new-ingest

Ingest with a CLI

scripts/old/ingest_rst.py

@@ -0,0 +1,85 @@
+from pathlib import Path
+from langchain.text_splitter import CharacterTextSplitter
+import faiss
+from langchain.vectorstores import FAISS
+from langchain.embeddings import OpenAIEmbeddings
+import pickle
+import dotenv
+import tiktoken
+import sys
+from argparse import ArgumentParser
+
+def num_tokens_from_string(string: str, encoding_name: str) -> int:
+# Function to convert string to tokens and estimate user cost.
+    encoding = tiktoken.get_encoding(encoding_name)
+    num_tokens = len(encoding.encode(string))
+    total_price = ((num_tokens/1000) * 0.0004)
+    return num_tokens, total_price
+
+def call_openai_api():
+# Function to create a vector store from the documents and save it to disk.
+    store = FAISS.from_texts(docs, OpenAIEmbeddings(), metadatas=metadatas)
+    faiss.write_index(store.index, "docs.index")
+    store.index = None
+    with open("faiss_store.pkl", "wb") as f:
+        pickle.dump(store, f)
+
+def get_user_permission():
+# Function to ask user permission to call the OpenAI api and spend their OpenAI funds.
+    # Here we convert the docs list to a string and calculate the number of OpenAI tokens the string represents.
+    docs_content = (" ".join(docs))
+    tokens, total_price = num_tokens_from_string(string=docs_content, encoding_name="cl100k_base")
+    # Here we print the number of tokens and the approx user cost with some visually appealing formatting.
+    print(f"Number of Tokens = {format(tokens, ',d')}")
+    print(f"Approx Cost = ${format(total_price, ',.2f')}")
+    #Here we check for user permission before calling the API.
+    user_input = input("Price Okay? (Y/N) \n").lower()
+    if user_input == "y":
+        call_openai_api()
+    elif user_input == "":
+        call_openai_api()
+    else:
+        print("The API was not called. No money was spent.")
+
+#Load .env file
+dotenv.load_dotenv()
+
+ap = ArgumentParser("Script for training DocsGPT on .rst documentation files.")
+ap.add_argument("-i", "--inputs",
+                type=str,
+                default="inputs",
+                help="Directory containing documentation files")
+args = ap.parse_args()
+
+# Here we load in the data in the format that Notion exports it in.
+ps = list(Path(args.inputs).glob("**/*.rst"))
+
+# parse all child directories
+data = []
+sources = []
+for p in ps:
+    with open(p) as f:
+        data.append(f.read())
+    sources.append(p)
+
+# Here we split the documents, as needed, into smaller chunks.
+# We do this due to the context limits of the LLMs.
+text_splitter = CharacterTextSplitter(chunk_size=1500, separator="\n")
+docs = []
+metadatas = []
+for i, d in enumerate(data):
+    splits = text_splitter.split_text(d)
+    docs.extend(splits)
+    metadatas.extend([{"source": sources[i]}] * len(splits))
+
+# Here we check for command line arguments for bot calls.
+# If no argument exists or the permission_bypass_flag argument is not '-y',
+# user permission is requested to call the API.
+if len(sys.argv) > 1:
+    permission_bypass_flag = sys.argv[1]
+    if permission_bypass_flag == '-y':
+        call_openai_api()
+    else:
+        get_user_permission()
+else:
+    get_user_permission()