Open LJ-Hao opened 2 months ago
To resolve the timeout issue when running your local multimodal RAG chatbot code, you can set the request_timeout
parameter in the OllamaMultiModal
class. This parameter specifies the timeout for making HTTP requests to the Ollama API server.
Here's how you can set the request_timeout
parameter:
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={}
)
In your code, you have already set the request_timeout
to 3600 seconds, which should be sufficient for most cases. However, if you are still experiencing timeouts, consider the following additional steps:
Check Network Stability: Ensure that your network connection is stable and that there are no interruptions during the request.
Optimize Video Processing: Large video files can take a significant amount of time to process. Ensure that your video processing steps (audio extraction, text extraction, and frame extraction) are optimized for performance.
Increase Timeout Further: If necessary, you can increase the request_timeout
value further to accommodate longer processing times.
Debugging and Logging: Enable detailed logging to diagnose where the timeout is occurring. You can enable debug logging in your application as follows:
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
Asynchronous Operations: If possible, consider using asynchronous operations to handle long-running tasks without blocking the main thread.
By following these steps, you should be able to diagnose and resolve the timeout issue in your multimodal RAG chatbot code [1].
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So someone helps, did I use some function with a wrong way?
To resolve the timeout issue in your local multimodal RAG chatbot code, ensure that the request_timeout
parameter is correctly passed to the Client
initialization. The provided code already sets the timeout
parameter in the Client
initialization using the request_timeout
value.
Here is the relevant part of the code:
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)
If you are still experiencing timeout issues, consider checking the following:
request_timeout
is correctly set when initializing the OllamaMultiModal
instance.Additionally, ensure that the ollama
library is correctly installed and imported, as shown in the OllamaMultiModal
class:
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)
If all configurations are correct and the issue persists, you might need to investigate further into the server logs or network conditions to identify the root cause of the timeout.
Additionally, ensure that the httpx.Client
is being used correctly with the Timeout
object, as shown in the Ollama
class from the llama-index
library:
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",)),
)
Ensure that the Timeout
object is correctly instantiated and passed to the httpx.Client
to handle the request timeout properly [1][2][3].
To continue talking to Dosu, mention @dosu.
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
Hi, I want use clip to embedding my images and text, how can I use it, could you give me an example.
To switch to using CLIP to embed your images and text in your local multimodal RAG chatbot, you can use the ClipEmbedding
class provided in the llama_index/embeddings/clip/base.py
file. Here is a step-by-step guide:
Install the required packages:
pip install git+https://github.com/openai/CLIP.git
pip install torch pillow
Initialize the ClipEmbedding
class:
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
)
Generate text embeddings:
text = "Your text here"
text_embedding = clip_embedding._get_text_embedding(text)
Generate image embeddings:
img_file_path = "path/to/your/image.jpg"
image_embedding = await clip_embedding._aget_image_embedding(img_file_path)
Here is a complete example:
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())
This code will initialize the CLIP model, generate embeddings for the provided text and image, and print the embeddings [1][2].
To continue talking to Dosu, mention @dosu.
To switch to using CLIP to embed your images and text in your local multimodal RAG chatbot, you can use the
ClipEmbedding
class provided in thellama_index/embeddings/clip/base.py
file. Here is a step-by-step guide:
- Install the required packages:
pip install git+https://github.com/openai/CLIP.git pip install torch pillow
Initialize the
ClipEmbedding
class: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 )
- Generate text embeddings:
text = "Your text here" text_embedding = clip_embedding._get_text_embedding(text)
- Generate image embeddings:
img_file_path = "path/to/your/image.jpg" image_embedding = await clip_embedding._aget_image_embedding(img_file_path)
Here is a complete example:
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())
This code will initialize the CLIP model, generate embeddings for the provided text and image, and print the embeddings [1][2].
To continue talking to Dosu, mention @dosu.
And use clip with LanceDBVectorStore, give me an example
To replace the current embedding model with CLIP and use it with LanceDBVectorStore
in your local multimodal RAG chatbot, follow these steps:
Install the required packages:
pip install git+https://github.com/openai/CLIP.git
pip install torch pillow
Define the ClipEmbedding
class:
Use the provided ClipEmbedding
class to generate embeddings using the CLIP model. Here is the complete class definition:
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]
Integrate with LanceDBVectorStore
:
Assuming you have a LanceDBVectorStore
class, you need to integrate the ClipEmbedding
class with it. Here is a simplified example of how you might do this:
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)
This code demonstrates how to initialize the ClipEmbedding
class, generate text and image embeddings, and add them to the LanceDBVectorStore
. Adjust the integration according to your specific implementation of LanceDBVectorStore
[1][2][3].
To continue talking to Dosu, mention @dosu.
Question Validation
Question
But I get problem like below:
I am sure that my computer is inferencing my question, but I don’t get a response to the question.