mirror of
https://github.com/khoj-ai/khoj.git
synced 2024-11-23 15:38:55 +01:00
Merge branch 'master' of github.com:khoj-ai/khoj into features/improve-tool-selection
This commit is contained in:
sabaimran
commit
7e662a05f8
13 changed files with 263 additions and 112 deletions
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@ -37,7 +37,7 @@ ENV PYTHONPATH=/app/src:$PYTHONPATH
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# Go to the directory src/interface/web and export the built Next.js assets
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WORKDIR /app/src/interface/web
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RUN bash -c "yarn install --frozen-lockfile --verbose && yarn ciexport && yarn cache clean"
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RUN bash -c "yarn install --frozen-lockfile && yarn ciexport && yarn cache clean"
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WORKDIR /app
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# Run the Application
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|
|
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@ -37,6 +37,7 @@ services:
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volumes:
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- khoj_config:/root/.khoj/
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- khoj_models:/root/.cache/torch/sentence_transformers
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- khoj_models:/root/.cache/huggingface
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# Use 0.0.0.0 to explicitly set the host ip for the service on the container. https://pythonspeed.com/articles/docker-connection-refused/
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environment:
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- POSTGRES_DB=postgres
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@ -48,12 +49,17 @@ services:
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- KHOJ_DEBUG=False
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- KHOJ_ADMIN_EMAIL=username@example.com
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- KHOJ_ADMIN_PASSWORD=password
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# Uncomment lines below to use chat models by each provider.
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# Uncomment line below to use with Ollama running on your local machine at localhost:11434.
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# Change URL to use with other OpenAI API compatible providers like VLLM, LMStudio etc.
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# - OPENAI_API_BASE=http://host.docker.internal:11434/v1/
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#
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# Uncomment appropriate lines below to use chat models by OpenAI, Anthropic, Google.
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# Ensure you set your provider specific API keys.
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# ---
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# - OPENAI_API_KEY=your_openai_api_key
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# - GEMINI_API_KEY=your_gemini_api_key
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# - ANTHROPIC_API_KEY=your_anthropic_api_key
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#
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# Uncomment the necessary lines below to make your instance publicly accessible.
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# Replace the KHOJ_DOMAIN with either your domain or IP address (no http/https prefix).
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# Proceed with caution, especially if you are using anonymous mode.
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@ -1,33 +0,0 @@
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# Ollama
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:::info
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This is only helpful for self-hosted users. If you're using [Khoj Cloud](https://app.khoj.dev), you're limited to our first-party models.
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:::
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:::info
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Khoj natively supports local LLMs [available on HuggingFace in GGUF format](https://huggingface.co/models?library=gguf). Using an OpenAI API proxy with Khoj maybe useful for ease of setup, trying new models or using commercial LLMs via API.
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:::
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Ollama allows you to run [many popular open-source LLMs](https://ollama.com/library) locally from your terminal.
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For folks comfortable with the terminal, Ollama's terminal based flows can ease setup and management of chat models.
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Ollama exposes a local [OpenAI API compatible server](https://github.com/ollama/ollama/blob/main/docs/openai.md#models). This makes it possible to use chat models from Ollama to create your personal AI agents with Khoj.
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## Setup
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1. Setup Ollama: https://ollama.com/
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2. Start your preferred model with Ollama. For example,
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```bash
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ollama run llama3.1
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```
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3. Create a new [OpenAI Processor Conversation Config](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/add) on your Khoj admin panel
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- Name: `ollama`
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- Api Key: `any string`
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- Api Base Url: `http://localhost:11434/v1/` (default for Ollama)
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4. Create a new [Chat Model Option](http://localhost:42110/server/admin/database/chatmodeloptions/add) on your Khoj admin panel.
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- Name: `llama3.1` (replace with the name of your local model)
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- Model Type: `Openai`
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- Openai Config: `<the ollama config you created in step 3>`
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- Max prompt size: `20000` (replace with the max prompt size of your model)
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5. Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
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That's it! You should now be able to chat with your Ollama model from Khoj. If you want to add additional models running on Ollama, repeat step 6 for each model.
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78
documentation/docs/advanced/ollama.mdx
Normal file
78
documentation/docs/advanced/ollama.mdx
Normal file
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@ -0,0 +1,78 @@
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|||
# Ollama
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||||
|
||||
```mdx-code-block
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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||||
```
|
||||
|
||||
:::info
|
||||
This is only helpful for self-hosted users. If you're using [Khoj Cloud](https://app.khoj.dev), you can use our first-party supported models.
|
||||
:::
|
||||
|
||||
:::info
|
||||
Khoj can directly run local LLMs [available on HuggingFace in GGUF format](https://huggingface.co/models?library=gguf). The integration with Ollama is useful to run Khoj on Docker and have the chat models use your GPU or to try new models via CLI.
|
||||
:::
|
||||
|
||||
Ollama allows you to run [many popular open-source LLMs](https://ollama.com/library) locally from your terminal.
|
||||
For folks comfortable with the terminal, Ollama's terminal based flows can ease setup and management of chat models.
|
||||
|
||||
Ollama exposes a local [OpenAI API compatible server](https://github.com/ollama/ollama/blob/main/docs/openai.md#models). This makes it possible to use chat models from Ollama with Khoj.
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|
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## Setup
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||||
:::info
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||||
Restart your Khoj server after first run or update to the settings below to ensure all settings are applied correctly.
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:::
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||||
|
||||
<Tabs groupId="type" queryString>
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<TabItem value="first-run" label="First Run">
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||||
<Tabs groupId="server" queryString>
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<TabItem value="docker" label="Docker">
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1. Setup Ollama: https://ollama.com/
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2. Download your preferred chat model with Ollama. For example,
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||||
```bash
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ollama pull llama3.1
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```
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3. Uncomment `OPENAI_API_BASE` environment variable in your downloaded Khoj [docker-compose.yml](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml#:~:text=OPENAI_API_BASE)
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4. Start Khoj docker for the first time to automatically integrate and load models from the Ollama running on your host machine
|
||||
```bash
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# run below command in the directory where you downloaded the Khoj docker-compose.yml
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docker-compose up
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```
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</TabItem>
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|
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<TabItem value="pip" label="Pip">
|
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1. Setup Ollama: https://ollama.com/
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2. Download your preferred chat model with Ollama. For example,
|
||||
```bash
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ollama pull llama3.1
|
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```
|
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3. Set `OPENAI_API_BASE` environment variable to `http://localhost:11434/v1` in your shell before starting Khoj for the first time
|
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```bash
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export OPENAI_API_BASE="http://localhost:11434/v1"
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khoj --anonymous-mode
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```
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||||
</TabItem>
|
||||
</Tabs>
|
||||
</TabItem>
|
||||
<TabItem value="update" label="Update">
|
||||
1. Setup Ollama: https://ollama.com/
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||||
2. Download your preferred chat model with Ollama. For example,
|
||||
```bash
|
||||
ollama pull llama3.1
|
||||
```
|
||||
3. Create a new [OpenAI Processor Conversation Config](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/add) on your Khoj admin panel
|
||||
- Name: `ollama`
|
||||
- Api Key: `any string`
|
||||
- Api Base Url: `http://localhost:11434/v1/` (default for Ollama)
|
||||
4. Create a new [Chat Model Option](http://localhost:42110/server/admin/database/chatmodeloptions/add) on your Khoj admin panel.
|
||||
- Name: `llama3.1` (replace with the name of your local model)
|
||||
- Model Type: `Openai`
|
||||
- Openai Config: `<the ollama config you created in step 3>`
|
||||
- Max prompt size: `20000` (replace with the max prompt size of your model)
|
||||
5. Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
|
||||
|
||||
If you want to add additional models running on Ollama, repeat step 4 for each model.
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
That's it! You should now be able to chat with your Ollama model from Khoj.
|
||||
|
|
@ -10,6 +10,17 @@ To generate images, you just need to provide a prompt to Khoj in which the image
|
|||
|
||||
## Setup (Self-Hosting)
|
||||
|
||||
Right now, we only support integration with OpenAI's DALL-E. You need to have an OpenAI API key to use this feature. Here's how you can set it up:
|
||||
1. Setup your OpenAI API key. See instructions [here](/get-started/setup#2-configure)
|
||||
2. Create a text to image config at http://localhost:42110/server/admin/database/texttoimagemodelconfig/. We recommend the value `dall-e-3`.
|
||||
You have a couple of image generation options.
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||||
|
||||
### Image Generation Models
|
||||
|
||||
We support most state of the art image generation models, including Ideogram, Flux, and Stable Diffusion. These will run using [Replicate](https://replicate.com). Here's how to set them up:
|
||||
|
||||
1. Get a Replicate API key [here](https://replicate.com/account/api-tokens).
|
||||
1. Create a new [Text to Image Model](https://app.khoj.dev/server/admin/database/texttoimagemodelconfig/). Set the `type` to `Replicate`. Use any of the model names you see [on this list](https://replicate.com/pricing#image-models).
|
||||
|
||||
### OpenAI
|
||||
|
||||
1. Get [an OpenAI API key](https://platform.openai.com/settings/organization/api-keys).
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||||
2. Setup your OpenAI API key, if you haven't already. See instructions [here](/get-started/setup#2-configure)
|
||||
3. Create a text to image config at http://localhost:42110/server/admin/database/texttoimagemodelconfig/. We recommend the `model name` `dall-e-3`. Make sure to associate it with the OpenAI API chat configuration you setup in step 2 with `Openai config` field.
|
||||
|
|
|
|||
|
|
@ -19,7 +19,11 @@ These are the general setup instructions for self-hosted Khoj.
|
|||
You can install the Khoj server using either [Docker](?server=docker) or [Pip](?server=pip).
|
||||
|
||||
:::info[Offline Model + GPU]
|
||||
If you want to use the offline chat model and you have a GPU, you should use Installation Option 2 - local setup via the Python package directly. Our Docker image doesn't currently support running the offline chat model on GPU, making inference times really slow.
|
||||
To use the offline chat model with your GPU, we recommend using the Docker setup with Ollama . You can also use the local Khoj setup via the Python package directly.
|
||||
:::
|
||||
|
||||
:::info[First Run]
|
||||
Restart your Khoj server after the first run to ensure all settings are applied correctly.
|
||||
:::
|
||||
|
||||
<Tabs groupId="server" queryString>
|
||||
|
|
@ -28,9 +32,9 @@ If you want to use the offline chat model and you have a GPU, you should use Ins
|
|||
<TabItem value="macos" label="MacOS">
|
||||
<h3>Prerequisites</h3>
|
||||
<h4>Docker</h4>
|
||||
(Option 1) Click here to install [Docker Desktop](https://docs.docker.com/desktop/install/mac-install/). Make sure you also install the [Docker Compose](https://docs.docker.com/desktop/install/mac-install/) tool.
|
||||
- *Option 1*: Click here to install [Docker Desktop](https://docs.docker.com/desktop/install/mac-install/). Make sure you also install the [Docker Compose](https://docs.docker.com/desktop/install/mac-install/) tool.
|
||||
|
||||
(Option 2) Use [Homebrew](https://brew.sh/) to install Docker and Docker Compose.
|
||||
- *Option 2*: Use [Homebrew](https://brew.sh/) to install Docker and Docker Compose.
|
||||
```shell
|
||||
brew install --cask docker
|
||||
brew install docker-compose
|
||||
|
|
@ -41,9 +45,10 @@ If you want to use the offline chat model and you have a GPU, you should use Ins
|
|||
mkdir ~/.khoj && cd ~/.khoj
|
||||
wget https://raw.githubusercontent.com/khoj-ai/khoj/master/docker-compose.yml
|
||||
```
|
||||
2. Configure the environment variables in the docker-compose.yml
|
||||
2. Configure the environment variables in the `docker-compose.yml`
|
||||
- Set `KHOJ_ADMIN_PASSWORD`, `KHOJ_DJANGO_SECRET_KEY` (and optionally the `KHOJ_ADMIN_EMAIL`) to something secure. This allows you to customize Khoj later via the admin panel.
|
||||
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini chat models respectively.
|
||||
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini commercial chat models respectively.
|
||||
- Uncomment `OPENAI_API_BASE` to use [Ollama](/advanced/ollama?type=first-run&server=docker#setup) running on your host machine. Or set it to the URL of your OpenAI compatible API like vLLM or [LMStudio](/advanced/lmstudio).
|
||||
3. Start Khoj by running the following command in the same directory as your docker-compose.yml file.
|
||||
```shell
|
||||
cd ~/.khoj
|
||||
|
|
@ -66,9 +71,10 @@ If you want to use the offline chat model and you have a GPU, you should use Ins
|
|||
mkdir ~/.khoj && cd ~/.khoj
|
||||
wget https://raw.githubusercontent.com/khoj-ai/khoj/master/docker-compose.yml
|
||||
```
|
||||
2. Configure the environment variables in the docker-compose.yml
|
||||
2. Configure the environment variables in the `docker-compose.yml`
|
||||
- Set `KHOJ_ADMIN_PASSWORD`, `KHOJ_DJANGO_SECRET_KEY` (and optionally the `KHOJ_ADMIN_EMAIL`) to something secure. This allows you to customize Khoj later via the admin panel.
|
||||
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini chat models respectively.
|
||||
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini commercial chat models respectively.
|
||||
- Uncomment `OPENAI_API_BASE` to use [Ollama](/advanced/ollama) running on your host machine. Or set it to the URL of your OpenAI compatible API like vLLM or [LMStudio](/advanced/lmstudio).
|
||||
3. Start Khoj by running the following command in the same directory as your docker-compose.yml file.
|
||||
```shell
|
||||
# Windows users should use their WSL2 terminal to run these commands
|
||||
|
|
@ -87,9 +93,10 @@ If you want to use the offline chat model and you have a GPU, you should use Ins
|
|||
mkdir ~/.khoj && cd ~/.khoj
|
||||
wget https://raw.githubusercontent.com/khoj-ai/khoj/master/docker-compose.yml
|
||||
```
|
||||
2. Configure the environment variables in the docker-compose.yml
|
||||
2. Configure the environment variables in the `docker-compose.yml`
|
||||
- Set `KHOJ_ADMIN_PASSWORD`, `KHOJ_DJANGO_SECRET_KEY` (and optionally the `KHOJ_ADMIN_EMAIL`) to something secure. This allows you to customize Khoj later via the admin panel.
|
||||
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini chat models respectively.
|
||||
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini commercial chat models respectively.
|
||||
- Uncomment `OPENAI_API_BASE` to use [Ollama](/advanced/ollama) running on your host machine. Or set it to the URL of your OpenAI compatible API like vLLM or [LMStudio](/advanced/lmstudio).
|
||||
3. Start Khoj by running the following command in the same directory as your docker-compose.yml file.
|
||||
```shell
|
||||
cd ~/.khoj
|
||||
|
|
|
|||
|
|
@ -170,7 +170,12 @@ export const ChatInputArea = forwardRef<HTMLTextAreaElement, ChatInputProps>((pr
|
|||
}
|
||||
|
||||
let messageToSend = message.trim();
|
||||
if (useResearchMode && !messageToSend.startsWith("/research")) {
|
||||
// Check if message starts with an explicit slash command
|
||||
const startsWithSlashCommand =
|
||||
props.chatOptionsData &&
|
||||
Object.keys(props.chatOptionsData).some((cmd) => messageToSend.startsWith(`/${cmd}`));
|
||||
// Only add /research if useResearchMode is enabled and message doesn't already use a slash command
|
||||
if (useResearchMode && !startsWithSlashCommand) {
|
||||
messageToSend = `/research ${messageToSend}`;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -108,7 +108,6 @@ admin.site.register(GithubConfig)
|
|||
admin.site.register(NotionConfig)
|
||||
admin.site.register(UserVoiceModelConfig)
|
||||
admin.site.register(VoiceModelOption)
|
||||
admin.site.register(UserConversationConfig)
|
||||
admin.site.register(UserRequests)
|
||||
|
||||
|
||||
|
|
@ -326,3 +325,35 @@ class ConversationAdmin(admin.ModelAdmin):
|
|||
if "export_selected_minimal_objects" in actions:
|
||||
del actions["export_selected_minimal_objects"]
|
||||
return actions
|
||||
|
||||
|
||||
@admin.register(UserConversationConfig)
|
||||
class UserConversationConfigAdmin(admin.ModelAdmin):
|
||||
list_display = (
|
||||
"id",
|
||||
"get_user_email",
|
||||
"get_chat_model",
|
||||
"get_subscription_type",
|
||||
)
|
||||
search_fields = ("id", "user__email", "setting__chat_model", "user__subscription__type")
|
||||
ordering = ("-updated_at",)
|
||||
|
||||
def get_user_email(self, obj):
|
||||
return obj.user.email
|
||||
|
||||
get_user_email.short_description = "User Email" # type: ignore
|
||||
get_user_email.admin_order_field = "user__email" # type: ignore
|
||||
|
||||
def get_chat_model(self, obj):
|
||||
return obj.setting.chat_model if obj.setting else None
|
||||
|
||||
get_chat_model.short_description = "Chat Model" # type: ignore
|
||||
get_chat_model.admin_order_field = "setting__chat_model" # type: ignore
|
||||
|
||||
def get_subscription_type(self, obj):
|
||||
if hasattr(obj.user, "subscription"):
|
||||
return obj.user.subscription.type
|
||||
return None
|
||||
|
||||
get_subscription_type.short_description = "Subscription Type" # type: ignore
|
||||
get_subscription_type.admin_order_field = "user__subscription__type" # type: ignore
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 200 KiB |
|
|
@ -101,7 +101,7 @@ class PdfToEntries(TextToEntries):
|
|||
tmpf.flush() # Ensure all data is written
|
||||
|
||||
# Load the content using PyMuPDFLoader
|
||||
loader = PyMuPDFLoader(tmpf.name, extract_images=True)
|
||||
loader = PyMuPDFLoader(tmpf.name)
|
||||
pdf_entries_per_file = loader.load()
|
||||
|
||||
# Convert the loaded entries into the desired format
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ default_offline_chat_models = [
|
|||
]
|
||||
default_openai_chat_models = ["gpt-4o-mini", "gpt-4o"]
|
||||
default_gemini_chat_models = ["gemini-1.5-flash", "gemini-1.5-pro"]
|
||||
default_anthropic_chat_models = ["claude-3-5-sonnet-20240620", "claude-3-opus-20240229"]
|
||||
default_anthropic_chat_models = ["claude-3-5-sonnet-20241022", "claude-3-5-haiku-20241022"]
|
||||
|
||||
empty_config = {
|
||||
"search-type": {
|
||||
|
|
|
|||
|
|
@ -2,12 +2,13 @@ import logging
|
|||
import os
|
||||
from typing import Tuple
|
||||
|
||||
import openai
|
||||
|
||||
from khoj.database.adapters import ConversationAdapters
|
||||
from khoj.database.models import (
|
||||
ChatModelOptions,
|
||||
KhojUser,
|
||||
OpenAIProcessorConversationConfig,
|
||||
ServerChatSettings,
|
||||
SpeechToTextModelOptions,
|
||||
TextToImageModelConfig,
|
||||
)
|
||||
|
|
@ -42,14 +43,32 @@ def initialization(interactive: bool = True):
|
|||
"🗣️ Configure chat models available to your server. You can always update these at /server/admin using your admin account"
|
||||
)
|
||||
|
||||
openai_api_base = os.getenv("OPENAI_API_BASE")
|
||||
provider = "Ollama" if openai_api_base and openai_api_base.endswith(":11434/v1/") else "OpenAI"
|
||||
openai_api_key = os.getenv("OPENAI_API_KEY", "placeholder" if openai_api_base else None)
|
||||
default_chat_models = default_openai_chat_models
|
||||
if openai_api_base:
|
||||
# Get available chat models from OpenAI compatible API
|
||||
try:
|
||||
openai_client = openai.OpenAI(api_key=openai_api_key, base_url=openai_api_base)
|
||||
default_chat_models = [model.id for model in openai_client.models.list()]
|
||||
# Put the available default OpenAI models at the top
|
||||
valid_default_models = [model for model in default_openai_chat_models if model in default_chat_models]
|
||||
other_available_models = [model for model in default_chat_models if model not in valid_default_models]
|
||||
default_chat_models = valid_default_models + other_available_models
|
||||
except Exception:
|
||||
logger.warning(f"⚠️ Failed to fetch {provider} chat models. Fallback to default models. Error: {e}")
|
||||
|
||||
# Set up OpenAI's online chat models
|
||||
openai_configured, openai_provider = _setup_chat_model_provider(
|
||||
ChatModelOptions.ModelType.OPENAI,
|
||||
default_openai_chat_models,
|
||||
default_api_key=os.getenv("OPENAI_API_KEY"),
|
||||
default_chat_models,
|
||||
default_api_key=openai_api_key,
|
||||
api_base_url=openai_api_base,
|
||||
vision_enabled=True,
|
||||
is_offline=False,
|
||||
interactive=interactive,
|
||||
provider_name=provider,
|
||||
)
|
||||
|
||||
# Setup OpenAI speech to text model
|
||||
|
|
@ -87,7 +106,7 @@ def initialization(interactive: bool = True):
|
|||
ChatModelOptions.ModelType.GOOGLE,
|
||||
default_gemini_chat_models,
|
||||
default_api_key=os.getenv("GEMINI_API_KEY"),
|
||||
vision_enabled=False,
|
||||
vision_enabled=True,
|
||||
is_offline=False,
|
||||
interactive=interactive,
|
||||
provider_name="Google Gemini",
|
||||
|
|
@ -98,7 +117,7 @@ def initialization(interactive: bool = True):
|
|||
ChatModelOptions.ModelType.ANTHROPIC,
|
||||
default_anthropic_chat_models,
|
||||
default_api_key=os.getenv("ANTHROPIC_API_KEY"),
|
||||
vision_enabled=False,
|
||||
vision_enabled=True,
|
||||
is_offline=False,
|
||||
interactive=interactive,
|
||||
)
|
||||
|
|
@ -154,11 +173,14 @@ def initialization(interactive: bool = True):
|
|||
default_chat_models: list,
|
||||
default_api_key: str,
|
||||
interactive: bool,
|
||||
api_base_url: str = None,
|
||||
vision_enabled: bool = False,
|
||||
is_offline: bool = False,
|
||||
provider_name: str = None,
|
||||
) -> Tuple[bool, OpenAIProcessorConversationConfig]:
|
||||
supported_vision_models = ["gpt-4o-mini", "gpt-4o"]
|
||||
supported_vision_models = (
|
||||
default_openai_chat_models + default_anthropic_chat_models + default_gemini_chat_models
|
||||
)
|
||||
provider_name = provider_name or model_type.name.capitalize()
|
||||
default_use_model = {True: "y", False: "n"}[default_api_key is not None or is_offline]
|
||||
use_model_provider = (
|
||||
|
|
@ -170,14 +192,16 @@ def initialization(interactive: bool = True):
|
|||
|
||||
logger.info(f"️💬 Setting up your {provider_name} chat configuration")
|
||||
|
||||
chat_model_provider = None
|
||||
chat_provider = None
|
||||
if not is_offline:
|
||||
if interactive:
|
||||
user_api_key = input(f"Enter your {provider_name} API key (default: {default_api_key}): ")
|
||||
api_key = user_api_key if user_api_key != "" else default_api_key
|
||||
else:
|
||||
api_key = default_api_key
|
||||
chat_model_provider = OpenAIProcessorConversationConfig.objects.create(api_key=api_key, name=provider_name)
|
||||
chat_provider = OpenAIProcessorConversationConfig.objects.create(
|
||||
api_key=api_key, name=provider_name, api_base_url=api_base_url
|
||||
)
|
||||
|
||||
if interactive:
|
||||
chat_model_names = input(
|
||||
|
|
@ -199,13 +223,13 @@ def initialization(interactive: bool = True):
|
|||
"max_prompt_size": default_max_tokens,
|
||||
"vision_enabled": vision_enabled,
|
||||
"tokenizer": default_tokenizer,
|
||||
"openai_config": chat_model_provider,
|
||||
"openai_config": chat_provider,
|
||||
}
|
||||
|
||||
ChatModelOptions.objects.create(**chat_model_options)
|
||||
|
||||
logger.info(f"🗣️ {provider_name} chat model configuration complete")
|
||||
return True, chat_model_provider
|
||||
return True, chat_provider
|
||||
|
||||
admin_user = KhojUser.objects.filter(is_staff=True).first()
|
||||
if admin_user is None:
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import logging
|
|||
import os
|
||||
import time
|
||||
from datetime import datetime
|
||||
from io import StringIO
|
||||
from typing import Any, Dict
|
||||
|
||||
import pandas as pd
|
||||
|
|
@ -36,11 +37,21 @@ SLEEP_SECONDS = 1 # Delay between API calls to avoid rate limiting
|
|||
|
||||
|
||||
def load_frames_dataset():
|
||||
"""Load the FRAMES benchmark dataset from HuggingFace"""
|
||||
"""
|
||||
Load the Google FRAMES benchmark dataset from HuggingFace
|
||||
|
||||
FRAMES is a benchmark dataset to evaluate retrieval and answering capabilities of agents.
|
||||
It contains ~800 requiring multi-hop retrieval and reasoning across various topics.
|
||||
|
||||
### Data Fields
|
||||
- Prompt: The question to be answered
|
||||
- Answer: The ground truth answer
|
||||
- reasoning_types: The type of reasoning required to answer the question
|
||||
"""
|
||||
try:
|
||||
dataset = load_dataset("google/frames-benchmark")
|
||||
dataset = dataset.shuffle() if RANDOMIZE else dataset
|
||||
# Use test split for evaluation. Sample and shuffle dataset if configured
|
||||
dataset = dataset.shuffle() if RANDOMIZE else dataset
|
||||
return dataset["test"][: int(SAMPLE_SIZE)] if SAMPLE_SIZE else dataset["test"]
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -48,24 +59,36 @@ def load_frames_dataset():
|
|||
return None
|
||||
|
||||
|
||||
def load_talc_dataset():
|
||||
def load_simpleqa_dataset():
|
||||
"""
|
||||
Load the TALC dataset from Github.
|
||||
Load the OpenAI SimpleQA benchmark dataset from their public bucket.
|
||||
|
||||
Normalize it into the FRAMES benchmark structure and the HuggingFace Dataset format.
|
||||
SimpleQA is a dataset of moderately difficult q&a for 2024 models to answer across various topics.
|
||||
It contains ~4000 human vetted questions and answers with additional metadata.
|
||||
Its usage can be seen in openai/simple-evals github repository as well.
|
||||
|
||||
### Data Fields
|
||||
- problem: The question to be answered
|
||||
- answer: The ground truth answer
|
||||
- metadata: Additional metadata including topic information
|
||||
"""
|
||||
|
||||
try:
|
||||
# Load TALC search benchmark from Github
|
||||
raw_url = "https://raw.githubusercontent.com/Talc-AI/search-bench/3fd5b0858e2effa4c1578c7d046bee0a3895c488/data/searchbench_08_30_2024.jsonl"
|
||||
# Load SimpleQA benchmark from OpenAI public bucket
|
||||
raw_url = "https://openaipublic.blob.core.windows.net/simple-evals/simple_qa_test_set.csv"
|
||||
response = requests.get(raw_url)
|
||||
response.raise_for_status()
|
||||
|
||||
# Parse benchmark from raw JSONL response
|
||||
jsonl_data = [json.loads(line) for line in response.text.splitlines()]
|
||||
|
||||
# Rename keys to match FRAMES format
|
||||
# Parse benchmark from raw CSV response
|
||||
csv_data = pd.read_csv(StringIO(response.text))
|
||||
# Normalize it into FRAMES format
|
||||
formatted_data = [
|
||||
{"Prompt": d["question"], "Answer": d["expected_answer"], "reasoning_types": "talc"} for d in jsonl_data
|
||||
{
|
||||
"Prompt": d["problem"],
|
||||
"Answer": d["answer"],
|
||||
"reasoning_types": json.loads(csv_data.to_dict("records")[0]["metadata"].replace("'", '"'))["topic"],
|
||||
}
|
||||
for d in csv_data.to_dict("records")
|
||||
]
|
||||
|
||||
# Convert benchmark to HF Dataset
|
||||
|
|
@ -74,9 +97,8 @@ def load_talc_dataset():
|
|||
dataset = dataset.select(range(int(SAMPLE_SIZE))) if SAMPLE_SIZE else dataset
|
||||
|
||||
return dataset
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading dataset: {e}")
|
||||
logger.error(f"Error loading simpleqa dataset: {e}")
|
||||
return None
|
||||
|
||||
|
||||
|
|
@ -208,7 +230,7 @@ def parse_args():
|
|||
"--dataset",
|
||||
"-d",
|
||||
default="frames",
|
||||
choices=["frames", "talc"],
|
||||
choices=["frames", "simpleqa"],
|
||||
help="Dataset to use for evaluation (default: frames)",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
|
@ -220,11 +242,11 @@ def main():
|
|||
dataset = None
|
||||
|
||||
# Load dataset
|
||||
with timer(f"Loaded {args.dataset} dataset in", logger):
|
||||
with timer(f"Loaded {args.dataset} dataset in", logger, log_level=logging.INFO):
|
||||
if args.dataset == "frames":
|
||||
dataset = load_frames_dataset()
|
||||
elif args.dataset == "talc":
|
||||
dataset = load_talc_dataset()
|
||||
elif args.dataset == "simpleqa":
|
||||
dataset = load_simpleqa_dataset()
|
||||
if dataset is None:
|
||||
return
|
||||
|
||||
Loading…
Reference in a new issue