Contextual Generate model

llama-index-integrations/llms/llama-index-llms-contextual/README.md

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+# Contextual LLM Integration for LlamaIndex
+
+This package provides a Contextual LLM integration for LlamaIndex.
+
+## Installation
+
+```bash
+pip install llama-index-llms-contextual
+```
+
+## Usage
+
+```python
+from llama_index.llms.contextual import Contextual
+
+llm = Contextual(model="contextual-clm", api_key="your_api_key")
+
+response = llm.complete("Explain the importance of Grounded Language Models.")
+```

llama-index-integrations/llms/llama-index-llms-contextual/llama_index/llms/contextual/BUILD

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+python_sources()

llama-index-integrations/llms/llama-index-llms-contextual/llama_index/llms/contextual/__init__.py

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+from llama_index.llms.contextual.base import Contextual
+
+__all__ = ["Contextual"]

llama-index-integrations/llms/llama-index-llms-contextual/llama_index/llms/contextual/base.py

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+from typing import Any, Optional
+
+from llama_index.llms.openai_like import OpenAILike
+from pydantic import Field
+from typing import List
+from llama_index.core.llms.callbacks import (
+    llm_chat_callback,
+    llm_completion_callback,
+)
+from llama_index.core.base.llms.types import (
+    CompletionResponse,
+    ChatResponse,
+    ChatResponseGen,
+    MessageRole,
+    ChatMessage,
+)
+
+
+from contextual import ContextualAI
+
+
+class Contextual(OpenAILike):
+    """
+    Generate a response using Contextual's Grounded Language Model (GLM), an LLM engineered specifically to prioritize faithfulness to in-context retrievals over parametric knowledge to reduce hallucinations in Retrieval-Augmented Generation.
+
+    The total request cannot exceed 32,000 tokens. Email [email protected] with any feedback or questions.
+
+    Examples:
+        `pip install llama-index-llms-contextual`
+
+        ```python
+        from llama_index.llms.contextual import Contextual
+
+        # Set up the Contextual class with the required model and API key
+        llm = Contextual(model="contextual-clm", api_key="your_api_key")
+
+        # Call the complete method with a query
+        response = llm.complete("Explain the importance of low latency LLMs")
+
+        print(response)
+        ```
+    """
+
+    model: str = Field(
+        description="The model to use. Currently only supports `v1`.", default="v1"
+    )
+    api_key: str = Field(description="The API key to use.", default=None)
+    base_url: str = Field(
+        description="The base URL to use.",
+        default="https://api.contextual.ai/v1/generate",
+    )
+    avoid_commentary: bool = Field(
+        description="Flag to indicate whether the model should avoid providing additional commentary in responses. Commentary is conversational in nature and does not contain verifiable claims; therefore, commentary is not strictly grounded in available context. However, commentary may provide useful context which improves the helpfulness of responses.",
+        default=False,
+    )
+    client: Any = Field(default=None, description="Contextual AI Client")
+
+    def __init__(
+        self,
+        model: str,
+        api_key: str,
+        base_url: str = None,
+        avoid_commentary: bool = False,
+        **openai_llm_kwargs: Any,
+    ) -> None:
+        super().__init__(
+            model=model,
+            api_key=api_key,
+            api_base=base_url,
+            is_chat_model=openai_llm_kwargs.pop("is_chat_model", True),
+            **openai_llm_kwargs,
+        )
+
+        try:
+            self.client = ContextualAI(api_key=api_key, base_url=base_url)
+        except Exception as e:
+            raise ValueError(f"Error initializing ContextualAI client: {e}")
+
+    @classmethod
+    def class_name(cls) -> str:
+        """Get class name."""
+        return "contextual-clm"
+
+    # Synchronous Methods
+    @llm_completion_callback()
+    def complete(
+        self, prompt: str, knowledge: Optional[List[str]] = None, **kwargs
+    ) -> CompletionResponse:
+        """
+        Generate completion for the given prompt.
+
+        Args:
+            prompt (str): The input prompt to generate completion for.
+            **kwargs: Additional keyword arguments for the API request.
+
+        Returns:
+            str: The generated text completion.
+        """
+        messages_list = [{"role": MessageRole.USER, "content": prompt}]
+        response = self._generate(
+            knowledge=knowledge,
+            messages=messages_list,
+            model=self.model,
+            system_prompt=self.system_prompt,
+            **kwargs,
+        )
+        return CompletionResponse(text=response)
+
+    @llm_chat_callback()
+    def chat(self, messages: List[ChatMessage], **kwargs) -> ChatResponse:
+        """
+        Generate a chat response for the given messages.
+        """
+        messages_list = [
+            {"role": msg.role, "content": msg.blocks[0].text} for msg in messages
+        ]
+        response = self._generate(
+            knowledge=kwargs.get("knowledge_base", None),
+            messages=messages_list,
+            model=self.model,
+            system_prompt=self.system_prompt,
+            **kwargs,
+        )
+        return ChatResponse(
+            message=ChatMessage(role=MessageRole.ASSISTANT, content=response)
+        )
+
+    @llm_chat_callback()
+    def stream_chat(self, messages: List[ChatMessage], **kwargs) -> ChatResponseGen:
+        """
+        Generate a chat response for the given messages.
+        """
+        raise NotImplementedError("stream methods not implemented in Contextual")
+
+    @llm_completion_callback()
+    def stream_complete(self, prompt: str, **kwargs) -> ChatResponseGen:
+        """
+        Generate a chat response for the given messages.
+        """
+        raise NotImplementedError("stream methods not implemented in Contextual")
+
+    # ===== Async Endpoints =====
+    @llm_chat_callback()
+    async def achat(
+        self,
+        messages: Sequence[ChatMessage],
+        **kwargs: Any,
+    ) -> ChatResponse:
+        raise NotImplementedError("async methods not implemented in Contextual")
+
+    @llm_chat_callback()
+    async def astream_chat(
+        self,
+        messages: Sequence[ChatMessage],
+        **kwargs: Any,
+    ) -> ChatResponseAsyncGen:
+        raise NotImplementedError("async methods not implemented in Contextual")
+
+    @llm_completion_callback()
+    async def acomplete(
+        self, prompt: str, formatted: bool = False, **kwargs: Any
+    ) -> CompletionResponse:
+        raise NotImplementedError("async methods not implemented in Contextual")
+
+    @llm_completion_callback()
+    async def astream_complete(
+        self, prompt: str, formatted: bool = False, **kwargs: Any
+    ) -> CompletionResponseAsyncGen:
+        raise NotImplementedError("async methods not implemented in Contextual")
+
+    def _generate(
+        self, knowledge, messages, system_prompt, **kwargs
+    ) -> CompletionResponse:
+        """
+        Generate completion for the given prompt.
+        """
+        raw_message = self.client.generate.create(
+            messages=messages,
+            knowledge=knowledge or [],
+            model=self.model,
+            system_prompt=system_prompt,
+            avoid_commentary=self.avoid_commentary,
+            temperature=kwargs.get("temperature", 0.0),
+            max_new_tokens=kwargs.get("max_tokens", 1024),
+            top_p=kwargs.get("top_p", 1),
+        )
+        return raw_message.response