2023-11-03 00:59:15 +00:00
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import os
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from langchain.prompts import ChatPromptTemplate
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2024-01-02 20:32:16 +00:00
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.embeddings import OpenAIEmbeddings
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2024-01-02 21:47:11 +00:00
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from langchain_community.vectorstores import MomentoVectorIndex
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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'
```
2023-12-12 00:49:10 +00:00
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnablePassthrough
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2023-11-03 00:59:15 +00:00
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from momento import (
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CredentialProvider,
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PreviewVectorIndexClient,
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VectorIndexConfigurations,
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)
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API_KEY_ENV_VAR_NAME = "MOMENTO_API_KEY"
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if os.environ.get(API_KEY_ENV_VAR_NAME, None) is None:
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raise Exception(f"Missing `{API_KEY_ENV_VAR_NAME}` environment variable.")
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MOMENTO_INDEX_NAME = os.environ.get("MOMENTO_INDEX_NAME", "langchain-test")
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### Sample Ingest Code - this populates the vector index with data
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### Run this on the first time to seed with data
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# from rag_momento_vector_index import ingest
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# ingest.load(API_KEY_ENV_VAR_NAME, MOMENTO_INDEX_NAME)
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vectorstore = MomentoVectorIndex(
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embedding=OpenAIEmbeddings(),
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client=PreviewVectorIndexClient(
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configuration=VectorIndexConfigurations.Default.latest(),
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credential_provider=CredentialProvider.from_environment_variable(
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API_KEY_ENV_VAR_NAME
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),
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),
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index_name=MOMENTO_INDEX_NAME,
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)
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retriever = vectorstore.as_retriever()
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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model = ChatOpenAI()
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chain = (
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{"context": retriever, "question": RunnablePassthrough()}
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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