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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 |
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from langchain.llms import Anthropic | |
from langchain.agents import load_tools, initialize_agent | |
from langchain.tools import AIPluginTool | |
PREFIX = """\n\nHuman: Answer the following questions as best you can. You have access to the following tools:""" | |
SUFFIX = """Begin! | |
Question: {input} | |
\n\nAssistant: | |
Thought:{agent_scratchpad}""" |
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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") |
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class AgentState(TypedDict): | |
code: str | |
tests: str | |
errors: Optional[str] | |
def initial_writer(state): | |
... | |
return {"code": ..., "tests": ...} | |
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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] | |
}) |
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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. |
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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") |
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# STEP 1: Load | |
# Load documents using LangChain's DocumentLoaders | |
# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html | |
from langchain.document_loaders.csv_loader import CSVLoader | |
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv') | |
data = loader.load() |
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System: Respond to the human as helpfully and accurately as possible. You have access to the following tools: | |
click_element: Click on an element with the given CSS selector, args: {{'selector': {{'title': 'Selector', 'description': 'CSS selector for the element to click', 'type': 'string'}}}} | |
navigate_browser: Navigate a browser to the specified URL, args: {{'url': {{'title': 'Url', 'description': 'url to navigate to', 'type': 'string'}}}} | |
previous_webpage: Navigate back to the previous page in the browser history, args: {{}} | |
extract_text: Extract all the text on the current webpage, args: {{}} | |
extract_hyperlinks: Extract all hyperlinks on the current webpage, args: {{'absolute_urls': {{'title': 'Absolute Urls', 'description': 'Return absolute URLs instead of relative URLs', 'default': False, 'type': 'boolean'}}}} | |
get_elements: Retrieve elements in the current web page matching the given CSS selector, args: {{'selector': {{'title': 'Selector', 'description': "CSS selector, such as '*', 'div', 'p', 'a', #id, |
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from langchain.llms.base import LLM | |
from typing import Optional, List, Mapping, Any | |
import requests | |
from langchain.llms.utils import enforce_stop_tokens | |
class CustomLLM(LLM): | |
def __init__(self, url: str): | |
self.url = url |
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