{"id":22410866,"url":"https://github.com/runtime-error786/hypothetical-document-embedding","last_synced_at":"2026-05-01T15:39:24.249Z","repository":{"id":254666357,"uuid":"847205731","full_name":"runtime-error786/Hypothetical-Document-Embedding","owner":"runtime-error786","description":null,"archived":false,"fork":false,"pushed_at":"2024-08-25T06:56:56.000Z","size":6500,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T02:45:46.182Z","etag":null,"topics":["huggingface-transformers","langchain","llama3-meta-ai"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/runtime-error786.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-25T06:38:16.000Z","updated_at":"2024-12-31T19:23:24.000Z","dependencies_parsed_at":"2024-08-25T07:50:11.720Z","dependency_job_id":"f0455560-1020-49d0-a106-4f483ea0725b","html_url":"https://github.com/runtime-error786/Hypothetical-Document-Embedding","commit_stats":null,"previous_names":["runtime-error786/hypothetical-document-embedding"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/runtime-error786/Hypothetical-Document-Embedding","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2FHypothetical-Document-Embedding","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2FHypothetical-Document-Embedding/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2FHypothetical-Document-Embedding/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2FHypothetical-Document-Embedding/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/runtime-error786","download_url":"https://codeload.github.com/runtime-error786/Hypothetical-Document-Embedding/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2FHypothetical-Document-Embedding/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32503203,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-30T13:12:12.517Z","status":"online","status_checked_at":"2026-05-01T02:00:05.856Z","response_time":64,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["huggingface-transformers","langchain","llama3-meta-ai"],"created_at":"2024-12-05T13:11:55.897Z","updated_at":"2026-05-01T15:39:24.223Z","avatar_url":"https://github.com/runtime-error786.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Hypothetical Document Embedding\n\n## Project Description\n\nThis project leverages LangChain to process and retrieve relevant information from PDF documents. By embedding the contents of multiple PDFs, it enables efficient and contextually accurate question-answering. The system incorporates state-of-the-art models like Hugging Face for embedding and Ollama for generating responses. Additionally, it employs a Chroma vector store to facilitate the retrieval of document segments that are most relevant to user queries.\n\n## Features\n\n- **PDF Document Loading:** Automatically loads and processes PDF documents from a specified directory.\n- **Text Chunking:** Utilizes RecursiveCharacterTextSplitter to divide large text into manageable chunks for better retrieval performance.\n- **Document Embedding:** Uses Hugging Face embeddings to create vector representations of text chunks for efficient retrieval.\n- **Vector Store Integration:** Stores and retrieves document embeddings using Chroma, allowing for quick and accurate search operations.\n- **Contextual Question Answering:** Employs Ollama to generate responses based on retrieved document segments, providing answers in context.\n- **Customizable Prompts:** Supports custom prompt templates to fine-tune the response generation according to specific requirements.\n- **Dynamic Document Retrieval:** Automatically retrieves and provides contextually relevant document segments in response to queries.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruntime-error786%2Fhypothetical-document-embedding","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fruntime-error786%2Fhypothetical-document-embedding","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruntime-error786%2Fhypothetical-document-embedding/lists"}