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