docs: update docxsearchtool.mdx

- add `docx2txt` as a dependency requirement for the tool

docs/concepts/llms.mdx

@@ -27,23 +27,19 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
   </Card>
 </CardGroup>
 
-## Setting Up Your LLM
+## Setting up your LLM
 
-There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
+There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.
 
 <Tabs>
   <Tab title="1. Environment Variables">
-    The simplest way to get started. Set these variables in your environment:
+    The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.
 
-    ```bash
-    # Required: Your API key for authentication
-    OPENAI_API_KEY=<your-api-key>
+    ```bash .env
+    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...
 
-    # Optional: Default model selection
-    OPENAI_MODEL_NAME=gpt-4o-mini  # Default if not set
-
-    # Optional: Organization ID (if applicable)
-    OPENAI_ORGANIZATION_ID=<your-org-id>
+    # Be sure to set your API keys here too. See the Provider
+    # section below.
     ```
 
     <Warning>
@@ -53,13 +49,13 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
   <Tab title="2. YAML Configuration">
     Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
 
-    ```yaml
+    ```yaml agents.yaml {6}
     researcher:
         role: Research Specialist
         goal: Conduct comprehensive research and analysis
         backstory: A dedicated research professional with years of experience
         verbose: true
-        llm: openai/gpt-4o-mini # your model here
+        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
         # (see provider configuration examples below for more)
     ```
 
@@ -74,23 +70,23 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
   <Tab title="3. Direct Code">
     For maximum flexibility, configure LLMs directly in your Python code:
 
-    ```python
+    ```python {4,8}
     from crewai import LLM
 
     # Basic configuration
-    llm = LLM(model="gpt-4")
+    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...
 
     # Advanced configuration with detailed parameters
     llm = LLM(
-        model="gpt-4o-mini",
+        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
         temperature=0.7,        # Higher for more creative outputs
-        timeout=120,           # Seconds to wait for response
-        max_tokens=4000,       # Maximum length of response
-        top_p=0.9,            # Nucleus sampling parameter
-        frequency_penalty=0.1, # Reduce repetition
-        presence_penalty=0.1,  # Encourage topic diversity
+        timeout=120,            # Seconds to wait for response
+        max_tokens=4000,        # Maximum length of response
+        top_p=0.9,              # Nucleus sampling parameter
+        frequency_penalty=0.1 , # Reduce repetition
+        presence_penalty=0.1,   # Encourage topic diversity
         response_format={"type": "json"},  # For structured outputs
-        seed=42               # For reproducible results
+        seed=42                 # For reproducible results
     )
     ```
 
@@ -110,7 +106,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
 
 ## Provider Configuration Examples
 
-
 CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
 In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
 
@@ -383,7 +378,7 @@ In this section, you'll find detailed examples that help you select, configure,
     | microsoft/phi-3-medium-4k-instruct                                  | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
     | microsoft/phi-3-medium-128k-instruct                                | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
     | microsoft/phi-3.5-mini-instruct                                     | 128K tokens    | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
-    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
+    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
     | microsoft/kosmos-2                                                  | 1,024 tokens   | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
     | microsoft/phi-3-vision-128k-instruct                               | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
     | microsoft/phi-3.5-vision-instruct                                  | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
@@ -407,19 +402,19 @@ In this section, you'll find detailed examples that help you select, configure,
   </Accordion>
 
   <Accordion title="Local NVIDIA NIM Deployed using WSL2">
-    
-    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux). 
-    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services. 
+
+    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
+    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
     Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
-    
+
     Here is a step-by-step guide to setting up a local NVIDIA NIM model:
-    
+
     1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)
 
     2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)
 
     3. Configure your crewai local models:
-  
+
     ```python Code
     from crewai.llm import LLM
 
@@ -441,7 +436,7 @@ In this section, you'll find detailed examples that help you select, configure,
                 config=self.agents_config['researcher'], # type: ignore[index]
                 llm=local_nvidia_nim_llm
             )
-        
+
         # ...
     ```
   </Accordion>
@@ -637,19 +632,19 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
 
     When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
   </Tab>
-  
+
   <Tab title="Event Handling">
     CrewAI emits events for each chunk received during streaming:
-    
+
     ```python
     from crewai import LLM
     from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
-    
+
     class MyEventHandler(EventHandler):
         def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
             # Process each chunk as it arrives
             print(f"Received chunk: {event.chunk}")
-    
+
     # Register the event handler
     from crewai.utilities.events import crewai_event_bus
     crewai_event_bus.register_handler(MyEventHandler())
@@ -785,7 +780,7 @@ Learn how to get the most out of your LLM configuration:
     <Tip>
       Use larger context models for extensive tasks
     </Tip>
-    
+
     ```python
     # Large context model
     llm = LLM(model="openai/gpt-4o")  # 128K tokens

docs/installation.mdx

@@ -71,6 +71,10 @@ If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on
             ```
           </Warning>
 
+          <Warning>
+            If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install (Visual Studio Build Tools)[https://visualstudio.microsoft.com/downloads/] with *Desktop development with C++*.
+          </Warning>
+
       - To verify that `crewai` is installed, run:
         ```shell
         uv tool list

docs/tools/docxsearchtool.mdx

@@ -22,7 +22,7 @@ streamlining the process of finding specific information within large document c
 Install the crewai_tools package by running the following command in your terminal:
 
 ```shell
-pip install 'crewai[tools]'
+uv pip install docx2txt 'crewai[tools]'
 ```
 
 ## Example
@@ -76,4 +76,4 @@ tool = DOCXSearchTool(
         ),
     )
 )
-```
+```

docs/tools/ragtool.mdx

@@ -143,12 +143,30 @@ config = {
         "config": {
             "model": "text-embedding-ada-002"
         }
+    },
+    "vectordb": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "my-collection",
+            "cloud_id": "deployment-name:xxxx",
+            "api_key": "your-key",
+            "verify_certs": False
+        }
+    },
+    "chunker": {
+        "chunk_size": 400,
+        "chunk_overlap": 100,
+        "length_function": "len",
+        "min_chunk_size": 0
     }
 }
 
 rag_tool = RagTool(config=config, summarize=True)
 ```
 
-## Conclusion
+The internal RAG tool utilizes the Embedchain adapter, allowing you to pass any configuration options that are supported by Embedchain.
+You can refer to the [Embedchain documentation](https://docs.embedchain.ai/components/introduction) for details.
+Make sure to review the configuration options available in the .yaml file.
 
+## Conclusion
 The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.67.1",
+    "litellm==1.68.0",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",

src/crewai/cli/install_crew.py

@@ -4,7 +4,7 @@ import click
 
 
 # Be mindful about changing this.
-# on some enviorments we don't use this command but instead uv sync directly
+# on some environments we don't use this command but instead uv sync directly
 # so if you expect this to support more things you will need to replicate it there
 # ask @joaomdmoura if you are unsure
 def install_crew(proxy_options: list[str]) -> None:

tests/cassettes/test_lite_agent_structured_output.yaml

@@ -16,7 +16,7 @@ interactions:
       answer MUST contain all the information requested in the following format: {\n  \"summary\":
       str,\n  \"confidence\": int\n}\n\nIMPORTANT: Ensure the final output does not
       include any code block markers like ```json or ```python."}, {"role": "user",
-      "content": "What is the population of Tokyo? Return your strucutred output in
+      "content": "What is the population of Tokyo? Return your structured output in
       JSON format with the following fields: summary, confidence"}], "model": "gpt-4o-mini",
       "stop": []}'
     headers:

uv.lock

@@ -835,7 +835,7 @@ requires-dist = [
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "json5", specifier = ">=0.10.0" },
     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.67.1" },
+    { name = "litellm", specifier = "==1.68.0" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.94" },
     { name = "openai", specifier = ">=1.13.3" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -2387,7 +2387,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.67.1"
+version = "1.68.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -2402,9 +2402,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/54/a4/bb3e9ae59e5a9857443448de7c04752630dc84cddcbd8cee037c0976f44f/litellm-1.67.1.tar.gz", hash = "sha256:78eab1bd3d759ec13aa4a05864356a4a4725634e78501db609d451bf72150ee7", size = 7242044 }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/22/138545b646303ca3f4841b69613c697b9d696322a1386083bb70bcbba60b/litellm-1.68.0.tar.gz", hash = "sha256:9fb24643db84dfda339b64bafca505a2eef857477afbc6e98fb56512c24dbbfa", size = 7314051 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/88/86/c14d3c24ae13c08296d068e6f79fd4bd17a0a07bddbda94990b87c35d20e/litellm-1.67.1-py3-none-any.whl", hash = "sha256:8fff5b2a16b63bb594b94d6c071ad0f27d3d8cd4348bd5acea2fd40c8e0c11e8", size = 7607266 },
+    { url = "https://files.pythonhosted.org/packages/10/af/1e344bc8aee41445272e677d802b774b1f8b34bdc3bb5697ba30f0fb5d52/litellm-1.68.0-py3-none-any.whl", hash = "sha256:3bca38848b1a5236b11aa6b70afa4393b60880198c939e582273f51a542d4759", size = 7684460 },
 ]
 
 [[package]]