hwchase17

class AgentState(TypedDict):
    code: str
    tests: str
    errors: Optional[str]

def initial_writer(state):
    ...
    return {"code": ..., "tests": ...}

hwchase17

from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone

# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(
    api_key="...", environment="..."
)
index = pinecone.Index("test123")

hwchase17

from langchain.chains.openai_functions import create_structured_output_runnable
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel, Field
 
class Insight(BaseModel):
    insight: str = Field(description="""insight""")

chat_model = ChatOpenAI(model_name="gpt-4-1106-preview")

hwchase17

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
import requests
from bs4 import BeautifulSoup
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from langchain.utilities import DuckDuckGoSearchAPIWrapper
import json

RESULTS_PER_QUESTION = 3

hwchase17

from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatAnthropic
from langchain.schema.output_parser import StrOutputParser


#### ROUTER
# This is the router - responsible for chosing what to do
chain = PromptTemplate.from_template("""Given the user question below, classify it as either being about `weather` or `other`.
                                     
Do not respond with more than one word.

hwchase17

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI

llm = OpenAI(temperature=0, model="gpt-3.5-turbo-instruct")

from metaphor_python import Metaphor

client = Metaphor("")

hwchase17

from langchain import PromptTemplate, OpenAI, LLMChain

prompt_template = "What is a good name for a company that makes {product}?"

llm1 = OpenAI(model="foo", temperature=0)
llm_chain1 = LLMChain(
    llm=llm1,
    prompt=PromptTemplate.from_template(prompt_template)
)

hwchase17

from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.schema.output_parser import StrOutputParser
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.runnable import RunnableMap
from langchain.schema import format_document
from typing import AsyncGenerator

hwchase17

from langchain.chat_models import ChatAnthropic
from kor import create_extraction_chain, Object, Text

llm = ChatAnthropic(
    model_name="claude-v2",
    temperature=0,
    max_tokens=2000,
)

schema = Object(

hwchase17

import pandas as pd
from pandasai import PandasAI

# Sample DataFrame
df = pd.DataFrame({
    "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
    "gdp": [19294482071552, 2891615567872, 2411255037952, 3435817336832, 1745433788416, 1181205135360, 1607402389504, 1490967855104, 4380756541440, 14631844184064],
    "happiness_index": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12]
})