langchain/templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py

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import os
import cassio
import langchain
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from langchain.cache import CassandraCache
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import BaseMessage
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from langchain_core.prompts import ChatPromptTemplate
docs[patch], templates[patch]: Import from core (#14575) 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' ```
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from langchain_core.runnables import RunnableLambda
use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0"))
if use_cassandra:
from .cassandra_cluster_init import get_cassandra_connection
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session, keyspace = get_cassandra_connection()
cassio.init(
session=session,
keyspace=keyspace,
)
else:
cassio.init(
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
database_id=os.environ["ASTRA_DB_ID"],
keyspace=os.environ.get("ASTRA_DB_KEYSPACE"),
)
# inits
langchain.llm_cache = CassandraCache(session=None, keyspace=None)
llm = ChatOpenAI()
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# custom runnables
def msg_splitter(msg: BaseMessage):
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return [w.strip() for w in msg.content.split(",") if w.strip()]
# synonym-route preparation
synonym_prompt = ChatPromptTemplate.from_template(
"List up to five comma-separated synonyms of this word: {word}"
)
chain = synonym_prompt | llm | RunnableLambda(msg_splitter)