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dbkinder authored Sep 17, 2024
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4 changes: 4 additions & 0 deletions latest/_sources/GenAIComps/comps/embeddings/README.md.txt
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Expand Up @@ -31,3 +31,7 @@ For details, please refer to this [readme](multimodal/README.md).
## Embeddings Microservice with Multimodal Clip

For details, please refer to this [readme](multimodal_clip/README.md).

## Embeddings Microservice with Prediction Guard

For details, please refer to this [readme](predictionguard/README.md).
Original file line number Diff line number Diff line change
@@ -0,0 +1,39 @@
# Embedding Generation Prediction Guard Microservice

[Prediction Guard](https://docs.predictionguard.com) allows you to utilize hosted open access LLMs, LVMs, and embedding functionality with seamlessly integrated safeguards. In addition to providing a scalable access to open models, Prediction Guard allows you to configure factual consistency checks, toxicity filters, PII filters, and prompt injection blocking. Join the [Prediction Guard Discord channel](https://discord.gg/TFHgnhAFKd) and request an API key to get started.

This embedding microservice is designed to efficiently convert text into vectorized embeddings using the [BridgeTower model](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-itc). Thus, it is ideal for both RAG or semantic search applications.

**Note** - The BridgeTower model implemented in Prediction Guard can actually embed text, images, or text + images (jointly). For now this service only embeds text, but a follow on contribution will enable the multimodal functionality.

# 🚀 Start Microservice with Docker

## Setup Environment Variables

Setup the following environment variables first

```bash
export PREDICTIONGUARD_API_KEY=${your_predictionguard_api_key}
```

## Build Docker Images

```bash
cd ../../..
docker build -t opea/embedding-predictionguard:latest -f comps/embeddings/predictionguard/Dockerfile .
```

## Start Service

```bash
docker run -d --name="embedding-predictionguard" -p 6000:6000 -e PREDICTIONGUARD_API_KEY=$PREDICTIONGUARD_API_KEY opea/embedding-predictionguard:latest
```

# 🚀 Consume Embeddings Service

```bash
curl localhost:6000/v1/embeddings \
-X POST \
-d '{"text":"Hello, world!"}' \
-H 'Content-Type: application/json'
```
1 change: 1 addition & 0 deletions latest/_sources/GenAIComps/comps/guardrails/README.md.txt
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Expand Up @@ -7,5 +7,6 @@ The Guardrails service enhances the security of LLM-based applications by offeri
| [Llama Guard](./llama_guard/langchain/README.md) | Provides guardrails for inputs and outputs to ensure safe interactions |
| [PII Detection](./pii_detection/README.md) | Detects Personally Identifiable Information (PII) and Business Sensitive Information (BSI) |
| [Toxicity Detection](./toxicity_detection/README.md) | Detects Toxic language (rude, disrespectful, or unreasonable language that is likely to make someone leave a discussion) |
| [Bias Detection](./bias_detection/README.md) | Detects Biased language (framing bias, epistemological bias, and demographic bias) |

Additional safety-related microservices will be available soon.
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# Bias Detection Microservice

## Introduction

Bias Detection Microservice allows AI Application developers to safeguard user input and LLM output from biased language in a RAG environment. By leveraging a smaller fine-tuned Transformer model for bias classification (e.g. DistilledBERT, RoBERTa, etc.), we maintain a lightweight guardrails microservice without significantly sacrificing performance making it readily deployable on both Intel Gaudi and Xeon.

Bias erodes our collective trust and fuels social conflict. Bias can be defined as inappropriate subjectivity in the form of one of the following:

- Framing bias -- using subjective words or phrases linked with a particular point of view
- Epistemological bias -- linguistic features that subtly modify the believability of a proposition
- Demographic bias -- text with presuppositions about particular genders, races, or other demographic categories

## Future Development

- Add a "neutralizing bias" microservice to neutralizing any detected bias in the RAG serving, guarding the RAG usage.

## 🚀1. Start Microservice with Python(Option 1)

### 1.1 Install Requirements

```bash
pip install -r requirements.txt
```

### 1.2 Start Bias Detection Microservice with Python Script

```bash
python bias_detection.py
```

## 🚀2. Start Microservice with Docker (Option 2)

### 2.1 Prepare bias detection model

export HUGGINGFACEHUB_API_TOKEN=${HP_TOKEN}

### 2.2 Build Docker Image

```bash
cd ../../../ # back to GenAIComps/ folder
docker build -t opea/guardrails-bias-detection:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/bias_detection/Dockerfile .
```

### 2.3 Run Docker Container with Microservice

```bash
docker run -d --rm --runtime=runc --name="guardrails-bias-detection" -p 9092:9092 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} opea/guardrails-bias-detection:latest
```

## 🚀3. Get Status of Microservice

```bash
docker container logs -f guardrails-bias-detection
```

## 🚀4. Consume Microservice Pre-LLM/Post-LLM

Once microservice starts, users can use examples (bash or python) below to apply bias detection for both user's query (Pre-LLM) or LLM's response (Post-LLM)

**Bash:**

```bash
curl localhost:9092/v1/bias
-X POST
-d '{"text":"John McCain exposed as an unprincipled politician"}'
-H 'Content-Type: application/json'
```

Example Output:

```bash
"\nI'm sorry, but your query or LLM's response is BIASED with an score of 0.74 (0-1)!!!\n"
```

**Python Script:**

```python
import requests
import json

proxies = {"http": ""}
url = "http://localhost:9092/v1/bias"
data = {"text": "John McCain exposed as an unprincipled politician"}


try:
resp = requests.post(url=url, data=data, proxies=proxies)
print(resp.text)
resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
print("Request successful!")
except requests.exceptions.RequestException as e:
print("An error occurred:", e)
```
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@@ -0,0 +1,54 @@
# Introduction

[Prediction Guard](https://docs.predictionguard.com) allows you to utilize hosted open access LLMs, LVMs, and embedding functionality with seamlessly integrated safeguards. In addition to providing a scalable access to open models, Prediction Guard allows you to configure factual consistency checks, toxicity filters, PII filters, and prompt injection blocking. Join the [Prediction Guard Discord channel](https://discord.gg/TFHgnhAFKd) and request an API key to get started.

# Get Started

## Build Docker Image

```bash
cd ../../..
docker build -t opea/llm-textgen-predictionguard:latest -f comps/llms/text-generation/predictionguard/Dockerfile .
```

## Run the Predictionguard Microservice

```bash
docker run -d -p 9000:9000 -e PREDICTIONGUARD_API_KEY=$PREDICTIONGUARD_API_KEY --name llm-textgen-predictionguard opea/llm-textgen-predictionguard:latest
```

# Consume the Prediction Guard Microservice

See the [Prediction Guard docs](https://docs.predictionguard.com/) for available model options.

## Without streaming

```bash
curl -X POST http://localhost:9000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Hermes-2-Pro-Llama-3-8B",
"query": "Tell me a joke.",
"max_new_tokens": 100,
"temperature": 0.7,
"top_p": 0.9,
"top_k": 50,
"stream": false
}'
```

## With streaming

```bash
curl -N -X POST http://localhost:9000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Hermes-2-Pro-Llama-3-8B",
"query": "Tell me a joke.",
"max_new_tokens": 100,
"temperature": 0.7,
"top_p": 0.9,
"top_k": 50,
"stream": true
}'
```
70 changes: 31 additions & 39 deletions latest/_sources/GenAIExamples/ChatQnA/README.md.txt
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Expand Up @@ -10,16 +10,15 @@ ChatQnA architecture shows below:

The ChatQnA example is implemented using the component-level microservices defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart below shows the information flow between different microservices for this example.

```{mermaid}
```mermaid
---
config:
flowchart:
nodeSpacing: 100
nodeSpacing: 400
rankSpacing: 100
curve: linear
theme: base
themeVariables:
fontSize: 42px
fontSize: 50px
---
flowchart LR
%% Colors %%
Expand All @@ -28,67 +27,60 @@ flowchart LR
classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef invisible fill:transparent,stroke:transparent;
style ChatQnA-MegaService stroke:#000000

%% Subgraphs %%
subgraph ChatQnA-MegaService["ChatQnA-MegaService"]
subgraph ChatQnA-MegaService["ChatQnA MegaService "]
direction LR
EM([Embedding <br>]):::blue
RET([Retrieval <br>]):::blue
RER([Rerank <br>]):::blue
LLM([LLM <br>]):::blue
EM([Embedding MicroService]):::blue
RET([Retrieval MicroService]):::blue
RER([Rerank MicroService]):::blue
LLM([LLM MicroService]):::blue
end
subgraph User Interface
direction TB
subgraph UserInterface[" User Interface "]
direction LR
a([User Input Query]):::orchid
Ingest([Ingest data]):::orchid
UI([UI server<br>]):::orchid
end
subgraph ChatQnA GateWay
direction LR
invisible1[ ]:::invisible
GW([ChatQnA GateWay<br>]):::orange
end
subgraph .
X([OPEA Micsrservice]):::blue
Y{{Open Source Service}}
Z([OPEA Gateway]):::orange
Z1([UI]):::orchid
end



TEI_RER{{Reranking service<br>}}
TEI_EM{{Embedding service <br>}}
VDB{{Vector DB<br><br>}}
R_RET{{Retriever service <br>}}
DP([Data Preparation<br>]):::blue
DP([Data Preparation MicroService]):::blue
LLM_gen{{LLM Service <br>}}
GW([ChatQnA GateWay<br>]):::orange

%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] -->|a| UI
UI -->|b| DP
DP <-.->|c| TEI_EM
Ingest[Ingest data] --> UI
UI --> DP
DP <-.-> TEI_EM

%% Questions interaction
direction LR
a[User Input Query] -->|1| UI
UI -->|2| GW
GW <==>|3| ChatQnA-MegaService
EM ==>|4| RET
RET ==>|5| RER
RER ==>|6| LLM
a[User Input Query] --> UI
UI --> GW
GW <==> ChatQnA-MegaService
EM ==> RET
RET ==> RER
RER ==> LLM


%% Embedding service flow
direction TB
EM <-.->|3'| TEI_EM
RET <-.->|4'| R_RET
RER <-.->|5'| TEI_RER
LLM <-.->|6'| LLM_gen
direction LR
EM <-.-> TEI_EM
RET <-.-> R_RET
RER <-.-> TEI_RER
LLM <-.-> LLM_gen

direction TB
%% Vector DB interaction
R_RET <-.->|d|VDB
DP <-.->|d|VDB
R_RET <-.->VDB
DP <-.->VDB



Expand Down
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Expand Up @@ -76,7 +76,7 @@ The CodeGen megaservice manages a single microservice called LLM within a Direct

The mega flow of the CodeGen application, from user's input query to the application's output response, is as follows:

```{mermaid}
```mermaid
flowchart LR
subgraph CodeGen
direction LR
Expand Down
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Expand Up @@ -62,7 +62,7 @@ The CodeGen megaservice manages a single microservice called LLM within a Direct

The mega flow of the CodeGen application, from user's input query to the application's output response, is as follows:

```{mermaid}
```mermaid
flowchart LR
subgraph CodeGen
direction LR
Expand Down
2 changes: 1 addition & 1 deletion latest/_sources/GenAIExamples/DocSum/README.md.txt
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Expand Up @@ -104,7 +104,7 @@ Refer to the [DocSum helm chart](https://github.com/opea-project/GenAIInfra/tree

The workflow of the Document Summarization Service, from user's input query to the application's output response, is as follows:

```{mermaid}
```mermaid
flowchart LR
subgraph DocSum
direction LR
Expand Down
2 changes: 1 addition & 1 deletion latest/_sources/GenAIExamples/MultimodalQnA/README.md.txt
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Expand Up @@ -10,7 +10,7 @@ The MultimodalQnA architecture shows below:

MultimodalQnA is implemented on top of [GenAIComps](https://github.com/opea-project/GenAIComps), the MultimodalQnA Flow Chart shows below:

```{mermaid}
```mermaid
---
config:
flowchart:
Expand Down
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Expand Up @@ -4,7 +4,7 @@ VideoQnA is a framework that retrieves video based on provided user prompt. It u

VideoQnA is implemented on top of [GenAIComps](https://github.com/opea-project/GenAIComps), with the architecture flow chart shows below:

```{mermaid}
```mermaid
---
config:
flowchart:
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Expand Up @@ -6,7 +6,7 @@ Guardrails in OPEA Pipeline Proxy supports both rule-based detection and AI-powe

## Architecture

```{mermaid}
```mermaid
flowchart LR
subgraph Filter[HTTP Filter]
direction BT
Expand Down
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