要解决在运行本地多模态RAG聊天机器人代码时遇到的超时问题，你可以在 OllamaMultiModal 类中设置 request_timeout 参数。此参数指定向 Ollama API 服务器发出 HTTP 请求的超时时间。
以下是如何设置 request_timeout 参数的方法：

ollama_model = OllamaMultiModal(
    base_url="http://localhost:11434",
    model="llava",
    temperature=0.75,
    context_window=DEFAULT_CONTEXT_WINDOW,
    request_timeout=3600,  # Set the timeout to 3600 seconds (1 hour) or any other value you prefer
    additional_kwargs={}
)

在你的代码中，你已经将 request_timeout 设置为 3600 秒，这对于大多数情况来说应该足够了。然而，如果你仍然遇到超时问题，可以考虑以下附加步骤：

1. 检查网络稳定性:确保你的网络连接稳定，请求过程中没有中断。
2. 优化视频处理:大型视频文件需要花费大量时间进行处理。确保你的视频处理步骤(音频提取、文本提取和帧提取)针对性能进行了优化。
3. 进一步增加超时:如果必要，你可以进一步增加 request_timeout 值以容纳更长的处理时间。
4. 调试和日志记录:启用详细的日志记录以诊断超时发生的位置。你可以按照以下方式在应用程序中启用调试日志记录：

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

1. 异步操作:如果可能，考虑使用异步操作来处理长时间运行的任务，而不阻塞主线程。
   通过遵循这些步骤，你应该能够诊断并解决多模态 RAG 聊天机器人代码中的超时问题。

• 要继续与 Dosu 交谈，请提及 @dosu。*

所以有人帮忙，我是不是用了一些错误的功能？

要解决本地多模态RAG聊天机器人代码中的超时问题，请确保将 request_timeout 参数正确传递给 Client 初始化。提供的代码已经使用 request_timeout 值在 Client 初始化中设置了 timeout 参数。
以下是相关代码部分：

class OllamaMultiModal(MultiModalLLM):
    # ... other fields and methods ...

    def __init__(self, **kwargs: Any) -> None:
        """Init params."""
        super().__init__(**kwargs)
        self._client = Client(host=self.base_url, timeout=self.request_timeout)

如果您仍然遇到超时问题，请考虑检查以下内容：

1. 网络问题:确保没有网络问题导致超时。
2. 服务器性能:验证托管模型的服务器是否能够在指定的超时时间内处理请求。
3. 客户端配置:在初始化 OllamaMultiModal 示例时，请仔细检查 request_timeout 是否正确设置。
   此外，请确保 ollama 库已正确安装和导入，如 OllamaMultiModal 类所示：

def __init__(self, **kwargs: Any) -> None:
    """Init params."""
    # make sure that ollama is installed
    try:
        import ollama  # noqa: F401
    except ImportError:
        raise ImportError(
            "Ollama is not installed. Please install it using `pip install ollama`."
        )
    super().__init__(**kwargs)

如果所有配置都正确，问题仍然存在，您可能需要进一步调查服务器日志或网络条件，以确定超时的根源。
此外，请确保 httpx.Client 与 Timeout 对象正确使用，如 Ollama 类所示(来自 llama-index 库):

with httpx.Client(timeout=Timeout(self.request_timeout)) as client:
    response = client.post(
        url=f"{self.base_url}/api/chat",
        json=payload,
    )
    response.raise_for_status()
    raw = response.json()
    message = raw["message"]
    return ChatResponse(
        message=ChatMessage(
            content=message.get("content"),
            role=MessageRole(message.get("role")),
            additional_kwargs=get_additional_kwargs(
                message, ("content", "role")
            ),
        ),
        raw=raw,
        additional_kwargs=get_additional_kwargs(raw, ("message",)),
    )

请确保正确示例化并将 Timeout 对象传递给 httpx.Client 以正确处理请求超时。要继续与 Dosu 交谈，请提及 @dosu。

你好，我想使用 clip 来嵌入我的图片和文本，我该如何使用它？你能给我一个例子吗？

要切换到使用CLIP将您的图像和文本嵌入到本地多模态RAG聊天机器人中，您可以使用llama_index/embeddings/clip/base.py文件中的ClipEmbedding类。以下是逐步指南：

1. 安装所需软件包:

pip install git+https://github.com/openai/CLIP.git
pip install torch pillow

1. 初始化ClipEmbedding类:

from llama_index.embeddings.clip import ClipEmbedding

clip_embedding = ClipEmbedding(
    embed_batch_size=10,  # You can adjust the batch size as needed
    model_name="ViT-B/32"  # You can choose from the available CLIP models
)

1. 生成文本嵌入:

text = "Your text here"
text_embedding = clip_embedding._get_text_embedding(text)

1. 生成图像嵌入:

img_file_path = "path/to/your/image.jpg"
image_embedding = await clip_embedding._aget_image_embedding(img_file_path)

以下是一个完整示例：

import asyncio
from llama_index.embeddings.clip import ClipEmbedding

# Initialize the ClipEmbedding class
clip_embedding = ClipEmbedding(
    embed_batch_size=10,  # Adjust the batch size as needed
    model_name="ViT-B/32"  # Choose from the available CLIP models
)

# Generate text embedding
text = "Your text here"
text_embedding = clip_embedding._get_text_embedding(text)
print("Text Embedding:", text_embedding)

# Generate image embedding
async def get_image_embedding():
    img_file_path = "path/to/your/image.jpg"
    image_embedding = await clip_embedding._aget_image_embedding(img_file_path)
    print("Image Embedding:", image_embedding)

# Run the async function to get image embedding
asyncio.run(get_image_embedding())

此代码将初始化CLIP模型，为提供的文本和图像生成嵌入，并打印嵌入1(https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/embeddings/clip.py)2(https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py)。

• 要继续与Dosu交谈，请提及@dosu。*

要切换到使用CLIP将您的图像和文本嵌入到本地多模态RAG聊天机器人中，您可以使用llama_index/embeddings/clip/base.py文件中的ClipEmbedding类。以下是逐步指南：

1. 安装所需的包:

pip install git+https://github.com/openai/CLIP.git
pip install torch pillow

1. 初始化ClipEmbedding类:

from llama_index.embeddings.clip import ClipEmbedding

clip_embedding = ClipEmbedding(
    embed_batch_size=10,  # You can adjust the batch size as needed
    model_name="ViT-B/32"  # You can choose from the available CLIP models
)

1. 生成文本嵌入:

text = "Your text here"
text_embedding = clip_embedding._get_text_embedding(text)

1. 生成图像嵌入:

img_file_path = "path/to/your/image.jpg"
image_embedding = await clip_embedding._aget_image_embedding(img_file_path)

以下是一个完整的例子：

import asyncio
from llama_index.embeddings.clip import ClipEmbedding

# Initialize the ClipEmbedding class
clip_embedding = ClipEmbedding(
    embed_batch_size=10,  # Adjust the batch size as needed
    model_name="ViT-B/32"  # Choose from the available CLIP models
)

# Generate text embedding
text = "Your text here"
text_embedding = clip_embedding._get_text_embedding(text)
print("Text Embedding:", text_embedding)

# Generate image embedding
async def get_image_embedding():
    img_file_path = "path/to/your/image.jpg"
    image_embedding = await clip_embedding._aget_image_embedding(img_file_path)
    print("Image Embedding:", image_embedding)

# Run the async function to get image embedding
asyncio.run(get_image_embedding())

这段代码将初始化CLIP模型，为提供的文本和图像生成嵌入，并打印嵌入 1(https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/embeddings/clip.py) 和 2(https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py) 。

• 要继续与 Dosu 交谈，请提及 @dosu。*

使用clip与LanceDBVectorStore的示例

将当前的嵌入模型替换为CLIP并在本地多模态RAG聊天机器人中使用它，请按照以下步骤操作：

1. 安装所需的包:

pip install git+https://github.com/openai/CLIP.git
pip install torch pillow

1. 定义 ClipEmbedding 类:
   使用提供的 ClipEmbedding 类，使用CLIP模型生成嵌入。这里是完整的类定义：

import logging
from typing import Any, List
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.constants import DEFAULT_EMBED_BATCH_SIZE
from llama_index.core.embeddings.multi_modal_base import MultiModalEmbedding
from llama_index.core.schema import ImageType
from PIL import Image
import os

logger = logging.getLogger(__name__)

AVAILABLE_CLIP_MODELS = (
    "RN50",
    "RN101",
    "RN50x4",
    "RN50x16",
    "RN50x64",
    "ViT-B/32",
    "ViT-B/16",
    "ViT-L/14",
    "ViT-L/14@336px",
)
DEFAULT_CLIP_MODEL = "ViT-B/32"

class ClipEmbedding(MultiModalEmbedding):
    """CLIP embedding models for encoding text and image for Multi-Modal purpose.

This class provides an interface to generate embeddings using a model
deployed in OpenAI CLIP. At the initialization it requires a model name
of CLIP.

Note:
Requires `clip` package to be available in the PYTHONPATH. It can be installed with
`pip install git+https://github.com/openai/CLIP.git`.
"""

    embed_batch_size: int = Field(default=DEFAULT_EMBED_BATCH_SIZE, gt=0)

    _clip: Any = PrivateAttr()
    _model: Any = PrivateAttr()
    _preprocess: Any = PrivateAttr()
    _device: Any = PrivateAttr()

    @classmethod
    def class_name(cls) -> str:
        return "ClipEmbedding"

    def __init__(
        self,
        *,
        embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
        model_name: str = DEFAULT_CLIP_MODEL,
        **kwargs: Any,
    ):
        """Initializes the ClipEmbedding class.

During the initialization the `clip` package is imported.

Args:
embed_batch_size (int, optional): The batch size for embedding generation. Defaults to 10,
must be > 0 and <= 100.
model_name (str): The model name of Clip model.

Raises:
ImportError: If the `clip` package is not available in the PYTHONPATH.
ValueError: If the model cannot be fetched from Open AI. or if the embed_batch_size
is not in the range (0, 100].
"""
        if embed_batch_size <= 0:
            raise ValueError(f"Embed batch size {embed_batch_size}  must be > 0.")

        try:
            import clip
            import torch
        except ImportError:
            raise ImportError(
                "ClipEmbedding requires `pip install git+https://github.com/openai/CLIP.git` and torch."
            )

        super().__init__(
            embed_batch_size=embed_batch_size, model_name=model_name, **kwargs
        )

        try:
            self._device = "cuda" if torch.cuda.is_available() else "cpu"
            is_local_path = os.path.exists(self.model_name)
            if not is_local_path and self.model_name not in AVAILABLE_CLIP_MODELS:
                raise ValueError(
                    f"Model name {self.model_name} is not available in CLIP."
                )
            self._model, self._preprocess = clip.load(
                self.model_name, device=self._device
            )

        except Exception as e:
            logger.error("Error while loading clip model.")
            raise ValueError("Unable to fetch the requested embeddings model") from e

    # TEXT EMBEDDINGS

    async def _aget_query_embedding(self, query: str) -> Embedding:
        return self._get_query_embedding(query)

    def _get_text_embedding(self, text: str) -> Embedding:
        return self._get_text_embeddings([text])[0]

    def _get_text_embeddings(self, texts: List[str]) -> List<Embedding]:
        results = []
        for text in texts:
            try:
                import clip
            except ImportError:
                raise ImportError(
                    "ClipEmbedding requires `pip install git+https://github.com/openai/CLIP.git` and torch."
                )
            text_embedding = self._model.encode_text(
                clip.tokenize(text).to(self._device)
            )
            results.append(text_embedding.tolist()[0])

        return results

    def _get_query_embedding(self, query: str) -> Embedding:
        return self._get_text_embedding(query)

    # IMAGE EMBEDDINGS

    async def _aget_image_embedding(self, img_file_path: ImageType) -> Embedding:
        return self._get_image_embedding(img_file_path)

    def _get_image_embedding(self, img_file_path: ImageType) -> Embedding:
        import torch
        with torch.no_grad():
            image = (
                self._preprocess(Image.open(img_file_path))
                .unsqueeze(0)
                .to(self._device)
            )
            return self._model.encode_image(image).tolist()[0]

1. 与 LanceDBVectorStore 集成:
   假设你有一个 LanceDBVectorStore 类，你需要将其与 ClipEmbedding 类集成。下面是一个简化的示例，展示了如何进行此操作：

from lance_db_vector_store import LanceDBVectorStore  # Assuming you have this class
from llama_index.embeddings.clip import ClipEmbedding  # Importing ClipEmbedding from llama_index

# Initialize the ClipEmbedding
clip_embedding = ClipEmbedding(model_name="ViT-B/32")

# Create an instance of LanceDBVectorStore with the ClipEmbedding
vector_store = LanceDBVectorStore(embedding_model=clip_embedding)

# Example usage
text = "A sample text to encode"
text_embedding = clip_embedding._get_text_embedding(text)
vector_store.add_embedding(text, text_embedding)

# For image embeddings
image_path = "path/to/your/image.jpg"
image_embedding = clip_embedding._get_image_embedding(image_path)
vector_store.add_embedding(image_path, image_embedding)

这段代码演示了如何初始化 ClipEmbedding 类，生成文本和图像嵌入，并将它们添加到 LanceDBVectorStore 中。根据你对 LanceDBVectorStore1(https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/embeddings/clip.py)2(https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py)3(https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/init.py) 的具体实现，调整集成方式。

• 要继续与 Dosu 对话，请提及 @dosu。*