pgpt_python 0.1.2

PrivateGPT Python SDK

Python SDK for PrivateGPT API

pgpt_python is an open-source Python SDK designed to interact with the PrivateGPT API. PrivateGPT is a popular AI Open Source project that provides secure and private access to advanced natural language processing capabilities. This SDK simplifies the integration of PrivateGPT into Python applications, allowing developers to harness the power of PrivateGPT for various language-related tasks.

This SDK has been created using Fern.

Installation

To install the pgpt_python SDK, use the following pip command:

pip install pgpt_python

Getting Started

To begin using pgpt_python with the PrivateGPT API, follow these steps:

1. Import the PrivateGPTApi class:

   from pgpt_python.client import PrivateGPTApi
   

2. Create a PrivateGPTApi instance. Point it to your PrivateGPT url:

   client = PrivateGPTApi(base_url="http://localhost:8001")
   

3. Certainly! Here's the completed "Perform API calls" section:

Perform API Calls

Once you have set up the PrivateGPTApi instance, you can perform various API calls using the pgpt_python SDK. Below are examples of common API calls:

1. Health Check:

print(client.health.health())

> status='ok'

This call checks the health of the PrivateGPT API and returns information about its status.

2. Completion:

prompt_result = client.contextual_completions.prompt_completion(
    prompt="Answer with just the result: 2+2"
)
print(prompt_result.choices[0].message.content)

> The answer is 4.

This call performs contextual completions based on the provided prompt and retrieves the completion result.

3. Streaming Completion:

for i in client.contextual_completions.prompt_completion_stream(
    prompt="Answer with just the result: 2+2"
):
    print(i.choices[0].delta.content, end="")

> The answer i...

This example demonstrates contextual completions using a streaming approach, allowing you to process results incrementally.

4. Chat Completion:

chat_result = client.contextual_completions.chat_completion(
    messages=[{"role": "user", "content": "Answer with just the result: 2+2"}]
)
print(chat_result.choices[0].message.content)

> The answer is 4.

Perform a chat completion using a list of messages to simulate a conversation.

5. Streaming Chat Completion:

for i in client.contextual_completions.chat_completion_stream(
    messages=[{"role": "user", "content": "Answer with just the result: 2+2"}]
):
    print(i.choices[0].delta.content, end="")

> The answer i...

This example demonstrates streaming chat completions using a streaming approach, allowing you to process results incrementally.

6. Embeddings:

embedding_result = client.embeddings.embeddings_generation(input="Hello world")
print(embedding_result.data[0].embedding)

> [0.015196125954389572, -0.022570695728063583, 0.008547102101147175, -0.07417059689760208, 0.0038364222273230553, ... ]

Retrieve embeddings for a given input using synchronous embeddings generation.

7. Ingestion of Text:

text_to_ingest = "Books bombarded his shoulder, ... (your long text here)"
ingested_text_doc_id = (
    client.ingestion.ingest_text(file_name="Fahrenheit 451", text=text_to_ingest)
    .data[0]
    .doc_id
)
print("Ingested text doc id: ", ingested_text_doc_id)

> Ingested text doc id:  8cfc93fa-01dd-4644-82d4-e12dfff54dcf

Ingest text content and obtain a document ID for future reference.

8. Ingestion of File:

with open("example_file.txt", "rb") as f:
    ingested_file_doc_id = client.ingestion.ingest_file(file=f).data[0].doc_id
print("Ingested file doc id: ", ingested_file_doc_id)

> Ingested file doc id:  e2989f56-1729-4557-b30a-e4b023628629

Ingest a file and obtain a document ID for future reference.

9. List Ingested Documents:

for doc in client.ingestion.list_ingested().data:
    print(doc.doc_id)

>List ingested documents
e2989f56-1729-4557-b30a-e4b023628629
8cfc93fa-01dd-4644-82d4-e12dfff54dcf

Retrieve a list of ingested documents along with their document IDs.

10. Chunks Retrieval:

chunks_result = client.context_chunks.chunks_retrieval(text="Pigeon fluttering")
print(chunks_result.data[0].text)

> A book lit, almost obediently, like a white pigeon, in his hands, wings fluttering.

Retrieve related chunks of text from ingested docs based on a specified query.

11. Contextual Completion:

result = client.contextual_completions.prompt_completion(
    prompt="What did Montage do?",
    use_context=True,
    context_filter={"docs_ids": ["8cfc93fa-01dd-4644-82d4-e12dfff54dcf"]},
    include_sources=True,
).choices[0]

print("\n>Contextual completion:")
print(result.message.content)
print(f" # Source: {result.sources[0].document.doc_metadata['file_name']}")

> Montage was surrounded by falling books when one of them landed in his hands.
  Source: Fahrenheit 451

Perform a contextual completion using the specified context from ingested documents, including in the response the sources used during inference.

12. Deletion of Ingested Documents:

client.ingestion.delete_ingested("8cfc93fa-01dd-4644-82d4-e12dfff54dcf")
client.ingestion.delete_ingested("e2989f56-1729-4557-b30a-e4b023628629")

Delete previously ingested documents using their document IDs.

Examples

The provided examples_script.py showcases various features of the pgpt_python SDK. Feel free to explore and modify this script to suit your specific use cases.

Documentation

For detailed information on available methods and their parameters, refer to the official documentation.

Contributing

We welcome contributions from the community! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This project is licensed under the Apache License.

Acknowledgments

• Fern - For providing a powerful SDK generation tool, and supporting PrivateGPT with a free forever license.
• PrivateGPT Community - For being the fuel of PrivateGPT and providing valuable feedback.