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Code Issues Packages Projects Releases Wiki Activity

notebook fmt (#12498)

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This commit is contained in:
Bagatur
2023-10-29 15:50:09 -07:00
committed by GitHub
parent 56cc5b847c
commit 2424fff3f1
342 changed files with 8261 additions and 6796 deletions
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3
templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py
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@@ -7,9 +7,10 @@ def _format_docs(docs):
)
return result
def format_agent_scratchpad(intermediate_steps):
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += '</search_query>' + _format_docs(observation)
thoughts += "</search_query>" + _format_docs(observation)
return thoughts

12
templates/anthropic-iterative-search/anthropic_iterative_search/chain.py
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@@ -10,16 +10,18 @@ prompt = ChatPromptTemplate.from_template(answer_prompt)
model = ChatAnthropic(model="claude-2", temperature=0, max_tokens_to_sample=1000)
chain = {
"query": lambda x: x["query"],
"information": executor | (lambda x: x["output"])
} | prompt | model | StrOutputParser()
chain = (
{"query": lambda x: x["query"], "information": executor | (lambda x: x["output"])}
| prompt
| model
| StrOutputParser()
)
# Add typing for the inputs to be used in the playground
class Inputs(BaseModel):
query: str
query: str
chain = chain.with_types(input_type=Inputs)

36
templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py
View File

@@ -9,21 +9,33 @@ from .output_parser import parse_output
from .prompts import retrieval_prompt
from .retriever import retriever_description, search
prompt = ChatPromptTemplate.from_messages([
("user", retrieval_prompt),
("ai", "{agent_scratchpad}"),
])
prompt = ChatPromptTemplate.from_messages(
[
("user", retrieval_prompt),
("ai", "{agent_scratchpad}"),
]
)
prompt = prompt.partial(retriever_description=retriever_description)
model = ChatAnthropic(model="claude-2", temperature=0, max_tokens_to_sample=1000)
chain = RunnablePassthrough.assign(
agent_scratchpad=lambda x: format_agent_scratchpad(x['intermediate_steps'])
) | prompt | model.bind( stop_sequences=['</search_query>']) | StrOutputParser()
chain = (
RunnablePassthrough.assign(
agent_scratchpad=lambda x: format_agent_scratchpad(x["intermediate_steps"])
)
| prompt
| model.bind(stop_sequences=["</search_query>"])
| StrOutputParser()
)
agent_chain = RunnableMap({
"partial_completion": chain,
"intermediate_steps": lambda x: x['intermediate_steps']
}) | parse_output
agent_chain = (
RunnableMap(
{
"partial_completion": chain,
"intermediate_steps": lambda x: x["intermediate_steps"],
}
)
| parse_output
)
executor = AgentExecutor(agent=agent_chain, tools = [search], verbose=True)
executor = AgentExecutor(agent=agent_chain, tools=[search], verbose=True)