These steps provide a structured approach to processing, analyzing, and searching video files using LangChain and related tools. This workflow leverages the strengths of various components to provide comprehensive video analysis capabilities.

langchain_video_analysis.md

To process and analyze video files using LangChain, you can follow some best practices and examples from the LangChain cookbook and related resources. Here's a comprehensive approach to accomplish video analysis and search:

Extracting Frames from Video

First, you need to extract frames from the video, which can be done using a library like OpenCV:

import cv2
import os

def extract_frames(video_path, output_folder):
    cap = cv2.VideoCapture(video_path)
    frame_rate = cap.get(cv2.CAP_PROP_FPS)
    success, frame = cap.read()
    count = 0
    while success:
        cv2.imwrite(os.path.join(output_folder, f"frame_{count}.jpg"), frame)
        success, frame = cap.read()
        count += 1
    cap.release()

Extracting Features from Frames

Use a pre-trained model like ResNet to extract features from each frame. These features can be embedded into a vector space for similarity search:

import numpy as np
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.preprocessing import image

model = ResNet50(weights='imagenet', include_top=False)

def extract_features(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    img_data = image.img_to_array(img)
    img_data = np.expand_dims(img_data, axis=0)
    img_data = preprocess_input(img_data)
    features = model.predict(img_data)
    return features.flatten()

Storing and Querying Vectors in Weaviate

Store the extracted features in Weaviate, and use LangChain to manage and query the data:

import weaviate
from langchain.vectorstores import WeaviateVectorStore
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable

client = weaviate.Client("http://localhost:8080")

schema = {
    "classes": [
        {
            "class": "Frame",
            "properties": [
                {"name": "frame_id", "dataType": ["int"]},
                {"name": "features", "dataType": ["blob"]}
            ]
        }
    ]
}
client.schema.create(schema)

def store_features(frame_id, features):
    client.data_object.create({
        "frame_id": frame_id,
        "features": features.tolist()
    }, "Frame")

output_folder = "path_to_extracted_frames"
for frame_file in os.listdir(output_folder):
    frame_path = os.path.join(output_folder, frame_file)
    features = extract_features(frame_path)
    frame_id = int(frame_file.split('_')[1].split('.')[0])
    store_features(frame_id, features)

query_vector = extract_features("path_to_query_frame.jpg")
result = client.query.get("Frame", ["frame_id"]).with_near_vector(query_vector).do()
print(result)

Using LangChain for Video Captioning

You can use LangChain's components to handle video captioning, as shown in this example from the LangChain cookbook:

import openai
from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers.openai_tools import PydanticToolsParser
from langchain.language_models import OpenAI

video_path = "path_to_video.mp4"
with open(video_path, "rb") as video_file:
    video_data = video_file.read()

prompt = ChatPromptTemplate.from_messages([
    ("system", "Caption the following video."),
    ("user", "{input}")
])

llm = OpenAI(model_name="text-davinci-002")

chain = prompt | llm | PydanticToolsParser()
response = chain.invoke({"input": video_data})
print(response)

Resources

• LangChain GitHub Repository
• LangChain Audio/Video Structured Extraction
• Video Captioning with LangChain

These steps provide a structured approach to processing, analyzing, and searching video files using LangChain and related tools. This workflow leverages the strengths of various components to provide comprehensive video analysis capabilities.