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get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-kvstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-firestore')
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, StorageContext
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.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() | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core.query_pipeline import (
QueryPipeline as QP,
Link,
InputComponent,
)
from llama_index.core.query_engine.pandas import PandasInstructionParser
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
get_ipython().system("wget 'https://raw.githubusercontent.com/jerryjliu/llama_index/main/docs/examples/data/csv/titanic_train.csv' -O 'titanic_train.csv'")
import pandas as pd
df = pd.read_csv("./titanic_train.csv")
instruction_str = (
"1. Convert the query to executable Python code using Pandas.\n"
"2. The final line of code should be a Python expression that can be called with the `eval()` function.\n"
"3. The code should represent a solution to the query.\n"
"4. PRINT ONLY THE EXPRESSION.\n"
"5. Do not quote the expression.\n"
)
pandas_prompt_str = (
"You are working with a pandas dataframe in Python.\n"
"The name of the dataframe is `df`.\n"
"This is the result of `print(df.head())`:\n"
"{df_str}\n\n"
"Follow these instructions:\n"
"{instruction_str}\n"
"Query: {query_str}\n\n"
"Expression:"
)
response_synthesis_prompt_str = (
"Given an input question, synthesize a response from the query results.\n"
"Query: {query_str}\n\n"
"Pandas Instructions (optional):\n{pandas_instructions}\n\n"
"Pandas Output: {pandas_output}\n\n"
"Response: "
)
pandas_prompt = PromptTemplate(pandas_prompt_str).partial_format(
instruction_str=instruction_str, df_str=df.head(5)
)
pandas_output_parser = PandasInstructionParser(df)
response_synthesis_prompt = | PromptTemplate(response_synthesis_prompt_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install llama-index-callbacks-uptrain')
get_ipython().run_line_magic('pip', 'install -q html2text llama-index pandas tqdm uptrain torch sentence-transformers')
from llama_index.core import Settings, VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.readers.web import SimpleWebPageReader
from llama_index.core.callbacks import CallbackManager
from llama_index.callbacks.uptrain.base import UpTrainCallbackHandler
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.postprocessor import SentenceTransformerRerank
from llama_index.llms.openai import OpenAI
import os
os.environ[
"OPENAI_API_KEY"
] = "sk-************" # Replace with your OpenAI API key
callback_handler = UpTrainCallbackHandler(
key_type="openai",
api_key=os.environ["OPENAI_API_KEY"],
project_name_prefix="llama",
)
Settings.callback_manager = | CallbackManager([callback_handler]) | llama_index.core.callbacks.CallbackManager |
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"]).load_data()
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
client = chromadb.PersistentClient(path="./raptor_paper_db")
collection = client.get_or_create_collection("raptor")
vector_store = ChromaVectorStore(chroma_collection=collection)
raptor_pack = RaptorPack(
documents,
embed_model=OpenAIEmbedding(
model="text-embedding-3-small"
), # used for embedding clusters
llm=OpenAI(model="gpt-3.5-turbo", temperature=0.1), # used for generating summaries
vector_store=vector_store, # used for storage
similarity_top_k=2, # top k for each layer, or overall top-k for collapsed
mode="collapsed", # sets default mode
transformations=[
SentenceSplitter(chunk_size=400, chunk_overlap=50)
], # transformations applied for ingestion
)
nodes = raptor_pack.run("What baselines is raptor compared against?", mode="collapsed")
print(len(nodes))
print(nodes[0].text)
nodes = raptor_pack.run(
"What baselines is raptor compared against?", mode="tree_traversal"
)
print(len(nodes))
print(nodes[0].text)
from llama_index.packs.raptor import RaptorRetriever
retriever = RaptorRetriever(
[],
embed_model=OpenAIEmbedding(
model="text-embedding-3-small"
), # used for embedding clusters
llm=OpenAI(model="gpt-3.5-turbo", temperature=0.1), # used for generating summaries
vector_store=vector_store, # used for storage
similarity_top_k=2, # top k for each layer, or overall top-k for collapsed
mode="tree_traversal", # sets default mode
)
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = RetrieverQueryEngine.from_args(
raptor_pack.retriever, llm= | OpenAI(model="gpt-3.5-turbo", temperature=0.1) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install duckdb duckdb-engine')
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 SQLDatabase, SimpleDirectoryReader, Document
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
from IPython.display import Markdown, display
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("duckdb:///: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)
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": "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)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
from llama_index.core import SQLDatabase
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
query_engine = | NLSQLTableQueryEngine(sql_database) | llama_index.core.query_engine.NLSQLTableQueryEngine |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
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')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
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
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core import SummaryIndex
Settings.llm = OpenAI()
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-mistralai')
get_ipython().system('pip install llama-index')
from llama_index.llms.mistralai import MistralAI
llm = MistralAI()
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(role="system", content="You are CEO of MistralAI."),
ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = | MistralAI() | llama_index.llms.mistralai.MistralAI |
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,
ArgPackComponent,
)
from typing import Dict, Any, List, Optional
from llama_index.core.llama_pack import BaseLlamaPack
from llama_index.core.llms import LLM
from llama_index.llms.openai import OpenAI
from llama_index.core import Document, VectorStoreIndex
from llama_index.core.response_synthesizers import TreeSummarize
from llama_index.core.schema import NodeWithScore, TextNode
from llama_index.core.node_parser import SentenceSplitter
llm = OpenAI(model="gpt-3.5-turbo")
chunk_sizes = [128, 256, 512, 1024]
query_engines = {}
for chunk_size in chunk_sizes:
splitter = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=0)
nodes = splitter.get_nodes_from_documents(documents)
vector_index = | VectorStoreIndex(nodes) | llama_index.core.VectorStoreIndex |
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"]).load_data()
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
client = chromadb.PersistentClient(path="./raptor_paper_db")
collection = client.get_or_create_collection("raptor")
vector_store = ChromaVectorStore(chroma_collection=collection)
raptor_pack = RaptorPack(
documents,
embed_model=OpenAIEmbedding(
model="text-embedding-3-small"
), # used for embedding clusters
llm= | OpenAI(model="gpt-3.5-turbo", temperature=0.1) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-konko')
get_ipython().system('pip install llama-index')
import os
os.environ["KONKO_API_KEY"] = "<your-api-key>"
from llama_index.llms.konko import Konko
from llama_index.core.llms import ChatMessage
llm = Konko(model="meta-llama/llama-2-13b-chat")
messages = ChatMessage(role="user", content="Explain Big Bang Theory briefly")
resp = llm.chat([messages])
print(resp)
import os
os.environ["OPENAI_API_KEY"] = "<your-api-key>"
llm = Konko(model="gpt-3.5-turbo")
message = ChatMessage(role="user", content="Explain Big Bang Theory briefly")
resp = llm.chat([message])
print(resp)
message = | ChatMessage(role="user", content="Tell me a story in 250 words") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-postgres')
get_ipython().system('pip install llama-index')
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.postgres import PGVectorStore
import textwrap
import openai
import os
os.environ["OPENAI_API_KEY"] = "<your key>"
openai.api_key = "<your 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'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
import psycopg2
connection_string = "postgresql://postgres:password@localhost:5432"
db_name = "vector_db"
conn = psycopg2.connect(connection_string)
conn.autocommit = True
with conn.cursor() as c:
c.execute(f"DROP DATABASE IF EXISTS {db_name}")
c.execute(f"CREATE DATABASE {db_name}")
from sqlalchemy import make_url
url = make_url(connection_string)
vector_store = PGVectorStore.from_params(
database=db_name,
host=url.host,
password=url.password,
port=url.port,
user=url.username,
table_name="paul_graham_essay",
embed_dim=1536, # openai embedding dimension
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, show_progress=True
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What happened in the mid 1980s?")
print(textwrap.fill(str(response), 100))
vector_store = PGVectorStore.from_params(
database="vector_db",
host="localhost",
password="password",
port=5432,
user="postgres",
table_name="paul_graham_essay",
embed_dim=1536, # openai embedding dimension
)
index = | VectorStoreIndex.from_vector_store(vector_store=vector_store) | llama_index.core.VectorStoreIndex.from_vector_store |
import openai
openai.api_key = "sk-xxx"
from llama_index.agent.openai import OpenAIAgent
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
agent = OpenAIAgent.from_tools(
| DuckDuckGoSearchToolSpec() | llama_index.tools.duckduckgo.DuckDuckGoSearchToolSpec |
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) | llama_index.core.Settings.text_splitter.get_nodes_from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-cohere')
get_ipython().system('pip install llama-index')
from llama_index.llms.cohere import Cohere
api_key = "Your api key"
resp = Cohere(api_key=api_key).complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.cohere import Cohere
messages = [
ChatMessage(role="user", content="hello there"),
ChatMessage(
role="assistant", content="Arrrr, matey! How can I help ye today?"
),
ChatMessage(role="user", content="What is your name"),
]
resp = Cohere(api_key=api_key).chat(
messages, preamble_override="You are a pirate with a colorful personality"
)
print(resp)
from llama_index.llms.openai import OpenAI
llm = Cohere(api_key=api_key)
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.openai import OpenAI
llm = Cohere(api_key=api_key)
messages = [
ChatMessage(role="user", content="hello there"),
ChatMessage(
role="assistant", content="Arrrr, matey! How can I help ye today?"
),
ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(
messages, preamble_override="You are a pirate with a colorful personality"
)
for r in resp:
print(r.delta, end="")
from llama_index.llms.cohere import Cohere
llm = Cohere(model="command", api_key=api_key)
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.llms.cohere import Cohere
llm = Cohere(model="command", api_key=api_key)
resp = await llm.acomplete("Paul Graham is ")
print(resp)
resp = await llm.astream_complete("Paul Graham is ")
async for delta in resp:
print(delta.delta, end="")
from llama_index.llms.cohere import Cohere
llm_good = Cohere(api_key=api_key)
llm_bad = | Cohere(model="command", api_key="BAD_KEY") | llama_index.llms.cohere.Cohere |
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=YOUR_OPENAI_KEY')
get_ipython().system('pip install llama-index pypdf')
get_ipython().system("mkdir -p '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)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
for idx, node in enumerate(base_nodes):
node.id_ = f"node-{idx}"
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-small-en")
llm = OpenAI(model="gpt-3.5-turbo")
base_index = VectorStoreIndex(base_nodes, embed_model=embed_model)
base_retriever = base_index.as_retriever(similarity_top_k=2)
retrievals = base_retriever.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for n in retrievals:
display_source_node(n, source_length=1500)
query_engine_base = RetrieverQueryEngine.from_args(base_retriever, llm=llm)
response = query_engine_base.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
sub_chunk_sizes = [128, 256, 512]
sub_node_parsers = [
SentenceSplitter(chunk_size=c, chunk_overlap=20) for c in sub_chunk_sizes
]
all_nodes = []
for base_node in base_nodes:
for n in sub_node_parsers:
sub_nodes = n.get_nodes_from_documents([base_node])
sub_inodes = [
IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes
]
all_nodes.extend(sub_inodes)
original_node = IndexNode.from_text_node(base_node, base_node.node_id)
all_nodes.append(original_node)
all_nodes_dict = {n.node_id: n for n in all_nodes}
vector_index_chunk = VectorStoreIndex(all_nodes, embed_model=embed_model)
vector_retriever_chunk = vector_index_chunk.as_retriever(similarity_top_k=2)
retriever_chunk = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_chunk},
node_dict=all_nodes_dict,
verbose=True,
)
nodes = retriever_chunk.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for node in nodes:
display_source_node(node, source_length=2000)
query_engine_chunk = RetrieverQueryEngine.from_args(retriever_chunk, llm=llm)
response = query_engine_chunk.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
import nest_asyncio
nest_asyncio.apply()
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
extractors = [
SummaryExtractor(summaries=["self"], show_progress=True),
QuestionsAnsweredExtractor(questions=5, show_progress=True),
]
node_to_metadata = {}
for extractor in extractors:
metadata_dicts = extractor.extract(base_nodes)
for node, metadata in zip(base_nodes, metadata_dicts):
if node.node_id not in node_to_metadata:
node_to_metadata[node.node_id] = metadata
else:
node_to_metadata[node.node_id].update(metadata)
def save_metadata_dicts(path, data):
with open(path, "w") as fp:
json.dump(data, fp)
def load_metadata_dicts(path):
with open(path, "r") as fp:
data = json.load(fp)
return data
save_metadata_dicts("data/llama2_metadata_dicts.json", node_to_metadata)
metadata_dicts = load_metadata_dicts("data/llama2_metadata_dicts.json")
import copy
all_nodes = copy.deepcopy(base_nodes)
for node_id, metadata in node_to_metadata.items():
for val in metadata.values():
all_nodes.append(IndexNode(text=val, index_id=node_id))
all_nodes_dict = {n.node_id: n for n in all_nodes}
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system('curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf')
from llama_index.core import SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import DatasetGenerator
documents = SimpleDirectoryReader(
input_files=["IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()
import random
random.seed(42)
random.shuffle(documents)
gpt_35_llm = | OpenAI(model="gpt-3.5-turbo", temperature=0.3) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-cassandra')
get_ipython().system('pip install --quiet "astrapy>=0.5.8"')
import os
from getpass import getpass
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
Document,
StorageContext,
)
from llama_index.vector_stores.cassandra import CassandraVectorStore
from cassandra.cluster import Cluster
cluster = Cluster(["127.0.0.1"])
session = cluster.connect()
import cassio
CASSANDRA_KEYSPACE = input("CASSANDRA_KEYSPACE = ")
cassio.init(session=session, keyspace=CASSANDRA_KEYSPACE)
ASTRA_DB_ID = input("ASTRA_DB_ID = ")
ASTRA_DB_TOKEN = getpass("ASTRA_DB_TOKEN = ")
desired_keyspace = input("ASTRA_DB_KEYSPACE (optional, can be left empty) = ")
if desired_keyspace:
ASTRA_DB_KEYSPACE = desired_keyspace
else:
ASTRA_DB_KEYSPACE = None
import cassio
cassio.init(
database_id=ASTRA_DB_ID,
token=ASTRA_DB_TOKEN,
keyspace=ASTRA_DB_KEYSPACE,
)
os.environ["OPENAI_API_KEY"] = getpass("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'")
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().system('pip install -q llama-index google-generativeai')
get_ipython().run_line_magic('env', 'GOOGLE_API_KEY=...')
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.llms.gemini import Gemini
resp = | Gemini() | llama_index.llms.gemini.Gemini |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
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.postprocessor import (
PIINodePostprocessor,
NERPIINodePostprocessor,
)
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.core import Document, VectorStoreIndex
from llama_index.core.schema import TextNode
text = """
Hello Paulo Santos. The latest statement for your credit card account \
1111-0000-1111-0000 was mailed to 123 Any Street, Seattle, WA 98109.
"""
node = TextNode(text=text)
processor = NERPIINodePostprocessor()
from llama_index.core.schema import NodeWithScore
new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)])
new_nodes[0].node.get_text()
new_nodes[0].node.metadata["__pii_node_info__"]
from llama_index.llms.openai import OpenAI
processor = PIINodePostprocessor(llm=OpenAI())
from llama_index.core.schema import NodeWithScore
new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)])
new_nodes[0].node.get_text()
new_nodes[0].node.metadata["__pii_node_info__"]
text = """
Hello Paulo Santos. The latest statement for your credit card account \
4095-2609-9393-4932 was mailed to Seattle, WA 98109. \
IBAN GB90YNTU67299444055881 and social security number is 474-49-7577 were verified on the system. \
Further communications will be sent to [email protected]
"""
presidio_node = TextNode(text=text)
from llama_index.postprocessor.presidio import PresidioPIINodePostprocessor
processor = | PresidioPIINodePostprocessor() | llama_index.postprocessor.presidio.PresidioPIINodePostprocessor |
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)
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) | llama_index.core.load_index_from_storage |
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
get_ipython().run_line_magic('pip', 'install -q html2text llama-index pandas pyarrow tqdm')
get_ipython().run_line_magic('pip', 'install -q llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install -q llama-index-callbacks-openinference')
import hashlib
import json
from pathlib import Path
import os
import textwrap
from typing import List, Union
import llama_index.core
from llama_index.readers.web import SimpleWebPageReader
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.callbacks import CallbackManager
from llama_index.callbacks.openinference import OpenInferenceCallbackHandler
from llama_index.callbacks.openinference.base import (
as_dataframe,
QueryData,
NodeData,
)
from llama_index.core.node_parser import SimpleNodeParser
import pandas as pd
from tqdm import tqdm
documents = SimpleWebPageReader().load_data(
[
"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt"
]
)
print(documents[0].text)
parser = SentenceSplitter()
nodes = parser.get_nodes_from_documents(documents)
print(nodes[0].text)
callback_handler = OpenInferenceCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llama_index.core.Settings.callback_manager = callback_manager
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
max_characters_per_line = 80
queries = [
"What did Paul Graham do growing up?",
"When and how did Paul Graham's mother die?",
"What, in Paul Graham's opinion, is the most distinctive thing about YC?",
"When and how did Paul Graham meet Jessica Livingston?",
"What is Bel, and when and where was it written?",
]
for query in queries:
response = query_engine.query(query)
print("Query")
print("=====")
print(textwrap.fill(query, max_characters_per_line))
print()
print("Response")
print("========")
print(textwrap.fill(str(response), max_characters_per_line))
print()
query_data_buffer = callback_handler.flush_query_data_buffer()
query_dataframe = as_dataframe(query_data_buffer)
query_dataframe
class ParquetCallback:
def __init__(
self, data_path: Union[str, Path], max_buffer_length: int = 1000
):
self._data_path = Path(data_path)
self._data_path.mkdir(parents=True, exist_ok=False)
self._max_buffer_length = max_buffer_length
self._batch_index = 0
def __call__(
self,
query_data_buffer: List[QueryData],
node_data_buffer: List[NodeData],
) -> None:
if len(query_data_buffer) >= self._max_buffer_length:
query_dataframe = as_dataframe(query_data_buffer)
file_path = self._data_path / f"log-{self._batch_index}.parquet"
query_dataframe.to_parquet(file_path)
self._batch_index += 1
query_data_buffer.clear() # ⚠️ clear the buffer or it will keep growing forever!
node_data_buffer.clear() # didn't log node_data_buffer, but still need to clear it
data_path = "data"
parquet_writer = ParquetCallback(
data_path=data_path,
max_buffer_length=1,
)
callback_handler = OpenInferenceCallbackHandler(callback=parquet_writer)
callback_manager = CallbackManager([callback_handler])
llama_index.core.Settings.callback_manager = callback_manager
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().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
from llama_index.core import PromptTemplate
from IPython.display import Markdown, display
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.llms.openai import OpenAI
gpt35_llm = OpenAI(model="gpt-3.5-turbo")
gpt4_llm = OpenAI(model="gpt-4")
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-chroma')
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 os
import getpass
import openai
openai.api_key = "sk-"
import chromadb
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.chroma import ChromaVectorStore
from IPython.display import Markdown, display
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text="The Shawshank Redemption",
metadata={
"author": "Stephen King",
"theme": "Friendship",
"year": 1994,
},
),
TextNode(
text="The Godfather",
metadata={
"director": "Francis Ford Coppola",
"theme": "Mafia",
"year": 1972,
},
),
TextNode(
text="Inception",
metadata={
"director": "Christopher Nolan",
"theme": "Fiction",
"year": 2010,
},
),
TextNode(
text="To Kill a Mockingbird",
metadata={
"author": "Harper Lee",
"theme": "Mafia",
"year": 1960,
},
),
TextNode(
text="1984",
metadata={
"author": "George Orwell",
"theme": "Totalitarianism",
"year": 1949,
},
),
TextNode(
text="The Great Gatsby",
metadata={
"author": "F. Scott Fitzgerald",
"theme": "The American Dream",
"year": 1925,
},
),
TextNode(
text="Harry Potter and the Sorcerer's Stone",
metadata={
"author": "J.K. Rowling",
"theme": "Fiction",
"year": 1997,
},
),
]
from llama_index.core import StorageContext
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)
from llama_index.core.vector_stores import (
MetadataFilter,
MetadataFilters,
FilterOperator,
)
filters = MetadataFilters(
filters=[
| MetadataFilter(key="theme", operator=FilterOperator.EQ, value="Mafia") | llama_index.core.vector_stores.MetadataFilter |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-replicate')
get_ipython().run_line_magic('pip', 'install unstructured replicate')
get_ipython().run_line_magic('pip', 'install llama_index ftfy regex tqdm')
get_ipython().run_line_magic('pip', 'install git+https://github.com/openai/CLIP.git')
get_ipython().run_line_magic('pip', 'install torch torchvision')
get_ipython().run_line_magic('pip', 'install matplotlib scikit-image')
get_ipython().run_line_magic('pip', 'install -U qdrant_client')
import os
REPLICATE_API_TOKEN = "..." # Your Relicate API token here
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1UU0xc3uLXs-WG0aDQSXjGacUkp142rLS" -O texas.jpg')
from llama_index.readers.file import FlatReader
from pathlib import Path
from llama_index.core.node_parser import UnstructuredElementNodeParser
reader = FlatReader()
docs_2021 = reader.load_data(Path("tesla_2021_10k.htm"))
node_parser = UnstructuredElementNodeParser()
import openai
OPENAI_API_TOKEN = "..."
openai.api_key = OPENAI_API_TOKEN # add your openai api key here
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
import os
import pickle
if not os.path.exists("2021_nodes.pkl"):
raw_nodes_2021 = node_parser.get_nodes_from_documents(docs_2021)
pickle.dump(raw_nodes_2021, open("2021_nodes.pkl", "wb"))
else:
raw_nodes_2021 = pickle.load(open("2021_nodes.pkl", "rb"))
nodes_2021, objects_2021 = node_parser.get_nodes_and_objects(raw_nodes_2021)
from llama_index.core import VectorStoreIndex
vector_index = VectorStoreIndex(nodes=nodes_2021, objects=objects_2021)
query_engine = vector_index.as_query_engine(similarity_top_k=5, verbose=True)
from PIL import Image
import matplotlib.pyplot as plt
imageUrl = "./texas.jpg"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.schema import ImageDocument
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
print(imageUrl)
llava_multi_modal_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=200,
temperature=0.1,
)
prompt = "which Tesla factory is shown in the image? Please answer just the name of the factory."
llava_response = llava_multi_modal_llm.complete(
prompt=prompt,
image_documents=[ImageDocument(image_path=imageUrl)],
)
print(llava_response.text)
rag_response = query_engine.query(llava_response.text)
print(rag_response)
input_image_path = Path("instagram_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=12ZpBBFkYu-jzz1iz356U5kMikn4uN9ww" -O ./instagram_images/jordan.png')
from pydantic import BaseModel
class InsAds(BaseModel):
"""Data model for a Ins Ads."""
account: str
brand: str
product: str
category: str
discount: str
price: str
comments: str
review: str
description: str
from PIL import Image
import matplotlib.pyplot as plt
ins_imageUrl = "./instagram_images/jordan.png"
image = Image.open(ins_imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
prompt_template_str = """\
can you summarize what is in the image\
and return the answer with json format \
"""
def pydantic_llava(
model_name, output_class, image_documents, prompt_template_str
):
mm_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=1000,
)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_class),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=mm_llm,
verbose=True,
)
response = llm_program()
print(f"Model: {model_name}")
for res in response:
print(res)
return response
from llama_index.core import SimpleDirectoryReader
ins_image_documents = SimpleDirectoryReader("./instagram_images").load_data()
pydantic_response = pydantic_llava(
"llava-13b", InsAds, ins_image_documents, prompt_template_str
)
print(pydantic_response.brand)
from pathlib import Path
import requests
wiki_titles = [
"batman",
"Vincent van Gogh",
"San Francisco",
"iPhone",
"Tesla Model S",
"BTS",
"Air Jordan",
]
data_path = Path("data_wiki")
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"]
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
import wikipedia
import urllib.request
image_path = Path("data_wiki")
image_uuid = 0
image_metadata_dict = {}
MAX_IMAGES_PER_WIKI = 30
wiki_titles = [
"Air Jordan",
"San Francisco",
"Batman",
"Vincent van Gogh",
"iPhone",
"Tesla Model S",
"BTS band",
]
if not image_path.exists():
Path.mkdir(image_path)
for title in wiki_titles:
images_per_wiki = 0
print(title)
try:
page_py = wikipedia.page(title)
list_img_urls = page_py.images
for url in list_img_urls:
if url.endswith(".jpg") or url.endswith(".png"):
image_uuid += 1
image_file_name = title + "_" + url.split("/")[-1]
image_metadata_dict[image_uuid] = {
"filename": image_file_name,
"img_path": "./" + str(image_path / f"{image_uuid}.jpg"),
}
urllib.request.urlretrieve(
url, image_path / f"{image_uuid}.jpg"
)
images_per_wiki += 1
if images_per_wiki > MAX_IMAGES_PER_WIKI:
break
except:
print(str(Exception("No images found for Wikipedia page: ")) + title)
continue
import qdrant_client
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.indices import MultiModalVectorStoreIndex
client = qdrant_client.QdrantClient(path="qdrant_mm_db")
text_store = QdrantVectorStore(
client=client, collection_name="text_collection"
)
image_store = QdrantVectorStore(
client=client, collection_name="image_collection"
)
storage_context = StorageContext.from_defaults(
vector_store=text_store, image_store=image_store
)
documents = | SimpleDirectoryReader("./data_wiki/") | llama_index.core.SimpleDirectoryReader |
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)
json_prompt_str = """\
Please generate related movies to {movie_name}. Output with the following JSON format:
"""
json_prompt_str = output_parser.format(json_prompt_str)
json_prompt_tmpl = PromptTemplate(json_prompt_str)
p = QueryPipeline(chain=[json_prompt_tmpl, llm, output_parser], verbose=True)
output = p.run(movie_name="Toy Story")
output
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
prompt_str2 = """\
Here's some text:
{text}
Can you rewrite this with a summary of each movie?
"""
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = OpenAI(model="gpt-3.5-turbo")
llm_c = llm.as_query_component(streaming=True)
p = QueryPipeline(
chain=[prompt_tmpl, llm_c, prompt_tmpl2, llm_c], verbose=True
)
output = p.run(movie_name="The Dark Knight")
for o in output:
print(o.delta, end="")
p = QueryPipeline(
chain=[
json_prompt_tmpl,
llm.as_query_component(streaming=True),
output_parser,
],
verbose=True,
)
output = p.run(movie_name="Toy Story")
print(output)
from llama_index.postprocessor.cohere_rerank import CohereRerank
prompt_str1 = "Please generate a concise question about Paul Graham's life regarding the following topic {topic}"
prompt_tmpl1 = | PromptTemplate(prompt_str1) | llama_index.core.PromptTemplate |
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system('curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf')
from llama_index.core import SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import DatasetGenerator
documents = SimpleDirectoryReader(
input_files=["IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()
import random
random.seed(42)
random.shuffle(documents)
gpt_35_llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
question_gen_query = (
"You are a Teacher/ Professor. Your task is to setup "
"a quiz/examination. Using the provided context from a "
"report on climate change and the oceans, formulate "
"a single question that captures an important fact from the "
"context. Restrict the question to the context information provided."
)
dataset_generator = DatasetGenerator.from_documents(
documents[:50],
question_gen_query=question_gen_query,
llm=gpt_35_llm,
)
questions = dataset_generator.generate_questions_from_nodes(num=40)
print("Generated ", len(questions), " questions")
with open("train_questions.txt", "w") as f:
for question in questions:
f.write(question + "\n")
dataset_generator = DatasetGenerator.from_documents(
documents[
50:
], # since we generated ~1 question for 40 documents, we can skip the first 40
question_gen_query=question_gen_query,
llm=gpt_35_llm,
)
questions = dataset_generator.generate_questions_from_nodes(num=40)
print("Generated ", len(questions), " questions")
with open("eval_questions.txt", "w") as f:
for question in questions:
f.write(question + "\n")
questions = []
with open("eval_questions.txt", "r") as f:
for line in f:
questions.append(line.strip())
from llama_index.core import VectorStoreIndex, Settings
Settings.context_window = 2048
gpt_35_llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-callbacks-wandb')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks import LlamaDebugHandler
from llama_index.callbacks.wandb import WandbCallbackHandler
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
SimpleKeywordTableIndex,
StorageContext,
)
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-4", temperature=0)
import llama_index.core
from llama_index.core import set_global_handler
set_global_handler("wandb", run_args={"project": "llamaindex"})
wandb_callback = llama_index.core.global_handler
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
run_args = dict(
project="llamaindex",
)
wandb_callback = WandbCallbackHandler(run_args=run_args)
Settings.callback_manager = CallbackManager([llama_debug, wandb_callback])
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/") | llama_index.core.SimpleDirectoryReader |
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/").load_data()
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, embed_model=embed_model
)
query_engine = index.as_query_engine(llm=llm)
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))
db = chromadb.PersistentClient(path="./chroma_db")
chroma_collection = db.get_or_create_collection("quickstart")
vector_store = | ChromaVectorStore(chroma_collection=chroma_collection) | llama_index.vector_stores.chroma.ChromaVectorStore |
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
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)
chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)
response = chat_engine.chat("What did Paul Graham do after YC?")
print(response)
response = chat_engine.chat("What about after that?")
print(response)
response = chat_engine.chat("Can you tell me more?")
print(response)
chat_engine.reset()
response = chat_engine.chat("What about after that?")
print(response)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", temperature=0)
data = SimpleDirectoryReader(input_dir="../data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(data) | llama_index.core.VectorStoreIndex.from_documents |
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)
query_engine = vector_index.as_query_engine(similarity_top_k=3)
assert len(query_list) == len(reference_answers_list)
pairwise_local_score = 0
for index in range(0, len(query_list)):
query = query_list[index]
reference = reference_answers_list[index]
if reference != "Unacceptable":
number_of_accepted_queries += 1
response = str(query_engine.query(query))
no_reranker_dict = {
"query": query,
"response": response,
"reference": reference,
}
no_reranker_dict_list.append(no_reranker_dict)
pairwise_eval_result = await evaluator_gpt4_pairwise.aevaluate(
query, response=response, reference=reference
)
pairwise_score = pairwise_eval_result.score
pairwise_local_score += pairwise_score
else:
pass
if number_of_accepted_queries > 0:
avg_pairwise_local_score = (
pairwise_local_score / number_of_accepted_queries
)
pairwise_scores_list.append(avg_pairwise_local_score)
overal_pairwise_average_score = sum(pairwise_scores_list) / len(
pairwise_scores_list
)
df_responses = pd.DataFrame(no_reranker_dict_list)
df_responses.to_csv("No_Reranker_Responses.csv")
results_dict = {
"name": ["Without Reranker"],
"pairwise score": [overal_pairwise_average_score],
}
results_df = pd.DataFrame(results_dict)
display(results_df)
from llama_index.core.postprocessor import SentenceTransformerRerank
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
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
rerank = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-12-v2", top_n=3
)
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4_pairwise = PairwiseComparisonEvaluator(llm=gpt4)
pairwise_scores_list = []
base_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)
query_engine = vector_index.as_query_engine(
similarity_top_k=8, node_postprocessors=[rerank]
)
assert len(query_list) == len(reference_answers_list)
pairwise_local_score = 0
for index in range(0, len(query_list)):
query = query_list[index]
reference = reference_answers_list[index]
if reference != "Unacceptable":
number_of_accepted_queries += 1
response = str(query_engine.query(query))
base_reranker_dict = {
"query": query,
"response": response,
"reference": reference,
}
base_reranker_dict_list.append(base_reranker_dict)
pairwise_eval_result = await evaluator_gpt4_pairwise.aevaluate(
query=query, response=response, reference=reference
)
pairwise_score = pairwise_eval_result.score
pairwise_local_score += pairwise_score
else:
pass
if number_of_accepted_queries > 0:
avg_pairwise_local_score = (
pairwise_local_score / number_of_accepted_queries
)
pairwise_scores_list.append(avg_pairwise_local_score)
overal_pairwise_average_score = sum(pairwise_scores_list) / len(
pairwise_scores_list
)
df_responses = pd.DataFrame(base_reranker_dict_list)
df_responses.to_csv("Base_Reranker_Responses.csv")
results_dict = {
"name": ["With base cross-encoder/ms-marco-MiniLM-L-12-v2 as Reranker"],
"pairwise score": [overal_pairwise_average_score],
}
results_df = pd.DataFrame(results_dict)
display(results_df)
from llama_index.core.postprocessor import SentenceTransformerRerank
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
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
rerank = SentenceTransformerRerank(
model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2", top_n=3
)
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4_pairwise = PairwiseComparisonEvaluator(llm=gpt4)
pairwise_scores_list = []
finetuned_reranker_dict_list = []
for index, row in df_test.iterrows():
documents = [ | Document(text=row["paper"]) | 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)
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=llm_gpt4,
verbose=True,
)
movie_names = [
"The Shining",
"The Departed",
"Titanic",
"Goodfellas",
"Pretty Woman",
"Home Alone",
"Caged Fury",
"Edward Scissorhands",
"Total Recall",
"Ghost",
"Tremors",
"RoboCop",
"Rocky V",
]
from tqdm.notebook import tqdm
for movie_name in tqdm(movie_names):
output = openai_program(movie_name=movie_name)
print(output.json())
events = finetuning_handler.get_finetuning_events()
events
finetuning_handler.save_finetuning_events("mock_finetune_songs.jsonl")
get_ipython().system('cat mock_finetune_songs.jsonl')
base_model_slug = "llama2-7b-chat"
base_llm = GradientBaseModelLLM(
base_model_slug=base_model_slug, max_tokens=500, is_chat_model=True
)
from llama_index.finetuning import GradientFinetuneEngine
finetune_engine = GradientFinetuneEngine(
base_model_slug=base_model_slug,
name="movies_structured",
data_path="mock_finetune_songs.jsonl",
verbose=True,
max_steps=200,
batch_size=1,
)
finetune_engine.model_adapter_id
epochs = 2
for i in range(epochs):
print(f"** EPOCH {i} **")
finetune_engine.finetune()
ft_llm = finetune_engine.get_finetuned_model(
max_tokens=500, is_chat_model=True
)
from llama_index.llms.gradient import GradientModelAdapterLLM
new_prompt_template_str = """\
Generate an example album, with an artist and a list of songs. \
Using the movie {movie_name} as inspiration.\
Please only generate one album.
"""
gradient_program = LLMTextCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Album),
prompt_template_str=new_prompt_template_str,
llm=ft_llm,
verbose=True,
)
gradient_program(movie_name="Goodfellas")
gradient_program(movie_name="Chucky")
base_gradient_program = LLMTextCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Album),
prompt_template_str=prompt_template_str,
llm=base_llm,
verbose=True,
)
base_gradient_program(movie_name="Goodfellas")
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pydantic import Field
from typing import List
class Citation(BaseModel):
"""Citation class."""
author: str = Field(
..., description="Inferred first author (usually last name"
)
year: int = Field(..., description="Inferred year")
desc: str = Field(
...,
description=(
"Inferred description from the text of the work that the author is"
" cited for"
),
)
class Response(BaseModel):
"""List of author citations.
Extracted over unstructured text.
"""
citations: List[Citation] = Field(
...,
description=(
"List of author citations (organized by author, year, and"
" description)."
),
)
from llama_index.readers.file import PyMuPDFReader
from llama_index.core import Document
from llama_index.core.node_parser import SimpleNodeParser
from pathlib import Path
from llama_index.core.callbacks import GradientAIFineTuningHandler
loader = PyMuPDFReader()
docs0 = loader.load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
metadata = {
"paper_title": "Llama 2: Open Foundation and Fine-Tuned Chat Models"
}
docs = [Document(text=doc_text, metadata=metadata)]
chunk_size = 1024
node_parser = SimpleNodeParser.from_defaults(chunk_size=chunk_size)
nodes = node_parser.get_nodes_from_documents(docs)
len(nodes)
finetuning_handler = GradientAIFineTuningHandler()
callback_manager = CallbackManager([finetuning_handler])
llm_gpt4 = OpenAI(model="gpt-4-0613", temperature=0.3)
llm_gpt4.pydantic_program_mode = "llm"
base_model_slug = "llama2-7b-chat"
base_llm = GradientBaseModelLLM(
base_model_slug=base_model_slug, max_tokens=500, is_chat_model=True
)
base_llm.pydantic_program_mode = "llm"
eval_llm = OpenAI(model="gpt-4-0613", temperature=0)
from llama_index.core.evaluation import DatasetGenerator
from llama_index.core import SummaryIndex
from llama_index.core import PromptTemplate
from tqdm.notebook import tqdm
from tqdm.asyncio import tqdm_asyncio
fp = open("data/qa_pairs.jsonl", "w")
question_gen_prompt = PromptTemplate(
"""
{query_str}
Context:
{context_str}
Questions:
"""
)
question_gen_query = """\
Snippets from a research paper is given below. It contains citations.
Please generate questions from the text asking about these citations.
For instance, here are some sample questions:
Which citations correspond to related works on transformer models?
Tell me about authors that worked on advancing RLHF.
Can you tell me citations corresponding to all computer vision works? \
"""
qr_pairs = []
node_questions_tasks = []
for idx, node in enumerate(nodes[:39]):
num_questions = 1 # change this number to increase number of nodes
dataset_generator = DatasetGenerator(
[node],
question_gen_query=question_gen_query,
text_question_template=question_gen_prompt,
llm=eval_llm,
metadata_mode="all",
num_questions_per_chunk=num_questions,
)
task = dataset_generator.agenerate_questions_from_nodes(num=num_questions)
node_questions_tasks.append(task)
node_questions_lists = await tqdm_asyncio.gather(*node_questions_tasks)
len(node_questions_lists)
node_questions_lists[1]
import pickle
pickle.dump(node_questions_lists, open("llama2_questions.pkl", "wb"))
node_questions_lists = pickle.load(open("llama2_questions.pkl", "rb"))
from llama_index.core import VectorStoreIndex
gpt4_index = VectorStoreIndex(nodes[:39], callback_manager=callback_manager)
gpt4_query_engine = gpt4_index.as_query_engine(
output_cls=Response, llm=llm_gpt4, similarity_top_k=1
)
from json import JSONDecodeError
for idx, node in enumerate(tqdm(nodes[:39])):
node_questions_0 = node_questions_lists[idx]
for question in node_questions_0:
try:
gpt4_query_engine.query(question)
except Exception as e:
print(f"Error for question {question}, {repr(e)}")
pass
finetuning_handler.save_finetuning_events("llama2_citation_events.jsonl")
from llama_index.finetuning import GradientFinetuneEngine
finetune_engine = GradientFinetuneEngine(
base_model_slug=base_model_slug,
name="llama2_structured",
data_path="llama2_citation_events.jsonl",
verbose=True,
max_steps=200,
batch_size=1,
)
finetune_engine.model_adapter_id
epochs = 2
for i in range(epochs):
print(f"** EPOCH {i} **")
finetune_engine.finetune()
ft_llm = finetune_engine.get_finetuned_model(max_tokens=500)
from llama_index.core import VectorStoreIndex
vector_index = | VectorStoreIndex(nodes) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
get_ipython().system("mkdir -p 'data/'")
get_ipython().system("curl 'https://arxiv.org/pdf/2307.09288.pdf' -o 'data/llama2.pdf'")
from llama_index.readers.file import UnstructuredReader
documents = | UnstructuredReader() | llama_index.readers.file.UnstructuredReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-packs-rag-fusion-query-pipeline')
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"]) | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-kvstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-firestore')
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, StorageContext
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.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)
from llama_index.storage.kvstore.firestore import FirestoreKVStore
from llama_index.storage.docstore.firestore import FirestoreDocumentStore
from llama_index.storage.index_store.firestore import FirestoreIndexStore
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore=FirestoreDocumentStore(kvstore),
index_store=FirestoreIndexStore(kvstore),
)
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
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore= | FirestoreDocumentStore(kvstore) | llama_index.storage.docstore.firestore.FirestoreDocumentStore |
get_ipython().run_line_magic('pip', 'install llama-index-question-gen-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from IPython.display import Markdown, display
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>"))
from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
from llama_index.core.selectors import (
PydanticMultiSelector,
PydanticSingleSelector,
)
selector = LLMMultiSelector.from_defaults()
from llama_index.core.tools import ToolMetadata
tool_choices = [
ToolMetadata(
name="covid_nyt",
description=("This tool contains a NYT news article about COVID-19"),
),
ToolMetadata(
name="covid_wiki",
description=("This tool contains the Wikipedia page about COVID-19"),
),
ToolMetadata(
name="covid_tesla",
description=("This tool contains the Wikipedia page about apples"),
),
]
display_prompt_dict(selector.get_prompts())
selector_result = selector.select(
tool_choices, query="Tell me more about COVID-19"
)
selector_result.selections
from llama_index.core import PromptTemplate
from llama_index.llms.openai import OpenAI
query_gen_str = """\
You are a helpful assistant that generates multiple search queries based on a \
single input query. Generate {num_queries} search queries, one on each line, \
related to the following input query:
Query: {query}
Queries:
"""
query_gen_prompt = PromptTemplate(query_gen_str)
llm = OpenAI(model="gpt-3.5-turbo")
def generate_queries(query: str, llm, num_queries: int = 4):
response = llm.predict(
query_gen_prompt, num_queries=num_queries, query=query
)
queries = response.split("\n")
queries_str = "\n".join(queries)
print(f"Generated queries:\n{queries_str}")
return queries
queries = generate_queries("What happened at Interleaf and Viaweb?", llm)
queries
from llama_index.core.indices.query.query_transform import HyDEQueryTransform
from llama_index.llms.openai import OpenAI
hyde = | HyDEQueryTransform(include_original=True) | llama_index.core.indices.query.query_transform.HyDEQueryTransform |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.chat_engine import SimpleChatEngine
chat_engine = SimpleChatEngine.from_defaults()
chat_engine.chat_repl()
from llama_index.llms.openai import OpenAI
llm = | OpenAI(temperature=0.0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index-multi-modal-llms-ollama')
get_ipython().system('pip install llama-index-readers-file')
get_ipython().system('pip install unstructured')
get_ipython().system('pip install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index-vector-stores-qdrant')
get_ipython().system('pip install llama-index-embeddings-clip')
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
mm_model = OllamaMultiModal(model="llava:13b")
from pathlib import Path
from llama_index.core import SimpleDirectoryReader
from PIL import Image
import matplotlib.pyplot as plt
input_image_path = Path("restaurant_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1GlqcNJhGGbwLKjJK1QJ_nyswCTQ2K2Fq" -O ./restaurant_images/fried_chicken.png')
image_documents = SimpleDirectoryReader("./restaurant_images").load_data()
imageUrl = "./restaurant_images/fried_chicken.png"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from pydantic import BaseModel
class Restaurant(BaseModel):
"""Data model for an restaurant."""
restaurant: str
food: str
discount: str
price: str
rating: str
review: str
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
{query_str}
Return the answer as a Pydantic object. The Pydantic schema is given below:
"""
mm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser= | PydanticOutputParser(Restaurant) | llama_index.core.output_parsers.PydanticOutputParser |
get_ipython().system('pip install llama-index-llms-dashscope')
get_ipython().run_line_magic('env', 'DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY')
import os
os.environ["DASHSCOPE_API_KEY"] = "YOUR_DASHSCOPE_API_KEY"
from llama_index.llms.dashscope import DashScope, DashScopeGenerationModels
dashscope_llm = DashScope(model_name=DashScopeGenerationModels.QWEN_MAX)
resp = dashscope_llm.complete("How to make cake?")
print(resp)
responses = dashscope_llm.stream_complete("How to make cake?")
for response in responses:
print(response.delta, end="")
from llama_index.core.base.llms.types import MessageRole, ChatMessage
messages = [
ChatMessage(
role=MessageRole.SYSTEM, content="You are a helpful assistant."
),
ChatMessage(role=MessageRole.USER, content="How to make cake?"),
]
resp = dashscope_llm.chat(messages)
print(resp)
responses = dashscope_llm.stream_chat(messages)
for response in responses:
print(response.delta, end="")
messages = [
ChatMessage(
role=MessageRole.SYSTEM, content="You are a helpful assistant."
),
| ChatMessage(role=MessageRole.USER, content="How to make cake?") | llama_index.core.base.llms.types.ChatMessage |
get_ipython().system('pip install llama-index-multi-modal-llms-ollama')
get_ipython().system('pip install llama-index-readers-file')
get_ipython().system('pip install unstructured')
get_ipython().system('pip install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index-vector-stores-qdrant')
get_ipython().system('pip install llama-index-embeddings-clip')
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
mm_model = OllamaMultiModal(model="llava:13b")
from pathlib import Path
from llama_index.core import SimpleDirectoryReader
from PIL import Image
import matplotlib.pyplot as plt
input_image_path = Path("restaurant_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1GlqcNJhGGbwLKjJK1QJ_nyswCTQ2K2Fq" -O ./restaurant_images/fried_chicken.png')
image_documents = SimpleDirectoryReader("./restaurant_images").load_data()
imageUrl = "./restaurant_images/fried_chicken.png"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from pydantic import BaseModel
class Restaurant(BaseModel):
"""Data model for an restaurant."""
restaurant: str
food: str
discount: str
price: str
rating: str
review: str
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
{query_str}
Return the answer as a Pydantic object. The Pydantic schema is given below:
"""
mm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(Restaurant),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=mm_model,
verbose=True,
)
response = mm_program(query_str="Can you summarize what is in the image?")
for res in response:
print(res)
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1THe1qqM61lretr9N3BmINc_NWDvuthYf" -O shanghai.jpg')
from pathlib import Path
from llama_index.readers.file import UnstructuredReader
from llama_index.core.schema import ImageDocument
loader = UnstructuredReader()
documents = loader.load_data(file=Path("tesla_2021_10k.htm"))
image_doc = ImageDocument(image_path="./shanghai.jpg")
from llama_index.core import VectorStoreIndex
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-m3")
vector_index = VectorStoreIndex.from_documents(
documents, embed_model=embed_model
)
query_engine = vector_index.as_query_engine()
from llama_index.core.prompts import PromptTemplate
from llama_index.core.query_pipeline import QueryPipeline, FnComponent
query_prompt_str = """\
Please expand the initial statement using the provided context from the Tesla 10K report.
{initial_statement}
"""
query_prompt_tmpl = PromptTemplate(query_prompt_str)
qp = QueryPipeline(
modules={
"mm_model": mm_model.as_query_component(
partial={"image_documents": [image_doc]}
),
"query_prompt": query_prompt_tmpl,
"query_engine": query_engine,
},
verbose=True,
)
qp.add_chain(["mm_model", "query_prompt", "query_engine"])
rag_response = qp.run("Which Tesla Factory is shown in the image?")
print(f"> Retrieval Augmented Response: {rag_response}")
rag_response.source_nodes[1].get_content()
get_ipython().system('wget "https://drive.usercontent.google.com/download?id=1qQDcaKuzgRGuEC1kxgYL_4mx7vG-v4gC&export=download&authuser=1&confirm=t&uuid=f944e95f-a31f-4b55-b68f-8ea67a6e90e5&at=APZUnTVZ6n1aOg7rtkcjBjw7Pt1D:1707010667927" -O mixed_wiki.zip')
get_ipython().system('unzip mixed_wiki.zip')
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O ./mixed_wiki/tesla_2021_10k.htm')
from llama_index.core.indices.multi_modal.base import (
MultiModalVectorStoreIndex,
)
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.embeddings.clip import ClipEmbedding
import qdrant_client
from llama_index import (
SimpleDirectoryReader,
)
client = qdrant_client.QdrantClient(path="qdrant_mm_db")
text_store = QdrantVectorStore(
client=client, collection_name="text_collection"
)
image_store = QdrantVectorStore(
client=client, collection_name="image_collection"
)
storage_context = StorageContext.from_defaults(
vector_store=text_store, image_store=image_store
)
image_embed_model = ClipEmbedding()
documents = SimpleDirectoryReader("./mixed_wiki/").load_data()
index = MultiModalVectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
image_embed_model=image_embed_model,
)
from llama_index.core.prompts import PromptTemplate
from llama_index.core.query_engine import SimpleMultiModalQueryEngine
qa_tmpl_str = (
"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: "
)
qa_tmpl = | PromptTemplate(qa_tmpl_str) | llama_index.core.prompts.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.node_parser import SentenceWindowNodeParser
from llama_index.core.node_parser import SentenceSplitter
node_parser = SentenceWindowNodeParser.from_defaults(
window_size=3,
window_metadata_key="window",
original_text_metadata_key="original_text",
)
text_splitter = SentenceSplitter()
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)
embed_model = HuggingFaceEmbedding(
model_name="sentence-transformers/all-mpnet-base-v2", max_length=512
)
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.text_splitter = text_splitter
get_ipython().system('curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf')
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader(
input_files=["./IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()
nodes = node_parser.get_nodes_from_documents(documents)
base_nodes = text_splitter.get_nodes_from_documents(documents)
from llama_index.core import VectorStoreIndex
sentence_index = VectorStoreIndex(nodes)
base_index = | VectorStoreIndex(base_nodes) | llama_index.core.VectorStoreIndex |
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() | llama_index.core.agent.ReActChatFormatter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
from llama_index.core import Settings
Settings.llm = | OpenAI(temperature=0.2, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
import nest_asyncio
nest_asyncio.apply()
from llama_index.core import SimpleDirectoryReader, Document
from llama_index.core import SummaryIndex
from llama_index.llms.openai import OpenAI
from llama_index.llms.anthropic import Anthropic
from llama_index.core.evaluation import CorrectnessEvaluator
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'")
uber_docs0 = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
uber_doc = Document(text="\n\n".join([d.get_content() for d in uber_docs0]))
from llama_index.core.utils import globals_helper
num_tokens = len(globals_helper.tokenizer(uber_doc.get_content()))
print(f"NUM TOKENS: {num_tokens}")
context_str = "Jerry's favorite snack is Hot Cheetos."
query_str = "What is Jerry's favorite snack?"
def augment_doc(doc_str, context, position):
"""Augment doc with additional context at a given position."""
doc_str1 = doc_str[:position]
doc_str2 = doc_str[position:]
return f"{doc_str1}...\n\n{context}\n\n...{doc_str2}"
test_str = augment_doc(
uber_doc.get_content(), context_str, int(0.5 * len(uber_doc.get_content()))
)
async def run_experiments(
doc, position_percentiles, context_str, query, llm, response_mode="compact"
):
eval_llm = OpenAI(model="gpt-4-1106-preview")
correctness_evaluator = CorrectnessEvaluator(llm=eval_llm)
eval_scores = {}
for idx, position_percentile in enumerate(position_percentiles):
print(f"Position percentile: {position_percentile}")
position_idx = int(position_percentile * len(uber_doc.get_content()))
new_doc_str = augment_doc(
uber_doc.get_content(), context_str, position_idx
)
new_doc = | Document(text=new_doc_str) | llama_index.core.Document |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-flag-embedding-reranker')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install git+https://github.com/FlagOpen/FlagEmbedding.git')
from llama_index.core import VectorStoreIndex, 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'")
import os
OPENAI_API_TOKEN = "sk-"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-langchain')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gradient')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('pip', 'install llama-index --quiet')
get_ipython().run_line_magic('pip', 'install gradientai --quiet')
import os
os.environ["GRADIENT_ACCESS_TOKEN"] = "{GRADIENT_ACCESS_TOKEN}"
os.environ["GRADIENT_WORKSPACE_ID"] = "{GRADIENT_WORKSPACE_ID}"
from llama_index.llms.gradient import GradientBaseModelLLM
llm = GradientBaseModelLLM(
base_model_slug="llama2-7b-chat",
max_tokens=400,
)
result = llm.complete("Can you tell me about large language models?")
print(result)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.langchain import LangchainEmbedding
from langchain.embeddings import HuggingFaceEmbeddings
from llama_index.core.node_parser import SentenceSplitter
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/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-github')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = ""
import os
from llama_index.readers.github import GitHubRepositoryIssuesReader, GitHubIssuesClient
github_client = | GitHubIssuesClient() | llama_index.readers.github.GitHubIssuesClient |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
import os
from getpass import getpass
if os.getenv("HONEYHIVE_API_KEY") is None:
os.environ["HONEYHIVE_API_KEY"] = getpass(
"Paste your HoneyHive key from:"
" https://app.honeyhive.ai/settings/account\n"
)
print("HoneyHive API key configured")
get_ipython().system('pip install llama-index')
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks import LlamaDebugHandler
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
SimpleKeywordTableIndex,
StorageContext,
)
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from honeyhive.utils.llamaindex_tracer import HoneyHiveLlamaIndexTracer
from llama_index.core import Settings
Settings.llm = | OpenAI(model="gpt-4", temperature=0) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().system('pip install llama-index qdrant_client pyMuPDF tools frontend git+https://github.com/openai/CLIP.git easyocr')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import Patch
import io
from PIL import Image, ImageDraw
import numpy as np
import csv
import pandas as pd
from torchvision import transforms
from transformers import AutoModelForObjectDetection
import torch
import openai
import os
import fitz
device = "cuda" if torch.cuda.is_available() else "cpu"
OPENAI_API_TOKEN = "sk-<your-openai-api-token>"
openai.api_key = OPENAI_API_TOKEN
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "llama2.pdf"')
pdf_file = "llama2.pdf"
output_directory_path, _ = os.path.splitext(pdf_file)
if not os.path.exists(output_directory_path):
os.makedirs(output_directory_path)
pdf_document = fitz.open(pdf_file)
for page_number in range(pdf_document.page_count):
page = pdf_document[page_number]
pix = page.get_pixmap()
image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
image.save(f"./{output_directory_path}/page_{page_number + 1}.png")
pdf_document.close()
from PIL import Image
import matplotlib.pyplot as plt
import os
image_paths = []
for img_path in os.listdir("./llama2"):
image_paths.append(str(os.path.join("./llama2", img_path)))
def plot_images(image_paths):
images_shown = 0
plt.figure(figsize=(16, 9))
for img_path in image_paths:
if os.path.isfile(img_path):
image = Image.open(img_path)
plt.subplot(3, 3, images_shown + 1)
plt.imshow(image)
plt.xticks([])
plt.yticks([])
images_shown += 1
if images_shown >= 9:
break
plot_images(image_paths[9:12])
import qdrant_client
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.indices import MultiModalVectorStoreIndex
from llama_index.core.schema import ImageDocument
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.schema import ImageNode
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
openai_mm_llm = OpenAIMultiModal(
model="gpt-4-vision-preview", api_key=OPENAI_API_TOKEN, max_new_tokens=1500
)
documents_images = SimpleDirectoryReader("./llama2/").load_data()
client = qdrant_client.QdrantClient(path="qdrant_index")
text_store = QdrantVectorStore(
client=client, collection_name="text_collection"
)
image_store = QdrantVectorStore(
client=client, collection_name="image_collection"
)
storage_context = StorageContext.from_defaults(
vector_store=text_store, image_store=image_store
)
index = MultiModalVectorStoreIndex.from_documents(
documents_images,
storage_context=storage_context,
)
retriever_engine = index.as_retriever(image_similarity_top_k=2)
from llama_index.core.indices.multi_modal.retriever import (
MultiModalVectorIndexRetriever,
)
query = "Compare llama2 with llama1?"
assert isinstance(retriever_engine, MultiModalVectorIndexRetriever)
retrieval_results = retriever_engine.text_to_image_retrieve(query)
retrieved_images = []
for res_node in retrieval_results:
if isinstance(res_node.node, ImageNode):
retrieved_images.append(res_node.node.metadata["file_path"])
else:
display_source_node(res_node, source_length=200)
plot_images(retrieved_images)
retrieved_images
image_documents = [
| ImageDocument(image_path=image_path) | llama_index.core.schema.ImageDocument |
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)
summaries = [
(
"This node provides information about the world's richest billionaires"
" in 2023"
),
(
"This node provides information on the number of billionaires and"
" their combined net worth from 2000 to 2023."
),
]
df_nodes = [
IndexNode(text=summary, index_id=f"pandas{idx}")
for idx, summary in enumerate(summaries)
]
df_id_query_engine_mapping = {
f"pandas{idx}": df_query_engine
for idx, df_query_engine in enumerate(df_query_engines)
}
vector_index = VectorStoreIndex(doc_nodes + df_nodes)
vector_retriever = vector_index.as_retriever(similarity_top_k=1)
vector_index0 = VectorStoreIndex(doc_nodes)
vector_query_engine0 = vector_index0.as_query_engine()
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import get_response_synthesizer
recursive_retriever = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever},
query_engine_dict=df_id_query_engine_mapping,
verbose=True,
)
response_synthesizer = | get_response_synthesizer(response_mode="compact") | llama_index.core.get_response_synthesizer |
get_ipython().run_line_magic('pip', 'install llama-index-llms-azure-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-nebula')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-azure-openai')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import logging
import sys
logging.basicConfig(
stream=sys.stdout, level=logging.INFO
) # logging.DEBUG for more verbose output
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
api_key = "<api-key>"
azure_endpoint = "https://<your-resource-name>.openai.azure.com/"
api_version = "2023-07-01-preview"
llm = AzureOpenAI(
model="gpt-35-turbo-16k",
deployment_name="my-custom-llm",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
embed_model = AzureOpenAIEmbedding(
model="text-embedding-ada-002",
deployment_name="my-custom-embedding",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512
get_ipython().run_line_magic('pip', 'install ipython-ngql nebula3-python')
os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula" # default is "nebula"
os.environ[
"NEBULA_ADDRESS"
] = "127.0.0.1:9669" # assumed we have NebulaGraph installed locally
space_name = "llamaindex"
edge_types, rel_prop_names = ["relationship"], [
"relationship"
] # default, could be omit if create from an empty kg
tags = ["entity"] # default, could be omit if create from an empty kg
from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore
graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
)
storage_context = | StorageContext.from_defaults(graph_store=graph_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-callbacks-wandb')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks import LlamaDebugHandler
from llama_index.callbacks.wandb import WandbCallbackHandler
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
SimpleKeywordTableIndex,
StorageContext,
)
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = | OpenAI(model="gpt-4", temperature=0) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
from llama_index.core.evaluation import generate_question_context_pairs
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import 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'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
node_parser = SentenceSplitter(chunk_size=512)
nodes = node_parser.get_nodes_from_documents(documents)
for idx, node in enumerate(nodes):
node.id_ = f"node_{idx}"
llm = OpenAI(model="gpt-4")
vector_index = VectorStoreIndex(nodes)
retriever = vector_index.as_retriever(similarity_top_k=2)
retrieved_nodes = retriever.retrieve("What did the author do growing up?")
from llama_index.core.response.notebook_utils import display_source_node
for node in retrieved_nodes:
display_source_node(node, source_length=1000)
from llama_index.core.evaluation import (
generate_question_context_pairs,
EmbeddingQAFinetuneDataset,
)
qa_dataset = generate_question_context_pairs(
nodes, llm=llm, num_questions_per_chunk=2
)
queries = qa_dataset.queries.values()
print(list(queries)[2])
qa_dataset.save_json("pg_eval_dataset.json")
qa_dataset = | EmbeddingQAFinetuneDataset.from_json("pg_eval_dataset.json") | llama_index.core.evaluation.EmbeddingQAFinetuneDataset.from_json |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install clickhouse_connect')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from os import environ
import clickhouse_connect
environ["OPENAI_API_KEY"] = "sk-*"
client = clickhouse_connect.get_client(
host="localhost",
port=8123,
username="default",
password="",
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.clickhouse import ClickHouseVectorStore
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
print("Number of Documents: ", len(documents))
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'")
loader = SimpleDirectoryReader("./data/paul_graham/")
documents = loader.load_data()
for file in loader.input_files:
print(file)
from llama_index.core import StorageContext
for document in documents:
document.metadata = {"user_id": "123", "favorite_color": "blue"}
vector_store = ClickHouseVectorStore(clickhouse_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-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=YOUR_OPENAI_KEY')
get_ipython().system('pip install llama-index pypdf')
get_ipython().system("mkdir -p '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() | llama_index.readers.file.PDFReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-epsilla')
get_ipython().system('pip/pip3 install pyepsilla')
get_ipython().system('pip install llama-index')
import logging
import sys
from llama_index.core import SimpleDirectoryReader, Document, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.epsilla import EpsillaVectorStore
import textwrap
import openai
import getpass
OPENAI_API_KEY = getpass.getpass("OpenAI API Key:")
openai.api_key = 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'")
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import SimpleDirectoryReader
documents_1 = SimpleDirectoryReader(
input_files=["../../community/integrations/vector_stores.md"]
).load_data()
documents_2 = SimpleDirectoryReader(
input_files=["../../module_guides/storing/vector_stores.md"]
).load_data()
from llama_index.core import VectorStoreIndex
index_1 = VectorStoreIndex.from_documents(documents_1)
index_2 = VectorStoreIndex.from_documents(documents_2)
from llama_index.core.retrievers import QueryFusionRetriever
retriever = QueryFusionRetriever(
[index_1.as_retriever(), index_2.as_retriever()],
similarity_top_k=2,
num_queries=4, # set this to 1 to disable query generation
use_async=True,
verbose=True,
)
import nest_asyncio
nest_asyncio.apply()
nodes_with_scores = retriever.retrieve("How do I setup a chroma vector store?")
for node in nodes_with_scores:
print(f"Score: {node.score:.2f} - {node.text[:100]}...")
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = RetrieverQueryEngine.from_args(retriever)
response = query_engine.query(
"How do I setup a chroma vector store? Can you give an example?"
)
from llama_index.core.response.notebook_utils import display_response
| display_response(response) | llama_index.core.response.notebook_utils.display_response |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
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
from llama_index.core import PromptTemplate
from IPython.display import Markdown, display
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.llms.openai import OpenAI
gpt35_llm = OpenAI(model="gpt-3.5-turbo")
gpt4_llm = OpenAI(model="gpt-4")
index = VectorStoreIndex.from_documents(documents)
query_str = "What are the potential risks associated with the use of Llama 2 as mentioned in the context?"
query_engine = index.as_query_engine(similarity_top_k=2, llm=gpt35_llm)
vector_retriever = index.as_retriever(similarity_top_k=2)
response = query_engine.query(query_str)
print(str(response))
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)
from langchain import hub
langchain_prompt = hub.pull("rlm/rag-prompt")
from llama_index.core.prompts import LangchainPromptTemplate
lc_prompt_tmpl = LangchainPromptTemplate(
template=langchain_prompt,
template_var_mappings={"query_str": "question", "context_str": "context"},
)
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": lc_prompt_tmpl}
)
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
response = query_engine.query(query_str)
print(str(response))
from llama_index.core.schema import TextNode
few_shot_nodes = []
for line in open("../llama2_qa_citation_events.jsonl", "r"):
few_shot_nodes.append(TextNode(text=line))
few_shot_index = VectorStoreIndex(few_shot_nodes)
few_shot_retriever = few_shot_index.as_retriever(similarity_top_k=2)
import json
def few_shot_examples_fn(**kwargs):
query_str = kwargs["query_str"]
retrieved_nodes = few_shot_retriever.retrieve(query_str)
result_strs = []
for n in retrieved_nodes:
raw_dict = json.loads(n.get_content())
query = raw_dict["query"]
response_dict = json.loads(raw_dict["response"])
result_str = f"""\
Query: {query}
Response: {response_dict}"""
result_strs.append(result_str)
return "\n\n".join(result_strs)
qa_prompt_tmpl_str = """\
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge, \
answer the query asking about citations over different topics.
Please provide your answer in the form of a structured JSON format containing \
a list of authors as the citations. Some examples are given below.
{few_shot_examples}
Query: {query_str}
Answer: \
"""
qa_prompt_tmpl = PromptTemplate(
qa_prompt_tmpl_str,
function_mappings={"few_shot_examples": few_shot_examples_fn},
)
citation_query_str = (
"Which citations are mentioned in the section on Safety RLHF?"
)
print(
qa_prompt_tmpl.format(
query_str=citation_query_str, context_str="test_context"
)
)
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": qa_prompt_tmpl}
)
display_prompt_dict(query_engine.get_prompts())
response = query_engine.query(citation_query_str)
print(str(response))
print(response.source_nodes[1].get_content())
from llama_index.core.postprocessor import (
NERPIINodePostprocessor,
SentenceEmbeddingOptimizer,
)
from llama_index.core import QueryBundle
from llama_index.core.schema import NodeWithScore, TextNode
pii_processor = NERPIINodePostprocessor(llm=gpt4_llm)
def filter_pii_fn(**kwargs):
query_bundle = | QueryBundle(query_str=kwargs["query_str"]) | llama_index.core.QueryBundle |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-docarray')
get_ipython().system('pip install llama-index')
import os
import sys
import logging
import textwrap
import warnings
warnings.filterwarnings("ignore")
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from llama_index.core import (
GPTVectorStoreIndex,
SimpleDirectoryReader,
Document,
)
from llama_index.vector_stores.docarray import DocArrayHnswVectorStore
from IPython.display import Markdown, display
import os
os.environ["OPENAI_API_KEY"] = "<your openai 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'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(
"Document ID:",
documents[0].doc_id,
"Document Hash:",
documents[0].doc_hash,
)
from llama_index.core import StorageContext
vector_store = DocArrayHnswVectorStore(work_dir="hnsw_index")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = GPTVectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text="The Shawshank Redemption",
metadata={
"author": "Stephen King",
"theme": "Friendship",
},
),
TextNode(
text="The Godfather",
metadata={
"director": "Francis Ford Coppola",
"theme": "Mafia",
},
),
TextNode(
text="Inception",
metadata={
"director": "Christopher Nolan",
},
),
]
from llama_index.core import StorageContext
vector_store = | DocArrayHnswVectorStore(work_dir="hnsw_filters") | llama_index.vector_stores.docarray.DocArrayHnswVectorStore |
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)
query_engine = vector_index.as_query_engine(similarity_top_k=3)
assert len(query_list) == len(reference_answers_list)
pairwise_local_score = 0
for index in range(0, len(query_list)):
query = query_list[index]
reference = reference_answers_list[index]
if reference != "Unacceptable":
number_of_accepted_queries += 1
response = str(query_engine.query(query))
no_reranker_dict = {
"query": query,
"response": response,
"reference": reference,
}
no_reranker_dict_list.append(no_reranker_dict)
pairwise_eval_result = await evaluator_gpt4_pairwise.aevaluate(
query, response=response, reference=reference
)
pairwise_score = pairwise_eval_result.score
pairwise_local_score += pairwise_score
else:
pass
if number_of_accepted_queries > 0:
avg_pairwise_local_score = (
pairwise_local_score / number_of_accepted_queries
)
pairwise_scores_list.append(avg_pairwise_local_score)
overal_pairwise_average_score = sum(pairwise_scores_list) / len(
pairwise_scores_list
)
df_responses = pd.DataFrame(no_reranker_dict_list)
df_responses.to_csv("No_Reranker_Responses.csv")
results_dict = {
"name": ["Without Reranker"],
"pairwise score": [overal_pairwise_average_score],
}
results_df = pd.DataFrame(results_dict)
display(results_df)
from llama_index.core.postprocessor import SentenceTransformerRerank
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
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
rerank = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-12-v2", top_n=3
)
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4_pairwise = PairwiseComparisonEvaluator(llm=gpt4)
pairwise_scores_list = []
base_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)
query_engine = vector_index.as_query_engine(
similarity_top_k=8, node_postprocessors=[rerank]
)
assert len(query_list) == len(reference_answers_list)
pairwise_local_score = 0
for index in range(0, len(query_list)):
query = query_list[index]
reference = reference_answers_list[index]
if reference != "Unacceptable":
number_of_accepted_queries += 1
response = str(query_engine.query(query))
base_reranker_dict = {
"query": query,
"response": response,
"reference": reference,
}
base_reranker_dict_list.append(base_reranker_dict)
pairwise_eval_result = await evaluator_gpt4_pairwise.aevaluate(
query=query, response=response, reference=reference
)
pairwise_score = pairwise_eval_result.score
pairwise_local_score += pairwise_score
else:
pass
if number_of_accepted_queries > 0:
avg_pairwise_local_score = (
pairwise_local_score / number_of_accepted_queries
)
pairwise_scores_list.append(avg_pairwise_local_score)
overal_pairwise_average_score = sum(pairwise_scores_list) / len(
pairwise_scores_list
)
df_responses = pd.DataFrame(base_reranker_dict_list)
df_responses.to_csv("Base_Reranker_Responses.csv")
results_dict = {
"name": ["With base cross-encoder/ms-marco-MiniLM-L-12-v2 as Reranker"],
"pairwise score": [overal_pairwise_average_score],
}
results_df = pd.DataFrame(results_dict)
display(results_df)
from llama_index.core.postprocessor import SentenceTransformerRerank
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
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
rerank = SentenceTransformerRerank(
model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2", top_n=3
)
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4_pairwise = PairwiseComparisonEvaluator(llm=gpt4)
pairwise_scores_list = []
finetuned_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().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().handlers = []
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
SimpleKeywordTableIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import 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'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
llm = OpenAI(model="gpt-4")
splitter = SentenceSplitter(chunk_size=1024)
nodes = splitter.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)
list_retriever = summary_index.as_retriever()
vector_retriever = vector_index.as_retriever()
keyword_retriever = keyword_index.as_retriever()
from llama_index.core.tools import RetrieverTool
list_tool = RetrieverTool.from_defaults(
retriever=list_retriever,
description=(
"Will retrieve all context from Paul Graham's essay on What I Worked"
" On. Don't use if the question only requires more specific context."
),
)
vector_tool = RetrieverTool.from_defaults(
retriever=vector_retriever,
description=(
"Useful for retrieving specific context from Paul Graham essay on What"
" I Worked On."
),
)
keyword_tool = RetrieverTool.from_defaults(
retriever=keyword_retriever,
description=(
"Useful for retrieving specific context from Paul Graham essay on What"
" I Worked On (using entities mentioned in query)"
),
)
from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
from llama_index.core.selectors import (
PydanticMultiSelector,
PydanticSingleSelector,
)
from llama_index.core.retrievers import RouterRetriever
from llama_index.core.response.notebook_utils import display_source_node
retriever = RouterRetriever(
selector=PydanticSingleSelector.from_defaults(llm=llm),
retriever_tools=[
list_tool,
vector_tool,
],
)
nodes = retriever.retrieve(
"Can you give me all the context regarding the author's life?"
)
for node in nodes:
| display_source_node(node) | llama_index.core.response.notebook_utils.display_source_node |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().system('pip install llama-index')
from llama_index.core.node_parser import SimpleFileNodeParser
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
html_file = reader.load_data(Path("./stack-overflow.html"))
md_file = reader.load_data(Path("./README.md"))
print(html_file[0].metadata)
print(html_file[0])
print("----")
print(md_file[0].metadata)
print(md_file[0])
parser = SimpleFileNodeParser()
md_nodes = parser.get_nodes_from_documents(md_file)
html_nodes = parser.get_nodes_from_documents(html_file)
print(md_nodes[0].metadata)
print(md_nodes[0].text)
print(md_nodes[1].metadata)
print(md_nodes[1].text)
print("----")
print(html_nodes[0].metadata)
print(html_nodes[0].text)
from llama_index.core.node_parser import SentenceSplitter
splitting_parser = SentenceSplitter(chunk_size=200, chunk_overlap=0)
html_chunked_nodes = splitting_parser(html_nodes)
md_chunked_nodes = splitting_parser(md_nodes)
print(f"\n\nHTML parsed nodes: {len(html_nodes)}")
print(html_nodes[0].text)
print(f"\n\nHTML chunked nodes: {len(html_chunked_nodes)}")
print(html_chunked_nodes[0].text)
print(f"\n\nMD parsed nodes: {len(md_nodes)}")
print(md_nodes[0].text)
print(f"\n\nMD chunked nodes: {len(md_chunked_nodes)}")
print(md_chunked_nodes[0].text)
from llama_index.core.ingestion import IngestionPipeline
pipeline = IngestionPipeline(
documents=reader.load_data(Path("./README.md")),
transformations=[
| SimpleFileNodeParser() | llama_index.core.node_parser.SimpleFileNodeParser |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-milvus')
get_ipython().system(' pip install llama-index')
import logging
import sys
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document
from llama_index.vector_stores.milvus import MilvusVectorStore
from IPython.display import Markdown, display
import textwrap
import openai
openai.api_key = "sk-"
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()
print("Document ID:", documents[0].doc_id)
from llama_index.core import StorageContext
vector_store = MilvusVectorStore(dim=1536, overwrite=True)
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 learn?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))
vector_store = MilvusVectorStore(dim=1536, overwrite=True)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
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.core.tools import QueryEngineTool, ToolMetadata
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/lyft"
)
lyft_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/uber"
)
uber_index = | load_index_from_storage(storage_context) | llama_index.core.load_index_from_storage |
get_ipython().run_line_magic('pip', 'install llama-index-llms-watsonx')
from llama_index.llms.watsonx import WatsonX
credentials = {
"url": "https://enter.your-ibm.url",
"apikey": "insert_your_api_key",
}
project_id = "insert_your_project_id"
resp = WatsonX(credentials=credentials, project_id=project_id).complete(
"Paul Graham is"
)
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.watsonx import WatsonX
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="Tell me a story"),
]
resp = WatsonX(
model_id="meta-llama/llama-2-70b-chat",
credentials=credentials,
project_id=project_id,
).chat(messages)
print(resp)
from llama_index.llms.watsonx import WatsonX
llm = | WatsonX(credentials=credentials, project_id=project_id) | llama_index.llms.watsonx.WatsonX |
get_ipython().system('pip install llama-index')
import logging
import sys
from IPython.display import Markdown, display
import pandas as pd
from llama_index.core.query_engine import PandasQueryEngine
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
df = pd.DataFrame(
{
"city": ["Toronto", "Tokyo", "Berlin"],
"population": [2930000, 13960000, 3645000],
}
)
query_engine = | PandasQueryEngine(df=df, verbose=True) | llama_index.core.query_engine.PandasQueryEngine |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
from llama_index.readers.file import ImageTabularChartReader
from llama_index.core import SummaryIndex
from llama_index.core.response.notebook_utils import display_response
from pathlib import Path
loader = ImageTabularChartReader(keep_image=True)
documents = loader.load_data(file=Path("./marine_chart.png"))
print(documents[0].text)
summary_index = SummaryIndex.from_documents(documents)
response = summary_index.as_query_engine().query(
"What is the difference between the shares of Greenland and the share of"
" Mauritania?"
)
| display_response(response, show_source=True) | llama_index.core.response.notebook_utils.display_response |
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) | llama_index.core.query_pipeline.QueryPipeline |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
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
from llama_index.core import PromptTemplate
from IPython.display import Markdown, display
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.llms.openai import OpenAI
gpt35_llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-friendli')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('env', 'FRIENDLI_TOKEN=...')
from llama_index.llms.friendli import Friendli
llm = | Friendli() | llama_index.llms.friendli.Friendli |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core.agent import (
CustomSimpleAgentWorker,
Task,
AgentChatResponse,
)
from typing import Dict, Any, List, Tuple, Optional
from llama_index.core.tools import BaseTool, QueryEngineTool
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core import ChatPromptTemplate, PromptTemplate
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.bridge.pydantic import Field, BaseModel
from llama_index.core.llms import ChatMessage, MessageRole
DEFAULT_PROMPT_STR = """
Given previous question/response pairs, please determine if an error has occurred in the response, and suggest \
a modified question that will not trigger the error.
Examples of modified questions:
- The question itself is modified to elicit a non-erroneous response
- The question is augmented with context that will help the downstream system better answer the question.
- The question is augmented with examples of negative responses, or other negative questions.
An error means that either an exception has triggered, or the response is completely irrelevant to the question.
Please return the evaluation of the response in the following JSON format.
"""
def get_chat_prompt_template(
system_prompt: str, current_reasoning: Tuple[str, str]
) -> ChatPromptTemplate:
system_msg = ChatMessage(role=MessageRole.SYSTEM, content=system_prompt)
messages = [system_msg]
for raw_msg in current_reasoning:
if raw_msg[0] == "user":
messages.append(
ChatMessage(role=MessageRole.USER, content=raw_msg[1])
)
else:
messages.append(
| ChatMessage(role=MessageRole.ASSISTANT, content=raw_msg[1]) | llama_index.core.llms.ChatMessage |
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-finetuning')
import json
from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import MetadataMode
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'")
TRAIN_FILES = ["./data/10k/lyft_2021.pdf"]
VAL_FILES = ["./data/10k/uber_2021.pdf"]
TRAIN_CORPUS_FPATH = "./data/train_corpus.json"
VAL_CORPUS_FPATH = "./data/val_corpus.json"
def load_corpus(files, verbose=False):
if verbose:
print(f"Loading files {files}")
reader = SimpleDirectoryReader(input_files=files)
docs = reader.load_data()
if verbose:
print(f"Loaded {len(docs)} docs")
parser = SentenceSplitter()
nodes = parser.get_nodes_from_documents(docs, show_progress=verbose)
if verbose:
print(f"Parsed {len(nodes)} nodes")
return nodes
train_nodes = load_corpus(TRAIN_FILES, verbose=True)
val_nodes = load_corpus(VAL_FILES, verbose=True)
from llama_index.finetuning import generate_qa_embedding_pairs
from llama_index.core.evaluation import EmbeddingQAFinetuneDataset
import os
OPENAI_API_TOKEN = "sk-"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
from llama_index.llms.openai import OpenAI
train_dataset = generate_qa_embedding_pairs(
llm=OpenAI(model="gpt-3.5-turbo"), nodes=train_nodes
)
val_dataset = generate_qa_embedding_pairs(
llm=OpenAI(model="gpt-3.5-turbo"), nodes=val_nodes
)
train_dataset.save_json("train_dataset.json")
val_dataset.save_json("val_dataset.json")
train_dataset = EmbeddingQAFinetuneDataset.from_json("train_dataset.json")
val_dataset = EmbeddingQAFinetuneDataset.from_json("val_dataset.json")
from llama_index.finetuning import SentenceTransformersFinetuneEngine
finetune_engine = SentenceTransformersFinetuneEngine(
train_dataset,
model_id="BAAI/bge-small-en",
model_output_path="test_model",
val_dataset=val_dataset,
)
finetune_engine.finetune()
embed_model = finetune_engine.get_finetuned_model()
embed_model
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import VectorStoreIndex
from llama_index.core.schema import TextNode
from tqdm.notebook import tqdm
import pandas as pd
def evaluate(
dataset,
embed_model,
top_k=5,
verbose=False,
):
corpus = dataset.corpus
queries = dataset.queries
relevant_docs = dataset.relevant_docs
nodes = [ | TextNode(id_=id_, text=text) | llama_index.core.schema.TextNode |
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)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
for idx, node in enumerate(base_nodes):
node.id_ = f"node-{idx}"
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-small-en")
llm = OpenAI(model="gpt-3.5-turbo")
base_index = VectorStoreIndex(base_nodes, embed_model=embed_model)
base_retriever = base_index.as_retriever(similarity_top_k=2)
retrievals = base_retriever.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for n in retrievals:
display_source_node(n, source_length=1500)
query_engine_base = RetrieverQueryEngine.from_args(base_retriever, llm=llm)
response = query_engine_base.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
sub_chunk_sizes = [128, 256, 512]
sub_node_parsers = [SentenceSplitter(chunk_size=c) for c in sub_chunk_sizes]
all_nodes = []
for base_node in base_nodes:
for n in sub_node_parsers:
sub_nodes = n.get_nodes_from_documents([base_node])
sub_inodes = [
IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes
]
all_nodes.extend(sub_inodes)
original_node = IndexNode.from_text_node(base_node, base_node.node_id)
all_nodes.append(original_node)
all_nodes_dict = {n.node_id: n for n in all_nodes}
vector_index_chunk = VectorStoreIndex(all_nodes, embed_model=embed_model)
vector_retriever_chunk = vector_index_chunk.as_retriever(similarity_top_k=2)
retriever_chunk = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_chunk},
node_dict=all_nodes_dict,
verbose=True,
)
nodes = retriever_chunk.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for node in nodes:
display_source_node(node, source_length=2000)
query_engine_chunk = RetrieverQueryEngine.from_args(retriever_chunk, llm=llm)
response = query_engine_chunk.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
extractors = [
SummaryExtractor(summaries=["self"], show_progress=True),
QuestionsAnsweredExtractor(questions=5, show_progress=True),
]
metadata_dicts = []
for extractor in extractors:
metadata_dicts.extend(extractor.extract(base_nodes))
def save_metadata_dicts(path):
with open(path, "w") as fp:
for m in metadata_dicts:
fp.write(json.dumps(m) + "\n")
def load_metadata_dicts(path):
with open(path, "r") as fp:
metadata_dicts = [json.loads(l) for l in fp.readlines()]
return metadata_dicts
save_metadata_dicts("data/llama2_metadata_dicts.jsonl")
metadata_dicts = load_metadata_dicts("data/llama2_metadata_dicts.jsonl")
import copy
all_nodes = copy.deepcopy(base_nodes)
for idx, d in enumerate(metadata_dicts):
inode_q = IndexNode(
text=d["questions_this_excerpt_can_answer"],
index_id=base_nodes[idx].node_id,
)
inode_s = IndexNode(
text=d["section_summary"], index_id=base_nodes[idx].node_id
)
all_nodes.extend([inode_q, inode_s])
all_nodes_dict = {n.node_id: n for n in all_nodes}
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install clickhouse_connect')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from os import environ
import clickhouse_connect
environ["OPENAI_API_KEY"] = "sk-*"
client = clickhouse_connect.get_client(
host="localhost",
port=8123,
username="default",
password="",
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.clickhouse import ClickHouseVectorStore
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
print("Number of Documents: ", len(documents))
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'")
loader = SimpleDirectoryReader("./data/paul_graham/")
documents = loader.load_data()
for file in loader.input_files:
print(file)
from llama_index.core import StorageContext
for document in documents:
document.metadata = {"user_id": "123", "favorite_color": "blue"}
vector_store = ClickHouseVectorStore(clickhouse_client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
import textwrap
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
query_engine = index.as_query_engine(
filters=MetadataFilters(
filters=[
| ExactMatchFilter(key="user_id", value="123") | llama_index.core.vector_stores.ExactMatchFilter |
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")
resp = llm.complete("Paul Graham is ")
print(resp)
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_tool
class Song(BaseModel):
"""A song with name and artist"""
name: str
artist: str
song_fn = to_openai_tool(Song)
from llama_index.llms.openai import OpenAI
response = OpenAI().complete("Generate a song", tools=[song_fn])
tool_calls = response.additional_kwargs["tool_calls"]
print(tool_calls)
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="text-davinci-003")
resp = await llm.acomplete("Paul Graham is ")
print(resp)
resp = await llm.astream_complete("Paul Graham is ")
async for delta in resp:
print(delta.delta, end="")
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="text-davinci-003", api_key="BAD_KEY") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install duckdb')
get_ipython().system('pip install llama-index-vector-stores-duckdb')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.duckdb import DuckDBVectorStore
from llama_index.core import StorageContext
from IPython.display import Markdown, display
import os
import openai
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'")
documents = SimpleDirectoryReader("data/paul_graham/").load_data()
vector_store = DuckDBVectorStore()
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>"))
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-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) | 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-index-postprocessor-cohere-rerank')
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().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().handlers = []
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
)
from llama_index.core import SummaryIndex
from llama_index.core.response.notebook_utils import display_response
from llama_index.llms.openai import OpenAI
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.core import Document
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
docs0 = loader.load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
llm = OpenAI(model="gpt-4")
chunk_sizes = [128, 256, 512, 1024]
nodes_list = []
vector_indices = []
for chunk_size in chunk_sizes:
print(f"Chunk Size: {chunk_size}")
splitter = SentenceSplitter(chunk_size=chunk_size)
nodes = splitter.get_nodes_from_documents(docs)
for node in nodes:
node.metadata["chunk_size"] = chunk_size
node.excluded_embed_metadata_keys = ["chunk_size"]
node.excluded_llm_metadata_keys = ["chunk_size"]
nodes_list.append(nodes)
vector_index = VectorStoreIndex(nodes)
vector_indices.append(vector_index)
from llama_index.core.tools import RetrieverTool
from llama_index.core.schema import IndexNode
retriever_dict = {}
retriever_nodes = []
for chunk_size, vector_index in zip(chunk_sizes, vector_indices):
node_id = f"chunk_{chunk_size}"
node = IndexNode(
text=(
"Retrieves relevant context from the Llama 2 paper (chunk size"
f" {chunk_size})"
),
index_id=node_id,
)
retriever_nodes.append(node)
retriever_dict[node_id] = vector_index.as_retriever()
from llama_index.core.selectors import PydanticMultiSelector
from llama_index.core.retrievers import RouterRetriever
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core import SummaryIndex
summary_index = SummaryIndex(retriever_nodes)
retriever = RecursiveRetriever(
root_id="root",
retriever_dict={"root": summary_index.as_retriever(), **retriever_dict},
)
nodes = await retriever.aretrieve(
"Tell me about the main aspects of safety fine-tuning"
)
print(f"Number of nodes: {len(nodes)}")
for node in nodes:
print(node.node.metadata["chunk_size"])
print(node.node.get_text())
from llama_index.core.postprocessor import LLMRerank, SentenceTransformerRerank
from llama_index.postprocessor.cohere_rerank import CohereRerank
reranker = CohereRerank(top_n=10)
from llama_index.core.query_engine import RetrieverQueryEngine
query_engine = RetrieverQueryEngine(retriever, node_postprocessors=[reranker])
response = query_engine.query(
"Tell me about the main aspects of safety fine-tuning"
)
display_response(
response, show_source=True, source_length=500, show_source_metadata=True
)
from collections import defaultdict
import pandas as pd
def mrr_all(metadata_values, metadata_key, source_nodes):
value_to_mrr_dict = {}
for metadata_value in metadata_values:
mrr = 0
for idx, source_node in enumerate(source_nodes):
if source_node.node.metadata[metadata_key] == metadata_value:
mrr = 1 / (idx + 1)
break
else:
continue
value_to_mrr_dict[metadata_value] = mrr
df = pd.DataFrame(value_to_mrr_dict, index=["MRR"])
df.style.set_caption("Mean Reciprocal Rank")
return df
print("Mean Reciprocal Rank for each Chunk Size")
mrr_all(chunk_sizes, "chunk_size", response.source_nodes)
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.llms.openai import OpenAI
import nest_asyncio
nest_asyncio.apply()
eval_llm = OpenAI(model="gpt-4")
dataset_generator = DatasetGenerator(
nodes_list[-1],
llm=eval_llm,
show_progress=True,
num_questions_per_chunk=2,
)
eval_dataset = await dataset_generator.agenerate_dataset_from_nodes(num=60)
eval_dataset.save_json("data/llama2_eval_qr_dataset.json")
eval_dataset = QueryResponseDataset.from_json(
"data/llama2_eval_qr_dataset.json"
)
import asyncio
import nest_asyncio
nest_asyncio.apply()
from llama_index.core.evaluation import (
CorrectnessEvaluator,
SemanticSimilarityEvaluator,
RelevancyEvaluator,
FaithfulnessEvaluator,
PairwiseComparisonEvaluator,
)
evaluator_c = CorrectnessEvaluator(llm=eval_llm)
evaluator_s = SemanticSimilarityEvaluator(llm=eval_llm)
evaluator_r = RelevancyEvaluator(llm=eval_llm)
evaluator_f = FaithfulnessEvaluator(llm=eval_llm)
pairwise_evaluator = PairwiseComparisonEvaluator(llm=eval_llm)
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
from llama_index.core.evaluation import BatchEvalRunner
max_samples = 60
eval_qs = eval_dataset.questions
qr_pairs = eval_dataset.qr_pairs
ref_response_strs = [r for (_, r) in qr_pairs]
base_query_engine = vector_indices[-1].as_query_engine(similarity_top_k=2)
reranker = CohereRerank(top_n=4)
query_engine = RetrieverQueryEngine(retriever, node_postprocessors=[reranker])
base_pred_responses = get_responses(
eval_qs[:max_samples], base_query_engine, show_progress=True
)
pred_responses = get_responses(
eval_qs[:max_samples], query_engine, show_progress=True
)
import numpy as np
pred_response_strs = [str(p) for p in pred_responses]
base_pred_response_strs = [str(p) for p in base_pred_responses]
evaluator_dict = {
"correctness": evaluator_c,
"faithfulness": evaluator_f,
"semantic_similarity": evaluator_s,
}
batch_runner = | BatchEvalRunner(evaluator_dict, workers=1, show_progress=True) | llama_index.core.evaluation.BatchEvalRunner |
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
get_ipython().system('pip install llama-index')
from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings
tokenizer = Anthropic().tokenizer
Settings.tokenizer = tokenizer
import os
os.environ["ANTHROPIC_API_KEY"] = "YOUR ANTHROPIC API KEY"
from llama_index.llms.anthropic import Anthropic
llm = Anthropic(model="claude-3-opus-20240229")
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.anthropic import Anthropic
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="Tell me a story"),
]
resp = Anthropic(model="claude-3-opus-20240229").chat(messages)
print(resp)
from llama_index.llms.anthropic import Anthropic
llm = Anthropic(model="claude-3-opus-20240229", max_tokens=100)
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.anthropic import Anthropic
llm = Anthropic(model="claude-3-opus-20240229")
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
| ChatMessage(role="user", content="Tell me a story") | llama_index.core.llms.ChatMessage |
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-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-program-openai')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.program.openai import OpenAIPydanticProgram
from pydantic import BaseModel
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
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 = | OpenAIFineTuningHandler() | llama_index.finetuning.callbacks.OpenAIFineTuningHandler |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
from llama_index.core.llama_dataset import (
LabelledRagDataExample,
CreatedByType,
CreatedBy,
)
query = "This is a test query, is it not?"
query_by = CreatedBy(type=CreatedByType.AI, model_name="gpt-4")
reference_answer = "Yes it is."
reference_answer_by = CreatedBy(type=CreatedByType.HUMAN)
reference_contexts = ["This is a sample context"]
rag_example = LabelledRagDataExample(
query=query,
query_by=query_by,
reference_contexts=reference_contexts,
reference_answer=reference_answer,
reference_answer_by=reference_answer_by,
)
print(rag_example.json())
LabelledRagDataExample.parse_raw(rag_example.json())
rag_example.dict()
LabelledRagDataExample.parse_obj(rag_example.dict())
query = "This is a test query, is it so?"
reference_answer = "I think yes, it is."
reference_contexts = ["This is a second sample context"]
rag_example_2 = LabelledRagDataExample(
query=query,
query_by=query_by,
reference_contexts=reference_contexts,
reference_answer=reference_answer,
reference_answer_by=reference_answer_by,
)
from llama_index.core.llama_dataset import LabelledRagDataset
rag_dataset = LabelledRagDataset(examples=[rag_example, rag_example_2])
rag_dataset.to_pandas()
rag_dataset.save_json("rag_dataset.json")
reload_rag_dataset = | LabelledRagDataset.from_json("rag_dataset.json") | llama_index.core.llama_dataset.LabelledRagDataset.from_json |
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"]).load_data()
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
client = chromadb.PersistentClient(path="./raptor_paper_db")
collection = client.get_or_create_collection("raptor")
vector_store = ChromaVectorStore(chroma_collection=collection)
raptor_pack = RaptorPack(
documents,
embed_model=OpenAIEmbedding(
model="text-embedding-3-small"
), # used for embedding clusters
llm=OpenAI(model="gpt-3.5-turbo", temperature=0.1), # used for generating summaries
vector_store=vector_store, # used for storage
similarity_top_k=2, # top k for each layer, or overall top-k for collapsed
mode="collapsed", # sets default mode
transformations=[
| SentenceSplitter(chunk_size=400, chunk_overlap=50) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
get_ipython().system('pip install llama-index')
from llama_index.core import SummaryIndex
from llama_index.readers.web import SimpleWebPageReader
from IPython.display import Markdown, display
import os
documents = | SimpleWebPageReader(html_to_text=True) | llama_index.readers.web.SimpleWebPageReader |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('pip -q install python-dotenv pinecone-client llama-index pymupdf')
dotenv_path = (
"env" # Google Colabs will not let you open a .env, but you can set
)
with open(dotenv_path, "w") as f:
f.write('PINECONE_API_KEY="<your api key>"\n')
f.write('PINECONE_ENVIRONMENT="gcp-starter"\n')
f.write('OPENAI_API_KEY="<your api key>"\n')
import os
from dotenv import load_dotenv
load_dotenv(dotenv_path=dotenv_path)
import pinecone
api_key = os.environ["PINECONE_API_KEY"]
environment = os.environ["PINECONE_ENVIRONMENT"]
pinecone.init(api_key=api_key, environment=environment)
index_name = "llamaindex-rag-fs"
pinecone.create_index(
index_name, dimension=1536, metric="euclidean", pod_type="p1"
)
pinecone_index = pinecone.Index(index_name)
pinecone_index.delete(deleteAll=True)
from llama_index.vector_stores.pinecone import PineconeVectorStore
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
import fitz
file_path = "./data/llama2.pdf"
doc = fitz.open(file_path)
from llama_index.core.node_parser import SentenceSplitter
text_parser = SentenceSplitter(
chunk_size=1024,
)
text_chunks = []
doc_idxs = []
for doc_idx, page in enumerate(doc):
page_text = page.get_text("text")
cur_text_chunks = text_parser.split_text(page_text)
text_chunks.extend(cur_text_chunks)
doc_idxs.extend([doc_idx] * len(cur_text_chunks))
from llama_index.core.schema import TextNode
nodes = []
for idx, text_chunk in enumerate(text_chunks):
node = TextNode(
text=text_chunk,
)
src_doc_idx = doc_idxs[idx]
src_page = doc[src_doc_idx]
nodes.append(node)
print(nodes[0].metadata)
print(nodes[0].get_content(metadata_mode="all"))
from llama_index.core.extractors import (
QuestionsAnsweredExtractor,
TitleExtractor,
)
from llama_index.core.ingestion import IngestionPipeline
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
extractors = [
TitleExtractor(nodes=5, llm=llm),
QuestionsAnsweredExtractor(questions=3, llm=llm),
]
pipeline = IngestionPipeline(
transformations=extractors,
)
nodes = await pipeline.arun(nodes=nodes, in_place=False)
print(nodes[0].metadata)
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = | OpenAIEmbedding() | llama_index.embeddings.openai.OpenAIEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-replicate')
get_ipython().run_line_magic('pip', 'install unstructured replicate')
get_ipython().run_line_magic('pip', 'install llama_index ftfy regex tqdm')
get_ipython().run_line_magic('pip', 'install git+https://github.com/openai/CLIP.git')
get_ipython().run_line_magic('pip', 'install torch torchvision')
get_ipython().run_line_magic('pip', 'install matplotlib scikit-image')
get_ipython().run_line_magic('pip', 'install -U qdrant_client')
import os
REPLICATE_API_TOKEN = "..." # Your Relicate API token here
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1UU0xc3uLXs-WG0aDQSXjGacUkp142rLS" -O texas.jpg')
from llama_index.readers.file import FlatReader
from pathlib import Path
from llama_index.core.node_parser import UnstructuredElementNodeParser
reader = FlatReader()
docs_2021 = reader.load_data(Path("tesla_2021_10k.htm"))
node_parser = UnstructuredElementNodeParser()
import openai
OPENAI_API_TOKEN = "..."
openai.api_key = OPENAI_API_TOKEN # add your openai api key here
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
import os
import pickle
if not os.path.exists("2021_nodes.pkl"):
raw_nodes_2021 = node_parser.get_nodes_from_documents(docs_2021)
pickle.dump(raw_nodes_2021, open("2021_nodes.pkl", "wb"))
else:
raw_nodes_2021 = pickle.load(open("2021_nodes.pkl", "rb"))
nodes_2021, objects_2021 = node_parser.get_nodes_and_objects(raw_nodes_2021)
from llama_index.core import VectorStoreIndex
vector_index = VectorStoreIndex(nodes=nodes_2021, objects=objects_2021)
query_engine = vector_index.as_query_engine(similarity_top_k=5, verbose=True)
from PIL import Image
import matplotlib.pyplot as plt
imageUrl = "./texas.jpg"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.schema import ImageDocument
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
print(imageUrl)
llava_multi_modal_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=200,
temperature=0.1,
)
prompt = "which Tesla factory is shown in the image? Please answer just the name of the factory."
llava_response = llava_multi_modal_llm.complete(
prompt=prompt,
image_documents=[ImageDocument(image_path=imageUrl)],
)
print(llava_response.text)
rag_response = query_engine.query(llava_response.text)
print(rag_response)
input_image_path = Path("instagram_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=12ZpBBFkYu-jzz1iz356U5kMikn4uN9ww" -O ./instagram_images/jordan.png')
from pydantic import BaseModel
class InsAds(BaseModel):
"""Data model for a Ins Ads."""
account: str
brand: str
product: str
category: str
discount: str
price: str
comments: str
review: str
description: str
from PIL import Image
import matplotlib.pyplot as plt
ins_imageUrl = "./instagram_images/jordan.png"
image = Image.open(ins_imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
prompt_template_str = """\
can you summarize what is in the image\
and return the answer with json format \
"""
def pydantic_llava(
model_name, output_class, image_documents, prompt_template_str
):
mm_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=1000,
)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_class),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=mm_llm,
verbose=True,
)
response = llm_program()
print(f"Model: {model_name}")
for res in response:
print(res)
return response
from llama_index.core import SimpleDirectoryReader
ins_image_documents = SimpleDirectoryReader("./instagram_images").load_data()
pydantic_response = pydantic_llava(
"llava-13b", InsAds, ins_image_documents, prompt_template_str
)
print(pydantic_response.brand)
from pathlib import Path
import requests
wiki_titles = [
"batman",
"Vincent van Gogh",
"San Francisco",
"iPhone",
"Tesla Model S",
"BTS",
"Air Jordan",
]
data_path = Path("data_wiki")
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"]
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
import wikipedia
import urllib.request
image_path = Path("data_wiki")
image_uuid = 0
image_metadata_dict = {}
MAX_IMAGES_PER_WIKI = 30
wiki_titles = [
"Air Jordan",
"San Francisco",
"Batman",
"Vincent van Gogh",
"iPhone",
"Tesla Model S",
"BTS band",
]
if not image_path.exists():
Path.mkdir(image_path)
for title in wiki_titles:
images_per_wiki = 0
print(title)
try:
page_py = wikipedia.page(title)
list_img_urls = page_py.images
for url in list_img_urls:
if url.endswith(".jpg") or url.endswith(".png"):
image_uuid += 1
image_file_name = title + "_" + url.split("/")[-1]
image_metadata_dict[image_uuid] = {
"filename": image_file_name,
"img_path": "./" + str(image_path / f"{image_uuid}.jpg"),
}
urllib.request.urlretrieve(
url, image_path / f"{image_uuid}.jpg"
)
images_per_wiki += 1
if images_per_wiki > MAX_IMAGES_PER_WIKI:
break
except:
print(str(Exception("No images found for Wikipedia page: ")) + title)
continue
import qdrant_client
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.indices import MultiModalVectorStoreIndex
client = qdrant_client.QdrantClient(path="qdrant_mm_db")
text_store = QdrantVectorStore(
client=client, collection_name="text_collection"
)
image_store = QdrantVectorStore(
client=client, collection_name="image_collection"
)
storage_context = StorageContext.from_defaults(
vector_store=text_store, image_store=image_store
)
documents = SimpleDirectoryReader("./data_wiki/").load_data()
index = MultiModalVectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
)
from PIL import Image
import matplotlib.pyplot as plt
import os
def plot_images(image_metadata_dict):
original_images_urls = []
images_shown = 0
for image_id in image_metadata_dict:
img_path = image_metadata_dict[image_id]["img_path"]
if os.path.isfile(img_path):
filename = image_metadata_dict[image_id]["filename"]
image = Image.open(img_path).convert("RGB")
plt.subplot(8, 8, len(original_images_urls) + 1)
plt.imshow(image)
plt.xticks([])
plt.yticks([])
original_images_urls.append(filename)
images_shown += 1
if images_shown >= 64:
break
plt.tight_layout()
plot_images(image_metadata_dict)
retriever = index.as_retriever(similarity_top_k=3, image_similarity_top_k=5)
retrieval_results = retriever.retrieve(pydantic_response.brand)
from llama_index.core.response.notebook_utils import (
display_source_node,
display_image_uris,
)
from llama_index.core.schema import ImageNode
retrieved_image = []
for res_node in retrieval_results:
if isinstance(res_node.node, ImageNode):
retrieved_image.append(res_node.node.metadata["file_path"])
else:
display_source_node(res_node, source_length=200)
display_image_uris(retrieved_image)
from llama_index.core import PromptTemplate
from llama_index.core.query_engine import SimpleMultiModalQueryEngine
qa_tmpl_str = (
"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: "
)
qa_tmpl = | PromptTemplate(qa_tmpl_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().system('pip install llama-index')
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
pprint_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()
index = VectorStoreIndex.from_documents(documents=documents)
import os
from llama_index.postprocessor.cohere_rerank import CohereRerank
api_key = os.environ["COHERE_API_KEY"]
cohere_rerank = | CohereRerank(api_key=api_key, top_n=2) | llama_index.postprocessor.cohere_rerank.CohereRerank |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().system('pip install llama-index')
from llama_index.core.node_parser import SimpleFileNodeParser
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
html_file = reader.load_data(Path("./stack-overflow.html"))
md_file = reader.load_data(Path("./README.md"))
print(html_file[0].metadata)
print(html_file[0])
print("----")
print(md_file[0].metadata)
print(md_file[0])
parser = SimpleFileNodeParser()
md_nodes = parser.get_nodes_from_documents(md_file)
html_nodes = parser.get_nodes_from_documents(html_file)
print(md_nodes[0].metadata)
print(md_nodes[0].text)
print(md_nodes[1].metadata)
print(md_nodes[1].text)
print("----")
print(html_nodes[0].metadata)
print(html_nodes[0].text)
from llama_index.core.node_parser import SentenceSplitter
splitting_parser = | SentenceSplitter(chunk_size=200, chunk_overlap=0) | llama_index.core.node_parser.SentenceSplitter |
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) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-extractors-entity')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
from llama_index.extractors.entity import EntityExtractor
from llama_index.core.node_parser import SentenceSplitter
entity_extractor = EntityExtractor(
prediction_threshold=0.5,
label_entities=False, # include the entity label in the metadata (can be erroneous)
device="cpu", # set to "cuda" if you have a GPU
)
node_parser = SentenceSplitter()
transformations = [node_parser, entity_extractor]
get_ipython().system('curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf')
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader(
input_files=["./IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()
from llama_index.core.ingestion import IngestionPipeline
import random
random.seed(42)
documents = random.sample(documents, 100)
pipeline = IngestionPipeline(transformations=transformations)
nodes = pipeline.run(documents=documents)
samples = random.sample(nodes, 5)
for node in samples:
print(node.metadata)
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = | OpenAI(model="gpt-3.5-turbo", temperature=0.2) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
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 VectorStoreIndex, SQLDatabase
from llama_index.readers.wikipedia import WikipediaReader
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')
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = | WikipediaReader() | llama_index.readers.wikipedia.WikipediaReader |
import os
from getpass import getpass
if os.getenv("OPENAI_API_KEY") is None:
os.environ["OPENAI_API_KEY"] = getpass(
"Paste your OpenAI key from:"
" https://platform.openai.com/account/api-keys\n"
)
assert os.getenv("OPENAI_API_KEY", "").startswith(
"sk-"
), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")
get_ipython().run_line_magic('pip', 'install -q html2text llama-index pandas pyarrow tqdm')
get_ipython().run_line_magic('pip', 'install -q llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install -q llama-index-callbacks-openinference')
import hashlib
import json
from pathlib import Path
import os
import textwrap
from typing import List, Union
import llama_index.core
from llama_index.readers.web import SimpleWebPageReader
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.callbacks import CallbackManager
from llama_index.callbacks.openinference import OpenInferenceCallbackHandler
from llama_index.callbacks.openinference.base import (
as_dataframe,
QueryData,
NodeData,
)
from llama_index.core.node_parser import SimpleNodeParser
import pandas as pd
from tqdm import tqdm
documents = SimpleWebPageReader().load_data(
[
"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt"
]
)
print(documents[0].text)
parser = | SentenceSplitter() | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-replicate')
get_ipython().system('pip install llama-index')
import os
os.environ["REPLICATE_API_TOKEN"] = "<your API key>"
from llama_index.llms.replicate import Replicate
llm = Replicate(
model="replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b"
)
resp = llm.complete("Who is Paul Graham?")
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)
response = llm.stream_complete("Who is Paul Graham?")
for r in response:
print(r.delta, end="")
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 |
from llama_index.agent import OpenAIAgent
import openai
openai.api_key = "sk-api-key"
from llama_index.tools.gmail.base import GmailToolSpec
from llama_index.tools.google_calendar.base import GoogleCalendarToolSpec
from llama_index.tools.google_search.base import GoogleSearchToolSpec
gmail_tools = | GmailToolSpec() | llama_index.tools.gmail.base.GmailToolSpec |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-retrievers-bm25')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
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().handlers = []
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
SimpleDirectoryReader,
StorageContext,
VectorStoreIndex,
)
from llama_index.retrievers.bm25 import BM25Retriever
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import 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'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
llm = OpenAI(model="gpt-4")
splitter = SentenceSplitter(chunk_size=1024)
nodes = splitter.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
index = VectorStoreIndex(
nodes=nodes,
storage_context=storage_context,
)
retriever = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=2)
from llama_index.core.response.notebook_utils import display_source_node
nodes = retriever.retrieve("What happened at Viaweb and Interleaf?")
for node in nodes:
display_source_node(node)
nodes = retriever.retrieve("What did Paul Graham do after RISD?")
for node in nodes:
display_source_node(node)
from llama_index.core.tools import RetrieverTool
vector_retriever = VectorIndexRetriever(index)
bm25_retriever = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=2)
retriever_tools = [
RetrieverTool.from_defaults(
retriever=vector_retriever,
description="Useful in most cases",
),
RetrieverTool.from_defaults(
retriever=bm25_retriever,
description="Useful if searching about specific information",
),
]
from llama_index.core.retrievers import RouterRetriever
retriever = RouterRetriever.from_defaults(
retriever_tools=retriever_tools,
llm=llm,
select_multi=True,
)
nodes = retriever.retrieve(
"Can you give me all the context regarding the author's life?"
)
for node in nodes:
display_source_node(node)
get_ipython().system('curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf')
from llama_index.core import (
VectorStoreIndex,
StorageContext,
SimpleDirectoryReader,
Document,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
documents = SimpleDirectoryReader(
input_files=["IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()
llm = OpenAI(model="gpt-3.5-turbo")
splitter = SentenceSplitter(chunk_size=256)
nodes = splitter.get_nodes_from_documents(
[Document(text=documents[0].get_content()[:1000000])]
)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-retrievers-bm25')
import os
import openai
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 SimpleDirectoryReader
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
from llama_index.llms.openai import OpenAI
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.postprocessor import LLMRerank
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import Settings
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.packs.koda_retriever import KodaRetriever
import os
from pinecone import Pinecone
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
index = pc.Index("sample-movies")
Settings.llm = OpenAI()
Settings.embed_model = OpenAIEmbedding()
vector_store = | PineconeVectorStore(pinecone_index=index, text_key="summary") | llama_index.vector_stores.pinecone.PineconeVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index')
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west4-gcp-free")
import os
import getpass
import openai
openai.api_key = "sk-<your-key>"
try:
pinecone.create_index(
"quickstart-index", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart-index")
pinecone_index.delete(deleteAll=True, namespace="test")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
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",
"gender": "male",
"born": 1963,
},
),
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",
"gender": "female",
"born": 1975,
},
),
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",
"gender": "male",
"born": 1971,
},
),
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",
"gender": "female",
"born": 1988,
},
),
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",
"gender": "male",
"born": 1985,
},
),
]
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="test"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().system('pip install -q llama-index google-generativeai')
get_ipython().run_line_magic('env', 'GOOGLE_API_KEY=...')
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.llms.gemini import Gemini
resp = Gemini().complete("Write a poem about a magic backpack")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.gemini import Gemini
messages = [
ChatMessage(role="user", content="Hello friend!"),
ChatMessage(role="assistant", content="Yarr what is shakin' matey?"),
ChatMessage(
role="user", content="Help me decide what to have for dinner."
),
]
resp = | Gemini() | llama_index.llms.gemini.Gemini |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-program-evaporate')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
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)
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
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
city_nodes = {}
for wiki_title in wiki_titles:
docs = city_docs[wiki_title]
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-vector-stores-txtai')
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 txtai
txtai_index = txtai.ann.ANNFactory.create({"backend": "numpy"})
from llama_index.core import (
SimpleDirectoryReader,
load_index_from_storage,
VectorStoreIndex,
StorageContext,
)
from llama_index.vector_stores.txtai import TxtaiVectorStore
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()
vector_store = | TxtaiVectorStore(txtai_index=txtai_index) | llama_index.vector_stores.txtai.TxtaiVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
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')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
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
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core import SummaryIndex
Settings.llm = | OpenAI() | llama_index.llms.openai.OpenAI |