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get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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) | 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-output-parsers-guardrails')
get_ipython().system('pip install guardrails-ai')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from IPython.display import Markdown, display
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents, chunk_size=512)
from llama_index.output_parsers.guardrails import GuardrailsOutputParser
from pydantic import BaseModel, Field
from typing import List
import guardrails as gd
class Point(BaseModel):
explanation: str = Field()
explanation2: str = Field()
explanation3: str = Field()
class BulletPoints(BaseModel):
points: List[Point] = Field(
description="Bullet points regarding events in the author's life."
)
prompt = """
Query string here.
${gr.xml_prefix_prompt}
${output_schema}
${gr.json_suffix_prompt_v2_wo_none}
"""
from llama_index.llms.openai import OpenAI
guard = gd.Guard.from_pydantic(output_class=BulletPoints, prompt=prompt)
output_parser = GuardrailsOutputParser(guard, llm=OpenAI())
llm = | OpenAI(output_parser=output_parser) | llama_index.llms.openai.OpenAI |
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import TimeWeightedPostprocessor
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 datetime import datetime, timedelta
from llama_index.core import StorageContext
now = datetime.now()
key = "__last_accessed__"
doc1 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v1.txt"]
).load_data()[0]
doc2 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v2.txt"]
).load_data()[0]
doc3 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v3.txt"]
).load_data()[0]
from llama_index.core import Settings
Settings.text_splitter = SentenceSplitter(chunk_size=512)
nodes1 = Settings.text_splitter.get_nodes_from_documents([doc1])
nodes2 = Settings.text_splitter.get_nodes_from_documents([doc2])
nodes3 = | Settings.text_splitter.get_nodes_from_documents([doc3]) | 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-readers-file')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama_hub')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.readers.file import UnstructuredReader
from llama_index.readers.file import PyMuPDFReader
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)
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")
index = | VectorStoreIndex(base_nodes) | llama_index.core.VectorStoreIndex |
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import TimeWeightedPostprocessor
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 datetime import datetime, timedelta
from llama_index.core import StorageContext
now = datetime.now()
key = "__last_accessed__"
doc1 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v1.txt"]
).load_data()[0]
doc2 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v2.txt"]
).load_data()[0]
doc3 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v3.txt"]
).load_data()[0]
from llama_index.core import Settings
Settings.text_splitter = SentenceSplitter(chunk_size=512)
nodes1 = Settings.text_splitter.get_nodes_from_documents([doc1])
nodes2 = Settings.text_splitter.get_nodes_from_documents([doc2])
nodes3 = Settings.text_splitter.get_nodes_from_documents([doc3])
nodes1[14].metadata[key] = (now - timedelta(hours=3)).timestamp()
nodes1[14].excluded_llm_metadata_keys = [key]
nodes2[14].metadata[key] = (now - timedelta(hours=2)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
nodes3[14].metadata[key] = (now - timedelta(hours=1)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
docstore = SimpleDocumentStore()
nodes = [nodes1[14], nodes2[14], nodes3[14]]
docstore.add_documents(nodes)
storage_context = | StorageContext.from_defaults(docstore=docstore) | llama_index.core.StorageContext.from_defaults |
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])
)
return | ChatPromptTemplate(message_templates=messages) | llama_index.core.ChatPromptTemplate |
import openai
openai.api_key = "sk-your-api-key"
from llama_index.agent import OpenAIAgent
import requests
import yaml
f = requests.get(
"https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/openai.com/1.2.0/openapi.yaml"
).text
open_api_spec = yaml.safe_load(f)
from llama_index.tools.openapi.base import OpenAPIToolSpec
from llama_index.tools.requests.base import RequestsToolSpec
from llama_index.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec
open_spec = OpenAPIToolSpec(open_api_spec)
open_spec = | OpenAPIToolSpec(
url="https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/openai.com/1.2.0/openapi.yaml"
) | llama_index.tools.openapi.base.OpenAPIToolSpec |
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)
prompt_str2 = (
"Please write a passage to answer the question\n"
"Try to include as many key details as possible.\n"
"\n"
"\n"
"{query_str}\n"
"\n"
"\n"
'Passage:"""\n'
)
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=5)
p = QueryPipeline(
chain=[prompt_tmpl1, llm, prompt_tmpl2, llm, retriever], verbose=True
)
nodes = p.run(topic="college")
len(nodes)
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.response_synthesizers import TreeSummarize
prompt_str = "Please generate a question about Paul Graham's life regarding the following topic {topic}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=3)
reranker = CohereRerank()
summarizer = TreeSummarize(llm=llm)
p = | QueryPipeline(verbose=True) | llama_index.core.query_pipeline.QueryPipeline |
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)
index_loaded = True
except:
index_loaded = False
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'")
if not index_loaded:
lyft_docs = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
lyft_index = VectorStoreIndex.from_documents(lyft_docs)
uber_index = VectorStoreIndex.from_documents(uber_docs)
lyft_index.storage_context.persist(persist_dir="./storage/lyft")
uber_index.storage_context.persist(persist_dir="./storage/uber")
lyft_engine = lyft_index.as_query_engine(similarity_top_k=3)
uber_engine = uber_index.as_query_engine(similarity_top_k=3)
query_engine_tools = [
QueryEngineTool(
query_engine=lyft_engine,
metadata=ToolMetadata(
name="lyft_10k",
description=(
"Provides information about Lyft financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
QueryEngineTool(
query_engine=uber_engine,
metadata=ToolMetadata(
name="uber_10k",
description=(
"Provides information about Uber financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
]
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo-0613") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
load_index_from_storage,
StorageContext,
)
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(response_mode="tree_summarize")
def display_prompt_dict(prompts_dict):
for k, p in prompts_dict.items():
text_md = f"**Prompt Key**: {k}<br>" f"**Text:** <br>"
display(Markdown(text_md))
print(p.get_template())
display(Markdown("<br><br>"))
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
prompts_dict = query_engine.response_synthesizer.get_prompts()
display_prompt_dict(prompts_dict)
query_engine = index.as_query_engine(response_mode="compact")
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
response = query_engine.query("What did the author do growing up?")
print(str(response))
from llama_index.core import PromptTemplate
query_engine = index.as_query_engine(response_mode="tree_summarize")
new_summary_tmpl_str = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the query in the style of a Shakespeare play.\n"
"Query: {query_str}\n"
"Answer: "
)
new_summary_tmpl = PromptTemplate(new_summary_tmpl_str)
query_engine.update_prompts(
{"response_synthesizer:summary_template": new_summary_tmpl}
)
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
response = query_engine.query("What did the author do growing up?")
print(str(response))
from llama_index.core.query_engine import (
RouterQueryEngine,
FLAREInstructQueryEngine,
)
from llama_index.core.selectors import LLMMultiSelector
from llama_index.core.evaluation import FaithfulnessEvaluator, DatasetGenerator
from llama_index.core.postprocessor import LLMRerank
from llama_index.core.tools import QueryEngineTool
query_tool = QueryEngineTool.from_defaults(
query_engine=query_engine, description="test description"
)
router_query_engine = RouterQueryEngine.from_defaults([query_tool])
prompts_dict = router_query_engine.get_prompts()
display_prompt_dict(prompts_dict)
flare_query_engine = FLAREInstructQueryEngine(query_engine)
prompts_dict = flare_query_engine.get_prompts()
display_prompt_dict(prompts_dict)
from llama_index.core.selectors import LLMSingleSelector
selector = | LLMSingleSelector.from_defaults() | llama_index.core.selectors.LLMSingleSelector.from_defaults |
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) | llama_index.postprocessor.cohere_rerank.CohereRerank |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
from llama_index.core.storage.docstore import SimpleDocumentStore
docstore = SimpleDocumentStore()
docstore.add_documents(nodes)
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults(docstore=docstore)
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-embeddings-jinaai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install Pillow')
import os
jinaai_api_key = "YOUR_JINAAI_API_KEY"
os.environ["JINAAI_API_KEY"] = jinaai_api_key
from llama_index.embeddings.jinaai import JinaEmbedding
embed_model = JinaEmbedding(
api_key=jinaai_api_key,
model="jina-embeddings-v2-base-en",
)
embeddings = embed_model.get_text_embedding("This is the text to embed")
print(len(embeddings))
print(embeddings[:5])
embeddings = embed_model.get_query_embedding("This is the query to embed")
print(len(embeddings))
print(embeddings[:5])
embed_model = JinaEmbedding(
api_key=jinaai_api_key,
model="jina-embeddings-v2-base-en",
embed_batch_size=16,
)
embeddings = embed_model.get_text_embedding_batch(
["This is the text to embed", "More text can be provided in a batch"]
)
print(len(embeddings))
print(embeddings[0][:5])
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_source_node
from IPython.display import Markdown, display
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
your_openai_key = "YOUR_OPENAI_KEY"
llm = OpenAI(api_key=your_openai_key)
embed_model = JinaEmbedding(
api_key=jinaai_api_key,
model="jina-embeddings-v2-base-en",
embed_batch_size=16,
)
index = VectorStoreIndex.from_documents(
documents=documents, embed_model=embed_model
)
search_query_retriever = index.as_retriever()
search_query_retrieved_nodes = search_query_retriever.retrieve(
"What happened after the thesis?"
)
for n in search_query_retrieved_nodes:
| display_source_node(n, source_length=2000) | llama_index.core.response.notebook_utils.display_source_node |
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 DocArrayInMemoryVectorStore
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 = | DocArrayInMemoryVectorStore() | llama_index.vector_stores.docarray.DocArrayInMemoryVectorStore |
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) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index llama-index-vector-stores-qdrant -q')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data')
get_ipython().system('wget "https://arxiv.org/pdf/2402.09353.pdf" -O "./data/dorav1.pdf"')
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4")
response = llm.complete("What is DoRA?")
print(response.text)
"""Load the data.
With llama-index, before any transformations are applied,
data is loaded in the `Document` abstraction, which is
a container that holds the text of the document.
"""
from llama_index.core import SimpleDirectoryReader
loader = SimpleDirectoryReader(input_dir="./data")
documents = loader.load_data()
"""Chunk, Encode, and Store into a Vector Store.
To streamline the process, we can make use of the IngestionPipeline
class that will apply your specified transformations to the
Document's.
"""
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client
client = qdrant_client.QdrantClient(location=":memory:")
vector_store = | QdrantVectorStore(client=client, collection_name="test_store") | llama_index.vector_stores.qdrant.QdrantVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
os.environ[
"PINECONE_API_KEY"
] = "<Your Pinecone API key, from app.pinecone.io>"
os.environ["OPENAI_API_KEY"] = "sk-..."
from pinecone import Pinecone
from pinecone import ServerlessSpec
api_key = os.environ["PINECONE_API_KEY"]
pc = Pinecone(api_key=api_key)
pc.create_index(
"quickstart-index",
dimension=1536,
metric="euclidean",
spec=ServerlessSpec(cloud="aws", region="us-west-2"),
)
pinecone_index = pc.Index("quickstart-index")
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="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,
},
),
]
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="test_05_14"
)
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="Fiction"
),
]
)
retriever = index.as_retriever(filters=filters)
retriever.retrieve("What is inception about?")
from llama_index.core.vector_stores import FilterOperator, FilterCondition
filters = MetadataFilters(
filters=[
| MetadataFilter(key="theme", value="Fiction") | llama_index.core.vector_stores.MetadataFilter |
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().system("pip install llama-index 'google-generativeai>=0.3.0' matplotlib qdrant_client")
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from pathlib import Path
import random
from typing import Optional
def get_image_files(
dir_path, sample: Optional[int] = 10, shuffle: bool = False
):
dir_path = Path(dir_path)
image_paths = []
for image_path in dir_path.glob("*.jpg"):
image_paths.append(image_path)
random.shuffle(image_paths)
if sample:
return image_paths[:sample]
else:
return image_paths
image_files = get_image_files("SROIE2019/test/img", sample=100)
from pydantic import BaseModel, Field
class ReceiptInfo(BaseModel):
company: str = Field(..., description="Company name")
date: str = Field(..., description="Date field in DD/MM/YYYY format")
address: str = Field(..., description="Address")
total: float = Field(..., description="total amount")
currency: str = Field(
..., description="Currency of the country (in abbreviations)"
)
summary: str = Field(
...,
description="Extracted text summary of the receipt, including items purchased, the type of store, the location, and any other notable salient features (what does the purchase seem to be for?).",
)
from llama_index.multi_modal_llms.gemini import GeminiMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
Can you summarize the image and return a response \
with the following JSON format: \
"""
async def pydantic_gemini(output_class, image_documents, prompt_template_str):
gemini_llm = GeminiMultiModal(
api_key=GOOGLE_API_KEY, model_name="models/gemini-pro-vision"
)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_class),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=gemini_llm,
verbose=True,
)
response = await llm_program.acall()
return response
from llama_index.core import SimpleDirectoryReader
from llama_index.core.async_utils import run_jobs
async def aprocess_image_file(image_file):
print(f"Image file: {image_file}")
img_docs = SimpleDirectoryReader(input_files=[image_file]).load_data()
output = await pydantic_gemini(ReceiptInfo, img_docs, prompt_template_str)
return output
async def aprocess_image_files(image_files):
"""Process metadata on image files."""
new_docs = []
tasks = []
for image_file in image_files:
task = aprocess_image_file(image_file)
tasks.append(task)
outputs = await | run_jobs(tasks, show_progress=True, workers=5) | llama_index.core.async_utils.run_jobs |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index>=0.9.31 pinecone-client>=3.0.0')
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from pinecone import Pinecone, ServerlessSpec
os.environ[
"PINECONE_API_KEY"
] = "<Your Pinecone API key, from app.pinecone.io>"
os.environ["OPENAI_API_KEY"] = "sk-..."
api_key = os.environ["PINECONE_API_KEY"]
pc = Pinecone(api_key=api_key)
pc.create_index(
name="quickstart",
dimension=1536,
metric="euclidean",
spec=ServerlessSpec(cloud="aws", region="us-west-2"),
)
pinecone_index = pc.Index("quickstart")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.pinecone import PineconeVectorStore
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
from llama_index.core import StorageContext
if "OPENAI_API_KEY" not in os.environ:
raise EnvironmentError(f"Environment variable OPENAI_API_KEY is not set")
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-program-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core import PromptTemplate
choices = [
"Useful for questions related to apples",
"Useful for questions related to oranges",
]
def get_choice_str(choices):
choices_str = "\n\n".join(
[f"{idx+1}. {c}" for idx, c in enumerate(choices)]
)
return choices_str
choices_str = get_choice_str(choices)
router_prompt0 = PromptTemplate(
"Some choices are given below. It is provided in a numbered list (1 to"
" {num_choices}), where each item in the list corresponds to a"
" summary.\n---------------------\n{context_list}\n---------------------\nUsing"
" only the choices above and not prior knowledge, return the top choices"
" (no more than {max_outputs}, but only select what is needed) that are"
" most relevant to the question: '{query_str}'\n"
)
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
def get_formatted_prompt(query_str):
fmt_prompt = router_prompt0.format(
num_choices=len(choices),
max_outputs=2,
context_list=choices_str,
query_str=query_str,
)
return fmt_prompt
query_str = "Can you tell me more about the amount of Vitamin C in apples"
fmt_prompt = get_formatted_prompt(query_str)
response = llm.complete(fmt_prompt)
print(str(response))
query_str = "What are the health benefits of eating orange peels?"
fmt_prompt = get_formatted_prompt(query_str)
response = llm.complete(fmt_prompt)
print(str(response))
query_str = (
"Can you tell me more about the amount of Vitamin C in apples and oranges."
)
fmt_prompt = get_formatted_prompt(query_str)
response = llm.complete(fmt_prompt)
print(str(response))
from dataclasses import fields
from pydantic import BaseModel
import json
class Answer(BaseModel):
choice: int
reason: str
print(json.dumps(Answer.schema(), indent=2))
from llama_index.core.types import BaseOutputParser
FORMAT_STR = """The output should be formatted as a JSON instance that conforms to
the JSON schema below.
Here is the output schema:
{
"type": "array",
"items": {
"type": "object",
"properties": {
"choice": {
"type": "integer"
},
"reason": {
"type": "string"
}
},
"required": [
"choice",
"reason"
],
"additionalProperties": false
}
}
"""
def _escape_curly_braces(input_string: str) -> str:
escaped_string = input_string.replace("{", "{{").replace("}", "}}")
return escaped_string
def _marshal_output_to_json(output: str) -> str:
output = output.strip()
left = output.find("[")
right = output.find("]")
output = output[left : right + 1]
return output
from typing import List
class RouterOutputParser(BaseOutputParser):
def parse(self, output: str) -> List[Answer]:
"""Parse string."""
json_output = _marshal_output_to_json(output)
json_dicts = json.loads(json_output)
answers = [Answer.from_dict(json_dict) for json_dict in json_dicts]
return answers
def format(self, prompt_template: str) -> str:
return prompt_template + "\n\n" + _escape_curly_braces(FORMAT_STR)
output_parser = RouterOutputParser()
from typing import List
def route_query(
query_str: str, choices: List[str], output_parser: RouterOutputParser
):
choices_str
fmt_base_prompt = router_prompt0.format(
num_choices=len(choices),
max_outputs=len(choices),
context_list=choices_str,
query_str=query_str,
)
fmt_json_prompt = output_parser.format(fmt_base_prompt)
raw_output = llm.complete(fmt_json_prompt)
parsed = output_parser.parse(str(raw_output))
return parsed
from pydantic import Field
class Answer(BaseModel):
"Represents a single choice with a reason."
choice: int
reason: str
class Answers(BaseModel):
"""Represents a list of answers."""
answers: List[Answer]
Answers.schema()
from llama_index.program.openai import OpenAIPydanticProgram
router_prompt1 = router_prompt0.partial_format(
num_choices=len(choices),
max_outputs=len(choices),
)
program = OpenAIPydanticProgram.from_defaults(
output_cls=Answers,
prompt=router_prompt1,
verbose=True,
)
query_str = "What are the health benefits of eating orange peels?"
output = program(context_list=choices_str, query_str=query_str)
output
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
from llama_index.core import VectorStoreIndex
from llama_index.core import SummaryIndex
from llama_index.core.node_parser import SentenceSplitter
splitter = | SentenceSplitter(chunk_size=1024) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-question-gen-openai')
get_ipython().system('pip install llama-index')
from llama_index.question_gen.openai import OpenAIQuestionGenerator
question_gen = | OpenAIQuestionGenerator.from_defaults() | llama_index.question_gen.openai.OpenAIQuestionGenerator.from_defaults |
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()
from llama_index.core.schema import NodeWithScore
presidio_new_nodes = processor.postprocess_nodes(
[ | NodeWithScore(node=presidio_node) | llama_index.core.schema.NodeWithScore |
get_ipython().run_line_magic('pip', 'install llama-index-readers-twitter')
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 VectorStoreIndex
from llama_index.readers.twitter import TwitterTweetReader
from IPython.display import Markdown, display
import os
BEARER_TOKEN = "<bearer_token>"
reader = TwitterTweetReader(BEARER_TOKEN)
documents = reader.load_data(["@twitter_handle1"])
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')
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 openai
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
llm = OpenAI(model="gpt-3.5-turbo-1106")
agent = OpenAIAgent.from_tools(
[multiply_tool, add_tool], llm=llm, verbose=True
)
response = agent.chat("What is (121 * 3) + 42?")
print(str(response))
response = agent.stream_chat("What is (121 * 3) + 42?")
import nest_asyncio
nest_asyncio.apply()
response = await agent.achat("What is (121 * 3) + 42?")
print(str(response))
response = await agent.astream_chat("What is (121 * 3) + 42?")
response_gen = response.response_gen
async for token in response.async_response_gen():
print(token, end="")
import json
def get_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
if "tokyo" in location.lower():
return json.dumps(
{"location": location, "temperature": "10", "unit": "celsius"}
)
elif "san francisco" in location.lower():
return json.dumps(
{"location": location, "temperature": "72", "unit": "fahrenheit"}
)
else:
return json.dumps(
{"location": location, "temperature": "22", "unit": "celsius"}
)
weather_tool = FunctionTool.from_defaults(fn=get_current_weather)
llm = OpenAI(model="gpt-3.5-turbo-1106")
agent = OpenAIAgent.from_tools([weather_tool], llm=llm, verbose=True)
response = agent.chat(
"What's the weather like in San Francisco, Tokyo, and Paris?"
)
llm = OpenAI(model="gpt-3.5-turbo-0613")
agent = | OpenAIAgent.from_tools([weather_tool], llm=llm, verbose=True) | llama_index.agent.openai.OpenAIAgent.from_tools |
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() | llama_index.readers.file.PDFReader |
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
get_ipython().system('pip install llama-index')
from llama_index.indices.managed.vectara import VectaraIndex
from llama_index.core import SimpleDirectoryReader
import os
documents = SimpleDirectoryReader(os.path.abspath("../data/10q/")).load_data()
print(f"documents loaded into {len(documents)} document objects")
print(f"Document ID of first doc is {documents[0].doc_id}")
index = | VectaraIndex.from_documents(documents) | llama_index.indices.managed.vectara.VectaraIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install llama-index-tools-google')
from llama_index.readers.web import SimpleWebPageReader
reader = SimpleWebPageReader(html_to_text=True)
docs = reader.load_data(urls=["https://eugeneyan.com/writing/llm-patterns/"])
print(docs[0].get_content()[:400])
from llama_index.core import VectorStoreIndex
index = | VectorStoreIndex.from_documents(docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index')
import pandas as pd
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", None)
pd.set_option("display.max_colwidth", None)
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://www.dropbox.com/scl/fi/rkw0u959yb4w8vlzz76sa/tesla_2020_10k.htm?rlkey=tfkdshswpoupav5tqigwz1mp7&dl=1" -O tesla_2020_10k.htm')
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
docs = reader.load_data(Path("./tesla_2020_10k.htm"))
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.readers.file import FlatReader
from llama_index.core.node_parser import HTMLNodeParser, SentenceSplitter
from llama_index.core.ingestion import IngestionPipeline
from pathlib import Path
import nest_asyncio
nest_asyncio.apply()
reader = FlatReader()
docs = reader.load_data(Path("./tesla_2020_10k.htm"))
pipeline = IngestionPipeline(
documents=docs,
transformations=[
HTMLNodeParser.from_defaults(),
SentenceSplitter(chunk_size=1024, chunk_overlap=200),
OpenAIEmbedding(),
],
)
eval_nodes = pipeline.run(documents=docs)
eval_llm = OpenAI(model="gpt-3.5-turbo")
dataset_generator = DatasetGenerator(
eval_nodes[:100],
llm=eval_llm,
show_progress=True,
num_questions_per_chunk=3,
)
eval_dataset = await dataset_generator.agenerate_dataset_from_nodes(num=100)
len(eval_dataset.qr_pairs)
eval_dataset.save_json("data/tesla10k_eval_dataset.json")
eval_dataset = QueryResponseDataset.from_json(
"data/tesla10k_eval_dataset.json"
)
eval_qs = eval_dataset.questions
qr_pairs = eval_dataset.qr_pairs
ref_response_strs = [r for (_, r) in qr_pairs]
from llama_index.core.evaluation import (
CorrectnessEvaluator,
SemanticSimilarityEvaluator,
)
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
from llama_index.core.evaluation import BatchEvalRunner
evaluator_c = CorrectnessEvaluator(llm=eval_llm)
evaluator_s = SemanticSimilarityEvaluator(llm=eval_llm)
evaluator_dict = {
"correctness": evaluator_c,
"semantic_similarity": evaluator_s,
}
batch_eval_runner = BatchEvalRunner(
evaluator_dict, workers=2, show_progress=True
)
from llama_index.core import VectorStoreIndex
async def run_evals(
pipeline, batch_eval_runner, docs, eval_qs, eval_responses_ref
):
nodes = pipeline.run(documents=docs)
vector_index = VectorStoreIndex(nodes)
query_engine = vector_index.as_query_engine()
pred_responses = get_responses(eval_qs, query_engine, show_progress=True)
eval_results = await batch_eval_runner.aevaluate_responses(
eval_qs, responses=pred_responses, reference=eval_responses_ref
)
return eval_results
from llama_index.core.node_parser import HTMLNodeParser, SentenceSplitter
sent_parser_o0 = SentenceSplitter(chunk_size=1024, chunk_overlap=0)
sent_parser_o200 = SentenceSplitter(chunk_size=1024, chunk_overlap=200)
sent_parser_o500 = SentenceSplitter(chunk_size=1024, chunk_overlap=600)
html_parser = HTMLNodeParser.from_defaults()
parser_dict = {
"sent_parser_o0": sent_parser_o0,
"sent_parser_o200": sent_parser_o200,
"sent_parser_o500": sent_parser_o500,
}
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.ingestion import IngestionPipeline
pipeline_dict = {}
for k, parser in parser_dict.items():
pipeline = IngestionPipeline(
documents=docs,
transformations=[
html_parser,
parser,
OpenAIEmbedding(),
],
)
pipeline_dict[k] = pipeline
eval_results_dict = {}
for k, pipeline in pipeline_dict.items():
eval_results = await run_evals(
pipeline, batch_eval_runner, docs, eval_qs, ref_response_strs
)
eval_results_dict[k] = eval_results
import pickle
pickle.dump(eval_results_dict, open("eval_results_1.pkl", "wb"))
eval_results_list = list(eval_results_dict.items())
results_df = get_results_df(
[v for _, v in eval_results_list],
[k for k, _ in eval_results_list],
["correctness", "semantic_similarity"],
)
display(results_df)
for k, pipeline in pipeline_dict.items():
pipeline.cache.persist(f"./cache/{k}.json")
from llama_index.core.extractors import (
TitleExtractor,
QuestionsAnsweredExtractor,
SummaryExtractor,
)
from llama_index.core.node_parser import HTMLNodeParser, SentenceSplitter
extractor_dict = {
"summary": SummaryExtractor(in_place=False),
"qa": QuestionsAnsweredExtractor(in_place=False),
"default": None,
}
html_parser = HTMLNodeParser.from_defaults()
sent_parser_o200 = | SentenceSplitter(chunk_size=1024, chunk_overlap=200) | llama_index.core.node_parser.SentenceSplitter |
from llama_index.tools.waii import WaiiToolSpec
waii_tool = WaiiToolSpec(
url="https://tweakit.waii.ai/api/",
api_key="3........",
database_key="snowflake://....",
verbose=True,
)
from llama_index import VectorStoreIndex
documents = waii_tool.load_data("Get all tables with their number of columns")
index = | VectorStoreIndex.from_documents(documents) | llama_index.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-litellm')
get_ipython().system('pip install llama-index')
import os
from llama_index.llms.litellm import LiteLLM
from llama_index.core.llms import ChatMessage
os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["COHERE_API_KEY"] = "your-api-key"
message = ChatMessage(role="user", content="Hey! how's it going?")
llm = LiteLLM("gpt-3.5-turbo")
chat_response = llm.chat([message])
llm = LiteLLM("command-nightly")
chat_response = llm.chat([message])
from llama_index.core.llms import ChatMessage
from llama_index.llms.litellm import LiteLLM
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="Tell me a story"),
]
resp = LiteLLM("gpt-3.5-turbo").chat(messages)
print(resp)
from llama_index.llms.litellm import LiteLLM
llm = LiteLLM("gpt-3.5-turbo")
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.litellm import LiteLLM
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-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) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-hub-llama-packs-agents-llm-compiler-step')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
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")
import nest_asyncio
nest_asyncio.apply()
from llama_index.packs.agents.llm_compiler.step import LLMCompilerAgentWorker
from llama_index.core.llama_pack import download_llama_pack
download_llama_pack(
"LLMCompilerAgentPack",
"./agent_pack",
skip_load=True,
)
from agent_pack.step import LLMCompilerAgentWorker
import json
from typing import Sequence, List
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
import nest_asyncio
nest_asyncio.apply()
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
tools = [multiply_tool, add_tool]
multiply_tool.metadata.fn_schema_str
from llama_index.core.agent import AgentRunner
llm = OpenAI(model="gpt-4")
callback_manager = llm.callback_manager
agent_worker = LLMCompilerAgentWorker.from_tools(
tools, llm=llm, verbose=True, callback_manager=callback_manager
)
agent = AgentRunner(agent_worker, callback_manager=callback_manager)
response = agent.chat("What is (121 * 3) + 42?")
response
agent.memory.get_all()
get_ipython().system('pip install llama-index-readers-wikipedia')
from llama_index.readers.wikipedia import WikipediaReader
wiki_titles = ["Toronto", "Seattle", "Chicago", "Boston", "Miami"]
city_docs = {}
reader = WikipediaReader()
for wiki_title in wiki_titles:
docs = reader.load_data(pages=[wiki_title])
city_docs[wiki_title] = docs
from llama_index.core import ServiceContext
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import CallbackManager
llm = | OpenAI(temperature=0, model="gpt-4") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install promptlayer')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["PROMPTLAYER_API_KEY"] = "pl_..."
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
docs = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
TABLE_NAME = os.environ["DYNAMODB_TABLE_NAME"]
from llama_index.storage.docstore.dynamodb import DynamoDBDocumentStore
from llama_index.storage.index_store.dynamodb import DynamoDBIndexStore
from llama_index.vector_stores.dynamodb import DynamoDBVectorStore
storage_context = StorageContext.from_defaults(
docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),
index_store=DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME),
vector_store=DynamoDBVectorStore.from_table_name(table_name=TABLE_NAME),
)
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_table_index = SimpleKeywordTableIndex(
nodes, storage_context=storage_context
)
len(storage_context.docstore.docs)
storage_context.persist()
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
from llama_index.core import load_index_from_storage
storage_context = StorageContext.from_defaults(
docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),
index_store= | DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME) | llama_index.storage.index_store.dynamodb.DynamoDBIndexStore.from_table_name |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-elasticsearch')
get_ipython().system('pip install llama-index')
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import StorageContext
vector_store = ElasticsearchStore(
es_url="http://localhost:9200",
index_name="paul_graham",
)
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 were his investments in Y Combinator?")
print(response)
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",
},
),
]
vector_store_metadata_example = ElasticsearchStore(
index_name="movies_metadata_example",
es_url="http://localhost:9200",
)
storage_context = StorageContext.from_defaults(
vector_store=vector_store_metadata_example
)
index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
domain = "docs.llamaindex.ai"
docs_url = "https://docs.llamaindex.ai/en/latest/"
get_ipython().system('wget -e robots=off --recursive --no-clobber --page-requisites --html-extension --convert-links --restrict-file-names=windows --domains {domain} --no-parent {docs_url}')
from llama_index.readers.file import UnstructuredReader
reader = UnstructuredReader()
from pathlib import Path
all_files_gen = Path("./docs.llamaindex.ai/").rglob("*")
all_files = [f.resolve() for f in all_files_gen]
all_html_files = [f for f in all_files if f.suffix.lower() == ".html"]
len(all_html_files)
from llama_index.core import Document
doc_limit = 100
docs = []
for idx, f in enumerate(all_html_files):
if idx > doc_limit:
break
print(f"Idx {idx}/{len(all_html_files)}")
loaded_docs = reader.load_data(file=f, split_documents=True)
start_idx = 72
loaded_doc = Document(
text="\n\n".join([d.get_content() for d in loaded_docs[72:]]),
metadata={"path": str(f)},
)
print(loaded_doc.metadata["path"])
docs.append(loaded_doc)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
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.agent.openai import OpenAIAgent
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.node_parser import SentenceSplitter
import os
from tqdm.notebook import tqdm
import pickle
async def build_agent_per_doc(nodes, file_base):
print(file_base)
vi_out_path = f"./data/llamaindex_docs/{file_base}"
summary_out_path = f"./data/llamaindex_docs/{file_base}_summary.pkl"
if not os.path.exists(vi_out_path):
Path("./data/llamaindex_docs/").mkdir(parents=True, exist_ok=True)
vector_index = VectorStoreIndex(nodes)
vector_index.storage_context.persist(persist_dir=vi_out_path)
else:
vector_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=vi_out_path),
)
summary_index = SummaryIndex(nodes)
vector_query_engine = vector_index.as_query_engine(llm=llm)
summary_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize", llm=llm
)
if not os.path.exists(summary_out_path):
Path(summary_out_path).parent.mkdir(parents=True, exist_ok=True)
summary = str(
await summary_query_engine.aquery(
"Extract a concise 1-2 line summary of this document"
)
)
pickle.dump(summary, open(summary_out_path, "wb"))
else:
summary = pickle.load(open(summary_out_path, "rb"))
query_engine_tools = [
QueryEngineTool(
query_engine=vector_query_engine,
metadata=ToolMetadata(
name=f"vector_tool_{file_base}",
description=f"Useful for questions related to specific facts",
),
),
QueryEngineTool(
query_engine=summary_query_engine,
metadata=ToolMetadata(
name=f"summary_tool_{file_base}",
description=f"Useful for summarization questions",
),
),
]
function_llm = OpenAI(model="gpt-4")
agent = OpenAIAgent.from_tools(
query_engine_tools,
llm=function_llm,
verbose=True,
system_prompt=f"""\
You are a specialized agent designed to answer queries about the `{file_base}.html` part of the LlamaIndex docs.
You must ALWAYS use at least one of the tools provided when answering a question; do NOT rely on prior knowledge.\
""",
)
return agent, summary
async def build_agents(docs):
node_parser = SentenceSplitter()
agents_dict = {}
extra_info_dict = {}
for idx, doc in enumerate(tqdm(docs)):
nodes = node_parser.get_nodes_from_documents([doc])
file_path = Path(doc.metadata["path"])
file_base = str(file_path.parent.stem) + "_" + str(file_path.stem)
agent, summary = await build_agent_per_doc(nodes, file_base)
agents_dict[file_base] = agent
extra_info_dict[file_base] = {"summary": summary, "nodes": nodes}
return agents_dict, extra_info_dict
agents_dict, extra_info_dict = await build_agents(docs)
all_tools = []
for file_base, agent in agents_dict.items():
summary = extra_info_dict[file_base]["summary"]
doc_tool = QueryEngineTool(
query_engine=agent,
metadata=ToolMetadata(
name=f"tool_{file_base}",
description=summary,
),
)
all_tools.append(doc_tool)
print(all_tools[0].metadata)
from llama_index.core import VectorStoreIndex
from llama_index.core.objects import (
ObjectIndex,
SimpleToolNodeMapping,
ObjectRetriever,
)
from llama_index.core.retrievers import BaseRetriever
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.llms.openai import OpenAI
llm = OpenAI(model_name="gpt-4-0613")
tool_mapping = SimpleToolNodeMapping.from_objects(all_tools)
obj_index = ObjectIndex.from_objects(
all_tools,
tool_mapping,
VectorStoreIndex,
)
vector_node_retriever = obj_index.as_node_retriever(similarity_top_k=10)
class CustomRetriever(BaseRetriever):
def __init__(self, vector_retriever, postprocessor=None):
self._vector_retriever = vector_retriever
self._postprocessor = postprocessor or CohereRerank(top_n=5)
super().__init__()
def _retrieve(self, query_bundle):
retrieved_nodes = self._vector_retriever.retrieve(query_bundle)
filtered_nodes = self._postprocessor.postprocess_nodes(
retrieved_nodes, query_bundle=query_bundle
)
return filtered_nodes
class CustomObjectRetriever(ObjectRetriever):
def __init__(self, retriever, object_node_mapping, all_tools, llm=None):
self._retriever = retriever
self._object_node_mapping = object_node_mapping
self._llm = llm or OpenAI("gpt-4-0613")
def retrieve(self, query_bundle):
nodes = self._retriever.retrieve(query_bundle)
tools = [self._object_node_mapping.from_node(n.node) for n in nodes]
sub_question_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=tools, llm=self._llm
)
sub_question_description = f"""\
Useful for any queries that involve comparing multiple documents. ALWAYS use this tool for comparison queries - make sure to call this \
tool with the original query. Do NOT use the other tools for any queries involving multiple documents.
"""
sub_question_tool = QueryEngineTool(
query_engine=sub_question_engine,
metadata=ToolMetadata(
name="compare_tool", description=sub_question_description
),
)
return tools + [sub_question_tool]
custom_node_retriever = CustomRetriever(vector_node_retriever)
custom_obj_retriever = CustomObjectRetriever(
custom_node_retriever, tool_mapping, all_tools, llm=llm
)
tmps = custom_obj_retriever.retrieve("hello")
print(len(tmps))
from llama_index.agent.openai_legacy import FnRetrieverOpenAIAgent
from llama_index.core.agent import ReActAgent
top_agent = FnRetrieverOpenAIAgent.from_retriever(
custom_obj_retriever,
system_prompt=""" \
You are an agent designed to answer queries about the documentation.
Please always use the tools provided to answer a question. Do not rely on prior knowledge.\
""",
llm=llm,
verbose=True,
)
all_nodes = [
n for extra_info in extra_info_dict.values() for n in extra_info["nodes"]
]
base_index = | VectorStoreIndex(all_nodes) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-timescalevector')
get_ipython().system('pip install llama-index')
import timescale_vector
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.timescalevector import TimescaleVectorStore
from llama_index.core.vector_stores import VectorStoreQuery, MetadataFilters
import textwrap
import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai.api_key = os.environ["OPENAI_API_KEY"]
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]
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)
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
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("Did the author work at YC?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What did the author work on before college?")
print(textwrap.fill(str(response), 100))
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do before YC?")
print(textwrap.fill(str(response), 100))
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
vector_store.create_index()
vector_store.drop_index()
vector_store.create_index("tsv", max_alpha=1.0, num_neighbors=50)
vector_store.drop_index()
vector_store.create_index("hnsw", m=16, ef_construction=64)
vector_store.drop_index()
vector_store.create_index("ivfflat", num_lists=20, num_records=1000)
vector_store.drop_index()
vector_store.create_index()
import pandas as pd
from pathlib import Path
file_path = Path("../data/csv/commit_history.csv")
df = pd.read_csv(file_path)
df.dropna(inplace=True)
df = df.astype(str)
df = df[:1000]
df.head()
from timescale_vector import client
def create_uuid(date_string: str):
if date_string is None:
return None
time_format = "%a %b %d %H:%M:%S %Y %z"
datetime_obj = datetime.strptime(date_string, time_format)
uuid = client.uuid_from_time(datetime_obj)
return str(uuid)
from typing import List, Tuple
def split_name(input_string: str) -> Tuple[str, str]:
if input_string is None:
return None, None
start = input_string.find("<")
end = input_string.find(">")
name = input_string[:start].strip()
return name
from datetime import datetime, timedelta
def create_date(input_string: str) -> datetime:
if input_string is None:
return None
month_dict = {
"Jan": "01",
"Feb": "02",
"Mar": "03",
"Apr": "04",
"May": "05",
"Jun": "06",
"Jul": "07",
"Aug": "08",
"Sep": "09",
"Oct": "10",
"Nov": "11",
"Dec": "12",
}
components = input_string.split()
day = components[2]
month = month_dict[components[1]]
year = components[4]
time = components[3]
timezone_offset_minutes = int(
components[5]
) # Convert the offset to minutes
timezone_hours = timezone_offset_minutes // 60 # Calculate the hours
timezone_minutes = (
timezone_offset_minutes % 60
) # Calculate the remaining minutes
timestamp_tz_str = (
f"{year}-{month}-{day} {time}+{timezone_hours:02}{timezone_minutes:02}"
)
return timestamp_tz_str
from llama_index.core.schema import TextNode, NodeRelationship, RelatedNodeInfo
def create_node(row):
record = row.to_dict()
record_name = split_name(record["author"])
record_content = (
str(record["date"])
+ " "
+ record_name
+ " "
+ str(record["change summary"])
+ " "
+ str(record["change details"])
)
node = TextNode(
id_=create_uuid(record["date"]),
text=record_content,
metadata={
"commit": record["commit"],
"author": record_name,
"date": create_date(record["date"]),
},
)
return node
nodes = [create_node(row) for _, row in df.iterrows()]
from llama_index.embeddings.openai import OpenAIEmbedding
embedding_model = OpenAIEmbedding()
for node in nodes:
node_embedding = embedding_model.get_text_embedding(
node.get_content(metadata_mode="all")
)
node.embedding = node_embedding
print(nodes[0].get_content(metadata_mode="all"))
print(nodes[0].get_embedding())
ts_vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="li_commit_history",
time_partition_interval=timedelta(days=7),
)
_ = ts_vector_store.add(nodes)
query_str = "What's new with TimescaleDB functions?"
embed_model = OpenAIEmbedding()
query_embedding = embed_model.get_query_embedding(query_str)
start_dt = datetime(
2023, 8, 1, 22, 10, 35
) # Start date = 1 August 2023, 22:10:35
end_dt = datetime(
2023, 8, 30, 22, 10, 35
) # End date = 30 August 2023, 22:10:35
td = timedelta(days=7) # Time delta = 7 days
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=5
)
query_result = ts_vector_store.query(
vector_store_query, start_date=start_dt, end_date=end_dt
)
query_result
for node in query_result.nodes:
print("-" * 80)
print(node.metadata["date"])
print(node.get_content(metadata_mode="all"))
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=5
)
query_result = ts_vector_store.query(
vector_store_query, start_date=start_dt, time_delta=td
)
for node in query_result.nodes:
print("-" * 80)
print(node.metadata["date"])
print(node.get_content(metadata_mode="all"))
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=5
)
query_result = ts_vector_store.query(
vector_store_query, end_date=end_dt, time_delta=td
)
for node in query_result.nodes:
print("-" * 80)
print(node.metadata["date"])
print(node.get_content(metadata_mode="all"))
from llama_index.core import VectorStoreIndex
from llama_index.core import StorageContext
index = | VectorStoreIndex.from_vector_store(ts_vector_store) | llama_index.core.VectorStoreIndex.from_vector_store |
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)
index = | VectorStoreIndex(nodes=nodes) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
domain = "docs.llamaindex.ai"
docs_url = "https://docs.llamaindex.ai/en/latest/"
get_ipython().system('wget -e robots=off --recursive --no-clobber --page-requisites --html-extension --convert-links --restrict-file-names=windows --domains {domain} --no-parent {docs_url}')
from llama_index.readers.file import UnstructuredReader
reader = UnstructuredReader()
from pathlib import Path
all_files_gen = Path("./docs.llamaindex.ai/").rglob("*")
all_files = [f.resolve() for f in all_files_gen]
all_html_files = [f for f in all_files if f.suffix.lower() == ".html"]
len(all_html_files)
from llama_index.core import Document
doc_limit = 100
docs = []
for idx, f in enumerate(all_html_files):
if idx > doc_limit:
break
print(f"Idx {idx}/{len(all_html_files)}")
loaded_docs = reader.load_data(file=f, split_documents=True)
start_idx = 72
loaded_doc = Document(
text="\n\n".join([d.get_content() for d in loaded_docs[72:]]),
metadata={"path": str(f)},
)
print(loaded_doc.metadata["path"])
docs.append(loaded_doc)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
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.agent.openai import OpenAIAgent
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.node_parser import SentenceSplitter
import os
from tqdm.notebook import tqdm
import pickle
async def build_agent_per_doc(nodes, file_base):
print(file_base)
vi_out_path = f"./data/llamaindex_docs/{file_base}"
summary_out_path = f"./data/llamaindex_docs/{file_base}_summary.pkl"
if not os.path.exists(vi_out_path):
Path("./data/llamaindex_docs/").mkdir(parents=True, exist_ok=True)
vector_index = VectorStoreIndex(nodes)
vector_index.storage_context.persist(persist_dir=vi_out_path)
else:
vector_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=vi_out_path),
)
summary_index = SummaryIndex(nodes)
vector_query_engine = vector_index.as_query_engine(llm=llm)
summary_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize", llm=llm
)
if not os.path.exists(summary_out_path):
Path(summary_out_path).parent.mkdir(parents=True, exist_ok=True)
summary = str(
await summary_query_engine.aquery(
"Extract a concise 1-2 line summary of this document"
)
)
pickle.dump(summary, open(summary_out_path, "wb"))
else:
summary = pickle.load(open(summary_out_path, "rb"))
query_engine_tools = [
QueryEngineTool(
query_engine=vector_query_engine,
metadata=ToolMetadata(
name=f"vector_tool_{file_base}",
description=f"Useful for questions related to specific facts",
),
),
QueryEngineTool(
query_engine=summary_query_engine,
metadata=ToolMetadata(
name=f"summary_tool_{file_base}",
description=f"Useful for summarization questions",
),
),
]
function_llm = OpenAI(model="gpt-4")
agent = OpenAIAgent.from_tools(
query_engine_tools,
llm=function_llm,
verbose=True,
system_prompt=f"""\
You are a specialized agent designed to answer queries about the `{file_base}.html` part of the LlamaIndex docs.
You must ALWAYS use at least one of the tools provided when answering a question; do NOT rely on prior knowledge.\
""",
)
return agent, summary
async def build_agents(docs):
node_parser = SentenceSplitter()
agents_dict = {}
extra_info_dict = {}
for idx, doc in enumerate(tqdm(docs)):
nodes = node_parser.get_nodes_from_documents([doc])
file_path = Path(doc.metadata["path"])
file_base = str(file_path.parent.stem) + "_" + str(file_path.stem)
agent, summary = await build_agent_per_doc(nodes, file_base)
agents_dict[file_base] = agent
extra_info_dict[file_base] = {"summary": summary, "nodes": nodes}
return agents_dict, extra_info_dict
agents_dict, extra_info_dict = await build_agents(docs)
all_tools = []
for file_base, agent in agents_dict.items():
summary = extra_info_dict[file_base]["summary"]
doc_tool = QueryEngineTool(
query_engine=agent,
metadata=ToolMetadata(
name=f"tool_{file_base}",
description=summary,
),
)
all_tools.append(doc_tool)
print(all_tools[0].metadata)
from llama_index.core import VectorStoreIndex
from llama_index.core.objects import (
ObjectIndex,
SimpleToolNodeMapping,
ObjectRetriever,
)
from llama_index.core.retrievers import BaseRetriever
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model_name="gpt-4-0613") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
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/'")
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, VectorStoreIndex
from llama_index.readers.file import PyMuPDFReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.llms.openai import OpenAI
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)]
node_parser = SimpleNodeParser.from_defaults()
nodes = node_parser.get_nodes_from_documents(docs)
len(nodes)
get_ipython().system('wget "https://www.dropbox.com/scl/fi/fh9vsmmm8vu0j50l3ss38/llama2_eval_qr_dataset.json?rlkey=kkoaez7aqeb4z25gzc06ak6kb&dl=1" -O data/llama2_eval_qr_dataset.json')
from llama_index.core.evaluation import QueryResponseDataset
eval_dataset = QueryResponseDataset.from_json(
"data/llama2_eval_qr_dataset.json"
)
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4-1106-preview")
dataset_generator = DatasetGenerator(
nodes[:20],
llm=llm,
show_progress=True,
num_questions_per_chunk=3,
)
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"
)
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
from llama_index.core.evaluation import (
CorrectnessEvaluator,
SemanticSimilarityEvaluator,
BatchEvalRunner,
)
from llama_index.llms.openai import OpenAI
eval_llm = OpenAI(model="gpt-4-1106-preview")
evaluator_c = CorrectnessEvaluator(llm=eval_llm)
evaluator_s = SemanticSimilarityEvaluator(llm=eval_llm)
evaluator_dict = {
"correctness": evaluator_c,
"semantic_similarity": evaluator_s,
}
batch_runner = BatchEvalRunner(evaluator_dict, workers=2, show_progress=True)
import numpy as np
import time
import os
import pickle
from tqdm import tqdm
def get_responses_sync(
eval_qs, query_engine, show_progress=True, save_path=None
):
if show_progress:
eval_qs_iter = tqdm(eval_qs)
else:
eval_qs_iter = eval_qs
pred_responses = []
start_time = time.time()
for eval_q in eval_qs_iter:
print(f"eval q: {eval_q}")
pred_response = agent.query(eval_q)
print(f"predicted response: {pred_response}")
pred_responses.append(pred_response)
if save_path is not None:
avg_time = (time.time() - start_time) / len(pred_responses)
pickle.dump(
{"pred_responses": pred_responses, "avg_time": avg_time},
open(save_path, "wb"),
)
return pred_responses
async def run_evals(
query_engine,
eval_qa_pairs,
batch_runner,
disable_async_for_preds=False,
save_path=None,
):
eval_qs = [q for q, _ in eval_qa_pairs]
eval_answers = [a for _, a in eval_qa_pairs]
if save_path is not None:
if not os.path.exists(save_path):
start_time = time.time()
if disable_async_for_preds:
pred_responses = get_responses_sync(
eval_qs,
query_engine,
show_progress=True,
save_path=save_path,
)
else:
pred_responses = get_responses(
eval_qs, query_engine, show_progress=True
)
avg_time = (time.time() - start_time) / len(eval_qs)
pickle.dump(
{"pred_responses": pred_responses, "avg_time": avg_time},
open(save_path, "wb"),
)
else:
pickled_dict = pickle.load(open(save_path, "rb"))
pred_responses = pickled_dict["pred_responses"]
avg_time = pickled_dict["avg_time"]
else:
start_time = time.time()
pred_responses = get_responses(
eval_qs, query_engine, show_progress=True
)
avg_time = (time.time() - start_time) / len(eval_qs)
eval_results = await batch_runner.aevaluate_responses(
eval_qs, responses=pred_responses, reference=eval_answers
)
return eval_results, {"avg_time": avg_time}
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="SEC Analyst",
instructions="You are a QA assistant designed to analyze sec filings.",
openai_tools=[{"type": "retrieval"}],
instructions_prefix="Please address the user as Jerry.",
files=["data/llama2.pdf"],
verbose=True,
)
response = agent.query(
"What are the key differences between Llama 2 and Llama 2-Chat?"
)
print(str(response))
llm = | OpenAI(model="gpt-4-1106-preview") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence, List
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
import nest_asyncio
nest_asyncio.apply()
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
class YourOpenAIAgent:
def __init__(
self,
tools: Sequence[BaseTool] = [],
llm: OpenAI = OpenAI(temperature=0, model="gpt-3.5-turbo-0613"),
chat_history: List[ChatMessage] = [],
) -> None:
self._llm = llm
self._tools = {tool.metadata.name: tool for tool in tools}
self._chat_history = chat_history
def reset(self) -> None:
self._chat_history = []
def chat(self, message: str) -> str:
chat_history = self._chat_history
chat_history.append(ChatMessage(role="user", content=message))
tools = [
tool.metadata.to_openai_tool() for _, tool in self._tools.items()
]
ai_message = self._llm.chat(chat_history, tools=tools).message
additional_kwargs = ai_message.additional_kwargs
chat_history.append(ai_message)
tool_calls = ai_message.additional_kwargs.get("tool_calls", None)
if tool_calls is not None:
for tool_call in tool_calls:
function_message = self._call_function(tool_call)
chat_history.append(function_message)
ai_message = self._llm.chat(chat_history).message
chat_history.append(ai_message)
return ai_message.content
def _call_function(self, tool_call: dict) -> ChatMessage:
id_ = tool_call["id"]
function_call = tool_call["function"]
tool = self._tools[function_call["name"]]
output = tool(**json.loads(function_call["arguments"]))
return ChatMessage(
name=function_call["name"],
content=str(output),
role="tool",
additional_kwargs={
"tool_call_id": id_,
"name": function_call["name"],
},
)
agent = YourOpenAIAgent(tools=[multiply_tool, add_tool])
agent.chat("Hi")
agent.chat("What is 2123 * 215123")
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo-0613") | llama_index.llms.openai.OpenAI |
from llama_index.core import SQLDatabase
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("sqlite:///chinook.db")
sql_database = SQLDatabase(engine)
from llama_index.core.query_pipeline import QueryPipeline
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('curl "https://www.sqlitetutorial.net/wp-content/uploads/2018/03/chinook.zip" -O ./chinook.zip')
get_ipython().system('unzip ./chinook.zip')
from llama_index.core.settings import Settings
from llama_index.core.callbacks import CallbackManager
callback_manager = CallbackManager()
Settings.callback_manager = callback_manager
import phoenix as px
import llama_index.core
px.launch_app()
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core.tools import QueryEngineTool
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["albums", "tracks", "artists"],
verbose=True,
)
sql_tool = QueryEngineTool.from_defaults(
query_engine=sql_query_engine,
name="sql_tool",
description=(
"Useful for translating a natural language query into a SQL query"
),
)
from llama_index.core.query_pipeline import QueryPipeline as QP
qp = QP(verbose=True)
from llama_index.core.agent.react.types import (
ActionReasoningStep,
ObservationReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.agent import Task, AgentChatResponse
from llama_index.core.query_pipeline import (
AgentInputComponent,
AgentFnComponent,
CustomAgentComponent,
QueryComponent,
ToolRunnerComponent,
)
from llama_index.core.llms import MessageRole
from typing import Dict, Any, Optional, Tuple, List, cast
def agent_input_fn(task: Task, state: Dict[str, Any]) -> Dict[str, Any]:
"""Agent input function.
Returns:
A Dictionary of output keys and values. If you are specifying
src_key when defining links between this component and other
components, make sure the src_key matches the specified output_key.
"""
if "current_reasoning" not in state:
state["current_reasoning"] = []
reasoning_step = ObservationReasoningStep(observation=task.input)
state["current_reasoning"].append(reasoning_step)
return {"input": task.input}
agent_input_component = AgentInputComponent(fn=agent_input_fn)
from llama_index.core.agent import ReActChatFormatter
from llama_index.core.query_pipeline import InputComponent, Link
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool
def react_prompt_fn(
task: Task, state: Dict[str, Any], input: str, tools: List[BaseTool]
) -> List[ChatMessage]:
chat_formatter = ReActChatFormatter()
return chat_formatter.format(
tools,
chat_history=task.memory.get() + state["memory"].get_all(),
current_reasoning=state["current_reasoning"],
)
react_prompt_component = AgentFnComponent(
fn=react_prompt_fn, partial_dict={"tools": [sql_tool]}
)
from typing import Set, Optional
from llama_index.core.agent.react.output_parser import ReActOutputParser
from llama_index.core.llms import ChatResponse
from llama_index.core.agent.types import Task
def parse_react_output_fn(
task: Task, state: Dict[str, Any], chat_response: ChatResponse
):
"""Parse ReAct output into a reasoning step."""
output_parser = ReActOutputParser()
reasoning_step = output_parser.parse(chat_response.message.content)
return {"done": reasoning_step.is_done, "reasoning_step": reasoning_step}
parse_react_output = AgentFnComponent(fn=parse_react_output_fn)
def run_tool_fn(
task: Task, state: Dict[str, Any], reasoning_step: ActionReasoningStep
):
"""Run tool and process tool output."""
tool_runner_component = ToolRunnerComponent(
[sql_tool], callback_manager=task.callback_manager
)
tool_output = tool_runner_component.run_component(
tool_name=reasoning_step.action,
tool_input=reasoning_step.action_input,
)
observation_step = ObservationReasoningStep(observation=str(tool_output))
state["current_reasoning"].append(observation_step)
return {"response_str": observation_step.get_content(), "is_done": False}
run_tool = | AgentFnComponent(fn=run_tool_fn) | llama_index.core.query_pipeline.AgentFnComponent |
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) | llama_index.core.evaluation.PairwiseComparisonEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-kuzu')
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
import shutil
shutil.rmtree("./test1", ignore_errors=True)
shutil.rmtree("./test2", ignore_errors=True)
shutil.rmtree("./test3", ignore_errors=True)
get_ipython().run_line_magic('pip', 'install kuzu')
import kuzu
db = kuzu.Database("test1")
from llama_index.graph_stores.kuzu import KuzuGraphStore
graph_store = KuzuGraphStore(db)
from llama_index.core import SimpleDirectoryReader, KnowledgeGraphIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from IPython.display import Markdown, display
import kuzu
documents = SimpleDirectoryReader(
"../../../../examples/paul_graham_essay/data"
).load_data()
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = llm
Settings.chunk_size = 512
from llama_index.core import StorageContext
storage_context = | StorageContext.from_defaults(graph_store=graph_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
embed_model = OpenAIEmbedding(embed_batch_size=10)
Settings.embed_model = embed_model
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = | OpenAIEmbedding(model="text-embedding-3-large") | llama_index.embeddings.openai.OpenAIEmbedding |
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)
query_engine = index.as_query_engine(similarity_top_k=2, llm=gpt_35_llm)
contexts = []
answers = []
for question in questions:
response = query_engine.query(question)
contexts.append([x.node.get_content() for x in response.source_nodes])
answers.append(str(response))
from datasets import Dataset
from ragas import evaluate
from ragas.metrics import answer_relevancy, faithfulness
ds = Dataset.from_dict(
{
"question": questions,
"answer": answers,
"contexts": contexts,
}
)
result = evaluate(ds, [answer_relevancy, faithfulness])
print(result)
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = CallbackManager([finetuning_handler])
llm = OpenAI(model="gpt-4", temperature=0.3)
Settings.callback_manager = (callback_manager,)
questions = []
with open("train_questions.txt", "r") as f:
for line in f:
questions.append(line.strip())
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=2, llm=llm)
for question in questions:
response = query_engine.query(question)
finetuning_handler.save_finetuning_events("finetuning_events.jsonl")
get_ipython().system('python ./launch_training.py ./finetuning_events.jsonl')
ft_model_name = "ft:gpt-3.5-turbo-0613:..."
from llama_index.llms.openai import OpenAI
ft_llm = OpenAI(model=ft_model_name, temperature=0.3)
questions = []
with open("eval_questions.txt", "r") as f:
for line in f:
questions.append(line.strip())
from llama_index import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=2, llm=ft_llm)
contexts = []
answers = []
for question in questions:
response = query_engine.query(question)
contexts.append([x.node.get_content() for x in response.source_nodes])
answers.append(str(response))
from datasets import Dataset
from ragas import evaluate
from ragas.metrics import answer_relevancy, faithfulness
ds = Dataset.from_dict(
{
"question": questions,
"answer": answers,
"contexts": contexts,
}
)
result = evaluate(ds, [answer_relevancy, faithfulness])
print(result)
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)
questions = []
with open("eval_questions.txt", "r") as f:
for line in f:
questions.append(line.strip())
print(questions[12])
from llama_index.core.response.notebook_utils import display_response
from llama_index.llms.openai import OpenAI
gpt_35_llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
query_engine = index.as_query_engine(llm=gpt_35_llm)
response = query_engine.query(questions[12])
| display_response(response) | llama_index.core.response.notebook_utils.display_response |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai-legacy')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
def useless(a: int, b: int) -> int:
"""Toy useless function."""
pass
multiply_tool = | FunctionTool.from_defaults(fn=multiply, name="multiply") | llama_index.core.tools.FunctionTool.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-colbert')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install torch sentence-transformers')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.indices.managed.google import GoogleIndex
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
project_name = "TODO-your-project-name" # @param {type:"string"}
email = "[email protected]" # @param {type:"string"}
client_file_name = "client_secret.json"
get_ipython().system('gcloud config set project $project_name')
get_ipython().system('gcloud config set account $email')
get_ipython().system('gcloud auth application-default login --no-browser --client-id-file=$client_file_name --scopes="https://www.googleapis.com/auth/generative-language.retriever,https://www.googleapis.com/auth/cloud-platform"')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
google_index = GoogleIndex.create_corpus(display_name="My first corpus!")
print(f"Newly created corpus ID is {google_index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
google_index.insert_documents(documents)
google_index = GoogleIndex.from_corpus(corpus_id="")
query_engine = google_index.as_query_engine()
response = query_engine.query("which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.VERBOSE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
| display_source_node(r, source_length=1000) | llama_index.core.response.notebook_utils.display_source_node |
get_ipython().system('pip install llama-index-postprocessor-jinaai-rerank')
get_ipython().system('pip install llama-index-embeddings-jinaai')
get_ipython().system('pip install llama-index')
import os
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
)
from llama_index.embeddings.jinaai import JinaEmbedding
api_key = os.environ["JINA_API_KEY"]
jina_embeddings = JinaEmbedding(api_key=api_key)
import requests
url = "https://niketeam-asset-download.nike.net/catalogs/2024/2024_Nike%20Kids_02_09_24.pdf?cb=09302022"
response = requests.get(url)
with open("Nike_Catalog.pdf", "wb") as f:
f.write(response.content)
reader = SimpleDirectoryReader(input_files=["Nike_Catalog.pdf"])
documents = reader.load_data()
index = VectorStoreIndex.from_documents(
documents=documents, embed_model=jina_embeddings
)
query_engine = index.as_query_engine(similarity_top_k=10)
response = query_engine.query(
"What is the best jersey by Nike in terms of fabric?",
)
print(response.source_nodes[0].text, response.source_nodes[0].score)
print("\n")
print(response.source_nodes[1].text, response.source_nodes[1].score)
import os
from llama_index.postprocessor.jinaai_rerank import JinaRerank
jina_rerank = | JinaRerank(api_key=api_key, top_n=2) | llama_index.postprocessor.jinaai_rerank.JinaRerank |
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) | llama_index.core.response.notebook_utils.display_source_node |
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) | llama_index.core.SimpleKeywordTableIndex |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence, List
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
import nest_asyncio
nest_asyncio.apply()
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = | FunctionTool.from_defaults(fn=multiply) | llama_index.core.tools.FunctionTool.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
from llama_index.core import set_global_handler
set_global_handler("wandb", run_args={"project": "llamaindex"})
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import MetadataMode
llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo", max_tokens=512)
from llama_index.core.node_parser import TokenTextSplitter
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
node_parser = TokenTextSplitter(
separator=" ", chunk_size=256, chunk_overlap=128
)
extractors_1 = [
QuestionsAnsweredExtractor(
questions=3, llm=llm, metadata_mode=MetadataMode.EMBED
),
]
extractors_2 = [
| SummaryExtractor(summaries=["prev", "self", "next"], llm=llm) | llama_index.core.extractors.SummaryExtractor |
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") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
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-1106", temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
from llama_index.core import Settings
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
from llama_index.core import SummaryIndex
from llama_index.core import VectorStoreIndex
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
list_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize",
use_async=True,
)
vector_query_engine = vector_index.as_query_engine()
from llama_index.core.tools import QueryEngineTool
list_tool = QueryEngineTool.from_defaults(
query_engine=list_query_engine,
description=(
"Useful for summarization questions related to Paul Graham eassy on"
" What I Worked On."
),
)
vector_tool = QueryEngineTool.from_defaults(
query_engine=vector_query_engine,
description=(
"Useful for retrieving specific context from Paul Graham essay on What"
" I Worked On."
),
)
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
from llama_index.core.selectors import (
PydanticMultiSelector,
PydanticSingleSelector,
)
query_engine = RouterQueryEngine(
selector=PydanticSingleSelector.from_defaults(),
query_engine_tools=[
list_tool,
vector_tool,
],
)
response = query_engine.query("What is the summary of the document?")
print(str(response))
response = query_engine.query("What did Paul Graham do after RICS?")
print(str(response))
query_engine = RouterQueryEngine(
selector=LLMSingleSelector.from_defaults(),
query_engine_tools=[
list_tool,
vector_tool,
],
)
response = query_engine.query("What is the summary of the document?")
print(str(response))
response = query_engine.query("What did Paul Graham do after RICS?")
print(str(response))
print(str(response.metadata["selector_result"]))
from llama_index.core import SimpleKeywordTableIndex
keyword_index = | SimpleKeywordTableIndex(nodes, storage_context=storage_context) | llama_index.core.SimpleKeywordTableIndex |
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() | llama_index.embeddings.clip.ClipEmbedding |
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) | llama_index.llms.cohere.Cohere |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt' -O pg_essay.txt")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader(input_files=["pg_essay.txt"])
documents = reader.load_data()
from llama_index.core.query_pipeline import QueryPipeline, InputComponent
from typing import Dict, Any, List, Optional
from llama_index.llms.openai import OpenAI
from llama_index.core import Document, VectorStoreIndex
from llama_index.core import SummaryIndex
from llama_index.core.response_synthesizers import TreeSummarize
from llama_index.core.schema import NodeWithScore, TextNode
from llama_index.core import PromptTemplate
from llama_index.core.selectors import LLMSingleSelector
hyde_str = """\
Please write a passage to answer the question: {query_str}
Try to include as many key details as possible.
Passage: """
hyde_prompt = PromptTemplate(hyde_str)
llm = OpenAI(model="gpt-3.5-turbo")
summarizer = TreeSummarize(llm=llm)
vector_index = VectorStoreIndex.from_documents(documents)
vector_query_engine = vector_index.as_query_engine(similarity_top_k=2)
summary_index = SummaryIndex.from_documents(documents)
summary_qrewrite_str = """\
Here's a question:
{query_str}
You are responsible for feeding the question to an agent that given context will try to answer the question.
The context may or may not be relevant. Rewrite the question to highlight the fact that
only some pieces of context (or none) maybe be relevant.
"""
summary_qrewrite_prompt = PromptTemplate(summary_qrewrite_str)
summary_query_engine = summary_index.as_query_engine()
selector = | LLMSingleSelector.from_defaults() | llama_index.core.selectors.LLMSingleSelector.from_defaults |
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)
prompt_str2 = (
"Please write a passage to answer the question\n"
"Try to include as many key details as possible.\n"
"\n"
"\n"
"{query_str}\n"
"\n"
"\n"
'Passage:"""\n'
)
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=5)
p = QueryPipeline(
chain=[prompt_tmpl1, llm, prompt_tmpl2, llm, retriever], verbose=True
)
nodes = p.run(topic="college")
len(nodes)
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.response_synthesizers import TreeSummarize
prompt_str = "Please generate a question about Paul Graham's life regarding the following topic {topic}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=3)
reranker = CohereRerank()
summarizer = TreeSummarize(llm=llm)
p = QueryPipeline(verbose=True)
p.add_modules(
{
"llm": llm,
"prompt_tmpl": prompt_tmpl,
"retriever": retriever,
"summarizer": summarizer,
"reranker": reranker,
}
)
p.add_link("prompt_tmpl", "llm")
p.add_link("llm", "retriever")
p.add_link("retriever", "reranker", dest_key="nodes")
p.add_link("llm", "reranker", dest_key="query_str")
p.add_link("reranker", "summarizer", dest_key="nodes")
p.add_link("llm", "summarizer", dest_key="query_str")
print(summarizer.as_query_component().input_keys)
from pyvis.network import Network
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(p.dag)
net.show("rag_dag.html")
response = p.run(topic="YC")
print(str(response))
response = await p.arun(topic="YC")
print(str(response))
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.response_synthesizers import TreeSummarize
from llama_index.core.query_pipeline import InputComponent
retriever = index.as_retriever(similarity_top_k=5)
summarizer = TreeSummarize(llm=OpenAI(model="gpt-3.5-turbo"))
reranker = CohereRerank()
p = QueryPipeline(verbose=True)
p.add_modules(
{
"input": InputComponent(),
"retriever": retriever,
"summarizer": summarizer,
}
)
p.add_link("input", "retriever")
p.add_link("input", "summarizer", dest_key="query_str")
p.add_link("retriever", "summarizer", dest_key="nodes")
output = p.run(input="what did the author do in YC")
print(str(output))
from llama_index.core.query_pipeline import (
CustomQueryComponent,
InputKeys,
OutputKeys,
)
from typing import Dict, Any
from llama_index.core.llms.llm import LLM
from pydantic import Field
class RelatedMovieComponent(CustomQueryComponent):
"""Related movie component."""
llm: LLM = Field(..., description="OpenAI LLM")
def _validate_component_inputs(
self, input: Dict[str, Any]
) -> Dict[str, Any]:
"""Validate component inputs during run_component."""
return input
@property
def _input_keys(self) -> set:
"""Input keys dict."""
return {"movie"}
@property
def _output_keys(self) -> set:
return {"output"}
def _run_component(self, **kwargs) -> Dict[str, Any]:
"""Run the component."""
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
p = | QueryPipeline(chain=[prompt_tmpl, llm]) | llama_index.core.query_pipeline.QueryPipeline |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-elasticsearch')
get_ipython().system('pip install llama-index')
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import StorageContext
vector_store = ElasticsearchStore(
es_url="http://localhost:9200",
index_name="paul_graham",
)
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 were his investments in Y Combinator?")
print(response)
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",
},
),
]
vector_store_metadata_example = ElasticsearchStore(
index_name="movies_metadata_example",
es_url="http://localhost:9200",
)
storage_context = StorageContext.from_defaults(
vector_store=vector_store_metadata_example
)
index = VectorStoreIndex(nodes, storage_context=storage_context)
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
filters = MetadataFilters(
filters=[ | ExactMatchFilter(key="theme", value="Mafia") | llama_index.core.vector_stores.ExactMatchFilter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-kuzu')
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
import shutil
shutil.rmtree("./test1", ignore_errors=True)
shutil.rmtree("./test2", ignore_errors=True)
shutil.rmtree("./test3", ignore_errors=True)
get_ipython().run_line_magic('pip', 'install kuzu')
import kuzu
db = kuzu.Database("test1")
from llama_index.graph_stores.kuzu import KuzuGraphStore
graph_store = KuzuGraphStore(db)
from llama_index.core import SimpleDirectoryReader, KnowledgeGraphIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from IPython.display import Markdown, display
import kuzu
documents = SimpleDirectoryReader(
"../../../../examples/paul_graham_essay/data"
).load_data()
llm = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
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 SimpleDirectoryReader
reader = SimpleDirectoryReader(
input_files=["./data/paul_graham/paul_graham_essay.txt"]
)
docs = reader.load_data()
text = docs[0].text
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
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))
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
reader = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
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)
index_loaded = True
except:
index_loaded = False
if not index_loaded:
march_docs = SimpleDirectoryReader(
input_files=["../../data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_docs = SimpleDirectoryReader(
input_files=["../../data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_docs = SimpleDirectoryReader(
input_files=["../../data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = VectorStoreIndex.from_documents(
march_docs,
)
june_index = VectorStoreIndex.from_documents(
june_docs,
)
sept_index = VectorStoreIndex.from_documents(
sept_docs,
)
march_index.storage_context.persist(persist_dir="./storage/march")
june_index.storage_context.persist(persist_dir="./storage/june")
sept_index.storage_context.persist(persist_dir="./storage/sept")
march_engine = march_index.as_query_engine(similarity_top_k=3, llm=llm_35)
june_engine = june_index.as_query_engine(similarity_top_k=3, llm=llm_35)
sept_engine = sept_index.as_query_engine(similarity_top_k=3, llm=llm_35)
from llama_index.core.tools import QueryEngineTool
query_tool_sept = QueryEngineTool.from_defaults(
query_engine=sept_engine,
name="sept_2022",
description=(
f"Provides information about Uber quarterly financials ending"
f" September 2022"
),
)
query_tool_june = QueryEngineTool.from_defaults(
query_engine=june_engine,
name="june_2022",
description=(
f"Provides information about Uber quarterly financials ending June"
f" 2022"
),
)
query_tool_march = QueryEngineTool.from_defaults(
query_engine=march_engine,
name="march_2022",
description=(
f"Provides information about Uber quarterly financials ending March"
f" 2022"
),
)
query_engine_tools = [query_tool_march, query_tool_june, query_tool_sept]
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo-0613")
base_agent = ReActAgent.from_tools(query_engine_tools, llm=llm, verbose=True)
response = base_agent.chat(
"Analyze Uber revenue growth over the last few quarters"
)
print(str(response))
print(str(response))
response = base_agent.chat(
"Can you tell me about the risk factors in the quarter with the highest"
" revenue growth?"
)
print(str(response))
from llama_index.core.evaluation import DatasetGenerator
base_question_gen_query = (
"You are a Teacher/ Professor. Your task is to setup a quiz/examination."
" Using the provided context from the Uber March 10Q filing, formulate a"
" single question that captures an important fact from the context."
" context. Restrict the question to the context information provided."
)
dataset_generator = DatasetGenerator.from_documents(
march_docs,
question_gen_query=base_question_gen_query,
llm=llm_35,
)
questions = dataset_generator.generate_questions_from_nodes(num=20)
questions
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
vary_question_tmpl = """\
You are a financial assistant. Given a question over a 2023 Uber 10Q filing, your goal
is to generate up to {num_vary} variations of that question that might span multiple 10Q's.
This can include compare/contrasting different 10Qs, replacing the current quarter with
another quarter, or generating questions that can only be answered over multiple quarters (be creative!)
You are given a valid set of 10Q filings. Please only generate question variations that can be
answered in that set.
For example:
Base Question: What was the free cash flow of Uber in March 2023?
Valid 10Qs: [March 2023, June 2023, September 2023]
Question Variations:
What was the free cash flow of Uber in June 2023?
Can you compare/contrast the free cash flow of Uber in June/September 2023 and offer explanations for the change?
Did the free cash flow of Uber increase of decrease in 2023?
Now let's give it a shot!
Base Question: {base_question}
Valid 10Qs: {valid_10qs}
Question Variations:
"""
def gen_question_variations(base_questions, num_vary=3):
"""Generate question variations."""
VALID_10Q_STR = "[March 2022, June 2022, September 2022]"
llm = OpenAI(model="gpt-4")
prompt_tmpl = PromptTemplate(vary_question_tmpl)
new_questions = []
for idx, question in enumerate(base_questions):
new_questions.append(question)
response = llm.complete(
prompt_tmpl.format(
num_vary=num_vary,
base_question=question,
valid_10qs=VALID_10Q_STR,
)
)
raw_lines = str(response).split("\n")
cur_new_questions = [l for l in raw_lines if l != ""]
print(f"[{idx}] Original Question: {question}")
print(f"[{idx}] Generated Question Variations: {cur_new_questions}")
new_questions.extend(cur_new_questions)
return new_questions
def save_questions(questions, path):
with open(path, "w") as f:
for question in questions:
f.write(question + "\n")
def load_questions(path):
questions = []
with open(path, "r") as f:
for line in f:
questions.append(line.strip())
return questions
new_questions = gen_question_variations(questions)
len(new_questions)
train_questions, eval_questions = new_questions[:60], new_questions[60:]
save_questions(train_questions, "train_questions_10q.txt")
save_questions(eval_questions, "eval_questions_10q.txt")
train_questions = load_questions("train_questions_10q.txt")
eval_questions = load_questions("eval_questions_10q.txt")
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core.agent import ReActAgent
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = | CallbackManager([finetuning_handler]) | llama_index.core.callbacks.CallbackManager |
get_ipython().run_line_magic('pip', 'install llama-index-evaluation-tonic-validate')
import json
import pandas as pd
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.evaluation.tonic_validate import (
AnswerConsistencyEvaluator,
AnswerSimilarityEvaluator,
AugmentationAccuracyEvaluator,
AugmentationPrecisionEvaluator,
RetrievalPrecisionEvaluator,
TonicValidateEvaluator,
)
question = "What makes Sam Altman a good founder?"
reference_answer = "He is smart and has a great force of will."
llm_answer = "He is a good founder because he is smart."
retrieved_context_list = [
"Sam Altman is a good founder. He is very smart.",
"What makes Sam Altman such a good founder is his great force of will.",
]
answer_similarity_evaluator = AnswerSimilarityEvaluator()
score = await answer_similarity_evaluator.aevaluate(
question,
llm_answer,
retrieved_context_list,
reference_response=reference_answer,
)
score
answer_consistency_evaluator = AnswerConsistencyEvaluator()
score = await answer_consistency_evaluator.aevaluate(
question, llm_answer, retrieved_context_list
)
score
augmentation_accuracy_evaluator = AugmentationAccuracyEvaluator()
score = await augmentation_accuracy_evaluator.aevaluate(
question, llm_answer, retrieved_context_list
)
score
augmentation_precision_evaluator = AugmentationPrecisionEvaluator()
score = await augmentation_precision_evaluator.aevaluate(
question, llm_answer, retrieved_context_list
)
score
retrieval_precision_evaluator = | RetrievalPrecisionEvaluator() | llama_index.evaluation.tonic_validate.RetrievalPrecisionEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = | OpenAI(model="gpt-4", temperature=0) | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = | FunctionTool.from_defaults(fn=add) | llama_index.core.tools.FunctionTool.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-portkey')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install -U llama_index')
get_ipython().system('pip install -U portkey-ai')
from llama_index.llms.portkey import Portkey
from llama_index.core.llms import ChatMessage
import portkey as pk
import os
os.environ["PORTKEY_API_KEY"] = "PORTKEY_API_KEY"
openai_virtual_key_a = ""
openai_virtual_key_b = ""
anthropic_virtual_key_a = ""
anthropic_virtual_key_b = ""
cohere_virtual_key_a = ""
cohere_virtual_key_b = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["ANTHROPIC_API_KEY"] = ""
portkey_client = Portkey(
mode="single",
)
openai_llm = pk.LLMOptions(
provider="openai",
model="gpt-4",
virtual_key=openai_virtual_key_a,
)
portkey_client.add_llms(openai_llm)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("Testing Portkey Llamaindex integration:")
response = portkey_client.chat(messages)
print(response)
prompt = "Why is the sky blue?"
print("\nTesting Stream Complete:\n")
response = portkey_client.stream_complete(prompt)
for i in response:
print(i.delta, end="", flush=True)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("\nTesting Stream Chat:\n")
response = portkey_client.stream_chat(messages)
for i in response:
print(i.delta, end="", flush=True)
portkey_client = Portkey(mode="fallback")
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
llm1 = pk.LLMOptions(
provider="openai",
model="gpt-4",
retry_settings={"on_status_codes": [429, 500], "attempts": 2},
virtual_key=openai_virtual_key_a,
)
llm2 = pk.LLMOptions(
provider="openai",
model="gpt-3.5-turbo",
virtual_key=openai_virtual_key_b,
)
portkey_client.add_llms(llm_params=[llm1, llm2])
print("Testing Fallback & Retry functionality:")
response = portkey_client.chat(messages)
print(response)
portkey_client = | Portkey(mode="ab_test") | llama_index.llms.portkey.Portkey |
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") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-readers-pathway')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install pathway')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/'")
get_ipython().system("wget 'https://gist.githubusercontent.com/janchorowski/dd22a293f3d99d1b726eedc7d46d2fc0/raw/pathway_readme.md' -O 'data/pathway_readme.md'")
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import getpass
import os
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import pathway as pw
data_sources = []
data_sources.append(
pw.io.fs.read(
"./data",
format="binary",
mode="streaming",
with_metadata=True,
) # This creates a `pathway` connector that tracks
)
from llama_index.core.retrievers import PathwayVectorServer
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.node_parser import TokenTextSplitter
embed_model = | OpenAIEmbedding(embed_batch_size=10) | llama_index.embeddings.openai.OpenAIEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().system("pip install llama-index 'google-generativeai>=0.3.0' matplotlib qdrant_client")
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from pathlib import Path
import random
from typing import Optional
def get_image_files(
dir_path, sample: Optional[int] = 10, shuffle: bool = False
):
dir_path = Path(dir_path)
image_paths = []
for image_path in dir_path.glob("*.jpg"):
image_paths.append(image_path)
random.shuffle(image_paths)
if sample:
return image_paths[:sample]
else:
return image_paths
image_files = get_image_files("SROIE2019/test/img", sample=100)
from pydantic import BaseModel, Field
class ReceiptInfo(BaseModel):
company: str = Field(..., description="Company name")
date: str = Field(..., description="Date field in DD/MM/YYYY format")
address: str = Field(..., description="Address")
total: float = Field(..., description="total amount")
currency: str = Field(
..., description="Currency of the country (in abbreviations)"
)
summary: str = Field(
...,
description="Extracted text summary of the receipt, including items purchased, the type of store, the location, and any other notable salient features (what does the purchase seem to be for?).",
)
from llama_index.multi_modal_llms.gemini import GeminiMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
Can you summarize the image and return a response \
with the following JSON format: \
"""
async def pydantic_gemini(output_class, image_documents, prompt_template_str):
gemini_llm = GeminiMultiModal(
api_key=GOOGLE_API_KEY, model_name="models/gemini-pro-vision"
)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_class),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=gemini_llm,
verbose=True,
)
response = await llm_program.acall()
return response
from llama_index.core import SimpleDirectoryReader
from llama_index.core.async_utils import run_jobs
async def aprocess_image_file(image_file):
print(f"Image file: {image_file}")
img_docs = SimpleDirectoryReader(input_files=[image_file]).load_data()
output = await pydantic_gemini(ReceiptInfo, img_docs, prompt_template_str)
return output
async def aprocess_image_files(image_files):
"""Process metadata on image files."""
new_docs = []
tasks = []
for image_file in image_files:
task = aprocess_image_file(image_file)
tasks.append(task)
outputs = await run_jobs(tasks, show_progress=True, workers=5)
return outputs
outputs = await aprocess_image_files(image_files)
outputs[4]
from llama_index.core.schema import TextNode
from typing import List
def get_nodes_from_objs(
objs: List[ReceiptInfo], image_files: List[str]
) -> TextNode:
"""Get nodes from objects."""
nodes = []
for image_file, obj in zip(image_files, objs):
node = TextNode(
text=obj.summary,
metadata={
"company": obj.company,
"date": obj.date,
"address": obj.address,
"total": obj.total,
"currency": obj.currency,
"image_file": str(image_file),
},
excluded_embed_metadata_keys=["image_file"],
excluded_llm_metadata_keys=["image_file"],
)
nodes.append(node)
return nodes
nodes = get_nodes_from_objs(outputs, image_files)
print(nodes[0].get_content(metadata_mode="all"))
import qdrant_client
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.gemini import GeminiEmbedding
from llama_index.llms.gemini import Gemini
from llama_index.core import Settings
client = qdrant_client.QdrantClient(path="qdrant_gemini")
vector_store = QdrantVectorStore(client=client, collection_name="collection")
Settings.embed_model = GeminiEmbedding(
model_name="models/embedding-001", api_key=GOOGLE_API_KEY
)
Settings.llm = (Gemini(api_key=GOOGLE_API_KEY),)
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-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index ipywidgets')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from IPython.display import Markdown, display
import torch
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.core import PromptTemplate
LLAMA2_7B = "meta-llama/Llama-2-7b-hf"
LLAMA2_7B_CHAT = "meta-llama/Llama-2-7b-chat-hf"
LLAMA2_13B = "meta-llama/Llama-2-13b-hf"
LLAMA2_13B_CHAT = "meta-llama/Llama-2-13b-chat-hf"
LLAMA2_70B = "meta-llama/Llama-2-70b-hf"
LLAMA2_70B_CHAT = "meta-llama/Llama-2-70b-chat-hf"
selected_model = LLAMA2_13B_CHAT
SYSTEM_PROMPT = """You are an AI assistant that answers questions in a friendly manner, based on the given source documents. Here are some rules you always follow:
- Generate human readable output, avoid creating output with gibberish text.
- Generate only the requested output, don't include any other language before or after the requested output.
- Never say thank you, that you are happy to help, that you are an AI agent, etc. Just answer directly.
- Generate professional language typically used in business documents in North America.
- Never generate offensive or foul language.
"""
query_wrapper_prompt = PromptTemplate(
"[INST]<<SYS>>\n" + SYSTEM_PROMPT + "<</SYS>>\n\n{query_str}[/INST] "
)
llm = HuggingFaceLLM(
context_window=4096,
max_new_tokens=2048,
generate_kwargs={"temperature": 0.0, "do_sample": False},
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name=selected_model,
model_name=selected_model,
device_map="auto",
model_kwargs={"torch_dtype": torch.float16, "load_in_8bit": True},
)
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
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 IPython.display import Image
Image(filename="img/airbyte_1.png")
Image(filename="img/github_1.png")
Image(filename="img/github_2.png")
Image(filename="img/snowflake_1.png")
Image(filename="img/snowflake_2.png")
Image(filename="img/airbyte_7.png")
Image(filename="img/github_3.png")
Image(filename="img/airbyte_9.png")
Image(filename="img/airbyte_8.png")
def snowflake_sqlalchemy_20_monkey_patches():
import sqlalchemy.util.compat
sqlalchemy.util.compat.string_types = (str,)
sqlalchemy.types.String.RETURNS_UNICODE = True
import snowflake.sqlalchemy.snowdialect
snowflake.sqlalchemy.snowdialect.SnowflakeDialect.returns_unicode_strings = (
True
)
import snowflake.sqlalchemy.snowdialect
def has_table(self, connection, table_name, schema=None, info_cache=None):
"""
Checks if the table exists
"""
return self._has_object(connection, "TABLE", table_name, schema)
snowflake.sqlalchemy.snowdialect.SnowflakeDialect.has_table = has_table
try:
snowflake_sqlalchemy_20_monkey_patches()
except Exception as e:
raise ValueError("Please run `pip install snowflake-sqlalchemy`")
snowflake_uri = "snowflake://<user_login_name>:<password>@<account_identifier>/<database_name>/<schema_name>?warehouse=<warehouse_name>&role=<role_name>"
from sqlalchemy import select, create_engine, MetaData, Table
engine = create_engine(snowflake_uri)
metadata = MetaData(bind=None)
table = Table("ZENDESK_TICKETS", metadata, autoload=True, autoload_with=engine)
stmt = select(table.columns)
with engine.connect() as connection:
results = connection.execute(stmt).fetchone()
print(results)
print(results.keys())
from llama_index import SQLDatabase
sql_database = SQLDatabase(engine)
from llama_index.indices.struct_store.sql_query import NLSQLTableQueryEngine
from IPython.display import Markdown, display
query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["github_issues", "github_comments", "github_users"],
)
query_str = "Which issues have the most comments? Give the top 10 and use a join on url."
response = query_engine.query(query_str)
display(Markdown(f"<b>{response}</b>"))
query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
synthesize_response=False,
tables=["github_issues", "github_comments", "github_users"],
)
response = query_engine.query(query_str)
display(Markdown(f"<b>{response}</b>"))
sql_query = response.metadata["sql_query"]
display(Markdown(f"<b>{sql_query}</b>"))
from llama_index.indices.struct_store.sql_query import (
SQLTableRetrieverQueryEngine,
)
from llama_index.objects import (
SQLTableNodeMapping,
ObjectIndex,
SQLTableSchema,
)
from llama_index import VectorStoreIndex
table_node_mapping = SQLTableNodeMapping(sql_database)
all_table_names = sql_database.get_usable_table_names()
table_schema_objs = []
for table_name in all_table_names:
table_schema_objs.append( | SQLTableSchema(table_name=table_name) | llama_index.objects.SQLTableSchema |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import PairwiseComparisonEvaluator
from llama_index.core.node_parser import SentenceSplitter
import pandas as pd
pd.set_option("display.max_colwidth", 0)
gpt4 = | OpenAI(temperature=0, model="gpt-4") | llama_index.llms.openai.OpenAI |
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) | llama_index.vector_stores.milvus.MilvusVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
from llama_index.core import SimpleDirectoryReader
from llama_index.core import SummaryIndex
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
wiki_titles = ["Michael Jordan", "Elon Musk", "Richard Branson", "Rihanna"]
wiki_metadatas = {
"Michael Jordan": {
"category": "Sports",
"country": "United States",
},
"Elon Musk": {
"category": "Business",
"country": "United States",
},
"Richard Branson": {
"category": "Business",
"country": "UK",
},
"Rihanna": {
"category": "Music",
"country": "Barbados",
},
}
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)
docs_dict = {}
for wiki_title in wiki_titles:
doc = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()[0]
doc.metadata.update(wiki_metadatas[wiki_title])
docs_dict[wiki_title] = doc
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import LlamaDebugHandler, CallbackManager
from llama_index.core.node_parser import SentenceSplitter
llm = | OpenAI("gpt-4") | llama_index.llms.openai.OpenAI |
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('wget "https://www.dropbox.com/s/f6bmb19xdg0xedm/paul_graham_essay.txt?dl=1" -O paul_graham_essay.txt')
from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SimpleNodeParser
reader = | SimpleDirectoryReader(input_files=["paul_graham_essay.txt"]) | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt' -O pg_essay.txt")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader(input_files=["pg_essay.txt"])
documents = reader.load_data()
from llama_index.core.query_pipeline import QueryPipeline, InputComponent
from typing import Dict, Any, List, Optional
from llama_index.llms.openai import OpenAI
from llama_index.core import Document, VectorStoreIndex
from llama_index.core import SummaryIndex
from llama_index.core.response_synthesizers import TreeSummarize
from llama_index.core.schema import NodeWithScore, TextNode
from llama_index.core import PromptTemplate
from llama_index.core.selectors import LLMSingleSelector
hyde_str = """\
Please write a passage to answer the question: {query_str}
Try to include as many key details as possible.
Passage: """
hyde_prompt = PromptTemplate(hyde_str)
llm = OpenAI(model="gpt-3.5-turbo")
summarizer = TreeSummarize(llm=llm)
vector_index = VectorStoreIndex.from_documents(documents)
vector_query_engine = vector_index.as_query_engine(similarity_top_k=2)
summary_index = SummaryIndex.from_documents(documents)
summary_qrewrite_str = """\
Here's a question:
{query_str}
You are responsible for feeding the question to an agent that given context will try to answer the question.
The context may or may not be relevant. Rewrite the question to highlight the fact that
only some pieces of context (or none) maybe be relevant.
"""
summary_qrewrite_prompt = PromptTemplate(summary_qrewrite_str)
summary_query_engine = summary_index.as_query_engine()
selector = LLMSingleSelector.from_defaults()
from llama_index.core.query_pipeline import RouterComponent
vector_chain = | QueryPipeline(chain=[vector_query_engine]) | llama_index.core.query_pipeline.QueryPipeline |
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()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("sqlite:///:memory:", future=True)
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
metadata_obj.tables.keys()
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{"city_name": "Berlin", "population": 3645000, "country": "Germany"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
get_ipython().system('pip install wikipedia')
from llama_index.readers.wikipedia import WikipediaReader
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = WikipediaReader().load_data(pages=cities)
from llama_index.core import SQLDatabase
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from llama_index.llms.openai import OpenAI
from llama_index.core import VectorStoreIndex
vector_indices = {}
vector_query_engines = {}
for city, wiki_doc in zip(cities, wiki_docs):
vector_index = VectorStoreIndex.from_documents([wiki_doc])
query_engine = vector_index.as_query_engine(
similarity_top_k=2, llm=OpenAI(model="gpt-3.5-turbo")
)
vector_indices[city] = vector_index
vector_query_engines[city] = query_engine
from llama_index.core.query_engine import NLSQLTableQueryEngine
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["city_stats"],
)
from llama_index.core.tools import QueryEngineTool
from llama_index.core.tools import ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
query_engine_tools = []
for city in cities:
query_engine = vector_query_engines[city]
query_engine_tool = QueryEngineTool(
query_engine=query_engine,
metadata=ToolMetadata(
name=city, description=f"Provides information about {city}"
),
)
query_engine_tools.append(query_engine_tool)
s_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools, llm=OpenAI(model="gpt-3.5-turbo")
)
from llama_index.core.retrievers import VectorIndexAutoRetriever
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo
from llama_index.core.query_engine import RetrieverQueryEngine
sql_tool = QueryEngineTool.from_defaults(
query_engine=sql_query_engine,
description=(
"Useful for translating a natural language query into a SQL query over"
" a table containing: city_stats, containing the population/country of"
" each city"
),
)
s_engine_tool = QueryEngineTool.from_defaults(
query_engine=s_engine,
description=(
f"Useful for answering semantic questions about different cities"
),
)
from llama_index.core.query_engine import SQLJoinQueryEngine
query_engine = SQLJoinQueryEngine(
sql_tool, s_engine_tool, llm= | OpenAI(model="gpt-4") | llama_index.llms.openai.OpenAI |
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import TimeWeightedPostprocessor
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 datetime import datetime, timedelta
from llama_index.core import StorageContext
now = datetime.now()
key = "__last_accessed__"
doc1 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v1.txt"]
).load_data()[0]
doc2 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v2.txt"]
).load_data()[0]
doc3 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v3.txt"]
).load_data()[0]
from llama_index.core import Settings
Settings.text_splitter = SentenceSplitter(chunk_size=512)
nodes1 = Settings.text_splitter.get_nodes_from_documents([doc1])
nodes2 = Settings.text_splitter.get_nodes_from_documents([doc2])
nodes3 = Settings.text_splitter.get_nodes_from_documents([doc3])
nodes1[14].metadata[key] = (now - timedelta(hours=3)).timestamp()
nodes1[14].excluded_llm_metadata_keys = [key]
nodes2[14].metadata[key] = (now - timedelta(hours=2)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
nodes3[14].metadata[key] = (now - timedelta(hours=1)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
docstore = SimpleDocumentStore()
nodes = [nodes1[14], nodes2[14], nodes3[14]]
docstore.add_documents(nodes)
storage_context = StorageContext.from_defaults(docstore=docstore)
index = VectorStoreIndex(nodes, storage_context=storage_context)
node_postprocessor = TimeWeightedPostprocessor(
time_decay=0.5, time_access_refresh=False, top_k=1
)
query_engine = index.as_query_engine(
similarity_top_k=3,
)
response = query_engine.query(
"How much did the author raise in seed funding from Idelle's husband"
" (Julian) for Viaweb?",
)
display_response(response)
query_engine = index.as_query_engine(
similarity_top_k=3, node_postprocessors=[node_postprocessor]
)
response = query_engine.query(
"How much did the author raise in seed funding from Idelle's husband"
" (Julian) for Viaweb?",
)
| display_response(response) | llama_index.core.response.notebook_utils.display_response |
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() | 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)
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)
query_engine = index.as_query_engine(similarity_top_k=2, llm=gpt_35_llm)
contexts = []
answers = []
for question in questions:
response = query_engine.query(question)
contexts.append([x.node.get_content() for x in response.source_nodes])
answers.append(str(response))
from datasets import Dataset
from ragas import evaluate
from ragas.metrics import answer_relevancy, faithfulness
ds = Dataset.from_dict(
{
"question": questions,
"answer": answers,
"contexts": contexts,
}
)
result = evaluate(ds, [answer_relevancy, faithfulness])
print(result)
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = | CallbackManager([finetuning_handler]) | llama_index.core.callbacks.CallbackManager |
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)
import llama_index.core
from llama_index.core import set_global_handler
set_global_handler(
"honeyhive",
project="My LlamaIndex Project",
name="My LlamaIndex Pipeline",
api_key=os.environ["HONEYHIVE_API_KEY"],
)
hh_tracer = llama_index.core.global_handler
llama_debug = | LlamaDebugHandler(print_trace_on_end=True) | llama_index.core.callbacks.LlamaDebugHandler |
get_ipython().run_line_magic('pip', 'install llama-index-llms-ai21')
get_ipython().system('pip install llama-index')
from llama_index.llms.ai21 import AI21
api_key = "Your api key"
resp = AI21(api_key=api_key).complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.ai21 import AI21
messages = [
| ChatMessage(role="user", content="hello there") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-redis')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-redis')
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.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)
REDIS_HOST = os.getenv("REDIS_HOST", "127.0.0.1")
REDIS_PORT = os.getenv("REDIS_PORT", 6379)
from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.index_store.redis import RedisIndexStore
storage_context = StorageContext.from_defaults(
docstore=RedisDocumentStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
index_store=RedisIndexStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
)
storage_context.docstore.add_documents(nodes)
len(storage_context.docstore.docs)
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(persist_dir="./storage")
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
from llama_index.core import load_index_from_storage
storage_context = StorageContext.from_defaults(
docstore=RedisDocumentStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
index_store=RedisIndexStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
)
summary_index = load_index_from_storage(
storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
storage_context=storage_context, index_id=vector_id
)
keyword_table_index = load_index_from_storage(
storage_context=storage_context, index_id=keyword_id
)
chatgpt = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = chatgpt
Settings.chunk_size = 1024
query_engine = summary_index.as_query_engine()
list_response = query_engine.query("What is a summary of this document?")
display_response(list_response)
query_engine = vector_index.as_query_engine()
vector_response = query_engine.query("What did the author do growing up?")
display_response(vector_response)
query_engine = keyword_table_index.as_query_engine()
keyword_response = query_engine.query(
"What did the author do after his time at YC?"
)
| display_response(keyword_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-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)
from llama_index.retrievers.bm25 import BM25Retriever
vector_retriever = index.as_retriever(similarity_top_k=10)
bm25_retriever = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=10)
from llama_index.core.retrievers import BaseRetriever
class HybridRetriever(BaseRetriever):
def __init__(self, vector_retriever, bm25_retriever):
self.vector_retriever = vector_retriever
self.bm25_retriever = bm25_retriever
super().__init__()
def _retrieve(self, query, **kwargs):
bm25_nodes = self.bm25_retriever.retrieve(query, **kwargs)
vector_nodes = self.vector_retriever.retrieve(query, **kwargs)
all_nodes = []
node_ids = set()
for n in bm25_nodes + vector_nodes:
if n.node.node_id not in node_ids:
all_nodes.append(n)
node_ids.add(n.node.node_id)
return all_nodes
index.as_retriever(similarity_top_k=5)
hybrid_retriever = HybridRetriever(vector_retriever, bm25_retriever)
get_ipython().system('pip install sentence-transformers')
from llama_index.core.postprocessor import SentenceTransformerRerank
reranker = | SentenceTransformerRerank(top_n=4, model="BAAI/bge-reranker-base") | llama_index.core.postprocessor.SentenceTransformerRerank |
get_ipython().run_line_magic('pip', 'install llama-index-llms-ai21')
get_ipython().system('pip install llama-index')
from llama_index.llms.ai21 import AI21
api_key = "Your api key"
resp = AI21(api_key=api_key).complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.ai21 import AI21
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 = AI21(api_key=api_key).chat(
messages, preamble_override="You are a pirate with a colorful personality"
)
print(resp)
from llama_index.llms.ai21 import AI21
llm = AI21(model="j2-mid", api_key=api_key)
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.llms.ai21 import AI21
llm_good = AI21(api_key=api_key)
llm_bad = | AI21(model="j2-mid", api_key="BAD_KEY") | llama_index.llms.ai21.AI21 |
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import TimeWeightedPostprocessor
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 datetime import datetime, timedelta
from llama_index.core import StorageContext
now = datetime.now()
key = "__last_accessed__"
doc1 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v1.txt"]
).load_data()[0]
doc2 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v2.txt"]
).load_data()[0]
doc3 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v3.txt"]
).load_data()[0]
from llama_index.core import Settings
Settings.text_splitter = SentenceSplitter(chunk_size=512)
nodes1 = Settings.text_splitter.get_nodes_from_documents([doc1])
nodes2 = Settings.text_splitter.get_nodes_from_documents([doc2])
nodes3 = Settings.text_splitter.get_nodes_from_documents([doc3])
nodes1[14].metadata[key] = (now - timedelta(hours=3)).timestamp()
nodes1[14].excluded_llm_metadata_keys = [key]
nodes2[14].metadata[key] = (now - timedelta(hours=2)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
nodes3[14].metadata[key] = (now - timedelta(hours=1)).timestamp()
nodes2[14].excluded_llm_metadata_keys = [key]
docstore = SimpleDocumentStore()
nodes = [nodes1[14], nodes2[14], nodes3[14]]
docstore.add_documents(nodes)
storage_context = StorageContext.from_defaults(docstore=docstore)
index = VectorStoreIndex(nodes, storage_context=storage_context)
node_postprocessor = TimeWeightedPostprocessor(
time_decay=0.5, time_access_refresh=False, top_k=1
)
query_engine = index.as_query_engine(
similarity_top_k=3,
)
response = query_engine.query(
"How much did the author raise in seed funding from Idelle's husband"
" (Julian) for Viaweb?",
)
display_response(response)
query_engine = index.as_query_engine(
similarity_top_k=3, node_postprocessors=[node_postprocessor]
)
response = query_engine.query(
"How much did the author raise in seed funding from Idelle's husband"
" (Julian) for Viaweb?",
)
display_response(response)
from llama_index.core import SummaryIndex
query_str = (
"How much did the author raise in seed funding from Idelle's husband"
" (Julian) for Viaweb?"
)
query_engine = index.as_query_engine(
similarity_top_k=3, response_mode="no_text"
)
init_response = query_engine.query(
query_str,
)
resp_nodes = [n for n in init_response.source_nodes]
new_resp_nodes = node_postprocessor.postprocess_nodes(resp_nodes)
summary_index = SummaryIndex([n.node for n in new_resp_nodes])
query_engine = summary_index.as_query_engine()
response = query_engine.query(query_str)
| display_response(response) | llama_index.core.response.notebook_utils.display_response |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-faiss')
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 faiss
d = 1536
faiss_index = faiss.IndexFlatL2(d)
from llama_index.core import (
SimpleDirectoryReader,
load_index_from_storage,
VectorStoreIndex,
StorageContext,
)
from llama_index.vector_stores.faiss import FaissVectorStore
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 = FaissVectorStore(faiss_index=faiss_index)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
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", temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install llama-index-packs-trulens-eval-packs')
get_ipython().system('pip install trulens-eval llama-hub html2text llama-index')
import os
from llama_index.packs.trulens_eval_packs import (
TruLensRAGTriadPack,
TruLensHarmlessPack,
TruLensHelpfulPack,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.readers.web import SimpleWebPageReader
from tqdm.auto import tqdm
os.environ["OPENAI_API_KEY"] = "sk-..."
documents = | SimpleWebPageReader(html_to_text=True) | llama_index.readers.web.SimpleWebPageReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
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.core import Document, VectorStoreIndex
from llama_index.readers.file import PyMuPDFReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.llms.openai import OpenAI
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)]
node_parser = SimpleNodeParser.from_defaults()
nodes = node_parser.get_nodes_from_documents(docs)
len(nodes)
get_ipython().system('wget "https://www.dropbox.com/scl/fi/fh9vsmmm8vu0j50l3ss38/llama2_eval_qr_dataset.json?rlkey=kkoaez7aqeb4z25gzc06ak6kb&dl=1" -O data/llama2_eval_qr_dataset.json')
from llama_index.core.evaluation import QueryResponseDataset
eval_dataset = QueryResponseDataset.from_json(
"data/llama2_eval_qr_dataset.json"
)
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4-1106-preview")
dataset_generator = DatasetGenerator(
nodes[:20],
llm=llm,
show_progress=True,
num_questions_per_chunk=3,
)
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"
)
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
from llama_index.core.evaluation import (
CorrectnessEvaluator,
SemanticSimilarityEvaluator,
BatchEvalRunner,
)
from llama_index.llms.openai import OpenAI
eval_llm = OpenAI(model="gpt-4-1106-preview")
evaluator_c = | CorrectnessEvaluator(llm=eval_llm) | llama_index.core.evaluation.CorrectnessEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
llm = | OpenAI(model="gpt-3.5-turbo-instruct") | llama_index.llms.openai.OpenAI |
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)
prompt_str2 = (
"Please write a passage to answer the question\n"
"Try to include as many key details as possible.\n"
"\n"
"\n"
"{query_str}\n"
"\n"
"\n"
'Passage:"""\n'
)
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=5)
p = QueryPipeline(
chain=[prompt_tmpl1, llm, prompt_tmpl2, llm, retriever], verbose=True
)
nodes = p.run(topic="college")
len(nodes)
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.response_synthesizers import TreeSummarize
prompt_str = "Please generate a question about Paul Graham's life regarding the following topic {topic}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=3)
reranker = | CohereRerank() | llama_index.postprocessor.cohere_rerank.CohereRerank |
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.callbacks.CallbackManager |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
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'")
documents = SimpleDirectoryReader("./data//paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents)
retriever = index.as_retriever()
from llama_index.core.query_engine import CustomQueryEngine
from llama_index.core.retrievers import BaseRetriever
from llama_index.core import get_response_synthesizer
from llama_index.core.response_synthesizers import BaseSynthesizer
class RAGQueryEngine(CustomQueryEngine):
"""RAG Query Engine."""
retriever: BaseRetriever
response_synthesizer: BaseSynthesizer
def custom_query(self, query_str: str):
nodes = self.retriever.retrieve(query_str)
response_obj = self.response_synthesizer.synthesize(query_str, nodes)
return response_obj
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
qa_prompt = PromptTemplate(
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the query.\n"
"Query: {query_str}\n"
"Answer: "
)
class RAGStringQueryEngine(CustomQueryEngine):
"""RAG String Query Engine."""
retriever: BaseRetriever
response_synthesizer: BaseSynthesizer
llm: OpenAI
qa_prompt: PromptTemplate
def custom_query(self, query_str: str):
nodes = self.retriever.retrieve(query_str)
context_str = "\n\n".join([n.node.get_content() for n in nodes])
response = self.llm.complete(
qa_prompt.format(context_str=context_str, query_str=query_str)
)
return str(response)
synthesizer = get_response_synthesizer(response_mode="compact")
query_engine = RAGQueryEngine(
retriever=retriever, response_synthesizer=synthesizer
)
response = query_engine.query("What did the author do growing up?")
print(str(response))
print(response.source_nodes[0].get_content())
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-packs-node-parser-semantic-chunking')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-hub-llama-packs-node-parser-semantic-chunking-base')
from llama_index.core import SimpleDirectoryReader
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'pg_essay.txt'")
documents = | SimpleDirectoryReader(input_files=["pg_essay.txt"]) | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai-legacy')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.core.tools import QueryEngineTool, ToolMetadata
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/march"
)
march_index = | load_index_from_storage(storage_context) | llama_index.core.load_index_from_storage |
get_ipython().system('pip install llama-index yfinance')
import openai
from llama_index.agent import OpenAIAgent
openai.api_key = "sk-..."
from llama_index.tools.yahoo_finance.base import YahooFinanceToolSpec
finance_tool = YahooFinanceToolSpec()
finance_tool_list = finance_tool.to_tool_list()
for tool in finance_tool_list:
print(tool.metadata.name)
print(finance_tool.balance_sheet("AAPL"))
agent = | OpenAIAgent.from_tools(finance_tool_list) | llama_index.agent.OpenAIAgent.from_tools |
Subsets and Splits