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127
.github/workflows/docker-build.yaml
vendored
127
.github/workflows/docker-build.yaml
vendored
@ -8,18 +8,12 @@ on:
|
||||
types: [published]
|
||||
|
||||
jobs:
|
||||
build-and-push:
|
||||
build-amd64:
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
service: [backend, app]
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v2
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v2
|
||||
with:
|
||||
@ -36,38 +30,109 @@ jobs:
|
||||
id: version
|
||||
run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
|
||||
|
||||
- name: Build and push Docker image for ${{ matrix.service }}
|
||||
- name: Build and push AMD64 Docker image
|
||||
if: github.ref == 'refs/heads/master' && github.event_name == 'push'
|
||||
run: |
|
||||
docker buildx create --use
|
||||
if [[ "${{ matrix.service }}" == "backend" ]]; then \
|
||||
DOCKERFILE=backend.dockerfile; \
|
||||
IMAGE_NAME=perplexica-backend; \
|
||||
else \
|
||||
DOCKERFILE=app.dockerfile; \
|
||||
IMAGE_NAME=perplexica-frontend; \
|
||||
fi
|
||||
docker buildx build --platform linux/amd64,linux/arm64 \
|
||||
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:main \
|
||||
DOCKERFILE=app.dockerfile
|
||||
IMAGE_NAME=perplexica
|
||||
docker buildx build --platform linux/amd64 \
|
||||
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:amd64 \
|
||||
--cache-to=type=inline \
|
||||
--provenance false \
|
||||
-f $DOCKERFILE \
|
||||
-t itzcrazykns1337/${IMAGE_NAME}:main \
|
||||
-t itzcrazykns1337/${IMAGE_NAME}:amd64 \
|
||||
--push .
|
||||
|
||||
- name: Build and push release Docker image for ${{ matrix.service }}
|
||||
- name: Build and push AMD64 release Docker image
|
||||
if: github.event_name == 'release'
|
||||
run: |
|
||||
docker buildx create --use
|
||||
if [[ "${{ matrix.service }}" == "backend" ]]; then \
|
||||
DOCKERFILE=backend.dockerfile; \
|
||||
IMAGE_NAME=perplexica-backend; \
|
||||
else \
|
||||
DOCKERFILE=app.dockerfile; \
|
||||
IMAGE_NAME=perplexica-frontend; \
|
||||
fi
|
||||
docker buildx build --platform linux/amd64,linux/arm64 \
|
||||
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
|
||||
DOCKERFILE=app.dockerfile
|
||||
IMAGE_NAME=perplexica
|
||||
docker buildx build --platform linux/amd64 \
|
||||
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
|
||||
--cache-to=type=inline \
|
||||
--provenance false \
|
||||
-f $DOCKERFILE \
|
||||
-t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
|
||||
-t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
|
||||
--push .
|
||||
|
||||
build-arm64:
|
||||
runs-on: ubuntu-24.04-arm
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v2
|
||||
with:
|
||||
install: true
|
||||
|
||||
- name: Log in to DockerHub
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKER_USERNAME }}
|
||||
password: ${{ secrets.DOCKER_PASSWORD }}
|
||||
|
||||
- name: Extract version from release tag
|
||||
if: github.event_name == 'release'
|
||||
id: version
|
||||
run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
|
||||
|
||||
- name: Build and push ARM64 Docker image
|
||||
if: github.ref == 'refs/heads/master' && github.event_name == 'push'
|
||||
run: |
|
||||
DOCKERFILE=app.dockerfile
|
||||
IMAGE_NAME=perplexica
|
||||
docker buildx build --platform linux/arm64 \
|
||||
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:arm64 \
|
||||
--cache-to=type=inline \
|
||||
--provenance false \
|
||||
-f $DOCKERFILE \
|
||||
-t itzcrazykns1337/${IMAGE_NAME}:arm64 \
|
||||
--push .
|
||||
|
||||
- name: Build and push ARM64 release Docker image
|
||||
if: github.event_name == 'release'
|
||||
run: |
|
||||
DOCKERFILE=app.dockerfile
|
||||
IMAGE_NAME=perplexica
|
||||
docker buildx build --platform linux/arm64 \
|
||||
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
|
||||
--cache-to=type=inline \
|
||||
--provenance false \
|
||||
-f $DOCKERFILE \
|
||||
-t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
|
||||
--push .
|
||||
|
||||
manifest:
|
||||
needs: [build-amd64, build-arm64]
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Log in to DockerHub
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
username: ${{ secrets.DOCKER_USERNAME }}
|
||||
password: ${{ secrets.DOCKER_PASSWORD }}
|
||||
|
||||
- name: Extract version from release tag
|
||||
if: github.event_name == 'release'
|
||||
id: version
|
||||
run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
|
||||
|
||||
- name: Create and push multi-arch manifest for main
|
||||
if: github.ref == 'refs/heads/master' && github.event_name == 'push'
|
||||
run: |
|
||||
IMAGE_NAME=perplexica
|
||||
docker manifest create itzcrazykns1337/${IMAGE_NAME}:main \
|
||||
--amend itzcrazykns1337/${IMAGE_NAME}:amd64 \
|
||||
--amend itzcrazykns1337/${IMAGE_NAME}:arm64
|
||||
docker manifest push itzcrazykns1337/${IMAGE_NAME}:main
|
||||
|
||||
- name: Create and push multi-arch manifest for releases
|
||||
if: github.event_name == 'release'
|
||||
run: |
|
||||
IMAGE_NAME=perplexica
|
||||
docker manifest create itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
|
||||
--amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
|
||||
--amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64
|
||||
docker manifest push itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}
|
||||
|
6
.gitignore
vendored
6
.gitignore
vendored
@ -4,9 +4,9 @@ npm-debug.log
|
||||
yarn-error.log
|
||||
|
||||
# Build output
|
||||
/.next/
|
||||
/out/
|
||||
/dist/
|
||||
.next/
|
||||
out/
|
||||
dist/
|
||||
|
||||
# IDE/Editor specific
|
||||
.vscode/
|
||||
|
@ -6,7 +6,6 @@ const config = {
|
||||
endOfLine: 'auto',
|
||||
singleQuote: true,
|
||||
tabWidth: 2,
|
||||
semi: true,
|
||||
};
|
||||
|
||||
module.exports = config;
|
||||
|
@ -1,32 +1,43 @@
|
||||
# How to Contribute to Perplexica
|
||||
|
||||
Hey there, thanks for deciding to contribute to Perplexica. Anything you help with will support the development of Perplexica and will make it better. Let's walk you through the key aspects to ensure your contributions are effective and in harmony with the project's setup.
|
||||
Thanks for your interest in contributing to Perplexica! Your help makes this project better. This guide explains how to contribute effectively.
|
||||
|
||||
Perplexica is a modern AI chat application with advanced search capabilities.
|
||||
|
||||
## Project Structure
|
||||
|
||||
Perplexica's design consists of two main domains:
|
||||
Perplexica's codebase is organized as follows:
|
||||
|
||||
- **Frontend (`ui` directory)**: This is a Next.js application holding all user interface components. It's a self-contained environment that manages everything the user interacts with.
|
||||
- **Backend (root and `src` directory)**: The backend logic is situated in the `src` folder, but the root directory holds the main `package.json` for backend dependency management.
|
||||
- All of the focus modes are created using the Meta Search Agent class present in `src/search/metaSearchAgent.ts`. The main logic behind Perplexica lies there.
|
||||
- **UI Components and Pages**:
|
||||
- **Components (`src/components`)**: Reusable UI components.
|
||||
- **Pages and Routes (`src/app`)**: Next.js app directory structure with page components.
|
||||
- Main app routes include: home (`/`), chat (`/c`), discover (`/discover`), library (`/library`), and settings (`/settings`).
|
||||
- **API Routes (`src/app/api`)**: API endpoints implemented with Next.js API routes.
|
||||
- `/api/chat`: Handles chat interactions.
|
||||
- `/api/search`: Provides direct access to Perplexica's search capabilities.
|
||||
- Other endpoints for models, files, and suggestions.
|
||||
- **Backend Logic (`src/lib`)**: Contains all the backend functionality including search, database, and API logic.
|
||||
- The search functionality is present inside `src/lib/search` directory.
|
||||
- All of the focus modes are implemented using the Meta Search Agent class in `src/lib/search/metaSearchAgent.ts`.
|
||||
- Database functionality is in `src/lib/db`.
|
||||
- Chat model and embedding model providers are managed in `src/lib/providers`.
|
||||
- Prompt templates and LLM chain definitions are in `src/lib/prompts` and `src/lib/chains` respectively.
|
||||
|
||||
## API Documentation
|
||||
|
||||
Perplexica exposes several API endpoints for programmatic access, including:
|
||||
|
||||
- **Search API**: Access Perplexica's advanced search capabilities directly via the `/api/search` endpoint. For detailed documentation, see `docs/api/search.md`.
|
||||
|
||||
## Setting Up Your Environment
|
||||
|
||||
Before diving into coding, setting up your local environment is key. Here's what you need to do:
|
||||
|
||||
### Backend
|
||||
|
||||
1. In the root directory, locate the `sample.config.toml` file.
|
||||
2. Rename it to `config.toml` and fill in the necessary configuration fields specific to the backend.
|
||||
3. Run `npm install` to install dependencies.
|
||||
4. Run `npm run db:push` to set up the local sqlite.
|
||||
5. Use `npm run dev` to start the backend in development mode.
|
||||
|
||||
### Frontend
|
||||
|
||||
1. Navigate to the `ui` folder and repeat the process of renaming `.env.example` to `.env`, making sure to provide the frontend-specific variables.
|
||||
2. Execute `npm install` within the `ui` directory to get the frontend dependencies ready.
|
||||
3. Launch the frontend development server with `npm run dev`.
|
||||
2. Rename it to `config.toml` and fill in the necessary configuration fields.
|
||||
3. Run `npm install` to install all dependencies.
|
||||
4. Run `npm run db:push` to set up the local sqlite database.
|
||||
5. Use `npm run dev` to start the application in development mode.
|
||||
|
||||
**Please note**: Docker configurations are present for setting up production environments, whereas `npm run dev` is used for development purposes.
|
||||
|
||||
|
32
README.md
32
README.md
@ -1,7 +1,22 @@
|
||||
# 🚀 Perplexica - An AI-powered search engine 🔎 <!-- omit in toc -->
|
||||
|
||||
[](https://discord.gg/26aArMy8tT)
|
||||
<div align="center" markdown="1">
|
||||
<sup>Special thanks to:</sup>
|
||||
<br>
|
||||
<br>
|
||||
<a href="https://www.warp.dev/perplexica">
|
||||
<img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/775dd593-9b5f-40f1-bf48-479faff4c27b">
|
||||
</a>
|
||||
|
||||
### [Warp, the AI Devtool that lives in your terminal](https://www.warp.dev/perplexica)
|
||||
|
||||
[Available for MacOS, Linux, & Windows](https://www.warp.dev/perplexica)
|
||||
|
||||
</div>
|
||||
|
||||
<hr/>
|
||||
|
||||
[](https://discord.gg/26aArMy8tT)
|
||||
|
||||

|
||||
|
||||
@ -44,7 +59,7 @@ Want to know more about its architecture and how it works? You can read it [here
|
||||
- **Normal Mode:** Processes your query and performs a web search.
|
||||
- **Focus Modes:** Special modes to better answer specific types of questions. Perplexica currently has 6 focus modes:
|
||||
- **All Mode:** Searches the entire web to find the best results.
|
||||
- **Writing Assistant Mode:** Helpful for writing tasks that does not require searching the web.
|
||||
- **Writing Assistant Mode:** Helpful for writing tasks that do not require searching the web.
|
||||
- **Academic Search Mode:** Finds articles and papers, ideal for academic research.
|
||||
- **YouTube Search Mode:** Finds YouTube videos based on the search query.
|
||||
- **Wolfram Alpha Search Mode:** Answers queries that need calculations or data analysis using Wolfram Alpha.
|
||||
@ -94,14 +109,13 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
|
||||
|
||||
1. Install SearXNG and allow `JSON` format in the SearXNG settings.
|
||||
2. Clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. Ensure you complete all required fields in this file.
|
||||
3. Rename the `.env.example` file to `.env` in the `ui` folder and fill in all necessary fields.
|
||||
4. After populating the configuration and environment files, run `npm i` in both the `ui` folder and the root directory.
|
||||
5. Install the dependencies and then execute `npm run build` in both the `ui` folder and the root directory.
|
||||
6. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
|
||||
3. After populating the configuration run `npm i`.
|
||||
4. Install the dependencies and then execute `npm run build`.
|
||||
5. Finally, start the app by running `npm rum start`
|
||||
|
||||
**Note**: Using Docker is recommended as it simplifies the setup process, especially for managing environment variables and dependencies.
|
||||
|
||||
See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like exposing it your network, etc.
|
||||
See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like updating, etc.
|
||||
|
||||
### Ollama Connection Errors
|
||||
|
||||
@ -139,11 +153,13 @@ For more details, check out the full documentation [here](https://github.com/Itz
|
||||
|
||||
## Expose Perplexica to network
|
||||
|
||||
You can access Perplexica over your home network by following our networking guide [here](https://github.com/ItzCrazyKns/Perplexica/blob/master/docs/installation/NETWORKING.md).
|
||||
Perplexica runs on Next.js and handles all API requests. It works right away on the same network and stays accessible even with port forwarding.
|
||||
|
||||
## One-Click Deployment
|
||||
|
||||
[](https://usw.sealos.io/?openapp=system-template%3FtemplateName%3Dperplexica)
|
||||
[](https://repocloud.io/details/?app_id=267)
|
||||
[](https://template.run.claw.cloud/?referralCode=U11MRQ8U9RM4&openapp=system-fastdeploy%3FtemplateName%3Dperplexica)
|
||||
|
||||
## Upcoming Features
|
||||
|
||||
|
@ -1,15 +1,27 @@
|
||||
FROM node:20.18.0-alpine
|
||||
|
||||
ARG NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
|
||||
ARG NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
|
||||
ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
|
||||
ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
|
||||
FROM node:20.18.0-slim AS builder
|
||||
|
||||
WORKDIR /home/perplexica
|
||||
|
||||
COPY ui /home/perplexica/
|
||||
COPY package.json yarn.lock ./
|
||||
RUN yarn install --frozen-lockfile --network-timeout 600000
|
||||
|
||||
RUN yarn install --frozen-lockfile
|
||||
COPY tsconfig.json next.config.mjs next-env.d.ts postcss.config.js drizzle.config.ts tailwind.config.ts ./
|
||||
COPY src ./src
|
||||
COPY public ./public
|
||||
|
||||
RUN mkdir -p /home/perplexica/data
|
||||
RUN yarn build
|
||||
|
||||
CMD ["yarn", "start"]
|
||||
FROM node:20.18.0-slim
|
||||
|
||||
WORKDIR /home/perplexica
|
||||
|
||||
COPY --from=builder /home/perplexica/public ./public
|
||||
COPY --from=builder /home/perplexica/.next/static ./public/_next/static
|
||||
|
||||
COPY --from=builder /home/perplexica/.next/standalone ./
|
||||
COPY --from=builder /home/perplexica/data ./data
|
||||
|
||||
RUN mkdir /home/perplexica/uploads
|
||||
|
||||
CMD ["node", "server.js"]
|
@ -1,17 +0,0 @@
|
||||
FROM node:18-slim
|
||||
|
||||
WORKDIR /home/perplexica
|
||||
|
||||
COPY src /home/perplexica/src
|
||||
COPY tsconfig.json /home/perplexica/
|
||||
COPY drizzle.config.ts /home/perplexica/
|
||||
COPY package.json /home/perplexica/
|
||||
COPY yarn.lock /home/perplexica/
|
||||
|
||||
RUN mkdir /home/perplexica/data
|
||||
RUN mkdir /home/perplexica/uploads
|
||||
|
||||
RUN yarn install --frozen-lockfile --network-timeout 600000
|
||||
RUN yarn build
|
||||
|
||||
CMD ["yarn", "start"]
|
@ -9,41 +9,21 @@ services:
|
||||
- perplexica-network
|
||||
restart: unless-stopped
|
||||
|
||||
perplexica-backend:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: backend.dockerfile
|
||||
image: itzcrazykns1337/perplexica-backend:main
|
||||
environment:
|
||||
- SEARXNG_API_URL=http://searxng:8080
|
||||
depends_on:
|
||||
- searxng
|
||||
ports:
|
||||
- 3001:3001
|
||||
volumes:
|
||||
- backend-dbstore:/home/perplexica/data
|
||||
- uploads:/home/perplexica/uploads
|
||||
- ./config.toml:/home/perplexica/config.toml
|
||||
extra_hosts:
|
||||
- 'host.docker.internal:host-gateway'
|
||||
networks:
|
||||
- perplexica-network
|
||||
restart: unless-stopped
|
||||
|
||||
perplexica-frontend:
|
||||
app:
|
||||
image: itzcrazykns1337/perplexica:main
|
||||
build:
|
||||
context: .
|
||||
dockerfile: app.dockerfile
|
||||
args:
|
||||
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
|
||||
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
|
||||
image: itzcrazykns1337/perplexica-frontend:main
|
||||
depends_on:
|
||||
- perplexica-backend
|
||||
environment:
|
||||
- SEARXNG_API_URL=http://searxng:8080
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
- perplexica-network
|
||||
volumes:
|
||||
- backend-dbstore:/home/perplexica/data
|
||||
- uploads:/home/perplexica/uploads
|
||||
- ./config.toml:/home/perplexica/config.toml
|
||||
restart: unless-stopped
|
||||
|
||||
networks:
|
||||
|
@ -6,9 +6,9 @@ Perplexica’s Search API makes it easy to use our AI-powered search engine. You
|
||||
|
||||
## Endpoint
|
||||
|
||||
### **POST** `http://localhost:3001/api/search`
|
||||
### **POST** `http://localhost:3000/api/search`
|
||||
|
||||
**Note**: Replace `3001` with any other port if you've changed the default PORT
|
||||
**Note**: Replace `3000` with any other port if you've changed the default PORT
|
||||
|
||||
### Request
|
||||
|
||||
@ -20,11 +20,11 @@ The API accepts a JSON object in the request body, where you define the focus mo
|
||||
{
|
||||
"chatModel": {
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o-mini"
|
||||
"name": "gpt-4o-mini"
|
||||
},
|
||||
"embeddingModel": {
|
||||
"provider": "openai",
|
||||
"model": "text-embedding-3-large"
|
||||
"name": "text-embedding-3-large"
|
||||
},
|
||||
"optimizationMode": "speed",
|
||||
"focusMode": "webSearch",
|
||||
@ -32,24 +32,26 @@ The API accepts a JSON object in the request body, where you define the focus mo
|
||||
"history": [
|
||||
["human", "Hi, how are you?"],
|
||||
["assistant", "I am doing well, how can I help you today?"]
|
||||
]
|
||||
],
|
||||
"systemInstructions": "Focus on providing technical details about Perplexica's architecture.",
|
||||
"stream": false
|
||||
}
|
||||
```
|
||||
|
||||
### Request Parameters
|
||||
|
||||
- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
|
||||
- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
|
||||
|
||||
- `provider`: Specifies the provider for the chat model (e.g., `openai`, `ollama`).
|
||||
- `model`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
|
||||
- `name`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
|
||||
- Optional fields for custom OpenAI configuration:
|
||||
- `customOpenAIBaseURL`: If you’re using a custom OpenAI instance, provide the base URL.
|
||||
- `customOpenAIKey`: The API key for a custom OpenAI instance.
|
||||
|
||||
- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
|
||||
- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
|
||||
|
||||
- `provider`: The provider for the embedding model (e.g., `openai`).
|
||||
- `model`: The specific embedding model (e.g., `text-embedding-3-large`).
|
||||
- `name`: The specific embedding model (e.g., `text-embedding-3-large`).
|
||||
|
||||
- **`focusMode`** (string, required): Specifies which focus mode to use. Available modes:
|
||||
|
||||
@ -62,6 +64,8 @@ The API accepts a JSON object in the request body, where you define the focus mo
|
||||
|
||||
- **`query`** (string, required): The search query or question.
|
||||
|
||||
- **`systemInstructions`** (string, optional): Custom instructions provided by the user to guide the AI's response. These instructions are treated as user preferences and have lower priority than the system's core instructions. For example, you can specify a particular writing style, format, or focus area.
|
||||
|
||||
- **`history`** (array, optional): An array of message pairs representing the conversation history. Each pair consists of a role (either 'human' or 'assistant') and the message content. This allows the system to use the context of the conversation to refine results. Example:
|
||||
|
||||
```json
|
||||
@ -71,11 +75,13 @@ The API accepts a JSON object in the request body, where you define the focus mo
|
||||
]
|
||||
```
|
||||
|
||||
- **`stream`** (boolean, optional): When set to `true`, enables streaming responses. Default is `false`.
|
||||
|
||||
### Response
|
||||
|
||||
The response from the API includes both the final message and the sources used to generate that message.
|
||||
|
||||
#### Example Response
|
||||
#### Standard Response (stream: false)
|
||||
|
||||
```json
|
||||
{
|
||||
@ -100,6 +106,28 @@ The response from the API includes both the final message and the sources used t
|
||||
}
|
||||
```
|
||||
|
||||
#### Streaming Response (stream: true)
|
||||
|
||||
When streaming is enabled, the API returns a stream of newline-delimited JSON objects. Each line contains a complete, valid JSON object. The response has Content-Type: application/json.
|
||||
|
||||
Example of streamed response objects:
|
||||
|
||||
```
|
||||
{"type":"init","data":"Stream connected"}
|
||||
{"type":"sources","data":[{"pageContent":"...","metadata":{"title":"...","url":"..."}},...]}
|
||||
{"type":"response","data":"Perplexica is an "}
|
||||
{"type":"response","data":"innovative, open-source "}
|
||||
{"type":"response","data":"AI-powered search engine..."}
|
||||
{"type":"done"}
|
||||
```
|
||||
|
||||
Clients should process each line as a separate JSON object. The different message types include:
|
||||
|
||||
- **`init`**: Initial connection message
|
||||
- **`sources`**: All sources used for the response
|
||||
- **`response`**: Chunks of the generated answer text
|
||||
- **`done`**: Indicates the stream is complete
|
||||
|
||||
### Fields in the Response
|
||||
|
||||
- **`message`** (string): The search result, generated based on the query and focus mode.
|
||||
|
@ -4,7 +4,7 @@ Curious about how Perplexica works? Don't worry, we'll cover it here. Before we
|
||||
|
||||
We'll understand how Perplexica works by taking an example of a scenario where a user asks: "How does an A.C. work?". We'll break down the process into steps to make it easier to understand. The steps are as follows:
|
||||
|
||||
1. The message is sent via WS to the backend server where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
|
||||
1. The message is sent to the `/api/chat` route where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
|
||||
2. The chain is now invoked; first, the message is passed to another chain where it first predicts (using the chat history and the question) whether there is a need for sources and searching the web. If there is, it will generate a query (in accordance with the chat history) for searching the web that we'll take up later. If not, the chain will end there, and then the answer generator chain, also known as the response generator, will be started.
|
||||
3. The query returned by the first chain is passed to SearXNG to search the web for information.
|
||||
4. After the information is retrieved, it is based on keyword-based search. We then convert the information into embeddings and the query as well, then we perform a similarity search to find the most relevant sources to answer the query.
|
||||
|
@ -1,109 +0,0 @@
|
||||
# Expose Perplexica to a network
|
||||
|
||||
This guide will show you how to make Perplexica available over a network. Follow these steps to allow computers on the same network to interact with Perplexica. Choose the instructions that match the operating system you are using.
|
||||
|
||||
## Windows
|
||||
|
||||
1. Open PowerShell as Administrator
|
||||
|
||||
2. Navigate to the directory containing the `docker-compose.yaml` file
|
||||
|
||||
3. Stop and remove the existing Perplexica containers and images:
|
||||
|
||||
```bash
|
||||
docker compose down --rmi all
|
||||
```
|
||||
|
||||
4. Open the `docker-compose.yaml` file in a text editor like Notepad++
|
||||
|
||||
5. Replace `127.0.0.1` with the IP address of the server Perplexica is running on in these two lines:
|
||||
|
||||
```bash
|
||||
args:
|
||||
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
|
||||
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
|
||||
```
|
||||
|
||||
6. Save and close the `docker-compose.yaml` file
|
||||
|
||||
7. Rebuild and restart the Perplexica container:
|
||||
|
||||
```bash
|
||||
docker compose up -d --build
|
||||
```
|
||||
|
||||
## macOS
|
||||
|
||||
1. Open the Terminal application
|
||||
|
||||
2. Navigate to the directory with the `docker-compose.yaml` file:
|
||||
|
||||
```bash
|
||||
cd /path/to/docker-compose.yaml
|
||||
```
|
||||
|
||||
3. Stop and remove existing containers and images:
|
||||
|
||||
```bash
|
||||
docker compose down --rmi all
|
||||
```
|
||||
|
||||
4. Open `docker-compose.yaml` in a text editor like Sublime Text:
|
||||
|
||||
```bash
|
||||
nano docker-compose.yaml
|
||||
```
|
||||
|
||||
5. Replace `127.0.0.1` with the server IP in these lines:
|
||||
|
||||
```bash
|
||||
args:
|
||||
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
|
||||
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
|
||||
```
|
||||
|
||||
6. Save and exit the editor
|
||||
|
||||
7. Rebuild and restart Perplexica:
|
||||
|
||||
```bash
|
||||
docker compose up -d --build
|
||||
```
|
||||
|
||||
## Linux
|
||||
|
||||
1. Open the terminal
|
||||
|
||||
2. Navigate to the `docker-compose.yaml` directory:
|
||||
|
||||
```bash
|
||||
cd /path/to/docker-compose.yaml
|
||||
```
|
||||
|
||||
3. Stop and remove containers and images:
|
||||
|
||||
```bash
|
||||
docker compose down --rmi all
|
||||
```
|
||||
|
||||
4. Edit `docker-compose.yaml`:
|
||||
|
||||
```bash
|
||||
nano docker-compose.yaml
|
||||
```
|
||||
|
||||
5. Replace `127.0.0.1` with the server IP:
|
||||
|
||||
```bash
|
||||
args:
|
||||
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
|
||||
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
|
||||
```
|
||||
|
||||
6. Save and exit the editor
|
||||
|
||||
7. Rebuild and restart Perplexica:
|
||||
|
||||
```bash
|
||||
docker compose up -d --build
|
||||
```
|
@ -7,34 +7,40 @@ To update Perplexica to the latest version, follow these steps:
|
||||
1. Clone the latest version of Perplexica from GitHub:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ItzCrazyKns/Perplexica.git
|
||||
git clone https://github.com/ItzCrazyKns/Perplexica.git
|
||||
```
|
||||
|
||||
2. Navigate to the Project Directory.
|
||||
2. Navigate to the project directory.
|
||||
|
||||
3. Pull latest images from registry.
|
||||
3. Check for changes in the configuration files. If the `sample.config.toml` file contains new fields, delete your existing `config.toml` file, rename `sample.config.toml` to `config.toml`, and update the configuration accordingly.
|
||||
|
||||
4. Pull the latest images from the registry.
|
||||
|
||||
```bash
|
||||
docker compose pull
|
||||
```
|
||||
|
||||
4. Update and Recreate containers.
|
||||
5. Update and recreate the containers.
|
||||
|
||||
```bash
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
5. Once the command completes running go to http://localhost:3000 and verify the latest changes.
|
||||
6. Once the command completes, go to http://localhost:3000 and verify the latest changes.
|
||||
|
||||
## For non Docker users
|
||||
## For non-Docker users
|
||||
|
||||
1. Clone the latest version of Perplexica from GitHub:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ItzCrazyKns/Perplexica.git
|
||||
git clone https://github.com/ItzCrazyKns/Perplexica.git
|
||||
```
|
||||
|
||||
2. Navigate to the Project Directory
|
||||
3. Execute `npm i` in both the `ui` folder and the root directory.
|
||||
4. Once packages are updated, execute `npm run build` in both the `ui` folder and the root directory.
|
||||
5. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
|
||||
2. Navigate to the project directory.
|
||||
|
||||
3. Check for changes in the configuration files. If the `sample.config.toml` file contains new fields, delete your existing `config.toml` file, rename `sample.config.toml` to `config.toml`, and update the configuration accordingly.
|
||||
4. After populating the configuration run `npm i`.
|
||||
5. Install the dependencies and then execute `npm run build`.
|
||||
6. Finally, start the app by running `npm rum start`
|
||||
|
||||
---
|
||||
|
@ -2,7 +2,7 @@ import { defineConfig } from 'drizzle-kit';
|
||||
|
||||
export default defineConfig({
|
||||
dialect: 'sqlite',
|
||||
schema: './src/db/schema.ts',
|
||||
schema: './src/lib/db/schema.ts',
|
||||
out: './drizzle',
|
||||
dbCredentials: {
|
||||
url: './data/db.sqlite',
|
||||
|
5
next-env.d.ts
vendored
Normal file
5
next-env.d.ts
vendored
Normal file
@ -0,0 +1,5 @@
|
||||
/// <reference types="next" />
|
||||
/// <reference types="next/image-types/global" />
|
||||
|
||||
// NOTE: This file should not be edited
|
||||
// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.
|
@ -1,5 +1,6 @@
|
||||
/** @type {import('next').NextConfig} */
|
||||
const nextConfig = {
|
||||
output: 'standalone',
|
||||
images: {
|
||||
remotePatterns: [
|
||||
{
|
||||
@ -7,6 +8,7 @@ const nextConfig = {
|
||||
},
|
||||
],
|
||||
},
|
||||
serverExternalPackages: ['pdf-parse'],
|
||||
};
|
||||
|
||||
export default nextConfig;
|
11024
package-lock.json
generated
Normal file
11024
package-lock.json
generated
Normal file
File diff suppressed because it is too large
Load Diff
87
package.json
87
package.json
@ -1,53 +1,66 @@
|
||||
{
|
||||
"name": "perplexica-backend",
|
||||
"version": "1.10.0-rc3",
|
||||
"name": "perplexica-frontend",
|
||||
"version": "1.10.2",
|
||||
"license": "MIT",
|
||||
"author": "ItzCrazyKns",
|
||||
"scripts": {
|
||||
"start": "npm run db:push && node dist/app.js",
|
||||
"build": "tsc",
|
||||
"dev": "nodemon --ignore uploads/ src/app.ts ",
|
||||
"db:push": "drizzle-kit push sqlite",
|
||||
"format": "prettier . --check",
|
||||
"format:write": "prettier . --write"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/better-sqlite3": "^7.6.10",
|
||||
"@types/cors": "^2.8.17",
|
||||
"@types/express": "^4.17.21",
|
||||
"@types/html-to-text": "^9.0.4",
|
||||
"@types/multer": "^1.4.12",
|
||||
"@types/pdf-parse": "^1.1.4",
|
||||
"@types/readable-stream": "^4.0.11",
|
||||
"@types/ws": "^8.5.12",
|
||||
"drizzle-kit": "^0.22.7",
|
||||
"nodemon": "^3.1.0",
|
||||
"prettier": "^3.2.5",
|
||||
"ts-node": "^10.9.2",
|
||||
"typescript": "^5.4.3"
|
||||
"dev": "next dev",
|
||||
"build": "npm run db:push && next build",
|
||||
"start": "next start",
|
||||
"lint": "next lint",
|
||||
"format:write": "prettier . --write",
|
||||
"db:push": "drizzle-kit push"
|
||||
},
|
||||
"dependencies": {
|
||||
"@headlessui/react": "^2.2.0",
|
||||
"@iarna/toml": "^2.2.5",
|
||||
"@langchain/anthropic": "^0.2.3",
|
||||
"@langchain/community": "^0.2.16",
|
||||
"@icons-pack/react-simple-icons": "^12.3.0",
|
||||
"@langchain/anthropic": "^0.3.15",
|
||||
"@langchain/community": "^0.3.36",
|
||||
"@langchain/core": "^0.3.42",
|
||||
"@langchain/google-genai": "^0.1.12",
|
||||
"@langchain/openai": "^0.0.25",
|
||||
"@langchain/google-genai": "^0.0.23",
|
||||
"@xenova/transformers": "^2.17.1",
|
||||
"axios": "^1.6.8",
|
||||
"better-sqlite3": "^11.0.0",
|
||||
"@langchain/ollama": "^0.2.0",
|
||||
"@langchain/textsplitters": "^0.1.0",
|
||||
"@tailwindcss/typography": "^0.5.12",
|
||||
"@xenova/transformers": "^2.17.2",
|
||||
"axios": "^1.8.3",
|
||||
"better-sqlite3": "^11.9.1",
|
||||
"clsx": "^2.1.0",
|
||||
"compute-cosine-similarity": "^1.1.0",
|
||||
"compute-dot": "^1.1.0",
|
||||
"cors": "^2.8.5",
|
||||
"dotenv": "^16.4.5",
|
||||
"drizzle-orm": "^0.31.2",
|
||||
"express": "^4.19.2",
|
||||
"drizzle-orm": "^0.40.1",
|
||||
"html-to-text": "^9.0.5",
|
||||
"langchain": "^0.1.30",
|
||||
"mammoth": "^1.8.0",
|
||||
"multer": "^1.4.5-lts.1",
|
||||
"lucide-react": "^0.363.0",
|
||||
"markdown-to-jsx": "^7.7.2",
|
||||
"next": "^15.2.2",
|
||||
"next-themes": "^0.3.0",
|
||||
"pdf-parse": "^1.1.1",
|
||||
"winston": "^3.13.0",
|
||||
"ws": "^8.17.1",
|
||||
"react": "^18",
|
||||
"react-dom": "^18",
|
||||
"react-text-to-speech": "^0.14.5",
|
||||
"react-textarea-autosize": "^8.5.3",
|
||||
"sonner": "^1.4.41",
|
||||
"tailwind-merge": "^2.2.2",
|
||||
"winston": "^3.17.0",
|
||||
"yet-another-react-lightbox": "^3.17.2",
|
||||
"zod": "^3.22.4"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/better-sqlite3": "^7.6.12",
|
||||
"@types/html-to-text": "^9.0.4",
|
||||
"@types/node": "^20",
|
||||
"@types/pdf-parse": "^1.1.4",
|
||||
"@types/react": "^18",
|
||||
"@types/react-dom": "^18",
|
||||
"autoprefixer": "^10.0.1",
|
||||
"drizzle-kit": "^0.30.5",
|
||||
"eslint": "^8",
|
||||
"eslint-config-next": "14.1.4",
|
||||
"postcss": "^8",
|
||||
"prettier": "^3.2.5",
|
||||
"tailwindcss": "^3.3.0",
|
||||
"typescript": "^5"
|
||||
}
|
||||
}
|
||||
|
Before Width: | Height: | Size: 1.3 KiB After Width: | Height: | Size: 1.3 KiB |
Before Width: | Height: | Size: 629 B After Width: | Height: | Size: 629 B |
@ -1,5 +1,4 @@
|
||||
[GENERAL]
|
||||
PORT = 3001 # Port to run the server on
|
||||
SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
|
||||
KEEP_ALIVE = "5m" # How long to keep Ollama models loaded into memory. (Instead of using -1 use "-1m")
|
||||
|
||||
@ -18,9 +17,16 @@ API_KEY = ""
|
||||
[MODELS.CUSTOM_OPENAI]
|
||||
API_KEY = ""
|
||||
API_URL = ""
|
||||
MODEL_NAME = ""
|
||||
|
||||
[MODELS.OLLAMA]
|
||||
API_URL = "" # Ollama API URL - http://host.docker.internal:11434
|
||||
|
||||
[MODELS.DEEPSEEK]
|
||||
API_KEY = ""
|
||||
|
||||
[MODELS.LM_STUDIO]
|
||||
API_URL = "" # LM Studio API URL - http://host.docker.internal:1234
|
||||
|
||||
[API_ENDPOINTS]
|
||||
SEARXNG = "http://localhost:32768" # SearxNG API URL
|
||||
SEARXNG = "" # SearxNG API URL - http://localhost:32768
|
||||
|
38
src/app.ts
38
src/app.ts
@ -1,38 +0,0 @@
|
||||
import { startWebSocketServer } from './websocket';
|
||||
import express from 'express';
|
||||
import cors from 'cors';
|
||||
import http from 'http';
|
||||
import routes from './routes';
|
||||
import { getPort } from './config';
|
||||
import logger from './utils/logger';
|
||||
|
||||
const port = getPort();
|
||||
|
||||
const app = express();
|
||||
const server = http.createServer(app);
|
||||
|
||||
const corsOptions = {
|
||||
origin: '*',
|
||||
};
|
||||
|
||||
app.use(cors(corsOptions));
|
||||
app.use(express.json());
|
||||
|
||||
app.use('/api', routes);
|
||||
app.get('/api', (_, res) => {
|
||||
res.status(200).json({ status: 'ok' });
|
||||
});
|
||||
|
||||
server.listen(port, () => {
|
||||
logger.info(`Server is running on port ${port}`);
|
||||
});
|
||||
|
||||
startWebSocketServer(server);
|
||||
|
||||
process.on('uncaughtException', (err, origin) => {
|
||||
logger.error(`Uncaught Exception at ${origin}: ${err}`);
|
||||
});
|
||||
|
||||
process.on('unhandledRejection', (reason, promise) => {
|
||||
logger.error(`Unhandled Rejection at: ${promise}, reason: ${reason}`);
|
||||
});
|
313
src/app/api/chat/route.ts
Normal file
313
src/app/api/chat/route.ts
Normal file
@ -0,0 +1,313 @@
|
||||
import prompts from '@/lib/prompts';
|
||||
import MetaSearchAgent from '@/lib/search/metaSearchAgent';
|
||||
import crypto from 'crypto';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { EventEmitter } from 'stream';
|
||||
import {
|
||||
chatModelProviders,
|
||||
embeddingModelProviders,
|
||||
getAvailableChatModelProviders,
|
||||
getAvailableEmbeddingModelProviders,
|
||||
} from '@/lib/providers';
|
||||
import db from '@/lib/db';
|
||||
import { chats, messages as messagesSchema } from '@/lib/db/schema';
|
||||
import { and, eq, gt } from 'drizzle-orm';
|
||||
import { getFileDetails } from '@/lib/utils/files';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '@/lib/config';
|
||||
import { ChatOllama } from '@langchain/ollama';
|
||||
import { searchHandlers } from '@/lib/search';
|
||||
|
||||
export const runtime = 'nodejs';
|
||||
export const dynamic = 'force-dynamic';
|
||||
|
||||
type Message = {
|
||||
messageId: string;
|
||||
chatId: string;
|
||||
content: string;
|
||||
};
|
||||
|
||||
type ChatModel = {
|
||||
provider: string;
|
||||
name: string;
|
||||
ollamaContextWindow?: number;
|
||||
};
|
||||
|
||||
type EmbeddingModel = {
|
||||
provider: string;
|
||||
name: string;
|
||||
};
|
||||
|
||||
type Body = {
|
||||
message: Message;
|
||||
optimizationMode: 'speed' | 'balanced' | 'quality';
|
||||
focusMode: string;
|
||||
history: Array<[string, string]>;
|
||||
files: Array<string>;
|
||||
chatModel: ChatModel;
|
||||
embeddingModel: EmbeddingModel;
|
||||
systemInstructions: string;
|
||||
};
|
||||
|
||||
const handleEmitterEvents = async (
|
||||
stream: EventEmitter,
|
||||
writer: WritableStreamDefaultWriter,
|
||||
encoder: TextEncoder,
|
||||
aiMessageId: string,
|
||||
chatId: string,
|
||||
) => {
|
||||
let recievedMessage = '';
|
||||
let sources: any[] = [];
|
||||
|
||||
stream.on('data', (data) => {
|
||||
const parsedData = JSON.parse(data);
|
||||
if (parsedData.type === 'response') {
|
||||
writer.write(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'message',
|
||||
data: parsedData.data,
|
||||
messageId: aiMessageId,
|
||||
}) + '\n',
|
||||
),
|
||||
);
|
||||
|
||||
recievedMessage += parsedData.data;
|
||||
} else if (parsedData.type === 'sources') {
|
||||
writer.write(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'sources',
|
||||
data: parsedData.data,
|
||||
messageId: aiMessageId,
|
||||
}) + '\n',
|
||||
),
|
||||
);
|
||||
|
||||
sources = parsedData.data;
|
||||
}
|
||||
});
|
||||
stream.on('end', () => {
|
||||
writer.write(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'messageEnd',
|
||||
messageId: aiMessageId,
|
||||
}) + '\n',
|
||||
),
|
||||
);
|
||||
writer.close();
|
||||
|
||||
db.insert(messagesSchema)
|
||||
.values({
|
||||
content: recievedMessage,
|
||||
chatId: chatId,
|
||||
messageId: aiMessageId,
|
||||
role: 'assistant',
|
||||
metadata: JSON.stringify({
|
||||
createdAt: new Date(),
|
||||
...(sources && sources.length > 0 && { sources }),
|
||||
}),
|
||||
})
|
||||
.execute();
|
||||
});
|
||||
stream.on('error', (data) => {
|
||||
const parsedData = JSON.parse(data);
|
||||
writer.write(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'error',
|
||||
data: parsedData.data,
|
||||
}),
|
||||
),
|
||||
);
|
||||
writer.close();
|
||||
});
|
||||
};
|
||||
|
||||
const handleHistorySave = async (
|
||||
message: Message,
|
||||
humanMessageId: string,
|
||||
focusMode: string,
|
||||
files: string[],
|
||||
) => {
|
||||
const chat = await db.query.chats.findFirst({
|
||||
where: eq(chats.id, message.chatId),
|
||||
});
|
||||
|
||||
if (!chat) {
|
||||
await db
|
||||
.insert(chats)
|
||||
.values({
|
||||
id: message.chatId,
|
||||
title: message.content,
|
||||
createdAt: new Date().toString(),
|
||||
focusMode: focusMode,
|
||||
files: files.map(getFileDetails),
|
||||
})
|
||||
.execute();
|
||||
}
|
||||
|
||||
const messageExists = await db.query.messages.findFirst({
|
||||
where: eq(messagesSchema.messageId, humanMessageId),
|
||||
});
|
||||
|
||||
if (!messageExists) {
|
||||
await db
|
||||
.insert(messagesSchema)
|
||||
.values({
|
||||
content: message.content,
|
||||
chatId: message.chatId,
|
||||
messageId: humanMessageId,
|
||||
role: 'user',
|
||||
metadata: JSON.stringify({
|
||||
createdAt: new Date(),
|
||||
}),
|
||||
})
|
||||
.execute();
|
||||
} else {
|
||||
await db
|
||||
.delete(messagesSchema)
|
||||
.where(
|
||||
and(
|
||||
gt(messagesSchema.id, messageExists.id),
|
||||
eq(messagesSchema.chatId, message.chatId),
|
||||
),
|
||||
)
|
||||
.execute();
|
||||
}
|
||||
};
|
||||
|
||||
export const POST = async (req: Request) => {
|
||||
try {
|
||||
const body = (await req.json()) as Body;
|
||||
const { message } = body;
|
||||
|
||||
if (message.content === '') {
|
||||
return Response.json(
|
||||
{
|
||||
message: 'Please provide a message to process',
|
||||
},
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
|
||||
getAvailableChatModelProviders(),
|
||||
getAvailableEmbeddingModelProviders(),
|
||||
]);
|
||||
|
||||
const chatModelProvider =
|
||||
chatModelProviders[
|
||||
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
|
||||
];
|
||||
const chatModel =
|
||||
chatModelProvider[
|
||||
body.chatModel?.name || Object.keys(chatModelProvider)[0]
|
||||
];
|
||||
|
||||
const embeddingProvider =
|
||||
embeddingModelProviders[
|
||||
body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0]
|
||||
];
|
||||
const embeddingModel =
|
||||
embeddingProvider[
|
||||
body.embeddingModel?.name || Object.keys(embeddingProvider)[0]
|
||||
];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
let embedding = embeddingModel.model;
|
||||
|
||||
if (body.chatModel?.provider === 'custom_openai') {
|
||||
llm = new ChatOpenAI({
|
||||
openAIApiKey: getCustomOpenaiApiKey(),
|
||||
modelName: getCustomOpenaiModelName(),
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
|
||||
// Set context window size for Ollama models
|
||||
if (llm instanceof ChatOllama && body.chatModel?.provider === 'ollama') {
|
||||
llm.numCtx = body.chatModel.ollamaContextWindow || 2048;
|
||||
}
|
||||
}
|
||||
|
||||
if (!llm) {
|
||||
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!embedding) {
|
||||
return Response.json(
|
||||
{ error: 'Invalid embedding model' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const humanMessageId =
|
||||
message.messageId ?? crypto.randomBytes(7).toString('hex');
|
||||
const aiMessageId = crypto.randomBytes(7).toString('hex');
|
||||
|
||||
const history: BaseMessage[] = body.history.map((msg) => {
|
||||
if (msg[0] === 'human') {
|
||||
return new HumanMessage({
|
||||
content: msg[1],
|
||||
});
|
||||
} else {
|
||||
return new AIMessage({
|
||||
content: msg[1],
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
const handler = searchHandlers[body.focusMode];
|
||||
|
||||
if (!handler) {
|
||||
return Response.json(
|
||||
{
|
||||
message: 'Invalid focus mode',
|
||||
},
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const stream = await handler.searchAndAnswer(
|
||||
message.content,
|
||||
history,
|
||||
llm,
|
||||
embedding,
|
||||
body.optimizationMode,
|
||||
body.files,
|
||||
body.systemInstructions,
|
||||
);
|
||||
|
||||
const responseStream = new TransformStream();
|
||||
const writer = responseStream.writable.getWriter();
|
||||
const encoder = new TextEncoder();
|
||||
|
||||
handleEmitterEvents(stream, writer, encoder, aiMessageId, message.chatId);
|
||||
handleHistorySave(message, humanMessageId, body.focusMode, body.files);
|
||||
|
||||
return new Response(responseStream.readable, {
|
||||
headers: {
|
||||
'Content-Type': 'text/event-stream',
|
||||
Connection: 'keep-alive',
|
||||
'Cache-Control': 'no-cache, no-transform',
|
||||
},
|
||||
});
|
||||
} catch (err) {
|
||||
console.error('An error occurred while processing chat request:', err);
|
||||
return Response.json(
|
||||
{ message: 'An error occurred while processing chat request' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
69
src/app/api/chats/[id]/route.ts
Normal file
69
src/app/api/chats/[id]/route.ts
Normal file
@ -0,0 +1,69 @@
|
||||
import db from '@/lib/db';
|
||||
import { chats, messages } from '@/lib/db/schema';
|
||||
import { eq } from 'drizzle-orm';
|
||||
|
||||
export const GET = async (
|
||||
req: Request,
|
||||
{ params }: { params: Promise<{ id: string }> },
|
||||
) => {
|
||||
try {
|
||||
const { id } = await params;
|
||||
|
||||
const chatExists = await db.query.chats.findFirst({
|
||||
where: eq(chats.id, id),
|
||||
});
|
||||
|
||||
if (!chatExists) {
|
||||
return Response.json({ message: 'Chat not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
const chatMessages = await db.query.messages.findMany({
|
||||
where: eq(messages.chatId, id),
|
||||
});
|
||||
|
||||
return Response.json(
|
||||
{
|
||||
chat: chatExists,
|
||||
messages: chatMessages,
|
||||
},
|
||||
{ status: 200 },
|
||||
);
|
||||
} catch (err) {
|
||||
console.error('Error in getting chat by id: ', err);
|
||||
return Response.json(
|
||||
{ message: 'An error has occurred.' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
export const DELETE = async (
|
||||
req: Request,
|
||||
{ params }: { params: Promise<{ id: string }> },
|
||||
) => {
|
||||
try {
|
||||
const { id } = await params;
|
||||
|
||||
const chatExists = await db.query.chats.findFirst({
|
||||
where: eq(chats.id, id),
|
||||
});
|
||||
|
||||
if (!chatExists) {
|
||||
return Response.json({ message: 'Chat not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
await db.delete(chats).where(eq(chats.id, id)).execute();
|
||||
await db.delete(messages).where(eq(messages.chatId, id)).execute();
|
||||
|
||||
return Response.json(
|
||||
{ message: 'Chat deleted successfully' },
|
||||
{ status: 200 },
|
||||
);
|
||||
} catch (err) {
|
||||
console.error('Error in deleting chat by id: ', err);
|
||||
return Response.json(
|
||||
{ message: 'An error has occurred.' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
15
src/app/api/chats/route.ts
Normal file
15
src/app/api/chats/route.ts
Normal file
@ -0,0 +1,15 @@
|
||||
import db from '@/lib/db';
|
||||
|
||||
export const GET = async (req: Request) => {
|
||||
try {
|
||||
let chats = await db.query.chats.findMany();
|
||||
chats = chats.reverse();
|
||||
return Response.json({ chats: chats }, { status: 200 });
|
||||
} catch (err) {
|
||||
console.error('Error in getting chats: ', err);
|
||||
return Response.json(
|
||||
{ message: 'An error has occurred.' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
119
src/app/api/config/route.ts
Normal file
119
src/app/api/config/route.ts
Normal file
@ -0,0 +1,119 @@
|
||||
import {
|
||||
getAnthropicApiKey,
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
getGeminiApiKey,
|
||||
getGroqApiKey,
|
||||
getOllamaApiEndpoint,
|
||||
getOpenaiApiKey,
|
||||
getDeepseekApiKey,
|
||||
getLMStudioApiEndpoint,
|
||||
updateConfig,
|
||||
} from '@/lib/config';
|
||||
import {
|
||||
getAvailableChatModelProviders,
|
||||
getAvailableEmbeddingModelProviders,
|
||||
} from '@/lib/providers';
|
||||
|
||||
export const GET = async (req: Request) => {
|
||||
try {
|
||||
const config: Record<string, any> = {};
|
||||
|
||||
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
|
||||
getAvailableChatModelProviders(),
|
||||
getAvailableEmbeddingModelProviders(),
|
||||
]);
|
||||
|
||||
config['chatModelProviders'] = {};
|
||||
config['embeddingModelProviders'] = {};
|
||||
|
||||
for (const provider in chatModelProviders) {
|
||||
config['chatModelProviders'][provider] = Object.keys(
|
||||
chatModelProviders[provider],
|
||||
).map((model) => {
|
||||
return {
|
||||
name: model,
|
||||
displayName: chatModelProviders[provider][model].displayName,
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
for (const provider in embeddingModelProviders) {
|
||||
config['embeddingModelProviders'][provider] = Object.keys(
|
||||
embeddingModelProviders[provider],
|
||||
).map((model) => {
|
||||
return {
|
||||
name: model,
|
||||
displayName: embeddingModelProviders[provider][model].displayName,
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
config['openaiApiKey'] = getOpenaiApiKey();
|
||||
config['ollamaApiUrl'] = getOllamaApiEndpoint();
|
||||
config['lmStudioApiUrl'] = getLMStudioApiEndpoint();
|
||||
config['anthropicApiKey'] = getAnthropicApiKey();
|
||||
config['groqApiKey'] = getGroqApiKey();
|
||||
config['geminiApiKey'] = getGeminiApiKey();
|
||||
config['deepseekApiKey'] = getDeepseekApiKey();
|
||||
config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
|
||||
config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
|
||||
config['customOpenaiModelName'] = getCustomOpenaiModelName();
|
||||
|
||||
return Response.json({ ...config }, { status: 200 });
|
||||
} catch (err) {
|
||||
console.error('An error occurred while getting config:', err);
|
||||
return Response.json(
|
||||
{ message: 'An error occurred while getting config' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
export const POST = async (req: Request) => {
|
||||
try {
|
||||
const config = await req.json();
|
||||
|
||||
const updatedConfig = {
|
||||
MODELS: {
|
||||
OPENAI: {
|
||||
API_KEY: config.openaiApiKey,
|
||||
},
|
||||
GROQ: {
|
||||
API_KEY: config.groqApiKey,
|
||||
},
|
||||
ANTHROPIC: {
|
||||
API_KEY: config.anthropicApiKey,
|
||||
},
|
||||
GEMINI: {
|
||||
API_KEY: config.geminiApiKey,
|
||||
},
|
||||
OLLAMA: {
|
||||
API_URL: config.ollamaApiUrl,
|
||||
},
|
||||
DEEPSEEK: {
|
||||
API_KEY: config.deepseekApiKey,
|
||||
},
|
||||
LM_STUDIO: {
|
||||
API_URL: config.lmStudioApiUrl,
|
||||
},
|
||||
CUSTOM_OPENAI: {
|
||||
API_URL: config.customOpenaiApiUrl,
|
||||
API_KEY: config.customOpenaiApiKey,
|
||||
MODEL_NAME: config.customOpenaiModelName,
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
updateConfig(updatedConfig);
|
||||
|
||||
return Response.json({ message: 'Config updated' }, { status: 200 });
|
||||
} catch (err) {
|
||||
console.error('An error occurred while updating config:', err);
|
||||
return Response.json(
|
||||
{ message: 'An error occurred while updating config' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
61
src/app/api/discover/route.ts
Normal file
61
src/app/api/discover/route.ts
Normal file
@ -0,0 +1,61 @@
|
||||
import { searchSearxng } from '@/lib/searxng';
|
||||
|
||||
const articleWebsites = [
|
||||
'yahoo.com',
|
||||
'www.exchangewire.com',
|
||||
'businessinsider.com',
|
||||
/* 'wired.com',
|
||||
'mashable.com',
|
||||
'theverge.com',
|
||||
'gizmodo.com',
|
||||
'cnet.com',
|
||||
'venturebeat.com', */
|
||||
];
|
||||
|
||||
const topics = ['AI', 'tech']; /* TODO: Add UI to customize this */
|
||||
|
||||
export const GET = async (req: Request) => {
|
||||
try {
|
||||
const data = (
|
||||
await Promise.all([
|
||||
...new Array(articleWebsites.length * topics.length)
|
||||
.fill(0)
|
||||
.map(async (_, i) => {
|
||||
return (
|
||||
await searchSearxng(
|
||||
`site:${articleWebsites[i % articleWebsites.length]} ${
|
||||
topics[i % topics.length]
|
||||
}`,
|
||||
{
|
||||
engines: ['bing news'],
|
||||
pageno: 1,
|
||||
},
|
||||
)
|
||||
).results;
|
||||
}),
|
||||
])
|
||||
)
|
||||
.map((result) => result)
|
||||
.flat()
|
||||
.sort(() => Math.random() - 0.5);
|
||||
|
||||
return Response.json(
|
||||
{
|
||||
blogs: data,
|
||||
},
|
||||
{
|
||||
status: 200,
|
||||
},
|
||||
);
|
||||
} catch (err) {
|
||||
console.error(`An error occurred in discover route: ${err}`);
|
||||
return Response.json(
|
||||
{
|
||||
message: 'An error has occurred',
|
||||
},
|
||||
{
|
||||
status: 500,
|
||||
},
|
||||
);
|
||||
}
|
||||
};
|
89
src/app/api/images/route.ts
Normal file
89
src/app/api/images/route.ts
Normal file
@ -0,0 +1,89 @@
|
||||
import handleImageSearch from '@/lib/chains/imageSearchAgent';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '@/lib/config';
|
||||
import { getAvailableChatModelProviders } from '@/lib/providers';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { ChatOllama } from '@langchain/ollama';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
|
||||
interface ChatModel {
|
||||
provider: string;
|
||||
model: string;
|
||||
ollamaContextWindow?: number;
|
||||
}
|
||||
|
||||
interface ImageSearchBody {
|
||||
query: string;
|
||||
chatHistory: any[];
|
||||
chatModel?: ChatModel;
|
||||
}
|
||||
|
||||
export const POST = async (req: Request) => {
|
||||
try {
|
||||
const body: ImageSearchBody = await req.json();
|
||||
|
||||
const chatHistory = body.chatHistory
|
||||
.map((msg: any) => {
|
||||
if (msg.role === 'user') {
|
||||
return new HumanMessage(msg.content);
|
||||
} else if (msg.role === 'assistant') {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
})
|
||||
.filter((msg) => msg !== undefined) as BaseMessage[];
|
||||
|
||||
const chatModelProviders = await getAvailableChatModelProviders();
|
||||
|
||||
const chatModelProvider =
|
||||
chatModelProviders[
|
||||
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
|
||||
];
|
||||
const chatModel =
|
||||
chatModelProvider[
|
||||
body.chatModel?.model || Object.keys(chatModelProvider)[0]
|
||||
];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
|
||||
if (body.chatModel?.provider === 'custom_openai') {
|
||||
llm = new ChatOpenAI({
|
||||
openAIApiKey: getCustomOpenaiApiKey(),
|
||||
modelName: getCustomOpenaiModelName(),
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
// Set context window size for Ollama models
|
||||
if (llm instanceof ChatOllama && body.chatModel?.provider === 'ollama') {
|
||||
llm.numCtx = body.chatModel.ollamaContextWindow || 2048;
|
||||
}
|
||||
}
|
||||
|
||||
if (!llm) {
|
||||
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
|
||||
}
|
||||
|
||||
const images = await handleImageSearch(
|
||||
{
|
||||
chat_history: chatHistory,
|
||||
query: body.query,
|
||||
},
|
||||
llm,
|
||||
);
|
||||
|
||||
return Response.json({ images }, { status: 200 });
|
||||
} catch (err) {
|
||||
console.error(`An error occurred while searching images: ${err}`);
|
||||
return Response.json(
|
||||
{ message: 'An error occurred while searching images' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
47
src/app/api/models/route.ts
Normal file
47
src/app/api/models/route.ts
Normal file
@ -0,0 +1,47 @@
|
||||
import {
|
||||
getAvailableChatModelProviders,
|
||||
getAvailableEmbeddingModelProviders,
|
||||
} from '@/lib/providers';
|
||||
|
||||
export const GET = async (req: Request) => {
|
||||
try {
|
||||
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
|
||||
getAvailableChatModelProviders(),
|
||||
getAvailableEmbeddingModelProviders(),
|
||||
]);
|
||||
|
||||
Object.keys(chatModelProviders).forEach((provider) => {
|
||||
Object.keys(chatModelProviders[provider]).forEach((model) => {
|
||||
delete (chatModelProviders[provider][model] as { model?: unknown })
|
||||
.model;
|
||||
});
|
||||
});
|
||||
|
||||
Object.keys(embeddingModelProviders).forEach((provider) => {
|
||||
Object.keys(embeddingModelProviders[provider]).forEach((model) => {
|
||||
delete (embeddingModelProviders[provider][model] as { model?: unknown })
|
||||
.model;
|
||||
});
|
||||
});
|
||||
|
||||
return Response.json(
|
||||
{
|
||||
chatModelProviders,
|
||||
embeddingModelProviders,
|
||||
},
|
||||
{
|
||||
status: 200,
|
||||
},
|
||||
);
|
||||
} catch (err) {
|
||||
console.error('An error occurred while fetching models', err);
|
||||
return Response.json(
|
||||
{
|
||||
message: 'An error has occurred.',
|
||||
},
|
||||
{
|
||||
status: 500,
|
||||
},
|
||||
);
|
||||
}
|
||||
};
|
276
src/app/api/search/route.ts
Normal file
276
src/app/api/search/route.ts
Normal file
@ -0,0 +1,276 @@
|
||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import type { Embeddings } from '@langchain/core/embeddings';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import {
|
||||
getAvailableChatModelProviders,
|
||||
getAvailableEmbeddingModelProviders,
|
||||
} from '@/lib/providers';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { MetaSearchAgentType } from '@/lib/search/metaSearchAgent';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '@/lib/config';
|
||||
import { searchHandlers } from '@/lib/search';
|
||||
import { ChatOllama } from '@langchain/ollama';
|
||||
|
||||
interface chatModel {
|
||||
provider: string;
|
||||
name: string;
|
||||
customOpenAIKey?: string;
|
||||
customOpenAIBaseURL?: string;
|
||||
ollamaContextWindow?: number;
|
||||
}
|
||||
|
||||
interface embeddingModel {
|
||||
provider: string;
|
||||
name: string;
|
||||
}
|
||||
|
||||
interface ChatRequestBody {
|
||||
optimizationMode: 'speed' | 'balanced';
|
||||
focusMode: string;
|
||||
chatModel?: chatModel;
|
||||
embeddingModel?: embeddingModel;
|
||||
query: string;
|
||||
history: Array<[string, string]>;
|
||||
stream?: boolean;
|
||||
systemInstructions?: string;
|
||||
}
|
||||
|
||||
export const POST = async (req: Request) => {
|
||||
try {
|
||||
const body: ChatRequestBody = await req.json();
|
||||
|
||||
if (!body.focusMode || !body.query) {
|
||||
return Response.json(
|
||||
{ message: 'Missing focus mode or query' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
body.history = body.history || [];
|
||||
body.optimizationMode = body.optimizationMode || 'balanced';
|
||||
body.stream = body.stream || false;
|
||||
|
||||
const history: BaseMessage[] = body.history.map((msg) => {
|
||||
return msg[0] === 'human'
|
||||
? new HumanMessage({ content: msg[1] })
|
||||
: new AIMessage({ content: msg[1] });
|
||||
});
|
||||
|
||||
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
|
||||
getAvailableChatModelProviders(),
|
||||
getAvailableEmbeddingModelProviders(),
|
||||
]);
|
||||
|
||||
const chatModelProvider =
|
||||
body.chatModel?.provider || Object.keys(chatModelProviders)[0];
|
||||
const chatModel =
|
||||
body.chatModel?.name ||
|
||||
Object.keys(chatModelProviders[chatModelProvider])[0];
|
||||
|
||||
const embeddingModelProvider =
|
||||
body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0];
|
||||
const embeddingModel =
|
||||
body.embeddingModel?.name ||
|
||||
Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
let embeddings: Embeddings | undefined;
|
||||
|
||||
if (body.chatModel?.provider === 'custom_openai') {
|
||||
llm = new ChatOpenAI({
|
||||
modelName: body.chatModel?.name || getCustomOpenaiModelName(),
|
||||
openAIApiKey:
|
||||
body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL:
|
||||
body.chatModel?.customOpenAIBaseURL || getCustomOpenaiApiUrl(),
|
||||
},
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (
|
||||
chatModelProviders[chatModelProvider] &&
|
||||
chatModelProviders[chatModelProvider][chatModel]
|
||||
) {
|
||||
llm = chatModelProviders[chatModelProvider][chatModel]
|
||||
.model as unknown as BaseChatModel | undefined;
|
||||
}
|
||||
|
||||
if (llm instanceof ChatOllama && body.chatModel?.provider === 'ollama') {
|
||||
llm.numCtx = body.chatModel.ollamaContextWindow || 2048;
|
||||
}
|
||||
|
||||
if (
|
||||
embeddingModelProviders[embeddingModelProvider] &&
|
||||
embeddingModelProviders[embeddingModelProvider][embeddingModel]
|
||||
) {
|
||||
embeddings = embeddingModelProviders[embeddingModelProvider][
|
||||
embeddingModel
|
||||
].model as Embeddings | undefined;
|
||||
}
|
||||
|
||||
if (!llm || !embeddings) {
|
||||
return Response.json(
|
||||
{ message: 'Invalid model selected' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
|
||||
|
||||
if (!searchHandler) {
|
||||
return Response.json({ message: 'Invalid focus mode' }, { status: 400 });
|
||||
}
|
||||
|
||||
const emitter = await searchHandler.searchAndAnswer(
|
||||
body.query,
|
||||
history,
|
||||
llm,
|
||||
embeddings,
|
||||
body.optimizationMode,
|
||||
[],
|
||||
body.systemInstructions || '',
|
||||
);
|
||||
|
||||
if (!body.stream) {
|
||||
return new Promise(
|
||||
(
|
||||
resolve: (value: Response) => void,
|
||||
reject: (value: Response) => void,
|
||||
) => {
|
||||
let message = '';
|
||||
let sources: any[] = [];
|
||||
|
||||
emitter.on('data', (data: string) => {
|
||||
try {
|
||||
const parsedData = JSON.parse(data);
|
||||
if (parsedData.type === 'response') {
|
||||
message += parsedData.data;
|
||||
} else if (parsedData.type === 'sources') {
|
||||
sources = parsedData.data;
|
||||
}
|
||||
} catch (error) {
|
||||
reject(
|
||||
Response.json(
|
||||
{ message: 'Error parsing data' },
|
||||
{ status: 500 },
|
||||
),
|
||||
);
|
||||
}
|
||||
});
|
||||
|
||||
emitter.on('end', () => {
|
||||
resolve(Response.json({ message, sources }, { status: 200 }));
|
||||
});
|
||||
|
||||
emitter.on('error', (error: any) => {
|
||||
reject(
|
||||
Response.json(
|
||||
{ message: 'Search error', error },
|
||||
{ status: 500 },
|
||||
),
|
||||
);
|
||||
});
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
const encoder = new TextEncoder();
|
||||
|
||||
const abortController = new AbortController();
|
||||
const { signal } = abortController;
|
||||
|
||||
const stream = new ReadableStream({
|
||||
start(controller) {
|
||||
let sources: any[] = [];
|
||||
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'init',
|
||||
data: 'Stream connected',
|
||||
}) + '\n',
|
||||
),
|
||||
);
|
||||
|
||||
signal.addEventListener('abort', () => {
|
||||
emitter.removeAllListeners();
|
||||
|
||||
try {
|
||||
controller.close();
|
||||
} catch (error) {}
|
||||
});
|
||||
|
||||
emitter.on('data', (data: string) => {
|
||||
if (signal.aborted) return;
|
||||
|
||||
try {
|
||||
const parsedData = JSON.parse(data);
|
||||
|
||||
if (parsedData.type === 'response') {
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'response',
|
||||
data: parsedData.data,
|
||||
}) + '\n',
|
||||
),
|
||||
);
|
||||
} else if (parsedData.type === 'sources') {
|
||||
sources = parsedData.data;
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'sources',
|
||||
data: sources,
|
||||
}) + '\n',
|
||||
),
|
||||
);
|
||||
}
|
||||
} catch (error) {
|
||||
controller.error(error);
|
||||
}
|
||||
});
|
||||
|
||||
emitter.on('end', () => {
|
||||
if (signal.aborted) return;
|
||||
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'done',
|
||||
}) + '\n',
|
||||
),
|
||||
);
|
||||
controller.close();
|
||||
});
|
||||
|
||||
emitter.on('error', (error: any) => {
|
||||
if (signal.aborted) return;
|
||||
|
||||
controller.error(error);
|
||||
});
|
||||
},
|
||||
cancel() {
|
||||
abortController.abort();
|
||||
},
|
||||
});
|
||||
|
||||
return new Response(stream, {
|
||||
headers: {
|
||||
'Content-Type': 'text/event-stream',
|
||||
'Cache-Control': 'no-cache, no-transform',
|
||||
Connection: 'keep-alive',
|
||||
},
|
||||
});
|
||||
} catch (err: any) {
|
||||
console.error(`Error in getting search results: ${err.message}`);
|
||||
return Response.json(
|
||||
{ message: 'An error has occurred.' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
87
src/app/api/suggestions/route.ts
Normal file
87
src/app/api/suggestions/route.ts
Normal file
@ -0,0 +1,87 @@
|
||||
import generateSuggestions from '@/lib/chains/suggestionGeneratorAgent';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '@/lib/config';
|
||||
import { getAvailableChatModelProviders } from '@/lib/providers';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { ChatOllama } from '@langchain/ollama';
|
||||
|
||||
interface ChatModel {
|
||||
provider: string;
|
||||
model: string;
|
||||
ollamaContextWindow?: number;
|
||||
}
|
||||
|
||||
interface SuggestionsGenerationBody {
|
||||
chatHistory: any[];
|
||||
chatModel?: ChatModel;
|
||||
}
|
||||
|
||||
export const POST = async (req: Request) => {
|
||||
try {
|
||||
const body: SuggestionsGenerationBody = await req.json();
|
||||
|
||||
const chatHistory = body.chatHistory
|
||||
.map((msg: any) => {
|
||||
if (msg.role === 'user') {
|
||||
return new HumanMessage(msg.content);
|
||||
} else if (msg.role === 'assistant') {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
})
|
||||
.filter((msg) => msg !== undefined) as BaseMessage[];
|
||||
|
||||
const chatModelProviders = await getAvailableChatModelProviders();
|
||||
|
||||
const chatModelProvider =
|
||||
chatModelProviders[
|
||||
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
|
||||
];
|
||||
const chatModel =
|
||||
chatModelProvider[
|
||||
body.chatModel?.model || Object.keys(chatModelProvider)[0]
|
||||
];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
|
||||
if (body.chatModel?.provider === 'custom_openai') {
|
||||
llm = new ChatOpenAI({
|
||||
openAIApiKey: getCustomOpenaiApiKey(),
|
||||
modelName: getCustomOpenaiModelName(),
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
// Set context window size for Ollama models
|
||||
if (llm instanceof ChatOllama && body.chatModel?.provider === 'ollama') {
|
||||
llm.numCtx = body.chatModel.ollamaContextWindow || 2048;
|
||||
}
|
||||
}
|
||||
|
||||
if (!llm) {
|
||||
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
|
||||
}
|
||||
|
||||
const suggestions = await generateSuggestions(
|
||||
{
|
||||
chat_history: chatHistory,
|
||||
},
|
||||
llm,
|
||||
);
|
||||
|
||||
return Response.json({ suggestions }, { status: 200 });
|
||||
} catch (err) {
|
||||
console.error(`An error occurred while generating suggestions: ${err}`);
|
||||
return Response.json(
|
||||
{ message: 'An error occurred while generating suggestions' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
134
src/app/api/uploads/route.ts
Normal file
134
src/app/api/uploads/route.ts
Normal file
@ -0,0 +1,134 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import crypto from 'crypto';
|
||||
import { getAvailableEmbeddingModelProviders } from '@/lib/providers';
|
||||
import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
|
||||
import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
|
||||
import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
|
||||
import { Document } from 'langchain/document';
|
||||
|
||||
interface FileRes {
|
||||
fileName: string;
|
||||
fileExtension: string;
|
||||
fileId: string;
|
||||
}
|
||||
|
||||
const uploadDir = path.join(process.cwd(), 'uploads');
|
||||
|
||||
if (!fs.existsSync(uploadDir)) {
|
||||
fs.mkdirSync(uploadDir, { recursive: true });
|
||||
}
|
||||
|
||||
const splitter = new RecursiveCharacterTextSplitter({
|
||||
chunkSize: 500,
|
||||
chunkOverlap: 100,
|
||||
});
|
||||
|
||||
export async function POST(req: Request) {
|
||||
try {
|
||||
const formData = await req.formData();
|
||||
|
||||
const files = formData.getAll('files') as File[];
|
||||
const embedding_model = formData.get('embedding_model');
|
||||
const embedding_model_provider = formData.get('embedding_model_provider');
|
||||
|
||||
if (!embedding_model || !embedding_model_provider) {
|
||||
return NextResponse.json(
|
||||
{ message: 'Missing embedding model or provider' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const embeddingModels = await getAvailableEmbeddingModelProviders();
|
||||
const provider =
|
||||
embedding_model_provider ?? Object.keys(embeddingModels)[0];
|
||||
const embeddingModel =
|
||||
embedding_model ?? Object.keys(embeddingModels[provider as string])[0];
|
||||
|
||||
let embeddingsModel =
|
||||
embeddingModels[provider as string]?.[embeddingModel as string]?.model;
|
||||
if (!embeddingsModel) {
|
||||
return NextResponse.json(
|
||||
{ message: 'Invalid embedding model selected' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const processedFiles: FileRes[] = [];
|
||||
|
||||
await Promise.all(
|
||||
files.map(async (file: any) => {
|
||||
const fileExtension = file.name.split('.').pop();
|
||||
if (!['pdf', 'docx', 'txt'].includes(fileExtension!)) {
|
||||
return NextResponse.json(
|
||||
{ message: 'File type not supported' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const uniqueFileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
|
||||
const filePath = path.join(uploadDir, uniqueFileName);
|
||||
|
||||
const buffer = Buffer.from(await file.arrayBuffer());
|
||||
fs.writeFileSync(filePath, new Uint8Array(buffer));
|
||||
|
||||
let docs: any[] = [];
|
||||
if (fileExtension === 'pdf') {
|
||||
const loader = new PDFLoader(filePath);
|
||||
docs = await loader.load();
|
||||
} else if (fileExtension === 'docx') {
|
||||
const loader = new DocxLoader(filePath);
|
||||
docs = await loader.load();
|
||||
} else if (fileExtension === 'txt') {
|
||||
const text = fs.readFileSync(filePath, 'utf-8');
|
||||
docs = [
|
||||
new Document({ pageContent: text, metadata: { title: file.name } }),
|
||||
];
|
||||
}
|
||||
|
||||
const splitted = await splitter.splitDocuments(docs);
|
||||
|
||||
const extractedDataPath = filePath.replace(/\.\w+$/, '-extracted.json');
|
||||
fs.writeFileSync(
|
||||
extractedDataPath,
|
||||
JSON.stringify({
|
||||
title: file.name,
|
||||
contents: splitted.map((doc) => doc.pageContent),
|
||||
}),
|
||||
);
|
||||
|
||||
const embeddings = await embeddingsModel.embedDocuments(
|
||||
splitted.map((doc) => doc.pageContent),
|
||||
);
|
||||
const embeddingsDataPath = filePath.replace(
|
||||
/\.\w+$/,
|
||||
'-embeddings.json',
|
||||
);
|
||||
fs.writeFileSync(
|
||||
embeddingsDataPath,
|
||||
JSON.stringify({
|
||||
title: file.name,
|
||||
embeddings,
|
||||
}),
|
||||
);
|
||||
|
||||
processedFiles.push({
|
||||
fileName: file.name,
|
||||
fileExtension: fileExtension,
|
||||
fileId: uniqueFileName.replace(/\.\w+$/, ''),
|
||||
});
|
||||
}),
|
||||
);
|
||||
|
||||
return NextResponse.json({
|
||||
files: processedFiles,
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error uploading file:', error);
|
||||
return NextResponse.json(
|
||||
{ message: 'An error has occurred.' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
}
|
89
src/app/api/videos/route.ts
Normal file
89
src/app/api/videos/route.ts
Normal file
@ -0,0 +1,89 @@
|
||||
import handleVideoSearch from '@/lib/chains/videoSearchAgent';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '@/lib/config';
|
||||
import { getAvailableChatModelProviders } from '@/lib/providers';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { ChatOllama } from '@langchain/ollama';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
|
||||
interface ChatModel {
|
||||
provider: string;
|
||||
model: string;
|
||||
ollamaContextWindow?: number;
|
||||
}
|
||||
|
||||
interface VideoSearchBody {
|
||||
query: string;
|
||||
chatHistory: any[];
|
||||
chatModel?: ChatModel;
|
||||
}
|
||||
|
||||
export const POST = async (req: Request) => {
|
||||
try {
|
||||
const body: VideoSearchBody = await req.json();
|
||||
|
||||
const chatHistory = body.chatHistory
|
||||
.map((msg: any) => {
|
||||
if (msg.role === 'user') {
|
||||
return new HumanMessage(msg.content);
|
||||
} else if (msg.role === 'assistant') {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
})
|
||||
.filter((msg) => msg !== undefined) as BaseMessage[];
|
||||
|
||||
const chatModelProviders = await getAvailableChatModelProviders();
|
||||
|
||||
const chatModelProvider =
|
||||
chatModelProviders[
|
||||
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
|
||||
];
|
||||
const chatModel =
|
||||
chatModelProvider[
|
||||
body.chatModel?.model || Object.keys(chatModelProvider)[0]
|
||||
];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
|
||||
if (body.chatModel?.provider === 'custom_openai') {
|
||||
llm = new ChatOpenAI({
|
||||
openAIApiKey: getCustomOpenaiApiKey(),
|
||||
modelName: getCustomOpenaiModelName(),
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
// Set context window size for Ollama models
|
||||
if (llm instanceof ChatOllama && body.chatModel?.provider === 'ollama') {
|
||||
llm.numCtx = body.chatModel.ollamaContextWindow || 2048;
|
||||
}
|
||||
}
|
||||
|
||||
if (!llm) {
|
||||
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
|
||||
}
|
||||
|
||||
const videos = await handleVideoSearch(
|
||||
{
|
||||
chat_history: chatHistory,
|
||||
query: body.query,
|
||||
},
|
||||
llm,
|
||||
);
|
||||
|
||||
return Response.json({ videos }, { status: 200 });
|
||||
} catch (err) {
|
||||
console.error(`An error occurred while searching videos: ${err}`);
|
||||
return Response.json(
|
||||
{ message: 'An error occurred while searching videos' },
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
};
|
9
src/app/c/[chatId]/page.tsx
Normal file
9
src/app/c/[chatId]/page.tsx
Normal file
@ -0,0 +1,9 @@
|
||||
import ChatWindow from '@/components/ChatWindow';
|
||||
import React from 'react';
|
||||
|
||||
const Page = ({ params }: { params: Promise<{ chatId: string }> }) => {
|
||||
const { chatId } = React.use(params);
|
||||
return <ChatWindow id={chatId} />;
|
||||
};
|
||||
|
||||
export default Page;
|
@ -19,7 +19,7 @@ const Page = () => {
|
||||
useEffect(() => {
|
||||
const fetchData = async () => {
|
||||
try {
|
||||
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/discover`, {
|
||||
const res = await fetch(`/api/discover`, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
Before Width: | Height: | Size: 25 KiB After Width: | Height: | Size: 25 KiB |
@ -21,7 +21,7 @@ const Page = () => {
|
||||
const fetchChats = async () => {
|
||||
setLoading(true);
|
||||
|
||||
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/chats`, {
|
||||
const res = await fetch(`/api/chats`, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
@ -7,6 +7,7 @@ import { Switch } from '@headlessui/react';
|
||||
import ThemeSwitcher from '@/components/theme/Switcher';
|
||||
import { ImagesIcon, VideoIcon } from 'lucide-react';
|
||||
import Link from 'next/link';
|
||||
import { PROVIDER_METADATA } from '@/lib/providers';
|
||||
|
||||
interface SettingsType {
|
||||
chatModelProviders: {
|
||||
@ -20,9 +21,12 @@ interface SettingsType {
|
||||
anthropicApiKey: string;
|
||||
geminiApiKey: string;
|
||||
ollamaApiUrl: string;
|
||||
lmStudioApiUrl: string;
|
||||
deepseekApiKey: string;
|
||||
customOpenaiApiKey: string;
|
||||
customOpenaiApiUrl: string;
|
||||
customOpenaiModelName: string;
|
||||
ollamaContextWindow: number;
|
||||
}
|
||||
|
||||
interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
|
||||
@ -54,6 +58,38 @@ const Input = ({ className, isSaving, onSave, ...restProps }: InputProps) => {
|
||||
);
|
||||
};
|
||||
|
||||
interface TextareaProps extends React.InputHTMLAttributes<HTMLTextAreaElement> {
|
||||
isSaving?: boolean;
|
||||
onSave?: (value: string) => void;
|
||||
}
|
||||
|
||||
const Textarea = ({
|
||||
className,
|
||||
isSaving,
|
||||
onSave,
|
||||
...restProps
|
||||
}: TextareaProps) => {
|
||||
return (
|
||||
<div className="relative">
|
||||
<textarea
|
||||
placeholder="Any special instructions for the LLM"
|
||||
className="placeholder:text-sm text-sm w-full flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors"
|
||||
rows={4}
|
||||
onBlur={(e) => onSave?.(e.target.value)}
|
||||
{...restProps}
|
||||
/>
|
||||
{isSaving && (
|
||||
<div className="absolute right-3 top-3">
|
||||
<Loader2
|
||||
size={16}
|
||||
className="animate-spin text-black/70 dark:text-white/70"
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
const Select = ({
|
||||
className,
|
||||
options,
|
||||
@ -111,18 +147,25 @@ const Page = () => {
|
||||
const [isLoading, setIsLoading] = useState(false);
|
||||
const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
|
||||
const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
|
||||
const [systemInstructions, setSystemInstructions] = useState<string>('');
|
||||
const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
|
||||
const [contextWindowSize, setContextWindowSize] = useState(2048);
|
||||
const [isCustomContextWindow, setIsCustomContextWindow] = useState(false);
|
||||
const predefinedContextSizes = [
|
||||
1024, 2048, 3072, 4096, 8192, 16384, 32768, 65536, 131072,
|
||||
];
|
||||
|
||||
useEffect(() => {
|
||||
const fetchConfig = async () => {
|
||||
setIsLoading(true);
|
||||
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
|
||||
const res = await fetch(`/api/config`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
});
|
||||
|
||||
const data = (await res.json()) as SettingsType;
|
||||
|
||||
setConfig(data);
|
||||
|
||||
const chatModelProvidersKeys = Object.keys(data.chatModelProviders || {});
|
||||
@ -171,6 +214,15 @@ const Page = () => {
|
||||
setAutomaticVideoSearch(
|
||||
localStorage.getItem('autoVideoSearch') === 'true',
|
||||
);
|
||||
const storedContextWindow = parseInt(
|
||||
localStorage.getItem('ollamaContextWindow') ?? '2048',
|
||||
);
|
||||
setContextWindowSize(storedContextWindow);
|
||||
setIsCustomContextWindow(
|
||||
!predefinedContextSizes.includes(storedContextWindow),
|
||||
);
|
||||
|
||||
setSystemInstructions(localStorage.getItem('systemInstructions')!);
|
||||
|
||||
setIsLoading(false);
|
||||
};
|
||||
@ -187,16 +239,13 @@ const Page = () => {
|
||||
[key]: value,
|
||||
} as SettingsType;
|
||||
|
||||
const response = await fetch(
|
||||
`${process.env.NEXT_PUBLIC_API_URL}/config`,
|
||||
{
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify(updatedConfig),
|
||||
const response = await fetch(`/api/config`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
);
|
||||
body: JSON.stringify(updatedConfig),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error('Failed to update config');
|
||||
@ -208,7 +257,7 @@ const Page = () => {
|
||||
key.toLowerCase().includes('api') ||
|
||||
key.toLowerCase().includes('url')
|
||||
) {
|
||||
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/config`, {
|
||||
const res = await fetch(`/api/config`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
@ -223,11 +272,11 @@ const Page = () => {
|
||||
setChatModels(data.chatModelProviders || {});
|
||||
setEmbeddingModels(data.embeddingModelProviders || {});
|
||||
|
||||
const currentProvider = selectedChatModelProvider;
|
||||
const newProviders = Object.keys(data.chatModelProviders || {});
|
||||
const currentChatProvider = selectedChatModelProvider;
|
||||
const newChatProviders = Object.keys(data.chatModelProviders || {});
|
||||
|
||||
if (!currentProvider && newProviders.length > 0) {
|
||||
const firstProvider = newProviders[0];
|
||||
if (!currentChatProvider && newChatProviders.length > 0) {
|
||||
const firstProvider = newChatProviders[0];
|
||||
const firstModel = data.chatModelProviders[firstProvider]?.[0]?.name;
|
||||
|
||||
if (firstModel) {
|
||||
@ -237,11 +286,11 @@ const Page = () => {
|
||||
localStorage.setItem('chatModel', firstModel);
|
||||
}
|
||||
} else if (
|
||||
currentProvider &&
|
||||
currentChatProvider &&
|
||||
(!data.chatModelProviders ||
|
||||
!data.chatModelProviders[currentProvider] ||
|
||||
!Array.isArray(data.chatModelProviders[currentProvider]) ||
|
||||
data.chatModelProviders[currentProvider].length === 0)
|
||||
!data.chatModelProviders[currentChatProvider] ||
|
||||
!Array.isArray(data.chatModelProviders[currentChatProvider]) ||
|
||||
data.chatModelProviders[currentChatProvider].length === 0)
|
||||
) {
|
||||
const firstValidProvider = Object.entries(
|
||||
data.chatModelProviders || {},
|
||||
@ -267,6 +316,55 @@ const Page = () => {
|
||||
}
|
||||
}
|
||||
|
||||
const currentEmbeddingProvider = selectedEmbeddingModelProvider;
|
||||
const newEmbeddingProviders = Object.keys(
|
||||
data.embeddingModelProviders || {},
|
||||
);
|
||||
|
||||
if (!currentEmbeddingProvider && newEmbeddingProviders.length > 0) {
|
||||
const firstProvider = newEmbeddingProviders[0];
|
||||
const firstModel =
|
||||
data.embeddingModelProviders[firstProvider]?.[0]?.name;
|
||||
|
||||
if (firstModel) {
|
||||
setSelectedEmbeddingModelProvider(firstProvider);
|
||||
setSelectedEmbeddingModel(firstModel);
|
||||
localStorage.setItem('embeddingModelProvider', firstProvider);
|
||||
localStorage.setItem('embeddingModel', firstModel);
|
||||
}
|
||||
} else if (
|
||||
currentEmbeddingProvider &&
|
||||
(!data.embeddingModelProviders ||
|
||||
!data.embeddingModelProviders[currentEmbeddingProvider] ||
|
||||
!Array.isArray(
|
||||
data.embeddingModelProviders[currentEmbeddingProvider],
|
||||
) ||
|
||||
data.embeddingModelProviders[currentEmbeddingProvider].length === 0)
|
||||
) {
|
||||
const firstValidProvider = Object.entries(
|
||||
data.embeddingModelProviders || {},
|
||||
).find(
|
||||
([_, models]) => Array.isArray(models) && models.length > 0,
|
||||
)?.[0];
|
||||
|
||||
if (firstValidProvider) {
|
||||
setSelectedEmbeddingModelProvider(firstValidProvider);
|
||||
setSelectedEmbeddingModel(
|
||||
data.embeddingModelProviders[firstValidProvider][0].name,
|
||||
);
|
||||
localStorage.setItem('embeddingModelProvider', firstValidProvider);
|
||||
localStorage.setItem(
|
||||
'embeddingModel',
|
||||
data.embeddingModelProviders[firstValidProvider][0].name,
|
||||
);
|
||||
} else {
|
||||
setSelectedEmbeddingModelProvider(null);
|
||||
setSelectedEmbeddingModel(null);
|
||||
localStorage.removeItem('embeddingModelProvider');
|
||||
localStorage.removeItem('embeddingModel');
|
||||
}
|
||||
}
|
||||
|
||||
setConfig(data);
|
||||
}
|
||||
|
||||
@ -278,6 +376,14 @@ const Page = () => {
|
||||
localStorage.setItem('chatModelProvider', value);
|
||||
} else if (key === 'chatModel') {
|
||||
localStorage.setItem('chatModel', value);
|
||||
} else if (key === 'embeddingModelProvider') {
|
||||
localStorage.setItem('embeddingModelProvider', value);
|
||||
} else if (key === 'embeddingModel') {
|
||||
localStorage.setItem('embeddingModel', value);
|
||||
} else if (key === 'ollamaContextWindow') {
|
||||
localStorage.setItem('ollamaContextWindow', value.toString());
|
||||
} else if (key === 'systemInstructions') {
|
||||
localStorage.setItem('systemInstructions', value);
|
||||
}
|
||||
} catch (err) {
|
||||
console.error('Failed to save:', err);
|
||||
@ -423,6 +529,19 @@ const Page = () => {
|
||||
</div>
|
||||
</SettingsSection>
|
||||
|
||||
<SettingsSection title="System Instructions">
|
||||
<div className="flex flex-col space-y-4">
|
||||
<Textarea
|
||||
value={systemInstructions}
|
||||
isSaving={savingStates['systemInstructions']}
|
||||
onChange={(e) => {
|
||||
setSystemInstructions(e.target.value);
|
||||
}}
|
||||
onSave={(value) => saveConfig('systemInstructions', value)}
|
||||
/>
|
||||
</div>
|
||||
</SettingsSection>
|
||||
|
||||
<SettingsSection title="Model Settings">
|
||||
{config.chatModelProviders && (
|
||||
<div className="flex flex-col space-y-4">
|
||||
@ -436,7 +555,6 @@ const Page = () => {
|
||||
const value = e.target.value;
|
||||
setSelectedChatModelProvider(value);
|
||||
saveConfig('chatModelProvider', value);
|
||||
// Auto-select first model of new provider
|
||||
const firstModel =
|
||||
config.chatModelProviders[value]?.[0]?.name;
|
||||
if (firstModel) {
|
||||
@ -448,8 +566,9 @@ const Page = () => {
|
||||
(provider) => ({
|
||||
value: provider,
|
||||
label:
|
||||
(PROVIDER_METADATA as any)[provider]?.displayName ||
|
||||
provider.charAt(0).toUpperCase() +
|
||||
provider.slice(1),
|
||||
provider.slice(1),
|
||||
}),
|
||||
)}
|
||||
/>
|
||||
@ -496,6 +615,78 @@ const Page = () => {
|
||||
];
|
||||
})()}
|
||||
/>
|
||||
{selectedChatModelProvider === 'ollama' && (
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-black/70 dark:text-white/70 text-sm">
|
||||
Chat Context Window Size
|
||||
</p>
|
||||
<Select
|
||||
value={
|
||||
isCustomContextWindow
|
||||
? 'custom'
|
||||
: contextWindowSize.toString()
|
||||
}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value;
|
||||
if (value === 'custom') {
|
||||
setIsCustomContextWindow(true);
|
||||
} else {
|
||||
setIsCustomContextWindow(false);
|
||||
const numValue = parseInt(value);
|
||||
setContextWindowSize(numValue);
|
||||
setConfig((prev) => ({
|
||||
...prev!,
|
||||
ollamaContextWindow: numValue,
|
||||
}));
|
||||
saveConfig('ollamaContextWindow', numValue);
|
||||
}
|
||||
}}
|
||||
options={[
|
||||
...predefinedContextSizes.map((size) => ({
|
||||
value: size.toString(),
|
||||
label: `${size.toLocaleString()} tokens`,
|
||||
})),
|
||||
{ value: 'custom', label: 'Custom...' },
|
||||
]}
|
||||
/>
|
||||
{isCustomContextWindow && (
|
||||
<div className="mt-2">
|
||||
<Input
|
||||
type="number"
|
||||
min={512}
|
||||
value={contextWindowSize}
|
||||
placeholder="Custom context window size (minimum 512)"
|
||||
isSaving={savingStates['ollamaContextWindow']}
|
||||
onChange={(e) => {
|
||||
// Allow any value to be typed
|
||||
const value =
|
||||
parseInt(e.target.value) ||
|
||||
contextWindowSize;
|
||||
setContextWindowSize(value);
|
||||
}}
|
||||
onSave={(value) => {
|
||||
// Validate only when saving
|
||||
const numValue = Math.max(
|
||||
512,
|
||||
parseInt(value) || 2048,
|
||||
);
|
||||
setContextWindowSize(numValue);
|
||||
setConfig((prev) => ({
|
||||
...prev!,
|
||||
ollamaContextWindow: numValue,
|
||||
}));
|
||||
saveConfig('ollamaContextWindow', numValue);
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
<p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
|
||||
{isCustomContextWindow
|
||||
? 'Adjust the context window size for Ollama models (minimum 512 tokens)'
|
||||
: 'Adjust the context window size for Ollama models'}
|
||||
</p>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
@ -511,12 +702,16 @@ const Page = () => {
|
||||
<Input
|
||||
type="text"
|
||||
placeholder="Model name"
|
||||
defaultValue={config.customOpenaiModelName}
|
||||
onChange={(e) =>
|
||||
setConfig({
|
||||
...config,
|
||||
value={config.customOpenaiModelName}
|
||||
isSaving={savingStates['customOpenaiModelName']}
|
||||
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
|
||||
setConfig((prev) => ({
|
||||
...prev!,
|
||||
customOpenaiModelName: e.target.value,
|
||||
})
|
||||
}));
|
||||
}}
|
||||
onSave={(value) =>
|
||||
saveConfig('customOpenaiModelName', value)
|
||||
}
|
||||
/>
|
||||
</div>
|
||||
@ -527,12 +722,16 @@ const Page = () => {
|
||||
<Input
|
||||
type="text"
|
||||
placeholder="Custom OpenAI API Key"
|
||||
defaultValue={config.customOpenaiApiKey}
|
||||
onChange={(e) =>
|
||||
setConfig({
|
||||
...config,
|
||||
value={config.customOpenaiApiKey}
|
||||
isSaving={savingStates['customOpenaiApiKey']}
|
||||
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
|
||||
setConfig((prev) => ({
|
||||
...prev!,
|
||||
customOpenaiApiKey: e.target.value,
|
||||
})
|
||||
}));
|
||||
}}
|
||||
onSave={(value) =>
|
||||
saveConfig('customOpenaiApiKey', value)
|
||||
}
|
||||
/>
|
||||
</div>
|
||||
@ -543,17 +742,97 @@ const Page = () => {
|
||||
<Input
|
||||
type="text"
|
||||
placeholder="Custom OpenAI Base URL"
|
||||
defaultValue={config.customOpenaiApiUrl}
|
||||
onChange={(e) =>
|
||||
setConfig({
|
||||
...config,
|
||||
value={config.customOpenaiApiUrl}
|
||||
isSaving={savingStates['customOpenaiApiUrl']}
|
||||
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
|
||||
setConfig((prev) => ({
|
||||
...prev!,
|
||||
customOpenaiApiUrl: e.target.value,
|
||||
})
|
||||
}));
|
||||
}}
|
||||
onSave={(value) =>
|
||||
saveConfig('customOpenaiApiUrl', value)
|
||||
}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{config.embeddingModelProviders && (
|
||||
<div className="flex flex-col space-y-4 mt-4 pt-4 border-t border-light-200 dark:border-dark-200">
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-black/70 dark:text-white/70 text-sm">
|
||||
Embedding Model Provider
|
||||
</p>
|
||||
<Select
|
||||
value={selectedEmbeddingModelProvider ?? undefined}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value;
|
||||
setSelectedEmbeddingModelProvider(value);
|
||||
saveConfig('embeddingModelProvider', value);
|
||||
const firstModel =
|
||||
config.embeddingModelProviders[value]?.[0]?.name;
|
||||
if (firstModel) {
|
||||
setSelectedEmbeddingModel(firstModel);
|
||||
saveConfig('embeddingModel', firstModel);
|
||||
}
|
||||
}}
|
||||
options={Object.keys(config.embeddingModelProviders).map(
|
||||
(provider) => ({
|
||||
value: provider,
|
||||
label:
|
||||
(PROVIDER_METADATA as any)[provider]?.displayName ||
|
||||
provider.charAt(0).toUpperCase() +
|
||||
provider.slice(1),
|
||||
}),
|
||||
)}
|
||||
/>
|
||||
</div>
|
||||
|
||||
{selectedEmbeddingModelProvider && (
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-black/70 dark:text-white/70 text-sm">
|
||||
Embedding Model
|
||||
</p>
|
||||
<Select
|
||||
value={selectedEmbeddingModel ?? undefined}
|
||||
onChange={(e) => {
|
||||
const value = e.target.value;
|
||||
setSelectedEmbeddingModel(value);
|
||||
saveConfig('embeddingModel', value);
|
||||
}}
|
||||
options={(() => {
|
||||
const embeddingModelProvider =
|
||||
config.embeddingModelProviders[
|
||||
selectedEmbeddingModelProvider
|
||||
];
|
||||
return embeddingModelProvider
|
||||
? embeddingModelProvider.length > 0
|
||||
? embeddingModelProvider.map((model) => ({
|
||||
value: model.name,
|
||||
label: model.displayName,
|
||||
}))
|
||||
: [
|
||||
{
|
||||
value: '',
|
||||
label: 'No models available',
|
||||
disabled: true,
|
||||
},
|
||||
]
|
||||
: [
|
||||
{
|
||||
value: '',
|
||||
label:
|
||||
'Invalid provider, please check backend logs',
|
||||
disabled: true,
|
||||
},
|
||||
];
|
||||
})()}
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</SettingsSection>
|
||||
|
||||
<SettingsSection title="API Keys">
|
||||
@ -652,6 +931,44 @@ const Page = () => {
|
||||
onSave={(value) => saveConfig('geminiApiKey', value)}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-black/70 dark:text-white/70 text-sm">
|
||||
Deepseek API Key
|
||||
</p>
|
||||
<Input
|
||||
type="text"
|
||||
placeholder="Deepseek API Key"
|
||||
value={config.deepseekApiKey}
|
||||
isSaving={savingStates['deepseekApiKey']}
|
||||
onChange={(e) => {
|
||||
setConfig((prev) => ({
|
||||
...prev!,
|
||||
deepseekApiKey: e.target.value,
|
||||
}));
|
||||
}}
|
||||
onSave={(value) => saveConfig('deepseekApiKey', value)}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-black/70 dark:text-white/70 text-sm">
|
||||
LM Studio API URL
|
||||
</p>
|
||||
<Input
|
||||
type="text"
|
||||
placeholder="LM Studio API URL"
|
||||
value={config.lmStudioApiUrl}
|
||||
isSaving={savingStates['lmStudioApiUrl']}
|
||||
onChange={(e) => {
|
||||
setConfig((prev) => ({
|
||||
...prev!,
|
||||
lmStudioApiUrl: e.target.value,
|
||||
}));
|
||||
}}
|
||||
onSave={(value) => saveConfig('lmStudioApiUrl', value)}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</SettingsSection>
|
||||
</div>
|
@ -16,6 +16,8 @@ const Chat = ({
|
||||
setFileIds,
|
||||
files,
|
||||
setFiles,
|
||||
optimizationMode,
|
||||
setOptimizationMode,
|
||||
}: {
|
||||
messages: Message[];
|
||||
sendMessage: (message: string) => void;
|
||||
@ -26,6 +28,8 @@ const Chat = ({
|
||||
setFileIds: (fileIds: string[]) => void;
|
||||
files: File[];
|
||||
setFiles: (files: File[]) => void;
|
||||
optimizationMode: string;
|
||||
setOptimizationMode: (mode: string) => void;
|
||||
}) => {
|
||||
const [dividerWidth, setDividerWidth] = useState(0);
|
||||
const dividerRef = useRef<HTMLDivElement | null>(null);
|
||||
@ -48,11 +52,17 @@ const Chat = ({
|
||||
});
|
||||
|
||||
useEffect(() => {
|
||||
messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
|
||||
const scroll = () => {
|
||||
messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
|
||||
};
|
||||
|
||||
if (messages.length === 1) {
|
||||
document.title = `${messages[0].content.substring(0, 30)} - Perplexica`;
|
||||
}
|
||||
|
||||
if (messages[messages.length - 1]?.role == 'user') {
|
||||
scroll();
|
||||
}
|
||||
}, [messages]);
|
||||
|
||||
return (
|
||||
@ -93,6 +103,8 @@ const Chat = ({
|
||||
setFileIds={setFileIds}
|
||||
files={files}
|
||||
setFiles={setFiles}
|
||||
optimizationMode={optimizationMode}
|
||||
setOptimizationMode={setOptimizationMode}
|
||||
/>
|
||||
</div>
|
||||
)}
|
@ -29,280 +29,154 @@ export interface File {
|
||||
fileId: string;
|
||||
}
|
||||
|
||||
const useSocket = (
|
||||
url: string,
|
||||
setIsWSReady: (ready: boolean) => void,
|
||||
setError: (error: boolean) => void,
|
||||
interface ChatModelProvider {
|
||||
name: string;
|
||||
provider: string;
|
||||
}
|
||||
|
||||
interface EmbeddingModelProvider {
|
||||
name: string;
|
||||
provider: string;
|
||||
}
|
||||
|
||||
const checkConfig = async (
|
||||
setChatModelProvider: (provider: ChatModelProvider) => void,
|
||||
setEmbeddingModelProvider: (provider: EmbeddingModelProvider) => void,
|
||||
setIsConfigReady: (ready: boolean) => void,
|
||||
setHasError: (hasError: boolean) => void,
|
||||
) => {
|
||||
const wsRef = useRef<WebSocket | null>(null);
|
||||
const reconnectTimeoutRef = useRef<NodeJS.Timeout>();
|
||||
const retryCountRef = useRef(0);
|
||||
const isCleaningUpRef = useRef(false);
|
||||
const MAX_RETRIES = 3;
|
||||
const INITIAL_BACKOFF = 1000; // 1 second
|
||||
const isConnectionErrorRef = useRef(false);
|
||||
try {
|
||||
let chatModel = localStorage.getItem('chatModel');
|
||||
let chatModelProvider = localStorage.getItem('chatModelProvider');
|
||||
let embeddingModel = localStorage.getItem('embeddingModel');
|
||||
let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
|
||||
|
||||
const getBackoffDelay = (retryCount: number) => {
|
||||
return Math.min(INITIAL_BACKOFF * Math.pow(2, retryCount), 10000); // Cap at 10 seconds
|
||||
};
|
||||
const autoImageSearch = localStorage.getItem('autoImageSearch');
|
||||
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
|
||||
|
||||
useEffect(() => {
|
||||
const connectWs = async () => {
|
||||
if (wsRef.current?.readyState === WebSocket.OPEN) {
|
||||
wsRef.current.close();
|
||||
if (!autoImageSearch) {
|
||||
localStorage.setItem('autoImageSearch', 'true');
|
||||
}
|
||||
|
||||
if (!autoVideoSearch) {
|
||||
localStorage.setItem('autoVideoSearch', 'false');
|
||||
}
|
||||
|
||||
const providers = await fetch(`/api/models`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
}).then(async (res) => {
|
||||
if (!res.ok)
|
||||
throw new Error(
|
||||
`Failed to fetch models: ${res.status} ${res.statusText}`,
|
||||
);
|
||||
return res.json();
|
||||
});
|
||||
|
||||
if (
|
||||
!chatModel ||
|
||||
!chatModelProvider ||
|
||||
!embeddingModel ||
|
||||
!embeddingModelProvider
|
||||
) {
|
||||
if (!chatModel || !chatModelProvider) {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
|
||||
chatModelProvider =
|
||||
chatModelProvider || Object.keys(chatModelProviders)[0];
|
||||
|
||||
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
|
||||
|
||||
if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
|
||||
return toast.error('No chat models available');
|
||||
}
|
||||
|
||||
try {
|
||||
let chatModel = localStorage.getItem('chatModel');
|
||||
let chatModelProvider = localStorage.getItem('chatModelProvider');
|
||||
let embeddingModel = localStorage.getItem('embeddingModel');
|
||||
let embeddingModelProvider = localStorage.getItem(
|
||||
'embeddingModelProvider',
|
||||
);
|
||||
|
||||
const autoImageSearch = localStorage.getItem('autoImageSearch');
|
||||
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
|
||||
|
||||
if (!autoImageSearch) {
|
||||
localStorage.setItem('autoImageSearch', 'true');
|
||||
}
|
||||
|
||||
if (!autoVideoSearch) {
|
||||
localStorage.setItem('autoVideoSearch', 'false');
|
||||
}
|
||||
|
||||
const providers = await fetch(
|
||||
`${process.env.NEXT_PUBLIC_API_URL}/models`,
|
||||
{
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
},
|
||||
).then(async (res) => {
|
||||
if (!res.ok)
|
||||
throw new Error(
|
||||
`Failed to fetch models: ${res.status} ${res.statusText}`,
|
||||
);
|
||||
return res.json();
|
||||
});
|
||||
if (!embeddingModel || !embeddingModelProvider) {
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (
|
||||
!chatModel ||
|
||||
!chatModelProvider ||
|
||||
!embeddingModel ||
|
||||
!embeddingModelProvider
|
||||
) {
|
||||
if (!chatModel || !chatModelProvider) {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
!embeddingModelProviders ||
|
||||
Object.keys(embeddingModelProviders).length === 0
|
||||
)
|
||||
return toast.error('No embedding models available');
|
||||
|
||||
chatModelProvider =
|
||||
chatModelProvider || Object.keys(chatModelProviders)[0];
|
||||
|
||||
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
|
||||
|
||||
if (
|
||||
!chatModelProviders ||
|
||||
Object.keys(chatModelProviders).length === 0
|
||||
)
|
||||
return toast.error('No chat models available');
|
||||
}
|
||||
|
||||
if (!embeddingModel || !embeddingModelProvider) {
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (
|
||||
!embeddingModelProviders ||
|
||||
Object.keys(embeddingModelProviders).length === 0
|
||||
)
|
||||
return toast.error('No embedding models available');
|
||||
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
}
|
||||
|
||||
localStorage.setItem('chatModel', chatModel!);
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
localStorage.setItem('embeddingModel', embeddingModel!);
|
||||
localStorage.setItem(
|
||||
'embeddingModelProvider',
|
||||
embeddingModelProvider,
|
||||
);
|
||||
} else {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (
|
||||
Object.keys(chatModelProviders).length > 0 &&
|
||||
!chatModelProviders[chatModelProvider]
|
||||
) {
|
||||
const chatModelProvidersKeys = Object.keys(chatModelProviders);
|
||||
chatModelProvider =
|
||||
chatModelProvidersKeys.find(
|
||||
(key) => Object.keys(chatModelProviders[key]).length > 0,
|
||||
) || chatModelProvidersKeys[0];
|
||||
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
}
|
||||
|
||||
if (
|
||||
chatModelProvider &&
|
||||
!chatModelProviders[chatModelProvider][chatModel]
|
||||
) {
|
||||
chatModel = Object.keys(
|
||||
chatModelProviders[
|
||||
Object.keys(chatModelProviders[chatModelProvider]).length > 0
|
||||
? chatModelProvider
|
||||
: Object.keys(chatModelProviders)[0]
|
||||
],
|
||||
)[0];
|
||||
localStorage.setItem('chatModel', chatModel);
|
||||
}
|
||||
|
||||
if (
|
||||
Object.keys(embeddingModelProviders).length > 0 &&
|
||||
!embeddingModelProviders[embeddingModelProvider]
|
||||
) {
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
localStorage.setItem(
|
||||
'embeddingModelProvider',
|
||||
embeddingModelProvider,
|
||||
);
|
||||
}
|
||||
|
||||
if (
|
||||
embeddingModelProvider &&
|
||||
!embeddingModelProviders[embeddingModelProvider][embeddingModel]
|
||||
) {
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
localStorage.setItem('embeddingModel', embeddingModel);
|
||||
}
|
||||
}
|
||||
|
||||
const wsURL = new URL(url);
|
||||
const searchParams = new URLSearchParams({});
|
||||
|
||||
searchParams.append('chatModel', chatModel!);
|
||||
searchParams.append('chatModelProvider', chatModelProvider);
|
||||
|
||||
if (chatModelProvider === 'custom_openai') {
|
||||
searchParams.append(
|
||||
'openAIApiKey',
|
||||
localStorage.getItem('openAIApiKey')!,
|
||||
);
|
||||
searchParams.append(
|
||||
'openAIBaseURL',
|
||||
localStorage.getItem('openAIBaseURL')!,
|
||||
);
|
||||
}
|
||||
|
||||
searchParams.append('embeddingModel', embeddingModel!);
|
||||
searchParams.append('embeddingModelProvider', embeddingModelProvider);
|
||||
|
||||
wsURL.search = searchParams.toString();
|
||||
|
||||
const ws = new WebSocket(wsURL.toString());
|
||||
wsRef.current = ws;
|
||||
|
||||
const timeoutId = setTimeout(() => {
|
||||
if (ws.readyState !== 1) {
|
||||
toast.error(
|
||||
'Failed to connect to the server. Please try again later.',
|
||||
);
|
||||
}
|
||||
}, 10000);
|
||||
|
||||
ws.addEventListener('message', (e) => {
|
||||
const data = JSON.parse(e.data);
|
||||
if (data.type === 'signal' && data.data === 'open') {
|
||||
const interval = setInterval(() => {
|
||||
if (ws.readyState === 1) {
|
||||
setIsWSReady(true);
|
||||
setError(false);
|
||||
if (retryCountRef.current > 0) {
|
||||
toast.success('Connection restored.');
|
||||
}
|
||||
retryCountRef.current = 0;
|
||||
clearInterval(interval);
|
||||
}
|
||||
}, 5);
|
||||
clearTimeout(timeoutId);
|
||||
console.debug(new Date(), 'ws:connected');
|
||||
}
|
||||
if (data.type === 'error') {
|
||||
isConnectionErrorRef.current = true;
|
||||
setError(true);
|
||||
toast.error(data.data);
|
||||
}
|
||||
});
|
||||
|
||||
ws.onerror = () => {
|
||||
clearTimeout(timeoutId);
|
||||
setIsWSReady(false);
|
||||
toast.error('WebSocket connection error.');
|
||||
};
|
||||
|
||||
ws.onclose = () => {
|
||||
clearTimeout(timeoutId);
|
||||
setIsWSReady(false);
|
||||
console.debug(new Date(), 'ws:disconnected');
|
||||
if (!isCleaningUpRef.current && !isConnectionErrorRef.current) {
|
||||
toast.error('Connection lost. Attempting to reconnect...');
|
||||
attemptReconnect();
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
console.debug(new Date(), 'ws:error', error);
|
||||
setIsWSReady(false);
|
||||
attemptReconnect();
|
||||
}
|
||||
};
|
||||
|
||||
const attemptReconnect = () => {
|
||||
retryCountRef.current += 1;
|
||||
|
||||
if (retryCountRef.current > MAX_RETRIES) {
|
||||
console.debug(new Date(), 'ws:max_retries');
|
||||
setError(true);
|
||||
toast.error(
|
||||
'Unable to connect to server after multiple attempts. Please refresh the page to try again.',
|
||||
);
|
||||
return;
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
}
|
||||
|
||||
const backoffDelay = getBackoffDelay(retryCountRef.current);
|
||||
console.debug(
|
||||
new Date(),
|
||||
`ws:retry attempt=${retryCountRef.current}/${MAX_RETRIES} delay=${backoffDelay}ms`,
|
||||
);
|
||||
localStorage.setItem('chatModel', chatModel!);
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
localStorage.setItem('embeddingModel', embeddingModel!);
|
||||
localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
|
||||
} else {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (reconnectTimeoutRef.current) {
|
||||
clearTimeout(reconnectTimeoutRef.current);
|
||||
if (
|
||||
Object.keys(chatModelProviders).length > 0 &&
|
||||
!chatModelProviders[chatModelProvider]
|
||||
) {
|
||||
const chatModelProvidersKeys = Object.keys(chatModelProviders);
|
||||
chatModelProvider =
|
||||
chatModelProvidersKeys.find(
|
||||
(key) => Object.keys(chatModelProviders[key]).length > 0,
|
||||
) || chatModelProvidersKeys[0];
|
||||
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
}
|
||||
|
||||
reconnectTimeoutRef.current = setTimeout(() => {
|
||||
connectWs();
|
||||
}, backoffDelay);
|
||||
};
|
||||
|
||||
connectWs();
|
||||
|
||||
return () => {
|
||||
if (reconnectTimeoutRef.current) {
|
||||
clearTimeout(reconnectTimeoutRef.current);
|
||||
if (
|
||||
chatModelProvider &&
|
||||
!chatModelProviders[chatModelProvider][chatModel]
|
||||
) {
|
||||
chatModel = Object.keys(
|
||||
chatModelProviders[
|
||||
Object.keys(chatModelProviders[chatModelProvider]).length > 0
|
||||
? chatModelProvider
|
||||
: Object.keys(chatModelProviders)[0]
|
||||
],
|
||||
)[0];
|
||||
localStorage.setItem('chatModel', chatModel);
|
||||
}
|
||||
if (wsRef.current?.readyState === WebSocket.OPEN) {
|
||||
wsRef.current.close();
|
||||
isCleaningUpRef.current = true;
|
||||
console.debug(new Date(), 'ws:cleanup');
|
||||
}
|
||||
};
|
||||
}, [url, setIsWSReady, setError]);
|
||||
|
||||
return wsRef.current;
|
||||
if (
|
||||
Object.keys(embeddingModelProviders).length > 0 &&
|
||||
!embeddingModelProviders[embeddingModelProvider]
|
||||
) {
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
|
||||
}
|
||||
|
||||
if (
|
||||
embeddingModelProvider &&
|
||||
!embeddingModelProviders[embeddingModelProvider][embeddingModel]
|
||||
) {
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
localStorage.setItem('embeddingModel', embeddingModel);
|
||||
}
|
||||
}
|
||||
|
||||
setChatModelProvider({
|
||||
name: chatModel!,
|
||||
provider: chatModelProvider,
|
||||
});
|
||||
|
||||
setEmbeddingModelProvider({
|
||||
name: embeddingModel!,
|
||||
provider: embeddingModelProvider,
|
||||
});
|
||||
|
||||
setIsConfigReady(true);
|
||||
} catch (err) {
|
||||
console.error('An error occurred while checking the configuration:', err);
|
||||
setIsConfigReady(false);
|
||||
setHasError(true);
|
||||
}
|
||||
};
|
||||
|
||||
const loadMessages = async (
|
||||
@ -315,15 +189,12 @@ const loadMessages = async (
|
||||
setFiles: (files: File[]) => void,
|
||||
setFileIds: (fileIds: string[]) => void,
|
||||
) => {
|
||||
const res = await fetch(
|
||||
`${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
|
||||
{
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
const res = await fetch(`/api/chats/${chatId}`, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
if (res.status === 404) {
|
||||
setNotFound(true);
|
||||
@ -373,15 +244,32 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
const [chatId, setChatId] = useState<string | undefined>(id);
|
||||
const [newChatCreated, setNewChatCreated] = useState(false);
|
||||
|
||||
const [chatModelProvider, setChatModelProvider] = useState<ChatModelProvider>(
|
||||
{
|
||||
name: '',
|
||||
provider: '',
|
||||
},
|
||||
);
|
||||
|
||||
const [embeddingModelProvider, setEmbeddingModelProvider] =
|
||||
useState<EmbeddingModelProvider>({
|
||||
name: '',
|
||||
provider: '',
|
||||
});
|
||||
|
||||
const [isConfigReady, setIsConfigReady] = useState(false);
|
||||
const [hasError, setHasError] = useState(false);
|
||||
const [isReady, setIsReady] = useState(false);
|
||||
|
||||
const [isWSReady, setIsWSReady] = useState(false);
|
||||
const ws = useSocket(
|
||||
process.env.NEXT_PUBLIC_WS_URL!,
|
||||
setIsWSReady,
|
||||
setHasError,
|
||||
);
|
||||
useEffect(() => {
|
||||
checkConfig(
|
||||
setChatModelProvider,
|
||||
setEmbeddingModelProvider,
|
||||
setIsConfigReady,
|
||||
setHasError,
|
||||
);
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, []);
|
||||
|
||||
const [loading, setLoading] = useState(false);
|
||||
const [messageAppeared, setMessageAppeared] = useState(false);
|
||||
@ -399,7 +287,15 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
|
||||
const [notFound, setNotFound] = useState(false);
|
||||
|
||||
const [isSettingsOpen, setIsSettingsOpen] = useState(false);
|
||||
useEffect(() => {
|
||||
const savedOptimizationMode = localStorage.getItem('optimizationMode');
|
||||
|
||||
if (savedOptimizationMode !== null) {
|
||||
setOptimizationMode(savedOptimizationMode);
|
||||
} else {
|
||||
localStorage.setItem('optimizationMode', optimizationMode);
|
||||
}
|
||||
}, []);
|
||||
|
||||
useEffect(() => {
|
||||
if (
|
||||
@ -426,16 +322,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, []);
|
||||
|
||||
useEffect(() => {
|
||||
return () => {
|
||||
if (ws?.readyState === 1) {
|
||||
ws.close();
|
||||
console.debug(new Date(), 'ws:cleanup');
|
||||
}
|
||||
};
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, []);
|
||||
|
||||
const messagesRef = useRef<Message[]>([]);
|
||||
|
||||
useEffect(() => {
|
||||
@ -443,18 +329,22 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
}, [messages]);
|
||||
|
||||
useEffect(() => {
|
||||
if (isMessagesLoaded && isWSReady) {
|
||||
if (isMessagesLoaded && isConfigReady) {
|
||||
setIsReady(true);
|
||||
console.debug(new Date(), 'app:ready');
|
||||
} else {
|
||||
setIsReady(false);
|
||||
}
|
||||
}, [isMessagesLoaded, isWSReady]);
|
||||
}, [isMessagesLoaded, isConfigReady]);
|
||||
|
||||
const sendMessage = async (message: string, messageId?: string) => {
|
||||
const sendMessage = async (
|
||||
message: string,
|
||||
messageId?: string,
|
||||
options?: { rewriteIndex?: number },
|
||||
) => {
|
||||
if (loading) return;
|
||||
if (!ws || ws.readyState !== WebSocket.OPEN) {
|
||||
toast.error('Cannot send message while disconnected');
|
||||
if (!isConfigReady) {
|
||||
toast.error('Cannot send message before the configuration is ready');
|
||||
return;
|
||||
}
|
||||
|
||||
@ -464,24 +354,23 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
let sources: Document[] | undefined = undefined;
|
||||
let recievedMessage = '';
|
||||
let added = false;
|
||||
let messageChatHistory = chatHistory;
|
||||
|
||||
if (options?.rewriteIndex !== undefined) {
|
||||
const rewriteIndex = options.rewriteIndex;
|
||||
setMessages((prev) => {
|
||||
return [...prev.slice(0, messages.length > 2 ? rewriteIndex - 1 : 0)];
|
||||
});
|
||||
|
||||
messageChatHistory = chatHistory.slice(
|
||||
0,
|
||||
messages.length > 2 ? rewriteIndex - 1 : 0,
|
||||
);
|
||||
setChatHistory(messageChatHistory);
|
||||
}
|
||||
|
||||
messageId = messageId ?? crypto.randomBytes(7).toString('hex');
|
||||
|
||||
ws.send(
|
||||
JSON.stringify({
|
||||
type: 'message',
|
||||
message: {
|
||||
messageId: messageId,
|
||||
chatId: chatId!,
|
||||
content: message,
|
||||
},
|
||||
files: fileIds,
|
||||
focusMode: focusMode,
|
||||
optimizationMode: optimizationMode,
|
||||
history: [...chatHistory, ['human', message]],
|
||||
}),
|
||||
);
|
||||
|
||||
setMessages((prevMessages) => [
|
||||
...prevMessages,
|
||||
{
|
||||
@ -493,9 +382,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
},
|
||||
]);
|
||||
|
||||
const messageHandler = async (e: MessageEvent) => {
|
||||
const data = JSON.parse(e.data);
|
||||
|
||||
const messageHandler = async (data: any) => {
|
||||
if (data.type === 'error') {
|
||||
toast.error(data.data);
|
||||
setLoading(false);
|
||||
@ -558,11 +445,25 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
['assistant', recievedMessage],
|
||||
]);
|
||||
|
||||
ws?.removeEventListener('message', messageHandler);
|
||||
setLoading(false);
|
||||
|
||||
const lastMsg = messagesRef.current[messagesRef.current.length - 1];
|
||||
|
||||
const autoImageSearch = localStorage.getItem('autoImageSearch');
|
||||
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
|
||||
|
||||
if (autoImageSearch === 'true') {
|
||||
document
|
||||
.getElementById(`search-images-${lastMsg.messageId}`)
|
||||
?.click();
|
||||
}
|
||||
|
||||
if (autoVideoSearch === 'true') {
|
||||
document
|
||||
.getElementById(`search-videos-${lastMsg.messageId}`)
|
||||
?.click();
|
||||
}
|
||||
|
||||
if (
|
||||
lastMsg.role === 'assistant' &&
|
||||
lastMsg.sources &&
|
||||
@ -579,46 +480,87 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
}),
|
||||
);
|
||||
}
|
||||
|
||||
const autoImageSearch = localStorage.getItem('autoImageSearch');
|
||||
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
|
||||
|
||||
if (autoImageSearch === 'true') {
|
||||
document.getElementById('search-images')?.click();
|
||||
}
|
||||
|
||||
if (autoVideoSearch === 'true') {
|
||||
document.getElementById('search-videos')?.click();
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
ws?.addEventListener('message', messageHandler);
|
||||
const ollamaContextWindow =
|
||||
localStorage.getItem('ollamaContextWindow') || '2048';
|
||||
|
||||
const res = await fetch('/api/chat', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
content: message,
|
||||
message: {
|
||||
messageId: messageId,
|
||||
chatId: chatId!,
|
||||
content: message,
|
||||
},
|
||||
chatId: chatId!,
|
||||
files: fileIds,
|
||||
focusMode: focusMode,
|
||||
optimizationMode: optimizationMode,
|
||||
history: messageChatHistory,
|
||||
chatModel: {
|
||||
name: chatModelProvider.name,
|
||||
provider: chatModelProvider.provider,
|
||||
...(chatModelProvider.provider === 'ollama' && {
|
||||
ollamaContextWindow: parseInt(ollamaContextWindow),
|
||||
}),
|
||||
},
|
||||
embeddingModel: {
|
||||
name: embeddingModelProvider.name,
|
||||
provider: embeddingModelProvider.provider,
|
||||
},
|
||||
systemInstructions: localStorage.getItem('systemInstructions'),
|
||||
}),
|
||||
});
|
||||
|
||||
if (!res.body) throw new Error('No response body');
|
||||
|
||||
const reader = res.body?.getReader();
|
||||
const decoder = new TextDecoder('utf-8');
|
||||
|
||||
let partialChunk = '';
|
||||
|
||||
while (true) {
|
||||
const { value, done } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
partialChunk += decoder.decode(value, { stream: true });
|
||||
|
||||
try {
|
||||
const messages = partialChunk.split('\n');
|
||||
for (const msg of messages) {
|
||||
if (!msg.trim()) continue;
|
||||
const json = JSON.parse(msg);
|
||||
messageHandler(json);
|
||||
}
|
||||
partialChunk = '';
|
||||
} catch (error) {
|
||||
console.warn('Incomplete JSON, waiting for next chunk...');
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const rewrite = (messageId: string) => {
|
||||
const index = messages.findIndex((msg) => msg.messageId === messageId);
|
||||
|
||||
if (index === -1) return;
|
||||
|
||||
const message = messages[index - 1];
|
||||
|
||||
setMessages((prev) => {
|
||||
return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
|
||||
const messageIndex = messages.findIndex(
|
||||
(msg) => msg.messageId === messageId,
|
||||
);
|
||||
if (messageIndex == -1) return;
|
||||
sendMessage(messages[messageIndex - 1].content, messageId, {
|
||||
rewriteIndex: messageIndex,
|
||||
});
|
||||
setChatHistory((prev) => {
|
||||
return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
|
||||
});
|
||||
|
||||
sendMessage(message.content, message.messageId);
|
||||
};
|
||||
|
||||
useEffect(() => {
|
||||
if (isReady && initialMessage && ws?.readyState === 1) {
|
||||
if (isReady && initialMessage && isConfigReady) {
|
||||
sendMessage(initialMessage);
|
||||
}
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, [ws?.readyState, isReady, initialMessage, isWSReady]);
|
||||
}, [isConfigReady, isReady, initialMessage]);
|
||||
|
||||
if (hasError) {
|
||||
return (
|
||||
@ -655,6 +597,8 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
setFileIds={setFileIds}
|
||||
files={files}
|
||||
setFiles={setFiles}
|
||||
optimizationMode={optimizationMode}
|
||||
setOptimizationMode={setOptimizationMode}
|
||||
/>
|
||||
</>
|
||||
) : (
|
@ -29,15 +29,12 @@ const DeleteChat = ({
|
||||
const handleDelete = async () => {
|
||||
setLoading(true);
|
||||
try {
|
||||
const res = await fetch(
|
||||
`${process.env.NEXT_PUBLIC_API_URL}/chats/${chatId}`,
|
||||
{
|
||||
method: 'DELETE',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
const res = await fetch(`/api/chats/${chatId}`, {
|
||||
method: 'DELETE',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
if (res.status != 200) {
|
||||
throw new Error('Failed to delete chat');
|
@ -12,13 +12,18 @@ import {
|
||||
Layers3,
|
||||
Plus,
|
||||
} from 'lucide-react';
|
||||
import Markdown from 'markdown-to-jsx';
|
||||
import Markdown, { MarkdownToJSX } from 'markdown-to-jsx';
|
||||
import Copy from './MessageActions/Copy';
|
||||
import Rewrite from './MessageActions/Rewrite';
|
||||
import MessageSources from './MessageSources';
|
||||
import SearchImages from './SearchImages';
|
||||
import SearchVideos from './SearchVideos';
|
||||
import { useSpeech } from 'react-text-to-speech';
|
||||
import ThinkBox from './ThinkBox';
|
||||
|
||||
const ThinkTagProcessor = ({ children }: { children: React.ReactNode }) => {
|
||||
return <ThinkBox content={children as string} />;
|
||||
};
|
||||
|
||||
const MessageBox = ({
|
||||
message,
|
||||
@ -43,32 +48,83 @@ const MessageBox = ({
|
||||
const [speechMessage, setSpeechMessage] = useState(message.content);
|
||||
|
||||
useEffect(() => {
|
||||
const citationRegex = /\[([^\]]+)\]/g;
|
||||
const regex = /\[(\d+)\]/g;
|
||||
let processedMessage = message.content;
|
||||
|
||||
if (message.role === 'assistant' && message.content.includes('<think>')) {
|
||||
const openThinkTag = processedMessage.match(/<think>/g)?.length || 0;
|
||||
const closeThinkTag = processedMessage.match(/<\/think>/g)?.length || 0;
|
||||
|
||||
if (openThinkTag > closeThinkTag) {
|
||||
processedMessage += '</think> <a> </a>'; // The extra <a> </a> is to prevent the the think component from looking bad
|
||||
}
|
||||
}
|
||||
|
||||
if (
|
||||
message.role === 'assistant' &&
|
||||
message?.sources &&
|
||||
message.sources.length > 0
|
||||
) {
|
||||
return setParsedMessage(
|
||||
message.content.replace(
|
||||
regex,
|
||||
(_, number) =>
|
||||
`<a href="${message.sources?.[number - 1]?.metadata?.url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${number}</a>`,
|
||||
setParsedMessage(
|
||||
processedMessage.replace(
|
||||
citationRegex,
|
||||
(_, capturedContent: string) => {
|
||||
const numbers = capturedContent
|
||||
.split(',')
|
||||
.map((numStr) => numStr.trim());
|
||||
|
||||
const linksHtml = numbers
|
||||
.map((numStr) => {
|
||||
const number = parseInt(numStr);
|
||||
|
||||
if (isNaN(number) || number <= 0) {
|
||||
return `[${numStr}]`;
|
||||
}
|
||||
|
||||
const source = message.sources?.[number - 1];
|
||||
const url = source?.metadata?.url;
|
||||
|
||||
if (url) {
|
||||
return `<a href="${url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${numStr}</a>`;
|
||||
} else {
|
||||
return `[${numStr}]`;
|
||||
}
|
||||
})
|
||||
.join('');
|
||||
|
||||
return linksHtml;
|
||||
},
|
||||
),
|
||||
);
|
||||
setSpeechMessage(message.content.replace(regex, ''));
|
||||
return;
|
||||
}
|
||||
|
||||
setSpeechMessage(message.content.replace(regex, ''));
|
||||
setParsedMessage(message.content);
|
||||
setParsedMessage(processedMessage);
|
||||
}, [message.content, message.sources, message.role]);
|
||||
|
||||
const { speechStatus, start, stop } = useSpeech({ text: speechMessage });
|
||||
|
||||
const markdownOverrides: MarkdownToJSX.Options = {
|
||||
overrides: {
|
||||
think: {
|
||||
component: ThinkTagProcessor,
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
return (
|
||||
<div>
|
||||
{message.role === 'user' && (
|
||||
<div className={cn('w-full', messageIndex === 0 ? 'pt-16' : 'pt-8')}>
|
||||
<div
|
||||
className={cn(
|
||||
'w-full',
|
||||
messageIndex === 0 ? 'pt-16' : 'pt-8',
|
||||
'break-words',
|
||||
)}
|
||||
>
|
||||
<h2 className="text-black dark:text-white font-medium text-3xl lg:w-9/12">
|
||||
{message.content}
|
||||
</h2>
|
||||
@ -105,11 +161,13 @@ const MessageBox = ({
|
||||
Answer
|
||||
</h3>
|
||||
</div>
|
||||
|
||||
<Markdown
|
||||
className={cn(
|
||||
'prose prose-h1:mb-3 prose-h2:mb-2 prose-h2:mt-6 prose-h2:font-[800] prose-h3:mt-4 prose-h3:mb-1.5 prose-h3:font-[600] dark:prose-invert prose-p:leading-relaxed prose-pre:p-0 font-[400]',
|
||||
'max-w-none break-words text-black dark:text-white',
|
||||
)}
|
||||
options={markdownOverrides}
|
||||
>
|
||||
{parsedMessage}
|
||||
</Markdown>
|
||||
@ -187,10 +245,12 @@ const MessageBox = ({
|
||||
<SearchImages
|
||||
query={history[messageIndex - 1].content}
|
||||
chatHistory={history.slice(0, messageIndex - 1)}
|
||||
messageId={message.messageId}
|
||||
/>
|
||||
<SearchVideos
|
||||
chatHistory={history.slice(0, messageIndex - 1)}
|
||||
query={history[messageIndex - 1].content}
|
||||
messageId={message.messageId}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
178
src/components/MessageInput.tsx
Normal file
178
src/components/MessageInput.tsx
Normal file
@ -0,0 +1,178 @@
|
||||
import { cn } from '@/lib/utils';
|
||||
import { ArrowUp } from 'lucide-react';
|
||||
import { useEffect, useRef, useState } from 'react';
|
||||
import TextareaAutosize from 'react-textarea-autosize';
|
||||
import Attach from './MessageInputActions/Attach';
|
||||
import CopilotToggle from './MessageInputActions/Copilot';
|
||||
import Optimization from './MessageInputActions/Optimization';
|
||||
import { File } from './ChatWindow';
|
||||
import AttachSmall from './MessageInputActions/AttachSmall';
|
||||
|
||||
const MessageInput = ({
|
||||
sendMessage,
|
||||
loading,
|
||||
fileIds,
|
||||
setFileIds,
|
||||
files,
|
||||
setFiles,
|
||||
optimizationMode,
|
||||
setOptimizationMode,
|
||||
}: {
|
||||
sendMessage: (message: string) => void;
|
||||
loading: boolean;
|
||||
fileIds: string[];
|
||||
setFileIds: (fileIds: string[]) => void;
|
||||
files: File[];
|
||||
setFiles: (files: File[]) => void;
|
||||
optimizationMode: string;
|
||||
setOptimizationMode: (mode: string) => void;
|
||||
}) => {
|
||||
const [copilotEnabled, setCopilotEnabled] = useState(false);
|
||||
const [message, setMessage] = useState('');
|
||||
const [textareaRows, setTextareaRows] = useState(1);
|
||||
const [mode, setMode] = useState<'multi' | 'single'>('single');
|
||||
|
||||
useEffect(() => {
|
||||
if (textareaRows >= 2 && message && mode === 'single') {
|
||||
setMode('multi');
|
||||
} else if (!message && mode === 'multi') {
|
||||
setMode('single');
|
||||
}
|
||||
}, [textareaRows, mode, message]);
|
||||
|
||||
const inputRef = useRef<HTMLTextAreaElement | null>(null);
|
||||
|
||||
useEffect(() => {
|
||||
const handleKeyDown = (e: KeyboardEvent) => {
|
||||
const activeElement = document.activeElement;
|
||||
const isInputFocused =
|
||||
activeElement?.tagName === 'INPUT' ||
|
||||
activeElement?.tagName === 'TEXTAREA' ||
|
||||
activeElement?.hasAttribute('contenteditable');
|
||||
if (e.key === '/' && !isInputFocused) {
|
||||
e.preventDefault();
|
||||
inputRef.current?.focus();
|
||||
}
|
||||
};
|
||||
document.addEventListener('keydown', handleKeyDown);
|
||||
return () => {
|
||||
document.removeEventListener('keydown', handleKeyDown);
|
||||
};
|
||||
}, []);
|
||||
|
||||
return (
|
||||
<form
|
||||
onSubmit={(e) => {
|
||||
if (loading) return;
|
||||
e.preventDefault();
|
||||
sendMessage(message);
|
||||
setMessage('');
|
||||
}}
|
||||
onKeyDown={(e) => {
|
||||
if (e.key === 'Enter' && !e.shiftKey && !loading) {
|
||||
e.preventDefault();
|
||||
sendMessage(message);
|
||||
setMessage('');
|
||||
}
|
||||
}}
|
||||
className={cn(
|
||||
'bg-light-secondary dark:bg-dark-secondary p-4 flex items-center border border-light-200 dark:border-dark-200',
|
||||
mode === 'multi'
|
||||
? 'flex-col rounded-lg'
|
||||
: 'flex-col md:flex-row rounded-lg md:rounded-full',
|
||||
)}
|
||||
>
|
||||
{mode === 'single' && (
|
||||
<div className="flex flex-row items-center justify-between w-full mb-2 md:mb-0 md:w-auto">
|
||||
<div className="flex flex-row items-center space-x-2">
|
||||
<AttachSmall
|
||||
fileIds={fileIds}
|
||||
setFileIds={setFileIds}
|
||||
files={files}
|
||||
setFiles={setFiles}
|
||||
/>
|
||||
<Optimization
|
||||
optimizationMode={optimizationMode}
|
||||
setOptimizationMode={setOptimizationMode}
|
||||
/>
|
||||
</div>
|
||||
<div className="md:hidden">
|
||||
<CopilotToggle
|
||||
copilotEnabled={copilotEnabled}
|
||||
setCopilotEnabled={setCopilotEnabled}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
<div className="flex flex-row items-center w-full">
|
||||
<TextareaAutosize
|
||||
ref={inputRef}
|
||||
value={message}
|
||||
onChange={(e) => setMessage(e.target.value)}
|
||||
onHeightChange={(height, props) => {
|
||||
setTextareaRows(Math.ceil(height / props.rowHeight));
|
||||
}}
|
||||
className="transition bg-transparent dark:placeholder:text-white/50 placeholder:text-sm text-sm dark:text-white resize-none focus:outline-none w-full px-2 max-h-24 lg:max-h-36 xl:max-h-48 flex-grow flex-shrink"
|
||||
placeholder="Ask a follow-up"
|
||||
/>
|
||||
{mode === 'single' && (
|
||||
<div className="flex flex-row items-center space-x-4">
|
||||
<div className="hidden md:block">
|
||||
<CopilotToggle
|
||||
copilotEnabled={copilotEnabled}
|
||||
setCopilotEnabled={setCopilotEnabled}
|
||||
/>
|
||||
</div>
|
||||
<button
|
||||
disabled={message.trim().length === 0 || loading}
|
||||
className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
|
||||
>
|
||||
<ArrowUp className="bg-background" size={17} />
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{mode === 'multi' && (
|
||||
<div className="flex flex-col md:flex-row items-start md:items-center justify-between w-full pt-2">
|
||||
<div className="flex flex-row items-center justify-between w-full md:w-auto mb-2 md:mb-0">
|
||||
<div className="flex flex-row items-center space-x-2">
|
||||
<AttachSmall
|
||||
fileIds={fileIds}
|
||||
setFileIds={setFileIds}
|
||||
files={files}
|
||||
setFiles={setFiles}
|
||||
/>
|
||||
<Optimization
|
||||
optimizationMode={optimizationMode}
|
||||
setOptimizationMode={setOptimizationMode}
|
||||
/>
|
||||
</div>
|
||||
<div className="md:hidden">
|
||||
<CopilotToggle
|
||||
copilotEnabled={copilotEnabled}
|
||||
setCopilotEnabled={setCopilotEnabled}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex flex-row items-center space-x-4 self-end">
|
||||
<div className="hidden md:block">
|
||||
<CopilotToggle
|
||||
copilotEnabled={copilotEnabled}
|
||||
setCopilotEnabled={setCopilotEnabled}
|
||||
/>
|
||||
</div>
|
||||
<button
|
||||
disabled={message.trim().length === 0 || loading}
|
||||
className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
|
||||
>
|
||||
<ArrowUp className="bg-background" size={17} />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</form>
|
||||
);
|
||||
};
|
||||
|
||||
export default MessageInput;
|
@ -41,7 +41,7 @@ const Attach = ({
|
||||
data.append('embedding_model_provider', embeddingModelProvider!);
|
||||
data.append('embedding_model', embeddingModel!);
|
||||
|
||||
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
|
||||
const res = await fetch(`/api/uploads`, {
|
||||
method: 'POST',
|
||||
body: data,
|
||||
});
|
||||
@ -110,7 +110,7 @@ const Attach = ({
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => fileInputRef.current.click()}
|
||||
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
|
||||
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
|
||||
>
|
||||
<input
|
||||
type="file"
|
||||
@ -128,7 +128,7 @@ const Attach = ({
|
||||
setFiles([]);
|
||||
setFileIds([]);
|
||||
}}
|
||||
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
|
||||
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
|
||||
>
|
||||
<Trash size={14} />
|
||||
<p className="text-xs">Clear</p>
|
||||
@ -145,7 +145,7 @@ const Attach = ({
|
||||
<div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
|
||||
<File size={16} className="text-white/70" />
|
||||
</div>
|
||||
<p className="text-white/70 text-sm">
|
||||
<p className="text-black/70 dark:text-white/70 text-sm">
|
||||
{file.fileName.length > 25
|
||||
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
|
||||
'...' +
|
@ -39,7 +39,7 @@ const AttachSmall = ({
|
||||
data.append('embedding_model_provider', embeddingModelProvider!);
|
||||
data.append('embedding_model', embeddingModel!);
|
||||
|
||||
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/uploads`, {
|
||||
const res = await fetch(`/api/uploads`, {
|
||||
method: 'POST',
|
||||
body: data,
|
||||
});
|
||||
@ -82,7 +82,7 @@ const AttachSmall = ({
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => fileInputRef.current.click()}
|
||||
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
|
||||
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
|
||||
>
|
||||
<input
|
||||
type="file"
|
||||
@ -100,7 +100,7 @@ const AttachSmall = ({
|
||||
setFiles([]);
|
||||
setFileIds([]);
|
||||
}}
|
||||
className="flex flex-row items-center space-x-1 text-white/70 hover:text-white transition duration-200"
|
||||
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
|
||||
>
|
||||
<Trash size={14} />
|
||||
<p className="text-xs">Clear</p>
|
||||
@ -117,7 +117,7 @@ const AttachSmall = ({
|
||||
<div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
|
||||
<File size={16} className="text-white/70" />
|
||||
</div>
|
||||
<p className="text-white/70 text-sm">
|
||||
<p className="text-black/70 dark:text-white/70 text-sm">
|
||||
{file.fileName.length > 25
|
||||
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
|
||||
'...' +
|
@ -45,25 +45,13 @@ const focusModes = [
|
||||
key: 'youtubeSearch',
|
||||
title: 'Youtube',
|
||||
description: 'Search and watch videos',
|
||||
icon: (
|
||||
<SiYoutube
|
||||
className="h-5 w-auto mr-0.5"
|
||||
onPointerEnterCapture={undefined}
|
||||
onPointerLeaveCapture={undefined}
|
||||
/>
|
||||
),
|
||||
icon: <SiYoutube className="h-5 w-auto mr-0.5" />,
|
||||
},
|
||||
{
|
||||
key: 'redditSearch',
|
||||
title: 'Reddit',
|
||||
description: 'Search for discussions and opinions',
|
||||
icon: (
|
||||
<SiReddit
|
||||
className="h-5 w-auto mr-0.5"
|
||||
onPointerEnterCapture={undefined}
|
||||
onPointerLeaveCapture={undefined}
|
||||
/>
|
||||
),
|
||||
icon: <SiReddit className="h-5 w-auto mr-0.5" />,
|
||||
},
|
||||
];
|
||||
|
@ -1,4 +1,4 @@
|
||||
import { ChevronDown, Sliders, Star, Zap } from 'lucide-react';
|
||||
import { ChevronDown, Minimize2, Sliders, Star, Zap } from 'lucide-react';
|
||||
import { cn } from '@/lib/utils';
|
||||
import {
|
||||
Popover,
|
||||
@ -7,7 +7,6 @@ import {
|
||||
Transition,
|
||||
} from '@headlessui/react';
|
||||
import { Fragment } from 'react';
|
||||
|
||||
const OptimizationModes = [
|
||||
{
|
||||
key: 'speed',
|
||||
@ -41,8 +40,13 @@ const Optimization = ({
|
||||
optimizationMode: string;
|
||||
setOptimizationMode: (mode: string) => void;
|
||||
}) => {
|
||||
const handleOptimizationChange = (mode: string) => {
|
||||
setOptimizationMode(mode);
|
||||
localStorage.setItem('optimizationMode', mode);
|
||||
};
|
||||
|
||||
return (
|
||||
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
|
||||
<Popover className="relative">
|
||||
<PopoverButton
|
||||
type="button"
|
||||
className="p-2 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
|
||||
@ -70,11 +74,11 @@ const Optimization = ({
|
||||
leaveFrom="opacity-100 translate-y-0"
|
||||
leaveTo="opacity-0 translate-y-1"
|
||||
>
|
||||
<PopoverPanel className="absolute z-10 w-64 md:w-[250px] right-0">
|
||||
<div className="flex flex-col gap-2 bg-light-primary dark:bg-dark-primary border rounded-lg border-light-200 dark:border-dark-200 w-full p-4 max-h-[200px] md:max-h-none overflow-y-auto">
|
||||
<PopoverPanel className="absolute z-10 bottom-[100%] mb-2 left-1/2 transform -translate-x-1/2">
|
||||
<div className="flex flex-col gap-2 bg-light-primary dark:bg-dark-primary border rounded-lg border-light-200 dark:border-dark-200 w-max max-w-[300px] p-4 max-h-[200px] md:max-h-none overflow-y-auto">
|
||||
{OptimizationModes.map((mode, i) => (
|
||||
<PopoverButton
|
||||
onClick={() => setOptimizationMode(mode.key)}
|
||||
onClick={() => handleOptimizationChange(mode.key)}
|
||||
key={i}
|
||||
disabled={mode.key === 'quality'}
|
||||
className={cn(
|
@ -69,11 +69,15 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
|
||||
<div className="flex flex-row items-center space-x-1">
|
||||
{sources.slice(3, 6).map((source, i) => {
|
||||
return source.metadata.url === 'File' ? (
|
||||
<div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
|
||||
<div
|
||||
key={i}
|
||||
className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full"
|
||||
>
|
||||
<File size={12} className="text-white/70" />
|
||||
</div>
|
||||
) : (
|
||||
<img
|
||||
key={i}
|
||||
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
|
||||
width={16}
|
||||
height={16}
|
@ -14,9 +14,11 @@ type Image = {
|
||||
const SearchImages = ({
|
||||
query,
|
||||
chatHistory,
|
||||
messageId,
|
||||
}: {
|
||||
query: string;
|
||||
chatHistory: Message[];
|
||||
messageId: string;
|
||||
}) => {
|
||||
const [images, setImages] = useState<Image[] | null>(null);
|
||||
const [loading, setLoading] = useState(false);
|
||||
@ -27,37 +29,38 @@ const SearchImages = ({
|
||||
<>
|
||||
{!loading && images === null && (
|
||||
<button
|
||||
id="search-images"
|
||||
id={`search-images-${messageId}`}
|
||||
onClick={async () => {
|
||||
setLoading(true);
|
||||
|
||||
const chatModelProvider = localStorage.getItem('chatModelProvider');
|
||||
const chatModel = localStorage.getItem('chatModel');
|
||||
|
||||
const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
|
||||
const customOpenAIKey = localStorage.getItem('openAIApiKey');
|
||||
const ollamaContextWindow =
|
||||
localStorage.getItem('ollamaContextWindow') || '2048';
|
||||
|
||||
const res = await fetch(
|
||||
`${process.env.NEXT_PUBLIC_API_URL}/images`,
|
||||
{
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
query: query,
|
||||
chatHistory: chatHistory,
|
||||
chatModel: {
|
||||
provider: chatModelProvider,
|
||||
model: chatModel,
|
||||
...(chatModelProvider === 'custom_openai' && {
|
||||
customOpenAIBaseURL: customOpenAIBaseURL,
|
||||
customOpenAIKey: customOpenAIKey,
|
||||
}),
|
||||
},
|
||||
}),
|
||||
const res = await fetch(`/api/images`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
);
|
||||
body: JSON.stringify({
|
||||
query: query,
|
||||
chatHistory: chatHistory,
|
||||
chatModel: {
|
||||
provider: chatModelProvider,
|
||||
model: chatModel,
|
||||
...(chatModelProvider === 'custom_openai' && {
|
||||
customOpenAIBaseURL: customOpenAIBaseURL,
|
||||
customOpenAIKey: customOpenAIKey,
|
||||
}),
|
||||
...(chatModelProvider === 'ollama' && {
|
||||
ollamaContextWindow: parseInt(ollamaContextWindow),
|
||||
}),
|
||||
},
|
||||
}),
|
||||
});
|
||||
|
||||
const data = await res.json();
|
||||
|
@ -27,9 +27,11 @@ declare module 'yet-another-react-lightbox' {
|
||||
const Searchvideos = ({
|
||||
query,
|
||||
chatHistory,
|
||||
messageId,
|
||||
}: {
|
||||
query: string;
|
||||
chatHistory: Message[];
|
||||
messageId: string;
|
||||
}) => {
|
||||
const [videos, setVideos] = useState<Video[] | null>(null);
|
||||
const [loading, setLoading] = useState(false);
|
||||
@ -42,37 +44,38 @@ const Searchvideos = ({
|
||||
<>
|
||||
{!loading && videos === null && (
|
||||
<button
|
||||
id="search-videos"
|
||||
id={`search-videos-${messageId}`}
|
||||
onClick={async () => {
|
||||
setLoading(true);
|
||||
|
||||
const chatModelProvider = localStorage.getItem('chatModelProvider');
|
||||
const chatModel = localStorage.getItem('chatModel');
|
||||
|
||||
const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
|
||||
const customOpenAIKey = localStorage.getItem('openAIApiKey');
|
||||
const ollamaContextWindow =
|
||||
localStorage.getItem('ollamaContextWindow') || '2048';
|
||||
|
||||
const res = await fetch(
|
||||
`${process.env.NEXT_PUBLIC_API_URL}/videos`,
|
||||
{
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
query: query,
|
||||
chatHistory: chatHistory,
|
||||
chatModel: {
|
||||
provider: chatModelProvider,
|
||||
model: chatModel,
|
||||
...(chatModelProvider === 'custom_openai' && {
|
||||
customOpenAIBaseURL: customOpenAIBaseURL,
|
||||
customOpenAIKey: customOpenAIKey,
|
||||
}),
|
||||
},
|
||||
}),
|
||||
const res = await fetch(`/api/videos`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
);
|
||||
body: JSON.stringify({
|
||||
query: query,
|
||||
chatHistory: chatHistory,
|
||||
chatModel: {
|
||||
provider: chatModelProvider,
|
||||
model: chatModel,
|
||||
...(chatModelProvider === 'custom_openai' && {
|
||||
customOpenAIBaseURL: customOpenAIBaseURL,
|
||||
customOpenAIKey: customOpenAIKey,
|
||||
}),
|
||||
...(chatModelProvider === 'ollama' && {
|
||||
ollamaContextWindow: parseInt(ollamaContextWindow),
|
||||
}),
|
||||
},
|
||||
}),
|
||||
});
|
||||
|
||||
const data = await res.json();
|
||||
|
@ -16,8 +16,6 @@ const VerticalIconContainer = ({ children }: { children: ReactNode }) => {
|
||||
const Sidebar = ({ children }: { children: React.ReactNode }) => {
|
||||
const segments = useSelectedLayoutSegments();
|
||||
|
||||
const [isSettingsOpen, setIsSettingsOpen] = useState(false);
|
||||
|
||||
const navLinks = [
|
||||
{
|
||||
icon: Home,
|
43
src/components/ThinkBox.tsx
Normal file
43
src/components/ThinkBox.tsx
Normal file
@ -0,0 +1,43 @@
|
||||
'use client';
|
||||
|
||||
import { useState } from 'react';
|
||||
import { cn } from '@/lib/utils';
|
||||
import { ChevronDown, ChevronUp, BrainCircuit } from 'lucide-react';
|
||||
|
||||
interface ThinkBoxProps {
|
||||
content: string;
|
||||
}
|
||||
|
||||
const ThinkBox = ({ content }: ThinkBoxProps) => {
|
||||
const [isExpanded, setIsExpanded] = useState(false);
|
||||
|
||||
return (
|
||||
<div className="my-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200 overflow-hidden">
|
||||
<button
|
||||
onClick={() => setIsExpanded(!isExpanded)}
|
||||
className="w-full flex items-center justify-between px-4 py-1 text-black/90 dark:text-white/90 hover:bg-light-200 dark:hover:bg-dark-200 transition duration-200"
|
||||
>
|
||||
<div className="flex items-center space-x-2">
|
||||
<BrainCircuit
|
||||
size={20}
|
||||
className="text-[#9C27B0] dark:text-[#CE93D8]"
|
||||
/>
|
||||
<p className="font-medium text-sm">Thinking Process</p>
|
||||
</div>
|
||||
{isExpanded ? (
|
||||
<ChevronUp size={18} className="text-black/70 dark:text-white/70" />
|
||||
) : (
|
||||
<ChevronDown size={18} className="text-black/70 dark:text-white/70" />
|
||||
)}
|
||||
</button>
|
||||
|
||||
{isExpanded && (
|
||||
<div className="px-4 py-3 text-black/80 dark:text-white/80 text-sm border-t border-light-200 dark:border-dark-200 bg-light-100/50 dark:bg-dark-100/50 whitespace-pre-wrap">
|
||||
{content}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
export default ThinkBox;
|
@ -6,8 +6,10 @@ export const getSuggestions = async (chatHisory: Message[]) => {
|
||||
|
||||
const customOpenAIKey = localStorage.getItem('openAIApiKey');
|
||||
const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
|
||||
const ollamaContextWindow =
|
||||
localStorage.getItem('ollamaContextWindow') || '2048';
|
||||
|
||||
const res = await fetch(`${process.env.NEXT_PUBLIC_API_URL}/suggestions`, {
|
||||
const res = await fetch(`/api/suggestions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
@ -21,6 +23,9 @@ export const getSuggestions = async (chatHisory: Message[]) => {
|
||||
customOpenAIKey,
|
||||
customOpenAIBaseURL,
|
||||
}),
|
||||
...(chatModelProvider === 'ollama' && {
|
||||
ollamaContextWindow: parseInt(ollamaContextWindow),
|
||||
}),
|
||||
},
|
||||
}),
|
||||
});
|
@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
|
||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||
import { BaseMessage } from '@langchain/core/messages';
|
||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
||||
import { searchSearxng } from '../lib/searxng';
|
||||
import { searchSearxng } from '../searxng';
|
||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const imageSearchChainPrompt = `
|
||||
@ -36,6 +36,12 @@ type ImageSearchChainInput = {
|
||||
query: string;
|
||||
};
|
||||
|
||||
interface ImageSearchResult {
|
||||
img_src: string;
|
||||
url: string;
|
||||
title: string;
|
||||
}
|
||||
|
||||
const strParser = new StringOutputParser();
|
||||
|
||||
const createImageSearchChain = (llm: BaseChatModel) => {
|
||||
@ -52,11 +58,13 @@ const createImageSearchChain = (llm: BaseChatModel) => {
|
||||
llm,
|
||||
strParser,
|
||||
RunnableLambda.from(async (input: string) => {
|
||||
input = input.replace(/<think>.*?<\/think>/g, '');
|
||||
|
||||
const res = await searchSearxng(input, {
|
||||
engines: ['bing images', 'google images'],
|
||||
});
|
||||
|
||||
const images = [];
|
||||
const images: ImageSearchResult[] = [];
|
||||
|
||||
res.results.forEach((result) => {
|
||||
if (result.img_src && result.url && result.title) {
|
@ -1,5 +1,5 @@
|
||||
import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
|
||||
import ListLineOutputParser from '../lib/outputParsers/listLineOutputParser';
|
||||
import ListLineOutputParser from '../outputParsers/listLineOutputParser';
|
||||
import { PromptTemplate } from '@langchain/core/prompts';
|
||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||
import { BaseMessage } from '@langchain/core/messages';
|
@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
|
||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||
import { BaseMessage } from '@langchain/core/messages';
|
||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
||||
import { searchSearxng } from '../lib/searxng';
|
||||
import { searchSearxng } from '../searxng';
|
||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const VideoSearchChainPrompt = `
|
||||
@ -36,6 +36,13 @@ type VideoSearchChainInput = {
|
||||
query: string;
|
||||
};
|
||||
|
||||
interface VideoSearchResult {
|
||||
img_src: string;
|
||||
url: string;
|
||||
title: string;
|
||||
iframe_src: string;
|
||||
}
|
||||
|
||||
const strParser = new StringOutputParser();
|
||||
|
||||
const createVideoSearchChain = (llm: BaseChatModel) => {
|
||||
@ -52,11 +59,13 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
|
||||
llm,
|
||||
strParser,
|
||||
RunnableLambda.from(async (input: string) => {
|
||||
input = input.replace(/<think>.*?<\/think>/g, '');
|
||||
|
||||
const res = await searchSearxng(input, {
|
||||
engines: ['youtube'],
|
||||
});
|
||||
|
||||
const videos = [];
|
||||
const videos: VideoSearchResult[] = [];
|
||||
|
||||
res.results.forEach((result) => {
|
||||
if (
|
@ -1,12 +1,18 @@
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import toml from '@iarna/toml';
|
||||
|
||||
// Use dynamic imports for Node.js modules to prevent client-side errors
|
||||
let fs: any;
|
||||
let path: any;
|
||||
if (typeof window === 'undefined') {
|
||||
// We're on the server
|
||||
fs = require('fs');
|
||||
path = require('path');
|
||||
}
|
||||
|
||||
const configFileName = 'config.toml';
|
||||
|
||||
interface Config {
|
||||
GENERAL: {
|
||||
PORT: number;
|
||||
SIMILARITY_MEASURE: string;
|
||||
KEEP_ALIVE: string;
|
||||
};
|
||||
@ -26,6 +32,12 @@ interface Config {
|
||||
OLLAMA: {
|
||||
API_URL: string;
|
||||
};
|
||||
DEEPSEEK: {
|
||||
API_KEY: string;
|
||||
};
|
||||
LM_STUDIO: {
|
||||
API_URL: string;
|
||||
};
|
||||
CUSTOM_OPENAI: {
|
||||
API_URL: string;
|
||||
API_KEY: string;
|
||||
@ -41,12 +53,17 @@ type RecursivePartial<T> = {
|
||||
[P in keyof T]?: RecursivePartial<T[P]>;
|
||||
};
|
||||
|
||||
const loadConfig = () =>
|
||||
toml.parse(
|
||||
fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
|
||||
) as any as Config;
|
||||
const loadConfig = () => {
|
||||
// Server-side only
|
||||
if (typeof window === 'undefined') {
|
||||
return toml.parse(
|
||||
fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
|
||||
) as any as Config;
|
||||
}
|
||||
|
||||
export const getPort = () => loadConfig().GENERAL.PORT;
|
||||
// Client-side fallback - settings will be loaded via API
|
||||
return {} as Config;
|
||||
};
|
||||
|
||||
export const getSimilarityMeasure = () =>
|
||||
loadConfig().GENERAL.SIMILARITY_MEASURE;
|
||||
@ -66,6 +83,8 @@ export const getSearxngApiEndpoint = () =>
|
||||
|
||||
export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
|
||||
|
||||
export const getDeepseekApiKey = () => loadConfig().MODELS.DEEPSEEK.API_KEY;
|
||||
|
||||
export const getCustomOpenaiApiKey = () =>
|
||||
loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
|
||||
|
||||
@ -75,6 +94,9 @@ export const getCustomOpenaiApiUrl = () =>
|
||||
export const getCustomOpenaiModelName = () =>
|
||||
loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
|
||||
|
||||
export const getLMStudioApiEndpoint = () =>
|
||||
loadConfig().MODELS.LM_STUDIO.API_URL;
|
||||
|
||||
const mergeConfigs = (current: any, update: any): any => {
|
||||
if (update === null || update === undefined) {
|
||||
return current;
|
||||
@ -107,11 +129,13 @@ const mergeConfigs = (current: any, update: any): any => {
|
||||
};
|
||||
|
||||
export const updateConfig = (config: RecursivePartial<Config>) => {
|
||||
const currentConfig = loadConfig();
|
||||
const mergedConfig = mergeConfigs(currentConfig, config);
|
||||
|
||||
fs.writeFileSync(
|
||||
path.join(__dirname, `../${configFileName}`),
|
||||
toml.stringify(mergedConfig),
|
||||
);
|
||||
// Server-side only
|
||||
if (typeof window === 'undefined') {
|
||||
const currentConfig = loadConfig();
|
||||
const mergedConfig = mergeConfigs(currentConfig, config);
|
||||
fs.writeFileSync(
|
||||
path.join(path.join(process.cwd(), `${configFileName}`)),
|
||||
toml.stringify(mergedConfig),
|
||||
);
|
||||
}
|
||||
};
|
@ -1,8 +1,9 @@
|
||||
import { drizzle } from 'drizzle-orm/better-sqlite3';
|
||||
import Database from 'better-sqlite3';
|
||||
import * as schema from './schema';
|
||||
import path from 'path';
|
||||
|
||||
const sqlite = new Database('data/db.sqlite');
|
||||
const sqlite = new Database(path.join(process.cwd(), 'data/db.sqlite'));
|
||||
const db = drizzle(sqlite, {
|
||||
schema: schema,
|
||||
});
|
@ -28,7 +28,7 @@ export class HuggingFaceTransformersEmbeddings
|
||||
|
||||
timeout?: number;
|
||||
|
||||
private pipelinePromise: Promise<any>;
|
||||
private pipelinePromise: Promise<any> | undefined;
|
||||
|
||||
constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
|
||||
super(fields ?? {});
|
||||
|
@ -9,7 +9,7 @@ class LineOutputParser extends BaseOutputParser<string> {
|
||||
|
||||
constructor(args?: LineOutputParserArgs) {
|
||||
super();
|
||||
this.key = args.key ?? this.key;
|
||||
this.key = args?.key ?? this.key;
|
||||
}
|
||||
|
||||
static lc_name() {
|
||||
|
@ -9,7 +9,7 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
|
||||
|
||||
constructor(args?: LineListOutputParserArgs) {
|
||||
super();
|
||||
this.key = args.key ?? this.key;
|
||||
this.key = args?.key ?? this.key;
|
||||
}
|
||||
|
||||
static lc_name() {
|
||||
|
@ -51,6 +51,10 @@ export const academicSearchResponsePrompt = `
|
||||
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
|
||||
- You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
|
||||
|
||||
### User instructions
|
||||
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
|
||||
{systemInstructions}
|
||||
|
||||
### Example Output
|
||||
- Begin with a brief introduction summarizing the event or query topic.
|
||||
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
|
@ -51,6 +51,10 @@ export const redditSearchResponsePrompt = `
|
||||
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
|
||||
- You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
|
||||
|
||||
### User instructions
|
||||
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
|
||||
{systemInstructions}
|
||||
|
||||
### Example Output
|
||||
- Begin with a brief introduction summarizing the event or query topic.
|
||||
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
|
@ -1,6 +1,6 @@
|
||||
export const webSearchRetrieverPrompt = `
|
||||
You are an AI question rephraser. You will be given a conversation and a follow-up question, you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
|
||||
If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
|
||||
If it is a simple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
|
||||
If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
|
||||
You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
|
||||
|
||||
@ -92,6 +92,10 @@ export const webSearchResponsePrompt = `
|
||||
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
|
||||
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
|
||||
|
||||
### User instructions
|
||||
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
|
||||
{systemInstructions}
|
||||
|
||||
### Example Output
|
||||
- Begin with a brief introduction summarizing the event or query topic.
|
||||
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
|
@ -51,6 +51,10 @@ export const wolframAlphaSearchResponsePrompt = `
|
||||
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
|
||||
- You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
|
||||
|
||||
### User instructions
|
||||
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
|
||||
{systemInstructions}
|
||||
|
||||
### Example Output
|
||||
- Begin with a brief introduction summarizing the event or query topic.
|
||||
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
|
@ -7,6 +7,10 @@ You have to cite the answer using [number] notation. You must cite the sentences
|
||||
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
|
||||
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
|
||||
|
||||
### User instructions
|
||||
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
|
||||
{systemInstructions}
|
||||
|
||||
<context>
|
||||
{context}
|
||||
</context>
|
@ -51,6 +51,10 @@ export const youtubeSearchResponsePrompt = `
|
||||
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
|
||||
- You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcrip
|
||||
|
||||
### User instructions
|
||||
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
|
||||
{systemInstructions}
|
||||
|
||||
### Example Output
|
||||
- Begin with a brief introduction summarizing the event or query topic.
|
||||
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
|
@ -1,6 +1,43 @@
|
||||
import { ChatAnthropic } from '@langchain/anthropic';
|
||||
import { getAnthropicApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { ChatModel } from '.';
|
||||
import { getAnthropicApiKey } from '../config';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'anthropic',
|
||||
displayName: 'Anthropic',
|
||||
};
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const anthropicChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Claude 3.7 Sonnet',
|
||||
key: 'claude-3-7-sonnet-20250219',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3.5 Haiku',
|
||||
key: 'claude-3-5-haiku-20241022',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3.5 Sonnet v2',
|
||||
key: 'claude-3-5-sonnet-20241022',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3.5 Sonnet',
|
||||
key: 'claude-3-5-sonnet-20240620',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3 Opus',
|
||||
key: 'claude-3-opus-20240229',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3 Sonnet',
|
||||
key: 'claude-3-sonnet-20240229',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3 Haiku',
|
||||
key: 'claude-3-haiku-20240307',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadAnthropicChatModels = async () => {
|
||||
const anthropicApiKey = getAnthropicApiKey();
|
||||
@ -8,52 +45,22 @@ export const loadAnthropicChatModels = async () => {
|
||||
if (!anthropicApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'claude-3-5-sonnet-20241022': {
|
||||
displayName: 'Claude 3.5 Sonnet',
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
anthropicChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatAnthropic({
|
||||
apiKey: anthropicApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-5-sonnet-20241022',
|
||||
}),
|
||||
},
|
||||
'claude-3-5-haiku-20241022': {
|
||||
displayName: 'Claude 3.5 Haiku',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-5-haiku-20241022',
|
||||
}),
|
||||
},
|
||||
'claude-3-opus-20240229': {
|
||||
displayName: 'Claude 3 Opus',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-opus-20240229',
|
||||
}),
|
||||
},
|
||||
'claude-3-sonnet-20240229': {
|
||||
displayName: 'Claude 3 Sonnet',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-sonnet-20240229',
|
||||
}),
|
||||
},
|
||||
'claude-3-haiku-20240307': {
|
||||
displayName: 'Claude 3 Haiku',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-haiku-20240307',
|
||||
}),
|
||||
},
|
||||
};
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Anthropic models: ${err}`);
|
||||
console.error(`Error loading Anthropic models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
49
src/lib/providers/deepseek.ts
Normal file
49
src/lib/providers/deepseek.ts
Normal file
@ -0,0 +1,49 @@
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { getDeepseekApiKey } from '../config';
|
||||
import { ChatModel } from '.';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'deepseek',
|
||||
displayName: 'Deepseek AI',
|
||||
};
|
||||
|
||||
const deepseekChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Deepseek Chat (Deepseek V3)',
|
||||
key: 'deepseek-chat',
|
||||
},
|
||||
{
|
||||
displayName: 'Deepseek Reasoner (Deepseek R1)',
|
||||
key: 'deepseek-reasoner',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadDeepseekChatModels = async () => {
|
||||
const deepseekApiKey = getDeepseekApiKey();
|
||||
|
||||
if (!deepseekApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
deepseekChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey: deepseekApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: 'https://api.deepseek.com',
|
||||
},
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
console.error(`Error loading Deepseek models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
@ -2,8 +2,57 @@ import {
|
||||
ChatGoogleGenerativeAI,
|
||||
GoogleGenerativeAIEmbeddings,
|
||||
} from '@langchain/google-genai';
|
||||
import { getGeminiApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { getGeminiApiKey } from '../config';
|
||||
import { ChatModel, EmbeddingModel } from '.';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'gemini',
|
||||
displayName: 'Google Gemini',
|
||||
};
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { Embeddings } from '@langchain/core/embeddings';
|
||||
|
||||
const geminiChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Gemini 2.5 Pro Experimental',
|
||||
key: 'gemini-2.5-pro-exp-03-25',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 2.0 Flash',
|
||||
key: 'gemini-2.0-flash',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 2.0 Flash-Lite',
|
||||
key: 'gemini-2.0-flash-lite',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 2.0 Flash Thinking Experimental',
|
||||
key: 'gemini-2.0-flash-thinking-exp-01-21',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 1.5 Flash',
|
||||
key: 'gemini-1.5-flash',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 1.5 Flash-8B',
|
||||
key: 'gemini-1.5-flash-8b',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 1.5 Pro',
|
||||
key: 'gemini-1.5-pro',
|
||||
},
|
||||
];
|
||||
|
||||
const geminiEmbeddingModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Text Embedding 004',
|
||||
key: 'models/text-embedding-004',
|
||||
},
|
||||
{
|
||||
displayName: 'Embedding 001',
|
||||
key: 'models/embedding-001',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadGeminiChatModels = async () => {
|
||||
const geminiApiKey = getGeminiApiKey();
|
||||
@ -11,75 +60,47 @@ export const loadGeminiChatModels = async () => {
|
||||
if (!geminiApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'gemini-1.5-flash': {
|
||||
displayName: 'Gemini 1.5 Flash',
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
geminiChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-1.5-flash',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-1.5-flash-8b': {
|
||||
displayName: 'Gemini 1.5 Flash 8B',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-1.5-flash-8b',
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-1.5-pro': {
|
||||
displayName: 'Gemini 1.5 Pro',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-1.5-pro',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-2.0-flash-exp': {
|
||||
displayName: 'Gemini 2.0 Flash Exp',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-2.0-flash-exp',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-2.0-flash-thinking-exp-01-21': {
|
||||
displayName: 'Gemini 2.0 Flash Thinking Exp 01-21',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-2.0-flash-thinking-exp-01-21',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
};
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Gemini models: ${err}`);
|
||||
console.error(`Error loading Gemini models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
export const loadGeminiEmbeddingsModels = async () => {
|
||||
export const loadGeminiEmbeddingModels = async () => {
|
||||
const geminiApiKey = getGeminiApiKey();
|
||||
|
||||
if (!geminiApiKey) return {};
|
||||
|
||||
try {
|
||||
const embeddingModels = {
|
||||
'text-embedding-004': {
|
||||
displayName: 'Text Embedding',
|
||||
const embeddingModels: Record<string, EmbeddingModel> = {};
|
||||
|
||||
geminiEmbeddingModels.forEach((model) => {
|
||||
embeddingModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new GoogleGenerativeAIEmbeddings({
|
||||
apiKey: geminiApiKey,
|
||||
modelName: 'text-embedding-004',
|
||||
}),
|
||||
},
|
||||
};
|
||||
modelName: model.key,
|
||||
}) as unknown as Embeddings,
|
||||
};
|
||||
});
|
||||
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Gemini embeddings model: ${err}`);
|
||||
console.error(`Error loading OpenAI embeddings models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
@ -1,6 +1,91 @@
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { getGroqApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { getGroqApiKey } from '../config';
|
||||
import { ChatModel } from '.';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'groq',
|
||||
displayName: 'Groq',
|
||||
};
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const groqChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Gemma2 9B IT',
|
||||
key: 'gemma2-9b-it',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.3 70B Versatile',
|
||||
key: 'llama-3.3-70b-versatile',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.1 8B Instant',
|
||||
key: 'llama-3.1-8b-instant',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama3 70B 8192',
|
||||
key: 'llama3-70b-8192',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama3 8B 8192',
|
||||
key: 'llama3-8b-8192',
|
||||
},
|
||||
{
|
||||
displayName: 'Mixtral 8x7B 32768',
|
||||
key: 'mixtral-8x7b-32768',
|
||||
},
|
||||
{
|
||||
displayName: 'Qwen QWQ 32B (Preview)',
|
||||
key: 'qwen-qwq-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'Mistral Saba 24B (Preview)',
|
||||
key: 'mistral-saba-24b',
|
||||
},
|
||||
{
|
||||
displayName: 'Qwen 2.5 Coder 32B (Preview)',
|
||||
key: 'qwen-2.5-coder-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'Qwen 2.5 32B (Preview)',
|
||||
key: 'qwen-2.5-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
|
||||
key: 'deepseek-r1-distill-qwen-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
|
||||
key: 'deepseek-r1-distill-llama-70b',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.3 70B SpecDec (Preview)',
|
||||
key: 'llama-3.3-70b-specdec',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 1B Preview (Preview)',
|
||||
key: 'llama-3.2-1b-preview',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 3B Preview (Preview)',
|
||||
key: 'llama-3.2-3b-preview',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 11B Vision Preview (Preview)',
|
||||
key: 'llama-3.2-11b-vision-preview',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 90B Vision Preview (Preview)',
|
||||
key: 'llama-3.2-90b-vision-preview',
|
||||
},
|
||||
/* {
|
||||
displayName: 'Llama 4 Maverick 17B 128E Instruct (Preview)',
|
||||
key: 'meta-llama/llama-4-maverick-17b-128e-instruct',
|
||||
}, */
|
||||
{
|
||||
displayName: 'Llama 4 Scout 17B 16E Instruct (Preview)',
|
||||
key: 'meta-llama/llama-4-scout-17b-16e-instruct',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadGroqChatModels = async () => {
|
||||
const groqApiKey = getGroqApiKey();
|
||||
@ -8,129 +93,25 @@ export const loadGroqChatModels = async () => {
|
||||
if (!groqApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'llama-3.3-70b-versatile': {
|
||||
displayName: 'Llama 3.3 70B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.3-70b-versatile',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
groqChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.2-3b-preview': {
|
||||
displayName: 'Llama 3.2 3B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.2-3b-preview',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.2-11b-vision-preview': {
|
||||
displayName: 'Llama 3.2 11B Vision',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.2-11b-vision-preview',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.2-90b-vision-preview': {
|
||||
displayName: 'Llama 3.2 90B Vision',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.2-90b-vision-preview',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.1-8b-instant': {
|
||||
displayName: 'Llama 3.1 8B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.1-8b-instant',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama3-8b-8192': {
|
||||
displayName: 'LLaMA3 8B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama3-8b-8192',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama3-70b-8192': {
|
||||
displayName: 'LLaMA3 70B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama3-70b-8192',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'mixtral-8x7b-32768': {
|
||||
displayName: 'Mixtral 8x7B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'mixtral-8x7b-32768',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'gemma2-9b-it': {
|
||||
displayName: 'Gemma2 9B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'gemma2-9b-it',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
};
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Groq models: ${err}`);
|
||||
console.error(`Error loading Groq models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
@ -1,33 +1,97 @@
|
||||
import { loadGroqChatModels } from './groq';
|
||||
import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
|
||||
import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
|
||||
import { loadAnthropicChatModels } from './anthropic';
|
||||
import { loadTransformersEmbeddingsModels } from './transformers';
|
||||
import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
|
||||
import { Embeddings } from '@langchain/core/embeddings';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import {
|
||||
loadOpenAIChatModels,
|
||||
loadOpenAIEmbeddingModels,
|
||||
PROVIDER_INFO as OpenAIInfo,
|
||||
PROVIDER_INFO,
|
||||
} from './openai';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '../../config';
|
||||
} from '../config';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import {
|
||||
loadOllamaChatModels,
|
||||
loadOllamaEmbeddingModels,
|
||||
PROVIDER_INFO as OllamaInfo,
|
||||
} from './ollama';
|
||||
import { loadGroqChatModels, PROVIDER_INFO as GroqInfo } from './groq';
|
||||
import {
|
||||
loadAnthropicChatModels,
|
||||
PROVIDER_INFO as AnthropicInfo,
|
||||
} from './anthropic';
|
||||
import {
|
||||
loadGeminiChatModels,
|
||||
loadGeminiEmbeddingModels,
|
||||
PROVIDER_INFO as GeminiInfo,
|
||||
} from './gemini';
|
||||
import {
|
||||
loadTransformersEmbeddingsModels,
|
||||
PROVIDER_INFO as TransformersInfo,
|
||||
} from './transformers';
|
||||
import {
|
||||
loadDeepseekChatModels,
|
||||
PROVIDER_INFO as DeepseekInfo,
|
||||
} from './deepseek';
|
||||
import {
|
||||
loadLMStudioChatModels,
|
||||
loadLMStudioEmbeddingsModels,
|
||||
PROVIDER_INFO as LMStudioInfo,
|
||||
} from './lmstudio';
|
||||
|
||||
const chatModelProviders = {
|
||||
openai: loadOpenAIChatModels,
|
||||
groq: loadGroqChatModels,
|
||||
ollama: loadOllamaChatModels,
|
||||
anthropic: loadAnthropicChatModels,
|
||||
gemini: loadGeminiChatModels,
|
||||
export const PROVIDER_METADATA = {
|
||||
openai: OpenAIInfo,
|
||||
ollama: OllamaInfo,
|
||||
groq: GroqInfo,
|
||||
anthropic: AnthropicInfo,
|
||||
gemini: GeminiInfo,
|
||||
transformers: TransformersInfo,
|
||||
deepseek: DeepseekInfo,
|
||||
lmstudio: LMStudioInfo,
|
||||
custom_openai: {
|
||||
key: 'custom_openai',
|
||||
displayName: 'Custom OpenAI',
|
||||
},
|
||||
};
|
||||
|
||||
const embeddingModelProviders = {
|
||||
openai: loadOpenAIEmbeddingsModels,
|
||||
local: loadTransformersEmbeddingsModels,
|
||||
ollama: loadOllamaEmbeddingsModels,
|
||||
gemini: loadGeminiEmbeddingsModels,
|
||||
export interface ChatModel {
|
||||
displayName: string;
|
||||
model: BaseChatModel;
|
||||
}
|
||||
|
||||
export interface EmbeddingModel {
|
||||
displayName: string;
|
||||
model: Embeddings;
|
||||
}
|
||||
|
||||
export const chatModelProviders: Record<
|
||||
string,
|
||||
() => Promise<Record<string, ChatModel>>
|
||||
> = {
|
||||
openai: loadOpenAIChatModels,
|
||||
ollama: loadOllamaChatModels,
|
||||
groq: loadGroqChatModels,
|
||||
anthropic: loadAnthropicChatModels,
|
||||
gemini: loadGeminiChatModels,
|
||||
deepseek: loadDeepseekChatModels,
|
||||
lmstudio: loadLMStudioChatModels,
|
||||
};
|
||||
|
||||
export const embeddingModelProviders: Record<
|
||||
string,
|
||||
() => Promise<Record<string, EmbeddingModel>>
|
||||
> = {
|
||||
openai: loadOpenAIEmbeddingModels,
|
||||
ollama: loadOllamaEmbeddingModels,
|
||||
gemini: loadGeminiEmbeddingModels,
|
||||
transformers: loadTransformersEmbeddingsModels,
|
||||
lmstudio: loadLMStudioEmbeddingsModels,
|
||||
};
|
||||
|
||||
export const getAvailableChatModelProviders = async () => {
|
||||
const models = {};
|
||||
const models: Record<string, Record<string, ChatModel>> = {};
|
||||
|
||||
for (const provider in chatModelProviders) {
|
||||
const providerModels = await chatModelProviders[provider]();
|
||||
@ -52,7 +116,7 @@ export const getAvailableChatModelProviders = async () => {
|
||||
configuration: {
|
||||
baseURL: customOpenAiApiUrl,
|
||||
},
|
||||
}),
|
||||
}) as unknown as BaseChatModel,
|
||||
},
|
||||
}
|
||||
: {}),
|
||||
@ -62,7 +126,7 @@ export const getAvailableChatModelProviders = async () => {
|
||||
};
|
||||
|
||||
export const getAvailableEmbeddingModelProviders = async () => {
|
||||
const models = {};
|
||||
const models: Record<string, Record<string, EmbeddingModel>> = {};
|
||||
|
||||
for (const provider in embeddingModelProviders) {
|
||||
const providerModels = await embeddingModelProviders[provider]();
|
||||
|
100
src/lib/providers/lmstudio.ts
Normal file
100
src/lib/providers/lmstudio.ts
Normal file
@ -0,0 +1,100 @@
|
||||
import { getKeepAlive, getLMStudioApiEndpoint } from '../config';
|
||||
import axios from 'axios';
|
||||
import { ChatModel, EmbeddingModel } from '.';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'lmstudio',
|
||||
displayName: 'LM Studio',
|
||||
};
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { OpenAIEmbeddings } from '@langchain/openai';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { Embeddings } from '@langchain/core/embeddings';
|
||||
|
||||
interface LMStudioModel {
|
||||
id: string;
|
||||
name?: string;
|
||||
}
|
||||
|
||||
const ensureV1Endpoint = (endpoint: string): string =>
|
||||
endpoint.endsWith('/v1') ? endpoint : `${endpoint}/v1`;
|
||||
|
||||
const checkServerAvailability = async (endpoint: string): Promise<boolean> => {
|
||||
try {
|
||||
await axios.get(`${ensureV1Endpoint(endpoint)}/models`, {
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
});
|
||||
return true;
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
};
|
||||
|
||||
export const loadLMStudioChatModels = async () => {
|
||||
const endpoint = getLMStudioApiEndpoint();
|
||||
|
||||
if (!endpoint) return {};
|
||||
if (!(await checkServerAvailability(endpoint))) return {};
|
||||
|
||||
try {
|
||||
const response = await axios.get(`${ensureV1Endpoint(endpoint)}/models`, {
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
});
|
||||
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
response.data.data.forEach((model: LMStudioModel) => {
|
||||
chatModels[model.id] = {
|
||||
displayName: model.name || model.id,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey: 'lm-studio',
|
||||
configuration: {
|
||||
baseURL: ensureV1Endpoint(endpoint),
|
||||
},
|
||||
modelName: model.id,
|
||||
temperature: 0.7,
|
||||
streaming: true,
|
||||
maxRetries: 3,
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
console.error(`Error loading LM Studio models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
export const loadLMStudioEmbeddingsModels = async () => {
|
||||
const endpoint = getLMStudioApiEndpoint();
|
||||
|
||||
if (!endpoint) return {};
|
||||
if (!(await checkServerAvailability(endpoint))) return {};
|
||||
|
||||
try {
|
||||
const response = await axios.get(`${ensureV1Endpoint(endpoint)}/models`, {
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
});
|
||||
|
||||
const embeddingsModels: Record<string, EmbeddingModel> = {};
|
||||
|
||||
response.data.data.forEach((model: LMStudioModel) => {
|
||||
embeddingsModels[model.id] = {
|
||||
displayName: model.name || model.id,
|
||||
model: new OpenAIEmbeddings({
|
||||
openAIApiKey: 'lm-studio',
|
||||
configuration: {
|
||||
baseURL: ensureV1Endpoint(endpoint),
|
||||
},
|
||||
modelName: model.id,
|
||||
}) as unknown as Embeddings,
|
||||
};
|
||||
});
|
||||
|
||||
return embeddingsModels;
|
||||
} catch (err) {
|
||||
console.error(`Error loading LM Studio embeddings model: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
@ -1,74 +1,78 @@
|
||||
import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
|
||||
import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { ChatOllama } from '@langchain/community/chat_models/ollama';
|
||||
import axios from 'axios';
|
||||
import { getKeepAlive, getOllamaApiEndpoint } from '../config';
|
||||
import { ChatModel, EmbeddingModel } from '.';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'ollama',
|
||||
displayName: 'Ollama',
|
||||
};
|
||||
import { ChatOllama } from '@langchain/ollama';
|
||||
import { OllamaEmbeddings } from '@langchain/ollama';
|
||||
|
||||
export const loadOllamaChatModels = async () => {
|
||||
const ollamaEndpoint = getOllamaApiEndpoint();
|
||||
const keepAlive = getKeepAlive();
|
||||
const ollamaApiEndpoint = getOllamaApiEndpoint();
|
||||
|
||||
if (!ollamaEndpoint) return {};
|
||||
if (!ollamaApiEndpoint) return {};
|
||||
|
||||
try {
|
||||
const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
|
||||
const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
});
|
||||
|
||||
const { models: ollamaModels } = response.data;
|
||||
const { models } = res.data;
|
||||
|
||||
const chatModels = ollamaModels.reduce((acc, model) => {
|
||||
acc[model.model] = {
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
models.forEach((model: any) => {
|
||||
chatModels[model.model] = {
|
||||
displayName: model.name,
|
||||
model: new ChatOllama({
|
||||
baseUrl: ollamaEndpoint,
|
||||
baseUrl: ollamaApiEndpoint,
|
||||
model: model.model,
|
||||
temperature: 0.7,
|
||||
keepAlive: keepAlive,
|
||||
keepAlive: getKeepAlive(),
|
||||
}),
|
||||
};
|
||||
|
||||
return acc;
|
||||
}, {});
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Ollama models: ${err}`);
|
||||
console.error(`Error loading Ollama models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
export const loadOllamaEmbeddingsModels = async () => {
|
||||
const ollamaEndpoint = getOllamaApiEndpoint();
|
||||
export const loadOllamaEmbeddingModels = async () => {
|
||||
const ollamaApiEndpoint = getOllamaApiEndpoint();
|
||||
|
||||
if (!ollamaEndpoint) return {};
|
||||
if (!ollamaApiEndpoint) return {};
|
||||
|
||||
try {
|
||||
const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
|
||||
const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
});
|
||||
|
||||
const { models: ollamaModels } = response.data;
|
||||
const { models } = res.data;
|
||||
|
||||
const embeddingsModels = ollamaModels.reduce((acc, model) => {
|
||||
acc[model.model] = {
|
||||
const embeddingModels: Record<string, EmbeddingModel> = {};
|
||||
|
||||
models.forEach((model: any) => {
|
||||
embeddingModels[model.model] = {
|
||||
displayName: model.name,
|
||||
model: new OllamaEmbeddings({
|
||||
baseUrl: ollamaEndpoint,
|
||||
baseUrl: ollamaApiEndpoint,
|
||||
model: model.model,
|
||||
}),
|
||||
};
|
||||
});
|
||||
|
||||
return acc;
|
||||
}, {});
|
||||
|
||||
return embeddingsModels;
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Ollama embeddings model: ${err}`);
|
||||
console.error(`Error loading Ollama embeddings models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
@ -1,89 +1,95 @@
|
||||
import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
|
||||
import { getOpenaiApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { getOpenaiApiKey } from '../config';
|
||||
import { ChatModel, EmbeddingModel } from '.';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'openai',
|
||||
displayName: 'OpenAI',
|
||||
};
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { Embeddings } from '@langchain/core/embeddings';
|
||||
|
||||
const openaiChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'GPT-3.5 Turbo',
|
||||
key: 'gpt-3.5-turbo',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4',
|
||||
key: 'gpt-4',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4 turbo',
|
||||
key: 'gpt-4-turbo',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4 omni',
|
||||
key: 'gpt-4o',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4 omni mini',
|
||||
key: 'gpt-4o-mini',
|
||||
},
|
||||
];
|
||||
|
||||
const openaiEmbeddingModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Text Embedding 3 Small',
|
||||
key: 'text-embedding-3-small',
|
||||
},
|
||||
{
|
||||
displayName: 'Text Embedding 3 Large',
|
||||
key: 'text-embedding-3-large',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadOpenAIChatModels = async () => {
|
||||
const openAIApiKey = getOpenaiApiKey();
|
||||
const openaiApiKey = getOpenaiApiKey();
|
||||
|
||||
if (!openAIApiKey) return {};
|
||||
if (!openaiApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'gpt-3.5-turbo': {
|
||||
displayName: 'GPT-3.5 Turbo',
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
openaiChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-3.5-turbo',
|
||||
openAIApiKey: openaiApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4': {
|
||||
displayName: 'GPT-4',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4-turbo': {
|
||||
displayName: 'GPT-4 turbo',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4-turbo',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4o': {
|
||||
displayName: 'GPT-4 omni',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4o-mini': {
|
||||
displayName: 'GPT-4 omni mini',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4o-mini',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
};
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading OpenAI models: ${err}`);
|
||||
console.error(`Error loading OpenAI models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
export const loadOpenAIEmbeddingsModels = async () => {
|
||||
const openAIApiKey = getOpenaiApiKey();
|
||||
export const loadOpenAIEmbeddingModels = async () => {
|
||||
const openaiApiKey = getOpenaiApiKey();
|
||||
|
||||
if (!openAIApiKey) return {};
|
||||
if (!openaiApiKey) return {};
|
||||
|
||||
try {
|
||||
const embeddingModels = {
|
||||
'text-embedding-3-small': {
|
||||
displayName: 'Text Embedding 3 Small',
|
||||
const embeddingModels: Record<string, EmbeddingModel> = {};
|
||||
|
||||
openaiEmbeddingModels.forEach((model) => {
|
||||
embeddingModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new OpenAIEmbeddings({
|
||||
openAIApiKey,
|
||||
modelName: 'text-embedding-3-small',
|
||||
}),
|
||||
},
|
||||
'text-embedding-3-large': {
|
||||
displayName: 'Text Embedding 3 Large',
|
||||
model: new OpenAIEmbeddings({
|
||||
openAIApiKey,
|
||||
modelName: 'text-embedding-3-large',
|
||||
}),
|
||||
},
|
||||
};
|
||||
openAIApiKey: openaiApiKey,
|
||||
modelName: model.key,
|
||||
}) as unknown as Embeddings,
|
||||
};
|
||||
});
|
||||
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading OpenAI embeddings model: ${err}`);
|
||||
console.error(`Error loading OpenAI embeddings models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
@ -1,6 +1,10 @@
|
||||
import logger from '../../utils/logger';
|
||||
import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
|
||||
|
||||
export const PROVIDER_INFO = {
|
||||
key: 'transformers',
|
||||
displayName: 'Hugging Face',
|
||||
};
|
||||
|
||||
export const loadTransformersEmbeddingsModels = async () => {
|
||||
try {
|
||||
const embeddingModels = {
|
||||
@ -26,7 +30,7 @@ export const loadTransformersEmbeddingsModels = async () => {
|
||||
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Transformers embeddings model: ${err}`);
|
||||
console.error(`Error loading Transformers embeddings model: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
59
src/lib/search/index.ts
Normal file
59
src/lib/search/index.ts
Normal file
@ -0,0 +1,59 @@
|
||||
import MetaSearchAgent from '@/lib/search/metaSearchAgent';
|
||||
import prompts from '../prompts';
|
||||
|
||||
export const searchHandlers: Record<string, MetaSearchAgent> = {
|
||||
webSearch: new MetaSearchAgent({
|
||||
activeEngines: [],
|
||||
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
|
||||
responsePrompt: prompts.webSearchResponsePrompt,
|
||||
rerank: true,
|
||||
rerankThreshold: 0.3,
|
||||
searchWeb: true,
|
||||
summarizer: true,
|
||||
}),
|
||||
academicSearch: new MetaSearchAgent({
|
||||
activeEngines: ['arxiv', 'google scholar', 'pubmed'],
|
||||
queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
|
||||
responsePrompt: prompts.academicSearchResponsePrompt,
|
||||
rerank: true,
|
||||
rerankThreshold: 0,
|
||||
searchWeb: true,
|
||||
summarizer: false,
|
||||
}),
|
||||
writingAssistant: new MetaSearchAgent({
|
||||
activeEngines: [],
|
||||
queryGeneratorPrompt: '',
|
||||
responsePrompt: prompts.writingAssistantPrompt,
|
||||
rerank: true,
|
||||
rerankThreshold: 0,
|
||||
searchWeb: false,
|
||||
summarizer: false,
|
||||
}),
|
||||
wolframAlphaSearch: new MetaSearchAgent({
|
||||
activeEngines: ['wolframalpha'],
|
||||
queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
|
||||
responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
|
||||
rerank: false,
|
||||
rerankThreshold: 0,
|
||||
searchWeb: true,
|
||||
summarizer: false,
|
||||
}),
|
||||
youtubeSearch: new MetaSearchAgent({
|
||||
activeEngines: ['youtube'],
|
||||
queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
|
||||
responsePrompt: prompts.youtubeSearchResponsePrompt,
|
||||
rerank: true,
|
||||
rerankThreshold: 0.3,
|
||||
searchWeb: true,
|
||||
summarizer: false,
|
||||
}),
|
||||
redditSearch: new MetaSearchAgent({
|
||||
activeEngines: ['reddit'],
|
||||
queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
|
||||
responsePrompt: prompts.redditSearchResponsePrompt,
|
||||
rerank: true,
|
||||
rerankThreshold: 0.3,
|
||||
searchWeb: true,
|
||||
summarizer: false,
|
||||
}),
|
||||
};
|
@ -13,18 +13,17 @@ import {
|
||||
} from '@langchain/core/runnables';
|
||||
import { BaseMessage } from '@langchain/core/messages';
|
||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
||||
import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
|
||||
import LineOutputParser from '../lib/outputParsers/lineOutputParser';
|
||||
import LineListOutputParser from '../outputParsers/listLineOutputParser';
|
||||
import LineOutputParser from '../outputParsers/lineOutputParser';
|
||||
import { getDocumentsFromLinks } from '../utils/documents';
|
||||
import { Document } from 'langchain/document';
|
||||
import { searchSearxng } from '../lib/searxng';
|
||||
import path from 'path';
|
||||
import fs from 'fs';
|
||||
import { searchSearxng } from '../searxng';
|
||||
import path from 'node:path';
|
||||
import fs from 'node:fs';
|
||||
import computeSimilarity from '../utils/computeSimilarity';
|
||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||
import eventEmitter from 'events';
|
||||
import { StreamEvent } from '@langchain/core/tracers/log_stream';
|
||||
import { IterableReadableStream } from '@langchain/core/utils/stream';
|
||||
|
||||
export interface MetaSearchAgentType {
|
||||
searchAndAnswer: (
|
||||
@ -34,6 +33,7 @@ export interface MetaSearchAgentType {
|
||||
embeddings: Embeddings,
|
||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
||||
fileIds: string[],
|
||||
systemInstructions: string,
|
||||
) => Promise<eventEmitter>;
|
||||
}
|
||||
|
||||
@ -90,7 +90,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
question = 'summarize';
|
||||
}
|
||||
|
||||
let docs = [];
|
||||
let docs: Document[] = [];
|
||||
|
||||
const linkDocs = await getDocumentsFromLinks({ links });
|
||||
|
||||
@ -203,6 +203,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
|
||||
return { query: question, docs: docs };
|
||||
} else {
|
||||
question = question.replace(/<think>.*?<\/think>/g, '');
|
||||
|
||||
const res = await searchSearxng(question, {
|
||||
language: 'en',
|
||||
engines: this.config.activeEngines,
|
||||
@ -235,9 +237,11 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
fileIds: string[],
|
||||
embeddings: Embeddings,
|
||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
||||
systemInstructions: string,
|
||||
) {
|
||||
return RunnableSequence.from([
|
||||
RunnableMap.from({
|
||||
systemInstructions: () => systemInstructions,
|
||||
query: (input: BasicChainInput) => input.query,
|
||||
chat_history: (input: BasicChainInput) => input.chat_history,
|
||||
date: () => new Date().toISOString(),
|
||||
@ -311,7 +315,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
|
||||
|
||||
const fileSimilaritySearchObject = content.contents.map(
|
||||
(c: string, i) => {
|
||||
(c: string, i: number) => {
|
||||
return {
|
||||
fileName: content.title,
|
||||
content: c,
|
||||
@ -414,6 +418,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
|
||||
return sortedDocs;
|
||||
}
|
||||
|
||||
return [];
|
||||
}
|
||||
|
||||
private processDocs(docs: Document[]) {
|
||||
@ -426,7 +432,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
}
|
||||
|
||||
private async handleStream(
|
||||
stream: IterableReadableStream<StreamEvent>,
|
||||
stream: AsyncGenerator<StreamEvent, any, any>,
|
||||
emitter: eventEmitter,
|
||||
) {
|
||||
for await (const event of stream) {
|
||||
@ -465,6 +471,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
embeddings: Embeddings,
|
||||
optimizationMode: 'speed' | 'balanced' | 'quality',
|
||||
fileIds: string[],
|
||||
systemInstructions: string,
|
||||
) {
|
||||
const emitter = new eventEmitter();
|
||||
|
||||
@ -473,6 +480,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
|
||||
fileIds,
|
||||
embeddings,
|
||||
optimizationMode,
|
||||
systemInstructions,
|
||||
);
|
||||
|
||||
const stream = answeringChain.streamEvents(
|
@ -1,5 +1,5 @@
|
||||
import axios from 'axios';
|
||||
import { getSearxngApiEndpoint } from '../config';
|
||||
import { getSearxngApiEndpoint } from './config';
|
||||
|
||||
interface SearxngSearchOptions {
|
||||
categories?: string[];
|
||||
@ -30,11 +30,12 @@ export const searchSearxng = async (
|
||||
|
||||
if (opts) {
|
||||
Object.keys(opts).forEach((key) => {
|
||||
if (Array.isArray(opts[key])) {
|
||||
url.searchParams.append(key, opts[key].join(','));
|
||||
const value = opts[key as keyof SearxngSearchOptions];
|
||||
if (Array.isArray(value)) {
|
||||
url.searchParams.append(key, value.join(','));
|
||||
return;
|
||||
}
|
||||
url.searchParams.append(key, opts[key]);
|
||||
url.searchParams.append(key, value as string);
|
||||
});
|
||||
}
|
||||
|
||||
|
5
src/lib/types/compute-dot.d.ts
vendored
Normal file
5
src/lib/types/compute-dot.d.ts
vendored
Normal file
@ -0,0 +1,5 @@
|
||||
declare function computeDot(vectorA: number[], vectorB: number[]): number;
|
||||
|
||||
declare module 'compute-dot' {
|
||||
export default computeDot;
|
||||
}
|
@ -6,7 +6,7 @@ const computeSimilarity = (x: number[], y: number[]): number => {
|
||||
const similarityMeasure = getSimilarityMeasure();
|
||||
|
||||
if (similarityMeasure === 'cosine') {
|
||||
return cosineSimilarity(x, y);
|
||||
return cosineSimilarity(x, y) as number;
|
||||
} else if (similarityMeasure === 'dot') {
|
||||
return dot(x, y);
|
||||
}
|
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user