prompt
stringlengths 70
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stringlengths 23
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|---|---|---|
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-colbert')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install torch sentence-transformers')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.indices.managed.google import GoogleIndex
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
project_name = "TODO-your-project-name" # @param {type:"string"}
email = "[email protected]" # @param {type:"string"}
client_file_name = "client_secret.json"
get_ipython().system('gcloud config set project $project_name')
get_ipython().system('gcloud config set account $email')
get_ipython().system('gcloud auth application-default login --no-browser --client-id-file=$client_file_name --scopes="https://www.googleapis.com/auth/generative-language.retriever,https://www.googleapis.com/auth/cloud-platform"')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
google_index = GoogleIndex.create_corpus(display_name="My first corpus!")
print(f"Newly created corpus ID is {google_index.corpus_id}.")
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-output-parsers-guardrails')
get_ipython().system('pip install guardrails-ai')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from IPython.display import Markdown, display
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(documents, chunk_size=512) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
import nest_asyncio
nest_asyncio.apply()
import os
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
import pandas as pd
def display_eval_df(question, source, answer_a, answer_b, result) -> None:
"""Pretty print question/answer + gpt-4 judgement dataset."""
eval_df = pd.DataFrame(
{
"Question": question,
"Source": source,
"Model A": answer_a["model"],
"Answer A": answer_a["text"],
"Model B": answer_b["model"],
"Answer B": answer_b["text"],
"Score": result.score,
"Judgement": result.feedback,
},
index=[0],
)
eval_df = eval_df.style.set_properties(
**{
"inline-size": "300px",
"overflow-wrap": "break-word",
},
subset=["Answer A", "Answer B"]
)
display(eval_df)
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
train_cities = [
"San Francisco",
"Toronto",
"New York",
"Vancouver",
"Montreal",
"Boston",
]
test_cities = [
"Tokyo",
"Singapore",
"Paris",
]
train_documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in train_cities]
)
test_documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in test_cities]
)
QUESTION_GEN_PROMPT = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
train_dataset_generator = DatasetGenerator.from_documents(
train_documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=llm,
show_progress=True,
num_questions_per_chunk=25,
)
test_dataset_generator = DatasetGenerator.from_documents(
test_documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=llm,
show_progress=True,
num_questions_per_chunk=25,
)
train_questions = train_dataset_generator.generate_questions_from_nodes(
num=200
)
test_questions = test_dataset_generator.generate_questions_from_nodes(num=150)
len(train_questions), len(test_questions)
train_questions[:3]
test_questions[:3]
from llama_index.core import VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
train_index = | VectorStoreIndex.from_documents(documents=train_documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-program-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-llama-api')
get_ipython().system('pip install llama-index')
from llama_index.llms.llama_api import LlamaAPI
api_key = "LL-your-key"
llm = LlamaAPI(api_key=api_key)
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = llm.chat(messages)
print(resp)
from pydantic import BaseModel
from llama_index.core.llms.openai_utils import to_openai_function
class Song(BaseModel):
"""A song with name and artist"""
name: str
artist: str
song_fn = | to_openai_function(Song) | llama_index.core.llms.openai_utils.to_openai_function |
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.llms.anthropic import Anthropic
llm = OpenAI()
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)
chat_engine = index.as_chat_engine(chat_mode="react", llm=llm, verbose=True)
response = chat_engine.chat(
"Use the tool to answer what did Paul Graham do in the summer of 1995?"
)
print(response)
llm = | Anthropic() | llama_index.llms.anthropic.Anthropic |
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-cohere')
get_ipython().system('pip install llama-index cohere pypdf')
openai_api_key = "YOUR OPENAI API KEY"
cohere_api_key = "YOUR COHEREAI API KEY"
import os
os.environ["OPENAI_API_KEY"] = openai_api_key
os.environ["COHERE_API_KEY"] = cohere_api_key
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.cohere import CohereEmbedding
from llama_index.core.retrievers import BaseRetriever, VectorIndexRetriever
from llama_index.core import QueryBundle
from llama_index.core.indices.query.schema import QueryType
from llama_index.core.schema import NodeWithScore
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.evaluation import EmbeddingQAFinetuneDataset
from llama_index.finetuning import generate_cohere_reranker_finetuning_dataset
from llama_index.core.evaluation import generate_question_context_pairs
from llama_index.core.evaluation import RetrieverEvaluator
from llama_index.finetuning import CohereRerankerFinetuneEngine
from typing import List
import pandas as pd
import nest_asyncio
nest_asyncio.apply()
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
lyft_docs = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
node_parser = | SimpleNodeParser.from_defaults(chunk_size=400) | llama_index.core.node_parser.SimpleNodeParser.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openllm')
get_ipython().system('pip install "openllm" # use \'openllm[vllm]\' if you have access to GPU')
get_ipython().system('pip install llama-index')
import os
from typing import List, Optional
from llama_index.llms.openllm import OpenLLM, OpenLLMAPI
from llama_index.core.llms import ChatMessage
os.environ[
"OPENLLM_ENDPOINT"
] = "na" # Change this to a remote server that you might run OpenLLM at.
local_llm = OpenLLM("HuggingFaceH4/zephyr-7b-alpha")
remote_llm = OpenLLMAPI(address="http://localhost:3000")
remote_llm = OpenLLMAPI()
completion_response = remote_llm.complete("To infinity, and")
print(completion_response)
for it in remote_llm.stream_complete(
"The meaning of time is", max_new_tokens=128
):
print(it, end="", flush=True)
async for it in remote_llm.astream_chat(
[
ChatMessage(
role="system", content="You are acting as Ernest Hemmingway."
),
ChatMessage(role="user", content="Hi there!"),
| ChatMessage(role="assistant", content="Yes?") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("../data/paul_graham")
docs = reader.load_data()
import os
from llama_index.core import (
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
if not os.path.exists("storage"):
index = VectorStoreIndex.from_documents(docs)
index.set_index_id("vector_index")
index.storage_context.persist("./storage")
else:
storage_context = StorageContext.from_defaults(persist_dir="storage")
index = load_index_from_storage(storage_context, index_id="vector_index")
from llama_index.core.query_pipeline import QueryPipeline
from llama_index.core import PromptTemplate
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
p = QueryPipeline(chain=[prompt_tmpl, llm], verbose=True)
output = p.run(movie_name="The Departed")
print(str(output))
from typing import List
from pydantic import BaseModel, Field
from llama_index.core.output_parsers import PydanticOutputParser
class Movie(BaseModel):
"""Object representing a single movie."""
name: str = Field(..., description="Name of the movie.")
year: int = Field(..., description="Year of the movie.")
class Movies(BaseModel):
"""Object representing a list of movies."""
movies: List[Movie] = Field(..., description="List of movies.")
llm = OpenAI(model="gpt-3.5-turbo")
output_parser = | PydanticOutputParser(Movies) | llama_index.core.output_parsers.PydanticOutputParser |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-hub-llama-packs-tables-chain-of-table-base')
get_ipython().system('wget "https://github.com/ppasupat/WikiTableQuestions/releases/download/v1.0.2/WikiTableQuestions-1.0.2-compact.zip" -O data.zip')
get_ipython().system('unzip data.zip')
import pandas as pd
df = pd.read_csv("./WikiTableQuestions/csv/200-csv/3.csv")
df
from llama_index.packs.tables.chain_of_table.base import (
ChainOfTableQueryEngine,
serialize_table,
)
from llama_index.core.llama_pack import download_llama_pack
download_llama_pack(
"ChainOfTablePack",
"./chain_of_table_pack",
skip_load=True,
)
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4-1106-preview")
import phoenix as px
import llama_index.core
px.launch_app()
llama_index.core.set_global_handler("arize_phoenix")
import pandas as pd
df = pd.read_csv("~/Downloads/WikiTableQuestions/csv/200-csv/11.csv")
df
query_engine = ChainOfTableQueryEngine(df, llm=llm, verbose=True)
response = query_engine.query("Who won best Director in the 1972 Academy Awards?")
str(response.response)
import pandas as pd
df = pd.read_csv("./WikiTableQuestions/csv/200-csv/42.csv")
df
query_engine = ChainOfTableQueryEngine(df, llm=llm, verbose=True)
response = query_engine.query("What was the precipitation in inches during June?")
str(response)
from llama_index.core import PromptTemplate
from llama_index.core.query_pipeline import QueryPipeline
prompt_str = """\
Here's a serialized table.
{serialized_table}
Given this table please answer the question: {question}
Answer: """
prompt = PromptTemplate(prompt_str)
prompt_c = prompt.as_query_component(partial={"serialized_table": serialize_table(df)})
qp = QueryPipeline(chain=[prompt_c, llm])
response = qp.run("What was the precipitation in inches during June?")
print(str(response))
import pandas as pd
df = pd.read_csv("./WikiTableQuestions/csv/203-csv/114.csv")
df
query_engine = ChainOfTableQueryEngine(df, llm=llm, verbose=True)
response = query_engine.query("Which televised ABC game had the greatest attendance?")
print(str(response))
from llama_index.core import PromptTemplate
from llama_index.core.query_pipeline import QueryPipeline
prompt_str = """\
Here's a serialized table.
{serialized_table}
Given this table please answer the question: {question}
Answer: """
prompt = | PromptTemplate(prompt_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-.."
openai.api_key = os.environ["OPENAI_API_KEY"]
from IPython.display import Markdown, display
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
)
engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
from llama_index.core import SQLDatabase
from llama_index.llms.openai import OpenAI
llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo")
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{
"city_name": "Chicago",
"population": 2679000,
"country": "United States",
},
{"city_name": "Seoul", "population": 9776000, "country": "South Korea"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
stmt = select(
city_stats_table.c.city_name,
city_stats_table.c.population,
city_stats_table.c.country,
).select_from(city_stats_table)
with engine.connect() as connection:
results = connection.execute(stmt).fetchall()
print(results)
from sqlalchemy import text
with engine.connect() as con:
rows = con.execute(text("SELECT city_name from city_stats"))
for row in rows:
print(row)
from llama_index.core.query_engine import NLSQLTableQueryEngine
query_engine = NLSQLTableQueryEngine(
sql_database=sql_database, tables=["city_stats"], llm=llm
)
query_str = "Which city has the highest population?"
response = query_engine.query(query_str)
display(Markdown(f"<b>{response}</b>"))
from llama_index.core.indices.struct_store.sql_query import (
SQLTableRetrieverQueryEngine,
)
from llama_index.core.objects import (
SQLTableNodeMapping,
ObjectIndex,
SQLTableSchema,
)
from llama_index.core import VectorStoreIndex
table_node_mapping = SQLTableNodeMapping(sql_database)
table_schema_objs = [
(SQLTableSchema(table_name="city_stats"))
] # add a SQLTableSchema for each table
obj_index = ObjectIndex.from_objects(
table_schema_objs,
table_node_mapping,
VectorStoreIndex,
)
query_engine = SQLTableRetrieverQueryEngine(
sql_database, obj_index.as_retriever(similarity_top_k=1)
)
response = query_engine.query("Which city has the highest population?")
display(Markdown(f"<b>{response}</b>"))
response.metadata["result"]
city_stats_text = (
"This table gives information regarding the population and country of a"
" given city.\nThe user will query with codewords, where 'foo' corresponds"
" to population and 'bar'corresponds to city."
)
table_node_mapping = SQLTableNodeMapping(sql_database)
table_schema_objs = [
(SQLTableSchema(table_name="city_stats", context_str=city_stats_text))
]
from llama_index.core.retrievers import NLSQLRetriever
nl_sql_retriever = NLSQLRetriever(
sql_database, tables=["city_stats"], return_raw=True
)
results = nl_sql_retriever.retrieve(
"Return the top 5 cities (along with their populations) with the highest population."
)
from llama_index.core.response.notebook_utils import display_source_node
for n in results:
display_source_node(n)
nl_sql_retriever = NLSQLRetriever(
sql_database, tables=["city_stats"], return_raw=False
)
results = nl_sql_retriever.retrieve(
"Return the top 5 cities (along with their populations) with the highest population."
)
for n in results:
display_source_node(n, show_source_metadata=True)
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = | RetrieverQueryEngine.from_args(nl_sql_retriever) | llama_index.core.query_engine.RetrieverQueryEngine.from_args |
get_ipython().run_line_magic('pip', 'install llama-index-readers-make-com')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.readers.make_com import MakeWrapper
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents=documents)
query_str = "What did the author do growing up?"
query_engine = index.as_query_engine()
response = query_engine.query(query_str)
wrapper = | MakeWrapper() | llama_index.readers.make_com.MakeWrapper |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import camelot
from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import PandasQueryEngine
from llama_index.core.schema import IndexNode
from llama_index.llms.openai import OpenAI
from llama_index.readers.file import PyMuPDFReader
from typing import List
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
file_path = "billionaires_page.pdf"
reader = PyMuPDFReader()
docs = reader.load(file_path)
def get_tables(path: str, pages: List[int]):
table_dfs = []
for page in pages:
table_list = camelot.read_pdf(path, pages=str(page))
table_df = table_list[0].df
table_df = (
table_df.rename(columns=table_df.iloc[0])
.drop(table_df.index[0])
.reset_index(drop=True)
)
table_dfs.append(table_df)
return table_dfs
table_dfs = get_tables(file_path, pages=[3, 25])
table_dfs[0]
table_dfs[1]
llm = OpenAI(model="gpt-4")
df_query_engines = [
PandasQueryEngine(table_df, llm=llm) for table_df in table_dfs
]
response = df_query_engines[0].query(
"What's the net worth of the second richest billionaire in 2023?"
)
print(str(response))
response = df_query_engines[1].query(
"How many billionaires were there in 2009?"
)
print(str(response))
from llama_index.core import Settings
doc_nodes = | Settings.node_parser.get_nodes_from_documents(docs) | llama_index.core.Settings.node_parser.get_nodes_from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import QueryEngineTool, ToolMetadata
llm = | OpenAI(model="gpt-4-1106-preview") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-redis')
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-mongodb')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('mkdir -p data')
get_ipython().system('echo "This is a test file: one!" > data/test1.txt')
get_ipython().system('echo "This is a test file: two!" > data/test2.txt')
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("./data", filename_as_id=True).load_data()
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.docstore.mongodb import MongoDocumentStore
from llama_index.core.node_parser import SentenceSplitter
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(),
| HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") | llama_index.embeddings.huggingface.HuggingFaceEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai-legacy')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
def useless(a: int, b: int) -> int:
"""Toy useless function."""
pass
multiply_tool = FunctionTool.from_defaults(fn=multiply, name="multiply")
useless_tools = [
FunctionTool.from_defaults(fn=useless, name=f"useless_{str(idx)}")
for idx in range(28)
]
add_tool = | FunctionTool.from_defaults(fn=add, name="add") | llama_index.core.tools.FunctionTool.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import llama_index.core.vector_stores.google.generativeai.genai_extension as genaix
from typing import Iterable
from random import randrange
LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX = f"llama-index-colab"
SESSION_CORPUS_ID_PREFIX = (
f"{LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX}-{randrange(1000000)}"
)
def corpus_id(num_id: int) -> str:
return f"{SESSION_CORPUS_ID_PREFIX}-{num_id}"
SESSION_CORPUS_ID = corpus_id(1)
def list_corpora() -> Iterable[genaix.Corpus]:
client = genaix.build_semantic_retriever()
yield from genaix.list_corpora(client=client)
def delete_corpus(*, corpus_id: str) -> None:
client = genaix.build_semantic_retriever()
genaix.delete_corpus(corpus_id=corpus_id, client=client)
def cleanup_colab_corpora():
for corpus in list_corpora():
if corpus.corpus_id.startswith(LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX):
try:
delete_corpus(corpus_id=corpus.corpus_id)
print(f"Deleted corpus {corpus.corpus_id}.")
except Exception:
pass
cleanup_colab_corpora()
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
from llama_index.core import Response
import time
index = GoogleIndex.create_corpus(
corpus_id=SESSION_CORPUS_ID, display_name="My first corpus!"
)
print(f"Newly created corpus ID is {index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index.insert_documents(documents)
for corpus in list_corpora():
print(corpus)
query_engine = index.as_query_engine()
response = query_engine.query("What did Paul Graham do growing up?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
for cited_text in [node.text for node in response.source_nodes]:
print(f"Cited text: {cited_text}")
if response.metadata:
print(
f"Answerability: {response.metadata.get('answerable_probability', 0)}"
)
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
query_engine = index.as_query_engine()
response = query_engine.query("Which company did Paul Graham build?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
index.insert_nodes(
[
TextNode(
text="It was the best of times.",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="123",
metadata={"file_name": "Tale of Two Cities"},
)
},
),
TextNode(
text="It was the worst of times.",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="123",
metadata={"file_name": "Tale of Two Cities"},
)
},
),
TextNode(
text="Bugs Bunny: Wassup doc?",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="456",
metadata={"file_name": "Bugs Bunny Adventure"},
)
},
),
]
)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
HarmCategory,
SafetySetting,
)
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
query_engine = index.as_query_engine(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
safety_setting=[
SafetySetting(
category=HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
),
SafetySetting(
category=HarmCategory.HARM_CATEGORY_VIOLENCE,
threshold=SafetySetting.HarmBlockThreshold.BLOCK_ONLY_HIGH,
),
],
)
response = query_engine.query("What was Bugs Bunny's favorite saying?")
print(response)
from llama_index.core import Response
response = query_engine.query("What were Paul Graham's achievements?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
for cited_text in [node.text for node in response.source_nodes]:
print(f"Cited text: {cited_text}")
if response.metadata:
print(
f"Answerability: {response.metadata.get('answerable_probability', 0)}"
)
from llama_index.llms.gemini import Gemini
GEMINI_API_KEY = "" # @param {type:"string"}
gemini = Gemini(api_key=GEMINI_API_KEY)
from llama_index.response_synthesizers.google import GoogleTextSynthesizer
from llama_index.vector_stores.google import GoogleVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.postprocessor import LLMRerank
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
store = GoogleVectorStore.from_corpus(corpus_id=SESSION_CORPUS_ID)
index = VectorStoreIndex.from_vector_store(
vector_store=store,
)
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
reranker = LLMRerank(
top_n=10,
llm=gemini,
)
query_engine = RetrieverQueryEngine.from_args(
retriever=VectorIndexRetriever(
index=index,
similarity_top_k=20,
),
node_postprocessors=[reranker],
response_synthesizer=response_synthesizer,
)
response = query_engine.query("What were Paul Graham's achievements?")
print(response)
from llama_index.core.indices.query.query_transform.base import (
StepDecomposeQueryTransform,
)
from llama_index.core.query_engine import MultiStepQueryEngine
store = GoogleVectorStore.from_corpus(corpus_id=SESSION_CORPUS_ID)
index = VectorStoreIndex.from_vector_store(
vector_store=store,
)
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
single_step_query_engine = index.as_query_engine(
similarity_top_k=10,
response_synthesizer=response_synthesizer,
)
step_decompose_transform = StepDecomposeQueryTransform(
llm=gemini,
verbose=True,
)
query_engine = MultiStepQueryEngine(
query_engine=single_step_query_engine,
query_transform=step_decompose_transform,
response_synthesizer=response_synthesizer,
index_summary="Ask me anything.",
num_steps=6,
)
response = query_engine.query("What were Paul Graham's achievements?")
print(response)
from llama_index.core.indices.query.query_transform import HyDEQueryTransform
from llama_index.core.query_engine import TransformQueryEngine
store = GoogleVectorStore.from_corpus(corpus_id=SESSION_CORPUS_ID)
index = VectorStoreIndex.from_vector_store(
vector_store=store,
)
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
base_query_engine = index.as_query_engine(
similarity_top_k=10,
response_synthesizer=response_synthesizer,
)
hyde = HyDEQueryTransform(
llm=gemini,
include_original=False,
)
hyde_query_engine = | TransformQueryEngine(base_query_engine, hyde) | llama_index.core.query_engine.TransformQueryEngine |
get_ipython().system('pip install llama-index llama-index-packs-raptor llama-index-vector-stores-qdrant')
from llama_index.packs.raptor import RaptorPack
get_ipython().system('wget https://arxiv.org/pdf/2401.18059.pdf -O ./raptor_paper.pdf')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
from llama_index.core import SimpleDirectoryReader
documents = | SimpleDirectoryReader(input_files=["./raptor_paper.pdf"]) | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt' -O pg_essay.txt")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader(input_files=["pg_essay.txt"])
documents = reader.load_data()
from llama_index.core.query_pipeline import QueryPipeline, InputComponent
from typing import Dict, Any, List, Optional
from llama_index.llms.openai import OpenAI
from llama_index.core import Document, VectorStoreIndex
from llama_index.core import SummaryIndex
from llama_index.core.response_synthesizers import TreeSummarize
from llama_index.core.schema import NodeWithScore, TextNode
from llama_index.core import PromptTemplate
from llama_index.core.selectors import LLMSingleSelector
hyde_str = """\
Please write a passage to answer the question: {query_str}
Try to include as many key details as possible.
Passage: """
hyde_prompt = PromptTemplate(hyde_str)
llm = OpenAI(model="gpt-3.5-turbo")
summarizer = TreeSummarize(llm=llm)
vector_index = VectorStoreIndex.from_documents(documents)
vector_query_engine = vector_index.as_query_engine(similarity_top_k=2)
summary_index = | SummaryIndex.from_documents(documents) | llama_index.core.SummaryIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
TABLE_NAME = os.environ["DYNAMODB_TABLE_NAME"]
from llama_index.storage.docstore.dynamodb import DynamoDBDocumentStore
from llama_index.storage.index_store.dynamodb import DynamoDBIndexStore
from llama_index.vector_stores.dynamodb import DynamoDBVectorStore
storage_context = StorageContext.from_defaults(
docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),
index_store=DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME),
vector_store=DynamoDBVectorStore.from_table_name(table_name=TABLE_NAME),
)
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_table_index = SimpleKeywordTableIndex(
nodes, storage_context=storage_context
)
len(storage_context.docstore.docs)
storage_context.persist()
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
from llama_index.core import load_index_from_storage
storage_context = StorageContext.from_defaults(
docstore= | DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME) | llama_index.storage.docstore.dynamodb.DynamoDBDocumentStore.from_table_name |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai-legacy')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.core.tools import QueryEngineTool, ToolMetadata
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/march"
)
march_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/june"
)
june_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/sept"
)
sept_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
get_ipython().system("mkdir -p 'data/10q/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_march_2022.pdf' -O 'data/10q/uber_10q_march_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_june_2022.pdf' -O 'data/10q/uber_10q_june_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_sept_2022.pdf' -O 'data/10q/uber_10q_sept_2022.pdf'")
if not index_loaded:
march_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = VectorStoreIndex.from_documents(march_docs)
june_index = | VectorStoreIndex.from_documents(june_docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import openai
import os
os.environ["OPENAI_API_KEY"] = "[You API key]"
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp-free")
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.core import StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import VectorStoreIndex
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="wiki_cities"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
vector_index = | VectorStoreIndex([], storage_context=storage_context) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-rungpt')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install rungpt')
get_ipython().system('rungpt serve decapoda-research/llama-7b-hf --precision fp16 --device_map balanced')
from llama_index.llms.rungpt import RunGptLLM
llm = RunGptLLM()
promot = "What public transportation might be available in a city?"
response = llm.complete(promot)
print(response)
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.llms.rungpt import RunGptLLM
messages = [
ChatMessage(
role=MessageRole.USER,
content="Now, I want you to do some math for me.",
),
ChatMessage(
role=MessageRole.ASSISTANT, content="Sure, I would like to help you."
),
ChatMessage(
role=MessageRole.USER,
content="How many points determine a straight line?",
),
]
llm = | RunGptLLM() | llama_index.llms.rungpt.RunGptLLM |
get_ipython().run_line_magic('', 'pip install llama-index-llms-groq')
get_ipython().system('pip install llama-index')
from llama_index.llms.groq import Groq
llm = Groq(model="mixtral-8x7b-32768", api_key="your_api_key")
response = llm.complete("Explain the importance of low latency LLMs")
print(response)
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
| ChatMessage(role="user", content="What is your name") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"
openai.api_key = os.environ["OPENAI_API_KEY"]
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import weaviate
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client(
"https://llama-test-ezjahb4m.weaviate.network",
auth_client_secret=resource_owner_config,
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
from llama_index.core import StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="LlamaIndex"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client(
"https://llama-test-ezjahb4m.weaviate.network",
auth_client_secret=resource_owner_config,
)
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="LlamaIndex"
)
loaded_index = | VectorStoreIndex.from_vector_store(vector_store) | llama_index.core.VectorStoreIndex.from_vector_store |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import openai
import os
os.environ["OPENAI_API_KEY"] = "[You API key]"
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp-free")
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.core import StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import VectorStoreIndex
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="wiki_cities"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
vector_index = VectorStoreIndex([], storage_context=storage_context)
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("sqlite:///:memory:", future=True)
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
metadata_obj.tables.keys()
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{"city_name": "Berlin", "population": 3645000, "country": "Germany"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
get_ipython().system('pip install wikipedia')
from llama_index.readers.wikipedia import WikipediaReader
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = WikipediaReader().load_data(pages=cities)
from llama_index.core import SQLDatabase
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from llama_index.core.query_engine import NLSQLTableQueryEngine
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["city_stats"],
)
from llama_index.core import Settings
for city, wiki_doc in zip(cities, wiki_docs):
nodes = | Settings.node_parser.get_nodes_from_documents([wiki_doc]) | llama_index.core.Settings.node_parser.get_nodes_from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.llms.openai import OpenAI
resp = OpenAI().complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAI
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = OpenAI().chat(messages)
print(resp)
from llama_index.llms.openai import OpenAI
llm = OpenAI()
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
llm = | OpenAI() | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4")
node_parser = SentenceSplitter(chunk_size=1024)
nodes = node_parser.get_nodes_from_documents(documents)
index = VectorStoreIndex(nodes)
query_engine = index.as_query_engine(llm=llm)
from llama_index.core.schema import BaseNode
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core import ChatPromptTemplate, PromptTemplate
from typing import Tuple, List
import re
llm = OpenAI(model="gpt-4")
QA_PROMPT = PromptTemplate(
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the query.\n"
"Query: {query_str}\n"
"Answer: "
)
def generate_answers_for_questions(
questions: List[str], context: str, llm: OpenAI
) -> str:
"""Generate answers for questions given context."""
answers = []
for question in questions:
fmt_qa_prompt = QA_PROMPT.format(
context_str=context, query_str=question
)
response_obj = llm.complete(fmt_qa_prompt)
answers.append(str(response_obj))
return answers
QUESTION_GEN_USER_TMPL = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"generate the relevant questions. "
)
QUESTION_GEN_SYS_TMPL = """\
You are a Teacher/ Professor. Your task is to setup \
{num_questions_per_chunk} questions for an upcoming \
quiz/examination. The questions should be diverse in nature \
across the document. Restrict the questions to the \
context information provided.\
"""
question_gen_template = ChatPromptTemplate(
message_templates=[
ChatMessage(role=MessageRole.SYSTEM, content=QUESTION_GEN_SYS_TMPL),
ChatMessage(role=MessageRole.USER, content=QUESTION_GEN_USER_TMPL),
]
)
def generate_qa_pairs(
nodes: List[BaseNode], llm: OpenAI, num_questions_per_chunk: int = 10
) -> List[Tuple[str, str]]:
"""Generate questions."""
qa_pairs = []
for idx, node in enumerate(nodes):
print(f"Node {idx}/{len(nodes)}")
context_str = node.get_content(metadata_mode="all")
fmt_messages = question_gen_template.format_messages(
num_questions_per_chunk=10,
context_str=context_str,
)
chat_response = llm.chat(fmt_messages)
raw_output = chat_response.message.content
result_list = str(raw_output).strip().split("\n")
cleaned_questions = [
re.sub(r"^\d+[\).\s]", "", question).strip()
for question in result_list
]
answers = generate_answers_for_questions(
cleaned_questions, context_str, llm
)
cur_qa_pairs = list(zip(cleaned_questions, answers))
qa_pairs.extend(cur_qa_pairs)
return qa_pairs
qa_pairs
qa_pairs = generate_qa_pairs(
nodes,
llm,
num_questions_per_chunk=10,
)
import pickle
pickle.dump(qa_pairs, open("eval_dataset.pkl", "wb"))
import pickle
qa_pairs = pickle.load(open("eval_dataset.pkl", "rb"))
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core import ChatPromptTemplate, PromptTemplate
from typing import Dict
CORRECTNESS_SYS_TMPL = """
You are an expert evaluation system for a question answering chatbot.
You are given the following information:
- a user query,
- a reference answer, and
- a generated answer.
Your job is to judge the relevance and correctness of the generated answer.
Output a single score that represents a holistic evaluation.
You must return your response in a line with only the score.
Do not return answers in any other format.
On a separate line provide your reasoning for the score as well.
Follow these guidelines for scoring:
- Your score has to be between 1 and 5, where 1 is the worst and 5 is the best.
- If the generated answer is not relevant to the user query, \
you should give a score of 1.
- If the generated answer is relevant but contains mistakes, \
you should give a score between 2 and 3.
- If the generated answer is relevant and fully correct, \
you should give a score between 4 and 5.
"""
CORRECTNESS_USER_TMPL = """
{query}
{reference_answer}
{generated_answer}
"""
eval_chat_template = ChatPromptTemplate(
message_templates=[
| ChatMessage(role=MessageRole.SYSTEM, content=CORRECTNESS_SYS_TMPL) | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
get_ipython().system("mkdir -p 'data/'")
get_ipython().system("curl 'https://arxiv.org/pdf/2307.09288.pdf' -o 'data/llama2.pdf'")
get_ipython().system('pip install unstructured[pdf]')
from llama_index.core import VectorStoreIndex
from llama_index.readers.file import UnstructuredReader
documents = UnstructuredReader().load_data("data/llama2.pdf")
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
from llama_index.core.llama_pack import download_llama_pack
FuzzyCitationEnginePack = | download_llama_pack("FuzzyCitationEnginePack", "./fuzzy_pack") | llama_index.core.llama_pack.download_llama_pack |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
gpt35 = OpenAI(temperature=0, model="gpt-3.5-turbo")
gpt4 = | OpenAI(temperature=0, model="gpt-4") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
load_index_from_storage,
StorageContext,
)
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(response_mode="tree_summarize")
def display_prompt_dict(prompts_dict):
for k, p in prompts_dict.items():
text_md = f"**Prompt Key**: {k}<br>" f"**Text:** <br>"
display(Markdown(text_md))
print(p.get_template())
display(Markdown("<br><br>"))
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
prompts_dict = query_engine.response_synthesizer.get_prompts()
display_prompt_dict(prompts_dict)
query_engine = index.as_query_engine(response_mode="compact")
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
response = query_engine.query("What did the author do growing up?")
print(str(response))
from llama_index.core import PromptTemplate
query_engine = index.as_query_engine(response_mode="tree_summarize")
new_summary_tmpl_str = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the query in the style of a Shakespeare play.\n"
"Query: {query_str}\n"
"Answer: "
)
new_summary_tmpl = | PromptTemplate(new_summary_tmpl_str) | llama_index.core.PromptTemplate |
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import (
FixedRecencyPostprocessor,
EmbeddingRecencyPostprocessor,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import StorageContext
def get_file_metadata(file_name: str):
"""Get file metadata."""
if "v1" in file_name:
return {"date": "2020-01-01"}
elif "v2" in file_name:
return {"date": "2020-02-03"}
elif "v3" in file_name:
return {"date": "2022-04-12"}
else:
raise ValueError("invalid file")
documents = SimpleDirectoryReader(
input_files=[
"test_versioned_data/paul_graham_essay_v1.txt",
"test_versioned_data/paul_graham_essay_v2.txt",
"test_versioned_data/paul_graham_essay_v3.txt",
],
file_metadata=get_file_metadata,
).load_data()
from llama_index.core import Settings
Settings.text_splitter = SentenceSplitter(chunk_size=512)
nodes = Settings.text_splitter.get_nodes_from_documents(documents)
docstore = SimpleDocumentStore()
docstore.add_documents(nodes)
storage_context = StorageContext.from_defaults(docstore=docstore)
print(documents[2].get_text())
index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
qa_prompt_str = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {query_str}\n"
)
refine_prompt_str = (
"We have the opportunity to refine the original answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_msg}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question: {query_str}. "
"If the context isn't useful, output the original answer again.\n"
"Original Answer: {existing_answer}"
)
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core import ChatPromptTemplate
chat_text_qa_msgs = [
ChatMessage(
role=MessageRole.SYSTEM,
content=(
"Always answer the question, even if the context isn't helpful."
),
),
ChatMessage(role=MessageRole.USER, content=qa_prompt_str),
]
text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)
chat_refine_msgs = [
ChatMessage(
role=MessageRole.SYSTEM,
content=(
"Always answer the question, even if the context isn't helpful."
),
),
ChatMessage(role=MessageRole.USER, content=refine_prompt_str),
]
refine_template = ChatPromptTemplate(chat_refine_msgs)
from llama_index.core import ChatPromptTemplate
chat_text_qa_msgs = [
(
"system",
"Always answer the question, even if the context isn't helpful.",
),
("user", qa_prompt_str),
]
text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
chat_refine_msgs = [
(
"system",
"Always answer the question, even if the context isn't helpful.",
),
("user", refine_prompt_str),
]
refine_template = ChatPromptTemplate.from_messages(chat_refine_msgs)
import openai
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-portkey')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install -U llama_index')
get_ipython().system('pip install -U portkey-ai')
from llama_index.llms.portkey import Portkey
from llama_index.core.llms import ChatMessage
import portkey as pk
import os
os.environ["PORTKEY_API_KEY"] = "PORTKEY_API_KEY"
openai_virtual_key_a = ""
openai_virtual_key_b = ""
anthropic_virtual_key_a = ""
anthropic_virtual_key_b = ""
cohere_virtual_key_a = ""
cohere_virtual_key_b = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["ANTHROPIC_API_KEY"] = ""
portkey_client = Portkey(
mode="single",
)
openai_llm = pk.LLMOptions(
provider="openai",
model="gpt-4",
virtual_key=openai_virtual_key_a,
)
portkey_client.add_llms(openai_llm)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("Testing Portkey Llamaindex integration:")
response = portkey_client.chat(messages)
print(response)
prompt = "Why is the sky blue?"
print("\nTesting Stream Complete:\n")
response = portkey_client.stream_complete(prompt)
for i in response:
print(i.delta, end="", flush=True)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("\nTesting Stream Chat:\n")
response = portkey_client.stream_chat(messages)
for i in response:
print(i.delta, end="", flush=True)
portkey_client = Portkey(mode="fallback")
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
| ChatMessage(role="user", content="What can you do?") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import llama_index.core.vector_stores.google.generativeai.genai_extension as genaix
from typing import Iterable
from random import randrange
LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX = f"llama-index-colab"
SESSION_CORPUS_ID_PREFIX = (
f"{LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX}-{randrange(1000000)}"
)
def corpus_id(num_id: int) -> str:
return f"{SESSION_CORPUS_ID_PREFIX}-{num_id}"
SESSION_CORPUS_ID = corpus_id(1)
def list_corpora() -> Iterable[genaix.Corpus]:
client = genaix.build_semantic_retriever()
yield from genaix.list_corpora(client=client)
def delete_corpus(*, corpus_id: str) -> None:
client = genaix.build_semantic_retriever()
genaix.delete_corpus(corpus_id=corpus_id, client=client)
def cleanup_colab_corpora():
for corpus in list_corpora():
if corpus.corpus_id.startswith(LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX):
try:
delete_corpus(corpus_id=corpus.corpus_id)
print(f"Deleted corpus {corpus.corpus_id}.")
except Exception:
pass
cleanup_colab_corpora()
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
from llama_index.core import Response
import time
index = GoogleIndex.create_corpus(
corpus_id=SESSION_CORPUS_ID, display_name="My first corpus!"
)
print(f"Newly created corpus ID is {index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index.insert_documents(documents)
for corpus in list_corpora():
print(corpus)
query_engine = index.as_query_engine()
response = query_engine.query("What did Paul Graham do growing up?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
for cited_text in [node.text for node in response.source_nodes]:
print(f"Cited text: {cited_text}")
if response.metadata:
print(
f"Answerability: {response.metadata.get('answerable_probability', 0)}"
)
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
query_engine = index.as_query_engine()
response = query_engine.query("Which company did Paul Graham build?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
index.insert_nodes(
[
TextNode(
text="It was the best of times.",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="123",
metadata={"file_name": "Tale of Two Cities"},
)
},
),
TextNode(
text="It was the worst of times.",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="123",
metadata={"file_name": "Tale of Two Cities"},
)
},
),
TextNode(
text="Bugs Bunny: Wassup doc?",
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id="456",
metadata={"file_name": "Bugs Bunny Adventure"},
)
},
),
]
)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
HarmCategory,
SafetySetting,
)
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
query_engine = index.as_query_engine(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
safety_setting=[
SafetySetting(
category=HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
),
SafetySetting(
category=HarmCategory.HARM_CATEGORY_VIOLENCE,
threshold=SafetySetting.HarmBlockThreshold.BLOCK_ONLY_HIGH,
),
],
)
response = query_engine.query("What was Bugs Bunny's favorite saying?")
print(response)
from llama_index.core import Response
response = query_engine.query("What were Paul Graham's achievements?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
for cited_text in [node.text for node in response.source_nodes]:
print(f"Cited text: {cited_text}")
if response.metadata:
print(
f"Answerability: {response.metadata.get('answerable_probability', 0)}"
)
from llama_index.llms.gemini import Gemini
GEMINI_API_KEY = "" # @param {type:"string"}
gemini = Gemini(api_key=GEMINI_API_KEY)
from llama_index.response_synthesizers.google import GoogleTextSynthesizer
from llama_index.vector_stores.google import GoogleVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.postprocessor import LLMRerank
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
store = GoogleVectorStore.from_corpus(corpus_id=SESSION_CORPUS_ID)
index = VectorStoreIndex.from_vector_store(
vector_store=store,
)
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
reranker = LLMRerank(
top_n=10,
llm=gemini,
)
query_engine = RetrieverQueryEngine.from_args(
retriever=VectorIndexRetriever(
index=index,
similarity_top_k=20,
),
node_postprocessors=[reranker],
response_synthesizer=response_synthesizer,
)
response = query_engine.query("What were Paul Graham's achievements?")
print(response)
from llama_index.core.indices.query.query_transform.base import (
StepDecomposeQueryTransform,
)
from llama_index.core.query_engine import MultiStepQueryEngine
store = GoogleVectorStore.from_corpus(corpus_id=SESSION_CORPUS_ID)
index = VectorStoreIndex.from_vector_store(
vector_store=store,
)
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
single_step_query_engine = index.as_query_engine(
similarity_top_k=10,
response_synthesizer=response_synthesizer,
)
step_decompose_transform = StepDecomposeQueryTransform(
llm=gemini,
verbose=True,
)
query_engine = MultiStepQueryEngine(
query_engine=single_step_query_engine,
query_transform=step_decompose_transform,
response_synthesizer=response_synthesizer,
index_summary="Ask me anything.",
num_steps=6,
)
response = query_engine.query("What were Paul Graham's achievements?")
print(response)
from llama_index.core.indices.query.query_transform import HyDEQueryTransform
from llama_index.core.query_engine import TransformQueryEngine
store = GoogleVectorStore.from_corpus(corpus_id=SESSION_CORPUS_ID)
index = VectorStoreIndex.from_vector_store(
vector_store=store,
)
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.2,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
base_query_engine = index.as_query_engine(
similarity_top_k=10,
response_synthesizer=response_synthesizer,
)
hyde = HyDEQueryTransform(
llm=gemini,
include_original=False,
)
hyde_query_engine = TransformQueryEngine(base_query_engine, hyde)
response = query_engine.query("What were Paul Graham's achievements?")
print(response)
store = | GoogleVectorStore.from_corpus(corpus_id=SESSION_CORPUS_ID) | llama_index.vector_stores.google.GoogleVectorStore.from_corpus |
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-rankgpt-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-ollama')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
import os
OPENAI_API_TOKEN = "sk-"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
from llama_index.core import Settings
Settings.llm = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-jinaai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install Pillow')
import os
jinaai_api_key = "YOUR_JINAAI_API_KEY"
os.environ["JINAAI_API_KEY"] = jinaai_api_key
from llama_index.embeddings.jinaai import JinaEmbedding
embed_model = JinaEmbedding(
api_key=jinaai_api_key,
model="jina-embeddings-v2-base-en",
)
embeddings = embed_model.get_text_embedding("This is the text to embed")
print(len(embeddings))
print(embeddings[:5])
embeddings = embed_model.get_query_embedding("This is the query to embed")
print(len(embeddings))
print(embeddings[:5])
embed_model = JinaEmbedding(
api_key=jinaai_api_key,
model="jina-embeddings-v2-base-en",
embed_batch_size=16,
)
embeddings = embed_model.get_text_embedding_batch(
["This is the text to embed", "More text can be provided in a batch"]
)
print(len(embeddings))
print(embeddings[0][:5])
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_source_node
from IPython.display import Markdown, display
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
your_openai_key = "YOUR_OPENAI_KEY"
llm = | OpenAI(api_key=your_openai_key) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import QueryEngineTool, ToolMetadata
llm_35 = OpenAI(model="gpt-3.5-turbo-0613", temperature=0.3)
llm_4 = OpenAI(model="gpt-4-0613", temperature=0.3)
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/march"
)
march_index = | load_index_from_storage(storage_context) | llama_index.core.load_index_from_storage |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import openai
import os
os.environ["OPENAI_API_KEY"] = "[You API key]"
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp-free")
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.core import StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import VectorStoreIndex
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="wiki_cities"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
vector_index = VectorStoreIndex([], storage_context=storage_context)
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("sqlite:///:memory:", future=True)
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
metadata_obj.tables.keys()
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{"city_name": "Berlin", "population": 3645000, "country": "Germany"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
get_ipython().system('pip install wikipedia')
from llama_index.readers.wikipedia import WikipediaReader
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = | WikipediaReader() | llama_index.readers.wikipedia.WikipediaReader |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-cross-encoders')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install datasets --quiet')
get_ipython().system('pip install sentence-transformers --quiet')
get_ipython().system('pip install openai --quiet')
from datasets import load_dataset
import random
dataset = load_dataset("allenai/qasper")
train_dataset = dataset["train"]
validation_dataset = dataset["validation"]
test_dataset = dataset["test"]
random.seed(42) # Set a random seed for reproducibility
train_sampled_indices = random.sample(range(len(train_dataset)), 800)
train_samples = [train_dataset[i] for i in train_sampled_indices]
test_sampled_indices = random.sample(range(len(test_dataset)), 80)
test_samples = [test_dataset[i] for i in test_sampled_indices]
from typing import List
def get_full_text(sample: dict) -> str:
"""
:param dict sample: the row sample from QASPER
"""
title = sample["title"]
abstract = sample["abstract"]
sections_list = sample["full_text"]["section_name"]
paragraph_list = sample["full_text"]["paragraphs"]
combined_sections_with_paras = ""
if len(sections_list) == len(paragraph_list):
combined_sections_with_paras += title + "\t"
combined_sections_with_paras += abstract + "\t"
for index in range(0, len(sections_list)):
combined_sections_with_paras += str(sections_list[index]) + "\t"
combined_sections_with_paras += "".join(paragraph_list[index])
return combined_sections_with_paras
else:
print("Not the same number of sections as paragraphs list")
def get_questions(sample: dict) -> List[str]:
"""
:param dict sample: the row sample from QASPER
"""
questions_list = sample["qas"]["question"]
return questions_list
doc_qa_dict_list = []
for train_sample in train_samples:
full_text = get_full_text(train_sample)
questions_list = get_questions(train_sample)
local_dict = {"paper": full_text, "questions": questions_list}
doc_qa_dict_list.append(local_dict)
len(doc_qa_dict_list)
import pandas as pd
df_train = pd.DataFrame(doc_qa_dict_list)
df_train.to_csv("train.csv")
"""
The Answers field in the dataset follow the below format:-
Unanswerable answers have "unanswerable" set to true.
The remaining answers have exactly one of the following fields being non-empty.
"extractive_spans" are spans in the paper which serve as the answer.
"free_form_answer" is a written out answer.
"yes_no" is true iff the answer is Yes, and false iff the answer is No.
We accept only free-form answers and for all the other kind of answers we set their value to 'Unacceptable',
to better evaluate the performance of the query engine using pairwise comparision evaluator as it uses GPT-4 which is biased towards preferring long answers more.
https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
So in the case of 'yes_no' answers it can favour Query Engine answers more than reference answers.
Also in the case of extracted spans it can favour reference answers more than Query engine generated answers.
"""
eval_doc_qa_answer_list = []
def get_answers(sample: dict) -> List[str]:
"""
:param dict sample: the row sample from the train split of QASPER
"""
final_answers_list = []
answers = sample["qas"]["answers"]
for answer in answers:
local_answer = ""
types_of_answers = answer["answer"][0]
if types_of_answers["unanswerable"] == False:
if types_of_answers["free_form_answer"] != "":
local_answer = types_of_answers["free_form_answer"]
else:
local_answer = "Unacceptable"
else:
local_answer = "Unacceptable"
final_answers_list.append(local_answer)
return final_answers_list
for test_sample in test_samples:
full_text = get_full_text(test_sample)
questions_list = get_questions(test_sample)
answers_list = get_answers(test_sample)
local_dict = {
"paper": full_text,
"questions": questions_list,
"answers": answers_list,
}
eval_doc_qa_answer_list.append(local_dict)
len(eval_doc_qa_answer_list)
import pandas as pd
df_test = pd.DataFrame(eval_doc_qa_answer_list)
df_test.to_csv("test.csv")
get_ipython().system('pip install llama-index --quiet')
import os
from llama_index.core import SimpleDirectoryReader
import openai
from llama_index.finetuning.cross_encoders.dataset_gen import (
generate_ce_fine_tuning_dataset,
generate_synthetic_queries_over_documents,
)
from llama_index.finetuning.cross_encoders import CrossEncoderFinetuneEngine
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import Document
final_finetuning_data_list = []
for paper in doc_qa_dict_list:
questions_list = paper["questions"]
documents = [Document(text=paper["paper"])]
local_finetuning_dataset = generate_ce_fine_tuning_dataset(
documents=documents,
questions_list=questions_list,
max_chunk_length=256,
top_k=5,
)
final_finetuning_data_list.extend(local_finetuning_dataset)
len(final_finetuning_data_list)
import pandas as pd
df_finetuning_dataset = pd.DataFrame(final_finetuning_data_list)
df_finetuning_dataset.to_csv("fine_tuning.csv")
finetuning_dataset = final_finetuning_data_list
finetuning_dataset[0]
get_ipython().system('wget -O test.csv https://www.dropbox.com/scl/fi/3lmzn6714oy358mq0vawm/test.csv?rlkey=yz16080te4van7fvnksi9kaed&dl=0')
import pandas as pd
import ast # Used to safely evaluate the string as a list
df_test = pd.read_csv("/content/test.csv", index_col=0)
df_test["questions"] = df_test["questions"].apply(ast.literal_eval)
df_test["answers"] = df_test["answers"].apply(ast.literal_eval)
print(f"Number of papers in the test sample:- {len(df_test)}")
from llama_index.core import Document
final_eval_data_list = []
for index, row in df_test.iterrows():
documents = [Document(text=row["paper"])]
query_list = row["questions"]
local_eval_dataset = generate_ce_fine_tuning_dataset(
documents=documents,
questions_list=query_list,
max_chunk_length=256,
top_k=5,
)
relevant_query_list = []
relevant_context_list = []
for item in local_eval_dataset:
if item.score == 1:
relevant_query_list.append(item.query)
relevant_context_list.append(item.context)
if len(relevant_query_list) > 0:
final_eval_data_list.append(
{
"paper": row["paper"],
"questions": relevant_query_list,
"context": relevant_context_list,
}
)
len(final_eval_data_list)
import pandas as pd
df_finetuning_dataset = pd.DataFrame(final_eval_data_list)
df_finetuning_dataset.to_csv("reranking_test.csv")
get_ipython().system('pip install huggingface_hub --quiet')
from huggingface_hub import notebook_login
notebook_login()
from sentence_transformers import SentenceTransformer
finetuning_engine = CrossEncoderFinetuneEngine(
dataset=finetuning_dataset, epochs=2, batch_size=8
)
finetuning_engine.finetune()
finetuning_engine.push_to_hub(
repo_id="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2"
)
get_ipython().system('pip install nest-asyncio --quiet')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('wget -O reranking_test.csv https://www.dropbox.com/scl/fi/mruo5rm46k1acm1xnecev/reranking_test.csv?rlkey=hkniwowq0xrc3m0ywjhb2gf26&dl=0')
import pandas as pd
import ast
df_reranking = pd.read_csv("/content/reranking_test.csv", index_col=0)
df_reranking["questions"] = df_reranking["questions"].apply(ast.literal_eval)
df_reranking["context"] = df_reranking["context"].apply(ast.literal_eval)
print(f"Number of papers in the reranking eval dataset:- {len(df_reranking)}")
df_reranking.head(1)
from llama_index.core.postprocessor import SentenceTransformerRerank
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
from llama_index.core import Settings
import os
import openai
import pandas as pd
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
Settings.chunk_size = 256
rerank_base = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-12-v2", top_n=3
)
rerank_finetuned = SentenceTransformerRerank(
model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2", top_n=3
)
without_reranker_hits = 0
base_reranker_hits = 0
finetuned_reranker_hits = 0
total_number_of_context = 0
for index, row in df_reranking.iterrows():
documents = [Document(text=row["paper"])]
query_list = row["questions"]
context_list = row["context"]
assert len(query_list) == len(context_list)
vector_index = VectorStoreIndex.from_documents(documents)
retriever_without_reranker = vector_index.as_query_engine(
similarity_top_k=3, response_mode="no_text"
)
retriever_with_base_reranker = vector_index.as_query_engine(
similarity_top_k=8,
response_mode="no_text",
node_postprocessors=[rerank_base],
)
retriever_with_finetuned_reranker = vector_index.as_query_engine(
similarity_top_k=8,
response_mode="no_text",
node_postprocessors=[rerank_finetuned],
)
for index in range(0, len(query_list)):
query = query_list[index]
context = context_list[index]
total_number_of_context += 1
response_without_reranker = retriever_without_reranker.query(query)
without_reranker_nodes = response_without_reranker.source_nodes
for node in without_reranker_nodes:
if context in node.node.text or node.node.text in context:
without_reranker_hits += 1
response_with_base_reranker = retriever_with_base_reranker.query(query)
with_base_reranker_nodes = response_with_base_reranker.source_nodes
for node in with_base_reranker_nodes:
if context in node.node.text or node.node.text in context:
base_reranker_hits += 1
response_with_finetuned_reranker = (
retriever_with_finetuned_reranker.query(query)
)
with_finetuned_reranker_nodes = (
response_with_finetuned_reranker.source_nodes
)
for node in with_finetuned_reranker_nodes:
if context in node.node.text or node.node.text in context:
finetuned_reranker_hits += 1
assert (
len(with_finetuned_reranker_nodes)
== len(with_base_reranker_nodes)
== len(without_reranker_nodes)
== 3
)
without_reranker_scores = [without_reranker_hits]
base_reranker_scores = [base_reranker_hits]
finetuned_reranker_scores = [finetuned_reranker_hits]
reranker_eval_dict = {
"Metric": "Hits",
"OpenAI_Embeddings": without_reranker_scores,
"Base_cross_encoder": base_reranker_scores,
"Finetuned_cross_encoder": finetuned_reranker_hits,
"Total Relevant Context": total_number_of_context,
}
df_reranker_eval_results = pd.DataFrame(reranker_eval_dict)
display(df_reranker_eval_results)
get_ipython().system('wget -O test.csv https://www.dropbox.com/scl/fi/3lmzn6714oy358mq0vawm/test.csv?rlkey=yz16080te4van7fvnksi9kaed&dl=0')
import pandas as pd
import ast # Used to safely evaluate the string as a list
df_test = pd.read_csv("/content/test.csv", index_col=0)
df_test["questions"] = df_test["questions"].apply(ast.literal_eval)
df_test["answers"] = df_test["answers"].apply(ast.literal_eval)
print(f"Number of papers in the test sample:- {len(df_test)}")
df_test.head(1)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
from llama_index.core.evaluation import PairwiseComparisonEvaluator
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
import os
import openai
import pandas as pd
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4_pairwise = PairwiseComparisonEvaluator(llm=gpt4)
pairwise_scores_list = []
no_reranker_dict_list = []
for index, row in df_test.iterrows():
documents = [Document(text=row["paper"])]
query_list = row["questions"]
reference_answers_list = row["answers"]
number_of_accepted_queries = 0
vector_index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('env', 'OPENAI_API_KEY=')
get_ipython().run_line_magic('env', 'BRAINTRUST_API_KEY=')
get_ipython().run_line_magic('env', 'TOKENIZERS_PARALLELISM=true # This is needed to avoid a warning message from Chroma')
get_ipython().run_line_magic('pip', 'install -U llama_hub llama_index braintrust autoevals pypdf pillow transformers torch torchvision')
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [ | Document(text=doc_text) | llama_index.core.Document |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gradient')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().system('pip install llama-index gradientai -q')
import os
from llama_index.llms.gradient import GradientBaseModelLLM
from llama_index.finetuning import GradientFinetuneEngine
os.environ["GRADIENT_ACCESS_TOKEN"] = os.getenv("GRADIENT_API_KEY")
os.environ["GRADIENT_WORKSPACE_ID"] = "<insert_workspace_id>"
from pydantic import BaseModel
class Album(BaseModel):
"""Data model for an album."""
name: str
artist: str
from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler
from llama_index.llms.openai import OpenAI
from llama_index.llms.gradient import GradientBaseModelLLM
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
openai_handler = LlamaDebugHandler()
openai_callback = CallbackManager([openai_handler])
openai_llm = OpenAI(model="gpt-4", callback_manager=openai_callback)
gradient_handler = LlamaDebugHandler()
gradient_callback = CallbackManager([gradient_handler])
base_model_slug = "llama2-7b-chat"
gradient_llm = GradientBaseModelLLM(
base_model_slug=base_model_slug,
max_tokens=300,
callback_manager=gradient_callback,
is_chat_model=True,
)
from llama_index.core.llms import LLMMetadata
prompt_template_str = """\
Generate an example album, with an artist and a list of songs. \
Using the movie {movie_name} as inspiration.\
"""
openai_program = LLMTextCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Album),
prompt_template_str=prompt_template_str,
llm=openai_llm,
verbose=True,
)
gradient_program = LLMTextCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Album),
prompt_template_str=prompt_template_str,
llm=gradient_llm,
verbose=True,
)
response = openai_program(movie_name="The Shining")
print(str(response))
tmp = openai_handler.get_llm_inputs_outputs()
print(tmp[0][0].payload["messages"][0])
response = gradient_program(movie_name="The Shining")
print(str(response))
tmp = gradient_handler.get_llm_inputs_outputs()
print(tmp[0][0].payload["messages"][0])
from llama_index.core.program import LLMTextCompletionProgram
from pydantic import BaseModel
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import GradientAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core.output_parsers import PydanticOutputParser
from typing import List
class Song(BaseModel):
"""Data model for a song."""
title: str
length_seconds: int
class Album(BaseModel):
"""Data model for an album."""
name: str
artist: str
songs: List[Song]
finetuning_handler = GradientAIFineTuningHandler()
callback_manager = CallbackManager([finetuning_handler])
llm_gpt4 = | OpenAI(model="gpt-4", callback_manager=callback_manager) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-deeplake')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install deeplake')
import os
import textwrap
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document
from llama_index.vector_stores.deeplake import DeepLakeVectorStore
os.environ["OPENAI_API_KEY"] = "sk-********************************"
os.environ["ACTIVELOOP_TOKEN"] = "********************************"
import urllib.request
urllib.request.urlretrieve(
"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt",
"data/paul_graham/paul_graham_essay.txt",
)
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(
"Document ID:",
documents[0].doc_id,
"Document Hash:",
documents[0].hash,
)
from llama_index.core import StorageContext
dataset_path = "./dataset/paul_graham"
vector_store = | DeepLakeVectorStore(dataset_path=dataset_path, overwrite=True) | llama_index.vector_stores.deeplake.DeepLakeVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import llama_index.core.vector_stores.google.generativeai.genai_extension as genaix
from typing import Iterable
from random import randrange
LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX = f"llama-index-colab"
SESSION_CORPUS_ID_PREFIX = (
f"{LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX}-{randrange(1000000)}"
)
def corpus_id(num_id: int) -> str:
return f"{SESSION_CORPUS_ID_PREFIX}-{num_id}"
SESSION_CORPUS_ID = corpus_id(1)
def list_corpora() -> Iterable[genaix.Corpus]:
client = genaix.build_semantic_retriever()
yield from genaix.list_corpora(client=client)
def delete_corpus(*, corpus_id: str) -> None:
client = genaix.build_semantic_retriever()
genaix.delete_corpus(corpus_id=corpus_id, client=client)
def cleanup_colab_corpora():
for corpus in list_corpora():
if corpus.corpus_id.startswith(LLAMA_INDEX_COLAB_CORPUS_ID_PREFIX):
try:
delete_corpus(corpus_id=corpus.corpus_id)
print(f"Deleted corpus {corpus.corpus_id}.")
except Exception:
pass
cleanup_colab_corpora()
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
from llama_index.core import Response
import time
index = GoogleIndex.create_corpus(
corpus_id=SESSION_CORPUS_ID, display_name="My first corpus!"
)
print(f"Newly created corpus ID is {index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index.insert_documents(documents)
for corpus in list_corpora():
print(corpus)
query_engine = index.as_query_engine()
response = query_engine.query("What did Paul Graham do growing up?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
for cited_text in [node.text for node in response.source_nodes]:
print(f"Cited text: {cited_text}")
if response.metadata:
print(
f"Answerability: {response.metadata.get('answerable_probability', 0)}"
)
index = GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID)
query_engine = index.as_query_engine()
response = query_engine.query("Which company did Paul Graham build?")
assert isinstance(response, Response)
print(f"Response is {response.response}")
from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
index = | GoogleIndex.from_corpus(corpus_id=SESSION_CORPUS_ID) | llama_index.indices.managed.google.GoogleIndex.from_corpus |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
wiki_titles = ["Toronto", "Seattle", "Chicago", "Boston", "Houston"]
from pathlib import Path
import requests
for title in wiki_titles:
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"format": "json",
"titles": title,
"prop": "extracts",
"explaintext": True,
},
).json()
page = next(iter(response["query"]["pages"].values()))
wiki_text = page["extract"]
data_path = Path("data")
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
city_docs = {}
for wiki_title in wiki_titles:
city_docs[wiki_title] = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()
from llama_index.core import VectorStoreIndex
from llama_index.agent.openai import OpenAIAgent
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core import VectorStoreIndex
tool_dict = {}
for wiki_title in wiki_titles:
vector_index = VectorStoreIndex.from_documents(
city_docs[wiki_title],
)
vector_query_engine = vector_index.as_query_engine(llm=llm)
vector_tool = QueryEngineTool(
query_engine=vector_query_engine,
metadata=ToolMetadata(
name=wiki_title,
description=("Useful for questions related to" f" {wiki_title}"),
),
)
tool_dict[wiki_title] = vector_tool
from llama_index.core import VectorStoreIndex
from llama_index.core.objects import ObjectIndex, SimpleToolNodeMapping
tool_mapping = SimpleToolNodeMapping.from_objects(list(tool_dict.values()))
tool_index = ObjectIndex.from_objects(
list(tool_dict.values()),
tool_mapping,
VectorStoreIndex,
)
tool_retriever = tool_index.as_retriever(similarity_top_k=1)
from llama_index.core.llms import ChatMessage
from llama_index.core import ChatPromptTemplate
from typing import List
GEN_SYS_PROMPT_STR = """\
Task information is given below.
Given the task, please generate a system prompt for an OpenAI-powered bot to solve this task:
{task} \
"""
gen_sys_prompt_messages = [
ChatMessage(
role="system",
content="You are helping to build a system prompt for another bot.",
),
ChatMessage(role="user", content=GEN_SYS_PROMPT_STR),
]
GEN_SYS_PROMPT_TMPL = | ChatPromptTemplate(gen_sys_prompt_messages) | llama_index.core.ChatPromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-longllmlingua')
get_ipython().system('pip install llmlingua llama-index')
import openai
openai.api_key = "<insert_openai_key>"
get_ipython().system('wget "https://www.dropbox.com/s/f6bmb19xdg0xedm/paul_graham_essay.txt?dl=1" -O paul_graham_essay.txt')
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
load_index_from_storage,
StorageContext,
)
documents = SimpleDirectoryReader(
input_files=["paul_graham_essay.txt"]
).load_data()
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().system('pip install llama-index')
import time
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["OPENAI_API_KEY"] = "[YOUR_API_KEY]"
from llama_index.core import VectorStoreIndex, download_loader
from llama_index.readers.wikipedia import WikipediaReader
loader = WikipediaReader()
documents = loader.load_data(
pages=[
"Berlin",
"Santiago",
"Moscow",
"Tokyo",
"Jakarta",
"Cairo",
"Bogota",
"Shanghai",
"Damascus",
]
)
len(documents)
start_time = time.perf_counter()
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-monsterapi')
get_ipython().system('python3 -m pip install llama-index --quiet -y')
get_ipython().system('python3 -m pip install monsterapi --quiet')
get_ipython().system('python3 -m pip install sentence_transformers --quiet')
import os
from llama_index.llms.monsterapi import MonsterLLM
from llama_index.core.embeddings import resolve_embed_model
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
os.environ["MONSTER_API_KEY"] = ""
model = "llama2-7b-chat"
llm = MonsterLLM(model=model, temperature=0.75)
result = llm.complete("Who are you?")
print(result)
from llama_index.core.llms import ChatMessage
history_message = ChatMessage(
**{
"role": "user",
"content": (
"When asked 'who are you?' respond as 'I am qblocks llm model'"
" everytime."
),
}
)
current_message = ChatMessage(**{"role": "user", "content": "Who are you?"})
response = llm.chat([history_message, current_message])
print(response)
get_ipython().system('python3 -m pip install pypdf --quiet')
get_ipython().system('rm -r ./data')
get_ipython().system('mkdir -p data&&cd data&&curl \'https://arxiv.org/pdf/2005.11401.pdf\' -o "RAG.pdf"')
documents = | SimpleDirectoryReader("./data") | llama_index.core.SimpleDirectoryReader |
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
import nltk
nltk.download("stopwords")
import llama_index.core
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
load_index_from_storage,
StorageContext,
)
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents)
index.set_index_id("vector_index")
index.storage_context.persist("./storage")
storage_context = StorageContext.from_defaults(persist_dir="storage")
index = load_index_from_storage(storage_context, index_id="vector_index")
query_engine = index.as_query_engine(response_mode="tree_summarize")
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))
query_modes = [
"svm",
"linear_regression",
"logistic_regression",
]
for query_mode in query_modes:
query_engine = index.as_query_engine(vector_store_query_mode=query_mode)
response = query_engine.query("What did the author do growing up?")
print(f"Query mode: {query_mode}")
display(Markdown(f"<b>{response}</b>"))
display(Markdown(f"<b>{response}</b>"))
print(response.source_nodes[0].text)
from llama_index.core import QueryBundle
query_bundle = QueryBundle(
query_str="What did the author do growing up?",
custom_embedding_strs=["The author grew up painting."],
)
query_engine = index.as_query_engine()
response = query_engine.query(query_bundle)
display(Markdown(f"<b>{response}</b>"))
query_engine = index.as_query_engine(
vector_store_query_mode="mmr", vector_store_kwargs={"mmr_threshold": 0.2}
)
response = query_engine.query("What did the author do growing up?")
print(response.get_formatted_sources())
from llama_index.core import Document
doc = | Document(text="target", metadata={"tag": "target"}) | llama_index.core.Document |
from llama_index.core import SQLDatabase
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("sqlite:///chinook.db")
sql_database = SQLDatabase(engine)
from llama_index.core.query_pipeline import QueryPipeline
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('curl "https://www.sqlitetutorial.net/wp-content/uploads/2018/03/chinook.zip" -O ./chinook.zip')
get_ipython().system('unzip ./chinook.zip')
from llama_index.core.settings import Settings
from llama_index.core.callbacks import CallbackManager
callback_manager = CallbackManager()
Settings.callback_manager = callback_manager
import phoenix as px
import llama_index.core
px.launch_app()
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core.tools import QueryEngineTool
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["albums", "tracks", "artists"],
verbose=True,
)
sql_tool = QueryEngineTool.from_defaults(
query_engine=sql_query_engine,
name="sql_tool",
description=(
"Useful for translating a natural language query into a SQL query"
),
)
from llama_index.core.query_pipeline import QueryPipeline as QP
qp = QP(verbose=True)
from llama_index.core.agent.react.types import (
ActionReasoningStep,
ObservationReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.agent import Task, AgentChatResponse
from llama_index.core.query_pipeline import (
AgentInputComponent,
AgentFnComponent,
CustomAgentComponent,
QueryComponent,
ToolRunnerComponent,
)
from llama_index.core.llms import MessageRole
from typing import Dict, Any, Optional, Tuple, List, cast
def agent_input_fn(task: Task, state: Dict[str, Any]) -> Dict[str, Any]:
"""Agent input function.
Returns:
A Dictionary of output keys and values. If you are specifying
src_key when defining links between this component and other
components, make sure the src_key matches the specified output_key.
"""
if "current_reasoning" not in state:
state["current_reasoning"] = []
reasoning_step = ObservationReasoningStep(observation=task.input)
state["current_reasoning"].append(reasoning_step)
return {"input": task.input}
agent_input_component = AgentInputComponent(fn=agent_input_fn)
from llama_index.core.agent import ReActChatFormatter
from llama_index.core.query_pipeline import InputComponent, Link
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool
def react_prompt_fn(
task: Task, state: Dict[str, Any], input: str, tools: List[BaseTool]
) -> List[ChatMessage]:
chat_formatter = ReActChatFormatter()
return chat_formatter.format(
tools,
chat_history=task.memory.get() + state["memory"].get_all(),
current_reasoning=state["current_reasoning"],
)
react_prompt_component = AgentFnComponent(
fn=react_prompt_fn, partial_dict={"tools": [sql_tool]}
)
from typing import Set, Optional
from llama_index.core.agent.react.output_parser import ReActOutputParser
from llama_index.core.llms import ChatResponse
from llama_index.core.agent.types import Task
def parse_react_output_fn(
task: Task, state: Dict[str, Any], chat_response: ChatResponse
):
"""Parse ReAct output into a reasoning step."""
output_parser = ReActOutputParser()
reasoning_step = output_parser.parse(chat_response.message.content)
return {"done": reasoning_step.is_done, "reasoning_step": reasoning_step}
parse_react_output = AgentFnComponent(fn=parse_react_output_fn)
def run_tool_fn(
task: Task, state: Dict[str, Any], reasoning_step: ActionReasoningStep
):
"""Run tool and process tool output."""
tool_runner_component = ToolRunnerComponent(
[sql_tool], callback_manager=task.callback_manager
)
tool_output = tool_runner_component.run_component(
tool_name=reasoning_step.action,
tool_input=reasoning_step.action_input,
)
observation_step = ObservationReasoningStep(observation=str(tool_output))
state["current_reasoning"].append(observation_step)
return {"response_str": observation_step.get_content(), "is_done": False}
run_tool = AgentFnComponent(fn=run_tool_fn)
def process_response_fn(
task: Task, state: Dict[str, Any], response_step: ResponseReasoningStep
):
"""Process response."""
state["current_reasoning"].append(response_step)
response_str = response_step.response
state["memory"].put(ChatMessage(content=task.input, role=MessageRole.USER))
state["memory"].put(
ChatMessage(content=response_str, role=MessageRole.ASSISTANT)
)
return {"response_str": response_str, "is_done": True}
process_response = AgentFnComponent(fn=process_response_fn)
def process_agent_response_fn(
task: Task, state: Dict[str, Any], response_dict: dict
):
"""Process agent response."""
return (
AgentChatResponse(response_dict["response_str"]),
response_dict["is_done"],
)
process_agent_response = AgentFnComponent(fn=process_agent_response_fn)
from llama_index.core.query_pipeline import QueryPipeline as QP
from llama_index.llms.openai import OpenAI
qp.add_modules(
{
"agent_input": agent_input_component,
"react_prompt": react_prompt_component,
"llm": | OpenAI(model="gpt-4-1106-preview") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
import nest_asyncio
nest_asyncio.apply()
import os
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
import pandas as pd
def display_eval_df(question, source, answer_a, answer_b, result) -> None:
"""Pretty print question/answer + gpt-4 judgement dataset."""
eval_df = pd.DataFrame(
{
"Question": question,
"Source": source,
"Model A": answer_a["model"],
"Answer A": answer_a["text"],
"Model B": answer_b["model"],
"Answer B": answer_b["text"],
"Score": result.score,
"Judgement": result.feedback,
},
index=[0],
)
eval_df = eval_df.style.set_properties(
**{
"inline-size": "300px",
"overflow-wrap": "break-word",
},
subset=["Answer A", "Answer B"]
)
display(eval_df)
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
train_cities = [
"San Francisco",
"Toronto",
"New York",
"Vancouver",
"Montreal",
"Boston",
]
test_cities = [
"Tokyo",
"Singapore",
"Paris",
]
train_documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in train_cities]
)
test_documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in test_cities]
)
QUESTION_GEN_PROMPT = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
train_dataset_generator = DatasetGenerator.from_documents(
train_documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=llm,
show_progress=True,
num_questions_per_chunk=25,
)
test_dataset_generator = DatasetGenerator.from_documents(
test_documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=llm,
show_progress=True,
num_questions_per_chunk=25,
)
train_questions = train_dataset_generator.generate_questions_from_nodes(
num=200
)
test_questions = test_dataset_generator.generate_questions_from_nodes(num=150)
len(train_questions), len(test_questions)
train_questions[:3]
test_questions[:3]
from llama_index.core import VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
train_index = VectorStoreIndex.from_documents(documents=train_documents)
train_retriever = VectorIndexRetriever(
index=train_index,
similarity_top_k=2,
)
test_index = VectorStoreIndex.from_documents(documents=test_documents)
test_retriever = VectorIndexRetriever(
index=test_index,
similarity_top_k=2,
)
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
def create_query_engine(
hf_name: str, retriever: VectorIndexRetriever, hf_llm_generators: dict
) -> RetrieverQueryEngine:
"""Create a RetrieverQueryEngine using the HuggingFaceInferenceAPI LLM"""
if hf_name not in hf_llm_generators:
raise KeyError("model not listed in hf_llm_generators")
llm = HuggingFaceInferenceAPI(
model_name=hf_llm_generators[hf_name],
context_window=2048, # to use refine
token=HUGGING_FACE_TOKEN,
)
return RetrieverQueryEngine.from_args(retriever=retriever, llm=llm)
hf_llm_generators = {
"mistral-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
"llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf",
}
train_query_engines = {
mdl: create_query_engine(mdl, train_retriever, hf_llm_generators)
for mdl in hf_llm_generators.keys()
}
test_query_engines = {
mdl: create_query_engine(mdl, test_retriever, hf_llm_generators)
for mdl in hf_llm_generators.keys()
}
import tqdm
import random
train_dataset = []
for q in tqdm.tqdm(train_questions):
model_versus = random.sample(list(train_query_engines.items()), 2)
data_entry = {"question": q}
responses = []
source = None
for name, engine in model_versus:
response = engine.query(q)
response_struct = {}
response_struct["model"] = name
response_struct["text"] = str(response)
if source is not None:
assert source == response.source_nodes[0].node.text[:1000] + "..."
else:
source = response.source_nodes[0].node.text[:1000] + "..."
responses.append(response_struct)
data_entry["answers"] = responses
data_entry["source"] = source
train_dataset.append(data_entry)
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core.evaluation import PairwiseComparisonEvaluator
from llama_index.core import Settings
main_finetuning_handler = | OpenAIFineTuningHandler() | llama_index.finetuning.callbacks.OpenAIFineTuningHandler |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4", temperature=0)
Settings.chunk_size = 512
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
documents = | SimpleDirectoryReader("./data/paul_graham") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
import nest_asyncio
nest_asyncio.apply()
import os
HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
get_ipython().system('pip install wikipedia -q')
from llama_index.readers.wikipedia import WikipediaReader
cities = [
"San Francisco",
"Toronto",
"New York",
"Vancouver",
"Montreal",
"Tokyo",
"Singapore",
"Paris",
]
documents = WikipediaReader().load_data(
pages=[f"History of {x}" for x in cities]
)
QUESTION_GEN_PROMPT = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.llms.openai import OpenAI
gpt_35_llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
dataset_generator = DatasetGenerator.from_documents(
documents,
question_gen_query=QUESTION_GEN_PROMPT,
llm=gpt_35_llm,
num_questions_per_chunk=25,
)
qrd = dataset_generator.generate_dataset_from_nodes(num=350)
from llama_index.core import VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
the_index = | VectorStoreIndex.from_documents(documents=documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install sqlite-utils')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import openai
os.environ["OPENAI_API_KEY"] = "YOUR_KEY_HERE"
openai.api_key = os.environ["OPENAI_API_KEY"]
from IPython.display import Markdown, display
json_list = [
{
"name": "John Doe",
"age": 25,
"major": "Computer Science",
"email": "[email protected]",
"address": "123 Main St",
"city": "New York",
"state": "NY",
"country": "USA",
"phone": "+1 123-456-7890",
"occupation": "Software Engineer",
},
{
"name": "Jane Smith",
"age": 30,
"major": "Business Administration",
"email": "[email protected]",
"address": "456 Elm St",
"city": "San Francisco",
"state": "CA",
"country": "USA",
"phone": "+1 234-567-8901",
"occupation": "Marketing Manager",
},
{
"name": "Michael Johnson",
"age": 35,
"major": "Finance",
"email": "[email protected]",
"address": "789 Oak Ave",
"city": "Chicago",
"state": "IL",
"country": "USA",
"phone": "+1 345-678-9012",
"occupation": "Financial Analyst",
},
{
"name": "Emily Davis",
"age": 28,
"major": "Psychology",
"email": "[email protected]",
"address": "234 Pine St",
"city": "Los Angeles",
"state": "CA",
"country": "USA",
"phone": "+1 456-789-0123",
"occupation": "Psychologist",
},
{
"name": "Alex Johnson",
"age": 27,
"major": "Engineering",
"email": "[email protected]",
"address": "567 Cedar Ln",
"city": "Seattle",
"state": "WA",
"country": "USA",
"phone": "+1 567-890-1234",
"occupation": "Civil Engineer",
},
{
"name": "Jessica Williams",
"age": 32,
"major": "Biology",
"email": "[email protected]",
"address": "890 Walnut Ave",
"city": "Boston",
"state": "MA",
"country": "USA",
"phone": "+1 678-901-2345",
"occupation": "Biologist",
},
{
"name": "Matthew Brown",
"age": 26,
"major": "English Literature",
"email": "[email protected]",
"address": "123 Peach St",
"city": "Atlanta",
"state": "GA",
"country": "USA",
"phone": "+1 789-012-3456",
"occupation": "Writer",
},
{
"name": "Olivia Wilson",
"age": 29,
"major": "Art",
"email": "[email protected]",
"address": "456 Plum Ave",
"city": "Miami",
"state": "FL",
"country": "USA",
"phone": "+1 890-123-4567",
"occupation": "Artist",
},
{
"name": "Daniel Thompson",
"age": 31,
"major": "Physics",
"email": "[email protected]",
"address": "789 Apple St",
"city": "Denver",
"state": "CO",
"country": "USA",
"phone": "+1 901-234-5678",
"occupation": "Physicist",
},
{
"name": "Sophia Clark",
"age": 27,
"major": "Sociology",
"email": "[email protected]",
"address": "234 Orange Ln",
"city": "Austin",
"state": "TX",
"country": "USA",
"phone": "+1 012-345-6789",
"occupation": "Social Worker",
},
{
"name": "Christopher Lee",
"age": 33,
"major": "Chemistry",
"email": "[email protected]",
"address": "567 Mango St",
"city": "San Diego",
"state": "CA",
"country": "USA",
"phone": "+1 123-456-7890",
"occupation": "Chemist",
},
{
"name": "Ava Green",
"age": 28,
"major": "History",
"email": "[email protected]",
"address": "890 Cherry Ave",
"city": "Philadelphia",
"state": "PA",
"country": "USA",
"phone": "+1 234-567-8901",
"occupation": "Historian",
},
{
"name": "Ethan Anderson",
"age": 30,
"major": "Business",
"email": "[email protected]",
"address": "123 Lemon Ln",
"city": "Houston",
"state": "TX",
"country": "USA",
"phone": "+1 345-678-9012",
"occupation": "Entrepreneur",
},
{
"name": "Isabella Carter",
"age": 28,
"major": "Mathematics",
"email": "[email protected]",
"address": "456 Grape St",
"city": "Phoenix",
"state": "AZ",
"country": "USA",
"phone": "+1 456-789-0123",
"occupation": "Mathematician",
},
{
"name": "Andrew Walker",
"age": 32,
"major": "Economics",
"email": "[email protected]",
"address": "789 Berry Ave",
"city": "Portland",
"state": "OR",
"country": "USA",
"phone": "+1 567-890-1234",
"occupation": "Economist",
},
{
"name": "Mia Evans",
"age": 29,
"major": "Political Science",
"email": "[email protected]",
"address": "234 Lime St",
"city": "Washington",
"state": "DC",
"country": "USA",
"phone": "+1 678-901-2345",
"occupation": "Political Analyst",
},
]
from llama_index.llms.openai import OpenAI
from llama_index.core.query_engine import JSONalyzeQueryEngine
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-psychic')
get_ipython().system('pip install llama-index')
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SummaryIndex
from llama_index.readers.psychic import PsychicReader
from IPython.display import Markdown, display
psychic_key = "PSYCHIC_API_KEY"
account_id = "ACCOUNT_ID"
connector_id = "notion"
documents = | PsychicReader(psychic_key=psychic_key) | llama_index.readers.psychic.PsychicReader |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-redis')
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-mongodb')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('mkdir -p data')
get_ipython().system('echo "This is a test file: one!" > data/test1.txt')
get_ipython().system('echo "This is a test file: two!" > data/test2.txt')
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("./data", filename_as_id=True).load_data()
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.docstore.mongodb import MongoDocumentStore
from llama_index.core.node_parser import SentenceSplitter
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(),
HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
],
docstore=SimpleDocumentStore(),
)
nodes = pipeline.run(documents=documents)
print(f"Ingested {len(nodes)} Nodes")
pipeline.persist("./pipeline_storage")
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(),
HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
]
)
pipeline.load("./pipeline_storage")
get_ipython().system('echo "This is a test file: three!" > data/test3.txt')
get_ipython().system('echo "This is a NEW test file: one!" > data/test1.txt')
documents = | SimpleDirectoryReader("./data", filename_as_id=True) | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-rungpt')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install rungpt')
get_ipython().system('rungpt serve decapoda-research/llama-7b-hf --precision fp16 --device_map balanced')
from llama_index.llms.rungpt import RunGptLLM
llm = | RunGptLLM() | llama_index.llms.rungpt.RunGptLLM |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openllm')
get_ipython().system('pip install "openllm" # use \'openllm[vllm]\' if you have access to GPU')
get_ipython().system('pip install llama-index')
import os
from typing import List, Optional
from llama_index.llms.openllm import OpenLLM, OpenLLMAPI
from llama_index.core.llms import ChatMessage
os.environ[
"OPENLLM_ENDPOINT"
] = "na" # Change this to a remote server that you might run OpenLLM at.
local_llm = OpenLLM("HuggingFaceH4/zephyr-7b-alpha")
remote_llm = OpenLLMAPI(address="http://localhost:3000")
remote_llm = | OpenLLMAPI() | llama_index.llms.openllm.OpenLLMAPI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
from llama_index.core.storage.docstore import SimpleDocumentStore
docstore = | SimpleDocumentStore() | llama_index.core.storage.docstore.SimpleDocumentStore |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('pip install llama-index')
get_ipython().system('pip install spacy')
wiki_titles = [
"Toronto",
"Seattle",
"Chicago",
"Boston",
"Houston",
"Tokyo",
"Berlin",
"Lisbon",
]
from pathlib import Path
import requests
for title in wiki_titles:
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"format": "json",
"titles": title,
"prop": "extracts",
"explaintext": True,
},
).json()
page = next(iter(response["query"]["pages"].values()))
wiki_text = page["extract"]
data_path = Path("data")
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
from llama_index.core import SimpleDirectoryReader
city_docs = {}
for wiki_title in wiki_titles:
city_docs[wiki_title] = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
city_descs_dict = {}
choices = []
choice_to_id_dict = {}
for idx, wiki_title in enumerate(wiki_titles):
vector_desc = (
"Useful for questions related to specific aspects of"
f" {wiki_title} (e.g. the history, arts and culture,"
" sports, demographics, or more)."
)
summary_desc = (
"Useful for any requests that require a holistic summary"
f" of EVERYTHING about {wiki_title}. For questions about"
" more specific sections, please use the vector_tool."
)
doc_id_vector = f"{wiki_title}_vector"
doc_id_summary = f"{wiki_title}_summary"
city_descs_dict[doc_id_vector] = vector_desc
city_descs_dict[doc_id_summary] = summary_desc
choices.extend([vector_desc, summary_desc])
choice_to_id_dict[idx * 2] = f"{wiki_title}_vector"
choice_to_id_dict[idx * 2 + 1] = f"{wiki_title}_summary"
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
llm = OpenAI(model_name="gpt-3.5-turbo")
summary_q_tmpl = """\
You are a summary question generator. Given an existing question which asks for a summary of a given topic, \
generate {num_vary} related queries that also ask for a summary of the topic.
For example, assuming we're generating 3 related questions:
Base Question: Can you tell me more about Boston?
Question Variations:
Give me an overview of Boston as a city.
Can you describe different aspects of Boston, from the history to the sports scene to the food?
Write a concise summary of Boston; I've never been.
Now let's give it a shot!
Base Question: {base_question}
Question Variations:
"""
summary_q_prompt = PromptTemplate(summary_q_tmpl)
from collections import defaultdict
from llama_index.core.evaluation import DatasetGenerator
from llama_index.core.evaluation import EmbeddingQAFinetuneDataset
from llama_index.core.node_parser import SimpleNodeParser
from tqdm.notebook import tqdm
def generate_dataset(
wiki_titles,
city_descs_dict,
llm,
summary_q_prompt,
num_vector_qs_per_node=2,
num_summary_qs=4,
):
queries = {}
corpus = {}
relevant_docs = defaultdict(list)
for idx, wiki_title in enumerate(tqdm(wiki_titles)):
doc_id_vector = f"{wiki_title}_vector"
doc_id_summary = f"{wiki_title}_summary"
corpus[doc_id_vector] = city_descs_dict[doc_id_vector]
corpus[doc_id_summary] = city_descs_dict[doc_id_summary]
node_parser = SimpleNodeParser.from_defaults()
nodes = node_parser.get_nodes_from_documents(city_docs[wiki_title])
dataset_generator = DatasetGenerator(
nodes,
llm=llm,
num_questions_per_chunk=num_vector_qs_per_node,
)
doc_questions = dataset_generator.generate_questions_from_nodes(
num=len(nodes) * num_vector_qs_per_node
)
for query_idx, doc_question in enumerate(doc_questions):
query_id = f"{wiki_title}_{query_idx}"
relevant_docs[query_id] = [doc_id_vector]
queries[query_id] = doc_question
base_q = f"Give me a summary of {wiki_title}"
fmt_prompt = summary_q_prompt.format(
num_vary=num_summary_qs,
base_question=base_q,
)
raw_response = llm.complete(fmt_prompt)
raw_lines = str(raw_response).split("\n")
doc_summary_questions = [l for l in raw_lines if l != ""]
print(f"[{idx}] Original Question: {base_q}")
print(
f"[{idx}] Generated Question Variations: {doc_summary_questions}"
)
for query_idx, doc_summary_question in enumerate(
doc_summary_questions
):
query_id = f"{wiki_title}_{query_idx}"
relevant_docs[query_id] = [doc_id_summary]
queries[query_id] = doc_summary_question
return EmbeddingQAFinetuneDataset(
queries=queries, corpus=corpus, relevant_docs=relevant_docs
)
dataset = generate_dataset(
wiki_titles,
city_descs_dict,
llm,
summary_q_prompt,
num_vector_qs_per_node=4,
num_summary_qs=5,
)
dataset.save_json("dataset.json")
dataset = | EmbeddingQAFinetuneDataset.from_json("dataset.json") | llama_index.core.evaluation.EmbeddingQAFinetuneDataset.from_json |
from llama_hub.semanticscholar.base import SemanticScholarReader
import os
import openai
from llama_index.llms import OpenAI
from llama_index.query_engine import CitationQueryEngine
from llama_index import (
VectorStoreIndex,
StorageContext,
load_index_from_storage,
ServiceContext,
)
from llama_index.response.notebook_utils import display_response
s2reader = SemanticScholarReader()
openai.api_key = os.environ["OPENAI_API_KEY"]
service_context = ServiceContext.from_defaults(
llm=OpenAI(model="gpt-3.5-turbo", temperature=0)
)
query_space = "large language models"
full_text = True
total_papers = 50
persist_dir = (
"./citation_" + query_space + "_" + str(total_papers) + "_" + str(full_text)
)
if not os.path.exists(persist_dir):
documents = s2reader.load_data(query_space, total_papers, full_text=full_text)
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
index.storage_context.persist(persist_dir=persist_dir)
else:
index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=persist_dir),
service_context=service_context,
)
query_engine = CitationQueryEngine.from_args(
index,
similarity_top_k=3,
citation_chunk_size=512,
)
query_string = "limitations of using large language models"
response = query_engine.query(query_string)
display_response(
response, show_source=True, source_length=100, show_source_metadata=True
)
query_space = "covid 19 vaccine"
query_string = "List the efficacy numbers of the covid 19 vaccines"
full_text = True
total_papers = 50
persist_dir = (
"./citation_" + query_space + "_" + str(total_papers) + "_" + str(full_text)
)
if not os.path.exists(persist_dir):
documents = s2reader.load_data(query_space, total_papers, full_text=full_text)
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
index.storage_context.persist(persist_dir=persist_dir)
else:
index = load_index_from_storage(
| StorageContext.from_defaults(persist_dir=persist_dir) | llama_index.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
get_ipython().system('pip install "llama_index>=0.9.7"')
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.extractors import TitleExtractor, SummaryExtractor
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import MetadataMode
def build_pipeline():
llm = OpenAI(model="gpt-3.5-turbo-1106", temperature=0.1)
transformations = [
SentenceSplitter(chunk_size=1024, chunk_overlap=20),
TitleExtractor(
llm=llm, metadata_mode=MetadataMode.EMBED, num_workers=8
),
SummaryExtractor(
llm=llm, metadata_mode=MetadataMode.EMBED, num_workers=8
),
OpenAIEmbedding(),
]
return IngestionPipeline(transformations=transformations)
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
import time
times = []
for _ in range(3):
time.sleep(30) # help prevent rate-limits/timeouts, keeps each run fair
pipline = build_pipeline()
start = time.time()
nodes = await pipline.arun(documents=documents)
end = time.time()
times.append(end - start)
print(f"Average time: {sum(times) / len(times)}")
get_ipython().system('pip install "llama_index<0.9.6"')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.extractors import TitleExtractor, SummaryExtractor
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import MetadataMode
def build_pipeline():
llm = OpenAI(model="gpt-3.5-turbo-1106", temperature=0.1)
transformations = [
SentenceSplitter(chunk_size=1024, chunk_overlap=20),
TitleExtractor(llm=llm, metadata_mode=MetadataMode.EMBED),
SummaryExtractor(llm=llm, metadata_mode=MetadataMode.EMBED),
OpenAIEmbedding(),
]
return IngestionPipeline(transformations=transformations)
from llama_index.core import SimpleDirectoryReader
documents = | SimpleDirectoryReader("./data/paul_graham") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gradient')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().system('pip install llama-index gradientai -q')
import os
from llama_index.llms.gradient import GradientBaseModelLLM
from llama_index.finetuning import GradientFinetuneEngine
os.environ["GRADIENT_ACCESS_TOKEN"] = os.getenv("GRADIENT_API_KEY")
os.environ["GRADIENT_WORKSPACE_ID"] = ""
dialect = "sqlite"
from datasets import load_dataset
from pathlib import Path
import json
def load_jsonl(data_dir):
data_path = Path(data_dir).as_posix()
data = load_dataset("json", data_files=data_path)
return data
def save_jsonl(data_dicts, out_path):
with open(out_path, "w") as fp:
for data_dict in data_dicts:
fp.write(json.dumps(data_dict) + "\n")
def load_data_sql(data_dir: str = "data_sql"):
dataset = load_dataset("b-mc2/sql-create-context")
dataset_splits = {"train": dataset["train"]}
out_path = Path(data_dir)
out_path.parent.mkdir(parents=True, exist_ok=True)
for key, ds in dataset_splits.items():
with open(out_path, "w") as f:
for item in ds:
newitem = {
"input": item["question"],
"context": item["context"],
"output": item["answer"],
}
f.write(json.dumps(newitem) + "\n")
load_data_sql(data_dir="data_sql")
from math import ceil
def get_train_val_splits(
data_dir: str = "data_sql",
val_ratio: float = 0.1,
seed: int = 42,
shuffle: bool = True,
):
data = load_jsonl(data_dir)
num_samples = len(data["train"])
val_set_size = ceil(val_ratio * num_samples)
train_val = data["train"].train_test_split(
test_size=val_set_size, shuffle=shuffle, seed=seed
)
return train_val["train"].shuffle(), train_val["test"].shuffle()
raw_train_data, raw_val_data = get_train_val_splits(data_dir="data_sql")
save_jsonl(raw_train_data, "train_data_raw.jsonl")
save_jsonl(raw_val_data, "val_data_raw.jsonl")
raw_train_data[0]
text_to_sql_tmpl_str = """\
<s>### Instruction:\n{system_message}{user_message}\n\n### Response:\n{response}</s>"""
text_to_sql_inference_tmpl_str = """\
<s>### Instruction:\n{system_message}{user_message}\n\n### Response:\n"""
def _generate_prompt_sql(input, context, dialect="sqlite", output=""):
system_message = f"""You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.
You must output the SQL query that answers the question.
"""
user_message = f"""### Dialect:
{dialect}
{input}
{context}
"""
if output:
return text_to_sql_tmpl_str.format(
system_message=system_message,
user_message=user_message,
response=output,
)
else:
return text_to_sql_inference_tmpl_str.format(
system_message=system_message, user_message=user_message
)
def generate_prompt(data_point):
full_prompt = _generate_prompt_sql(
data_point["input"],
data_point["context"],
dialect="sqlite",
output=data_point["output"],
)
return {"inputs": full_prompt}
train_data = [
{"inputs": d["inputs"] for d in raw_train_data.map(generate_prompt)}
]
save_jsonl(train_data, "train_data.jsonl")
val_data = [{"inputs": d["inputs"] for d in raw_val_data.map(generate_prompt)}]
save_jsonl(val_data, "val_data.jsonl")
print(train_data[0]["inputs"])
base_model_slug = "llama2-7b-chat"
base_llm = GradientBaseModelLLM(
base_model_slug=base_model_slug, max_tokens=300
)
finetune_engine = GradientFinetuneEngine(
base_model_slug=base_model_slug,
name="text_to_sql",
data_path="train_data.jsonl",
verbose=True,
max_steps=200,
batch_size=4,
)
finetune_engine.model_adapter_id
epochs = 1
for i in range(epochs):
print(f"** EPOCH {i} **")
finetune_engine.finetune()
ft_llm = finetune_engine.get_finetuned_model(max_tokens=300)
def get_text2sql_completion(llm, raw_datapoint):
text2sql_tmpl_str = _generate_prompt_sql(
raw_datapoint["input"],
raw_datapoint["context"],
dialect="sqlite",
output=None,
)
response = llm.complete(text2sql_tmpl_str)
return str(response)
test_datapoint = raw_val_data[2]
display(test_datapoint)
get_text2sql_completion(base_llm, test_datapoint)
get_text2sql_completion(ft_llm, test_datapoint)
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
from llama_index.core import SQLDatabase
engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
from sqlalchemy.schema import CreateTable
table_create_stmt = str(CreateTable(city_stats_table))
print(table_create_stmt)
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{
"city_name": "Chicago",
"population": 2679000,
"country": "United States",
},
{"city_name": "Seoul", "population": 9776000, "country": "South Korea"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.connect() as connection:
cursor = connection.execute(stmt)
connection.commit()
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core import PromptTemplate
def get_text2sql_query_engine(llm, table_context, sql_database):
text2sql_tmpl_str = _generate_prompt_sql(
"{query_str}", "{schema}", dialect="{dialect}", output=""
)
sql_prompt = | PromptTemplate(text2sql_tmpl_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-callbacks-aim')
get_ipython().system('pip install llama-index')
from llama_index.core.callbacks import CallbackManager
from llama_index.callbacks.aim import AimCallback
from llama_index.core import SummaryIndex
from llama_index.core import SimpleDirectoryReader
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
docs = SimpleDirectoryReader("./data/paul_graham").load_data()
aim_callback = AimCallback(repo="./")
callback_manager = CallbackManager([aim_callback])
index = | SummaryIndex.from_documents(docs, callback_manager=callback_manager) | llama_index.core.SummaryIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.llms.openai import OpenAI
resp = OpenAI().complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAI
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = OpenAI().chat(messages)
print(resp)
from llama_index.llms.openai import OpenAI
llm = OpenAI()
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
llm = OpenAI()
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="text-davinci-003") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install -q llama-index-vector-stores-chroma llama-index-llms-fireworks llama-index-embeddings-fireworks==0.1.2')
get_ipython().run_line_magic('pip', 'install -q llama-index')
get_ipython().system('pip install llama-index chromadb --quiet')
get_ipython().system('pip install -q chromadb')
get_ipython().system('pip install -q pydantic==1.10.11')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.fireworks import FireworksEmbedding
from llama_index.llms.fireworks import Fireworks
from IPython.display import Markdown, display
import chromadb
import getpass
fw_api_key = getpass.getpass("Fireworks API Key:")
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.llms.fireworks import Fireworks
from llama_index.embeddings.fireworks import FireworksEmbedding
llm = Fireworks(
temperature=0, model="accounts/fireworks/models/mixtral-8x7b-instruct"
)
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
embed_model = FireworksEmbedding(
model_name="nomic-ai/nomic-embed-text-v1.5",
)
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import weaviate
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client("http://localhost:8080")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core.response.notebook_utils import display_response
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import StorageContext
vector_store = WeaviateVectorStore(weaviate_client=client)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index')
import weaviate
client = weaviate.Client("https://test-cluster-bbn8vqsn.weaviate.network")
try:
client.schema.delete_class("Book")
except:
pass
schema = {
"classes": [
{
"class": "Book",
"properties": [
{"name": "title", "dataType": ["text"]},
{"name": "author", "dataType": ["text"]},
{"name": "content", "dataType": ["text"]},
{"name": "year", "dataType": ["int"]},
],
},
]
}
if not client.schema.contains(schema):
client.schema.create(schema)
books = [
{
"title": "To Kill a Mockingbird",
"author": "Harper Lee",
"content": (
"To Kill a Mockingbird is a novel by Harper Lee published in"
" 1960..."
),
"year": 1960,
},
{
"title": "1984",
"author": "George Orwell",
"content": (
"1984 is a dystopian novel by George Orwell published in 1949..."
),
"year": 1949,
},
{
"title": "The Great Gatsby",
"author": "F. Scott Fitzgerald",
"content": (
"The Great Gatsby is a novel by F. Scott Fitzgerald published in"
" 1925..."
),
"year": 1925,
},
{
"title": "Pride and Prejudice",
"author": "Jane Austen",
"content": (
"Pride and Prejudice is a novel by Jane Austen published in"
" 1813..."
),
"year": 1813,
},
]
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding()
with client.batch as batch:
for book in books:
vector = embed_model.get_text_embedding(book["content"])
batch.add_data_object(
data_object=book, class_name="Book", vector=vector
)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_source_node
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="Book", text_key="content"
)
retriever = VectorStoreIndex.from_vector_store(vector_store).as_retriever(
similarity_top_k=1
)
nodes = retriever.retrieve("What is that book about a bird again?")
| pprint_source_node(nodes[0]) | llama_index.core.response.pprint_utils.pprint_source_node |
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.indices.query.query_transform import HyDEQueryTransform
from llama_index.core.query_engine import TransformQueryEngine
from IPython.display import Markdown, display
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-together')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
domain = "docs.llamaindex.ai"
docs_url = "https://docs.llamaindex.ai/en/latest/"
get_ipython().system('wget -e robots=off --recursive --no-clobber --page-requisites --html-extension --convert-links --restrict-file-names=windows --domains {domain} --no-parent {docs_url}')
from llama_index.readers.file import UnstructuredReader
from pathlib import Path
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
reader = UnstructuredReader()
all_html_files = [
"docs.llamaindex.ai/en/latest/index.html",
"docs.llamaindex.ai/en/latest/contributing/contributing.html",
"docs.llamaindex.ai/en/latest/understanding/understanding.html",
"docs.llamaindex.ai/en/latest/understanding/using_llms/using_llms.html",
"docs.llamaindex.ai/en/latest/understanding/using_llms/privacy.html",
"docs.llamaindex.ai/en/latest/understanding/loading/llamahub.html",
"docs.llamaindex.ai/en/latest/optimizing/production_rag.html",
"docs.llamaindex.ai/en/latest/module_guides/models/llms.html",
]
doc_limit = 10
docs = []
for idx, f in enumerate(all_html_files):
if idx > doc_limit:
break
print(f"Idx {idx}/{len(all_html_files)}")
loaded_docs = reader.load_data(file=f, split_documents=True)
start_idx = 64
loaded_doc = Document(
id_=str(f),
text="\n\n".join([d.get_content() for d in loaded_docs[start_idx:]]),
metadata={"path": str(f)},
)
print(str(f))
docs.append(loaded_doc)
from llama_index.embeddings.together import TogetherEmbedding
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
api_key = "<api_key>"
embed_model = TogetherEmbedding(
model_name="togethercomputer/m2-bert-80M-32k-retrieval", api_key=api_key
)
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
from llama_index.core.storage.docstore import SimpleDocumentStore
for doc in docs:
embedding = embed_model.get_text_embedding(doc.get_content())
doc.embedding = embedding
docstore = SimpleDocumentStore()
docstore.add_documents(docs)
from llama_index.core.schema import IndexNode
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import SummaryIndex
from llama_index.core.retrievers import RecursiveRetriever
import os
from tqdm.notebook import tqdm
import pickle
def build_index(docs, out_path: str = "storage/chunk_index"):
nodes = []
splitter = SentenceSplitter(chunk_size=512, chunk_overlap=70)
for idx, doc in enumerate(tqdm(docs)):
cur_nodes = splitter.get_nodes_from_documents([doc])
for cur_node in cur_nodes:
file_path = doc.metadata["path"]
new_node = IndexNode(
text=cur_node.text or "None",
index_id=str(file_path),
metadata=doc.metadata
)
nodes.append(new_node)
print("num nodes: " + str(len(nodes)))
if not os.path.exists(out_path):
index = | VectorStoreIndex(nodes, embed_model=embed_model) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-lantern')
get_ipython().system('pip install llama-index psycopg2-binary asyncpg')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
os.environ["OPENAI_API_KEY"] = "<your-api-key>"
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
import psycopg2
from sqlalchemy import make_url
connection_string = "postgresql://postgres:postgres@localhost:5432"
url = make_url(connection_string)
db_name = "postgres"
conn = psycopg2.connect(connection_string)
conn.autocommit = True
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.lantern import LanternVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
},
),
TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
),
metadata={
"category": "Entertainment",
"country": "United States",
},
),
TextNode(
text=(
"Elon Musk is a business magnate, industrial designer, and"
" engineer. He is the founder, CEO, and lead designer of SpaceX,"
" Tesla, Inc., Neuralink, and The Boring Company."
),
metadata={
"category": "Business",
"country": "United States",
},
),
TextNode(
text=(
"Rihanna is a Barbadian singer, actress, and businesswoman. She"
" has achieved significant success in the music industry and is"
" known for her versatile musical style."
),
metadata={
"category": "Music",
"country": "Barbados",
},
),
TextNode(
text=(
"Cristiano Ronaldo is a Portuguese professional footballer who is"
" considered one of the greatest football players of all time. He"
" has won numerous awards and set multiple records during his"
" career."
),
metadata={
"category": "Sports",
"country": "Portugal",
},
),
]
vector_store = LanternVectorStore.from_params(
database=db_name,
host=url.host,
password=url.password,
port=url.port,
user=url.username,
table_name="famous_people",
embed_dim=1536, # openai embedding dimension
m=16, # HNSW M parameter
ef_construction=128, # HNSW ef construction parameter
ef=64, # HNSW ef search parameter
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |