mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
9ffca3b92a
Update imports to use core for the low-hanging fruit changes. Ran following ```bash git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g' git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g' git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g' git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g' git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g' git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g' git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g' git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g' git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g' git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g' git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g' git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g' git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g' git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g' git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g' git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g' git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g' git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g' ```
38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import re
|
|
|
|
from langchain_core.agents import AgentAction, AgentFinish
|
|
|
|
from .agent_scratchpad import _format_docs
|
|
|
|
|
|
def extract_between_tags(tag: str, string: str, strip: bool = True) -> str:
|
|
ext_list = re.findall(f"<{tag}\s?>(.+?)</{tag}\s?>", string, re.DOTALL)
|
|
if strip:
|
|
ext_list = [e.strip() for e in ext_list]
|
|
if ext_list:
|
|
if len(ext_list) != 1:
|
|
raise ValueError
|
|
# 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>"
|
|
)
|
|
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 += partial_completion
|
|
return AgentFinish({"docs": docs, "output": str_output}, log=partial_completion)
|
|
else:
|
|
return AgentAction(
|
|
tool="search", tool_input=search_query, log=partial_completion
|
|
)
|