add template for hyde (#12390)

templates/hyde/LICENSE

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+MIT License
+
+Copyright (c) 2023 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

templates/hyde/README.md

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+# HyDE
+
+Hypothetical Document Embeddings (HyDE) are a method to improve retrieval.
+To do this, a hypothetical document is generated for an incoming query.
+That document is then embedded, and that embedding is used to look up real documents similar to that hypothetical document.
+The idea behind this is that the hypothetical document may be closer in the embedding space than the query.
+For a more detailed description, read the full paper [here](https://arxiv.org/abs/2212.10496).
+
+For this example, we use a simple RAG architecture, although you can easily use this technique in other more complicated architectures.

templates/hyde/hyde/chain.py

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+from langchain.prompts import ChatPromptTemplate
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough, RunnableParallel
+from langchain.vectorstores import Chroma
+from hyde.prompts import hyde_prompt
+
+# Example for document loading (from url), splitting, and creating vectostore
+
+''' 
+# Load
+from langchain.document_loaders import WebBaseLoader
+loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+data = loader.load()
+
+# Split
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+all_splits = text_splitter.split_documents(data)
+
+# Add to vectorDB
+vectorstore = Chroma.from_documents(documents=all_splits, 
+                                    collection_name="rag-chroma",
+                                    embedding=OpenAIEmbeddings(),
+                                    )
+retriever = vectorstore.as_retriever()
+'''
+
+# Embed a single document as a test
+vectorstore = Chroma.from_texts(
+    ["harrison worked at kensho"],
+    collection_name="rag-chroma",
+    embedding=OpenAIEmbeddings()
+)
+retriever = vectorstore.as_retriever()
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# LLM
+model = ChatOpenAI()
+
+# RAG chain
+chain = (
+    RunnableParallel({
+        # Configure the input, pass it the prompt, pass that to the model, and then the result to the retriever
+        "context": {"input": RunnablePassthrough()} | hyde_prompt | model | StrOutputParser() | retriever,
+        "question": RunnablePassthrough()
+    })
+    | prompt
+    | model
+    | StrOutputParser()
+)

templates/hyde/hyde/prompts.py

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+from langchain.prompts.prompt import PromptTemplate
+
+# There are a few different templates to choose from
+# These are just different ways to generate hypothetical documents
+web_search_template = """Please write a passage to answer the question 
+Question: {input}
+Passage:"""
+sci_fact_template = """Please write a scientific paper passage to support/refute the claim 
+Claim: {input}
+Passage:"""
+fiqa_template = """Please write a financial article passage to answer the question
+Question: {input}
+Passage:"""
+trec_news_template = """Please write a news passage about the topic.
+Topic: {input}
+Passage:"""
+
+# For the sake of this example we will use the web search template
+hyde_prompt = PromptTemplate.from_template(web_search_template)

templates/hyde/pyproject.toml

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+[tool.poetry]
+name = "hyde"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.313, <0.1"
+openai = "^0.28.1"
+
+[tool.poetry.group.dev.dependencies]
+poethepoet = "^0.24.1"
+langchain-cli = ">=0.0.4"
+fastapi = "^0.104.0"
+sse-starlette = "^1.6.5"
+
+[tool.langserve]
+export_module = "hyde.chain"
+export_attr = "chain"
+
+[tool.poe.tasks.start]
+cmd="uvicorn langchain_cli.dev_scripts:create_demo_server --reload --port $port --host $host"
+args = [
+    {name = "port", help = "port to run on", default = "8000"},
+    {name = "host", help = "host to run on", default = "127.0.0.1"}
+]
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"