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Add Litellm to supported providers in GenericLLMProvider #1060

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Jan 5, 2025
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Add Litellm to supported providers in GenericLLMProvider #1060

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Add Litellm to supported providers in GenericLLMProvider
moritalous Jan 4, 2025
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update document
moritalous Jan 5, 2025
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11 changes: 10 additions & 1 deletion docs/docs/gpt-researcher/llms/llms.md
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Expand Up @@ -3,7 +3,7 @@
As described in the [introduction](/docs/gpt-researcher/gptr/config), the default LLM and embedding is OpenAI due to its superior performance and speed.
With that said, GPT Researcher supports various open/closed source LLMs and embeddings, and you can easily switch between them by updating the `SMART_LLM`, `FAST_LLM` and `EMBEDDING` env variables. You might also need to include the provider API key and corresponding configuration params.

Current supported LLMs are `openai`, `anthropic`, `azure_openai`, `cohere`, `google_vertexai`, `google_genai`, `fireworks`, `ollama`, `together`, `mistralai`, `huggingface`, `groq` and `bedrock`.
Current supported LLMs are `openai`, `anthropic`, `azure_openai`, `cohere`, `google_vertexai`, `google_genai`, `fireworks`, `ollama`, `together`, `mistralai`, `huggingface`, `groq`, `bedrock` and `litellm`.

Current supported embeddings are `openai`, `azure_openai`, `cohere`, `google_vertexai`, `google_genai`, `fireworks`, `ollama`, `together`, `mistralai`, `huggingface`, `nomic` ,`voyageai` and `bedrock`.

Expand Down Expand Up @@ -242,6 +242,15 @@ EMBEDDING="bedrock:amazon.titan-embed-text-v2:0"
```


## LiteLLM

```bash
FAST_LLM="litellm:perplexity/pplx-7b-chat"
SMART_LLM="litellm:perplexity/pplx-70b-chat"
STRATEGIC_LLM="litellm:perplexity/pplx-70b-chat"
```


## xAI

```bash
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6 changes: 6 additions & 0 deletions gpt_researcher/llm_provider/generic/base.py
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Expand Up @@ -20,6 +20,7 @@
"dashscope",
"xai",
"deepseek",
"litellm",
}


Expand Down Expand Up @@ -123,6 +124,11 @@ def from_provider(cls, provider: str, **kwargs: Any):
openai_api_key=os.environ["DEEPSEEK_API_KEY"],
**kwargs
)
elif provider == "litellm":
_check_pkg("langchain_community")
from langchain_community.chat_models.litellm import ChatLiteLLM

llm = ChatLiteLLM(**kwargs)
else:
supported = ", ".join(_SUPPORTED_PROVIDERS)
raise ValueError(
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