Add RAG input types (#12684)

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/chat-bot-feedback/chat_bot_feedback/chain.py

@@ -164,9 +164,8 @@ def format_chat_history(chain_input: dict) -> dict:
 # if you update the name of this, you MUST also update ../pyproject.toml
 # with the new `tool.langserve.export_attr`
 chain = (
-    (format_chat_history | _prompt | _model | StrOutputParser()).with_types(
+    (format_chat_history | _prompt | _model | StrOutputParser())
-        input_type=ChainInput
+    .with_types(input_type=ChainInput)
-    )
     # This is to add the evaluators as "listeners"
     # and to customize the name of the chain.
     # Any chain that accepts a compatible input type works here.

templates/rag-matching-engine/rag_matching_engine/chain.py

@@ -3,6 +3,7 @@ import os
 from langchain.embeddings import VertexAIEmbeddings
 from langchain.llms import VertexAI
 from langchain.prompts import PromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
 from langchain.vectorstores import MatchingEngine
@@ -67,3 +68,11 @@ chain = (
     | model
     | StrOutputParser()
 )
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py

@@ -3,6 +3,7 @@ import os
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.retrievers.multi_query import MultiQueryRetriever
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
@@ -55,3 +56,11 @@ chain = (
     | model
     | StrOutputParser()
 )
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py

@@ -3,6 +3,7 @@ import os
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import CohereRerank
 from langchain.schema.output_parser import StrOutputParser
@@ -62,3 +63,11 @@ chain = (
     | model
     | StrOutputParser()
 )
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-pinecone/rag_pinecone/chain.py

@@ -3,6 +3,7 @@ import os
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
 from langchain.vectorstores import Pinecone
@@ -50,3 +51,11 @@ chain = (
     | model
     | StrOutputParser()
 )
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-semi-structured/rag_semi_structured/chain.py

@@ -4,6 +4,7 @@ import uuid
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.retrievers.multi_vector import MultiVectorRetriever
 from langchain.schema.document import Document
 from langchain.schema.output_parser import StrOutputParser
@@ -109,3 +110,11 @@ chain = (
     | model
     | StrOutputParser()
 )
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-supabase/rag_supabase/chain.py

@@ -3,6 +3,7 @@ import os
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
 from langchain.vectorstores.supabase import SupabaseVectorStore
@@ -39,3 +40,11 @@ chain = (
     | model
     | StrOutputParser()
 )
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-weaviate/rag_weaviate/chain.py

@@ -4,6 +4,7 @@ from langchain.chat_models import ChatOpenAI
 from langchain.document_loaders import WebBaseLoader
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
 from langchain.text_splitter import RecursiveCharacterTextSplitter
@@ -50,3 +51,11 @@ chain = (
     | model
     | StrOutputParser()
 )
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)