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…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
import os
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import cassio
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import langchain
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from langchain.cache import CassandraCache
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from langchain.prompts import ChatPromptTemplate
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from langchain.schema import BaseMessage
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from langchain_community.chat_models import ChatOpenAI
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from langchain_core.runnables import RunnableLambda
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use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0"))
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if use_cassandra:
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from .cassandra_cluster_init import get_cassandra_connection
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session, keyspace = get_cassandra_connection()
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cassio.init(
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session=session,
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keyspace=keyspace,
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)
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else:
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cassio.init(
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token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
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database_id=os.environ["ASTRA_DB_ID"],
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keyspace=os.environ.get("ASTRA_DB_KEYSPACE"),
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)
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# inits
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langchain.llm_cache = CassandraCache(session=None, keyspace=None)
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llm = ChatOpenAI()
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# custom runnables
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def msg_splitter(msg: BaseMessage):
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return [w.strip() for w in msg.content.split(",") if w.strip()]
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# synonym-route preparation
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synonym_prompt = ChatPromptTemplate.from_template(
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"List up to five comma-separated synonyms of this word: {word}"
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)
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chain = synonym_prompt | llm | RunnableLambda(msg_splitter)
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