2023-11-07 03:24:00 +00:00
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import os
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2024-01-02 21:47:11 +00:00
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from langchain_community.vectorstores import Vectara
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2024-06-04 19:57:28 +00:00
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from langchain_community.vectorstores.vectara import SummaryConfig, VectaraQueryConfig
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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.pydantic_v1 import BaseModel
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2023-11-07 03:24:00 +00:00
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if os.environ.get("VECTARA_CUSTOMER_ID", None) is None:
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raise Exception("Missing `VECTARA_CUSTOMER_ID` environment variable.")
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if os.environ.get("VECTARA_CORPUS_ID", None) is None:
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raise Exception("Missing `VECTARA_CORPUS_ID` environment variable.")
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if os.environ.get("VECTARA_API_KEY", None) is None:
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raise Exception("Missing `VECTARA_API_KEY` environment variable.")
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2024-06-04 19:57:28 +00:00
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# Setup the Vectara vectorstore with your Corpus ID and API Key
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vectara = Vectara()
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# Define the query configuration:
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summary_config = SummaryConfig(is_enabled=True, max_results=5, response_lang="eng")
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config = VectaraQueryConfig(k=10, lambda_val=0.005, summary_config=summary_config)
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rag = Vectara().as_rag(config)
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2023-11-07 03:24:00 +00:00
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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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2024-06-04 19:57:28 +00:00
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chain = rag.with_types(input_type=Question)
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