👐 Create agent tools

The easiest way to define custom tools for agents in LangChain is using the @tool decorator. The decorator makes tools out of functions by using the function name as the tool name by default, and the function's docstring as the tool's description.

We want the AI research agent to have access to the following tools:

• get_paper_metadata_arxiv: Fetches a list of research papers from Arxiv, given a topic

• get_paper_summary_from_arxiv: Fetches the summary for a single research paper, given a paper ID

• answer_questions_about_topics: Answer questions about a given topic based on information in the agent's knowledge base

Fill in any <CODE_BLOCK_N> placeholders and run the cells under the Step 6: Create agent tools section in the notebook to create tools for the agent to use.

tip

The get_paper_metadata_arxiv tool has been defined for you. Use this as inspiration to create the other two tools. The tool names and docstrings have been written out for you. All you have to do is code up the tool logic.

Similarly, use the sample test for the get_paper_metadata_arxiv tool to test out the other tools.

The answers for code blocks in this section are as follows:

CODE_BLOCK_9

Answer

doc = ArxivLoader(query=id, load_max_docs=1).get_summaries_as_docs()
if len(doc) == 0:
    return "No summary found for this paper."
return doc[0].page_content

CODE_BLOCK_10

Answer

MongoDBAtlasVectorSearch.from_connection_string(
    connection_string=MONGODB_URI,
    namespace=DB_NAME + "." + COLLECTION_NAME,
    embedding=embedding_model,
    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
    text_key="abstract",
)

CODE_BLOCK_11

Answer

context = vector_store.similarity_search_with_score(query=query)
context = [doc for doc, score in context if score > 0.8]
return context

CODE_BLOCK_12

Answer

retrieve = {
    "context": RunnableLambda(get_context)
    | (lambda docs: "\n\n".join([d.page_content for d in docs])),
    "question": RunnablePassthrough(),
}
# Defining the chat prompt
template = """Answer the question based only on the following context. IF NO CONTEXT IS PROVIDED, SAY I DO NOT KNOW: \
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# Parse output as a string
parse_output = StrOutputParser()
# Retrieval chain
retrieval_chain = retrieve | prompt | llm | parse_output

answer = retrieval_chain.invoke(query)

return answer

CODE_BLOCK_13

Answer

get_paper_summary_from_arxiv.invoke("1808.09236")

CODE_BLOCK_14

Answer

get_paper_summary_from_arxiv.invoke("808.09236")

CODE_BLOCK_15

Answer

answer_questions_about_topics.invoke("Partial cubes")

CODE_BLOCK_16

Answer

answer_questions_about_topics.invoke("Tree of Thoughts")