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feat(llm): Add openailike llm mode (#1447)
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This mode behaves the same as the openai mode, except that it allows setting custom models not
supported by OpenAI. It can be used with any tool that serves models from an OpenAI compatible API.

Implements #1424
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mchill authored Dec 26, 2023
1 parent fee9f08 commit 2d27a9f
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21 changes: 20 additions & 1 deletion fern/docs/pages/manual/llms.mdx
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Expand Up @@ -37,6 +37,7 @@ llm:
mode: openai

openai:
api_base: <openai-api-base-url> # Defaults to https://api.openai.com/v1
api_key: <your_openai_api_key> # You could skip this configuration and use the OPENAI_API_KEY env var instead
model: <openai_model_to_use> # Optional model to use. Default is "gpt-3.5-turbo"
# Note: Open AI Models are listed here: https://platform.openai.com/docs/models
Expand All @@ -55,6 +56,24 @@ Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:80
You'll notice the speed and quality of response is higher, given you are using OpenAI's servers for the heavy
computations.

### Using OpenAI compatible API

Many tools, including [LocalAI](https://localai.io/) and [vLLM](https://docs.vllm.ai/en/latest/),
support serving local models with an OpenAI compatible API. Even when overriding the `api_base`,
using the `openai` mode doesn't allow you to use custom models. Instead, you should use the `openailike` mode:

```yaml
llm:
mode: openailike
```
This mode uses the same settings as the `openai` mode.

As an example, you can follow the [vLLM quickstart guide](https://docs.vllm.ai/en/latest/getting_started/quickstart.html#openai-compatible-server)
to run an OpenAI compatible server. Then, you can run PrivateGPT using the `settings-vllm.yaml` profile:

`PGPT_PROFILES=vllm make run`

### Using AWS Sagemaker

For a fully private & performant setup, you can choose to have both your LLM and Embeddings model deployed using Sagemaker.
Expand Down Expand Up @@ -82,4 +101,4 @@ or
`PGPT_PROFILES=sagemaker poetry run python -m private_gpt`

When the server is started it will print a log *Application startup complete*.
Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.
Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.
16 changes: 15 additions & 1 deletion private_gpt/components/llm/llm_component.py
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Expand Up @@ -62,7 +62,21 @@ def __init__(self, settings: Settings) -> None:

openai_settings = settings.openai
self.llm = OpenAI(
api_key=openai_settings.api_key, model=openai_settings.model
api_base=openai_settings.api_base,
api_key=openai_settings.api_key,
model=openai_settings.model,
)
case "openailike":
from llama_index.llms import OpenAILike

openai_settings = settings.openai
self.llm = OpenAILike(
api_base=openai_settings.api_base,
api_key=openai_settings.api_key,
model=openai_settings.model,
is_chat_model=True,
max_tokens=None,
api_version="",
)
case "mock":
self.llm = MockLLM()
6 changes: 5 additions & 1 deletion private_gpt/settings/settings.py
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Expand Up @@ -81,7 +81,7 @@ class DataSettings(BaseModel):


class LLMSettings(BaseModel):
mode: Literal["local", "openai", "sagemaker", "mock"]
mode: Literal["local", "openai", "openailike", "sagemaker", "mock"]
max_new_tokens: int = Field(
256,
description="The maximum number of token that the LLM is authorized to generate in one completion.",
Expand Down Expand Up @@ -156,6 +156,10 @@ class SagemakerSettings(BaseModel):


class OpenAISettings(BaseModel):
api_base: str = Field(
None,
description="Base URL of OpenAI API. Example: 'https://api.openai.com/v1'.",
)
api_key: str
model: str = Field(
"gpt-3.5-turbo",
Expand Down
14 changes: 14 additions & 0 deletions settings-vllm.yaml
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@@ -0,0 +1,14 @@
llm:
mode: openailike

embedding:
mode: local
ingest_mode: simple

local:
embedding_hf_model_name: BAAI/bge-small-en-v1.5

openai:
api_base: http://localhost:8000/v1
api_key: EMPTY
model: facebook/opt-125m

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