Format Templates (#12396)

templates/anthropic-iterative-search/anthropic_iterative_search/__init__.py

@@ -1,7 +1,7 @@
 from langchain.schema.runnable import ConfigurableField
 
+from .chain import chain
 from .retriever_agent import executor
-from .chain import chain 
 
 final_chain = chain.configurable_alternatives(
     ConfigurableField(id="chain"),

templates/anthropic-iterative-search/anthropic_iterative_search/chain.py

@@ -1,5 +1,5 @@
-from langchain.prompts import ChatPromptTemplate
 from langchain.chat_models import ChatAnthropic
+from langchain.prompts import ChatPromptTemplate
 from langchain.schema.output_parser import StrOutputParser
 
 from .prompts import answer_prompt

templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py

@@ -1,6 +1,7 @@
-from langchain.schema.agent import AgentAction, AgentFinish
 import re
 
+from langchain.schema.agent import AgentAction, AgentFinish
+
 from .agent_scratchpad import _format_docs
 
 
@@ -14,18 +15,23 @@ def extract_between_tags(tag: str, string: str, strip: bool = True) -> str:
         # Only return the first one
         return ext_list[0]
 
+
 def parse_output(outputs):
     partial_completion = outputs["partial_completion"]
     steps = outputs["intermediate_steps"]
-    search_query = extract_between_tags('search_query', partial_completion + '</search_query>') 
+    search_query = extract_between_tags(
+        "search_query", partial_completion + "</search_query>"
+    )
     if search_query is None:
         docs = []
         str_output = ""
         for action, observation in steps:
             docs.extend(observation)
             str_output += action.log
-            str_output += '</search_query>' + _format_docs(observation)
+            str_output += "</search_query>" + _format_docs(observation)
         str_output += partial_completion
         return AgentFinish({"docs": docs, "output": str_output}, log=partial_completion)
     else:
-        return AgentAction(tool="search", tool_input=search_query, log=partial_completion)
+        return AgentAction(
+            tool="search", tool_input=search_query, log=partial_completion
+        )

templates/anthropic-iterative-search/anthropic_iterative_search/prompts.py

@@ -2,6 +2,6 @@ retrieval_prompt = """{retriever_description} Before beginning to research the u
 
 After each call to the Search Engine Tool, reflect briefly inside <search_quality></search_quality> tags about whether you now have enough information to answer, or whether more information is needed. If you have all the relevant information, write it in <information></information> tags, WITHOUT actually answering the question. Otherwise, issue a new search.
 
-Here is the user's question: <question>{query}</question> Remind yourself to make short queries in your scratchpad as you plan out your strategy."""
+Here is the user's question: <question>{query}</question> Remind yourself to make short queries in your scratchpad as you plan out your strategy."""  # noqa: E501
 
-answer_prompt = "Here is a user query: <query>{query}</query>. Here is some relevant information: <information>{information}</information>. Please answer the question using the relevant information."
+answer_prompt = "Here is a user query: <query>{query}</query>. Here is some relevant information: <information>{information}</information>. Please answer the question using the relevant information."  # noqa: E501

templates/anthropic-iterative-search/anthropic_iterative_search/retriever.py

@@ -3,13 +3,14 @@ from langchain.tools import tool
 
 # This is used to tell the model how to best use the retriever.
 
-retriever_description = """You will be asked a question by a human user. You have access to the following tool to help answer the question. <tool_description> Search Engine Tool * The search engine will exclusively search over Wikipedia for pages similar to your query. It returns for each page its title and full page content. Use this tool if you want to get up-to-date and comprehensive information on a topic to help answer queries. Queries should be as atomic as possible -- they only need to address one part of the user's question. For example, if the user's query is "what is the color of a basketball?", your search query should be "basketball". Here's another example: if the user's question is "Who created the first neural network?", your first query should be "neural network". As you can see, these queries are quite short. Think keywords, not phrases. * At any time, you can make a call to the search engine using the following syntax: <search_query>query_word</search_query>. * You'll then get results back in <search_result> tags.</tool_description>"""
+retriever_description = """You will be asked a question by a human user. You have access to the following tool to help answer the question. <tool_description> Search Engine Tool * The search engine will exclusively search over Wikipedia for pages similar to your query. It returns for each page its title and full page content. Use this tool if you want to get up-to-date and comprehensive information on a topic to help answer queries. Queries should be as atomic as possible -- they only need to address one part of the user's question. For example, if the user's query is "what is the color of a basketball?", your search query should be "basketball". Here's another example: if the user's question is "Who created the first neural network?", your first query should be "neural network". As you can see, these queries are quite short. Think keywords, not phrases. * At any time, you can make a call to the search engine using the following syntax: <search_query>query_word</search_query>. * You'll then get results back in <search_result> tags.</tool_description>"""  # noqa: E501
 
 retriever = WikipediaRetriever()
 
 # This should be the same as the function name below
 RETRIEVER_TOOL_NAME = "search"
 
+
 @tool
 def search(query):
     """Search with the retriever."""

templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py

@@ -1,13 +1,13 @@
+from langchain.agents import AgentExecutor
 from langchain.chat_models import ChatAnthropic
 from langchain.prompts import ChatPromptTemplate
-from langchain.schema.runnable import RunnablePassthrough, RunnableMap
 from langchain.schema.output_parser import StrOutputParser
-from langchain.agents import AgentExecutor
+from langchain.schema.runnable import RunnableMap, RunnablePassthrough
 
-from .retriever import search, RETRIEVER_TOOL_NAME, retriever_description
-from .prompts import retrieval_prompt
 from .agent_scratchpad import format_agent_scratchpad
 from .output_parser import parse_output
+from .prompts import retrieval_prompt
+from .retriever import retriever_description, search
 
 prompt = ChatPromptTemplate.from_messages([
     ("user", retrieval_prompt),

templates/anthropic-iterative-search/main.py

@@ -1,6 +1,12 @@
-from anthropic_iterative_search import final_chain 
-
+from anthropic_iterative_search import final_chain
 
 if __name__ == "__main__":
-	query = "Which movie came out first: Oppenheimer, or Are You There God It's Me Margaret?"
-	print(final_chain.with_config(configurable={"chain": "retrieve"}).invoke({"query": query}))
+    query = (
+        "Which movie came out first: Oppenheimer, or "
+        "Are You There God It's Me Margaret?"
+    )
+    print(
+        final_chain.with_config(configurable={"chain": "retrieve"}).invoke(
+            {"query": query}
+        )
+    )