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 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.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.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.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.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.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

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

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