Skip to main content

ChatReka

This notebook provides a quick overview for getting started with Reka chat models.

Reka has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the Reka docs.

Overview​

Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
[ChatReka]langchain_community✅❌❌PyPI - DownloadsPyPI - Version

Model features​

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
✅❌❌✅✅✅✅✅❌❌

Setup​

To access Reka models you'll need to create an Reka developer account, get an API key, and install the langchain_community integration package and the reka python package via 'pip install reka-api'.

Credentials​

Head to https://platform.reka.ai/ to sign up for Reka and generate an API key. Once you've done this set the REKA_API_KEY environment variable:

Installation​

The LangChain ModuleName integration lives in the langchain_community package:

%pip install -qU langchain_community reka-api
Note: you may need to restart the kernel to use updated packages.

Initialize a client

import getpass
import os

os.environ["REKA_API_KEY"] = getpass.getpass("Enter your Reka API key: ")

Optional: use Langsmith to trace the execution of the model

import getpass
import os

os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your Langsmith API key: ")
from langchain_community.chat_models import ChatReka

model = ChatReka()
API Reference:ChatReka

Single turn text message

model.invoke("hi")
AIMessage(content=' Hello! How can I help you today? If you have a question, need assistance, or just want to chat, feel free to let me know. Have a great day!\n\n', additional_kwargs={}, response_metadata={}, id='run-b40e505a-5110-451a-92e6-a2a34988472c-0')

Images input

from langchain_core.messages import HumanMessage

image_url = "https://v0.docs.reka.ai/_images/000000245576.jpg"

message = HumanMessage(
    content=[
        {"type": "text", "text": "describe the weather in this image"},
        {
            "type": "image_url",
            "image_url": {"url": image_url},
        },
    ],
)
response = model.invoke([message])
print(response.content)
API Reference:HumanMessage
 The image shows an indoor setting with no visible weather elements. It features a cat on a desk licking a computer keyboard. The background includes a computer monitor, a desk with a few items like a pen holder and a mobile phone, and a glimpse of a window with blinds partially drawn.

Multiple images as input

message = HumanMessage(
    content=[
        {"type": "text", "text": "What are the difference between the two images? "},
        {
            "type": "image_url",
            "image_url": {
                "url": "https://cdn.pixabay.com/photo/2019/07/23/13/51/shepherd-dog-4357790_1280.jpg"
            },
        },
        {
            "type": "image_url",
            "image_url": {
                "url": "https://cdn.pixabay.com/photo/2024/02/17/00/18/cat-8578562_1280.jpg"
            },
        },
    ],
)
response = model.invoke([message])
print(response.content)
 The first image shows two German Shepherds, one adult and one puppy, in a grassy field. The adult dog is carrying a large stick in its mouth, indicating playfulness or a game being played. The background features a natural, leafy environment, suggesting an outdoor setting conducive to activities like running or training.

The second image features a close-up of a single cat with striking blue eyes, set against a background of dry leaves or grass. The cat has a calm and somewhat intense expression, with its fur neatly groomed and whiskers prominently visible. The focus is on the cat's face, capturing its serene demeanor in a quiet, natural outdoor setting.

The main differences lie in the subjects (dogs vs. cat) and their expressions (playful vs. serene), as well as the composition and focus of the images (outdoor play vs. close-up portrait).

Use use with tavtly api search

Tool use and agent creation

Define the tools​

We first need to create the tools we want to use. Our main tool of choice will be Tavily - a search engine. We have a built-in tool in LangChain to easily use Tavily search engine as tool.

import getpass
import os

os.environ["TAVILY_API_KEY"] = getpass.getpass("Enter your Tavily API key: ")
from langchain_community.tools.tavily_search import TavilySearchResults

search = TavilySearchResults(max_results=2)
search_results = search.invoke("what is the weather in SF")
print(search_results)
# If we want, we can create other tools.
# Once we have all the tools we want, we can put them in a list that we will reference later.
tools = [search]
API Reference:TavilySearchResults
[{'url': 'https://www.weatherapi.com/', 'content': "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1730484342, 'localtime': '2024-11-01 11:05'}, 'current': {'last_updated_epoch': 1730484000, 'last_updated': '2024-11-01 11:00', 'temp_c': 11.1, 'temp_f': 52.0, 'is_day': 1, 'condition': {'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/day/143.png', 'code': 1030}, 'wind_mph': 2.9, 'wind_kph': 4.7, 'wind_degree': 247, 'wind_dir': 'WSW', 'pressure_mb': 1019.0, 'pressure_in': 30.08, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 100, 'cloud': 100, 'feelslike_c': 11.1, 'feelslike_f': 52.0, 'windchill_c': 10.3, 'windchill_f': 50.5, 'heatindex_c': 10.8, 'heatindex_f': 51.5, 'dewpoint_c': 10.4, 'dewpoint_f': 50.6, 'vis_km': 2.8, 'vis_miles': 1.0, 'uv': 3.0, 'gust_mph': 3.8, 'gust_kph': 6.1}}"}, {'url': 'https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States', 'content': 'San Francisco Temperature History January 2024\nHourly Temperature in January 2024 in San Francisco\nCompare San Francisco to another city:\nCloud Cover in January 2024 in San Francisco\nDaily Precipitation in January 2024 in San Francisco\nObserved Weather in January 2024 in San Francisco\nHours of Daylight and Twilight in January 2024 in San Francisco\nSunrise & Sunset with Twilight in January 2024 in San Francisco\nSolar Elevation and Azimuth in January 2024 in San Francisco\nMoon Rise, Set & Phases in January 2024 in San Francisco\nHumidity Comfort Levels in January 2024 in San Francisco\nWind Speed in January 2024 in San Francisco\nHourly Wind Speed in January 2024 in San Francisco\nHourly Wind Direction in 2024 in San Francisco\nAtmospheric Pressure in January 2024 in San Francisco\nData Sources\n See all nearby weather stations\nLatest Report — 1:56 PM\nFri, Jan 12, 2024\xa0\xa0\xa0\xa04 min ago\xa0\xa0\xa0\xa0UTC 21:56\nCall Sign KSFO\nTemp.\n54.0°F\nPrecipitation\nNo Report\nWind\n8.1 mph\nCloud Cover\nMostly Cloudy\n14,000 ft\nRaw: KSFO 122156Z 08007KT 10SM FEW030 SCT050 BKN140 12/07 A3022 While having the tremendous advantages of temporal and spatial completeness, these reconstructions: (1) are based on computer models that may have model-based errors, (2) are coarsely sampled on a 50 km grid and are therefore unable to reconstruct the local variations of many microclimates, and (3) have particular difficulty with the weather in some coastal areas, especially small islands.\n We further caution that our travel scores are only as good as the data that underpin them, that weather conditions at any given location and time are unpredictable and variable, and that the definition of the scores reflects a particular set of preferences that may not agree with those of any particular reader.\n January 2024 Weather History in San Francisco California, United States\nThe data for this report comes from the San Francisco International Airport.'}]

We can now see what it is like to enable this model to do tool calling. In order to enable that we use .bind_tools to give the language model knowledge of these tools

model_with_tools = model.bind_tools(tools)

We can now call the model. Let's first call it with a normal message, and see how it responds. We can look at both the content field as well as the tool_calls field.

from langchain_core.messages import HumanMessage

response = model_with_tools.invoke([HumanMessage(content="Hi!")])

print(f"ContentString: {response.content}")
print(f"ToolCalls: {response.tool_calls}")
API Reference:HumanMessage
ContentString:  Hello! How can I help you today? If you have a question or need information on a specific topic, feel free to ask. Just type your search query and I'll do my best to assist using the available function.


ToolCalls: []

Now, let's try calling it with some input that would expect a tool to be called.

response = model_with_tools.invoke([HumanMessage(content="What's the weather in SF?")])

print(f"ContentString: {response.content}")
print(f"ToolCalls: {response.tool_calls}")
ContentString: 
ToolCalls: [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in SF'}, 'id': '2548c622-3553-42df-8220-39fde0632bdb', 'type': 'tool_call'}]

We can see that there's now no text content, but there is a tool call! It wants us to call the Tavily Search tool.

This isn't calling that tool yet - it's just telling us to. In order to actually call it, we'll want to create our agent.

Create the agent

Now that we have defined the tools and the LLM, we can create the agent. We will be using LangGraph to construct the agent. Currently, we are using a high level interface to construct the agent, but the nice thing about LangGraph is that this high-level interface is backed by a low-level, highly controllable API in case you want to modify the agent logic.

Now, we can initialize the agent with the LLM and the tools.

Note that we are passing in the model, not model_with_tools. That is because create_react_agent will call .bind_tools for us under the hood.

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(model, tools)
API Reference:create_react_agent

Let's now try it out on an example where it should be invoking the tool

response = agent_executor.invoke({"messages": [HumanMessage(content="hi!")]})

response["messages"]
[HumanMessage(content='hi!', additional_kwargs={}, response_metadata={}, id='0ab1f3c7-9079-42d4-8a8a-13af5f6c226b'),
 AIMessage(content=' Hello! How can I help you today? If you have a question or need information on a specific topic, feel free to ask. For example, you can start with a search query like "latest news on climate change" or "biography of Albert Einstein".\n\n', additional_kwargs={}, response_metadata={}, id='run-276d9dcd-13f3-481d-b562-8fe3962d9ba1-0')]

In order to see exactly what is happening under the hood (and to make sure it's not calling a tool) we can take a look at the LangSmith trace: https://smith.langchain.com/public/2372d9c5-855a-45ee-80f2-94b63493563d/r

response = agent_executor.invoke(
    {"messages": [HumanMessage(content="whats the weather in sf?")]}
)
response["messages"]
[HumanMessage(content='whats the weather in sf?', additional_kwargs={}, response_metadata={}, id='af276c61-3df7-4241-8cb0-81d1f1477bb3'),
 AIMessage(content='', additional_kwargs={'tool_calls': [{'id': '86da84b8-0d44-444f-8448-7f134f9afa41', 'type': 'function', 'function': {'name': 'tavily_search_results_json', 'arguments': '{"query": "weather in SF"}'}}]}, response_metadata={}, id='run-abe1b8e2-98a6-4f69-8f95-278ac8c141ff-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in SF'}, 'id': '86da84b8-0d44-444f-8448-7f134f9afa41', 'type': 'tool_call'}]),
 ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.775, \'lon\': -122.4183, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1730483436, \'localtime\': \'2024-11-01 10:50\'}, \'current\': {\'last_updated_epoch\': 1730483100, \'last_updated\': \'2024-11-01 10:45\', \'temp_c\': 11.4, \'temp_f\': 52.5, \'is_day\': 1, \'condition\': {\'text\': \'Mist\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/143.png\', \'code\': 1030}, \'wind_mph\': 2.2, \'wind_kph\': 3.6, \'wind_degree\': 237, \'wind_dir\': \'WSW\', \'pressure_mb\': 1019.0, \'pressure_in\': 30.08, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 100, \'cloud\': 100, \'feelslike_c\': 11.8, \'feelslike_f\': 53.2, \'windchill_c\': 11.2, \'windchill_f\': 52.1, \'heatindex_c\': 11.7, \'heatindex_f\': 53.0, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.1, \'vis_km\': 2.8, \'vis_miles\': 1.0, \'uv\': 3.0, \'gust_mph\': 3.0, \'gust_kph\': 4.9}}"}, {"url": "https://www.timeanddate.com/weather/@z-us-94134/ext", "content": "Forecasted weather conditions the coming 2 weeks for San Francisco. Sign in. News. News Home; Astronomy News; Time Zone News ... 01 pm: Mon Nov 11: 60 / 53 °F: Tstorms early. Broken clouds. 54 °F: 19 mph: ↑: 70%: 58%: 0.20\\" 0 (Low) 6:46 am: 5:00 pm * Updated Monday, October 28, 2024 2:24:10 pm San Francisco time - Weather by CustomWeather"}]', name='tavily_search_results_json', id='de8c8d78-ae24-4a8a-9c73-795c1e4fdd41', tool_call_id='86da84b8-0d44-444f-8448-7f134f9afa41', artifact={'query': 'weather in SF', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'title': 'Weather in San Francisco', 'url': 'https://www.weatherapi.com/', 'content': "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1730483436, 'localtime': '2024-11-01 10:50'}, 'current': {'last_updated_epoch': 1730483100, 'last_updated': '2024-11-01 10:45', 'temp_c': 11.4, 'temp_f': 52.5, 'is_day': 1, 'condition': {'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/day/143.png', 'code': 1030}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 237, 'wind_dir': 'WSW', 'pressure_mb': 1019.0, 'pressure_in': 30.08, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 100, 'cloud': 100, 'feelslike_c': 11.8, 'feelslike_f': 53.2, 'windchill_c': 11.2, 'windchill_f': 52.1, 'heatindex_c': 11.7, 'heatindex_f': 53.0, 'dewpoint_c': 10.1, 'dewpoint_f': 50.1, 'vis_km': 2.8, 'vis_miles': 1.0, 'uv': 3.0, 'gust_mph': 3.0, 'gust_kph': 4.9}}", 'score': 0.9989501, 'raw_content': None}, {'title': 'San Francisco, USA 14 day weather forecast - timeanddate.com', 'url': 'https://www.timeanddate.com/weather/@z-us-94134/ext', 'content': 'Forecasted weather conditions the coming 2 weeks for San Francisco. Sign in. News. News Home; Astronomy News; Time Zone News ... 01 pm: Mon Nov 11: 60 / 53 °F: Tstorms early. Broken clouds. 54 °F: 19 mph: ↑: 70%: 58%: 0.20" 0 (Low) 6:46 am: 5:00 pm * Updated Monday, October 28, 2024 2:24:10 pm San Francisco time - Weather by CustomWeather', 'score': 0.9938309, 'raw_content': None}], 'response_time': 3.56}),
 AIMessage(content=' The current weather in San Francisco is mist with a temperature of 11.4°C (52.5°F). There is a 100% humidity and the wind is blowing at 2.2 mph from the WSW direction. The forecast for the coming weeks shows a mix of cloudy and partly cloudy days with some chances of thunderstorms. Temperatures are expected to range between 53°F and 60°F.\n\n', additional_kwargs={}, response_metadata={}, id='run-de4207d6-e8e8-4382-ad16-4de0dcf0812a-0')]

We can check out the LangSmith trace to make sure it's calling the search tool effectively.

https://smith.langchain.com/public/013ef704-654b-4447-8428-637b343d646e/r

We've seen how the agent can be called with .invoke to get a final response. If the agent executes multiple steps, this may take a while. To show intermediate progress, we can stream back messages as they occur.

for chunk in agent_executor.stream(
    {"messages": [HumanMessage(content="whats the weather in sf?")]}
):
    print(chunk)
    print("----")
{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': '2457d3ea-f001-4b8c-a1ed-3dc3d1381639', 'type': 'function', 'function': {'name': 'tavily_search_results_json', 'arguments': '{"query": "weather in San Francisco"}'}}]}, response_metadata={}, id='run-0363deab-84d2-4319-bb1e-b55b47fe2274-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': '2457d3ea-f001-4b8c-a1ed-3dc3d1381639', 'type': 'tool_call'}])]}}
----
{'tools': {'messages': [ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.775, \'lon\': -122.4183, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1730483636, \'localtime\': \'2024-11-01 10:53\'}, \'current\': {\'last_updated_epoch\': 1730483100, \'last_updated\': \'2024-11-01 10:45\', \'temp_c\': 11.4, \'temp_f\': 52.5, \'is_day\': 1, \'condition\': {\'text\': \'Mist\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/143.png\', \'code\': 1030}, \'wind_mph\': 2.2, \'wind_kph\': 3.6, \'wind_degree\': 237, \'wind_dir\': \'WSW\', \'pressure_mb\': 1019.0, \'pressure_in\': 30.08, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 100, \'cloud\': 100, \'feelslike_c\': 11.8, \'feelslike_f\': 53.2, \'windchill_c\': 11.2, \'windchill_f\': 52.1, \'heatindex_c\': 11.7, \'heatindex_f\': 53.0, \'dewpoint_c\': 10.1, \'dewpoint_f\': 50.1, \'vis_km\': 2.8, \'vis_miles\': 1.0, \'uv\': 3.0, \'gust_mph\': 3.0, \'gust_kph\': 4.9}}"}, {"url": "https://weather.com/weather/monthly/l/69bedc6a5b6e977993fb3e5344e3c06d8bc36a1fb6754c3ddfb5310a3c6d6c87", "content": "Weather.com brings you the most accurate monthly weather forecast for San Francisco, CA with average/record and high/low temperatures, precipitation and more. ... 11. 66 ° 55 ° 12. 69 ° 60"}]', name='tavily_search_results_json', id='e675f99b-130f-4e98-8477-badd45938d9d', tool_call_id='2457d3ea-f001-4b8c-a1ed-3dc3d1381639', artifact={'query': 'weather in San Francisco', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'title': 'Weather in San Francisco', 'url': 'https://www.weatherapi.com/', 'content': "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1730483636, 'localtime': '2024-11-01 10:53'}, 'current': {'last_updated_epoch': 1730483100, 'last_updated': '2024-11-01 10:45', 'temp_c': 11.4, 'temp_f': 52.5, 'is_day': 1, 'condition': {'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/day/143.png', 'code': 1030}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 237, 'wind_dir': 'WSW', 'pressure_mb': 1019.0, 'pressure_in': 30.08, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 100, 'cloud': 100, 'feelslike_c': 11.8, 'feelslike_f': 53.2, 'windchill_c': 11.2, 'windchill_f': 52.1, 'heatindex_c': 11.7, 'heatindex_f': 53.0, 'dewpoint_c': 10.1, 'dewpoint_f': 50.1, 'vis_km': 2.8, 'vis_miles': 1.0, 'uv': 3.0, 'gust_mph': 3.0, 'gust_kph': 4.9}}", 'score': 0.9968992, 'raw_content': None}, {'title': 'Monthly Weather Forecast for San Francisco, CA - weather.com', 'url': 'https://weather.com/weather/monthly/l/69bedc6a5b6e977993fb3e5344e3c06d8bc36a1fb6754c3ddfb5310a3c6d6c87', 'content': 'Weather.com brings you the most accurate monthly weather forecast for San Francisco, CA with average/record and high/low temperatures, precipitation and more. ... 11. 66 ° 55 ° 12. 69 ° 60', 'score': 0.97644573, 'raw_content': None}], 'response_time': 3.16})]}}
----
{'agent': {'messages': [AIMessage(content=' The current weather in San Francisco is misty with a temperature of 11.4°C (52.5°F). The wind is blowing at 2.2 mph (3.6 kph) from the WSW direction. The humidity is at 100%, and the visibility is 2.8 km (1.0 miles). The monthly forecast shows average temperatures ranging from 55°F to 66°F (13°C to 19°C) with some precipitation expected.\n\n', additional_kwargs={}, response_metadata={}, id='run-99ccf444-d286-4244-a5a5-7b1b511153a6-0')]}}
----

Was this page helpful?