"functions = [\n",
|
||||
" {\n",
|
||||
" \"name\": \"sub_questions\",\n",
|
||||
" \"description\": \"List of sub questions\",\n",
|
||||
" \"parameters\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"questions\": {\n",
|
||||
" \"type\": \"array\",\n",
|
||||
" \"description\": \"List of sub questions to ask.\",\n",
|
||||
" \"items\": {\n",
|
||||
" \"type\": \"string\"\n",
|
||||
" }\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"name\": \"sub_questions\",\n",
|
||||
" \"description\": \"List of sub questions\",\n",
|
||||
" \"parameters\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"questions\": {\n",
|
||||
" \"type\": \"array\",\n",
|
||||
" \"description\": \"List of sub questions to ask.\",\n",
|
||||
" \"items\": {\"type\": \"string\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
"]\n",
|
||||
"prompt = ChatPromptTemplate.from_template(template)\n",
|
||||
"question_chain = prompt | ChatOpenAI(temperature=0).bind(functions=functions, function_call={\"name\":\"sub_questions\"}) | JsonKeyOutputFunctionsParser(key_name=\"questions\")"
|
||||
"question_chain = (\n",
|
||||
" prompt\n",
|
||||
" | ChatOpenAI(temperature=0).bind(\n",
|
||||
" functions=functions, function_call={\"name\": \"sub_questions\"}\n",
|
||||
" )\n",
|
||||
" | JsonKeyOutputFunctionsParser(key_name=\"questions\")\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -393,16 +403,12 @@
|
||||
" research_inbox.subscribe()\n",
|
||||
" | {\n",
|
||||
" \"research\": lambda x: web_researcher.batch(x),\n",
|
||||
" #\"research\": lambda x: [web_researcher.invoke({\"question\": i}) for i in x],\n",
|
||||
" # \"research\": lambda x: [web_researcher.invoke({\"question\": i}) for i in x],\n",
|
||||
" \"question\": Topic.IN.current() | itemgetter(\"question\"),\n",
|
||||
" }\n",
|
||||
" | writer_inbox.publish()\n",
|
||||
")\n",
|
||||
"write_actor = (\n",
|
||||
" writer_inbox.subscribe()\n",
|
||||
" | report_chain\n",
|
||||
" | Topic.OUT.publish()\n",
|
||||
")"
|
||||
"write_actor = writer_inbox.subscribe() | report_chain | Topic.OUT.publish()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Generated
+14
-1
@@ -360,10 +360,12 @@ files = [
|
||||
|
||||
[package.dependencies]
|
||||
click = ">=8.0.0"
|
||||
ipython = {version = ">=7.8.0", optional = true, markers = "extra == \"jupyter\""}
|
||||
mypy-extensions = ">=0.4.3"
|
||||
packaging = ">=22.0"
|
||||
pathspec = ">=0.9.0"
|
||||
platformdirs = ">=2"
|
||||
tokenize-rt = {version = ">=3.2.0", optional = true, markers = "extra == \"jupyter\""}
|
||||
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
|
||||
typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
|
||||
|
||||
@@ -3165,6 +3167,17 @@ webencodings = ">=0.4"
|
||||
doc = ["sphinx", "sphinx_rtd_theme"]
|
||||
test = ["flake8", "isort", "pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "tokenize-rt"
|
||||
version = "5.2.0"
|
||||
description = "A wrapper around the stdlib `tokenize` which roundtrips."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "tokenize_rt-5.2.0-py2.py3-none-any.whl", hash = "sha256:b79d41a65cfec71285433511b50271b05da3584a1da144a0752e9c621a285289"},
|
||||
{file = "tokenize_rt-5.2.0.tar.gz", hash = "sha256:9fe80f8a5c1edad2d3ede0f37481cc0cc1538a2f442c9c2f9e4feacd2792d054"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
@@ -3457,4 +3470,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "3dc1864f26be637b3e52cdfef9a2bbbb2b1d4feeb245b5a3e105044e04dec2c8"
|
||||
content-hash = "efcc0b5e1b923cc732d3f214a48e9e5b362ceb415839307fa82ac91839ac5aa7"
|
||||
|
||||
+1
-1
@@ -25,7 +25,7 @@ syrupy = "^4.0.2"
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.0.249"
|
||||
black = "^23.1.0"
|
||||
black = {extras = ["jupyter"], version = "^23.7.0"}
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^0.991"
|
||||
|
||||
Reference in New Issue
Block a user