Cleaning up
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Dockerfile
10
Dockerfile
@ -116,14 +116,12 @@ RUN { \
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# Activate the pip environment on all shell calls
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SHELL [ "/opt/backstory/shell" ]
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# From https://pytorch-extension.intel.com/installation?platform=gpu&version=v2.6.10%2Bxpu&os=linux%2Fwsl2&package=pip
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RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu
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#RUN pip install intel-extension-for-pytorch==2.6.10+xpu oneccl_bind_pt==2.6.0+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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# From https://huggingface.co/docs/bitsandbytes/main/en/installation?backend=Intel+CPU+%2B+GPU#multi-backend
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RUN pip install "transformers>=4.45.1"
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#RUN pip install 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-manylinux_2_24_x86_64.whl'
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RUN pip install 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.45.3.dev272-py3-none-manylinux_2_24_x86_64.whl'
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# Note, if you don't want to reinstall BNBs dependencies, append the `--no-deps` flag!
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#RUN pip install --force-reinstall 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-manylinux_2_24_x86_64.whl'
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# Install ollama python module
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RUN pip install ollama langchain-ollama
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@ -475,6 +473,10 @@ FROM ubuntu:oracular AS frontend
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# easy creation of an Android app as well as web app
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RUN apt-get update \
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&& DEBIAN_FRONTEND=noninteractive apt-get install -y \
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rsync \
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nano \
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wget \
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curl \
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nodejs \
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npm \
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&& apt-get clean \
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@ -58,7 +58,7 @@ services:
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- ./chromadb-prod:/opt/backstory/chromadb:rw # Persist ChromaDB
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- ./sessions-prod:/opt/backstory/sessions:rw # Persist sessions
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- ./docs-prod:/opt/backstory/docs:rw # Live mount of RAG content
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- ./frontend/deployed:/opt/backstory/deployed:ro # Live mount built frontend
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- ./frontend/deployed:/opt/backstory/frontend/deployed:ro # Live mount built frontend
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cap_add: # used for running ze-monitor within container
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- CAP_DAC_READ_SEARCH # Bypass all filesystem read access checks
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- CAP_PERFMON # Access to perf_events (vs. overloaded CAP_SYS_ADMIN)
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1
frontend/.gitignore
vendored
1
frontend/.gitignore
vendored
@ -1,5 +1,6 @@
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# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
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build
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deployed
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# dependencies
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/node_modules
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|
||||
Backstory is developed using:
|
||||
|
||||
## Frontend
|
||||
|
||||
* React
|
||||
* MUI
|
||||
* Plotly.js
|
||||
* MuiMarkdown
|
||||
* Mermaid
|
||||
|
||||
## Backend
|
||||
|
||||
* Python
|
||||
* FastAPI
|
||||
* HuggingFace Transformers
|
||||
* Ollama
|
||||
* Backstory Agent Framework
|
||||
* Prometheus
|
||||
* Grafana
|
||||
* ze-monitor
|
||||
* Jupyter Notebook
|
||||
@ -1,28 +0,0 @@
|
||||
## Backstory is three things
|
||||
|
||||
1. **An interactive Q&A** -- let potential employers ask questions about an individual's work history (aka "Backstory".) Based on the content the job seeker has provided to the RAG system, that can provide insights into that individual's resume and curriculum vitae that are often left out when people are trying to fit everything onto one page.
|
||||
|
||||
|
||||
2. **A resume builder** -- if you have a job position, and you think this person might be a candidate, paste your job description and have a resume produced based on their data. If it looks interesting, reach out to them. If not, hopefully you've gained some insight into what drives them.
|
||||
|
||||
3. **A curated expert about you** -- as a potential job seeker, you can self host this environment and generate resumes for yourself.
|
||||
|
||||
While this project was generally built for self-hosting with open source models, you can use any of the frontier models. The API adapters in this project can be configured to use infrastructure hosted from Anthropic, Google, Grok, and OpenAI (alphabetical.) For information, see [https://github.com/jketreno/backstory/README.md](https://github.com/jketreno/backstory/README.md#Frontier_Models).
|
||||
|
||||
|
||||
## This application was developed to achieve a few goals:
|
||||
|
||||
1. See if it is realistic to self-host AI LLMs. Turns out, it is -- with constraints. I don't have the GPU hardware to run models larger than about 8 billion parameters, which puts my local deployment in the realm of a Small Language Model (SLM.) I've been meaning to write a blog post about what to buy to build an AI PC that can run the latest "small" (7B) parameter models.
|
||||
2. Provide a recent example of my capabilities; many of my projects while working for Intel were internally facing. The source code to this project is available on [GitHub](https://github.com/jketreno/backstory). It doesn't touch on much of my history of work, however it does represent the pace at which I can adapt and develop useful solutions to fill a gap. During this project's development I have had the opportunity to test and use many of the latest frontier models, which has allowed me to develop at a pace that far exceeds what I could have done even a year ago.
|
||||
3. Explore Stable Diffusion (SD), Reinforced Learning (RL), Large Language Models (LLM), Paramater-Efficient Fine-Tuning (PEFT), Quantized Low-Rank Adapters (QLORA), open source and frontier models, tokenizers, and the vast open-source ecosystem for Machine Learning (ML) and Artificial Intelligence (AI). I wanted to do this to understand the strengths, weakness, and state of the industry in its development and deployment of those technologies.
|
||||
4. My career at Intel was diverse. Over the years, I have worked on many projects almost everywhere in the computer ecosystem. That results in a resume that is either too long, or too short. This application is intended to provide a quick way for employers to interactively ask about me. You can view my resume in totality, or use the Resume Builder to post your job position to see how I fit. Or go the Backstory and ask questions about the projects mentioned in my resume.
|
||||
|
||||
## Some questions I've been asked
|
||||
|
||||
Q. <ChatQuery prompt="Why aren't you providing this as a Platform As a Service (PaaS) application?" tunables={{ "enable_tools": false }} />
|
||||
|
||||
A. I could; but I don't want to store your data. I also don't want to have to be on the hook for support of this service. I like it, it's fun, but it's not what I want as my day-gig, you know? If it was, I wouldn't be looking for a job...
|
||||
|
||||
Q. <ChatQuery prompt="Why can't I just ask Backstory these questions?" tunables={{ "enable_tools": false }} />
|
||||
|
||||
A. Try it. See what you find out :)
|
||||
@ -1,100 +0,0 @@
|
||||
The system follows a carefully designed pipeline with isolated stages to prevent fabrication:
|
||||
|
||||
## System Architecture Overview
|
||||
|
||||
The system uses a pipeline of isolated analysis and generation steps:
|
||||
|
||||
1. **Stage 1: Isolated Analysis** (three sub-stages)
|
||||
- **1A: Job Analysis** - Extracts requirements from job description only
|
||||
- **1B: Candidate Analysis** - Catalogs qualifications from resume/context only
|
||||
- **1C: Mapping Analysis** - Identifies legitimate matches between requirements and qualifications
|
||||
|
||||
2. **Stage 2: Resume Generation**
|
||||
- Uses mapping output to create a tailored resume with evidence-based content
|
||||
|
||||
3. **Stage 3: Verification**
|
||||
- Performs fact-checking to catch any remaining fabrications
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
subgraph "Stage 1: Isolated Analysis"
|
||||
subgraph "Stage 1A: Job Analysis"
|
||||
A1[Job Description Input] --> A2[Job Analysis LLM]
|
||||
A2 --> A3[Job Requirements JSON]
|
||||
end
|
||||
|
||||
subgraph "Stage 1B: Candidate Analysis"
|
||||
B1[Resume & Context Input] --> B2[Candidate Analysis LLM]
|
||||
B2 --> B3[Candidate Qualifications JSON]
|
||||
end
|
||||
|
||||
subgraph "Stage 1C: Mapping Analysis"
|
||||
C1[Job Requirements JSON] --> C2[Candidate Qualifications JSON]
|
||||
C2 --> C3[Mapping Analysis LLM]
|
||||
C3 --> C4[Skills Mapping JSON]
|
||||
end
|
||||
end
|
||||
|
||||
subgraph "Stage 2: Resume Generation"
|
||||
D1[Skills Mapping JSON] --> D2[Original Resume Reference]
|
||||
D2 --> D3[Resume Generation LLM]
|
||||
D3 --> D4[Tailored Resume Draft]
|
||||
end
|
||||
|
||||
subgraph "Stage 3: Verification"
|
||||
E1[Skills Mapping JSON] --> E2[Original Materials]
|
||||
E2 --> E3[Tailored Resume Draft]
|
||||
E3 --> E4[Verification LLM]
|
||||
E4 --> E5{Verification Check}
|
||||
E5 -->|PASS| E6[Approved Resume]
|
||||
E5 -->|FAIL| E7[Correction Instructions]
|
||||
E7 --> D3
|
||||
end
|
||||
|
||||
A3 --> C1
|
||||
B3 --> C2
|
||||
C4 --> D1
|
||||
D4 --> E3
|
||||
|
||||
style A2 fill:#f9d77e,stroke:#333,stroke-width:2px
|
||||
style B2 fill:#f9d77e,stroke:#333,stroke-width:2px
|
||||
style C3 fill:#f9d77e,stroke:#333,stroke-width:2px
|
||||
style D3 fill:#f9d77e,stroke:#333,stroke-width:2px
|
||||
style E4 fill:#f9d77e,stroke:#333,stroke-width:2px
|
||||
style E5 fill:#a3e4d7,stroke:#333,stroke-width:2px
|
||||
style E6 fill:#aed6f1,stroke:#333,stroke-width:2px
|
||||
style E7 fill:#f5b7b1,stroke:#333,stroke-width:2px
|
||||
```
|
||||
|
||||
## Stage 1: Isolated Analysis (three separate sub-stages)
|
||||
|
||||
1. **Job Analysis**: Extracts requirements from just the job description
|
||||
2. **Candidate Analysis**: Catalogs qualifications from just the resume/context
|
||||
3. **Mapping Analysis**: Identifies legitimate matches between requirements and qualifications
|
||||
|
||||
## Stage 2: Resume Generation
|
||||
|
||||
Creates a tailored resume using only verified information from the mapping
|
||||
|
||||
## Stage 3: Verification
|
||||
|
||||
1. Performs fact-checking to catch any remaining fabrications
|
||||
2. Corrects issues if needed and re-verifies
|
||||
|
||||
### Key Anti-Fabrication Mechanisms
|
||||
|
||||
The system uses several techniques to prevent fabrication:
|
||||
|
||||
* **Isolation of Analysis Stages**: By analyzing the job and candidate separately, the system prevents the LLM from prematurely creating connections that might lead to fabrication.
|
||||
* **Evidence Requirements**: Each qualification included must have explicit evidence from the original materials.
|
||||
* **Conservative Transferability**: The system is instructed to be conservative when claiming skills are transferable.
|
||||
* **Verification Layer**: A dedicated verification step acts as a safety check to catch any remaining fabrications.
|
||||
* **Strict JSON Structures**: Using structured JSON formats ensures information flows properly between stages.
|
||||
|
||||
## Implementation Details
|
||||
|
||||
* **Prompt Engineering**: Each stage has carefully designed prompts with clear instructions and output formats.
|
||||
* **Error Handling**: Comprehensive validation and error handling throughout the pipeline.
|
||||
* **Correction Loop**: If verification fails, the system attempts to correct issues and re-verify.
|
||||
* **Traceability**: Information in the final resume can be traced back to specific evidence in the original materials.
|
||||
|
||||
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||||
<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Interactive chat with an enhanced LLM."/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Backstory</title><script defer="defer" src="/static/js/main.b1c48cdf.js"></script><link href="/static/css/main.3c3ddc39.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>
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{
|
||||
"short_name": "ai.ketrenos.com",
|
||||
"name": "Ketrenos AI Chat",
|
||||
"icons": [
|
||||
{
|
||||
"src": "favicon.ico",
|
||||
"sizes": "64x64 32x32 24x24 16x16",
|
||||
"type": "image/x-icon"
|
||||
},
|
||||
{
|
||||
"src": "logo192.png",
|
||||
"type": "image/png",
|
||||
"sizes": "192x192"
|
||||
},
|
||||
{
|
||||
"src": "logo512.png",
|
||||
"type": "image/png",
|
||||
"sizes": "512x512"
|
||||
}
|
||||
],
|
||||
"start_url": ".",
|
||||
"display": "standalone",
|
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"theme_color": "#000000",
|
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"background_color": "#ffffff"
|
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}
|
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|
||||
# https://www.robotstxt.org/robotstxt.html
|
||||
User-agent: *
|
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Disallow:
|
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Block a user