## IMDB MOVIE REVIEWS LOADER
```bash
pip install llama-index-readers-imdb-review
```
This loader fetches all the reviews of a movie or a TV-series from IMDB official site. This loader is working on Windows machine and it requires further debug on Linux. Fixes are on the way
Install the required dependencies
```
pip install -r requirements.txt
```
The IMDB downloader takes in two attributes
- movie_name_year: The name of the movie or series and year
- webdriver_engine: To use edge, google or gecko (mozilla) webdriver
- generate_csv: Whether to generate csv file
- multithreading: whether to use multithreading or not
## Usage
```python
from llama_index.readers.imdb_review import IMDBReviews
loader = IMDBReviews(
movie_name_year="The Social Network 2010", webdriver_engine="edge"
)
docs = loader.load_data()
```
The metadata has the following information
- date of the review (date)
- title of the review (title)
- rating of the review (rating)
- link of the review (link)
- whether the review is spoiler or not (spoiler)
- number of people found the review helpful (found_helpful)
- total number of votes (total)
It will download the files inside the folder `movie_reviews` with the filename as the movie name
## EXAMPLES
This loader can be used with both Langchain and LlamaIndex.
### LlamaIndex
```python
from llama_index.core import VectorStoreIndex, download_loader
from llama_index.core import VectorStoreIndex
from llama_index.readers.imdb_review import IMDBReviews
loader = IMDBReviewsloader(
movie_name_year="The Social Network 2010",
webdriver_engine="edge",
generate_csv=False,
multithreading=False,
)
docs = loader.load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query(
"What did the movie say about Mark Zuckerberg?",
)
print(response)
```
### Langchain
```python
from langchain.llms import OpenAI
from langchain.agents.agent_toolkits.pandas import (
create_pandas_dataframe_agent,
)
from langchain.agents import Tool
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from llama_index.readers.imdb_review import IMDBReviews
loader = IMDBReviewsloader(
movie_name_year="The Social Network 2010",
webdriver_engine="edge",
generate_csv=False,
multithreading=False,
)
docs = loader.load_data()
tools = [
Tool(
name="LlamaIndex",
func=lambda q: str(index.as_query_engine().query(q)),
description="useful for when you want to answer questions about the movies and their reviews. The input to this tool should be a complete english sentence.",
return_direct=True,
),
]
llm = ChatOpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="conversational-react-description")
agent.run("What did the movie say about Mark Zuckerberg?")
```
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"description": "## IMDB MOVIE REVIEWS LOADER\n\n```bash\npip install llama-index-readers-imdb-review\n```\n\nThis loader fetches all the reviews of a movie or a TV-series from IMDB official site. This loader is working on Windows machine and it requires further debug on Linux. Fixes are on the way\n\nInstall the required dependencies\n\n```\npip install -r requirements.txt\n```\n\nThe IMDB downloader takes in two attributes\n\n- movie_name_year: The name of the movie or series and year\n- webdriver_engine: To use edge, google or gecko (mozilla) webdriver\n- generate_csv: Whether to generate csv file\n- multithreading: whether to use multithreading or not\n\n## Usage\n\n```python\nfrom llama_index.readers.imdb_review import IMDBReviews\n\nloader = IMDBReviews(\n movie_name_year=\"The Social Network 2010\", webdriver_engine=\"edge\"\n)\ndocs = loader.load_data()\n```\n\nThe metadata has the following information\n\n- date of the review (date)\n- title of the review (title)\n- rating of the review (rating)\n- link of the review (link)\n- whether the review is spoiler or not (spoiler)\n- number of people found the review helpful (found_helpful)\n- total number of votes (total)\n\nIt will download the files inside the folder `movie_reviews` with the filename as the movie name\n\n## EXAMPLES\n\nThis loader can be used with both Langchain and LlamaIndex.\n\n### LlamaIndex\n\n```python\nfrom llama_index.core import VectorStoreIndex, download_loader\nfrom llama_index.core import VectorStoreIndex\n\nfrom llama_index.readers.imdb_review import IMDBReviews\n\nloader = IMDBReviewsloader(\n movie_name_year=\"The Social Network 2010\",\n webdriver_engine=\"edge\",\n generate_csv=False,\n multithreading=False,\n)\ndocs = loader.load_data()\n\nindex = VectorStoreIndex.from_documents(documents)\nquery_engine = index.as_query_engine()\n\nresponse = query_engine.query(\n \"What did the movie say about Mark Zuckerberg?\",\n)\nprint(response)\n```\n\n### Langchain\n\n```python\nfrom langchain.llms import OpenAI\nfrom langchain.agents.agent_toolkits.pandas import (\n create_pandas_dataframe_agent,\n)\nfrom langchain.agents import Tool\nfrom langchain.agents import initialize_agent\nfrom langchain.chat_models import ChatOpenAI\n\nfrom llama_index.readers.imdb_review import IMDBReviews\n\nloader = IMDBReviewsloader(\n movie_name_year=\"The Social Network 2010\",\n webdriver_engine=\"edge\",\n generate_csv=False,\n multithreading=False,\n)\ndocs = loader.load_data()\ntools = [\n Tool(\n name=\"LlamaIndex\",\n func=lambda q: str(index.as_query_engine().query(q)),\n description=\"useful for when you want to answer questions about the movies and their reviews. The input to this tool should be a complete english sentence.\",\n return_direct=True,\n ),\n]\nllm = ChatOpenAI(temperature=0)\nagent = initialize_agent(tools, llm, agent=\"conversational-react-description\")\nagent.run(\"What did the movie say about Mark Zuckerberg?\")\n```\n",
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