Advanced RAG with LlamaParse

Building Advanced RAG With LlamaParse¶

In this notebook we will demonstrate the following:

1. Using LlamaParse.
2. Using Recursive Retrieval with LlamaParse to query tables/ text within a document hierarchically.

LlamaParse Documentation

Installation¶

In [ ]:

!pip install llama-index
!pip install llama-index-postprocessor-flag-embedding-reranker
!pip install git+https://github.com/FlagOpen/FlagEmbedding.git
!pip install llama-parse

!pip install llama-index !pip install llama-index-postprocessor-flag-embedding-reranker !pip install git+https://github.com/FlagOpen/FlagEmbedding.git !pip install llama-parse

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Installing collected packages: striprtf, pytz, dirtyjson, wrapt, tzdata, typing-extensions, tqdm, tenacity, regex, mypy-extensions, multidict, marshmallow, joblib, jiter, h11, greenlet, fsspec, frozenlist, click, beautifulsoup4, async-timeout, annotated-types, aiohappyeyeballs, yarl, typing-inspect, tiktoken, SQLAlchemy, pypdf, pydantic-core, pandas, nltk, httpcore, deprecated, aiosignal, pydantic, httpx, dataclasses-json, aiohttp, openai, llama-index-core, llama-cloud, llama-parse, llama-index-readers-file, llama-index-legacy, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-readers-llama-parse, llama-index-llms-openai, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama-index-multi-modal-llms-openai, llama-index-cli, llama-index
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Successfully installed SQLAlchemy-2.0.34 aiohappyeyeballs-2.4.0 aiohttp-3.10.5 aiosignal-1.3.1 annotated-types-0.7.0 async-timeout-4.0.3 beautifulsoup4-4.12.3 click-8.1.7 dataclasses-json-0.6.7 deprecated-1.2.14 dirtyjson-1.0.8 frozenlist-1.4.1 fsspec-2024.9.0 greenlet-3.0.3 h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jiter-0.5.0 joblib-1.4.2 llama-cloud-0.0.15 llama-index-0.11.5 llama-index-agent-openai-0.3.0 llama-index-cli-0.3.0 llama-index-core-0.11.5 llama-index-embeddings-openai-0.2.4 llama-index-indices-managed-llama-cloud-0.3.0 llama-index-legacy-0.9.48.post3 llama-index-llms-openai-0.2.2 llama-index-multi-modal-llms-openai-0.2.0 llama-index-program-openai-0.2.0 llama-index-question-gen-openai-0.2.0 llama-index-readers-file-0.2.0 llama-index-readers-llama-parse-0.3.0 llama-parse-0.5.2 marshmallow-3.22.0 multidict-6.0.5 mypy-extensions-1.0.0 nltk-3.9.1 openai-1.43.0 pandas-2.2.2 pydantic-2.8.2 pydantic-core-2.20.1 pypdf-4.3.1 pytz-2024.1 regex-2024.7.24 striprtf-0.0.26 tenacity-8.5.0 tiktoken-0.7.0 tqdm-4.66.5 typing-extensions-4.12.2 typing-inspect-0.9.0 tzdata-2024.1 wrapt-1.16.0 yarl-1.9.11
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.3.1 -> 24.2
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.3.1 -> 24.2
[notice] To update, run: python -m pip install --upgrade pip
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Successfully installed FlagEmbedding-1.2.11 accelerate-0.34.0 datasets-2.21.0 dill-0.3.8 fsspec-2024.6.1 huggingface-hub-0.24.6 multiprocess-0.70.16 peft-0.12.0 pyarrow-17.0.0 requests-2.32.3 safetensors-0.4.4 scikit-learn-1.5.1 scipy-1.14.1 sentence_transformers-3.0.1 threadpoolctl-3.5.0 tokenizers-0.19.1 transformers-4.44.2 xxhash-3.5.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.3.1 -> 24.2
[notice] To update, run: python -m pip install --upgrade pip
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.3.1 -> 24.2
[notice] To update, run: python -m pip install --upgrade pip

Download Data¶

In [ ]:

!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10q/uber_10q_march_2022.pdf' -O './uber_10q_march_2022.pdf'

!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10q/uber_10q_march_2022.pdf' -O './uber_10q_march_2022.pdf'

--2024-09-05 07:01:47--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10q/uber_10q_march_2022.pdf
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1260185 (1.2M) [application/octet-stream]
Saving to: ‘./uber_10q_march_2022.pdf’

./uber_10q_march_20 100%[===================>]   1.20M  --.-KB/s    in 0.02s   

2024-09-05 07:01:48 (77.6 MB/s) - ‘./uber_10q_march_2022.pdf’ saved [1260185/1260185]

Setting API Keys¶

In [ ]:

# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio
import nest_asyncio

nest_asyncio.apply()

import os

# API access to llama-cloud
os.environ["LLAMA_CLOUD_API_KEY"] = "llx-..."

# Using OpenAI API for embeddings/llms
os.environ["OPENAI_API_KEY"] = "sk-..."

# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio import nest_asyncio nest_asyncio.apply() import os # API access to llama-cloud os.environ["LLAMA_CLOUD_API_KEY"] = "llx-..." # Using OpenAI API for embeddings/llms os.environ["OPENAI_API_KEY"] = "sk-..."

Setting LLM and Embedding Model¶

In [ ]:

from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import VectorStoreIndex
from llama_index.core import Settings

embed_model = OpenAIEmbedding(model="text-embedding-3-small")
llm = OpenAI(model="gpt-3.5-turbo-0125")

Settings.llm = llm
Settings.embed_model = embed_model

from llama_index.llms.openai import OpenAI from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.core import VectorStoreIndex from llama_index.core import Settings embed_model = OpenAIEmbedding(model="text-embedding-3-small") llm = OpenAI(model="gpt-3.5-turbo-0125") Settings.llm = llm Settings.embed_model = embed_model

LlamaParse PDF reader for PDF Parsing¶

We compare two different retrieval/ queryengine strategies.

1. Using raw Markdown text as nodes for building index and applying a simple query engine for generating results.
2. Using MarkdownElementNodeParser for parsing the LlamaParse output Markdown results and building a recursive retriever query engine for generation.

In [ ]:

# LlamaParse PDF reader for PDF Parsing
from llama_parse import LlamaParse

documents = LlamaParse(result_type="markdown").load_data(
    "./uber_10q_march_2022.pdf"
)
# Started parsing the file under job_id b76a572b-d2bb-42ae-bad9-b9810049f1af

# LlamaParse PDF reader for PDF Parsing from llama_parse import LlamaParse documents = LlamaParse(result_type="markdown").load_data( "./uber_10q_march_2022.pdf" ) # Started parsing the file under job_id b76a572b-d2bb-42ae-bad9-b9810049f1af

Started parsing the file under job_id 0ef2f65b-9cab-4ca8-b221-d20f1f6d1336

In [ ]:

print(documents[0].text[:1000] + "...")

print(documents[0].text[:1000] + "...")

# UNITED STATES SECURITIES AND EXCHANGE COMMISSION

# Washington, D.C. 20549

# FORM 10-Q

(Mark One)

☒ QUARTERLY REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

For the quarterly period ended March 31, 2022

OR

☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

For the transition period from_____ to _____

Commission File Number: 001-38902

# UBER TECHNOLOGIES, INC.

(Exact name of registrant as specified in its charter)

Not Applicable

(Former name, former address and former fiscal year, if changed since last report)

|Delaware|45-2647441|
|---|---|
|(State or other jurisdiction of incorporation or organization)|(I.R.S. Employer Identification No.)|
|1515 3rd Street|San Francisco, California 94158|
|(Address of principal executive offices, including zip code)|(415) 612-8582|
|(Registrant’s telephone number, including area code)| |

# Securities registered pursuant to Section 12(b) of the Act:

|Title of each c...

In [ ]:

from llama_index.core.node_parser import MarkdownElementNodeParser

node_parser = MarkdownElementNodeParser(
    llm=OpenAI(model="gpt-3.5-turbo-0125"), num_workers=8
)

nodes = node_parser.get_nodes_from_documents(documents)

from llama_index.core.node_parser import MarkdownElementNodeParser node_parser = MarkdownElementNodeParser( llm=OpenAI(model="gpt-3.5-turbo-0125"), num_workers=8 ) nodes = node_parser.get_nodes_from_documents(documents)

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In [ ]:

text_nodes, index_nodes = node_parser.get_nodes_and_objects(nodes)

text_nodes, index_nodes = node_parser.get_nodes_and_objects(nodes)

In [ ]:

text_nodes[0]

text_nodes[0]

Out[ ]:

TextNode(id_='c6ffea61-1221-40e3-b0e0-5b24cfbd02d5', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='33b7b29c-8eba-458b-a25f-bb8f88951e92', node_type=<ObjectType.DOCUMENT: '4'>, metadata={}, hash='3d4ec5b02a042598b0ea47cdac56453869c17b531a10f60343e9598e05a9390e'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='de618b65-c78a-4390-8536-4e9e295c0e49', node_type=<ObjectType.INDEX: '3'>, metadata={'col_schema': 'Column: Delaware\nType: string\nSummary: State or other jurisdiction of incorporation or organization\n\nColumn: 45-2647441\nType: string\nSummary: I.R.S. Employer Identification No.'}, hash='c008153189b8dd031a3e5e694239a50ebd21f42602676f072d9746241fcef858')}, text='UNITED STATES SECURITIES AND EXCHANGE COMMISSION\n\n Washington, D.C. 20549\n\n FORM 10-Q\n\n(Mark One)\n\n☒ QUARTERLY REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\n\nFor the quarterly period ended March 31, 2022\n\nOR\n\n☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\n\nFor the transition period from_____ to _____\n\nCommission File Number: 001-38902\n\n UBER TECHNOLOGIES, INC.\n\n(Exact name of registrant as specified in its charter)\n\nNot Applicable\n\n(Former name, former address and former fiscal year, if changed since last report)', mimetype='text/plain', start_char_idx=1, end_char_idx=595, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n')

In [ ]:

index_nodes[0]

index_nodes[0]

Out[ ]:

IndexNode(id_='de618b65-c78a-4390-8536-4e9e295c0e49', embedding=None, metadata={'col_schema': 'Column: Delaware\nType: string\nSummary: State or other jurisdiction of incorporation or organization\n\nColumn: 45-2647441\nType: string\nSummary: I.R.S. Employer Identification No.'}, excluded_embed_metadata_keys=['col_schema'], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='33b7b29c-8eba-458b-a25f-bb8f88951e92', node_type=<ObjectType.DOCUMENT: '4'>, metadata={}, hash='3d4ec5b02a042598b0ea47cdac56453869c17b531a10f60343e9598e05a9390e'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='c6ffea61-1221-40e3-b0e0-5b24cfbd02d5', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='0cafbb2bbffe3085738e748c9ed19c5b88f6b300d876820fc3caa7afa8f0627f'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='c57f8dab-7b69-4850-8885-6a9cf0f531f9', node_type=<ObjectType.TEXT: '1'>, metadata={'table_df': "{'Delaware': {0: '(State or other jurisdiction of incorporation or organization)', 1: '1515 3rd Street', 2: '(Address of principal executive offices, including zip code)', 3: '(Registrant’s telephone number, including area code)'}, '45-2647441': {0: '(I.R.S. Employer Identification No.)', 1: 'San Francisco, California 94158', 2: '(415) 612-8582', 3: ' '}}", 'table_summary': "Table providing information about a company's incorporation details, address of principal executive offices, and contact information.,\nwith the following columns:\n- Delaware: State or other jurisdiction of incorporation or organization\n- 45-2647441: I.R.S. Employer Identification No.\n"}, hash='fadc844962620525c1d3c8d7ff1693a090642818928f9ce7600117258a39aa04')}, text="Table providing information about a company's incorporation details, address of principal executive offices, and contact information.,\nwith the following columns:\n- Delaware: State or other jurisdiction of incorporation or organization\n- 45-2647441: I.R.S. Employer Identification No.\n", mimetype='text/plain', start_char_idx=601, end_char_idx=919, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n', index_id='c57f8dab-7b69-4850-8885-6a9cf0f531f9', obj=TextNode(id_='c57f8dab-7b69-4850-8885-6a9cf0f531f9', embedding=None, metadata={'table_df': "{'Delaware': {0: '(State or other jurisdiction of incorporation or organization)', 1: '1515 3rd Street', 2: '(Address of principal executive offices, including zip code)', 3: '(Registrant’s telephone number, including area code)'}, '45-2647441': {0: '(I.R.S. Employer Identification No.)', 1: 'San Francisco, California 94158', 2: '(415) 612-8582', 3: ' '}}", 'table_summary': "Table providing information about a company's incorporation details, address of principal executive offices, and contact information.,\nwith the following columns:\n- Delaware: State or other jurisdiction of incorporation or organization\n- 45-2647441: I.R.S. Employer Identification No.\n"}, excluded_embed_metadata_keys=['table_df', 'table_summary'], excluded_llm_metadata_keys=['table_df', 'table_summary'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='33b7b29c-8eba-458b-a25f-bb8f88951e92', node_type=<ObjectType.DOCUMENT: '4'>, metadata={}, hash='3d4ec5b02a042598b0ea47cdac56453869c17b531a10f60343e9598e05a9390e'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='de618b65-c78a-4390-8536-4e9e295c0e49', node_type=<ObjectType.INDEX: '3'>, metadata={'col_schema': 'Column: Delaware\nType: string\nSummary: State or other jurisdiction of incorporation or organization\n\nColumn: 45-2647441\nType: string\nSummary: I.R.S. Employer Identification No.'}, hash='c008153189b8dd031a3e5e694239a50ebd21f42602676f072d9746241fcef858'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='c0fa90a9-fe14-46fa-8434-cd70e134d40e', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='cc6b1c09572e3a0bd06a93ccea32a5562b36a393a36ede3852f4a5fc946c51fd')}, text="Table providing information about a company's incorporation details, address of principal executive offices, and contact information.,\nwith the following columns:\n- Delaware: State or other jurisdiction of incorporation or organization\n- 45-2647441: I.R.S. Employer Identification No.\n\n|Delaware|45-2647441|\n|---|---|\n|(State or other jurisdiction of incorporation or organization)|(I.R.S. Employer Identification No.)|\n|1515 3rd Street|San Francisco, California 94158|\n|(Address of principal executive offices, including zip code)|(415) 612-8582|\n|(Registrant’s telephone number, including area code)| |\n", mimetype='text/plain', start_char_idx=601, end_char_idx=919, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'))

Build Index¶

In [ ]:

recursive_index = VectorStoreIndex(nodes=text_nodes + index_nodes)
raw_index = VectorStoreIndex.from_documents(documents)

recursive_index = VectorStoreIndex(nodes=text_nodes + index_nodes) raw_index = VectorStoreIndex.from_documents(documents)

Create Query Engines¶

In [ ]:

from llama_index.postprocessor.flag_embedding_reranker import (
    FlagEmbeddingReranker,
)

reranker = FlagEmbeddingReranker(
    top_n=5,
    model="BAAI/bge-reranker-large",
)

from llama_index.postprocessor.flag_embedding_reranker import ( FlagEmbeddingReranker, ) reranker = FlagEmbeddingReranker( top_n=5, model="BAAI/bge-reranker-large", )

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In [ ]:

recursive_query_engine = recursive_index.as_query_engine(
    similarity_top_k=15, node_postprocessors=[reranker], verbose=True
)

recursive_query_engine = recursive_index.as_query_engine( similarity_top_k=15, node_postprocessors=[reranker], verbose=True )

In [ ]:

raw_query_engine = raw_index.as_query_engine(
    similarity_top_k=15, node_postprocessors=[reranker]
)

raw_query_engine = raw_index.as_query_engine( similarity_top_k=15, node_postprocessors=[reranker] )

Querying with two different query engines¶

we compare base query engine vs recursive query engine with tables

Table Query Task: Queries for Table Question Answering¶

In [ ]:

query = "What is the change of free cash flow and what is the rate from the financial and operational highlights?"

response_1 = raw_query_engine.query(query)
print("\n************New LlamaParse+ Basic Query Engine************")
print(response_1)

response_2 = recursive_query_engine.query(query)
print(
    "\n************New LlamaParse+ Recursive Retriever Query Engine************"
)
print(response_2)

query = "What is the change of free cash flow and what is the rate from the financial and operational highlights?" response_1 = raw_query_engine.query(query) print("\n************New LlamaParse+ Basic Query Engine************") print(response_1) response_2 = recursive_query_engine.query(query) print( "\n************New LlamaParse+ Recursive Retriever Query Engine************" ) print(response_2)

************New LlamaParse+ Basic Query Engine************
The change in free cash flow from the financial and operational highlights is an increase of $826 million, from a net cash used in operating activities of $611 million in 2021 to net cash provided by operating activities of $215 million in 2022. The rate of this change is a positive improvement.
Retrieval entering 015f9778-1f7c-44cd-9e26-90f2c9e21550: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?
Retrieval entering 5e8febd0-0c43-4552-9499-9465674b8877: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?
Retrieval entering d3c8d59b-9d7e-4088-94e9-3e58aba09f10: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?
Retrieval entering 25385e8f-24df-4660-959b-c499cc220246: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?
Retrieval entering 6b48fe6f-a60e-425a-aba5-22b62d1b4512: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?
Retrieval entering aa60761d-1d8a-4896-aef9-e41553f17558: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?
Retrieval entering d0634d58-0589-47cb-9921-d2d57d88240f: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?
Retrieval entering 492b36c1-8b39-4db9-8dca-dd9f6c488a9d: TextNode
Retrieving from object TextNode with query What is the change of free cash flow and what is the rate from the financial and operational highlights?

************New LlamaParse+ Recursive Retriever Query Engine************
The change in free cash flow from 2021 to 2022 is an increase of $635 million. This change represents a significant improvement in free cash flow performance over the period.