2023-10-26 01:47:42 +00:00
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import os
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2023-10-27 02:44:30 +00:00
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from langchain.chains.query_constructor.base import AttributeInfo
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2023-10-26 01:47:42 +00:00
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from langchain.retrievers.self_query.base import SelfQueryRetriever
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2024-01-02 20:32:16 +00:00
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.llms.openai import OpenAI
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2024-01-02 21:47:11 +00:00
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from langchain_community.vectorstores.supabase import SupabaseVectorStore
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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.runnables import RunnableParallel, RunnablePassthrough
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2023-10-27 02:44:30 +00:00
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from supabase.client import create_client
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supabase_url = os.environ.get("SUPABASE_URL")
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supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
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supabase = create_client(supabase_url, supabase_key)
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embeddings = OpenAIEmbeddings()
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vectorstore = SupabaseVectorStore(
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client=supabase,
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embedding=embeddings,
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table_name="documents",
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2023-10-27 02:44:30 +00:00
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query_name="match_documents",
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2023-10-26 01:47:42 +00:00
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)
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# Adjust this based on the metadata you store in the `metadata` JSON column
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metadata_field_info = [
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AttributeInfo(
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name="genre",
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description="The genre of the movie",
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type="string or list[string]",
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),
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AttributeInfo(
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name="year",
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description="The year the movie was released",
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type="integer",
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),
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AttributeInfo(
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name="director",
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description="The name of the movie director",
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type="string",
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),
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AttributeInfo(
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name="rating", description="A 1-10 rating for the movie", type="float"
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),
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]
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# Adjust this based on the type of documents you store
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document_content_description = "Brief summary of a movie"
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llm = OpenAI(temperature=0)
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retriever = SelfQueryRetriever.from_llm(
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llm, vectorstore, document_content_description, metadata_field_info, verbose=True
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)
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2023-10-27 02:44:30 +00:00
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chain = RunnableParallel({"query": RunnablePassthrough()}) | retriever
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