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Chatbot with PDF for Semantic Search

Chatbot with PDF for Semantic Search is an open-source Python code example for building a Streamlit chatbot that answers natural-language questions about a document collection through semantic search. It combines LangChain with vector-store examples for Pinecone, Chroma, and Azure Cognitive Search, and includes Azure OpenAI embedding and chat examples.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Chatbot with PDF for Semantic Search — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when you want a starting point for a custom document-search chatbot and are prepared to configure its Python code and supporting services.

Who it is for

  • Developers prototyping question answering over PDFs or other document collections.
  • Teams exploring semantic search for internal document libraries.
  • Python developers comparing the example's Pinecone, Chroma, and Azure Cognitive Search patterns.

Documented uses

  • Prepare document content as embeddings and save it to a supported vector database.
  • Search a vector database for relevant document content in response to a question.
  • Build a Streamlit chat interface that summarizes retrieved results and responds to users.
Before you choose

Requirements & limits

The repository is identified as open source and publicly visible on GitHub.

  • The examples pair Azure OpenAI embeddings and chat with Pinecone, Chroma, or Azure Cognitive Search.
  • The README shows separate preprocessing notebooks, consumption notebooks, and app examples for each listed backend.
  • The documented launch command targets the Pinecone app; the README excerpt does not explain full environment setup.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Launch the Pinecone Streamlit example

Start the repository's Pinecone-backed chatbot app using the launch command in its README.

Before you start

  • The repository's Python code.
  • A Pinecone-backed app configuration suitable for your environment.

Steps

  1. Obtain the repository's code.

  2. From its project environment, run streamlit run app_pinecone.py.

Expected outcome

The README identifies this command as the way to run the Streamlit web app; the example is intended for semantic search over documents using Pinecone.

Guide limitations

  • The command is documented guidance, not a verified execution result.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
AI LLM PlaygroundAsk questions about PDF documents through an assistant interface.Web
Db Per TenantExplore a working application pattern for uploading PDFs and asking questions about their contents.WebThe repository is public and identified as open source. Its README links to a live demo.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
easonlai/chatbot_with_pdf_streamlit · Official homepage title · retained in its source language
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