mirror of
https://github.com/hwchase17/langchain.git
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Add openai v2 adapter (#14063)
### Description
Starting from [openai version
1.0.0](17ac677995 (module-level-client)
),
the camel case form of `openai.ChatCompletion` is no longer supported
and has been changed to lowercase `openai.chat.completions`. In
addition, the returned object only accepts attribute access instead of
index access:
```python
import openai
# optional; defaults to `os.environ['OPENAI_API_KEY']`
openai.api_key = '...'
# all client options can be configured just like the `OpenAI` instantiation counterpart
openai.base_url = "https://..."
openai.default_headers = {"x-foo": "true"}
completion = openai.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.choices[0].message.content)
```
So I implemented a compatible adapter that supports both attribute
access and index access:
```python
In [1]: from langchain.adapters import openai as lc_openai
...: messages = [{"role": "user", "content": "hi"}]
In [2]: result = lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [3]: result.choices[0].message
Out[3]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [4]: result["choices"][0]["message"]
Out[4]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [5]: result = await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [6]: result.choices[0].message
Out[6]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [7]: result["choices"][0]["message"]
Out[7]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [8]: for rs in lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
In [20]: async for rs in await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
...
```
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
This commit is contained in:
parent
0f02081392
commit
cd2028288e
285
docs/docs/integrations/adapters/openai-old.ipynb
Normal file
285
docs/docs/integrations/adapters/openai-old.ipynb
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@ -0,0 +1,285 @@
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{
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"cells": [
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{
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||||
"cell_type": "markdown",
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"id": "700a516b",
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||||
"metadata": {},
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||||
"source": [
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"# OpenAI Adapter(Old)\n",
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"\n",
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||||
"**Please ensure OpenAI library is less than 1.0.0; otherwise, refer to the newer doc [OpenAI Adapter](./openai.ipynb).**\n",
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"\n",
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"A lot of people get started with OpenAI but want to explore other models. LangChain's integrations with many model providers make this easy to do so. While LangChain has it's own message and model APIs, we've also made it as easy as possible to explore other models by exposing an adapter to adapt LangChain models to the OpenAI api.\n",
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"\n",
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"At the moment this only deals with output and does not return other information (token counts, stop reasons, etc)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6017f26a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import openai\n",
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"from langchain.adapters import openai as lc_openai"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b522ceda",
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"metadata": {},
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"source": [
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"## ChatCompletion.create"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "1d22eb61",
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"metadata": {},
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"outputs": [],
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"source": [
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"messages = [{\"role\": \"user\", \"content\": \"hi\"}]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d550d3ad",
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"metadata": {},
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"source": [
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"Original OpenAI call"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "012d81ae",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result = openai.ChatCompletion.create(\n",
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" messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
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")\n",
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"result[\"choices\"][0][\"message\"].to_dict_recursive()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "db5b5500",
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"metadata": {},
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"source": [
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"LangChain OpenAI wrapper call"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "c67a5ac8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lc_result = lc_openai.ChatCompletion.create(\n",
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" messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
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")\n",
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"lc_result[\"choices\"][0][\"message\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "034ba845",
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"metadata": {},
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"source": [
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"Swapping out model providers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "f7c94827",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'role': 'assistant', 'content': ' Hello!'}"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lc_result = lc_openai.ChatCompletion.create(\n",
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" messages=messages, model=\"claude-2\", temperature=0, provider=\"ChatAnthropic\"\n",
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")\n",
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"lc_result[\"choices\"][0][\"message\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cb3f181d",
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"metadata": {},
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"source": [
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"## ChatCompletion.stream"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7b8cd18",
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"metadata": {},
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"source": [
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"Original OpenAI call"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "fd8cb1ea",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'role': 'assistant', 'content': ''}\n",
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"{'content': 'Hello'}\n",
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"{'content': '!'}\n",
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"{'content': ' How'}\n",
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"{'content': ' can'}\n",
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"{'content': ' I'}\n",
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"{'content': ' assist'}\n",
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"{'content': ' you'}\n",
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"{'content': ' today'}\n",
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"{'content': '?'}\n",
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"{}\n"
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]
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}
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],
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"source": [
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"for c in openai.ChatCompletion.create(\n",
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" messages=messages, model=\"gpt-3.5-turbo\", temperature=0, stream=True\n",
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"):\n",
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" print(c[\"choices\"][0][\"delta\"].to_dict_recursive())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b2a076b",
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"metadata": {},
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"source": [
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"LangChain OpenAI wrapper call"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"id": "9521218c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'role': 'assistant', 'content': ''}\n",
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"{'content': 'Hello'}\n",
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"{'content': '!'}\n",
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"{'content': ' How'}\n",
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"{'content': ' can'}\n",
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"{'content': ' I'}\n",
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"{'content': ' assist'}\n",
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"{'content': ' you'}\n",
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"{'content': ' today'}\n",
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"{'content': '?'}\n",
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"{}\n"
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]
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}
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],
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"source": [
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"for c in lc_openai.ChatCompletion.create(\n",
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" messages=messages, model=\"gpt-3.5-turbo\", temperature=0, stream=True\n",
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"):\n",
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" print(c[\"choices\"][0][\"delta\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0fc39750",
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"metadata": {},
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"source": [
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"Swapping out model providers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "68f0214e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'role': 'assistant', 'content': ' Hello'}\n",
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"{'content': '!'}\n",
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"{}\n"
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]
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}
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],
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"source": [
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"for c in lc_openai.ChatCompletion.create(\n",
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" messages=messages,\n",
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" model=\"claude-2\",\n",
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" temperature=0,\n",
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" stream=True,\n",
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" provider=\"ChatAnthropic\",\n",
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"):\n",
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" print(c[\"choices\"][0][\"delta\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -7,6 +7,8 @@
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"source": [
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"# OpenAI Adapter\n",
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"\n",
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"**Please ensure OpenAI library is version 1.0.0 or higher; otherwise, refer to the older doc [OpenAI Adapter(Old)](./openai-old.ipynb).**\n",
|
||||
"\n",
|
||||
"A lot of people get started with OpenAI but want to explore other models. LangChain's integrations with many model providers make this easy to do so. While LangChain has it's own message and model APIs, we've also made it as easy as possible to explore other models by exposing an adapter to adapt LangChain models to the OpenAI api.\n",
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"\n",
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"At the moment this only deals with output and does not return other information (token counts, stop reasons, etc)."
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@ -28,12 +30,12 @@
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"id": "b522ceda",
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"metadata": {},
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"source": [
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"## ChatCompletion.create"
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"## chat.completions.create"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 2,
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"id": "1d22eb61",
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"metadata": {},
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"outputs": [],
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@ -51,26 +53,29 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 3,
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"id": "012d81ae",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
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"{'content': 'Hello! How can I assist you today?',\n",
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" 'role': 'assistant',\n",
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" 'function_call': None,\n",
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" 'tool_calls': None}"
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]
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},
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"execution_count": 15,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result = openai.ChatCompletion.create(\n",
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"result = openai.chat.completions.create(\n",
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" messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
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")\n",
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"result[\"choices\"][0][\"message\"].to_dict_recursive()"
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"result.choices[0].message.model_dump()"
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]
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},
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{
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@ -83,26 +88,48 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 4,
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"id": "c67a5ac8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
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"{'role': 'assistant', 'content': 'Hello! How can I help you today?'}"
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]
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},
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"execution_count": 17,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lc_result = lc_openai.ChatCompletion.create(\n",
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"lc_result = lc_openai.chat.completions.create(\n",
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" messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
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")\n",
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"lc_result[\"choices\"][0][\"message\"]"
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"\n",
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"lc_result.choices[0].message # Attribute access"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "37a6e461-8608-47f6-ac45-12ad753c062a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'role': 'assistant', 'content': 'Hello! How can I help you today?'}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lc_result[\"choices\"][0][\"message\"] # Also compatible with index access"
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]
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},
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{
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@ -115,26 +142,26 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 6,
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"id": "f7c94827",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'role': 'assistant', 'content': ' Hello!'}"
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"{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
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]
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},
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"execution_count": 19,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lc_result = lc_openai.ChatCompletion.create(\n",
|
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"lc_result = lc_openai.chat.completions.create(\n",
|
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" messages=messages, model=\"claude-2\", temperature=0, provider=\"ChatAnthropic\"\n",
|
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")\n",
|
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"lc_result[\"choices\"][0][\"message\"]"
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"lc_result.choices[0].message"
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]
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},
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{
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@ -142,7 +169,7 @@
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"id": "cb3f181d",
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"metadata": {},
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"source": [
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"## ChatCompletion.stream"
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"## chat.completions.stream"
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]
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},
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{
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@ -155,7 +182,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 7,
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"id": "fd8cb1ea",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -163,25 +190,25 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'role': 'assistant', 'content': ''}\n",
|
||||
"{'content': 'Hello'}\n",
|
||||
"{'content': '!'}\n",
|
||||
"{'content': ' How'}\n",
|
||||
"{'content': ' can'}\n",
|
||||
"{'content': ' I'}\n",
|
||||
"{'content': ' assist'}\n",
|
||||
"{'content': ' you'}\n",
|
||||
"{'content': ' today'}\n",
|
||||
"{'content': '?'}\n",
|
||||
"{}\n"
|
||||
"{'content': '', 'function_call': None, 'role': 'assistant', 'tool_calls': None}\n",
|
||||
"{'content': 'Hello', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': '!', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': ' How', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': ' can', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': ' I', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': ' assist', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': ' you', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': ' today', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': '?', 'function_call': None, 'role': None, 'tool_calls': None}\n",
|
||||
"{'content': None, 'function_call': None, 'role': None, 'tool_calls': None}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for c in openai.ChatCompletion.create(\n",
|
||||
"for c in openai.chat.completions.create(\n",
|
||||
" messages=messages, model=\"gpt-3.5-turbo\", temperature=0, stream=True\n",
|
||||
"):\n",
|
||||
" print(c[\"choices\"][0][\"delta\"].to_dict_recursive())"
|
||||
" print(c.choices[0].delta.model_dump())"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -194,7 +221,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 8,
|
||||
"id": "9521218c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -217,10 +244,10 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for c in lc_openai.ChatCompletion.create(\n",
|
||||
"for c in lc_openai.chat.completions.create(\n",
|
||||
" messages=messages, model=\"gpt-3.5-turbo\", temperature=0, stream=True\n",
|
||||
"):\n",
|
||||
" print(c[\"choices\"][0][\"delta\"])"
|
||||
" print(c.choices[0].delta)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -233,7 +260,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 9,
|
||||
"id": "68f0214e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -241,14 +268,22 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'role': 'assistant', 'content': ' Hello'}\n",
|
||||
"{'role': 'assistant', 'content': ''}\n",
|
||||
"{'content': 'Hello'}\n",
|
||||
"{'content': '!'}\n",
|
||||
"{'content': ' How'}\n",
|
||||
"{'content': ' can'}\n",
|
||||
"{'content': ' I'}\n",
|
||||
"{'content': ' assist'}\n",
|
||||
"{'content': ' you'}\n",
|
||||
"{'content': ' today'}\n",
|
||||
"{'content': '?'}\n",
|
||||
"{}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for c in lc_openai.ChatCompletion.create(\n",
|
||||
"for c in lc_openai.chat.completions.create(\n",
|
||||
" messages=messages,\n",
|
||||
" model=\"claude-2\",\n",
|
||||
" temperature=0,\n",
|
||||
@ -275,7 +310,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
@ -25,6 +25,7 @@ from langchain_core.messages import (
|
||||
SystemMessage,
|
||||
ToolMessage,
|
||||
)
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
from typing_extensions import Literal
|
||||
|
||||
|
||||
@ -38,6 +39,29 @@ async def aenumerate(
|
||||
i += 1
|
||||
|
||||
|
||||
class IndexableBaseModel(BaseModel):
|
||||
"""Allows a BaseModel to return its fields by string variable indexing"""
|
||||
|
||||
def __getitem__(self, item: str) -> Any:
|
||||
return getattr(self, item)
|
||||
|
||||
|
||||
class Choice(IndexableBaseModel):
|
||||
message: dict
|
||||
|
||||
|
||||
class ChatCompletions(IndexableBaseModel):
|
||||
choices: List[Choice]
|
||||
|
||||
|
||||
class ChoiceChunk(IndexableBaseModel):
|
||||
delta: dict
|
||||
|
||||
|
||||
class ChatCompletionChunk(IndexableBaseModel):
|
||||
choices: List[ChoiceChunk]
|
||||
|
||||
|
||||
def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
|
||||
"""Convert a dictionary to a LangChain message.
|
||||
|
||||
@ -129,7 +153,7 @@ def convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMess
|
||||
return [convert_dict_to_message(m) for m in messages]
|
||||
|
||||
|
||||
def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
|
||||
def _convert_message_chunk(chunk: BaseMessageChunk, i: int) -> dict:
|
||||
_dict: Dict[str, Any] = {}
|
||||
if isinstance(chunk, AIMessageChunk):
|
||||
if i == 0:
|
||||
@ -148,6 +172,11 @@ def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str
|
||||
# This only happens at the end of streams, and OpenAI returns as empty dict
|
||||
if _dict == {"content": ""}:
|
||||
_dict = {}
|
||||
return _dict
|
||||
|
||||
|
||||
def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
|
||||
_dict = _convert_message_chunk(chunk, i)
|
||||
return {"choices": [{"delta": _dict}]}
|
||||
|
||||
|
||||
@ -262,3 +291,109 @@ def convert_messages_for_finetuning(
|
||||
for session in sessions
|
||||
if _has_assistant_message(session)
|
||||
]
|
||||
|
||||
|
||||
class Completions:
|
||||
"""Completion."""
|
||||
|
||||
@overload
|
||||
@staticmethod
|
||||
def create(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[False] = False,
|
||||
**kwargs: Any,
|
||||
) -> ChatCompletions:
|
||||
...
|
||||
|
||||
@overload
|
||||
@staticmethod
|
||||
def create(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[True],
|
||||
**kwargs: Any,
|
||||
) -> Iterable:
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
def create(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Union[ChatCompletions, Iterable]:
|
||||
models = importlib.import_module("langchain.chat_models")
|
||||
model_cls = getattr(models, provider)
|
||||
model_config = model_cls(**kwargs)
|
||||
converted_messages = convert_openai_messages(messages)
|
||||
if not stream:
|
||||
result = model_config.invoke(converted_messages)
|
||||
return ChatCompletions(
|
||||
choices=[Choice(message=convert_message_to_dict(result))]
|
||||
)
|
||||
else:
|
||||
return (
|
||||
ChatCompletionChunk(
|
||||
choices=[ChoiceChunk(delta=_convert_message_chunk(c, i))]
|
||||
)
|
||||
for i, c in enumerate(model_config.stream(converted_messages))
|
||||
)
|
||||
|
||||
@overload
|
||||
@staticmethod
|
||||
async def acreate(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[False] = False,
|
||||
**kwargs: Any,
|
||||
) -> ChatCompletions:
|
||||
...
|
||||
|
||||
@overload
|
||||
@staticmethod
|
||||
async def acreate(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[True],
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator:
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
async def acreate(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Union[ChatCompletions, AsyncIterator]:
|
||||
models = importlib.import_module("langchain.chat_models")
|
||||
model_cls = getattr(models, provider)
|
||||
model_config = model_cls(**kwargs)
|
||||
converted_messages = convert_openai_messages(messages)
|
||||
if not stream:
|
||||
result = await model_config.ainvoke(converted_messages)
|
||||
return ChatCompletions(
|
||||
choices=[Choice(message=convert_message_to_dict(result))]
|
||||
)
|
||||
else:
|
||||
return (
|
||||
ChatCompletionChunk(
|
||||
choices=[ChoiceChunk(delta=_convert_message_chunk(c, i))]
|
||||
)
|
||||
async for i, c in aenumerate(model_config.astream(converted_messages))
|
||||
)
|
||||
|
||||
|
||||
class Chat:
|
||||
def __init__(self) -> None:
|
||||
self.completions = Completions()
|
||||
|
||||
|
||||
chat = Chat()
|
||||
|
Loading…
Reference in New Issue
Block a user