mirror of
https://github.com/hwchase17/langchain
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c010ec8b71
- `.get_relevant_documents(query)` -> `.invoke(query)` - `.get_relevant_documents(query=query)` -> `.invoke(query)` - `.get_relevant_documents(query, callbacks=callbacks)` -> `.invoke(query, config={"callbacks": callbacks})` - `.get_relevant_documents(query, **kwargs)` -> `.invoke(query, **kwargs)` --------- Co-authored-by: Erick Friis <erick@langchain.dev> |
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.. | ||
langchain_exa | ||
scripts | ||
tests | ||
.gitignore | ||
LICENSE | ||
Makefile | ||
poetry.lock | ||
pyproject.toml | ||
README.md |
langchain-exa
This package contains the LangChain integrations for Exa Cloud generative models.
Installation
pip install -U langchain-exa
Exa Search Retriever
You can retrieve search results as follows
from langchain_exa import ExaSearchRetriever
exa_api_key = "YOUR API KEY"
# Create a new instance of the ExaSearchRetriever
exa = ExaSearchRetriever(exa_api_key=exa_api_key)
# Search for a query and save the results
results = exa.invoke("What is the capital of France?")
# Print the results
print(results)
Exa Search Results
You can run the ExaSearchResults module as follows
from langchain_exa import ExaSearchResults
# Initialize the ExaSearchResults tool
search_tool = ExaSearchResults(exa_api_key="YOUR API KEY")
# Perform a search query
search_results = search_tool._run(
query="When was the last time the New York Knicks won the NBA Championship?",
num_results=5,
text_contents_options=True,
highlights=True
)
print("Search Results:", search_results)
Exa Find Similar Results
You can run the ExaFindSimilarResults module as follows
from langchain_exa import ExaFindSimilarResults
# Initialize the ExaFindSimilarResults tool
find_similar_tool = ExaFindSimilarResults(exa_api_key="YOUR API KEY")
# Find similar results based on a URL
similar_results = find_similar_tool._run(
url="http://espn.com",
num_results=5,
text_contents_options=True,
highlights=True
)
print("Similar Results:", similar_results)