merge with dev

Author: rohit-kadhe

README.md

@@ -246,6 +246,7 @@ Langtrace automatically captures traces from the following vendors:
 | Groq         | LLM             | :x:                | :white_check_mark:              |
 | Perplexity   | LLM             | :white_check_mark: | :white_check_mark:              |
 | Gemini       | LLM             | :x:                | :white_check_mark:              |
+| Mistral      | LLM             | :x:                | :white_check_mark:              |
 | Langchain    | Framework       | :x:                | :white_check_mark:              |
 | LlamaIndex   | Framework       | :white_check_mark: | :white_check_mark:              |
 | Langgraph    | Framework       | :x:                | :white_check_mark:              |

pyproject.toml

@@ -18,7 +18,7 @@ classifiers = [
   "Operating System :: OS Independent",
 ]
 dependencies = [
-  'trace-attributes==7.0.1',
+  'trace-attributes==7.0.4',
   'opentelemetry-api>=1.25.0',
   'opentelemetry-sdk>=1.25.0',
   'opentelemetry-instrumentation>=0.47b0',
@@ -54,6 +54,7 @@ dev = [
   "groq",
   "google-generativeai",
   "google-cloud-aiplatform",
+  "mistralai",
 ]
 
 test = ["pytest", "pytest-vcr", "pytest-asyncio"]

src/examples/crewai_example/trip_planner/agents.py

@@ -12,9 +12,9 @@ class TravelAgents:
     def __init__(self):
         self.OpenAIGPT35 = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.7)
         self.OpenAIGPT4 = ChatOpenAI(model_name="gpt-4", temperature=0.7)
-        self.Ollama = ChatOllama(model="openhermes")
+        self.Ollama = ChatOllama(model="llama3")
         self.Cohere = ChatCohere(model="command-r")
-        self.Anthropic = ChatAnthropic(model="claude-3-5-sonnet")
+        self.Anthropic = ChatAnthropic(model="claude-3-5-sonnet-20240620")
 
     def expert_travel_agent(self):
         return Agent(
@@ -28,7 +28,7 @@ def expert_travel_agent(self):
             # tools=[tool_1, tool_2],
             allow_delegation=False,
             verbose=True,
-            llm=self.OpenAIGPT4,
+            llm=self.Cohere,
         )
 
     def city_selection_expert(self):
@@ -39,7 +39,7 @@ def city_selection_expert(self):
             # tools=[tool_1, tool_2],
             allow_delegation=False,
             verbose=True,
-            llm=self.OpenAIGPT4,
+            llm=self.Cohere,
         )
 
     def local_tour_guide(self):
@@ -50,5 +50,5 @@ def local_tour_guide(self):
             # tools=[tool_1, tool_2],
             allow_delegation=False,
             verbose=True,
-            llm=self.OpenAIGPT4,
+            llm=self.Cohere,
         )

src/examples/litellm_example/basic.py

@@ -0,0 +1,98 @@
+from langtrace_python_sdk import with_langtrace_root_span, langtrace
+from dotenv import load_dotenv
+from litellm import completion, acompletion
+import litellm
+import asyncio
+
+load_dotenv()
+
+
+litellm.success_callback = ["langtrace"]
+langtrace.init()
+litellm.set_verbose = False
+
+
+@with_langtrace_root_span("Litellm Example OpenAI")
+def openAI(streaming=False):
+    response = completion(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"content": "respond only in Yoda speak.", "role": "system"},
+            {"content": "Hello, how are you?", "role": "user"},
+        ],
+        stream=streaming,
+        stream_options={"include_usage": True},
+    )
+    if streaming:
+        for _ in response:
+            pass
+    else:
+        return response
+
+
+# @with_langtrace_root_span("Litellm Example Anthropic Completion")
+def anthropic(streaming=False):
+    try:
+
+        response = completion(
+            model="claude-2.1",
+            messages=[
+                {"content": "respond only in Yoda speak.", "role": "system"},
+                {"content": "what is 2 + 2?", "role": "user"},
+            ],
+            temperature=0.5,
+            top_p=0.5,
+            n=1,
+            stream=streaming,
+            stream_options={"include_usage": True},
+        )
+        # print(response)
+        if streaming:
+            for _ in response:
+                pass
+        else:
+            return response
+    except Exception as e:
+        print("ERORRRR", e)
+
+
+# @with_langtrace_root_span("Litellm Example OpenAI Async Streaming")
+async def async_anthropic(streaming=False):
+    response = await acompletion(
+        model="claude-2.1",
+        messages=[{"content": "Hello, how are you?", "role": "user"}],
+        stream=streaming,
+        stream_options={"include_usage": True},
+        temperature=0.5,
+        top_p=0.5,
+        n=1,
+    )
+    if streaming:
+        async for _ in response:
+            pass
+    else:
+        return response
+
+
+def cohere(streaming=False):
+    response = completion(
+        model="command-r",
+        messages=[
+            {"content": "respond only in Yoda speak.", "role": "system"},
+            {"content": "Hello, how are you?", "role": "user"},
+        ],
+        stream=streaming,
+        stream_options={"include_usage": True},
+    )
+    if streaming:
+        for _ in response:
+            pass
+    else:
+        return response
+
+
+if __name__ == "__main__":
+    # openAI()
+    anthropic(streaming=False)
+    cohere(streaming=True)
+    # asyncio.run(async_anthropic(streaming=True))

src/examples/mistral_example/__init__.py

@@ -0,0 +1,14 @@
+import asyncio
+from examples.mistral_example.complete import chat_complete
+from examples.mistral_example.complete_async import complete_async
+from examples.mistral_example.embeddings import embeddings_create
+from langtrace_python_sdk import langtrace, with_langtrace_root_span
+
+langtrace.init()
+
+class MistralRunner:
+    @with_langtrace_root_span("Mistral")
+    def run(self):
+        chat_complete()
+        asyncio.run(complete_async())
+        embeddings_create()

src/examples/mistral_example/complete.py

@@ -0,0 +1,18 @@
+import os
+from langtrace_python_sdk import with_langtrace_root_span
+from mistralai import Mistral
+
+@with_langtrace_root_span("chat_complete")
+def chat_complete():
+    model = "mistral-large-latest"
+    client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
+    chat_response = client.chat.complete(
+        model= model,
+        messages = [
+            {
+                "role": "user",
+                "content": "I need 10 cocktail recipes with tequila other than the classics like margarita, tequila"
+            },
+        ]
+    )
+    print(chat_response.choices[0].message.content)

src/examples/mistral_example/complete_async.py

@@ -0,0 +1,16 @@
+import os
+from langtrace_python_sdk import with_langtrace_root_span
+from mistralai import Mistral
+
+@with_langtrace_root_span("chat_complete_async")
+async def complete_async():
+    client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
+    res = await client.chat.complete_async(model="mistral-small-latest", messages=[
+        {
+            "content": "Which locations should I visit when I travel to New york? Answer in one short sentence.",
+            "role": "user",
+        },
+    ])
+    if res is not None:
+        # handle response
+        print(res.choices[0].message.content)

src/examples/mistral_example/embeddings.py

@@ -0,0 +1,17 @@
+import os
+from langtrace_python_sdk import with_langtrace_root_span
+from mistralai import Mistral
+
+
+@with_langtrace_root_span("create_embeddings")
+def embeddings_create():
+    model = "mistral-embed"
+
+    client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
+
+    embeddings_batch_response = client.embeddings.create(
+        model=model,
+        inputs=["Embed this sentence.", "As well as this one."],
+    )
+
+    print(embeddings_batch_response.data[0].embedding)

src/examples/weaviate_example/query_text.py

@@ -15,13 +15,14 @@
 import requests
 import weaviate
 import weaviate.classes as wvc
+import weaviate.classes.config as wc
+from dotenv import load_dotenv
 from weaviate.classes.aggregate import GroupByAggregate
 from weaviate.classes.query import Filter, HybridFusion, MetadataQuery
 from weaviate.collections.classes.grpc import Move
 
 import langtrace_python_sdk.langtrace as langtrace
 from langtrace_python_sdk import with_langtrace_root_span
-from dotenv import load_dotenv
 
 load_dotenv()
 # Set these environment variables
@@ -44,11 +45,16 @@ def create():
     if not client.collections.get("Question"):
         questions = client.collections.create(
             name="Question",
-            # Set the vectorizer to "text2vec-openai" to use the OpenAI API for vector-related operations
-            vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_openai(),
-            # Ensure the `generative-openai` module is used for generative queries
-            generative_config=wvc.config.Configure.Generative.openai(),
-        )
+        properties=[
+            wc.Property(name="answer", data_type=wc.DataType.TEXT),
+            wc.Property(name="question", data_type=wc.DataType.TEXT),
+            wc.Property(name="category", data_type=wc.DataType.TEXT),
+        ],
+        # Define the vectorizer module
+        vectorizer_config=wc.Configure.Vectorizer.text2vec_openai(),
+        # Define the generative module
+        generative_config=wc.Configure.Generative.openai(),
+    )
 
 
 @with_langtrace_root_span("insert")

src/langtrace_python_sdk/constants/instrumentation/common.py

@@ -29,6 +29,7 @@
     "OLLAMA": "Ollama",
     "VERTEXAI": "VertexAI",
     "GEMINI": "Gemini",
+    "MISTRAL": "Mistral",
 }
 
 LANGTRACE_ADDITIONAL_SPAN_ATTRIBUTES_KEY = "langtrace_additional_attributes"