https://github.com/clemlesne/private-gpt
Deploy smart and secure conversational agents for your employees, using Azure. Able to use both private and public data.
https://github.com/clemlesne/private-gpt
ai-assisted-chat azure-openai business-productivity chatgpt cloud-based-chat conversational-interface deploy-chatbot employee-communication employee-engagement enterprise-grade-platform internal-communication local-chatbot natural-language-processing private-gpt virtual-assistant-for-employees
Last synced: about 1 year ago
JSON representation
Deploy smart and secure conversational agents for your employees, using Azure. Able to use both private and public data.
- Host: GitHub
- URL: https://github.com/clemlesne/private-gpt
- Owner: clemlesne
- License: apache-2.0
- Created: 2023-06-27T14:16:31.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-28T20:56:32.000Z (over 2 years ago)
- Last Synced: 2025-05-05T15:19:44.164Z (about 1 year ago)
- Topics: ai-assisted-chat, azure-openai, business-productivity, chatgpt, cloud-based-chat, conversational-interface, deploy-chatbot, employee-communication, employee-engagement, enterprise-grade-platform, internal-communication, local-chatbot, natural-language-processing, private-gpt, virtual-assistant-for-employees
- Language: Python
- Homepage:
- Size: 4.17 MB
- Stars: 60
- Watchers: 3
- Forks: 12
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
# Employee communication with Private GPT – AI-powered chatbot you can trust
> 👋🏻 Demo available at [private-gpt.shopping-cart-devops-demo.lesne.pro](https://private-gpt.shopping-cart-devops-demo.lesne.pro).
Private GPT is a local version of Chat GPT, using Azure OpenAI. It is an enterprise grade platform to deploy a ChatGPT-like interface for your employees.
Includes:
- Can be configured to use any Azure OpenAI completion API, including GPT-4
- Dark theme for better readability
- Dead simple interface
- Deployable on any Kubernetes cluster, with its Helm chart
- Every persistence layers (search, index, AI) is cached, for performance and low cost
- Manage users effortlessly with OpenID Connect
- Monitoring with Azure App Insights (logs, traces, user behaviors)
- More than 150 tones and personalities (accountant, advisor, debater, excel sheet, instructor, logistician, etc.) to better help employees in their specific daily tasks
- Plug and play storage system, including [Azure Cosmos DB](https://learn.microsoft.com/en-us/azure/cosmos-db/), [Redis](https://github.com/redis/redis) and [Qdrant](https://github.com/qdrant/qdrant).
- Possibility to send temporary messages, for confidentiality
- Salable system based on stateless APIs, cache, progressive web app and events
- Search engine for conversations, based on semantic similarity and AI embeddings
- Unlimited conversation history and number of users
- Usage tracking, for better understanding of your employees' usage
## How it works
### High level
```mermaid
sequenceDiagram
autonumber
actor User
participant PWA
participant API
participant OpenAI
PWA ->> API: Ask for conversations
activate API
API ->> API: Get conversations from storage
API ->> PWA: Answer with conversations
deactivate API
User ->> PWA: Select a conversation
User ->> PWA: Insert a message
PWA ->> API: Send the message
activate API
API ->> OpenAI: Ask for a completion
activate OpenAI
OpenAI ->> API: Send completion
deactivate OpenAI
API ->> API: Save conversation and message
API ->> PWA: Answer with the message
deactivate API
User ->> PWA: See results
```
### Architecture
```mermaid
graph
user(["User"])
api["Conversation service\n(REST API)"]
ui["Conversation UI\n(PWA)"]
subgraph tools["Tools"]
subgraph "Business data"
form_recognizer["Form recognizer"]
cognitive_services["Cognitive services"]
storage_blob["Blob storage"]
mssql["SQL Server"]
end
subgraph "Public data"
tmdb["TMDB"]
news["News"]
listen_notes["Listen notes"]
bing["Bing"]
end
end
subgraph "Persistence"
cosmosdb[("Cosmos DB\n(disk)")]
qdrant[("Qdrant\n(disk)")]
redis[("Redis\n(memory)")]
end
subgraph "Azure OpenAI services"
oai_ada["ADA embedding"]
oai_gpt["GPT completions"]
safety["Content Safety"]
end
api -- Cache low-level AI results --> redis
api -- Generate completions --> oai_gpt
api -- Generate embeddings --> oai_ada
api -- Index messages --> qdrant
api -- Persist conversations --> cosmosdb
api -- Test moderation --> safety
api -- Orchestrate external capabilities --> tools
ui -- Use APIs --> api
user -- Use UI --> ui
cognitive_services -- Index data --> mssql
cognitive_services -- Index data --> storage_blob
```
## How to use
### Run locally
Create a local configuration file, a file named `config.toml` at the root of the project. The TOML file can be placed anywhere in the folder or in any parent directory.
```toml
# config.toml
# /!\ All the file values are for example, you must change them
[api]
# root_path = "[api-path]"
[oidc]
algorithms = ["RS256"]
api_audience = "[aad_app_id]"
issuers = ["https://login.microsoftonline.com/[tenant_id]/v2.0"]
jwks = "https://login.microsoftonline.com/common/discovery/v2.0/keys"
[monitoring]
[monitoring.logging]
app_level = "DEBUG" # Enum: "NOSET", "DEBUG", "INFO", "WARN", "ERROR", "FATAL", "CRITICAL"
sys_level = "WARN" # Enum: "NOSET", "DEBUG", "INFO", "WARN", "ERROR", "FATAL", "CRITICAL"
[monitoring.azure_app_insights]
connection_str = "InstrumentationKey=[key];[...]"
[persistence]
cache = "redis" # Enum: "redis"
search = "qdrant" # Enum: "qdrant"
store = "cosmos" # Enum: "cache", "cosmos"
stream = "redis" # Enum: "redis"
[persistence.qdrant]
host = "[host]"
[persistence.redis]
db = 0
host = "[host]"
[persistence.cosmos]
# Containers "conversation" (/user_id), "message" (/conversation_id), "user" (/dummy), "usage" (/user_id) must exist
url = "https://[deployment].documents.azure.com:443"
database = "[db_name]"
[ai]
[ai.openai]
ada_deploy_id = "ada"
ada_max_tokens = 2049
api_base = "https://[deployment].openai.azure.com"
gpt_deploy_id = "gpt"
gpt_max_tokens = 4096
[ai.azure_content_safety]
api_base = "https://[deployment].cognitiveservices.azure.com"
api_token = "[api_token]"
max_length = 1000
[tools]
[tools.azure_form_recognizer]
api_base = "https://[deployment].cognitiveservices.azure.com"
api_token = "[api_token]"
[tools.bing]
search_url = "https://api.bing.microsoft.com/v7.0/search"
subscription_key = "[api_token]"
[tools.tmdb]
bearer_token = "[jwt_token]"
[tools.news]
api_key = "[api_token]"
[tools.listen_notes]
api_key = "[api_token]"
```
Now, you can either run the application as container or with live reload. For development, it is recommended to use live reload. For demo, it is recommended to use the container.
With live reload:
```bash
# In each "src/[...]" directory, example "src/conversation-api"
make install start
```
As container:
```bash
make build start logs
```
Then, go to [http://127.0.0.1:8081](http://127.0.0.1:8081).
### Deploy locally
WIP
### Deploy in production
Deployment is container based. Use Helm to install the latest released chart:
```bash
helm repo add clemlesne-private-gpt https://clemlesne.github.io/private-gpt
helm repo update
helm upgrade --install default clemlesne-private-gpt/private-gpt
```
### Get API docs
Go to [http://127.0.0.1:8081/redoc](http://127.0.0.1:8081/redoc).
## [Security](./SECURITY.md)
## Support
This project is open source and maintained by people like you. If you need help or found a bug, please feel free to open an issue on the [clemlesne/private-gpt](https://github.com/clemlesne/private-gpt) GitHub project.
## [Code of conduct](./CODE_OF_CONDUCT.md)
## [Authors](./AUTHORS.md)