Advanced RAG: Query Decomposition and Reasoning

!pip install haystack-ai
!pip install pyarrow==15.0.2
!pip install datasets
!pip install cohere-haystack

from haystack import Document, component
from haystack.components.builders import PromptBuilder
from haystack.components.retrievers import InMemoryEmbeddingRetriever
from haystack.components.generators import OpenAIGenerator
from haystack.components.generators.openai_utils import _convert_message_to_openai_format
from haystack.dataclasses import ChatMessage, StreamingChunk
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.cohere import CohereDocumentEmbedder, CohereTextEmbedder

from datasets import load_dataset
from openai import OpenAI, Stream
from openai.types.chat import ChatCompletion, ChatCompletionChunk
from typing import List, Any, Dict, Optional, Callable, Union

document_store = InMemoryDocumentStore()

dataset = load_dataset("Tuana/game-of-thrones", split="train")
docs = [Document(content=doc["content"], meta=doc["meta"]) for doc in dataset]

doc_embedder = CohereDocumentEmbedder(model="embed-multilingual-v3.0")
docs_with_embeddings = doc_embedder.run(docs)
document_store.write_documents(docs_with_embeddings["documents"])

class OpenAIGenerator(OpenAIGenerator):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    @component.output_types(replies=List[str], meta=List[Dict[str, Any]], structured_reply=BaseModel)
    def run(self, prompt: str, streaming_callback: Optional[Callable[[StreamingChunk], None]] = None, generation_kwargs: Optional[Dict[str, Any]] = None,):
      generation_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
      if "response_format" in generation_kwargs.keys():
        message = ChatMessage.from_user(prompt)
        if self.system_prompt:
            messages = [ChatMessage.from_system(self.system_prompt), message]
        else:
            messages = [message]

        streaming_callback = streaming_callback or self.streaming_callback
        openai_formatted_messages = [_convert_message_to_openai_format(message) for message in messages]
        completion: Union[Stream[ChatCompletionChunk], ChatCompletion] = self.client.beta.chat.completions.parse(
            model=self.model,
            messages=openai_formatted_messages,
            **generation_kwargs)
        completions = [self._build_structured_message(completion, choice) for choice in completion.choices]
        for response in completions:
            self._check_finish_reason(response)

        return {
            "replies": [message.content for message in completions],
            "meta": [message.meta for message in completions],
            "structured_reply": completions[0].content
        }
      else:
          return super().run(prompt, streaming_callback, generation_kwargs)

    def _build_structured_message(self, completion: Any, choice: Any) -> ChatMessage:
        chat_message = ChatMessage.from_assistant(choice.message.parsed or "")
        chat_message.meta.update(
            {
                "model": completion.model,
                "index": choice.index,
                "finish_reason": choice.finish_reason,
                "usage": dict(completion.usage),
            }
        )
        return chat_message

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
  os.environ["OPENAI_API_KEY"] = getpass("OpenAI API Key:")

if "COHERE_API_KEY" not in os.environ:
  os.environ["COHERE_API_KEY"] = getpass("Cohere API Key:")

from pydantic import BaseModel

class Question(BaseModel):
    question: str
    answer: Optional[str] = None

class Questions(BaseModel):
    questions: list[Question]

from haystack import Pipeline
from haystack.components.builders import PromptBuilder

splitter_prompt = """
You are a helpful assistant that prepares queries that will be sent to a search component.
Sometimes, these queries are very complex.
Your job is to simplify complex queries into multiple queries that can be answered
in isolation to eachother.

If the query is simple, then keep it as it is.
Examples
1. Query: Did Microsoft or Google make more money last year?
   Decomposed Questions: [Question(question='How much profit did Microsoft make last year?', answer=None), Question(question='How much profit did Google make last year?', answer=None)]
2. Query: What is the capital of France?
   Decomposed Questions: [Question(question='What is the capital of France?', answer=None)]
3. Query: {{question}}
   Decomposed Questions:
"""

builder = PromptBuilder(splitter_prompt)
llm = OpenAIGenerator(model="gpt-4o-mini", generation_kwargs={"response_format": Questions})

@component
class CohereMultiTextEmbedder:
    def __init__(self, model: str = "embed-multilingual-v3.0"):
        self.model = model
        self.query_embedder = CohereTextEmbedder(model=self.model)

    @component.output_types(embeddings=List[List[float]])
    def run(self, questions: BaseModel):
        embeddings = []
        for question in questions.questions:
          embeddings.append(self.query_embedder.run(question.question)["embedding"])
        return {"embeddings": embeddings}

@component
class MultiQueryInMemoryEmbeddingRetriever:

    def __init__(self, retriever: InMemoryEmbeddingRetriever, top_k: int = 3):

        self.retriever = retriever
        self.question_context_pairs = []
        self.top_k = top_k

    @component.output_types(question_context_pairs=List[Dict])
    def run(self, queries: BaseModel, query_embeddings: List[List[float]],top_k: int = None):
        if top_k != None:
          self.top_k = top_k
        for query, embedding in zip(queries.questions, query_embeddings):
          result = self.retriever.run(query_embedding = embedding, top_k = self.top_k)
          self.question_context_pairs.append({"question": query.question, "documents": {doc.content for doc in result["documents"]}})
        return {"question_context_pairs": self.question_context_pairs}

multi_query_template = """
You are a helpful assistant that can answer complex queries.
Here is the original question you were asked: {{question}}

And you have split the task into the following questions:
{% for pair in question_context_pairs %}
  {{pair.question}}
{% endfor %}

Here are the question and context pairs for each question.
For each question, generate the question answer pair as a structured output
{% for pair in question_context_pairs %}
  Question: {{pair.question}}
  Context: {{pair.documents}}
{% endfor %}
Answers:
"""

multi_query_prompt = PromptBuilder(multi_query_template)

reasoning_template = """
You are a helpful assistant that can answer complex queries.
Here is the original question you were asked: {{question}}

You have split this question up into simpler questoins that can be answered in
isolation.
Here are the questions and answers that you've generated
{% for pair in question_answer_pair %}
  {{pair}}
{% endfor %}

Reason about the final answer to the original query based on these questions and
aswers
Final Answer:
"""

resoning_prompt = PromptBuilder(reasoning_template)

query_decomposition_pipeline = Pipeline()

query_decomposition_pipeline.add_component("prompt", PromptBuilder(splitter_prompt))
query_decomposition_pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o-mini", generation_kwargs={"response_format": Questions}))

query_decomposition_pipeline.connect("prompt", "llm")

question = "Who has more siblings, Jamie or Sansa?"
result = query_decomposition_pipeline.run({"prompt":{"question": question}})
print(result["llm"]["structured_reply"])

pipeline = Pipeline()

pipeline.add_component("prompt", PromptBuilder(splitter_prompt))
pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o-mini", generation_kwargs={"response_format": Questions}))
pipeline.add_component("embedder", CohereMultiTextEmbedder(model="embed-multilingual-v3.0"))
pipeline.add_component("multi_query_retriever", MultiQueryInMemoryEmbeddingRetriever(InMemoryEmbeddingRetriever(document_store=document_store)))
pipeline.add_component("multi_query_prompt", PromptBuilder(multi_query_template))
pipeline.add_component("query_resolver_llm", OpenAIGenerator(model="gpt-4o-mini", generation_kwargs={"response_format": Questions}))
pipeline.add_component("reasoning_prompt", PromptBuilder(reasoning_template))
pipeline.add_component("reasoning_llm", OpenAIGenerator(model="gpt-4o-mini"))

pipeline.connect("prompt", "llm")
pipeline.connect("llm.structured_reply", "embedder.questions")
pipeline.connect("embedder.embeddings", "multi_query_retriever.query_embeddings")
pipeline.connect("llm.structured_reply", "multi_query_retriever.queries")
pipeline.connect("llm.structured_reply", "embedder.questions")
pipeline.connect("multi_query_retriever.question_context_pairs", "multi_query_prompt.question_context_pairs")
pipeline.connect("multi_query_prompt", "query_resolver_llm")
pipeline.connect("query_resolver_llm.structured_reply", "reasoning_prompt.question_answer_pair")
pipeline.connect("reasoning_prompt", "reasoning_llm")

question = "Who has more siblings, Jamie or Sansa?"
result = pipeline.run({"prompt":{"question": question},
              "multi_query_prompt": {"question": question},
              "reasoning_prompt": {"question": question}},
                      include_outputs_from=["query_resolver_llm"])

print("The original query was split and resolved:\n")
for pair in result["query_resolver_llm"]["structured_reply"].questions:
  print(pair)
print("\nSo the original query is answered as follows:\n")
print(result["reasoning_llm"]["replies"][0])

pipeline.show()