Add template for self-query-qdrant (#12795)

This PR adds a self-querying template using Qdrant as a vector store.
The template uses an artificial dataset and was implemented in a way
that simplifies passing different components and choosing LLM and
embedding providers.

---------

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

templates/self-query-qdrant/self_query_qdrant/chain.py

@@ -0,0 +1,92 @@
+import os
+from typing import List, Optional
+
+from langchain.chains.query_constructor.schema import AttributeInfo
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.llms import BaseLLM
+from langchain.llms.openai import OpenAI
+from langchain.pydantic_v1 import BaseModel
+from langchain.retrievers import SelfQueryRetriever
+from langchain.schema import Document, StrOutputParser
+from langchain.schema.embeddings import Embeddings
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain.vectorstores.qdrant import Qdrant
+from qdrant_client import QdrantClient
+
+from self_query_qdrant import defaults, helper, prompts
+
+
+class Query(BaseModel):
+    __root__: str
+
+
+def create_chain(
+    llm: Optional[BaseLLM] = None,
+    embeddings: Optional[Embeddings] = None,
+    document_contents: str = defaults.DEFAULT_DOCUMENT_CONTENTS,
+    metadata_field_info: List[AttributeInfo] = defaults.DEFAULT_METADATA_FIELD_INFO,
+    collection_name: str = defaults.DEFAULT_COLLECTION_NAME,
+):
+    """
+    Create a chain that can be used to query a Qdrant vector store with a self-querying
+    capability. By default, this chain will use the OpenAI LLM and OpenAIEmbeddings, and
+    work with the default document contents and metadata field info. You can override
+    these defaults by passing in your own values.
+    :param llm: an LLM to use for generating text
+    :param embeddings: an Embeddings to use for generating queries
+    :param document_contents: a description of the document set
+    :param metadata_field_info: list of metadata attributes
+    :param collection_name: name of the Qdrant collection to use
+    :return:
+    """
+    llm = llm or OpenAI()
+    embeddings = embeddings or OpenAIEmbeddings()
+
+    # Set up a vector store to store your vectors and metadata
+    client = QdrantClient(
+        url=os.environ.get("QDRANT_URL", "http://localhost:6333"),
+        api_key=os.environ.get("QDRANT_API_KEY"),
+    )
+    vectorstore = Qdrant(
+        client=client,
+        collection_name=collection_name,
+        embeddings=embeddings,
+    )
+
+    # Set up a retriever to query your vector store with self-querying capabilities
+    retriever = SelfQueryRetriever.from_llm(
+        llm, vectorstore, document_contents, metadata_field_info, verbose=True
+    )
+
+    context = RunnableParallel(
+        context=retriever | helper.combine_documents,
+        query=RunnablePassthrough(),
+    )
+    pipeline = context | prompts.LLM_CONTEXT_PROMPT | llm | StrOutputParser()
+    return pipeline.with_types(input_type=Query)
+
+
+def initialize(
+    embeddings: Optional[Embeddings] = None,
+    collection_name: str = defaults.DEFAULT_COLLECTION_NAME,
+    documents: List[Document] = defaults.DEFAULT_DOCUMENTS,
+):
+    """
+    Initialize a vector store with a set of documents. By default, the documents will be
+    compatible with the default metadata field info. You can override these defaults by
+    passing in your own values.
+    :param embeddings: an Embeddings to use for generating queries
+    :param collection_name: name of the Qdrant collection to use
+    :param documents: a list of documents to initialize the vector store with
+    :return:
+    """
+    embeddings = embeddings or OpenAIEmbeddings()
+
+    # Set up a vector store to store your vectors and metadata
+    Qdrant.from_documents(
+        documents, embedding=embeddings, collection_name=collection_name
+    )
+
+
+# Create the default chain
+chain = create_chain()