Best Open-Source Tool to Build a Chatbot That Answers Questions from PDF Files in Python
Open-source tools for building a Python chatbot that answers questions from PDF files, usually with retrieval-augmented generation (RAG), with live GitHub data on each.
Last updated Repo data refreshed 25 Oct 20263 options
Our pick · llama_index
Our pick is run-llama/llama_index for building a chatbot that answers questions from PDF files in Python. While langchain-ai/langchain offers broad integration capabilities, LlamaIndex's specialized focus on data ingestion, indexing, and retrieval makes it exceptionally efficient and intuitive for connecting custom data like PDFs to LLMs for Retrieval Augmented Generation (RAG). Its streamlined API allows for quicker development of a functional RAG system. For robustly connecting PDFs to an LLM for question answering, LlamaIndex provides a more direct path; however, for complex conversational agents or broad integrations beyond custom data, langchain-ai/langchain remains a powerful alternative.
A data framework designed to connect custom data sources, such as PDF files, with large language models, specializing in data ingestion, indexing, and efficient retrieval for RAG applications.
Best for: Developers whose primary challenge is effectively indexing and querying large volumes of diverse, unstructured data (like PDFs) to power LLM applications, especially for robust Retrieval Augmented Generation (RAG).
What's good
Strongly focused on data ingestion, indexing, and retrieval, making it exceptionally good at processing unstructured data from various sources like PDFs for RAG.
Offers a simpler and more intuitive API for creating and querying indices from custom data, often requiring less boilerplate than LangChain for data-centric tasks.
Provides excellent tools and abstractions specifically for evaluating retrieval quality and performance, which is crucial for building robust RAG systems.
Supports various advanced query engines and indexing strategies, including sub-querying and recursive retrieval, to handle complex data interaction.
Watch out for
While rapidly improving, its conversational capabilities and agent tooling are generally less mature or comprehensive compared to LangChain's dedicated conversational chains.
The project has undergone significant renaming and API updates, which can occasionally lead to outdated examples or documentation.
The community, though growing rapidly and very supportive, is still slightly smaller than that of LangChain.
★ 148,401 starsLicence: MITLast commit 25 Oct 2026Latest release 22 Oct 2026Python
LangChain functions as an agent engineering platform, offering a framework for building applications powered by large language models in Python. It enables chaining together interoperable components and various third-party integrations to streamline AI application development.
Best for: Developers building question-answering chatbots that process PDF files in Python, seeking a comprehensive platform for managing data ingestion, retrieval augmented generation, and intricate conversational logic with significant control over the LLM stack.
What's good
Provides an extensive collection of integrations for document loaders (including PDFs), vector stores, and diverse large language models, offering high adaptability for data processing and retrieval.
Offers abstractions like 'chains' and 'agents' to simplify the construction of complex LLM workflows, such as multi-step reasoning and conversational agents.
Maintained as an active project with a large community, supported by extensive documentation and examples that assist in developing and troubleshooting applications.
Its modular architecture allows for easy customization and swapping of components, enabling adaptation to specific project requirements and future technological shifts.
Watch out for
The broad scope and numerous abstractions can lead to a steep learning curve, potentially overwhelming new users.
Developing complex application logic may sometimes require substantial boilerplate code, which can obscure the core flow of the application.
The project's rapid evolution can lead to frequent API changes, requiring ongoing adaptation for developers.
★ 26,831 starsLicence: Apache-2.0Last commit 24 Oct 2026Latest release 21 Oct 2026Python
An open-source framework for building custom search and question-answering systems, emphasizing modular pipelines and enterprise-grade robustness for production environments.
Best for: Engineering teams and data scientists building enterprise-grade, custom search and question-answering systems that demand robust data pipelines, deep control over retrieval components, and high scalability.
What's good
Designed for building robust, production-ready RAG systems with a strong emphasis on modularity and a clear pipeline architecture, making complex workflows manageable.
Offers excellent support for advanced information retrieval tasks, including dense passage retrieval, semantic search, and sophisticated document ranking strategies.
Provides advanced features for document pre-processing, including OCR, data cleaning, and a wide array of specialized readers, retrievers, and generators.
Strong focus on scalability, monitoring, and evaluation, making it a reliable choice for enterprise-level applications with stringent requirements.
Watch out for
Has a steeper learning curve than LangChain or LlamaIndex, especially due to its explicit pipeline-centric design and more defined component definitions.
Can be overkill for simpler 'chat with a single PDF' use cases, as its true power becomes apparent with more complex, multi-stage pipelines and data sources.
The community, while dedicated and highly professional, is smaller compared to the broader LangChain ecosystem, potentially meaning fewer immediate online examples.
"Dormant" means no commit pushed to the default branch in the last 12 months. "Archived" means the maintainers have marked the repository read-only on GitHub. "Deprecated" means the project's README says it is deprecated or no longer maintained. Stars, licence, commit and release dates come from the GitHub API (GitLab for projects developed there) and are cached for up to 24 hours. The written analysis is AI-assisted and checked against that data.
Building a PDF Q&A chatbot (RAG) with these tools
A PDF chatbot usually follows the same steps: load and split the PDFs, embed the passages, retrieve the closest ones for each question, and pass them to a language model to answer. LlamaIndex's SimpleDirectoryReader reads .pdf files directly. LangChain lists PDF loaders among its document-loader integrations. Haystack has a PyPDFToDocument converter that turns PDFs into documents for its pipelines.
Are LlamaIndex, LangChain and Haystack actively maintained?
LlamaIndex, LangChain and Haystack are active: each has had a commit in the last 12 months (most recent: LlamaIndex on 23 Oct 2026, LangChain on 25 Oct 2026, Haystack on 24 Oct 2026). Status is checked against GitHub daily.
What licences do LlamaIndex, LangChain and Haystack use?
LlamaIndex: MIT; LangChain: MIT; Haystack: Apache-2.0. Check each project's licence file before use.
Which is easiest to learn: LlamaIndex, LangChain or Haystack?
By our estimate (1 = easiest, 5 = steepest): LlamaIndex 3/5, LangChain 4/5, Haystack 4/5. LlamaIndex has the gentlest learning curve. This is ExploreGit's AI-assisted estimate, not a measured figure.
