347 lines
10 KiB
Python
347 lines
10 KiB
Python
import os
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import re
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import logging
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from typing import List
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import requests
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from huggingface_hub import snapshot_download
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from apps.ollama.main import generate_ollama_embeddings, GenerateEmbeddingsForm
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from config import SRC_LOG_LEVELS, CHROMA_CLIENT
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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def query_doc(collection_name: str, query: str, k: int, embedding_function):
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try:
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# if you use docker use the model from the environment variable
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collection = CHROMA_CLIENT.get_collection(
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name=collection_name,
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embedding_function=embedding_function,
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)
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result = collection.query(
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query_texts=[query],
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n_results=k,
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)
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return result
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except Exception as e:
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raise e
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def query_embeddings_doc(collection_name: str, query_embeddings, k: int):
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try:
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# if you use docker use the model from the environment variable
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log.info(f"query_embeddings_doc {query_embeddings}")
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collection = CHROMA_CLIENT.get_collection(
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name=collection_name,
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)
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result = collection.query(
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query_embeddings=[query_embeddings],
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n_results=k,
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)
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log.info(f"query_embeddings_doc:result {result}")
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return result
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except Exception as e:
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raise e
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def merge_and_sort_query_results(query_results, k):
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# Initialize lists to store combined data
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combined_ids = []
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combined_distances = []
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combined_metadatas = []
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combined_documents = []
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# Combine data from each dictionary
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for data in query_results:
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combined_ids.extend(data["ids"][0])
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combined_distances.extend(data["distances"][0])
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combined_metadatas.extend(data["metadatas"][0])
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combined_documents.extend(data["documents"][0])
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# Create a list of tuples (distance, id, metadata, document)
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combined = list(
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zip(combined_distances, combined_ids, combined_metadatas, combined_documents)
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)
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# Sort the list based on distances
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combined.sort(key=lambda x: x[0])
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# Unzip the sorted list
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sorted_distances, sorted_ids, sorted_metadatas, sorted_documents = zip(*combined)
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# Slicing the lists to include only k elements
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sorted_distances = list(sorted_distances)[:k]
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sorted_ids = list(sorted_ids)[:k]
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sorted_metadatas = list(sorted_metadatas)[:k]
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sorted_documents = list(sorted_documents)[:k]
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# Create the output dictionary
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merged_query_results = {
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"ids": [sorted_ids],
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"distances": [sorted_distances],
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"metadatas": [sorted_metadatas],
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"documents": [sorted_documents],
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"embeddings": None,
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"uris": None,
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"data": None,
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}
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return merged_query_results
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def query_collection(
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collection_names: List[str], query: str, k: int, embedding_function
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):
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results = []
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for collection_name in collection_names:
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try:
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# if you use docker use the model from the environment variable
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collection = CHROMA_CLIENT.get_collection(
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name=collection_name,
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embedding_function=embedding_function,
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)
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result = collection.query(
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query_texts=[query],
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n_results=k,
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)
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results.append(result)
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except:
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pass
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return merge_and_sort_query_results(results, k)
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def query_embeddings_collection(collection_names: List[str], query_embeddings, k: int):
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results = []
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log.info(f"query_embeddings_collection {query_embeddings}")
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for collection_name in collection_names:
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try:
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collection = CHROMA_CLIENT.get_collection(name=collection_name)
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result = collection.query(
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query_embeddings=[query_embeddings],
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n_results=k,
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)
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results.append(result)
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except:
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pass
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return merge_and_sort_query_results(results, k)
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def rag_template(template: str, context: str, query: str):
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template = template.replace("[context]", context)
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template = template.replace("[query]", query)
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return template
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def rag_messages(
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docs,
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messages,
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template,
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k,
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embedding_engine,
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embedding_model,
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embedding_function,
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openai_key,
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openai_url,
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):
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log.debug(
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f"docs: {docs} {messages} {embedding_engine} {embedding_model} {embedding_function} {openai_key} {openai_url}"
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)
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last_user_message_idx = None
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for i in range(len(messages) - 1, -1, -1):
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if messages[i]["role"] == "user":
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last_user_message_idx = i
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break
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user_message = messages[last_user_message_idx]
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if isinstance(user_message["content"], list):
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# Handle list content input
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content_type = "list"
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query = ""
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for content_item in user_message["content"]:
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if content_item["type"] == "text":
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query = content_item["text"]
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break
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elif isinstance(user_message["content"], str):
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# Handle text content input
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content_type = "text"
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query = user_message["content"]
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else:
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# Fallback in case the input does not match expected types
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content_type = None
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query = ""
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relevant_contexts = []
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for doc in docs:
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context = None
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try:
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if doc["type"] == "text":
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context = doc["content"]
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else:
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if embedding_engine == "":
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if doc["type"] == "collection":
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context = query_collection(
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collection_names=doc["collection_names"],
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query=query,
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k=k,
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embedding_function=embedding_function,
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)
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else:
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context = query_doc(
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collection_name=doc["collection_name"],
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query=query,
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k=k,
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embedding_function=embedding_function,
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)
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else:
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if embedding_engine == "ollama":
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query_embeddings = generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{
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"model": embedding_model,
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"prompt": query,
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}
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)
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)
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elif embedding_engine == "openai":
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query_embeddings = generate_openai_embeddings(
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model=embedding_model,
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text=query,
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key=openai_key,
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url=openai_url,
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)
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if doc["type"] == "collection":
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context = query_embeddings_collection(
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collection_names=doc["collection_names"],
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query_embeddings=query_embeddings,
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k=k,
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)
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else:
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context = query_embeddings_doc(
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collection_name=doc["collection_name"],
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query_embeddings=query_embeddings,
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k=k,
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)
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except Exception as e:
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log.exception(e)
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context = None
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relevant_contexts.append(context)
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log.debug(f"relevant_contexts: {relevant_contexts}")
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context_string = ""
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for context in relevant_contexts:
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if context:
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context_string += " ".join(context["documents"][0]) + "\n"
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ra_content = rag_template(
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template=template,
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context=context_string,
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query=query,
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)
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if content_type == "list":
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new_content = []
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for content_item in user_message["content"]:
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if content_item["type"] == "text":
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# Update the text item's content with ra_content
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new_content.append({"type": "text", "text": ra_content})
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else:
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# Keep other types of content as they are
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new_content.append(content_item)
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new_user_message = {**user_message, "content": new_content}
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else:
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new_user_message = {
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**user_message,
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"content": ra_content,
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}
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messages[last_user_message_idx] = new_user_message
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return messages
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def get_embedding_model_path(
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embedding_model: str, update_embedding_model: bool = False
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):
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# Construct huggingface_hub kwargs with local_files_only to return the snapshot path
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cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
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local_files_only = not update_embedding_model
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snapshot_kwargs = {
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"cache_dir": cache_dir,
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"local_files_only": local_files_only,
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}
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log.debug(f"embedding_model: {embedding_model}")
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log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
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# Inspiration from upstream sentence_transformers
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if (
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os.path.exists(embedding_model)
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or ("\\" in embedding_model or embedding_model.count("/") > 1)
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and local_files_only
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):
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# If fully qualified path exists, return input, else set repo_id
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return embedding_model
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elif "/" not in embedding_model:
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# Set valid repo_id for model short-name
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embedding_model = "sentence-transformers" + "/" + embedding_model
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snapshot_kwargs["repo_id"] = embedding_model
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# Attempt to query the huggingface_hub library to determine the local path and/or to update
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try:
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embedding_model_repo_path = snapshot_download(**snapshot_kwargs)
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log.debug(f"embedding_model_repo_path: {embedding_model_repo_path}")
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return embedding_model_repo_path
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except Exception as e:
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log.exception(f"Cannot determine embedding model snapshot path: {e}")
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return embedding_model
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def generate_openai_embeddings(
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model: str, text: str, key: str, url: str = "https://api.openai.com/v1"
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):
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try:
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r = requests.post(
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f"{url}/embeddings",
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {key}",
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},
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json={"input": text, "model": model},
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)
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r.raise_for_status()
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data = r.json()
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if "data" in data:
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return data["data"][0]["embedding"]
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else:
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raise "Something went wrong :/"
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except Exception as e:
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print(e)
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return None
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