mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-11 04:07:52 +02:00
more research stuff
This commit is contained in:
Harrison Chase
@@ -0,0 +1,91 @@
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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 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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},
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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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template = """You are tasked with writing a research report to answer the following question:
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<question>
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{question}
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</question>
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In order to do that, you first came up with several sub questions and researched those. please find those below:
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<research>
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{research}
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</research>
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Now, write your final report answering the original question!"""
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prompt = ChatPromptTemplate.from_template(template)
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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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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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| question_chain
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# The draft always goes to the editors inbox
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| research_inbox.publish()
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)
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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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"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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)
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longer_researcher = PubSub(
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processes=(subquestion_actor, research_actor, write_actor),
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connection=InMemoryPubSubConnection(),
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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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@@ -0,0 +1,57 @@
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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 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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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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def retrieve_documents(query):
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response = requests.get("http://127.0.0.1:8080/query", params={"query": query})
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return response.json()
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summarizer_inbox = Topic("summarizer")
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search_actor = (
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Topic.IN.subscribe()
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| {
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"search_results": retrieve_documents,
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"question": Topic.IN.current(),
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}
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| summarizer_inbox.publish()
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)
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summ_actor = (
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summarizer_inbox.subscribe()
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| {"answer":summarizer_chain }
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| Topic.OUT.publish()
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)
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web_researcher = PubSub(
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processes=(search_actor, summ_actor),
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connection=InMemoryPubSubConnection(),
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)
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app = FastAPI()
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class Data(BaseModel):
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questions: List[str]
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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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@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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@@ -0,0 +1,36 @@
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# main.py
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from fastapi import FastAPI
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from langchain.document_loaders import AsyncHtmlLoader
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from langchain.document_transformers import Html2TextTransformer
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from duckduckgo_search import DDGS
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ddgs = DDGS()
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app = FastAPI()
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@app.get("/")
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def read_root():
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return {"Hello": "World"}
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@app.get("/query")
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def read_item(query: str):
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query = query.strip().strip('"')
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search_results = ddgs.text(query)
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urls_to_look = []
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for res in search_results:
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if res.get("href", None):
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urls_to_look.append(res["href"])
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if len(urls_to_look) >= 4:
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break
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# Relevant urls
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# Load, split, and add new urls to vectorstore
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if urls_to_look:
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loader = AsyncHtmlLoader(urls_to_look)
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html2text = Html2TextTransformer()
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docs = loader.load()
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docs = list(html2text.transform_documents(docs))
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else:
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docs = []
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return docs
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@@ -176,21 +176,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 43,
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"id": "2d4a6b51-bd93-47c2-a301-9593a47df7d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"summ_actor = (\n",
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" summarizer_inbox.subscribe()\n",
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" | summarizer_chain\n",
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" | {\"answer\": summarizer_chain}\n",
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" | Topic.OUT.publish()\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": 29,
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"execution_count": 44,
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"id": "11d3b066-7f95-4ad9-82d0-ba64bbf3e3fa",
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"metadata": {},
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"outputs": [],
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@@ -203,12 +203,68 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 45,
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"id": "bc022d51-69f0-4da9-8025-70afdc3cc6a8",
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"metadata": {},
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"outputs": [],
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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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"Fetching pages: 100%|#############################################################################################################################################################################################| 4/4 [00:00<00:00, 6.44it/s]\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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"[{'answer': 'LangSmith is a platform for building production-grade language model applications. It helps trace and evaluate language model applications and intelligent agents, making it easier to move from prototype to production. LangSmith is developed by LangChain, the company behind the open source LangChain framework. More information can be found in the LangSmith documentation.'}]"
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]
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},
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"execution_count": 45,
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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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"#web_researcher.invoke(\"What is langsmith?\")"
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"web_researcher.invoke(\"What is langsmith?\")"
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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": 47,
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"id": "000f4f24-15ba-476f-8a33-d023081b18d2",
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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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"Fetching pages: 0%| | 0/4 [00:00<?, ?it/s]\n",
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"Fetching pages: 100%|#############################################################################################################################################################################################| 4/4 [00:00<00:00, 6.93it/s]\u001b[A\n",
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"Fetching pages: 100%|#############################################################################################################################################################################################| 4/4 [00:00<00:00, 4.79it/s]\n"
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]
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},
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{
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"ename": "TypeError",
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"evalue": "unsupported operand type(s) for +=: 'dict' and '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;31mTypeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[47], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mweb_researcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwhat is langsmith\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwhat is llama\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/workplace/langchain/libs/langchain/langchain/schema/runnable/base.py:102\u001b[0m, in \u001b[0;36mRunnable.batch\u001b[0;34m(self, inputs, config, max_concurrency)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minvoke(inputs[\u001b[38;5;241m0\u001b[39m], configs[\u001b[38;5;241m0\u001b[39m])]\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ThreadPoolExecutor(max_workers\u001b[38;5;241m=\u001b[39mmax_concurrency) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[0;32m--> 102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mexecutor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfigs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/concurrent/futures/_base.py:608\u001b[0m, in \u001b[0;36mExecutor.map.<locals>.result_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m 605\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[1;32m 606\u001b[0m \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 608\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 609\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 610\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m fs\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mresult(end_time \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic())\n",
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"File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/concurrent/futures/_base.py:445\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 444\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 445\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 446\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n",
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"File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/concurrent/futures/_base.py:390\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 390\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 392\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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"File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
|
||||
"File \u001b[0;32m~/workplace/permchain/permchain/pubsub.py:67\u001b[0m, in \u001b[0;36mPubSub.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Any, config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 66\u001b[0m collected \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream(\u001b[38;5;28minput\u001b[39m, config):\n\u001b[1;32m 68\u001b[0m collected\u001b[38;5;241m.\u001b[39mappend(chunk)\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collected\n",
|
||||
"File \u001b[0;32m~/workplace/permchain/permchain/pubsub.py:176\u001b[0m, in \u001b[0;36mPubSub.stream\u001b[0;34m(self, input, config, max_concurrency)\u001b[0m\n\u001b[1;32m 174\u001b[0m final_output \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 176\u001b[0m final_output \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 178\u001b[0m \u001b[38;5;66;03m# Cleanup\u001b[39;00m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fut \u001b[38;5;129;01min\u001b[39;00m inflight:\n",
|
||||
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +=: 'dict' and 'dict'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"web_researcher.batch([\"what is langsmith\", \"what is llama\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user